Preferred Citation: Rawski, Thomas G., and Lillian M. Li, editors Chinese History in Economic Perspective. Berkeley:  University of California Press,  c1992 1992. http://ark.cdlib.org/ark:/13030/ft6489p0n6/


cover

Chinese History in Economic Perspective

Edited By
Thomas G. Rawski and Lillian M. Li

UNIVERSITY OF CALIFORNIA PRESS
Berkeley · Los Angeles · Oxford
© 1992 The Regents of the University of California


Preferred Citation: Rawski, Thomas G., and Lillian M. Li, editors Chinese History in Economic Perspective. Berkeley:  University of California Press,  c1992 1992. http://ark.cdlib.org/ark:/13030/ft6489p0n6/

WEIGHTS AND MEASURES

dan : a measure of weight equal to 100 jin , with local variation.

jin (or catty): a measure of weight, normally equal to 1.3 pounds, with local variation.

mu : a measure of area equal to 0.1647 acre or 0.0666 hectare, with local variation.

shi : a measure of volume for grain (1 shi of milled rice weighed approximately 175–195 pounds); also used interchangeably with dan as a measure of weight, with local variation. Gansu granaries recorded stocks in jingshi , a measure of volume seven-tenths as large as the cangshi , the standard granary measure used elsewhere in China. In Wuxi, shi was used to denote a weight of approximately 150 jin .

tael (or liang ): the Chinese ounce, a measure of weight equal to one-sixteenth of a jin ; also one of numerous units of account for uncoined silver money employed in China before 1932.

yuan (or dollar): refers to silver coinage issued in China from the late nineteenth century and to fiat money of the Republic of China following the demonetization of silver in 1935.

SOURCES: Han-sheng Chuan and Richard A. Kraus, Mid-Ch'ing Rice Markets and Trade: An Essay in Price History (Cambridge, 1975), pp. 79–98; Thomas G. Rawski, Economic Growth in Prewar China (Berkeley and Los Angeles, 1989), p. xv.


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PREFACE

This volume is based on papers and discussions from the Workshop and Conference on Economic Methods for Chinese Historical Research sponsored by the Henry Luce Foundation, the American Council of Learned Societies, and the National Science Foundation and held in Honolulu, Hawaii, in January 1987 and Oracle, Arizona, in January 1988. In the 1987 workshop, a number of economists who work in economic history were asked to prepare seminars on broad subjects, such as choice, long-term trends, macroeconomics, international and interregional issues, and economic institutions, for which the historian-participants had prepared by doing assigned readings. The fact that none of the economists, except Thomas G. Rawski, were China specialists added an important comparative dimension to the proceedings. The economists were Jon Cohen, University of Toronto; Peter H. Lindert, University of California, Davis; Donald N. McCloskey, University of Iowa; and Richard Sutch, University of California, Berkeley. The papers they delivered at this meeting, together with additional material on monetary and labor economics, will be published separately in a volume entitled Economics and the Historian .

The same group of historians and economists convened at the 1988 conference. This time, however, the historians presented their papers, which they had written on the basis of guidelines and suggestions from the previous year, and the economists served as the discussants. In addition to those participants whose papers are included in this volume, I-chun Fan, Bozhong Li, and Guangyuan Zhou also attended the conference. Their participation and the contributions of the economists are gratefully acknowledged by the editors and authors of this volume.

We would also like to express our gratitude to Julius Rubin, who helped us start the whole project, to two anonymous referees for their insightful


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reviews of the entire manuscript, to David Arkush, Philip Kuhn, Susan Naquin, Evelyn S. Rawski, and William T. Rowe for their assistance in revising the introductory essay, to Shu-jen Yeh for helping to prepare the glossary and the bibliography, and to Eleanor Bennett, Sarah S. Fought, William Karunaratne, Debbie Kwolek, Patty Huchber, Sharon Wetzel, and Debra Ziolkowski for their invaluable efforts behind the scenes.


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CONTRIBUTORS

Lynda S. Bell is Assistant Professor of History, University of California, Riverside.

Loren Brandt is Associate Professor of Economics, University of Toronto.

Cameron Campbell is with the Graduate Group in Demography, University of Pennsylvania.

Emily Honig is Associate Professor of History, Yale University.

James Lee is Associate Professor of History, California Institute of Technology.

Lillian M. Li is Professor of History, Swarthmore College.

Susan Mann is Professor of History, University of California, Davis.

Peter C. Perdue is Associate Professor of History, Massachusetts Institute of Technology.

Kenneth Pomeranz is Assistant Professor of History, University of California, Irvine.

Thomas G. Rawski is Professor of Economics and History, University of Pittsburgh.

Barbara Sands is Associate Professor of Economics, University of Arizona.

Guofu Tan is Assistant Professor of Economics, University of British Columbia.

Yeh-chien Wang is Professor of History, Kent State University.

R. Bin Wong is Associate Professor of History, University of California, Irvine.


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INTRODUCTION:
CHINESE HISTORY IN ECONOMIC PERSPECTIVE

Thomas G. Rawski and Lillian M. Li

Economics and economists tend to bring out strong emotions both in the general public and among (noneconomist) scholars. How often does one encounter the sentiment, "If economists are so smart, how come they couldn't predict such-and-such [the latest round of inflation, the October '87 stock market crash, etc.]?" Economics has always been a controversial field of study, and economists often exhibit a strong professional affinity for contentiousness among themselves. Yet, while society might conceivably get along without economists, it would be difficult to imagine a world in which economics did not play a role, even the mythical world of Robinson Crusoe. Nor can historians avoid the economic aspects of history even when they would like to do so. Embedded in all their common notions of how history has developed are views, conscious or unconscious, of economic forces: the prosperity of the Italian city-states prompted the cultural efflorescence of the Renaissance, the Chinese had a rural revolution because the peasants were so poor, Europeans conducted oceanic explorations because they needed spices, and so forth. But fundamentally, historians need to know about the material side of history because they are concerned with human welfare, social development, and national histories. The classic definition of economics, after all, is that it studies the allocation of scarce resources among alternative uses. Therefore, subjects such as agriculture, money, industry, and trade compel historians' interest for a variety of commendable reasons.

It is our contention, however, that the study of such subjects in economic history has not always employed a true economic approach or perspective, at least among historians of China. This book is dedicated to the idea that the history of China's economy has been written many times in many ways but that the economic history of China has not yet been written. This, indeed, is not such a history either, but the essays in this volume are intended to illus-


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trate how economic history is not the same as the history of an economy, and how an economic perspective involves more than an interest in some economic topic. Scholarship on China has excelled in studying the economy of China, but has barely begun to do so with a true economic perspective. The fundamental objective of this volume is to delineate and illustrate the potential contribution of systematically applying an economic approach to the study of China's economic history.

State of the Field

Traditional Chinese scholarship did not neglect economic topics. Indeed, in the standard dynastic histories, sections on population, land taxes, and money, for example, assumed a prominent position. Local histories also treated these topics, as well as listing or describing local products, grain storage, and the like. A well-functioning economy was the hallmark of a successful dynastic regime, a visible sign of the harmony of heaven, earth, and man. Economics and morality were linked; a prosperous economy was a sign of the essential morality of the ruler. The model of the economy, like that of society, was based on the notions of harmony and stability, and not on the desirability of growth and change. The golden age of the past was one in which men plowed the fields and women wove cloth. Wars and famines signified the disruption of stability. The goal was to restore the status quo ante, the golden age, not to surpass it, because it could not be surpassed.

In recent decades, a different paradigm, that of Chinese Marxism, has dominated Chinese scholarship. The three broad areas that receive the most attention from historians in the People's Republic of China are land tenure, foreign imperialism, and the "sprouts of capitalism." In the post-Mao era, the "Asiatic mode of production" was added to this list. Studies of land tenure are closely linked to issues of servitude and subordination among China's peasantry in each period of history. Studies of foreign imperialism stress the plundering of China's economic resources by Western powers and Japan in the nineteenth and twentieth centuries, and the obstacles to the development of a modern economy posed by the unequal treaties. Studies of the "sprouts of capitalism" focus on the signs of development in China's late imperial, or early modern, economy (roughly since the mid-sixteenth century), such as the expansion of handicraft production and the freeing of labor in the countryside, but the line of interpretation has shifted from time to time—sometimes emphasizing the sprouts themselves and, at other times, the smothering of the sprouts. The revival of interest in Marx's idea of the Asiatic mode of production highlighted the dilemma of Chinese Marxist historians: how to fit Chinese history into the scheme of world history. Previously discredited by party historians because it tended to suggest that Chinese development did not fit into a unilinear world pattern, the Asiatic mode attracted renewed attention in the 1980s in part because it helped


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legitimize China's recent economic policies, which may seem to transgress the stages of history normally posited in the Marxist scheme of history.

How one evaluates the Marxist scholarship on China is to a certain extent a function of one's ideological persuasion. Certainly the Marxist framework provides a compelling agenda for research. Critics think, and sometimes dare to say, that the agenda is limited and that the questions posed to some extent determine the outcome. But this criticism could be leveled at any paradigm or framework. What is striking to us, however, is the extent to which a materialist or economic interpretation of history has essentially transformed itself into social history. It is the struggle between social forces and the conflict of social classes that seem to determine the economic stage of history rather than the economic forces that determine the social. Marxist historiography has stood Marxism on its head.

Substituting modernization theory for Confucian or Marxist theory, the postwar generation of Western historians has also sought reasons for China's economic backwardness in modern times. American scholarship in the 1950s and 1960s tended to focus on treaty-port developments and the introduction of Western trade and technology into China, implying that contact with the West, even on unfavorable terms, offered an opportunity for positive change that was missed.[1] A second wave of scholarship has focused on the role of entrepreneurship and bureaucratic leadership (or the lack of it) in the nineteenth and twentieth centuries, finding in them a major reason for China's "failure to modernize" along Western lines, even when exposed to Western influence.[2] In a similar vein, scholarship in Taiwan has emphasized the institutional and bureaucratic aspects of China's economic development in the last two centuries.

In an innovative and influential interpretive history, Mark Elvin tried to break away from the yoke of Western periodization schemes to show that China's history followed a different "pattern," in which a medieval economic revolution led to a "high-level equilibrium trap" that did not prevent further growth, but did impede significant change—"economic development without technological change."[3] Yet like other Western scholars, and indeed like the Chinese scholars, his underlying preoccupation is with explaining China's poor economic performance in modern times.

Like Elvin, recent Western scholarship has tended to search back beyond

[1] E.g., Chi-ming Hou, Foreign Investment and Economic Development in China, 1840–1937 (Cambridge, Mass., 1965).

[2] E.g., Albert Feuerwerker, China's Early Industrialization: Sheng Hsuan-huai (1862–1874) and Mandarin Enterprise (Cambridge, Mass., 1958); Yen-p'ing Hao, The Comprador in Nineteenth-Century China: Bridge between East and West (Cambridge, Mass., 1970); Wellington K. K. Chan, Merchants, Mandarins, and Modern Enterprise in Late Ch'ing China (Cambridge, Mass., 1977); and Sherman Cochran, Big Business in China: Sino-Foreign Rivalry in the Cigarette Industry, 1890–1930 (Cambridge, Mass., 1980).

[3] Mark Elvin, The Pattern of the Chinese Past (Stanford, 1973), Part Three, pp. 203–319.


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the troubled modern period to find strengths and weaknesses in the Chinese economy before the nineteenth century that might help to explain its behavior after the Opium War. These studies have focused on the role of the traditional Chinese state in shaping the economy, particularly in the eighteenth century. Building on Ping-ti Ho's work on China's population,[4] these studies have, on the one hand, emphasized the positive role of the state in encouraging the settlement of undeveloped and frontier areas[5] and in maintaining granary stocks to stabilize prices and prevent famines[6] while, on the other hand, stressing the essential limitations of state power. Yeh-chien Wang's work on Qing land tax, Madeleine Zelin's work on tax surcharges, and Susan Mann's work on the merchants' role in collecting commercial taxes all tend to show how the Qing and Republican governments were unable, and sometimes unwilling, to capture a larger share of the country's wealth for their own purposes.[7]

Some American scholarship, as well as some Japanese scholarship, has shared the Chinese interest in the primacy of social forces in governing economic history. For example, standing on different sides of an ideological divide, Ramon H. Myers and Philip C. C. Huang have disagreed sharply on the extent to which the land tenure system in North China produced social inequalities.[8] The work of William T. Rowe and others on the growth of Chinese cities tends to emphasize the strength of commercial developments that took place largely outside the sphere of direct government influence.[9] And G. William Skinner's influential work on marketing and his macro-regions paradigm both stress the essential independence of economic activity from political trends as embodied in the dynastic cycle.[10]

Although there are notable exceptions not captured in this broad summary, it is striking how American scholarship on Chinese economic history,

[4] Ho, Studies on the Population of China, 1368–1953 (Cambridge, Mass., 1959).

[5] E.g., Peter C. Perdue, Exhausting the Earth: State and Peasant in Hunan, 1500–1850 (Cambridge, Mass., 1987).

[6] Pierre-Etienne Will, Bureaucratie et famine en Chine au 18e siècle (Paris, 1980), and Pierre-Etienne Will and R. Bin Wong, Nourish the People: The State Civilian Granary System in China, 1650–1850 (Ann Arbor, Mich., 1991).

[7] Yeh-chien Wang, Land Taxation in Imperial China, 1750–1911 (Cambridge, Mass., 1973); Madeleine Zelin, The Magistrate's Tael: Rationalizing Fiscal Reform in Eighteenth-Century Ch'ing China (Berkeley and Los Angeles, 1984); and Susan Mann, Local Merchants and the Chinese Bureaucracy, 1750–1850 (Stanford, 1987).

[8] Ramon H. Myers, The Chinese Peasant Economy: Agricultural Development in Hopei and Shantung, 1890–1949 (Cambridge, Mass., 1970) and Philip C. C. Huang, The Peasant Economy and Social Change in North China (Stanford, 1985).

[9] William T. Rowe, Hankow: Commerce and Society in a Chinese City, 1796–1889 (Stanford, 1984).

[10] See especially "Introduction: Urban Development in Imperial China" and "Regional Urbanization in Nineteenth-Century China," both in G. William Skinner, ed., The City in Late Imperial China (Stanford, 1977).


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somewhat like PRC scholarship, has really revolved around social and institutional history. In fact, the bulk of the work concerning the Chinese economy has been done, not by those trained in economics, but rather by social historians, anthropologists, and others. Most of these scholars—including some of the contributors to this volume—have not in the past made regular and systematic use of economic analysis to inform and structure their inquiries. In part this may be due to ideological or disciplinary predisposition, and in part it may reflect the types of sources available for the study of economic history. Traditional official records are strong on bureaucratic institutions and practices but weak in quantitative material. Even so, the tendency for researchers to neglect economic approaches in writing the history of China's economy may reflect their limited appreciation of how the economic perspective can sharpen an analysis of the historical record.

In the 1960s similar criticisms were raised by a group of "new economic historians" against the work of the earlier generation of economic historians in the West. Feeling that the traditional economic histories of Europe and the United States overemphasized the description of legal and other institutions, the new generation advocated the application of economic theory and quantitative methods to historical scholarship. With the advent of Robert Fogel and Stanley Engerman's study of slavery in the American South, and the ensuing controversies, the Cliometric revolution reached its heyday and, some have said, began to peak.[11] Nonetheless, a more quantitative and analytic approach continues to prevail in the leading journals of economic history.

Our goal is not to champion the introduction of Cliometrics into Chinese economic history but rather to advocate adopting a more self-conscious economic perspective that may or may not involve quantitative analysis. Our belief is that the use of economic theory can illuminate issues that might otherwise prove inaccessible. In addition, the contributors to this volume have reached the surprising conclusion that applying economic analysis to historical topics often enlarges the interpretive significance of phenomena that historians, and not economists, are best qualified to comprehend.

Economic Theory

What do we mean by an economic perspective ? We mean the application of economic theory and methods to the study of historical topics.

Classical economic theory, as developed in the West, rests on a number of key concepts, which some call principles and others may call assumptions. The most fundamental of these is the concept of choice . Donald N. McCloskey

[11] Robert Fogel and Stanley Engerman, Time on the Cross: The Economics of American Negro Slavery (Boston, 1974). The development of the new economic history is discussed in Alexander J. Field, ed., The Future of Economic History (Boston, 1987), in the editor's introductory essay.


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defines economics as "the study of human choice under constraints."[12] Income and wealth, the conventional measures of economic well-being, define the extent of choice available to consumers. In most economies, choice is exercised primarily in markets, which offer opportunities to sell commodities and human skills in return for income, which can be translated, again through the marketplace, into consumption goods. Prices signal the rates at which any individual's resources of money, time, and skill can be converted into desired commodities or services. For the economist, prices demand attention because they offer precise measures of both choice and constraint that (important for the historian) are often recorded in great detail. Markets and prices thus emerge from the centrality of choice as natural focal points for historical inquiry.

Rationality is a closely related concept. Rationality means that people are motivated by self-interest, primarily pecuniary. Economic rationality means that individuals, families, and organizations have well-defined ideas about how various opportunities affect their well-being and that choice rests upon comparison of the cost of available alternatives. Economic rationality suggests that people know how to calculate costs and benefits and that they are free to act according to their choices.

The centrality of choice in economics leads to the concept of opportunity cost , which defines the cost of a specific action in terms of the value of alternative options rather than actual monetary outlay. Or, in McCloskey's words, "choosing one thing means giving up another, because things are scarce, constrained."[13] In the economists' view, the cost of education, for example, includes the value of income-earning opportunities forsaken by the student as well as the actual tuition she or he pays. The opportunity cost of moving to a new location must comprehend the value of wages lost while on the road as well as transportation costs. Opportunity cost is quite literally the value of "the road not taken."

Much of economic analysis revolves around the concept of equilibrium , which portrays economic circumstance as the outcome of a balance of conflicting forces. Market price is determined through bidding, a process of organized struggle between buyers, who seek to force the price to the lowest possible level, and sellers, whose interest is served by attaining the highest possible price. Market forces ceaselessly push price and quantity in the direction of equilibrium. If demand exceeds supply at the current price, anxious buyers will bid up the price, simultaneously curbing demand and attracting additional supplies. If price is so high that supply exceeds demand, sellers' prices will be bid down, leading toward the balance between desired purchases and sales that characterizes an equilibrium position.

[12] McCloskey, "The Economics of Choice" (Unpublished paper prepared for the Workshop on Economic Methods for Chinese Historical Research, Honolulu, January 1987), p. 1.

[13] Ibid., 1.


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Together with the idea of entry and exit , which simply maintains that productive resources, including human labor, will abandon occupations offering low rewards and gravitate toward the areas of greatest opportunity, the economists' equilibrium notion offers a valuable tool for historical researchers. Even though the interaction of supply and demand in particular markets may not leave clear tracks in the historical record, the qualitative consequences of changes in equilibrium positions often generate shifts in the direction of resource flows that will not escape the historian's notice. As D. K. Lieu has observed, businessmen in China (and elsewhere) "are ready to clear out at any time" if they see better prospects in another trade. The appearance of new businesses and the abandonment of old trades thus become a sensitive barometer of relative profitability in different lines of endeavor.[14] Similarly, if large numbers of workers migrate from North China to Manchuria, or from the rust belt to California, no statistical analysis is required to verify the existence of regional differences in economic opportunity.

Objections to Economic Theory

When thus presented as a series of abstract concepts, economic theory often provokes the deepest skepticism, if not outright hostility, among noneconomists.

Some have charged that these ideas of neoclassical Western economics are not universal principles or absolute truths but are, instead, a series of assumptions that are largely a matter of perspective or even faith, not susceptible to proof or argument. Moreover, these ideas are culturally and historically specific, a product of a particular phase of Western history, and are not universally applicable. Some, like Karl Polanyi, have argued that these ideas themselves have shaped people's behavior and the development of economic institutions, especially markets, that they have been, in short, not descriptive but prescriptive.[15]

Others object to economic theory because they believe it to rest on a view of human nature that is self-fulfilling, possibly erroneous, and certainly repugnant. "Rational economic man as a reflection of human nature is a fiction. . . . But it is a powerful fiction, and it becomes less and less a fiction as more and more of our institutions get pervaded by its assumptions and other paths are closed," writes one recent critic.[16] Adam Smith's notion that individuals pursuing their own self-interest are "led by an invisible hand" toward improving the society and economy in which they live is difficult to reconcile with more flattering views of human nature and human good.

[14] Lieu, The Growth and Industrialization of Shanghai (Shanghai, 1936), p. 103.

[15] Karl Polanyi, The Great Transformation (Boston, 1957).

[16] Barry Schwartz, The Battle for Human Nature: Science, Morality and Modern Life (New York, 1986), p. 325.


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There are those who believe that the classical economists' view of human nature is not only incorrect but that it can be replaced by a superior form of morality. Amitai Etzioni, for example, argues for the replacement of utilitarianism with ethical principles that stress intention, not result, for the replacement of individual calculation with collective rationality, and for the replacement of economic rationality with values and emotions.[17]

Most others who object to economic theory do so on the grounds that it is empirically invalid. They say that simple observation will reveal that not everyone is motivated by monetary self-interest above all other considerations and that the notion of economic rationality must therefore be false. The economists reply that economic rationality need not imply ceaseless calculation of cost and benefit by households and businesses, nor must economic decisions rest exclusively on financial considerations. Although economists often construct theories on the assumption that individuals and business firms pursue maximum financial rewards, the notion of rationality encompasses the possibility that a desire for prestige or perhaps stability, as well as monetary gain, may motivate economic behavior. The recent debate about the "moral economy of the peasant" highlights this controversy, with James C. Scott arguing that in peasant societies the dominant motive is survival and security, so that risk minimization, not profit maximization, is the principal goal.[18] Economists respond that peasant rationality is essentially no different from anyone else's rationality and that avoidance of risk is not inconsistent with rational calculation.

Critics also protest that rational choice implies perfect information and intelligence. But what if someone does not have all the information needed, or what if he or she is stupid or, worse still, lazy? I could increase my financial resources if I thought about my investments all the time, but I do not choose to use my time that way. The opportunity cost, measured in work or recreational time lost, is simply too high. But economists reply that decisions based on limited information and crude calculations may in fact reflect rational behavior. After all, the time and expense required to collect further information or to conduct detailed studies of opportunity costs may outweigh the anticipated benefits of prolonged search and analysis.

Finally, skeptics reject the idea that people actually have a choice in economic matters and are free to enter into or exit from economic activities as some kind of economists' wonderland, full of Mad Hatters. Surely, in real life people are not always free to change jobs, change residences, or change investments according to the dictates of rational calculation.[19] Custom, law,

[17] Amitai Etzioni, The Moral Dimension: Toward a New Economics (New York, 1988).

[18] James C. Scott, The Moral Economy of the Peasant: Rebellion and Subsistence in Southeast Asia (New Haven, 1976).

[19] The Nobel Prize—winning economist George J. Stigler tells the story of an economist who carefully decided how far from the city to locate his country home by efficiently balancing the number of fresh eggs he could get against the number of friends who would still be willing to visit him. In his review of Stigler's memoirs, Robert Krulwich dryly comments, "Here, I say, is why more and more people ignore economists." New York Times Book Review , Oct. 23, 1988.


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social practice and prejudice, inertia, and any number of restrictions on behavior exist today and were even more decisive in premodern times.

Economists, however, recognize that market activity and price formation do not occur in a social or cultural vacuum. They see the institutional arrangements that circumscribe and encapsulate economic activity—the household, legal structures, customary market procedures, forms of contract arrangement, business organization, even ideology and morality—as constraining economic activity along with limitations on the stock of physical and financial resources.[20] But economists regard institutions as flexible rather than immutable. If costs exceed benefits, economists anticipate change (perhaps gradual) in the relations between individuals and social institutions, as well as between buyers and sellers. The post—World War II increase in female employment in the United States represents such an event, with the unorganized response of millions of women to altered labor market conditions leading to changes in marriage practices, family size, child rearing, educational patterns, eating habits, and many other aspects of life long regarded as determined by custom and tradition rather than the marketplace.

The clash between economists and noneconomists is perhaps best embodied in the economists' favorite term, ceteris paribus (literally, all other things being equal). While economists will acknowledge the importance of noneconomic factors, those bothersome factors are generally left in the background of their theories and models. Let others study politics, law, social class, injustice, and the like. Models can be pure and "elegant," a favorite expression of economists, because all those other factors can be held constant or set aside. And since such factors are not easily quantifiable, how much more convenient to leave them out. Quantification of the nonquantitative is best left to the "soft" social scientists—the sociologists, the political scientists, and the historians.[21]

It is ceteris paribus that allows economists to be optimists. Although economics is called the dismal science, in fact economists tend to maintain a rosy view of the world controlled by an invisible hand. If only the government and others would stay out of it, the rational response to opportunity could produce growth and a better life for everyone. In the field of Chinese studies, the

[20] Jon Cohen, "Institutions and Economic Analysis" (Unpublished paper prepared for the Workshop on Economic Methods for Chinese Historical Research, Honolulu, January 1987).

[21] In all fairness, it must be said that economists tend to recognize their professional weaknesses and know how to laugh at them. Evidence for this can be found in the rich store of economist jokes that end with the punch line, "Assume . . . "


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optimism of the economists stands in marked contrast to the gloomy prognostications of the political scientists. The Chinese economic reforms of the 1980s inspired great hope among most economists, who tended to see the possibility for continued growth and change, while political scientists warned of bureaucratic competition, political backlash, social discontent, and other dangers, which they said might thwart the reforms.

Theoretical Reasoning

The tendency of many economists to sweep noneconomic factors into the dustbin of ceteris paribus is indeed regrettable. Recently, however, a few economic theorists themselves have begun to question the basic assumptions of the approaches that have dominated their field. The study of macroeconomics has been described as "a religious battlefield," where the most fundamental beliefs are being challenged.[22] George A. Akerlof, who has contributed to this battle, has said

The unwritten rules that only economic phenomena be considered in economic models, with agents as individualistic, selfish maximizers, restrict the range of economic theory and in some cases even cause the economics profession to appear peculiarly absurd—because, without relaxation of these rules, certain almost indisputable economic facts, such as the existence of involuntary unemployment, become inconsistent with economic theory . . . . Individualistic maximizing behavior constitutes an assumption that sharply restricts the domain of possible economic models. It is an assumption that turns out to be surprisingly restrictive.[23]

While recognizing the importance of noneconomic factors in governing economic behavior, a theorist such as Akerlof is nevertheless concerned primarily with perfecting an economic model, albeit one that he considers reasonably consistent with reality. For some economic theorists, it might be said, the model is the reality. Many economists tend to value work that contributes to the building of economic theory and to dismiss the study of real data as mere "empirical work." Economic historians, however, have argued for the importance of economic history to the development of theory.[24] It is our contention that just as economists need to test their theories against historical reality, historians can and should enrich their work through the use of economic theory, as well as economic methods.

Economic theory can serve several purposes for historians. At a practical

[22] An insight attributed to Mark Kuperberg of the Economics Department, Swarthmore College, whom we also thank for the reference to Akerlof's work (see n. 23).

[23] George A. Akerlof, An Economic Theorist's Book of Tales: Essays That Entertain the Consequences of New Assumptions in Economic Theory (Cambridge, 1984), p. 2.

[24] The contributions that historical studies can make to economic theory are outlined in essays in William M. Parker, ed., Economic History and the Modern Economist (Oxford and New York, 1986).


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level, some knowledge of economic theory can provide essential context for interpreting evidence that would otherwise be misunderstood. Upon learning of the small share of imported grain (and more generally, of foreign trade) in the economic life of late Qing China, the historian (and even the economist)[25] naturally assumes that foreign trade must have played a small role in China's economy, especially in the interior. But this assumption overlooks the economists' "marginal principle," which teaches that market prices are determined by the behavior of "marginal" buyers and sellers, who are on the brink of indifference between patronizing the local market or doing business elsewhere. If the demand for and supply of a particular commodity is "inelastic," meaning the amount people will purchase or sell is relatively inflexible in the face of changes in market price (as in the case of heating oil, milk, or insulin), then small changes in quantity may lead to relatively large changes in the price. Alternatively, if the demand for a commodity is elastic, small changes in price may lead to relatively large changes in the quantities people desire to buy or sell. Thus shifts in the behavior of marginal buyers or sellers can generate large changes in the prices or quantities available to all buyers and sellers.

Loren Brandt's study of Yangzi rice markets nicely illustrates these ideas. Despite the small volume of overseas rice trade, Brandt finds that by the end of the nineteenth century, rice prices in interior markets, like Chongqing and Changsha, were quickly affected by fluctuations in Asian grain markets.[26] This means that the daily lives of rice farmers, rice consumers, would-be rice farmers, grain merchants and shippers, the families and suppliers of these agents, their customers and suppliers, and others in interior regions, like Sichuan and Human, were significantly affected by what seem at first glance to be minor economic phenomena. Brandt's study shows how actions in apparently insignificant components of an economy can produce significant reactions, even in distant places, through the medium of market forces. Many people can verify this "principle" from their personal memories of the oil crisis of the early 1970s, when rising energy costs affected travel habits, auto designs, building codes, and so forth in the United States, Japan, and even oil exporters, like Canada.

The economists' campaign to win the minds, if not the hearts, of historians can probably not succeed merely by reciting economic principles as abstractions or immutable laws. More persuasive, perhaps, is the reasoning that is derived from economic theory. Economic theory can serve as a lever for increasing the power of a given set of data and a tool for squeezing as much meaning and implication from it as possible. For economists, economic

[25] Thomas G. Rawski, "China's Republican Economy: An Introduction" (Toronto, 1978), pp. 2-5.

[26] Loren Brandt, "Chinese Agriculture and the International Economy, 1870s-1930s: A Reassessment," Explorations in Economic History 22 (1985): 168-93.


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theory will suggest a story, or sequence of implications, about sets of initial economic circumstances or facts. The predictions obtained from theoretical reasoning can range from simple propositions about the impact on relative prices of meat and fish of the Pope's decision to end the Catholic tradition of meatless Fridays to Karl Marx's grand vision of capitalist decline. The stories told by economic historians fall between these two extremes, typically using short chains of reasoning based on economic concepts to obtain predictions that can be tested with historical evidence.[27] Their method involves selecting a model, or analytic framework, based on assumptions that appear to fit the historical circumstances under investigation, studying the logical implications of the model in search of testable conclusions, and comparing these predictions, as well as the model's assumptions, with concrete evidence from historical sources.

Several examples can illustrate the value of theory-based analysis as a source of hypotheses for the historian to investigate. Consider the case of railway development, which, by reducing transport costs and transit time, creates new opportunities for trade among cities and between town and countryside. Construction of a new railway line should raise the price that farmers receive for fruit crops, which now gain unprecedented access to urban markets, and lower the cost to farmers of urban factory goods. Terms of trade (price of interregional "exports" divided by price of imports) should improve for both townspeople and farmers. But China's new railways became the focus of military strife among competing political groups, bringing death and destruction to hapless farmers caught between rival armies.

Lacking detailed information concerning changes in local production or the damage inflicted by military operations, how can the historian begin to determine the economic consequences of railway construction in rural China? Here is where recourse to economic theory, with its capacity to reveal causal links that may provide unexpected opportunities to examine the consequences of historical events, begins to display its potential. The concept of entry and exit immediately directs the researcher's attention to changes in population density and migration patterns as indicators of altered patterns of economic opportunity in regions affected by railway development. The economic theory of rent implies that trends in land rents and land prices can reveal whether, from the viewpoint of local farmers, the opportunities created by railway development outweighed the damage caused by periodic military incursions and, if so, by how much.[28] Another perspective

[27] Donald N. McCloskey, Econometric History (Houndsmills, Eng., 1987), chap. 2.

[28] The idea of using trends in land values to appraise the impact of transport innovation comes from Roger Ransom, "Social Returns from Public Transport Investment: A Case Study of the Ohio Canal," Journal of Political Economy 78 (1970): 1041-60. For Chinese evidence, see Ernest P. Liang, China: Railways and Agricultural Development, 1875-1935 (Chicago, 1982), pp. 141-44.


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on the consequences of railway expansion comes from Thomas R. Gottschang's finding that the coming of the railway apparently slowed the pace of out-migration from North China, despite reducing the cost of travel to and from Manchuria. Apparently the increased opportunity arising from proximity to rail transport outweighed the reduced cost of migration in the eyes of farm families in Hebei and Shandong.[29]

Further examples of how historians can benefit from thinking in terms of economic theory arise from applying the concept of market integration , also known as the law of one price, which postulates that the universal desire to buy cheap and sell dear attracts buyers to low-price markets and sellers to high-price outlets, thus squeezing interregional price differences toward the minimum necessitated by the costs of shipping goods between separate markets. Market integration is made possible by good and cheap transportation, adequate information about costs, and efficient commercial institutions. Consumers, as well as economists, like market integration because it gives them access to a wide range of products at low prices. Producers value market integration because it expands the actual and potential market for their goods. Historians should also be keenly interested in market integration not simply for what markets show about links among various segments of the economy but also because, as the work of Skinner copiously demonstrates, analysis of marketing relationships may affect a host of political and social factors ranging from taxation to marriage and even language.[30]

Here again, a dose of theory can help the historian to leap over documentary lacunae, as well as overcome skepticism about the heuristic value of economic principles or assumptions. Did agricultural wages, productivity, and incomes rise in China during the decades prior to World War II? To answer this question, one would hope to find reliable information on trends in agricultural production and farmers' incomes. Unfortunately, the information available to the researcher is both thin and of questionable validity. Wage data for nonfarm occupations, however, are relatively abundant. Can theory offer a useful link between agricultural circumstances and non-farm wages?

Unskilled workers in such nonfarm industries as cotton mills and coal mines often came directly from rural villages. China's cotton and coal magnates were profit-seeking entrepreneurs operating in fiercely competitive markets that offered little chance to "pass along" rising costs in the form of higher prices. They had every incentive to keep wages as low as possible. Unless forced to raise wages by government fiat or union pressure, employers sought to avoid raising wages except when it was necessary to assure an

[29] Thomas R. Gottschang, "Economic Change, Disasters, and Migration: The Historical Case of Manchuria," Economic Development and Cultural Change 35, no. 3 (1987): 461-90.

[30] Skinner, "Marketing and Social Structure in Rural China," in three parts, Journal of Asian Studies 24, no. 1 (1964): 3-43; 24, no. 2 (1965): 195-228; and 24, no. 3 (1965): 363-99.


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adequate work force. As long as rural labor incomes remain stable, mines and mills can attract workers without raising wages. If rural incomes begin to increase, mines and mills will find their labor supply drying up unless they offer higher wages to village recruits. Under these circumstances, a pattern of rising real wages for unskilled workers in China's cotton and coal industries can be taken as evidence of rising real incomes in the rural regions that supplied miners and mill hands and also in more remote areas linked through labor markets to the immediate supplying regions. Because inter-regional wage differentials induced large numbers of Chinese workers to cross provincial and even international boundaries in pursuit of economic opportunity, evidence of rising real wages for unskilled workers in the widely dispersed cotton and coal industries furnishes strong support for the view that the rising trend of labor income was national in scope.[31]

Underlying this reasoning is the economists' conception, or model, of how markets, in this case labor markets, function. Textile mills or coal mines located in city A customarily obtain unskilled workers (perhaps indirectly through the agency of labor recruiters) from rural areas B and C. The mills or mines pay wages that are higher than typical farm incomes. This premium compensates workers for the cost of journeying to an unfamiliar locale, separation from their families, and the risk of industrial accidents. If farm incomes in B or C begin to rise, mill or mine wages will look less attractive to potential recruits, who will become less willing to leave their villages. The mill or mine owners (or labor recruiters) can look elsewhere for job candidates or raise wages to encourage more volunteers from the customary locations. If young villagers elsewhere are willing to move in response to economic opportunity, nonfarm employers may prefer the cheap option of seeking recruits from alternate rural locations D and E by offering the standard wage. If the rise in farm incomes is a local phenomenon confined to B and C, this approach will prove successful in damping upward pressure on nonfarm wages for unskilled labor. If, on the other hand, farm incomes are increasing across a wide range of localities from which mills and mines might seek to recruit new workers, nonfarm employers will find themselves unable to maintain an adequate work force without raising the wages offered to unskilled recruits. If farm incomes—which provide the financial alternative against which potential miners and textile workers measure the benefit of leaving their home villages—continue to increase, wages paid by mines and mills will rise too.

Thus, once it is assumed that labor markets function in the manner specified, with employers seeking cheap labor supplies and villagers willing to migrate in response to premium wages, the theory of market integration, here

[31] Thomas G. Rawski, Economic Growth in Prewar China (Berkeley and Los Angeles, 1989), chap. 6.


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applied to the market for unskilled labor, encourages the historian to perceive the trend of unskilled workers' earnings in coal mines and cotton mills as a barometer of farm incomes, not only in the workers' home villages but also in other villages where mines and mills could easily have sought fresh recruits. The link between farm and nonfarm wages is not automatic. Application of this reasoning requires the historian to determine that the wage data pertain to occupations open to village recruits and to verify the historical relevance of the behavior patterns postulated in the framework, or model, outlined above. If these tasks can be accomplished, economic theory permits the historian to construct a powerful and revealing analysis of phenomena that are simply not amenable to study through conventional methods.

The theory of market integration can also help to estimate interest rates in historical situations. Interest rates are of historical significance because they are part of broader economic cycles, because they tell us something about trends in the economy, and because they influence individual choices between current and future consumption. Yet interest rates are difficult for historians to discern. Consequently Donald N. McCloskey and John Nash's suggestion that interest rates are inherent in the seasonal fluctuation of grain prices is useful for Chinese historians, since the Chinese historical record contains a great deal of detailed information about grain prices. Whoever holds grain harvested in autumn for resale or consumption in the spring sacrifices the use of the money that could be obtained by immediate sale of the autumn harvest. Whoever loans money during the winter months makes an identical sacrifice. In other words, the opportunity cost of holding grain is the cash that could be obtained from autumn sales, plus whatever interest could be earned by that cash over the winter. The law of one price, here applied to the market for money, insists that, over a suitably long number of years, the earnings from assigning funds to holding grain must match the returns from assigning funds to holding debtors' promissory notes. Thus, McCloskey and Nash explain, interest rates, and the variation of interest rates across time and space, can be calculated from the seasonal rise in grain prices that begins with the annual post-harvest trough and ends at the seasonal preharvest peak.[32]

To recognize the importance of market integration is one thing; to define and measure it is another. As some of the essays in this volume show, even with good price data, it may be difficult to discern whether and when true market integration existed in history. Even in today's world of data collection and widespread information networks, economists still have difficulty establishing what actually constitutes market integration.[33] In antitrust cases, the

[32] Donald N. McCloskey and John Nash, "Corn at Interest: The Extent and Cost of Grain Storage in Medieval England," American Economic Review 74, no. 1 (1984): 174–87.

[33] For one suggestion, see George J. Stigler and Robert A. Sherwin, "The Extent of the Market," Journal of Law and Economics 28 (1985): 555–85.


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appropriate definition of a market includes both the "product market" (i.e., whether the product has reasonable substitutes) and the geographic market. When Mobil Corporation tried to acquire the Marathon Oil Company in 1981, Marathon brought an antitrust suit against Mobil. Mobil attempted to demonstrate that the relevant market for oil was nationwide and that hence the merger would have only a slight impact on prices. For Marathon, on the other hand, the task was to demonstrate that the markets for oil were regional and that hence the merger was likely to have a great impact on prices. Marathon won the case because, in the words of the court, "the persistence of price differentials in various areas of the nation demonstrates that motor gasoline does not move from area to area in response to price changes easily or as readily as Mobil asserts. Rather, they indicate that the relevant geographic market for motor gasoline is something less than nationwide."[34] Here the debate among lawyers and economists centered, not on the theoretical importance of market integration, but on exactly how to define and measure it.

Economic Methods and the Data Problem

The second aspect of an economic perspective or approach involves method . Methodology in economics can mean different things. Broadly defined, it can mean a way of thinking or a general approach to hypothesis testing or problem solving. More narrowly conceived, it can refer to particular statistical techniques: the Gini coefficient, the Chow test, and so forth. Although economics often involves the use of numbers and quantification of some sort, its approach is not absolutely dependent on quantification. At least two of the articles in this volume (by Susan Mann and Emily Honig) involve little quantitative data, and yet they fully reflect an economist's way of thinking.

Historians of China may be discouraged from pursuing economic topics because of the apparent lack of data. And yet there are, as we shall describe later, many more data than meet the eye. Moreover, generations of historians have contributed fruitfully to the analysis of economic trends in Europe and North America without the benefit of careful compilation or systematic analysis of quantitative data. A generation of new economic historians, focusing its attention on the economies of North America and Great Britain, has demonstrated that better, fuller results and sounder interpretations are often available when research using conventional documentary sources is combined with diligent mining of quantitative materials, which are always deficient in a variety of dimensions. Before succumbing to the defeatist view

[34] F. M. Scherer, "Merger in the Petroleum Industry: The Mobil-Marathon Case (1981)," in John E. Kwoka, Jr., and Lawrence J. White, eds., The Antitrust Revolution (Glenview, Ill., 1989), p. 35.


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that certain data are uniquely defective, Chinese historians should consider the implication of Nicholas Crafts's new study claiming that the average annual growth of British per capita income between 1801 and 1831 should be reduced from the long-accepted Deane-Cole result of 1.6 percent to a much lower figure of 0.5 percent, implying that per capita incomes rose by 16 percent rather than 61 percent during 1801–31.[35] If British historians cannot yet determine whether industry and commerce grew slower (Deane and Cole) or faster (Crafts), or whether agriculture grew much faster (Deane and Cole) or slower (Crafts) during 1760–80 than during 1700–60, perhaps their data, which have supported hundreds of studies in what McCloskey calls "econometric history" are no better than the Chinese historians'.

Historians are particularly concerned with detecting trends and cycles. Contrary to the political scientists' old adage "In China if something happens twice, it's a trend," the identification of trends in economic history is a bit more complicated. Was the economy growing or stagnating? Were incomes rising or falling? Was land distribution becoming more equal or less equal? Was the standard of living rising or falling? Not only is this the stuff of which the truly important historical debates are made, but it should be apparent that this is also the material of present-day debates among political candidates. These are questions of measurement that are at the heart of economic methodology.

How economists can use incomplete and imperfect data in studying historical problems can perhaps be illustrated with an analysis of the fate of the traditional Chinese junk trade in the Republican period. As railways and steamships were introduced to the Chinese economy in the late nineteenth and early twentieth centuries, it has often been assumed that they displaced the traditional sailing vessels, or junks. But Thomas G. Rawski's hypothesis is that the junk trade not only survived the introduction of modern transport but actually increased its volume at the same time that modern transport grew.[36] If he can prove his case, it would be extremely significant for evaluating China's prewar economy because it would show that the rapidly growing freight carriage by railways and steamships represented trade creation —an important sign of commercialization and economic expansion—and not trade diversion —a mere substitution of new technology for old with no change in cargo volume.

Economic theory links changes in production (in this case, of transport services) to the level of capital formation (construction of new junks). Wooden sailing vessels have long service lives. If we assume that participants

[35] N. F. R. Crafts, British Economic Growth during the Industrial Revolution (Oxford, 1985), as reviewed in the Journal of Economic Literature 24, no. 2 (1986): 683–84; and Phyllis Deane and W. A. Cole, British Economic Growth, 1688–1959 (Cambridge, 1967).

[36] Based on Thomas G. Rawski, Economic Growth in Prewar China , chap. 4.


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in the shipping and boat-building trades display the income-seeking behavior that economists expect in any market economy (shippers do not abandon useful vessels in the absence of significant technological change; shipyards do not continue to operate if sales volume and price plummet), then we must expect any decline in the volume of junk traffic to quickly erode the demand for new ships. This is exactly what occurred on the Liao River in Manchuria, where diversion of riverine traffic to the railways prompted observers to note that "there are no new ships built for the river and [the] majority of the ships now being used are those constructed more than ten years ago."[37] Information about shipbuilding in the Yangzi Dalta (including Shanghai), however, shows that the industry continued to thrive despite unrestricted competition from new carriers. A 1941 survey at Suzhou found that 14 of 36 ships were less than ten years old.[38] Another study lists over 20 places near Shanghai and along both banks of the Yangzi where shipyards continued to operate even after 1940.[39]

If evidence from shipbuilding data indicates that junk traffic did not decline prior to 1937, how can we investigate the stronger proposition that junk shipping actually increased despite growing competition from steamships, motor launches, railways, and trucks? Fortunately, we have some data showing that the junk trade expanded in several important ports and fared well in competition with rail, steamship, and cart traffic in delivering cotton to the major textile center of Tianjin. This information may be supplemented by a series of calculations that estimate the volume of wheat arriving in Shanghai by junk. Wheat was one of the most important commodities shipped into Shanghai. If junk-borne shipments of wheat increased along with the expansion of railway and steamship carriage, the overall argument about the survival and growth of the junk trade is greatly strengthened.

Our estimate rests on an equation. The volume of wheat arriving by junk was roughly equal to (1) the wheat required by Shanghai flour mills, minus (2) the net import of wheat into Shanghai from abroad, minus (3) the net inflow of domestic wheat carried by steamship, minus (4) the inflow of domestic wheat into Shanghai by rail. Gathering together various pieces of admittedly imperfect data, we reach the conclusion that junk-borne shipments of wheat into Shanghai may have risen from 139,000 tons in 1914 to an average of 244,000 tons in the 1930s. But how can we defend our estimate in the face of the known imperfections in the data we have used? The key is to look very carefully at the assumptions employed in constructing the data from which these results are derived. For example, there are several different figures for overseas wheat imports (2) during 1931–33, the end point of our

[37] The Manchuria Year Book 1932–33 (Tokyo, 1932), p. 284.

[38] Chushi no minsengyo: Soshu minsen jittai chosa hokoku (Tokyo, 1943), 1:26–27.

[39] Shina no koun (Tokyo, 1944), pp. 83–84.


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time series. Our calculations employ the largest of these figures, a tactic that lowers the estimated junk inflow for 1931–33 and thus tends to undercut the working hypothesis. Second, lacking data on railway shipments (4) in 1914, the starting point of our time series, we assume the lowest possible figure—none at all. This raises the estimated inflow of junk-borne wheat in 1914, again in opposition to the proposed conclusion. Despite these two challenges, the calculations are still able to show that junk-borne shipments of wheat into Shanghai were substantially larger in 1931–33 than in 1914.

Historians unfamiliar with quantitative research may complain that this type of scholarship is no more than a tissue of assumptions, with results predetermined before pencil meets paper. Nothing could be further from the truth. Assumptions are all laid out for the readers' scrutiny and critical evaluation, and precisely to show that they do not control the conclusions. In a world of imperfect sources, the researcher must convince critical readers that empirical results are strong enough to overcome possible defects in the underlying data. The tactic of demonstrating an assertion to be valid even under assumptions that stack the deck against the proposed conclusion is commonly used in economics for precisely this purpose. Findings that can survive the impact even of contrary assumptions are called robust . Robustness is a characteristic eagerly sought by applied economists and carefully weighted by readers who find themselves suspicious of published results. If evidence favoring the proposed conclusion is so striking that it emerges even from data that are skewed in ways that suppress the very trend the researcher seeks to establish, even a skeptical audience should acquiesce.

In this way—as illustrated by the examples of using wage data to study farm incomes, deriving interest rates from grain prices, and seeking information about junk traffic by investigating the fortunes of shipbuilders—historians can use economic reasoning to assist them in separating historical fact from fiction. Although it was generally said that steam and rail transport displaced junk transport, economic theory led to the suspicion that this might not actually have been the case, and application of economic methods showed that it almost certainly was not the case.

Identifying trends can help economists and historians to distinguish how people behave from what people say. If professors complain of low incomes, we conclude that they desire higher salaries. It is only when significant numbers of teachers leave academe that we can identify professional wages as being too low in the equilibrium sense. Unlike intellectual historians, who interest themselves in conscious ideas, or cultural or social historians, who investigate attitudes and perceptions (mentalités ) that are unconscious, economists are skeptical about words and self-perception. Because acquiring information is costly in terms of both time and money, economists believe that people tend to be well informed only about matters of direct importance to their livelihood and may not see the larger picture. Further-


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more, what people say and write about their economic circumstances is often intended to change those circumstances and may not be a reliable guide to the circumstances themselves.

But in focusing on broad trends, economists may overlook cyclical events or regional variations that greatly affect the lives of those who experience them and therefore offer important material for historical studies. Thus the conclusion that junk traffic increased in China during the decades prior to World War II submerges the reality of a regional decline in junk activity along the Liao River. In a monumental and influential work on Chinese agriculture from 1368 to 1968, the economist Dwight H. Perkins estimates the expansion of agricultural output in this period on the basis of population size.[40] The key to his estimate was the assumption that everyone must have eaten a minimum diet (or they would not have been alive).

The story of a rapidly expanding population sustained by six centuries of increased agricultural productivity certainly paints a rosy picture of the Chinese economy and implies that everyone had at least a subsistence diet, contrary to a gloomy Malthusian picture that might otherwise be imagined. But in fact Perkins's calculations say only that on the whole people must have had enough to eat. His equation does not take into account those who may have died from undernutrition, nor does it consider patterns of regional development and decline or the possibility of extreme inequality of income and welfare. Economists seeking to define long-term trends in average income or consumption may not notice that a minority may have eaten, and lived, exceedingly well, while a larger group may have suffered from inadequate diets (since on the whole, or on average, people had enough to eat). As a result, the measurement of macroeconomic trends, while offering valuable information to historians, leaves much important work to be done in terms of investigating the distribution of gains and losses among regions, groups, and individuals.[41]

Economists and Historians Need Each Other

Our message, then, is not that the economic approach to historical research overshadows other types of inquiry or that economics is a panacea for scholarly problems. Indeed, the economic approach tends to have its own limitations, such as taking the whole to be the same as the sum of the parts or underrating the importance of noneconomic causation in history. Economists need to take historical realities into account. But historians need to adopt an

[40] Dwight H. Perkins, Agricultural Development in China, 1368–1968 (Chicago, 1969).

[41] This perception is shared by economists who report that "rapid growth in underdeveloped countries has been of little or no benefit to perhaps a third of the population." See Hollis Chenery, "Introduction," in Chenery et al., Redistribution with Growth (London, 1974), p. xiii.


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economic perspective, particularly when writing about the economy. We contend that history written without the insights that emerge from systematically applying economic theory and method will be incomplete and impoverished. If historians are willing to suspend a certain disbelief about the economists' principles and assumptions, systematic application of theory and methods may produce insights and results that will weaken the initial disbelief. The knowledge required for historians to make use of the economic approach is neither remote nor inaccessible. The successful completion of this volume demonstrates that a brief period of intensive preparation will enable historical researchers without extensive economic training to fruitfully apply the insights of economic analysis with results that will appeal to economists as well as historians. This volume stands as the proof of this assertion, and we now turn to a survey of its contents.

Contents of this Volume

The essays in this volume fall into two groups. The first group relies primarily on grain price data from the Qing dynasty to establish long-term trends in the Chinese economy, analyze the nature of market integration, and delineate the role of the Qing state. They could be described as studies of price behavior. The second group of papers focuses on the study of land, labor, and capital in more localized situations in the twentieth century. Narrower in focus and more recent in time, these papers center on the issue of market response. In different ways, these papers illustrate some of the general ideas about economic perspective and approach discussed above and point to further opportunities for work in Chinese economic history.

The essays on Qing price history make use of grain price data from the Qing period compiled from the holdings of the First Historical Archives in Beijing and the National Palace Museum in Taibei. These data form what is perhaps the richest, longest, and most detailed price series for the history of any national economy. Starting formally from the beginning of the Qianlong period in 1736, each governor was required to submit a monthly report of grain prices in his province. This included the high and low price of each major grain grown in each prefecture. This monthly provincial report was compiled from ten-day reports submitted by each prefecture (fu ), which in turn had collected ten-day reports from each county (xian ). Thus the high and low prices noted in the prefectural reports represent the highest and lowest prices reported by any county within a given prefecture during that month. The number of grains for which prices were collected varied with each province. In the South, as many as five or more different grades of rice were included. In North China, five to seven grains were reported, including wheat, millet, and sorghum.

Although the analysis of these grain price data has just begun, even the


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preliminary results have great importance for Chinese historical studies. The records of grain price behavior permit us to establish basic long-term trends in China's historical economy, as well as to identify some shorter-term cycles. Grain was the single most important commodity in the agrarian economy of imperial China and the best indicator of economic trends. Yeh-chien Wang's article, "Secular Trends of Rice Prices in the Yangzi Delta, 1638–1935," delineates two long-term cycles in that key region of China: a steep downtrend from the 1640s to the early 1680s followed by over a century of generally rising prices; and a second cycle of price declines followed by steep inflation from the 1880s until the world depression of the 1930s.

Lillian M. Li's paper, "Grain Prices in Zhili Province, 1736–1911: A Preliminary Study," shows broadly similar patterns for wheat, millet, and sorghum prices in North China, with a peak in the 1820s and another steady climb starting in the 1890s. Li's data suggest the presence of distinct short-term cycles of perhaps four- or five-year intervals for coarse grains in the eighteenth century, and possibly longer cycles for wheat prices. She also finds considerable price fluctuation in the late nineteenth century, before the steady upward climb of the early twentieth century.

Much more work remains to be done to determine whether Wang was correct in his earlier, pioneering work when he concluded that North and South China grain prices in the Qing moved essentially in a synchronic manner, thus contradicting Skinner's hypothesis of asynchronic regional cycles and also implying considerable interregional market integration as early as the eighteenth century. Also on the agenda is work that will connect the analysis of Qing grain prices with the work of Brandt, who dates China's integration with the international rice market from the late nineteenth century.[42]

Grain prices can provide insight into the functioning of markets. In particular, price data can illuminate the extent of market integration within, as well as among, regions. Peter C. Perdue's essay, "The Qing State and the Gansu Grain Market, 1739–1864," reveals that even the remote Northwest of China had achieved a considerable degree of market integration, primarily through strong state intervention. Because of the strategic importance of the Northwest, the Qing court maintained a heavy military presence there and, to support it, a granary system that kept relatively high per capita levels of grain reserves. Perdue believes that, in conjunction with private storage and commerce, public grain storage worked to support the integration of key markets of Gansu with each other and with neighboring Ningxia Province.

[42] Yeh-chien Wang, "Spatial and Temporal Patterns of Grain Prices in China, 1740–1910" (Paper presented at the Conference on Spatial and Temporal Trends and Cycles in Chinese Economic History, Bellagio, Italy, 1984); G. William Skinner, "Presidential Address: The Structure of Chinese History," Journal of Asian Studies 44, no. 2 (1985): 271–92; Brandt, "Chinese Agriculture and the International Economy."


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Although Perdue's conclusions must be described as tentative because his data series are far from complete, his Gansu data present a strong case for market integration even in relatively remote areas of China.

In "Grain Markets and Food Supplies in Eighteenth-Century Hunan," R. Bin Wong and Peter C. Perdue further pursue the issue of market integration, this time within Human Province, a major rice-exporting region in central China. Since the general outline and functioning of the Hunan grain markets is relatively well documented, Wong and Perdue utilize the grain price data to test whether high levels of integration actually existed among those prefectures known to be heavily engaged in the rice export trade. They find that separate analyses of high prices and low prices each tend to confirm integration among the exporting prefectures and lack of integration between them and the nonexporting prefectures. They also see in the separate reporting of high and low prices an opportunity to test for integration within each prefecture, since the high and low prices reported for any month from each prefecture presumably represent price quotations from two different counties within the prefecture. They find that, with one exception, exporting prefectures had high levels of internal integration. Intraprefectural integration was also high in relatively isolated prefectures. In short, Wong and Perdue's findings are reassuring because the "price data generally confirm the outlines of the export trade based on qualitative information."

Because there is a considerable amount of qualitative information about Hunan's rice trade, one may conclude with some degree of confidence that high correlations of prices or price differences (the difference between the price in the current period and the price in the previous period) do represent market integration. Without confirmation of trading patterns, the occurrence of high price correlations might arise from common climatic patterns or changes in the stock of money rather than from market integration. In the case of Gansu, for example, it might be argued that the strong military presence in the province produced a type of integration of prices based, not on true markets, but rather on a large measure of government intervention. Perhaps this could be seen as a kind of false or pseudo market integration, which is not to deny its historical significance. The same hypothesis could be advanced with respect to grain markets in Zhili, where the presence of the Imperial court, bannermen, and the military was so pervasive.

We can also use grain prices to examine the short-term fluctuations in periods of crises. Li, in her article on Zhili, uses grain prices to test the impact of crises in several different ways. Like Perdue, she uses regression analysis to try to measure the relative impact on prices of the passage of time, seasonality, and natural catastrophes. Overall, she finds that flood or drought did affect prices, as one would expect. But the differences between the price levels in normal years and those observed under crisis conditions were rather slight in comparison to the differences recorded during crises in


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seventeenth- and eighteenth-century Europe. Most likely, the operation of the government granary system helped to stabilize prices and avert catastrophes, as well as to provide relief during the crises themselves.

The topic of government grain storage raises a number of theoretical as well as historical problems. Recent scholarship on the Qing granary system has excited considerable interest. In fact, some of our contributors have completed a book describing the state granaries and offering important data on their management and holdings.[43] For Chinese historians, the state's major role in grain storage comes as no surprise, although the extent and efficiency of the Qing granary system is quite remarkable. For non-China economists and many historians, however, the notion of public storage requires considerable explanation. Economists, working from the principle of opportunity cost, will immediately question whether government effort had any significant effect on market circumstances. Private citizens store grain because they hope to profit from the regular differential between low autumn and high springtime grain prices and also from high prices that occur in the wake of disasters, such as flood, drought, and war. Government storage efforts intended to limit seasonal price fluctuations and to curtail irregular price peaks will reduce the profitability of private grain storage and lower the risk to private citizens of not holding grain stocks. Thus, economists will reason, public storage encourages a reduction of private storage, creating the possibility that energetic official intervention may have no significant effect on the total quantity of grain stored, seasonal price fluctuations, or the price consequences of periodic natural or manmade disasters.

Of course the Chinese historians have a response to the economists' skepticism. The economists' assumption that private and public grain storage are substitutes for each other is based on the premise that the private sector has the capacity to store grain as conveniently as the government. In fact, the forthcoming granary volume will show that in per capita terms, the granary stocks were highest in China's most remote and least commercialized provinces. The government, in short, appears to have intervened precisely where the private sector was least able to ensure market stability. Put another way, the government was subsidizing the storage of grain. In highly commercialized regions, such as the Lower Yangzi area, with its dense network of markets and transport arteries and extensive private commerce, the government could and did leave the job to private efforts and to the market. Larger public storage programs might merely have replaced private efforts rather than compressing the amplitude of fluctuations. The topic of grain storage thus illustrates the economic sophistication of Qing officials. It also provides a

[43] Pierre-Etienne Will and R. Bin Wong, Nourish the People: The State Civilian Granary System in China, 1650–1850 (Ann Arbor, 1991). James Lee, Jean Oi, and Peter Perdue also contributed to this book.


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fruitful example of how the economists' approach to a problem may help to structure historical inquiry and how, conversely, understanding the historical context helps to modify the predictions of economic theories. Although much work has already been done on granaries, the availability of price data now creates an opportunity to combine economic analysis and documentary research to test and measure the impact of Qing grain storage efforts on the economy.

Another theoretical problem raised by the grain price data, also illustrated in Li's article, is that of how to define and identify markets. Do wheat, millet, and sorghum in North China belong to the same market, or do they constitute separate markets? The principle of substitution teaches that a rise in the relative price of wheat or any other commodity will increase the demand for, and hence the price of, items that provide close substitutes for the initial product as buyers seek to maintain their living standards or contain costs in the face of adverse price change. The question here is to what extent people were willing to substitute one grain for another or, elsewhere, one type of cloth for another. Such questions bear closely on the question of market integration and call for further inquiry.

While the articles of Li, Perdue, and Wong and Perdue deal only with grain prices, the articles of Yeh-chien Wang and James Lee, Cameron Campbell, and Guofu Tan show how price data can be used in conjunction with other long-term data. In his article, Wang arrays his rice price data from the Lower Yangzi region together with population data, information about silver stocks, and weather trends, to consider what factors may have influenced long-term cycles of inflation and deflation in the Yangzi Delta. Wang's preliminary, and pathbreaking, estimates of China's monetary silver stocks indicate a roughly parallel growth of rice prices and the stock of monetary silver throughout the Qing period. He finds that in China, as in England, long-term trends in food prices display a substantial correlation with changes in population and population growth. Wang describes two long swings in rice prices during the three centuries prior to World War II. In both cases, periods of rising prices coincide with relatively rapid growth of both population and monetary silver, while interludes of deflation are associated with stagnant or declining population. Wang's discussion highlights opportunities for further study of major factors underlying the long-term path of China's economy. Can we sharpen the causal interrelations among population, money stock, commercialization, climatic change, food production, and the material well-being of the Chinese peasantry? Do available studies understate the long-term significance of international money flows for China's agrarian economy? How closely were the rice markets of the Yangzi Delta linked to the farm economies of other regions within China's vast land mass?

In "Infanticide and Family Planning in Late Imperial China: The Price


26

and Population History of Rural Liaoning, 1774–1873," James Lee, Cameron Campbell, and Guofu Tan employ price data to help analyze a unique and rich set of Qing dynasty population registers from Daoyi, a rural suburb of present-day Shenyang in Liaoning (formerly Fengtian) Province in southern Manchuria. Their major demographic findings are, first, that significantly higher levels of infant mortality were found among females than among males, although there were greater fluctuations in mortality among males; second, that most couples appear to have practiced a considerable degree of family planning; and, third, that infanticide, particularly female infanticide, was a principal means of family planning. In this article, the authors pose the question of how food prices might have influenced birth and death rates. In other words, were fertility or mortality affected by times of scarcity, as indicated by high food prices? Their answer is that there seems, on the whole, to have been little relationship between food prices and mortality, but there was a strong relationship between high food prices and infanticides, particularly, but not exclusively, female infanticides. While these conclusions are likely to be hotly debated, in part because it is unclear whether Liaoning or Manchurian family patterns are generalizable to the rest of China, Lee, Campbell, and Tan have pointed toward a direction in research that has not previously been pursued in Chinese history.

The second group of essays in this volume focuses on issues more familiar to modern Chinese history—urban and rural poverty, the economic consequences of political unrest, and economic growth or the lack of it. In dealing with the factors of land, labor, and capital in local or regional settings, these essays pursue large issues in a more focused, and perhaps more manageable, way than the first set of articles. In each case, we see the interplay between economic analysis and historical inquiry. Economic models open new avenues of inquiry for historians, while the historical context illuminates the social and institutional conditions that shape the impact of economic forces in particular times and places.

In the first of these essays, "Land Concentration and Income Distribution in Republican China," Loren Brandt and Barbara Sands—the only economists among our contributors—address the issue of land concentration and income distribution in twentieth-century China. They challenge the commonly held view that there was increasing concentration of land ownership in the late nineteenth and early twentieth centuries and that concentration of landed wealth necessarily produced wide inequalities in the distribution of income. Although the data from the 1920s and 1930s show a highly unequal distribution of land, Brandt and Sands show that shifting the statistical base from landholding per household into per capita terms narrows the gap between poor and wealthy households. They argue that without comparable data for earlier periods, there is no basis for claiming that the degree of concentration of land ownership was rising over time.


27

Their key point, however, is that in the complex economy of North China, the distribution of per capita income depended on earnings from the disposition of labor and goods as well as land, so that the concentration of landholdings need not have coincided with the concentration of incomes. If, as hypothesized by Peter H. Lindert, "the common folk," who specialized in producing and selling goods that embodied large components of unskilled labor, "were among the greatest gainers" from the expansion of China's domestic and international trade, the spread of commercial agriculture following the growth of trade and transport may emerge as a significant source of reduced income inequality in the North China countryside.[44]

Although Brandt and Sands analyze three villages selected for their distinct economic characteristics, skeptics will note the small size of their sample and the possible biases inherent in their principal source, the South Manchurian Railway Company village surveys, which form the basis for a number of controversial studies of peasant welfare in North China.[45] For our purposes, however, of greater interest than the ultimate correctness of their interpretation is the economic approach or perspective that they have employed. By posing a theoretical issue, then isolating a number of key variables and finding an appropriate set of data, the authors create a framework for systematic analysis of the issue at hand. Finally, by placing the Chinese issue in comparative, international terms, the authors provide a baseline or context within which to judge the issue of large or small. At what point should income inequality be considered large? Too large? Such judgments require not only quantification but also appropriate context.

Lynda S. Bell approaches the issue of rural income from another perspective: she looks at the silk industry in Wuxi, an area in the Lower Yangzi region that developed into a major sericultural and silk-reeling center in the nineteenth century after the Taiping Rebellion. In "Farming, Sericulture, and Peasant Rationality in Wuxi County in the Early Twentieth Century," Bell explores an apparent paradox: why, in one of the more prosperous regions of China, did peasants in the 1920s and 1930s experience low incomes from sericultural activity? And why should peasants continue to pursue sericulture even though, as she effectively demonstrates, the returns per unit of labor were lower for mulberry cultivation and silkworm raising than for rice or wheat farming? Does this mean that farm households were not acting rationally or that they were engaged in a kind of "self-exploitation" in the manner described by A. V. Chayanov for the Russian peasantry? The key to this paradox, Bell finds, is that women supplied most of the labor in seri-

[44] Peter H. Lindert, "International Economics and the Historian" (Revision of a paper prepared for the Workshop on Economic Methods for Chinese Historical Research, Honolulu, September 1987), p. 30.

[45] Myers, Chinese Peasant Economy , and Philip C. C. Huang, Peasant Economy , also rely on these surveys.


28

culture. Compared to other domestic industries they could engage in, such as cotton weaving, sericulture brought superior returns. Only factory work could have brought higher wages to women, but the opportunity cost—in terms of domestic labor lost to the family if the woman left for a factory—outweighed the extra income that might have been earned. Moreover, Bell's calculations reveal that even at the depressed silk prices of the 1930s, income from sericulture allowed Wuxi farm households to buy more rice than they could have grown on the land occupied by mulberry plants. So Bell finds peasant choices ultimately to be rational, but cautions that rationality need not imply that they were earning large profits; rather, rationality was what kept them going in an economy in which subsistence, rather than profit, was still the major preoccupation. Participation in an international market presented new opportunities, but Wuxi peasants found that it also presented new risks.

While the economic value of female labor implicitly figures in Bell's article, it is the main topic of Susan Mann's essay, "Women's Work in the Ningbo Area, 1990–1936." Using rich qualitative materials from a relatively commercialized region of China, Mann delineates the factors that affected both the demand for, and the supply of, female labor. On the demand side, she shows that there were many opportunities for female workers both within and outside the household in the Ningbo area and that the hierarchy of jobs, from the women's perspective, was less related to wage levels than to the perceptions of social respectability and the degree of personal convenience associated with each type of work. On the supply side, the availability of female labor from each household was dependent on three major factors: its size, its other resource endowments (these two were, of course, closely related), and its stage in the family cycle. Families with adult women who had no child-care responsibilities (young women before marriage or "able-bodied widows") were most likely to have labor to spare and therefore to benefit from new opportunities for female employment within the household. Factory employment, which violated social conventions that restricted respectable women to working within the household, was acceptable only to women from "poor households strategizing to keep their menfolk afloat."

In "Native-Place Hierarchy and Labor Market Segmentation: The Case of Subei People in Shanghai," Emily Honig addresses an apparent puzzle: why were people from Subei, the area of Jiangsu Province north of the Yangzi River and south of the Huai River, routinely barred from certain types of employment in Shanghai, even when they would have worked for lower wages than employers paid to natives of south Jiangsu? Regarded as inferior human beings, the Subei people in Shanghai were condemned to the least attractive and least remunerative forms of employment—rickshaw pulling, night soil and garbage collecting (literally, as she says, "shit work"), bar-bering, and so forth—within a clear hierarchy of jobs. Honig employs the


29

economists' concept of segmented labor markets to show that in Shanghai, it was not race, religion, or ethnicity that formed a barrier to free entry and exit, but native-place hierarchy.

Both Mann's and Honig's articles show that the economists' notion of choice has to be tempered by the social historians' understanding of gender, class, and native-place ties. Despite the heuristic value of the economists' notion of ceteris paribus, historians find that all other things are rarely equal and in fact it is the "other things" that may hold the key to understanding the flow of events. Still, the approach of these two papers is entirely consistent with an economic perspective. To start with the assumption that there should be a unified labor market with no barriers to exit or entry and with essentially one wage scale is not wrong; what would be wrong is to stop there. Looked at from the employers' perspective, labor market segmentation rests on their need to assess the qualifications and character of would-be employees or associates. With no access to data banks or credit histories, they must seek a quick and inexpensive screening device. Discrimination on the basis of ethnicity, place of origin, linguistic background, or education, can be partly understood simply as a cost- and risk-reducing business decision.

Susan Mann's Ningbo women benefited from their reputation for diligence, skill, and gentility. Employers preferred workers from Ningbo and other south Jiangsu communities over migrants from the north not only because of their superior technical and social skills but also because kinship ties and networks of regional association were available for disciplining and controlling south Jiangsu workers, making it more profitable to hire them, even when they might require higher pay than northerners. By the same token, Subei natives were discriminated against. Businessmen preferred to deal with those whose background seemed to increase the likelihood of the successful fulfillment of agreements. When disputes arise, the existence of voluntary organizations, such as native-place associations (huiguan ), increases the probability of speedy resolution of conflict by informal procedures acceptable to all parties. Drawing on the economic theory of clubs, Janet T. Landa has proposed just such an explanation for the tendency of Chinese businessmen in Southeast Asia to deal preferentially with Chinese whose ancestors migrated from the same district or province, secondarily with other Chinese, and only if other contacts are not available, with local non-Chinese or with foreign business partners.[46]

Finally, Kenneth Pomeranz's article, "Local Interest Story: Political Power and Regional Differences in the Shandong Capital Market, 1900–

[46] Janet T. Landa, "The Political Economy of the Ethnically Homogeneous Chinese Middleman Group in Southeast Asia: Ethnicity and Entrepreneurship in a Plural Society," in The Chinese in Southeast Asia , vol. 1, ed. Linda Y. C. Lim and Peter L. A. Gosling (Singapore, 1983), pp. 86–116.


30

1937," illustrates how political structures can decisively influence the outcome of economic change. During the early twentieth century, Shandong Province experienced an expansion of markets and commerce similar to that in the Wuxi region of Jiangsu Province in an earlier period. As in Jiangsu, Shandong villagers were quick to avail themselves of new economic opportunities, specializing in peanuts and other cash crops in some regions and, as Pomeranz documents, exporting large quantities of underpriced copper coins whenever it became possible to do so.

Shandong's political elite found themselves torn between the gains available from encouraging economic integration and the benefits for themselves and their mercantile allies of using military force to obstruct integration and then exploit the resulting regional price gaps for pecuniary gain. With leaders in different regions responding differently to market circumstances, Shandong's economy displayed lines of demarcation that reflected the impact of political decisions more than economic, social, or geographic forces. Despite a national trend toward economic integration, the needs of state making during this turbulent period of Shandong's history prompted local authorities to restrict the movement of specie across administrative boundaries, leading to marked regional variations in both the silver-copper ratio and local interest rates that illustrate a real political constraint on the spread of purely market forces.

Conclusion

The essays in this volume do not fall into any single neat line of interpretation about the economic history of China over the last two or three centuries. Pomeranz's detailed work on Shandong cautions us against any broad generalizations about the extent to which the treaty ports in nineteenth- and twentieth-century China affected the hinterland economy. Pomeranz shows us that the more advanced, coastal area did interact with the hinterland but that political intervention prevented a higher degree of market integration.

The works of Bell, Mann, and Honig also contain a cautionary message. Even in the Lower Yangzi macroregion, the most agriculturally prosperous and commercially advanced area of China, the opportunities for economic gain for individual peasants or workers, although often greater than ever before, could be undercut by international economic instability, gender differences in the returns to labor, and unequal access to the urban labor market. The story that Brandt and Sands tell, however, contains the reverse message. In the much more adverse conditions of North China, all may not have been so bad as it appeared. New employment opportunities provided more channels for a family's economic gain than just landholding. Entry to and exit from these lines of work appear unimpeded in the North China world they describe.


31

The lessons of the articles in Part 1 are somewhat different. In some cases, the findings of these grain price studies confirm previously known trends or previously advanced hypotheses. For example, Wong and Perdue's study of Hunan's grain price series confirms commercial patterns already discerned through qualitative sources. Li's case studies of crises parallel the results of Pierre-Etienne Will's documentary study. Perdue's delineation of marketing patterns in Gansu coincides with G. William Skinner's predictions about the spatial patterns of Gansu's commodity trade. In other cases, such as Wang's study of money supply or Lee, Campbell, and Tan's study of Liaoning, new materials have generated new hypotheses about long-term trends.

These essays also contain the potential for even bolder messages, perhaps revisions of current received wisdom, about China's economic history over the last two or three centuries. Some readers may derive from the essays in Part 1 a picture of the eighteenth-century economy as more advanced in commercial development and market integration than previously thought. Certainly, the quality of the Qing bureaucracy's price data seems higher than that of its population records, the systematic fabrication of which Skinner has recently exposed.[47] Wang's compilation of information on stocks of monetary silver creates an opportunity for using the equation of exchange to investigate the implications of Dwight Perkins's long-standing assertion that, on the average, Chinese living standards, as measured by the availability of grain, experienced no long-term upward or downward trend during the Ming and Qing dynasties.[48] The essays in Part 2 all illustrate, in varying ways, the extent to which commercialization, including the development of foreign as well as domestic trade, penetrated the local economies of many areas. The story of expanding commercial networks finds a basis in these papers, but there are other stories that have been, and will be, told about the modern economy.

Despite the many insights and contributions contained in the essays that follow, we believe, however, that the real lessons of this volume are not the substantive ones. In each case, economic theories and methods have been employed to clarify the facts of history and to advance its understanding.

[47] G. William Skinner, "Sichuan's Population in the Nineteenth Century: Lessons from Disaggregated Data," Late Imperial China 8, no. 1 (1987): 69.

[48] If we assume parallel growth between silver stocks and money supply, between grain prices and the general price level, and between foodgrain production and total output, the equation of exchange can be used to derive the time path for income velocity of monetary circulation implied by Wang's data on silver, grain prices, and population together with Perkins's hypothesis of stable per capita output. The plausibility of the resulting velocity estimates and of changes that might arise from adjustments reflecting known biases in the underlying data (we know, for example, that money supply grew faster than silver stocks in the late nineteenth and early twentieth centuries) should make it possible to evaluate the degree to which Perkins's results, the grain price data, and Wang's new monetary estimates provide a mutually consistent picture of overall economic trends.


32

Without a fundamental understanding of the laws of supply and demand and the significance of market integration, none of the essays in Part 1 could have been written. Without an appreciation of how factor markets operate, the essays in Part 2 would have been greatly weakened. Wang's article provides an excellent example of how economic theory, in this case the quantity theory of money, can inform both the construction and interpretation of economic data to help formulate new questions and hypotheses.

In many of the essays, however, a simple economic approach in itself would lead to an impasse or a seeming contradiction. These apparent puzzles, such as Shandong's lack of monetary integration or Wuxi's apparent poverty in one of China's most prosperous regions, can only be explained with reference to the institutional and social context that historians are uniquely qualified to understand and explain. Without knowledge of the social prejudices attached to Subei people, their lowly position in Shanghai's labor force would defy understanding. Without knowing the history of the Chinese bureaucracy and the fundamentals of Confucian political theory, Western-trained economists find it difficult to comprehend why the Chinese state should have maintained a vast civilian granary system in the Qing period. Often the results of economic analysis raise questions that compel us to further noneconomic inquiry. The surprising demographic behavior of the Han Banner population of Liaoning causes us to want to know more about their ethnic background, their family structure, and their food allocation habits and in particular to understand whether they were very different from Han Chinese who lived within the Great Wall. In short, economic analysis cannot stand alone and, in almost every case, offers rich opportunities for work with other disciplines—sociology, anthropology, politics, and history.

Chinese economic history is barely coming into its own as a field of study. What this volume is intended to show, to its authors as well as to our colleagues and students, is that further study of China's economic history that systematically utilizes the theories and methods of economics can generate new hypotheses and fresh perspectives that will enrich the study of all aspects of China's history as well as deepen our understanding of the structure and evolution of the Chinese economy itself.


33

PART ONE
PRICE BEHAVIOR


35

One
Secular Trends of Rice Prices in the Yangzi Delta, 1638–1935

Yeh-chien Wang

In an agrarian society like Qing China, grains are the most important commodities in domestic trade, and food consumption makes up more than half of the average household budget.[1] Grain prices are therefore the leading indicator in the market; the direction and the magnitude of their movement generally reflect conditions of inflation, deflation, or crises of major proportion. Moreover, persistent changes in grain prices relative to prices of other commodities give rise to a process of income redistribution affecting the welfare of virtually all groups of people and eventually the social and political stability of a country. As such, a clear knowledge of the trends of grain prices will provide not only a key to understanding the state of economy and society but also a basis for further research in real wages, the standard of living, and many other areas once data on other economic indicators are uncovered.

I would like to thank the participants of the Conference on Economic Methods for Chinese Historical Research held in Oracle, Arizona, in 1988 and especially Professors Jon Cohen, Peter H. Lindert, Lillian M. Li, and Thomas G. Rawski for their comments and suggestions. Most data for this paper were gathered at the First Historical Archives in Beijing and the National Palace Museum in Taibei. I feel greatly indebted to the staffs of these two institutions for their cooperation and assistance. I am also grateful to Fang Xing of the Institute of Economics, Chinese Academy of Social Sciences, and the late Wu Dange of Fudan University, who kindly showed me additional sources of price data from published works, and to Douglas E. Lewis of Computer Services at Kent State University for his assistance in data design and graphics. For financial support I wish to acknowledge assistance from the following institutions: the Committee on Scholarly Communication with the People's Republic of China, the National Science Council of the Republic of China, the Social Science Research Council, the American Council of Learned Societies, the Foundation for Scholarly Exchange (Fulbright Foundation), the Wang Institute for Graduate Studies, and Kent State University.

[1] See Wu Chengming, Zhongguo ziben zhuyi yu guonei shichang (Beijing, 1985), p. 253; John Lossing Buck, Chinese Farm Economy (Chicago, 1930), pp. 361–64, 386; Sidney Gamble, Ting Hsien: A North China Rural Community (Stanford, 1968), p.118.


36

Empirical studies in price history for imperial China are still in their infancy because of scarcity of data,[2] but the gradual opening of archival resources in both Beijing and Taibei offers us rich mines for historical exploration. What I am attempting to do in this paper is to delineate broadly the secular trends of the prices of rice, the single most important staple food of the Chinese people, in the Yangzi Delta for three centuries prior to World War II and to suggest some tentative explanations for the trends observed.

The Yangzi Delta is chosen as the focus of observation for two main reasons. First, price data for the area are more abundant and, by and large, of better quality than price data for other areas. I am thus able to construct a price series extending over three centuries. Second, because of its economic centrality, prices in the area reflected conditions of demand and supply in the national, not just regional, market. In late imperial China most of the long-distance trade used the waterways, of which the Yangzi River, the Grand Canal, and the sea route along the coast were by far the most important. Linking the eastern coast with the interior, the Yangzi River flows through China's most productive regions. Together with its tributaries and connecting lakes it provided the most efficient network of inland transportation. The Grand Canal joined the capital region to the resource-rich South, while the sea route tied together all of the coastal provinces from Hainan Island to the Liaodong Peninsula. Only the northwestern region and the southwestern corner of the empire remained relatively isolated. Before the Opium War (1840–42) there were, according to one study, more than 200,000 junks plying these waterways and other smaller rivers with a total carrying capacity amounting to 4–5 million tons.[3] Strategically situated at the focal point where the three principal arteries converged, the Yangazi Delta thus became the hub of interregional trade (see Map 1.1).

In addition to the advantage it possessed in geographic position, the industrial structure of the delta further enhanced its economic significance. It was, on the one hand, the center of the textile industry. On the other hand, its agriculture was unable to produce sufficient food to feed its inhabitants because it had the highest density of population in the country and much of its cultivated acreage was occupied by cash crops, such as cotton and mulberries.[4] These structural features of the delta economy gave rise to a

[2] A few works may be cited: Han-sheng Chuan and Richard A. Kraus, Mid-Ch'ing Rice Markets and Trade: An Essay in Price History (Cambridge, Mass., 1975); Hwang Kuo-shu and Yeh-chien Wang, "Qingdai liangjia de changji biandong, 1763–1910," Jingji lunwen 9, no. 1 (March 1981): 1–27; Yeh-chien Wang, "Food Supply in Eighteenth-Century Fukien," Late Imperial China 7, no. 2 (December 1986): 80–117.

[3] Fan Baichuan, Zhongguo lunchuan hangyunye de xingqi (Chengdu, 1985), pp. 35–83.

[4] Cf. Yeh-chien Wang, "Food Supply and Grain Prices in the Yangtze Delta in the Eighteenth Century," Proceedings of the Second Conference on Modern Chinese Economic History (Taibei: Academia Sinica, 1989), pp. 424–27.


37

figure

Map 1.1.
Grain Trade Routes in Qing China.


38

growing two-way traffic in which cotton cloth and silk, the staple products of the delta, were distributed to the rest of the country while surplus food from inland and the newly developed areas came to the delta for local consumption or for transshipment to other areas where food was also in short supply. In the latter part of the eighteenth century the annual volume of long-distance trade in rice down the Yangzi River to the delta was probably between 15 million and 20 million shi , of which 5–6 million was transshipped to North China and the southeast coast (including 3 million shi as grain tribute to the capital). In addition, around 15 million shi of soybeans, bean products, and a variety of grains and fruits was transported from Manchuria and North China to the delta via the coastal waters and the Grand Canal. Beyond this, grain trade across provincial borders in the rest of the country was, quantitatively speaking, insignificant.[5] It must be noted, furthermore, that grains and textiles formed an overwhelming proportion of commercial cargoes carried across provinces. Before the middle of the nineteenth century, those two categories, as estimated by Wu Chengming, accounted for 42 and 31 percent, respectively, of the total value of the seven major commodities that entered interregional trade.[6] As the principal supplier of textiles to, and the consumer of most of the surplus food from, other parts of the country, the delta inevitably assumed the central role in the domestic market.

In a study on food supply in the delta in the eighteenth century, I selected for observation and analysis rice prices for 1738–89 in Suzhou and Hangzhou, the most flourishing prefectures in the delta and two principal prefectures in the Lower Yangzi Region, in conjunction with Quanzhou Prefecture of the Southeast Coast, Hanyang Prefecture of the Middle Yangzi Region, Huaian Prefecture in North China, and Guangdong Province in the Lingnan Region. My findings lend strong support to the proposition that the delta had economic centrality. First, prices show a remarkable degree of synchronized movement across all of the five macroregions linked by the three major waterways. Second, a Pearson correlation analysis of the deseasonalized, decycled, and detrended prices in these regions gives coefficients that are all positive, most of them of relatively high value (0.6 and over), and degrees of association between Suzhou and the rest that are the most pronounced. Although the data are far from complete and perfect, this survey of the grain trade nationwide and of grain price movements in a large part of the country does indicate the central position Suzhou occupied in the country's grain market.[7]

Not only was Suzhou the national market for grain and textiles, it was also

[5] Wang Yejian (Wang Yeh-chien) and Hwang Guoshu (Hwang Kuo-shu), "Shiba shiji Zhongguo liangshi gongxu de kaocha" (Paper read at the Symposium on Rural Economy in Modern China, Taibei, Institute of Modern History, Academia Sinica, 1989).

[6] Wu Chengming, Zhongguo ziben , pp. 247–51.

[7] Wang, "Food Supply and Grain Prices," pp. 444–51.


39

the foremost emporium for many other commodities. In 1756, for example, Governor Gao Jin said in a memorial to the emperior that tung oil and black plums were produced in Huguang, white wax in Hunan and Guizhou, copper in Yunnan and Guangdong, coir fiber in Huguang, Jiangxi, and Zhejiang, and rattan in Guangdong. But all of these products were, he pointed out, shipped to Suzhou for distribution to other parts of the empire.[8]

In Table 1.1, I have compiled an annual price series for the delta from 1638 through 1935 by combining four shorter series as follows: a Shanghai series for 1638–95, a series for Suzhou City (the capital city of Suzhou Prefecture) covering 1696–1740, a Suzhou Prefecture series for 1741–1910, and a Shanghai series for 1911–35. There are, however, a number of years for which price data are missing. In such cases, I have filled out the missing data by extrapolation (marked with an asterisk in the third column); for the years 1862–64, when Suzhou was occupied by the Taiping rebels, I have used Shanghai prices.

The core of these data is the 170-year-long Suzhou Prefecture series plus the preceding Suzhou City series. Combined, these two series cover the entire Qing period except for the beginning decades. The data for the Suzhou City series are obtained from reports of governors and imperial commissioners of silk works residing in the city. Since the city was then the largest grain market in the country, to which early Manchu emperors paid close attention, more price reports came from there than elsewhere in the country. But it was not until the establishment of a nationwide grain-price-reporting system in the late 1730s that reports became regularly required of local administrations. Under this system, provincial authorities throughout the country were required to submit to the throne monthly reports on prices of major grains in every prefecture under their jurisdiction.[9] The Suzhou Prefecture series is based on these reports. My colleague and I have gathered 1,632 monthly reports for this period (1740–1910), of which 96 years are complete with 12 months of data, another 25 years with 11 months of data, and only 6 years without data at all (see column 3). Nonetheless, how reliable are these official data? Obviously we cannot proceed with our research unless we have some degree of confidence that they provide a good approximation of market prices.

For the present purpose of trend observation I shall employ two kinds of tests to evaluate the official data, first, to observe whether excessively high prices occur in, or are preceded by, years of major natural or man-made calamities in the area or other parts of the country and, second, to see whether the secular movements of prices as manifested in the official series

[8] Gongzhongdang Qianlongchao zouzhe (Taibei, 1979), 15:431.

[9] For monthly grain price reports, see James Lee, Cameron Campbell, and Guofu Tan, "Infanticide and Family Planning," in this collection.


40
 

TABLE 1.1 Rice Prices in the Yangzi Delta, 1638–1935 (taels of silver per shi unless noted otherwise)

Year

Annual Price

No. of Official Reports Available

Other Series

31-Year Moving Average

Deviation from Moving Average (%)

1638

1.90

       

1639

1.90

       

1640

2.80

       

1641

3.90

*

     

1642

5.00

       

1643

2.50

       

1644

2.50

       

1645

2.50

       

1646

3.00

       

1647

4.00

       

1648

3.10

*

     

1649

2.20

       

1650

1.80

       

1651

3.90

       

1652

3.30

       

1653

2.70

   

2.18

24

1654

2.50

   

2.14

17

1655

2.50

   

2.11

19

1656

1.60

*

 

2.06

-22

1657

0.70

   

1.97

-64

1658

1.35

*

 

1.83

-26

1659

2.00

   

1.76

13

1660

1.85

*

 

1.71

8

1661

1.70

   

1.65

3

1662

1.70

   

1.58

8

1663

0.90

   

1.48

-39

1664

0.95

*

 

1.44

-34

1665

1.00

   

1.44

-31

1666

0.70

   

1.43

-51

1667

0.60

   

1.33

-55

1668

0.55

*

 

1.25

-56

1669

0.50

   

1.19

-58

1670

1.00

   

1.14

-12

1671

1.30

   

1.09

19

1672

1.10

   

1.08

2

1673

0.60

   

1.07

-44

1674

0.60

   

1.07

-44

1675

0.70

*

 

1.03

-32

1676

0.80

   

1.01

-21

1677

0.80

   

0.97

-18

1678

0.90

   

0.95

- 5

1679

1.80

   

0.96

88

(Table continued on next page)


41
 

TABLE 1.1 (Continued)

Year

Annual Price

No. of Official Reports Available

Other Series

31-Year Moving Average

Deviation from Moving Average (%)

1680

2.30

   

0.95

142

1681

1.50

*

 

0.94

60

1682

0.70

   

0.94

- 26

1683

0.90

   

0.95

- 6

1684

0.90

 

0.90

0.96

- 7

1685

0.90

 

0.85

0.97

- 8

1686

1.00

*

0.90

0.97

3

1687

1.10

 

0.90

0.96

15

1688

0.60

 

0.80

0.95

- 37

1689

1.10

 

0.94

0.97

13

1690

1.00

 

0.90

0.99

1

1691

1.00

 

0.85

1.02

- 2

1692

0.70

 

0.80

1.03

- 32

1693

1.00

 

1.20

1.06

- 5

1694

1.10

 

0.75

1.07

3

1695

0.70

 

0.75

1.04

- 33

1696

0.70

1

0.75

0.99

- 29

1697

0.80

*

0.88

0.96

- 17

1698

0.90

1

0.85

0.97

- 7

1699

0.86

*

0.85

0.97

- 11

1700

0.83

*

0.83

0.98

- 15

1701

0.80

1

0.73

0.98

- 19

1702

0.92

*

0.73

0.98

- 6

1703

1.04

*

0.90

0.97

7

1704

1.16

*

0.95

0.98

19

1705

1.28

*

1.05

0.97

32

1706

1.40

1

1.02

0.96

45

1707

1.30

3

1.30

0.96

35

1708

1.60

4

1.40

0.97

64

1709

1.20

10

1.32

0.98

22

1710

0.90

7

1.02

0.98

- 8

1711

0.80

7

0.82

0.99

- 19

1712

0.70

2

0.80

1.01

- 31

1713

0.90

9

1.03

1.02

- 12

1714

0.90

9

1.10

1.03

- 13

1715

1.20

7

0.85

1.04

15

1716

1.00

11

1.50

1.05

- 5

1717

1.00

9

0.85

1.07

- 7

1718

0.80

10

0.76

1.09

- 26

1719

0.70

10

1.00

1.09

- 36

1720

0.80

14

0.95

1.09

- 26

(Table continued on next page)


42
 

TABLE 1.1 (Continued)

Year

Annual Price

No. of Official Reports Available

Other Series

31-Year Moving Average

Deviation from Moving Average (%)

1721

0.90

5

1.30

1.08

- 16

1722

1.00

9

1.10

1.07

- 6

1723

1.00

5

1.20

1.07

- 6

1724

1.20

7

1.30

1.06

13

1725

1.20

11

1.00

1.06

13

1726

0.90

11

1.10

1.08

- 16

1727

1.30

14

1.15

1.10

18

1728

1.20

1

1.00

1.13

6

1729

1.20

*

0.85

1.15

4

1730

1.20

*

1.00

1.17

3

1731

1.20

1

1.20

1.17

2

1732

1.30

*

1.40

1.19

9

1733

1.40

2

1.20

1.22

14

1734

1.20

1

0.90

1.25

- 4

1735

1.00

1

0.86

1.28

- 22

1736

1.00

11

0.80

1.32

- 24

1737

1.10

12

0.92

1.37

- 19

1738

1.30

12

1.20

1.39

- 6

1739

1.40

12

1.05

1.41

- 1

1740

1.20

12

1.00

1.43

- 16

1741

1.34

12

1.10

1.48

- 10

1742

1.53

12

1.25

1.51

2

1743

1.60

11

1.30

1.52

5

1744

1.55

12

1.05

1.55

0

1745

1.42

12

1.00

1.58

- 10

1746

1.37

12

1.10

1.60

- 14

1747

1.61

12

1.50

1.62

- 1

1748

2.04

12

1.60

1.64

25

1749

1.69

12

1.40

1.65

3

1750

1.64

11

1.30

1.67

- 2

1751

1.93

11

2.10

1.70

13

1752

2.31

12

1.40

1.72

34

1753

1.73

12

1.30

1.74

- 1

1754

1.64

12

1.20

1.76

- 7

1755

1.89

11

2.20

1.78

6

1756

2.73

11

1.60

1.80

52

1757

1.70

7

1.30

1.80

- 6

1758

1.75

12

1.40

1.80

- 3

1759

1.95

12

1.90

1.80

8

1760

2.18

12

1.30

1.82

20

(Table continued on next page)


43
 

TABLE 1.1 (Continued)

Year

Annual Price

No. of Official Reports Available

Other Series

31-Year Moving Average

Deviation from Moving Average (%)

1761

1.82

12

1.50

1.84

- 1

1762

1.91

11

1.60

1.85

3

1763

1.82

6

1.40

1.86

- 2

1764

1.74

8

1.60

1.87

- 7

1765

1.93

7

1.95

1.87

3

1766

1.92

12

1.70

1.87

2

1767

1.67

11

1.70

1.88

- 11

1768

1.73

12

1.85

1.87

- 7

1769

1.97

12

2.10

1.87

5

1770

2.00

11

2.10

1.88

6

1771

1.62

7

1.80

1.91

- 15

1772

1.55

11

1.30

1.89

- 18

1773

1.36

11

1.32

1.89

- 28

1774

1.63

12

2.00

1.88

- 13

1775

2.05

12

2.20

1.86

10

1776

2.09

12

1.57

1.84

14

1777

1.87

12

1.45

1.82

2

1778

1.75

12

2.00

1.81

- 3

1779

2.33

12

2.10

1.79

30

1780

1.87

9

1.90

1.78

5

1781

1.68

12

2.30

1.76

- 5

1782

1.98

12

2.00

1.74

14

1783

2.05

8

1.90

1.72

19

1784

1.77

10

2.05

1.70

4

1785

2.09

11

3.50

1.68

24

1786

2.63

9

3.50

1.67

58

1787

2.19

12

2.10

1.68

31

1788

1.62

11

2.10

1.71

- 5

1789

1.49

6

2.00

1.75

- 15

1790

1.42

9

2.00

1.79

- 21

1791

1.46

2

2.20

1.82

- 20

1792

1.37

4

2.50

1.82

- 25

1793

1.36

4

3.30

1.86

- 27

1794

1.44

5

3.50

1.90

- 24

1795

1.37

6

2.70

1.91

- 28

1796

1.23

12

2.20

1.93

- 36

1797

1.18

7

2.75

1.96

- 40

1798

1.16

10

2.18

1.98

- 41

1799

1.20

12

1.85

2.01

- 40

1800

1.26

11

2.85

2.05

- 38

(Table continued on next page)


44
 

TABLE 1.1. (Continued)

Year

Annual Price

No. of Official Reports Available

Other Series

31-Year Moving Average

Deviation from Moving Average (%)

1801

1.57

12

2.62

2.07

- 24

1802

2.00

12

2.60

2.06

- 3

1803

2.48

12

 

2.06

20

1804

2.69

12

 

2.08

29

1805

2.79

4

 

2.11

32

1806

2.92

3

 

2.14

36

1807

2.35

1

 

2.17

8

1808

2.98

4

 

2.21

35

1809

2.95

11

 

2.25

31

1810

2.63

12

 

2.28

15

1811

2.41

12

 

2.31

4

1812

2.64

12

 

2.34

13

1813

2.61

12

 

2.37

10

1814

2.90

11

 

2.41

20

1815

3.09

1

 

2.44

26

1816

2.78

12

 

2.48

12

1817

2.27

11

 

2.52

- 10

1818

2.37

5

 

2.54

- 7

1819

2.11

12

 

2.56

- 17

1820

2.33

12

 

2.55

- 9

1821

2.48

1

 

2.53

- 2

1822

2.49

*

 

2.50

- 1

1823

2.50

1

 

2.50

0

1824

2.50

*

 

2.47

1

1825

2.50

1

 

2.46

2

1826

2.28

4

 

2.46

- 7

1827

2.17

12

 

2.46

- 12

1828

2.18

7

 

2.45

- 11

1829

2.22

12

 

2.44

- 9

1830

2.28

10

 

2.42

- 6

1831

2.51

12

 

2.38

5

1832

2.60

11

 

2.35

10

1833

2.77

12

 

2.35

18

1834

2.96

12

 

2.34

- 4

1835

2.37

12

 

2.35

1

1836

2.25

12

1.54

2.34

- 4

1837

2.17

12

1.32

2.31

- 6

1838

2.08

12

 

2.27

- 8

1839

2.24

12

1.54

2.23

0

1840

2.51

12

1.61

2.20

14

(Table continued on next page)


45
 

TABLE 1.1 (Continued)

Year

Annual Price

No. of Official Reports Available

Other Series

31-Year Moving Average

Deviation from Moving Average (%)

1841

2.57

12

 

2.17

19

1842

2.55

12

1.61

2.18

17

1843

2.26

12

 

2.20

3

1844

2.38

12

1.25

2.21

8

1845

2.24

12

0.96

2.19

2

1846

1.91

12

0.96

2.21

- 14

1847

1.95

12

1.04

2.27

- 14

1848

1.98

11

1.04

2.31

- 14

1849

2.20

7

2.79

2.34

- 6

1850

2.39

12

1.43

2.34

2

1851

2.18

12

0.96

2.36

- 8

1852

1.32

12

1.32

2.36

- 44

1853

1.35

8

 

2.35

- 43

1854

1.38

1

 

2.35

- 41

1855

1.46

9

1.07

2.34

- 38

1856

1.48

1

2.64

2.32

- 36

1857

2.66

8

2.68

2.30

16

1858

2.79

10

3.82

2.27

23

1859

2.37

9

 

2.25

5

1860

1.68

3

1.75

2.22

- 24

1861

2.96

*

 

2.20

35

1862

4.24

 

4.24

2.20

93

1863

3.82

 

3.82

2.21

73

1864

3.96

 

3.96

2.20

80

1865

2.84

4

2.93

2.18

30

1866

3.00

12

2.83

2.15

40

1867

2.26

12

2.69

2.13

6

1868

1.81

11

2.73

2.14

- 16

1869

1.96

12

2.14

2.15

- 9

1870

2.08

12

2.71

2.16

- 4

1871

1.90

12

2.12

2.18

- 13

1872

1.79

12

1.91

2.19

- 18

1873

1.72

12

1.53

2.16

- 20

1874

1.72

12

1.53

2.13

- 19

1875

1.53

11

1.77

2.13

- 28

1876

1.53

9

1.77

2.13

- 28

1877

1.83

12

1.98

2.10

- 13

1878

2.27

12

2.08

2.03

12

1879

1.81

12

2.08

1.97

- 8

1880

1.50

12

1.70

1.91

- 22

(Table continued on next page)


46
 

TABLE 1.1 (Continued)

Year

Annual Price

No. of Official Reports Available

Other Series

31-Year Moving Average

Deviation from Moving Average (%)

1881

1.33

12

1.70

1.90

- 30

1882

1.63

12

1.70

1.88

- 14

1883

1.79

12

1.70

1.91

- 7

1884

1.69

12

1.70

1.95

- 13

1885

1.59

12

2.26

1.98

- 20

1886

1.97

12

2.55

1.99

- 1

1887

1.96

11

1.84

2.05

- 4

1888

1.73

1

1.98

2.10

- 18

1889

1.87

12

1.98

2.16

- 13

1890

2.14

12

1.84

2.20

- 3

1891

1.88

12

2.55

2.25

- 16

1892

2.01

12

2.12

2.33

- 14

1893

2.04

12

1.98

2.40

- 15

1894

2.02

12

2.55

2.43

- 17

1895

2.11

12

3.54

2.50

- 16

1896

2.36

12

2.97

2.65

- 11

1897

2.59

12

3.68

2.81

- 8

1898

3.20

12

3.39

2.94

9

1899

2.85

12

3.39

3.01

- 5

1900

2.80

12

2.83

3.14

- 11

1901

2.65

11

2.83

3.27

- 19

1902

3.54

11

3.82

3.37

5

1903

3.52

12

3.82

3.47

1

1904

3.47

10

2.97

3.59

- 3

1905

2.87

11

3.61

3.77

- 24

1907

3.12

10

3.28

3.95

- 21

1907

3.98

12

4.11

4.17

- 4

1908

4.06

12

4.24

4.38

- 7

1909

3.34

12

3.54

4.57

- 27

1910

3.91

12

4.24

4.78

- 18

1911

6.20

   

5.11

21

1912

6.16

   

5.40

14

1913

5.60

   

5.60

0

1914

4.21

   

5.83

- 28

1915

5.74

   

6.17

- 7

1916

5.53

   

6.39

- 13

1917

5.06

   

6.58

- 23

1918

5.14

   

6.67

- 23

1919

5.39

   

6.82

- 21

1920

7.47

   

7.01

7

(Table continued on next page)


47
 

TABLE 1.1 Continued)

Year

Annual Price

No. of Official Reports Available

Other Series

31-Year Moving Average

Deviation from Moving Average (%)

1921

7.51

       

1922

8.68

       

1923

8.74

       

1924

7.96

       

1925

8.50

       

1926

12.24

       

1927

11.48

       

1928

8.61

       

1929

10.50

       

1930

13.22

       

1931

9.54

       

1932

8.82

       

1933

6.26

       

1934

7.98

       

1935

9.56

       

SOURCES: Ye Mengzhu, Yueshi bian (Shanghai, 1981), pp. 153–56, 171–72; Yao Tinglin, Linian ji , in Qingdai riji huichao (Shanghai, 1982), pp. 39-161; Li Xu, Li Xu zouzhe (Beijing, 1976), pp. 1–293; Gongzhongdang Yongzhengchao zouzhe (Taibei, 1977–79); Gongzhongdang Qianlongchao zouzhe (Taibei, 1979–89); grain price lists preserved at the First Historical Archives in Beijing and the National Palace Museum in Taibei; Decennial Reports, 1892–1901 (Shanghai, 1904), vol. II, app. 1, p. 26; Decennial Reports, 1902–1911 (Shanghai, 1913), vol. II, p. 340; Ke Wuchi, Louwang yongyu ji (Beijing, 1986), pp. 3–105; Yinxian tongzhi (1935), pp. 219–29; "Shanghai wushiliu nianlai mijia tongji", Shehui yuekan 1, no. 2 (Feb. 1929): 1–18; Zhu Sihuang, comp., Minguo jingjishi (Shanghai, 1948), p. 543; Tanaka Issei, "Shindai Setto sozoku no soshiki keisei ni okeru soshi engeki no kino ni tsuite", Toyoshi kenkyu 44, no. 4 44, no. 4 (March 1986): 47–50.

NOTES: The tael is a unit of weight in Qing China, and its official standard (kuping) is equivalent to 37.3 grams. The shi (officially cangshi ) is a unit of capacity amounting to 1.035 hectoliters.

Price data from official reports are for second-grade rice. An asterisk marks years with missing data, filled in by extrapolation. Shanghai data are used for 1862–64.

Price data from unofficial sources are presumably stated in local units of measure, of which the exact weight and capacity are unknown. The Maritime Customs data and Shanghai data for the Republican period are converted into their equivalents in Qing official standards according to rates as follows: 1 tael = 0.99 Haikwan tael = 1.10 Shanghai taels = 1.39 yuan , and 1 shi = 1.4 piculs = 0.926 Shanghai shi .

Prices originally stated in copper cash are converted into prices in taels according to the exchange rate between silver and cash in the respective years.

The 31-year moving average is the arithmetic average of the annual prices for a 31-year period centered on the year under observation. For example, the moving average for 1653 is the arithmetic average of annual prices for the 31 years from 1638 to 1668.


48

figure

Fig. 1.1.
Rice Prices in the Yangzi Delta, 1638–1935 (taels per shi )


49

are in line with those exhibited in other price series for the area that are derived from different and yet reputable sources. As indicated in Table 1.1 and Figure 1.1, rice prices peaked in 1756, 1786, 1814–15, and the early 1860s. In 1755 heavy flooding afflicted the Lower Yangzi and Huai River valleys, and in 1785 a dreadful drought blanketed the Lower and Middle Yangzi Valley, the North China Plain, and Manchuria. In 1814 the country was hit by both—flooding in Zhejiang, Fujian, and Jiangxi and drought in Jiangsu, Anhui, Henan, Sichuan, and Shanxi.[10] Finally, it was at the height of the Taiping Rebellion (1850–64) that prices skyrocketed as never before in the Qing period.

In the fourth column of Table 1.1, I have assembled three more series of rice prices of shorter duration for Xiaoshan (a county in the neighborhood of Hangzhou), Changshu (a county in Suzhou Prefecture), and Shanghai. The Xiaoshan series for 1684–1802 is derived from the records of the Lai clan in the county; the Changshu series for 1836–1860 from casual notes of a local scholar; and the Shanghai series for 1862–1910 from the decennial reports of China's Maritime Customs. As shown in Figure 1.1, prices of these shorter series move mostly in step with those of the annual series. Moreover, the linear trends fitted respectively for the annual series and for the Xiaoshan series for a whole century (1684–1788) turn out to be virtually the same, that is, an increase at the rate of 0.0131 tael per year for the former series and 0.0136 for the latter. The same can be said of the annual series and the Maritime Customs series for the last three decades of the Qing period, with respective increases of 0.0848 and 0.0860 tael. There is, then, good reason to believe the general validity of official price data for the Qing so far as the secular trend is concerned.

For the last dozen years of the eighteenth century, however, the annual series and the Xiaoshan series diverge significantly from each other (see Fig. 1.1). I suspect that something went wrong with the price reports from Suzhou Prefecture for those years because they show a sudden and drastic decrease in the extent of price fluctuations compared with any period before or after. On the other hand, not only for Suzhou but also for many other prefectures in and out of Jiangsu, the movement of prices represents a V-shape between the mid-1780s and the early years of the nineteenth century and the trough in the Suzhou series appears to be among the deepest. I cannot at the moment give a satisfactory explanation for the divergence between the two series for this short period. Perhaps a real picture will emerge with the discovery of new data.

Of all the data in the annual series, those for 1911–35 are unquestionably

[10] Zhongguo jin wubainian hanlao fenbu tuji , comp. Zhongyang Qixiangju Qixiang Kexue Yenjiuyuan (Institute of Meteorology, the Central Bureau of Meteorology; Beijing; 1981), pp. 148, 163, 178.


50

figure

Fig. 1.2.
Thirty-one-Year Moving Average of Rice Prices in the Yangzi Delta, 1638–1935 (taels per shi )


51

the most solid because they are compiled on the basis of price quotations in the Shen Bao and Xinwen Bao , two widely read newspapers in prewar Shanghai. The most rudimentary data are those for the seventeenth century, which are derived from random notes left by two contemporary local scholars. These notes do not have price entries for every year, and price data therein lack statistical uniformity in terms of product and date. Nonetheless, prices recorded by one scholar are highly consistent with those by the other. Moreover, virtually all peak prices noted by either of them coincide with years of calamities or come immediately afterward. All in all, while the quality of the four sets of data that form the annual series is uneven, the data are good enough to be taken as a general indicator of price trends in the market.

To better observe the broad trends of prices, I have, as did W. G. Hoskins for wheat prices in England, smoothed the annual series by using 31-year moving averages.[11] The results are rendered in column 5 of Table 1.1 and plotted in ratio scale in Figure 1.2. The smoothed averages fit the original series rather well. In view of the probable defects of the series for the last dozen years of the eighteenth century, which may have exerted an undue influence over the projection of the price trend, the shallow trough in the mid-1780s may well be nothing more than a statistical illusion. I have therefore smoothed it out freehand with a broken line. So modified, the figure shows prices to have moved in two broad swings over the three centuries. Beginning in the 1640s, the first swing headed steeply downward, reaching bottom in the early 1680s, then changed direction, rising at a modest rate for over a century to its peak around 1820. Then came the second swing. Prices descended gradually through the early 1850s and, after experiencing drastic fluctuations caused by the Taiping Rebellion and its aftermath in the third quarter of the nineteenth century, shot up in the early 1880s and kept rising through the 1920s.

As pointed out before, prices moved generally in a synchronized fashion in the Yangzi Delta and most other regions. Indeed, we can find few exceptions to the long inflationary trend in the eighteenth century, the slight downturn in the second quarter of the nineteenth century, and the half-century upswing before the Great Depression.[12] It may be worth noting that while broad trends in grain prices are also recognizable in premodern Europe, the turning points are much less uniform from one region to another. There were the well-observed Price Revolution in the sixteenth century, the fall of prices in the seventeenth, renewed inflation in the eighteenth. Nonetheless, the sixteenth-century inflationary period drew to an end between 1590 and 1600 in the south but between 1620 and 1640 in the north; and the seventeenth-

[11] W. G. Hoskins, "Harvest Fluctuations and English Economic History, 1480–1619," Agricultural History Review 12 (1964): 28–46.

[12] See also the other papers in Part 1 of this volume.


52

century deflationary period reached its nadir in the 1690s in Würzburg and Vienna but not until the 1730s and 1740s in England.

More interesting is the contrast in regional price differentials between the two continents. In the last half of the fifteenth century the western Mediterranean region led Europe with the highest prices, eastern Europe stood at the other end, and the northern and Atlantic regions fell in between. The ratio of wheat prices then was about 6 or 7 to 1 between the two extremes. A century later the gap was narrowed to 4 to 1 because of the growth of the grain trade on the Baltic, which brought more and more Polish wheat to the Mediterranean region. Even more remarkable was the development of the Atlantic trade in the next one and a half centuries. By 1700 the north (England, France, the Low Countries) had taken the economic leadership from the Mediterranean and turned itself into the region of expensive wheat. By the middle of the eighteenth century prices in most regions had, by and large, merged into one another, with a somewhat higher level in the north.[13] Europe thus became a highly integrated economy before the advent of the Industrial Revolution.

Early eighteenth-century China was, on the whole, comparable with Europe in terms of market integration. As shown in Map 1.2, in South China, where most people lived and where rice was the staple food, the Yangzi Delta (Suzhou and Hangzhou) had the highest prices, that is, about 1.5 taels per shi , around 1740. Next came neighboring Anhui and the southeastern coast of Fujian and Guangdong, where rice prices stayed at 1.0 taels or somewhat higher. The third region was the vast food-producing area of Sichuan, Huguang, and Guangxi, where rice was sold at around 0.8–0.9 tael per shi , the cheapest of all. The ratio between the highest and the lowest prices is 2 to 1 in the South. North China produced little rice, and most people there ate wheat, millet, and kaoliang instead. Almost all of the rice that the well-to-do consumed in the North was shipped from the South; its price was therefore much higher than in the South. Should the North be added as the fourth region for observation, the ratio between the highest and the lowest prices would increase to 3 to 1 (note that rice prices in the North vary from 1.8 taels in Xi'an to 2.4 taels in Jinan and Chengde).[14]

In the latter part of the nineteenth century, modern transportation, such as steamships and trains, was introduced into China. A narrowing of price

[13] For European prices, cf. F. P. Braudel and F. Spooner, "Prices in Europe from 1450 to 1750," The Cambridge Economic History of Europe , vol. 4, ed. E. E. Rich and C. H. Wilson (Cambridge, 1967), pp. 378–486.

[14] All price data come from the First Historical Archives in Beijing and the National Palace Museum in Taibei. The 1909 price for Guangzhou (2.30 taels per shi ) is apparently unreliable. Because Guangdong had become the province with the severest shortage in food supply, the Guangzhou price should be at least as high as the Quanzhou price. The 1909 price for Tianjin is unavailable; instead I give the price of top-grade rice in Beijing for the same year.


53

figure

Map 1.2.
Rice Prices of Selected Prefectures in China, 1738–1740 and 1909 (taels per shi ).


54

differentials is observable, though the degree of uniformity was much less pronounced than in Europe on the eve of its industrial takeoff. Although in the latter half of the nineteenth century the highest prices still prevailed in the North, much change had occurred in the South. The Yangzi Delta, still the country's center of economic gravity, was superseded by the two southern coastal provinces of Fujian and Guangdong as the region of dear rice because the Taiping Rebellion took a heavy toll of lives in the delta. In the interior, Sichuan and Hubei became overpopulated, raising the price of rice, while Hunan, Anhui, and Jiangxi remained an area with large amounts of food for export. The ratio of prices between the two extremes stood at 1.5 to 1 in the South in 1909, or 2.3 to 1 if the North is included; both ratios are lower than around 1740. What this brief survey of price history suggests is that, given the inexpensive network of water transportation that radiated from the Yangzi Delta, China had a more integrated economy than Europe did in most of the seventeenth century. However, the emergence of the North Atlantic economy moved Europe along at an accelerated pace. By the middle of the eighteenth century the position of the two continents had been unequivocally reversed.

Economic historians consider population and the quantity of money to be the prime factors affecting the secular trend in prices. In addition, some scholars note the causal relationship between climatic changes and food supply, which may produce a significant impact on prices. We may observe these relationships with prices by the use of the well-known equation of exchange

figure

where P stands for the price level, M for the stock of money, T for the volume of transactions, Y for real output, and Vt and Vy for transaction velocity and income velocity respectively. If T or Y increases with no change in the money stock or in velocity, P will decline. On the other hand, given T or Y , if MVt or MVy increases, P will rise.

What follows in this section is a historical review of the major variables—population, the stock of silver (the primary component of money for most of the period), and climate cycles—that, individually or in combination, may in large measure account for the two long price swings noted above. However, our present state of knowledge on any of the variables is so incomplete that most estimates are subject to a wide margin of error. Accordingly, any conclusions we may reach cannot but be tentative.

Population affects prices in a variety of ways. Malthusians have long stressed the imbalance between population growth and food supply. Since cultivated land cannot be expanded indefinitely, population growth will inevitably result in the decrease of per capita acreage. Increased input of labor and capital may raise the yields of land per hectare and thus compen-


55

sate for its shortage. Given the state of technology, however, the marginal productivity of land will eventually diminish. From then on, the food supply will not increase in proportion to population growth. Food prices cannot but rise; the level of general prices must rise too. On the other hand, not only does population increase mean more labor, but it may also contribute to capital formation and technological progress. At the same time, higher population density may stimulate an intensification of the marketing system, leading to crop specialization and division of labor and thereby raising agricultural productivity. Higher productivity means more abundant goods and services, which may in turn bring down the general level of prices.

Nonetheless, in a historical study of English population and prices Peter H. Lindert does find a strong correlation between the two prior to 1815, that is, before industrialization. Specifically, he observes that prices rose greatly in periods of rapid population growth (1526–1603 and 1760–1801) but climbed less or fell "in the period of less dramatic population increase between the early seventeenth century and the middle of the eighteenth." He offers two theories to explain how population growth affects prices. According to the first theory, rapid population growth may bring about a higher ratio of children to adults. With more children to support, a household generally has fewer savings. Lower savings relative to household income implies a greater demand for consumer goods and services relative to demand for money holdings, raising the price level. The second theory is what Lindert calls the "Goldstone variation." According to Jack A. Goldstone, population growth brings increased population density, urbanization, and specialization within agriculture. Monetized transactions will increase at the expense of production for home consumption. In an economy where the marketplace is underdeveloped, more frequent and smaller individual transactions should bring economies in the holding of cash, leading to a rise in the velocity of monetary circulation and higher prices.[15]

Despite the pioneer works of Ping-ti Ho and Dwight H. Perkins, the size of China's population before the 1953 census is still a matter of much debate. At the end of the sixteenth century the total number of people in the country was, according to Ho's estimate, around 150 million.[16] After studying the data on famine relief in Henan for 1593–94, Shu-yuen Yim concludes that official statistics on population are substantially understated and that the total population in the empire was no less than 200 million in 1600.[17] Mass uprisings and unprecedented droughts hit the greater part of the country in

[15] Peter H. Lindert, "English Population, Wages, and Prices, 1541–1913," Journal of Interdisciplinary History 15, no. 4 (Spring 1985): 609–34.

[16] Ping-ti Ho, Studies on the Population of China, 1368–1953 (Cambridge, Mass., 1959), p. 264.

[17] Shu-yuen Yim, "Famine Relief Statistics as a Guide to the Population of Sixteenth-Century China: A Case Study of Honan Province," Ch'ing-shih Wen-t'i 3, no. 9 (November 1978): 1–30.


56

the second quarter of the seventeenth century, causing a drastic decrease in population. Scholars are still far apart as to the extent of loss in lives during the time of the Ming-Qing transition. Estimates of the number of people at the beginning of the Manchu period vary from 70 million to 100–150 million,[18] though I am inclined to think that the upper range, or, say, 120 million, is probably a better approximation.

Following the Manchu conquest in 1644, political and economic order gradually returned. Before the pacification of the Three Feudatories and Taiwan in the early 1680s, however, bitter fighting continued in one part of the country after another because of Chinese resistance to foreign rule. Therefore, economic recovery proceeded slowly in the latter part of the seventeenth century. By 1700 China's population was most likely still quite below the level attained a century before, and perhaps something around 150 million is not far off the mark.

The eighteenth century is one of a few periods in Chinese history in which the country enjoyed prolonged peace and prosperity, and its population grew as never before. The baojia system for annual registration of population started in 1741 and was to last until 1850. Nevertheless, the statistics before 1776 are incomplete, and some of those close to the mid-nineteenth century are believed to be much inflated. We are left with only those for the last quarter of the eighteenth century and perhaps the first two decades of the nineteenth century that can be considered relatively reliable. According to official reports cited by Ho, the number of people totaled 353 million in 1820. By 1850 it had risen to 430 million. But, after a thorough investigation of the county data of Sichuan and a survey of apparently exaggerated figures for several other provinces, G. William Skinner concludes that China's population then was around 380 million instead.[19]

In the third quarter of the nineteenth century probably as many as 40 million people perished in the Taiping Rebellion and other social upheavals that devastated a large part of the empire and particularly the populous Lower Yangzi Valley. However, by the end of the century China had apparently regained the population lost several decades before. Despite political instability that characterized the last years of Manchu rule and the early Republican period, the country witnessed an expansion of population. In the early 1930s the total reached 500 million.[20]

It is interesting to note that grain price movements and population move-

[18] Dwight H. Perkins, Agricultural Development in China, 1368–1968 (Chicago, 1969), p. 216; Kang Chao, Man and Land in Chinese History (Stanford, 1986), p. 40.

[19] Ho, Studies on Population , pp. 281–82; G. William Skinner, "Sichuan's Population in the Nineteenth Century: Lessons from Disaggregated Data," Late Imperial China 8, no. 1 (June 1987): 68–76. In the light of Perkins's and Skinner's evaluations, official population data for the early nineteenth century are in all probability exaggerated.

[20] For the 1933 population, see Perkins, Agricultural Development , p. 16.


57
 

TABLE 1.2 Trends of Population, Silver Stocks, and Rice Prices in China, 1650–1930

 

Population

Silver Stocks

Rice Prices

Year

Total
(millions)

Annual
Growth
(%)

Total
(millions of
silver yuan)

Annual
Growth
(%)

Price
(taels per shi )

Annual
Change
(%)

1650

120

290–330

1655

2.11

1680

300–350

0.16

0.95

-3.24

1700

150

0.45

1820

353

0.72

2.55

0.70

1830

1,140–1,330

0.89

1850

380

0.25

900–1,100

-1.06

1870

1875

340

-0.45

1880

1,500–1,600

1.47

1.91

-0.48

1920

7.01

3.30

1930

500

0.70

3,200

1.46

NOTE: Silver stocks for 1680 and 1880 are estimated by extrapolation.

ments in China appear to be in line with what Lindert finds in the English case. As shown in Table 1.2 and Figure 1.3, a fairly strong correlation between population and prices is observable over nearly three centuries. Moreover, periods of rising prices happened to be periods of relatively rapid population growth (the 1700s and 1880–1930), whereas downward movements in prices were mostly accompanied by slower or negative growth in population (1650–80, 1820–80).

China's monetary system in the Qing period was bimetallic, with silver and copper cash. Paper notes issued by native banks (qianzhuang ), pawnshops, and other merchants appeared in the late eighteenth century. After the Treaty of Nanjing (1842) foreign banknotes began to circulate in treaty ports. China established its first modern bank half a century later. Not until the early twentieth century did banknotes and demand deposits constitute the largest component of the money supply. To observe the possible relationship between the stock of money and prices, I have made an estimate of the total stock of silver, the most important monetary metal in China, for a number of years in the three centuries under study.

Although China did not possess rich silver mines, it was able to cope with the growing demand for specie by importing silver from abroad for most of the period. The arrival of Westerners in the Indian and Pacific oceans following the Great Discoveries opened a new era in relations between the East and


58

figure

Fig. 1.3.
Trends of Population, Silver Stocks, and Rice Prices in China, 1650–1930


59

the West. Several Chinese products, such as silk, tea, and porcelain, were in great demand in Europe, America, and Japan, though manufactures from abroad struck little enthusiasm in the Middle Kingdom. Nor could the products—spices, ivory, sandalwood, etc.—from Southeast Asia, then under the domination of Western powers, offset the trade deficit incurred. To settle the balance, foreign merchants could offer only one thing, silver, that hardly any Chinese would refuse to accept. Meanwhile the discovery of rich silver mines in Japan, Peru, and Mexico provided a timely means of payment. Portuguese, Spanish, Dutch, British, and American, as well as Chinese, merchants all took part in this thriving trade, and large quantities of silver were thus brought into the country.

From the latter part of the nineteenth century China's economy was more closely integrated with the world market. When prices of silver were lower in the world market than in China, large amounts of specie would find their way through arbitrage into its trading ports, and vice versa. Since other countries had demonetized silver one after another, while China continued to use it as a medium of exchange and means of payment until 1935, it commanded a premium in the country for most of the period. The result is that China continued to be one of the largest recipients of silver even though it suffered a perennial trade deficit from the 1870s on.

How much silver did China possess over the three centuries under discussion? Let us begin with the most recent holdings. Eduard Kann, an expert on China's finance, estimated in early 1931 that there was about 2.5 billion ounces of silver in the country, including 1.7 billion in the form of silver dollars, sycee, and subsidiary coins and 0.8 billion in ornaments and household objects.[21] At the rate of 1.30 silver dollars or yuan to an ounce this would amount to 3,250 million yuan , which accords well with the Bank of China estimate in the early 1930s.[22] During the next five years, however, China incurred a net loss of 650–700 million yuan , largely because of the United States Silver Purchase Act. On the eve of China's monetary reform in early November 1935 its total silver stocks probably stood in the neighborhood of 2,600 million yuan .[23]

Between 1893 and 1930, according to Maritime Customs statistics, China

[21] Eduard Kann, "How Much Silver Is There in China?" Chinese Economic Journal 8, no. 4 (April 1931): 410–20.

[22] See Thomas G. Rawski, Economic Growth in Prewar China (Berkeley and Los Angeles, 1989), pp. 363–65.

[23] The net outflow through Maritime Customs amounted to 292 million Haikwan taels, or 456 million silver dollars, in 1931–35. In addition, somewhere between 225 million and 250 million yuan was smuggled out of the country in 1934–35. Liang-lin Hsiao, China's Foreign Trade Statistics, 1864–1949 (Cambridge, 1974), p. 129; Lin Weiying, Zhongguo zhi xinhuobi zhidu (Changsha, 1939), pp. 4–5; Yu Jieqiong, 1700–1937 nian Zhongguo yinhuo shuchuru de yige guji (Changsha, 1940), p. 3.


60

received from abroad 1,284 million silver yuan (823 million Haikwan taels).[24] Such being the case, it may not be far off to put the accumulated amount of specie at 1,900–2,000 million yuan at the end of the nineteenth century.

For earlier periods we are on a far less certain ground. In 1643 Jiang Chen, a scholar interested in fiscal and monetary matters, submitted to the falling Ming government a proposal for the adoption of paper money. He figured the total silver stocks then held in the private sector to be around 250 million taels, or 350 million yuan .[25] In an essay on the crisis of silver drain in the Daoguang period (1821–50), Taiping Shanren maintained that before the crisis unfolded, the country must have possessed something more than a billion silver yuan .[26] Both figures appear to be plausible.

In a broad survey of the influx of silver between the 1570s and 1830s Sun Yutang gives the following breakdown: 100 million silver yuan from the Spanish Philippines (1571–1821), over 500 million from England and other European countries (1700–1828), 100 million from the United States (1784–1833), and 140 million from Japan (seventeenth through early nineteenth centuries).[27] It should be pointed out, however, that the second item most probably includes amounts imported from the Philippines and the United States,[28] and so it should be revised downward to 300 million. According to Ch'üan Han-sheng, however, the total shipment from the Philippines during that same period was likely to have been 200 million or more instead of 100 million.[29] Moreover, there were fairly regular shipments of Japanese bullion in the latter part of the sixteenth century. A. Kobata notes, for instance, that the Portuguese carried 500,000–600,000 taels out of Japan annually to finance their trade with China.[30] Therefore, the silver inflow may reasonably be assumed to be around 800 million for the two and a half centuries in question.

Of the 800 million, how much was imported before the fall of the Ming dynasty in 1644? Liang Fangzhong estimates the amount at more than 100 million.[31] But Arai Hakuseki's study shows that Japan alone exported over

[24] Hsiao, China's Foreign Trade Statistics , pp. 128–29.

[25] Cited in Peng Xinwei, Zhongguo huobishi (Shanghai, 1965), p. 736.

[26] Taiping Shanren, "Daoguangchao yinhuang wenti," in Zhongguo jin sanbainian shehui jingjishi lunji (Hong Kong, 1974), 5:41–45.

[27] Sun Yutang, "Ming Qing shidai de baiyin neiliu yu fengjian shehui," Jinbu ribao , Feb. 3, 1951.

[28] Momose Hiromu, "Shindai ni okeru Supein doru no ryutsu," Shakai keizai shigaku 6, no. 2 (May 1936): 22.

[29] Quan Hansheng (Ch'üan Han-sheng), "Ming Qing jian Meizhou baiyin de shuru Zhongguo," in Zhongguo jingjishi luncong (Hong Kong, 1972), 1:435–50.

[30] A. Kobata, "The Production and Uses of Gold and Silver in Sixteenth- and Seventeenth-Century Japan," Economic History Review , 2d ser., 18, no. 2 (Nov. 1965): 245–66.

[31] Liang Fangzhong, "Mingdai guoji maoyi yu baiyin di shuchuru," Zhongguo shehui jingjishi jikan 6, no. 2 (Dec. 1939): 324.


61

100 million (748,000 kan ) in the first half of the seventeenth century, most of which ended up in China.[32] When the shipments from the Philippines between 1571 and 1644 and those from Japan before 1600 are added, the amount should, in the light of the foregoing discussion, be around 200 million instead.

While domestic production of silver played a secondary role in the supply of monetary metals, silver mining in Yunnan was in operation for most of the Ming-Qing period. Wei Yuan, one of the foremost scholars in the statecraft school and a keen observer of the nation's economy, wrote in 1842 that "of all silver stocks [in the country] 30–40 percent comes from mining and 60–70 percent from foreign vessels."[33] On the basis of his observation and the quantities of silver inflow, we may readily estimate the amount of bullion held in China to be in the range of 290–330 million silver yuan in the mid-seventeenth century and 1,140–1,330 million in the 1820s.

In the second quarter of the nineteenth century the balance of trade turned against China primarily because of the growing opium trade. According to H. B. Morse, there occurred a net drain of silver to the amount of 200 million yuan over the period.[34] On the eve of the Taiping Rebellion the country's holdings probably fell to the level of 900–1,100 million. In the latter half of the century, nonetheless, China resumed importing bullion from abroad. J. Laurence Laughlin notes that between 1852 and 1875 alone, at least 1 billion yuan of silver had been shipped from England and Mediterranean ports to India and the East.[35] So vast was the influx that China found its stocks of silver doubling in the half-century.

However rudimentary the estimate of silver stocks, the direction of change in the amount of bullion that China held over the three centuries is quite clear. We may, in light of the data presented in Table 1.2 and plotted in Figure 1.3, make two observations. First, not unlike prices and population, silver stocks in China generally followed a rising trend. Second, during the latter part of the seventeenth century and the second quarter of the nineteenth century, when the country's holdings of the white metal changed little or decreased, prices were on the decline. It appears, then, that silver stocks can serve as a crude indicator of the secular movements of prices.

To be sure, the stock of money included not only silver but also copper cash, paper notes, and, later, bank deposits. In the twentieth century, in particular, banknotes and bank deposits soon overtook silver as the dominant components of money. Nor did all silver stocks circulate as money; a

[32] Cited in Otake Fumio, "Min Shin jidai ni okeru gaikoku gin no ryunyu," in Kinsei Shina keizaishi kenkyu (Tokyo, 1942), p. 57.

[33] Wei Yuan, Shengwuji (1927), 14:33.

[34] Cited in Taiping Shanren, "Daoguangchao," pp. 45–46.

[35] J. Laurence Laughlin, The History of Bimetallism in the United States (New York, 1897), p. 125.


62

good part was hoarded in safes or underground or used for jewelry by the well-to-do. Therefore, other components of money as well as the velocity of circulation should, if possible, be taken into consideration when specific periods are examined.

Climatic conditions significantly affect the state of harvest and food supply. Various empirical studies show that relatively warmer temperature increases crop output by lengthening the growing season and by making uplands cultivable, and vice versa. Referring to sixteenth- and seventeenth-century Europe, for example, Andrew B. Appleby, G. Parker, and L. M. Smith suggest that a one-degree-centigrade decline in temperature will shorten the growing season by three to four weeks and is equivalent to raising land elevation by 500 feet.[36] A recent study by Wang Shaowu indicates, moreover, that low summer temperatures in a number of years between 1954 and 1976 reduced harvest yields by a third in Manchuria.[37] It was precisely because of concern for food supply in the empire that the Quing government required all local officials to submit regular weather reports along with reports on grain prices.

The degree of correlation between harvest yields and grain prices is nevertheless not so clear, because prices also depend on such other factors as access to food supply from other areas or from other parts of the world, grain storage, speculation, and government policy. To observe the possible relationships between climate, harvest yields, and secular prices, let us first look into climate cycles and harvest fluctuations in the delta over the centuries in question. Two groups of scientists in China have conducted original research on the long-term temperature changes in the Middle and Lower Yangzi Valley. One group, under the leadership of Zhang Peiyuan, has focused its attention on phenodata for the Qing period; the other, led by Yan Jiyuan, has made a historical investigation of the years when the Huangpu River and Lake Tai froze. They derived virtually the same climate cycles for the area.[38] Between the mid-1600s and the 1970s the country went through three long cycles. The cold period of the first cycle lasted through the first decade of the eighteenth century; the warm period that followed prevailed until 1780. The second cycle extended for almost a century (1781–1882), with 1830 as the turning point between the cold and warm periods. In the early 1870s another cycle started. It was also evenly split, with the 1920s as the transition years

[36] Andrew B. Appleby, "Epidemics and Famine in the Little Ice Age," Journal of Interdisciplinary History 10 (Spring 1980): 658; G. Parker and L. M. Smith, ed., The General Crisis of the Seventeenth Century (London, 1978), cited in Patrick G. Galloway, "Long-term Fluctuations in Climate and Population in the Preindustrial Era" (Paper read at the Ninth International Economic History Congress, Bern, Switzerland, 1986).

[37] Wang Shaowu, "Jin sibainian dongya de lengxia" (unpublished manuscript, 1988).

[38] Zhang Peiyuan, "Qingdai hannuan bianhua ji qi dui nongye de yingxiang" (Paper read at the Workshop on Qing Population History, Aug. 1985, California Institute of Technology, Pasadena, Calif.); Yan Jiyuan et al., "Changjiang sanjiaozhou de lengnuan tedian yu qushi zhanwang," in Quanguo qihou bianhua xueshu taolunhui wenji (Beijing, 1981), pp. 71–77.


63
 

TABLE 1.3 Price Trends, Climate Cycles, and Harvest Conditions in the Yangzi Delta, 1653–1920

Period

Price Trend

Climate Cycle

No. of Normal Harvests

No. of Good Harvests

No. of Deficient Harvests

Average Deviation of Harvests (%)

1653–1682

Down

Cold (1650–1710)

5

17

8

- 7.89

1683–1780

Up

Warm (1711–1780)

48

29

21

- 1.09

1781–1820

Up

Cold (1781–1830)

8

15

17

0.23

1821–1882

Down

Warm (1831–1872)

25

22

9a

- 7.10

1883–1920

Up

Cold (1873–1920s)

15

21

2

- 9.83

Total/ Average

   

101

104

57

- 5.14

a The skyrocketing prices in 1861–66 were caused mostly by the Taiping Rebellion, and therefore these years are excluded.

between the cold and warm periods (see Table 1.3). It is worthy of note that in the nineteenth and early twentieth centuries secular prices moved downward in the warm period (1831–72) but upward in the cold periods (1780–1830, 1873–1920), whereas the opposite is true in the seventeenth and eighteenth centuries.

I have not yet compiled data on harvest yields. In my study of grain prices in the Yangzi Delta, however, I do find a very strong association between harvest yields and grain prices. Over the half-century between 1738 and 1789 eleven cycles in both rice prices and wheat prices can be readily identified, and all peaks of these cycles occurred in years of crop failure or poor harvests caused by floods, droughts, or epidemics in the delta or in a large part of the country in the same year or the year before.[39] It is, however, another matter whether fluctuations in local harvests affect the secular movement of prices in an area with easy access to the food-exporting provinces of the country. We may take the deviations of annual prices from their trend values (31-year moving averages) as a measure of harvest fluctuations in the delta and then compare them with changes in secular prices. Assuming that years of normal harvests are those with rice prices within plus or minus 10 percent of the trend, that years of good harvests are those with prices 10 percent or more below the trend, and that years of deficient harvests are those with prices 10 percent or more above the trend, we find that years of good and normal

[39] Wang, "Food Supply and Grain Prices."


64

harvests outnumber years of poor harvests almost 4 to 1 and that average deviations are within the range for normal harvests for all periods (see the last four columns in Table 1.3). Accordingly, cyclical fluctuations in local harvests did not, in my judgment, have a significant impact on the long-term trends of prices, although grain prices were very sensitive to fluctuations in local harvests in the short run.

After reviewing the literature on population, silver stocks, and climate cycles, I can now offer some tentative explanations of the long-term trends of rice prices for the three centuries in question. The high level of prices at the time of the Ming-Qing transition clearly reflects the inflation and the shortage of food consequent to large-scale warfare and a series of natural catastrophes.[40] After the establishment of Manchu rule in 1644, peace and order were gradually restored in the country. Economic reconstruction followed, albeit slowly, with a concomitant improvement in the man-land ratio (depopulation had been drastic between 1600 and 1650). The supply of grain became plentiful relative to the demand for food, despite the cold climate that prevailed in the latter part of the seventeenth century.

During the same time the increase in the stock of money was at best moderate because the new Manchu regime imposed a total ban on coastal trade until the pacification of Taiwan in 1683. The shortage of exchange media received some relief, for the new government issued 1–2 million strings of copper cash annually between 1647–57; but the issuance was later progressively reduced to 200,000–300,000 strings a year.[41] The transaction velocity of money most probably fell after the decades-long inflationary spiral attending the demise of the Ming dynasty. When MV either decreased or remained rather stable and T expanded, prices could not but decline.

In the next phase of the price swing—from the early 1680s to 1820—silver stocks increased at an annual rate of about 0.9 percent, while the central government resumed the policy of expansion in the issuance of copper cash, which was facilitated by rising copper output from Yunnan. In 1724 the production from Yunnan copper mines was about 1 million catties; from the early 1740s through the first decade of the next century the annual output always went beyond the 10 million mark. Meanwhile, the quantity of cash issued by the two mints in the capital jumped from less than 0.5 million strings to well over 1 million a year, and counterfeit coins flooded a greater part of the country.[42] Furthermore, to put down the White Lotus Rebellion (1796–1804) the government spent 200 million taels, an amount equivalent

[40] Zhang Xiangong, "Zhongguo dongbanbu jin wubainian ganhan zhishu de fenxi," in Quanguo qihou bianhua , p. 48.

[41] Wang Yeh-chien, Zhongguo jindai huobi yu yinhang de yanjin, 1644–1937 (Taibei, 1981), p. 25.

[42] See Yeh-chien Wang, "Evolution of the Chinese Monetary System, 1644–1850," in Modern Chinese Economic History , ed. Chi-ming Hou and Tzong-shian Yu (Taibei, 1979), p. 442.


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to its budget expenditures for six or seven years in normal times, which added greater inflationary pressure to the economy.[43]

During the period 1680–1820 China's population grew at 0.72 percent a year, the highest rate in the three centuries under discussion (see Table 1.2). Moreover, eighteenth-century China was a time not only of rapid population growth but also of unprecedented commercialization. Cash crops, such as cotton, mulberries, sugar cane, and tobacco, were planted more widely in various provinces. Markets and towns proliferated as never before.[44] These developments would most likely, in view of Lindert's analysis, noted above, lead to a rise in the velocity of money. Thus both the growth of the money supply and the growth of population were building up strong pressures for inflation. At the same time the cultivated acreage expanded by a half or so; new food crops (sweet potatoes, corn, and peanuts) were spreading, and double-cropping was increasingly practiced.[45] Given generally warm climate the agriculture sector also experienced rather vigorous growth. Without large increases of real output the rate of the price rise would have been much higher during the upswing, which lasted for more than a century.

The second price swing followed a generally downward trend from about 1820, but turned sharply upward in the early 1880s and continued to rise through the 1920s. We may divide the half-century of downswing into two subperiods, with 1850 at the dividing line. Before 1850 warmer climate in combination with the silver drain had the effect of depressing prices. But the money supply may not have decreased because of the multiplication of paper notes issued privately by local banks, money shops, and various commercial stores and because of the increase of copper cash by debasement and counterfeiting.[46] What was more likely to bring prices down was, besides warm climate, the possible decline in the velocity of monetary circulation caused by the apparent drop in the rate of population growth and by the increased holding of silver by the public. Before 1850 the value of silver appreciated steadily in relation to commodities and copper cash.[47] In the expectation that its value was to rise, "wealthy people and rich merchants are striving to hoard silver"; thus observed Bao Shichen, a contemporary scholar with a great interest in political economy.[48] Not only did the silver crisis slow down the velocity of circulation, but it also severely strained the issue of private notes. (Without central banking, private notes were backed by the credit of the individual issuing banks or by the merchants themselves.)

[43] Wei Yuan, Shengwuji , 10:16.

[44] See, for example, Liu Shiji, Ming Qing shidai Jiangnan shizhen yanjiu (Beijing, 1987).

[45] Perkins, Agricultural Development , chaps. 2–3; Yeh-chien Wang, Land Taxation in Imperial China, 1750–1911 (Cambridge, Mass., 1973), p. 7.

[46] See Wang, "Evolution of the Chinese Monetary System," pp. 425–52.

[47] Ibid.

[48] Bao Shichen, Anwu sizhong (1872), 26:8.


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In the third quarter of the century mass uprisings took place in many parts of the country. Most virulent was the Taiping Rebellion, which lasted 15 years and devastated a large part of the Yangzi Valley, particularly the delta area. It cost the government more than 400 million taels to bring these peasant and minority revolts to an end.[49] Hyperinflation followed immediately, as is shown distinctly in Figure 1.2. But the war-induced inflation proved to be just an aberration, for the level of prices sank as quickly as it rose following the return of peace and order in one part of the country after another.

As in the late seventeenth century, economic reconstruction proceeded with an improved man-land ratio, but the pace of economic recovery was faster. For one thing, however ruinous were the mid-nineteenth-century uprisings, they were not accompanied by the natural calamities and the consequent famine that attended the fall of the Ming dynasty (the droughts that hit China four years in a row in 1641–44 were the worst in the last 500 years). For another, continued warm climate further aided agricultural production. Prices were going down as more and more land was brought back under cultivation and more irrigation works were restored or built.

Extending from the 1880s to 1920s, the second upswing was the most inflationary period of the three centuries. While rice prices rose annually at 0.7 percent in the first upswing, they ascended at the rate of 3.3 percent a year in the second (see Table 1.2). During the half-century before the Great Depression the money supply grew at a very rapid rate. In 1930 the total stock of money (currency and deposits) amounted to about 6 billion yuan , of which one sixth were metal currencies.[50] How much money was in circulation 50 years earlier? We can only hazard a guess. China's silver stocks then stood probably around 1.5–1.6 billion yuan (see Table 1.2). Assuming that a quarter of the silver was then in circulation and that the ratios of circulating silver stocks to copper cash and to paper notes were 2 to 1 and 5 to 1 respectively, the total amount of money in circulation would be in the range of 600–700 million silver yuan . It was probably not more than 800 million.[51] Accordingly, the money supply may have increased by eight to ten times, or at an annual rate of 4.2 percent to 4.7 percent in the half-century. Without doubt the accelerating growth of the money supply contributed significantly to the price inflation in the period.

On the other hand, the volume of transactions also grew at an unprecedented rate. The pace of commercialization sped up as the country was increasingly integrated with the world economy from the latter part of the

[49] Peng Zeyi, Shijiu shiji houban de Zhongguo caizheng yu jingji (Beijing, 1983), p. 136.

[50] Rawski, Economic Growth , p. 163.

[51] The ratios between the three components of money are quite close to Peng Xinwei's estimate for the last decades of the Qing period; see Peng Xinwei, Zhongguo huobishi , pp. 888–89. For the ratio of silver in circulation to its total stock, see Wang, Zhongguo jindai huobi , p. 35n.57.


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nineteenth century on. Many local products, such as soybeans, straw mats, tung oil, and bristles, found their way into the world market. The introduction of steamships and railroads lowered the cost of transportation and also contributed greatly to market expansion. Consequently, China's trade, external as well as internal, experienced a rapid expansion. The Nankai indexes show that both imports and exports tripled in volume between 1881 and 1930.[52] The interprovincial trade in real terms was, according to Perkins's estimate, more than three times in the 1920s what it was in the late nineteenth century.[53] In view of these facts, a rate of growth in the volume of transactions of 2 percent a year is probably a good approximation. Then, the transaction velocity of money must, by implication, have risen annually by around 0.5–1.0 percent over the period.[54]

There is good reason to believe that the velocity of transactions rose in the latest phase of secular price movements. First, population increased almost as fast as in the eighteenth century. Since the proportion of self-consumed farm output still approached nearly a half of farm output in prewar China,[55] Goldstone's thesis is applicable, perhaps more than ever before. Second, the development of telegraph communication and modern banking, as well as the continued growth of native banks, greatly facilitated business transactions and thereby had the effect of allowing merchants to conduct their business with reduced holdings of cash.

To recapitulate, the secular movements of rice prices in the Yangzi Delta exhibit two long swings over the three centuries prior to World War II. Starting in the 1640s, prices followed a steep downward trend until the 1680s; afterward they moved up gradually, at 0.7 percent a year, to peak around 1820. The second swing went downward at first, for more than half a century. At the beginning of the 1880s it once again shifted direction, rising swiftly, at the rate of more than 3 percent a year, through 1930.

Given the fact that the delta occupied a position of economic centrality in the country, it is reasonable to regard the trends exhibited by the price series as reflecting supply and demand forces at work in the country. Population, the stock of money, and climate cycles are the three principal variables affecting the long-term trends. I found, among other things, that inflation was

[52] Hsiao, China's Foreign Trade Statistics , pp. 274–75.

[53] Perkins, Agricultural Development , pp. 119–24.

[54] According to Thomas G. Rawski, the income velocity declined by 32–46 percent between 1914–18 and 1934. I consider the transaction velocity a better measure of monetary circulation for the reason that nearly one-half of farm output was still self-consumed in prewar China. But as Michael D. Bordo and Lars Jonung point out in their study of secular trends in the velocity of circulation in various countries, a fall in income velocity may coincide with a rise in transaction velocity. See Rawski, Economic Growth , pp. 161–65; Bordo and Jonung, The Long-term Behavior of the Velocity of Circulation (Cambridge, 1987), chaps. 2–3.

[55] Buck, Chinese Farm Economy , p. 199.


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nearly always accompanied by substantial expansion of the money supply, deflation by stagnation in monetary growth. The same was true for population. Rapid population growth appears to have contributed to inflation most likely because of changes in the age structure and because of the population-induced rise in the velocity of money. On the other hand, a decrease in population or a slowing down in its growth rate correlates strongly with deflation. Gradual changes in the climate probably helped moderate price inflation in the eighteenth century, pushing prices down in the nineteenth century and up in between; but the impact of falling temperatures on market transactions and hence on prices was neutralized or overbalanced by other factors in the latter part of the seventeenth century.


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Two
Grain Prices in Zhili Province, 1736–1911: a Preliminary Study

Lillian M. Li

The recent availability of grain price data for the Qing period now provides historians with an unprecedented opportunity to develop their understanding of the agricultural economy of every region of China. This study represents a preliminary attempt to apply the Qing period grain price data to an ongoing study of agriculture, food crises, natural disasters, and government relief in North China.[1] This rich series of data allows us to gain insight into the nature of the agricultural regime in the North, the long-term trends in the agricultural economy, the nature of short-term changes, the economic and social impact of natural disasters, and the extent of market integration and development. Because this is a first attempt, the methodology employed is exploratory, and the conclusions drawn should be regarded as tentative. In the future, completion of the data set and refinement of the methodology may

Many people have given me invaluable assistance with this project. In particular I would like to acknowledge the substantial contribution made by Keith Head (Swarthmore, '86), who helped analyze these data during the summers of 1986 and 1987 with the support of the Joel Dean Fund of Swarthmore College. Gudmund Iversen, Professor of Statistics at Swarthmore, has been generous with his time and expertise. A number of other Swarthmore colleagues have also generously offered guidance or assistance: John Boccio, Stefano Fenoaltea, Robinson Hollister, Jody Ann Malsbury, Frederic Pryor, F. M. Scherer, and Leah Smith. Several Swarthmore students have diligently assisted in the entering of data or with graphics: Patrick Awuah, Donald McMinn, Karen Neumer, Bonnie Spear, and Paul Talcott. I have benefited greatly from the comments of both economists and historians who participated in the Workshop and Conference on Economic Methods for Chinese Historical Research, held in January 1987 in Honolulu, Hawaii, and January 1988 in Oracle, Arizona.

[1] This essay is part of a projected book on this region, Flood and Famine in China: State Policy and Ecological Disaster in the Hai River Basin, 1690s–1990s . The present study should be regarded as preliminary in part because the set of grain price data that I have collected thus far, though extensive, is still incomplete.


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alter the conclusions, but in the interim these tentative results will, hopefully, generate some hypotheses for further analysis and research.

North China is the birthplace of China's civilization and the location of its imperial capitals throughout most of its history. Yet in recent centuries North China has also suffered unfavorable natural conditions and economic hardships. Unlike the fertile rice-growing regions of South China, the North practices mixed cultivation of dry-land grains under climatic conditions that have made the fate of each year's crop extremely uncertain.

Zhili Province (roughly equivalent to modern-day Hebei Province), the subject of this article, best embodies these contradictory characteristics. As the site of the imperial capital of Beijing since the thirteenth century, Zhili had economic advantages that derived more from its political centrality than from its natural resources. The emperors of the Qing dynasty (1644–1911), like their predecessors of the Yuan and Ming dynasties, sought to bring some of the material benefits of the South to the North. The Grand Canal was maintained specifically for the purpose of transporting rice and other products from the South to the capital. The grain tribute was intended to feed the court and officialdom, but it had the unintended consequence of linking the economies of North and South. It was the only significant North-South thoroughfare until railroads were introduced in the twentieth century. The strategic importance of Beijing also dictated that the court pay special attention to stocking both civilian and military granaries in the capital area as well as to the maintenance of the waterways in the province.

The care and protection of the Qing imperial court and its retainers took place, however, in the context of natural conditions that seem to have deteriorated through the centuries. The river system in particular has been a source of ongoing headaches. In ancient times many rivers flowed down from the Taihang mountain range, which forms a boundary between Zhili and Shanxi, its neighbor to the west. But the construction of the Yuan dynasty Grand Canal severed the normal channels of these waterways to the ocean, forcing them to flow into the canal itself. From that time on, the only outlet of five major waterways—the Bei, Yongding, Daqing, Ziya, and Nanyun rivers—was a single, short channel flowing from Tianjin to the ocean, known as the Hai River. This entire river system is known today as the Hai River Basin and is one of China's major river conservancy concerns.

Although smaller in scale, the Hai River Basin has many of the same characteristics as the larger Yellow River Basin, with which it became more closely linked after the Yellow River changed course in the 1850s to the north side of Shandong peninsula. Both have extremely shallow beds, which over the centuries have risen with increased siltation. In recent times deforestation in the mountains has accelerated the silting process. The control of these rivers requires intensive dredging, diking, and other engineering efforts. Over time, the vulnerability of these rivers to flooding has greatly increased, as has


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figure

Map 2.1.
Zhili Province in the Qing Period. Adapted from the end-map in Qingdai Haihe Luanhe honglao dang'an shiliao
(Beijing, 1981).


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their tendency to waterlog the soil, particularly given the flat, even concave, topography of the land in their lower reaches.

The uneven pattern of rainfall in North China further contributes to the danger of flooding. Most of the annual rainfall in the region is concentrated in the summer months of July, August, and September. It does not take very much rain to cause the rivers to overflow. Ironically the danger of flooding occurs in the context of the overall scarcity of rainfall in the North. As a rule, drought is a more pervasive and potentially serious problem than flood, but it is the river system, the nature of the topography, and the soil that make flooding such a frequent danger and waterlogging a near-chronic condition in some low-lying areas.

Throughout the Qing dynasty, the court and bureaucracy paid close attention to the dual threats of flood and drought. In part this reflected a general bureaucratic concern with disaster prevention that was empirewide, but Zhili Province clearly posed special problems because of its political importance. The bureaucratic record reflects the tremendous concern of the court and officials with river conservancy and with the stocking of granaries to guard against shortages. In the eighteenth century these efforts appear to have been quite successful in both the prevention and relief of famines, but vigilance was constantly required. During the nineteenth century, however, especially from mid-century, either the efforts were less successful or nature was harder to control. Starting with the great drought of the 1870s, North China was periodically beset by one disaster after another until the 1960s. In the 1890s there was hardly a year in which flooding did not occur somewhere in this region. In 1917 there was a massive, provincewide flood, followed closely by the North China drought of 1920–21. Since 1949 the government of the People's Republic has assigned high priority to the management of land and water resources in the area. The sinking of tube wells for irrigation in many parts of the province has proceeded together with engineering projects to prevent the recurrence of major floods, such as that of 1963.

Despite this somewhat unstable context, North China in general, and Zhili/Hebei in particular, have been able to sustain a large population increase in the last two centuries. In 1749 the population of Zhili was reported to be about 14 million.[2] In 1790 the population was recorded as 23.5 million.

[2] This 1749 figure almost certainly represents an underestimate. Before the baojia system of population registration was reformed in 1775, underestimation was common. See Ping-ti Ho, Studies on the Population of China, 1368–1953 (Cambridge, Mass., 1959), pp. 36–48. Ho concludes that the population figures of the 1741–1775 period were on the average underestimated by 20 percent. By that formula, Zhili's 1749 population may have been close to 16.8 million. Zhili did not submit its first detailed population return under the reformed system until 1778. If Zhili's population was 16.8 million in 1749 and 23.5 million in 1790, it experienced a 40 percent increase over 41 years.


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The rate of growth slackened in the next half-century; in 1850 the reported population was only 23.4 million. In 1933 Hebei's population was about 38.4 million, and in 1953, 46.6 million. In 1982 the population of the province, together with that of the independent municipalities of Beijing and Tianjin, was about 70 million.[3]

The history of the Zhili/Hebei area of North China poses questions of enormous consequence. How could this area maintain its political centrality for so many centuries despite a relatively weak economic base? How could substantial population growth be sustained in the face of what appear in the historical record to be frequent and regular occurrences of drought and flood?

The Price Data

During the Qing dynasty each provincial governor was required to submit to the throne a monthly report of grain prices in his province. This became a regular bureaucratic practice by the beginning of the Qianlong period in 1736. The Qing archives in Beijing and Taibei have a rather complete set of reports from Zhili for the eighteenth century and a more scattered sampling from the nineteenth century. I have collected approximately 609 of these monthly lists, including 233 monthly lists for 1738–64, 171 lists for 1765–95, and 205 lists for 1796–1910.[4]

In the first subperiod, 1738–64, the lists give the low and high prices of seven types of grain from each prefecture (fu ) or independent department (zhilizhou ) in the province: rice (daomi ), high-grade millet (shang sumi ), ordinary millet (cisumi or zhongsumi ), white wheat (baimai ), red wheat (hongmai ), black beans (heidou ), and sorghum, or kaoliang (gaoliang ). After 1765 only five grains were reported: millet (sumi ), sorghum (gaoliang ), a type of panicum millet (nimi ), wheat (mai ), and black beans (heidou ).[5]

Prices from seventeen prefectures or independent departments were reported by the governor-general of Zhili, although not all were reported in every period. Shuntian Prefecture (where Beijing was located) was not included until 1771. Chengde Prefecture was not included in the reports until

[3] The Qing figures are taken from Philip C. C. Huang, The Peasant Economy and Social Change in North China (Stanford, 1985), p. 322. The 1982 figure is reported in Judith Banister, China's Changing Population (Stanford, 1987), pp. 298–99, among other places.

[4] I am indebted to the staff of the Ming-Qing archives of the National Palace Museum in Taibei and the First Historical Archives in Beijing for allowing me access to these grain price lists.

[5] Sumi was Setaria italica , sometimes called foxtail millet, which was the most common type of millet grown in north China. Nimi was Panicum milaceum , sometimes called broomcorn millet. Heidou , lit. "black bean," was a type of soybean. See Francesca Bray, Agriculture , vol. 6, pt. 2 of Joseph Needham et al., Science and Civilization in China (Cambridge, Eng., 1984), pp. 434–48.


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1778. From 1736–68, the prices for Baoding Prefecture, the location of the provincial capital, were reported a month in advance of the other provinces.

These grain price reports were submitted monthly, according to the Chinese lunar calendar—with intercalary months ("leap months") added from time to time to make the lunar year catch up to the solar year. Since any given lunar month might lag behind its corresponding solar month by up to two months, solar months might be more appropriate to use in studying the agricultural cycle. In this study lunar-month prices have been used where aggregated data for year or multiyear periods would cancel out the variations in the months. However, where seasonality is an important concern, data are converted to correspond with the solar months.

Agriculture and Food Availability in Zhili

As the above lists suggest, many grain crops were grown in Zhili. Although rice was the subject of experimentation in the early eighteenth century, it was never very widely grown.[6] Wheat was the luxury grain in the North. Planted in the fall, it was harvested the following summer. Millets of various types were the staple of the poor people's diet. Like sorghum, millet was planted in late spring and harvested in the fall. It was hardy, having a tolerance for heat and drought. Sorghum, on the other hand, was more flood-resistant. Less desirable than millet as a food, sorghum was also used in making wine, and its stalks were burned for fuel.

Other crops were important too. Black beans, reported in the Qing grain price reports, were used both as a feedgrain for horses and as food for humans. They also became a cash crop, used in the production of oil. In the twentieth century corn became a major food crop, but it is not at all clear how extensively it was grown in the Qing period. Finally, cotton was the most important nonfood commercial crop in Zhili in Qing and later times, but the extent of its cultivation before the twentieth century is a matter of some uncertainty.[7]

There were numerous cropping systems in North China, with great variation within regions. One system was a three crop rotation over two years. As Philip C. C. Huang describes it, sorghum and millet were planted in May or June and harvested in September or October. Wheat was planted in the fall and harvested in July, too late for the planting of sorghum or millet, so soy-

[6] See Timothy Brook, "The Spread of Rice Cultivation and Rice Technology into the Hebei Region in the Ming and Qing," in Explorations in the History of Science and Technology in China (Shanghai, 1982), pp. 659–89, for an exhaustive study of experimentation in rice cultivation in North China.

[7] Philip C. C. Huang, pp. 111–14, asserts that cotton cultivation was widespread in Zhili by the late Ming period, but others have disputed this. See, for example, Loren Brandt's review of Huang, Peasant Economy , in Economic Development and Cultural Change 35 (April 1987):670–82.


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beans were planted for harvesting in October and November, after which the land would be left fallow. Other systems involved interplanting.[8]

It is not until the twentieth century that we have some idea of the acreage devoted to each of these major crops. John Lossing Buck's well-known 1929–33 farm study reports that in the winter wheat—kaoliang region, which included Hebei, wheat accounted for 45.5 percent of crop area, millet for 23.1 percent, sorghum for 18.5, corn for 16.3, soybeans for 13.4, and cotton for 8.6.[9] A survey conducted by Zhang Xinyi reports the following crop areas for Hebei in the 1930s: wheat, 31.3 million mu (28 percent), millet, 24.3 million mu (22 percent), sorghum, 21.7 million mu (20 percent), and corn, 15.5 million mu (14 percent). Beans (dadou ) accounted for 9.8 million mu (9 percent), and cotton for 8.1 million mu (7 percent).[10]

These two estimates are similar in that they show the primary importance of wheat, millet, and sorghum, in that order. There is, of course, every reason to believe that the situation in the Qing period differed in significant ways. Corn almost certainly played a lesser role, and perhaps the proportions of wheat, millet, and sorghum were different from the twentieth century. Unless further research uncovers new sources, it is unlikely that we shall ever have an exact picture of the production of these crops during the Qing period, but this general picture of the relative importance of these crops is unlikely to be changed.

This study analyzes the prices of three of the grains reported in the Qing memorials—wheat, millet (sumi or setaria millet), and sorghum—because of their centrality in the agriculture and the diet of the North. Black beans probably did not constitute a significant portion of the caloric content of the average diet. Panicum millet and rice were unlikely to have been of critical importance either.

Two additional factors probably influenced the price structure, although they were exogenous to Zhili's agricultural production. First, a significant portion of the grain consumed in Zhili during the Qing was not grown in the province but was imported from the South through the grain tribute system. During the Qing, 3–4 million shi of grain were transported annually to the metropolitan area.[11] Most of this was destined for the consumption of the court, bannermen, and soldiers stationed in the province. But it is quite likely

[8] Philip C. C. Huang, Peasant Economy , p. 61.

[9] John Lossing Buck, Land Utilization in China (Chicago and Nanking, 1937; repr. New York, 1956), 1:211–12. Because there was some double cropping, the percentages exceed 100.

[10] Cited in Shina nogyo kiso tokei shiryo , comp. comp. Toa kenkyujo (Shanghai, 1941), 1:41–43.

[11] The shi was a measure of grain volume. According to an authoritative estimate, "the likely weight of an imperial shih [shi ] of milled rice in the eighteenth century was about 185 pounds, with a margin of error unlikely to have been more than 5 percent either way (that is, the likely range was roughly 175 to 195 pounds)." Han-sheng Chuan and Richard A. Kraus, Mid-Ch'ing Rice Markets and Trade: An Essay in Price History (Cambridge, Mass., 1975), p. 98.


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that some quantity of grain reached the market, through either direct or indirect sales. Pierre-Etienne Will has estimated that 0.5 million shi a year was not used for direct consumption in Beijing.[12] Whether this amount was sufficient to have an impact on general grain price levels is not clear, and is a matter that deserves further investigation.

The second factor was an extensive state granary system, which flourished in the eighteenth century. The system included three types of granaries: the "ever-normal" granaries, the community granaries, and the charity granaries. During the eighteenth century these granaries were well stocked. In 1767, for example, the governor-general of the province reported that 3,534,536 shi of grain were actually stored in the province, 2,549,566 shi of which were in the ever-normal granaries.[13] By the nineteenth century, however, all granary holdings were down, especially those of the ever-normal granaries. In 1833, for example, the governor-general reported holdings of only about 616,000 shi , of which 275,719 shi were held in ever-normal granaries.[14]

Long-Term Trends

The Zhili price series affords an important opportunity to learn about the long-term behavior of prices over two centuries. This is important not only because of its relevance to a study of Zhili's economy in particular but also because it can serve as a general indicator of overall economic trends in North China. The price trend of these three major grains has several major characteristics. As Figure 2.1 shows, the overall price rise from 1738 to 1910 was not steep. During the eighteenth century prices rose very gradually. In the early part of the nineteenth century, there was a sharp increase in the price of wheat, followed by rises in the prices of millet and sorghum. From the 1830s to 1850, roughly during the Daoguang reign, prices fell precipitously, only to climb back up by 1870 and fall again. From 1890, prices rose steeply and steadily until the end of the dynasty. When these same prices, again grouped by four-year averages, are indexed to their base-period (1738–41) prices, the trends can be seen more clearly, as in Figure 2.2. At the end of the eighteenth century, the prices of wheat, millet, and sorghum were respectively only 134 percent, 122 percent, and 133 percent of the base-period prices. By the end of the dynasty, the three grains had risen to 258 percent, 243 percent, and 272

[12] Pierre-Etienne Will, Bureaucratie et famine en Chine au 18e siècle (Paris, 1980), pp. 241–44. Will points out that tribute grain surpluses were rarely used outside Zhili.

[13] Gongzhongdang, Palace Memorial Archives (Taibei), Qianlong 023616, 1767/12/12. These figures represent the actual holdings at the time of the report; the theoretical holdings, which took into account amounts loaned out but not yet paid back to the granaries, were larger.

[14] Junjidang, Grand Council Archives (Beijing), Daoguang 63339, 1833/4. These figures represent actual, not theoretical, holdings.


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figure

Fig. 2.1.
Grain Prices in Zhili Province, 1738–1910 (four-year averages, in tales per shi )
Note: See text for a description of the data used in this study. In Figs. 2.1, 2.2, and 2.4, there is a substantial amount of
missing data for the nineteenth century.


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figure

Fig. 2.2
Grain Prices in Zhili Province, 1738–1910: Indexed to the Base Period (four year averages; 1738–1741 = 100)


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figure

Fig. 2.3.
Annual Grain Prices in Zhili Province, 1738–1806 (taels per shi )


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percent of the base-period prices respectively, but if the final two decades of the dynasty are omitted, the increases are only 146 percent, 155 percent, and 165 percent respectively of the base-period price.

To show more clearly what the annual trends looked like, in Figure 2.3 I present the annual averages (unadjusted) for 1738–1806.[15] Here once again it is apparent that the trend for the eighteenth century was not very steep and that there seem to be cycles of about four to five years for millet and sorghum and possibly longer cycles in the price of wheat. Further, it appears that the prices of all three grains followed roughly the same trend, but there was at least one period—in the 1740s—in which the rise in the price of wheat was not accompanied by a rise in the prices of the coarse grains. A contrary trend is seen in the early 1760s, when a rise in the prices of the coarse grains was not accompanied by a continuous rise in the price of wheat.

[15] Only the eighteenth century is presented because that is the century for which the data are most complete. In this essay I have used the mean of the high and low prices reported from each prefecture each month. In analyzing the high and low prices separately, I found that their behavior generally followed the same pattern. This was true of all three grains. There seemed, then, little point in studying either high or low prices separately, particularly in a preliminary study.

The adjustment of annual averages to account for the seasonal variations in the missing data presents a greater challenge. Although it would be preferable to adjust the annual averages, in fact each possible procedure for adjustment produces its own problems. One way is to estimate, or "predict," missing data by using all the known data in combination with the seasonal coefficients produced by the regression equation discussed in the next section. Another method is to adjust each known piece of data by its relevant monthly coefficient. This creates not an annual average but an estimated January price based on all known data. Until a more accurate method of adjusting data can be found, however, leaving the data unadjusted does not, I believe, produce large distortions, because the annual seasonal range of prices in Zhili was not more than 0.14 tael in the extreme case of wheat (from a high of +0.08 taels in April to a low of -0.06 taels in September in the regression on data excluding Baoding, as presented in Table 2.1). For example, in 1740, a year for which there are no missing data, the mean price was 1.38 taels. If data were missing for the three low-price months of July, August, and September, the unadjusted mean of the other nine months of data would be 1.40, a distortion of only 0.02 tael, or 1.4 percent. If I use the regression coefficients to estimate the missing data, the annual mean would come to 1.39. The difference between adjusting or not adjusting the data is only 0.01 tael, or less than 1 percent of the actual mean price of 1.38. This example presents one of the worst possible cases since the mean price was rather low in comparison to the rest of the period 1738–1910. In a year when the prices were relatively high—over 2.50 taels, for example—the use of a seasonal coefficient would tend to underestimate the missing data (e.g., 0.06 tael would make less difference). The only time that a larger distortion might be introduced by not adjusting the data would be in a crisis year for which we have only one month of data in the early part of the year not yet affected by the crisis. In such a case, the price level would be very much underestimated. If, instead of "predicting" the price where data are missing, we use the seasonal coefficients to adjust the known data, the result in this example would be 1.37, only 0.01 less than the actual mean price. The fact that this hypothetical January price is close to the mean is somewhat accidental; the actual January price for that year was 1.42, which reflects the fact that the previous year was a crisis year (see n. 21 below) and that the price of wheat was abnormally high in the winter months.


81

figure

Fig. 2.4.
Prices of Coarse Grains Relative to Wheat in Zhili Province, 1738–1910 (5-year averages; price = % of wheat price in
concurrent period)


82

On the whole these graphs show that the prices of millet and sorghum followed each other very closely, with sorghum consistently being the cheaper of the two. Although millet was a less desirable grain than wheat, the figures show that there were years when its price approached that of wheat (and even exceeded it in the early 1760s, early 1780s, and early 1800s). Figure 2.4 charts the five-year averages of millet, sorghum, and black bean prices as a percentage of wheat price averages for the concurrent periods. This shows more clearly the few periods in which millet prices exceeded those of wheat (around 1760, 1800, 1870, and 1890). And it also shows that while millet, sorghum, and black bean prices maintained a spread in the eighteenth century, during the next century the price of black beans relative to wheat and millet steadily increased.

These trends suggest certain hypotheses about the roles played by these commodities in Zhili's agricultural markets. First, the generally higher price of wheat confirms that it was the luxury grain and suggests the possibility that its markets were relatively well developed. Second, the price trends of millet and sorghum seem to parallel each other, suggesting that they were responding to the same cropping cycle and weather patterns. On the other hand, millet's consistently higher price confirms our impressions from the twentieth century that millet was the staple grain and was valued more highly than sorghum. However, the fact that millet prices occasionally reached, and even surpassed, those of wheat raises the question of the extent to which it was a commercialized agricultural commodity and the extent to which it was considered a substitute for wheat. Finally, the price of black beans rose relative to wheat prices in a clear secular trend, probably because of a growing nationwide, perhaps even international, market for bean oil and other bean products produced primarily in Manchuria but also in North China.[16]

Regression Analysis

At any given month, the price of grain may have reflected not only the long-term price trend but also a place in the seasonal cycle, the impact of any irregularities in the weather that would affect output, as well as any exogenous factors affecting demand. To permit us to estimate, and separate out, the effects of time, seasonal variation, and crisis years on grain prices, a multiple regression of prices was run using the following equation:

figure

[16] The extent to which Zhili/Hebei was involved in this industry is a subject that awaits further research. Certainly in the twentieth century one of Manchuria's principal industries and export commodities was soybean oil and other soybean products. See, for example, Lien-en Tsao, "The Marketing of Soya Beans and Bean Oil," Chinese Economic Journal 7, no. 3 (Sept. 1930): 941–71.


83

Here the price in any given month (P ) is measured by a constant (a ), plus a coefficient (b ) multiplied by time (T ), plus dummy variables (d ) that are created for every month (M ) except January and another dummy variable created for crisis years (C ). The dummy variable C is entered as a l in a crisis year and as a 0 in noncrisis years.[17]

Since one of the purposes of this regression is to measure the effect of seasonality, all data used in the analysis were converted to solar prices using a method that weights the price by the number of appropriate lunar days in each solar month.[18] Since prices for Baoding, the seat of the provincial capital, were routinely reported a month in advance of prices from other prefectures during 1736–68, a period that represents 44 percent of my data (268 out of 609 monthly reports), I also ran the regression with Baoding prices excluded lest the months for which there are only Baoding prices unduly affect the results.[19]

The most problematic decision in setting up this regression concerned the crisis years. Although it would be ideal to use meteorological data (temperature and rainfall) to code years of crisis, I do not yet have access to such data. In their absence, I am forced to rely on reports of crises found in the historical records. Since such data represent the human (social and political) impact of natural disasters as seen through administrative lenses, they must necessarily have their limitations. The historical record itself is necessarily subjective, and our access to it is also incomplete, a function of what documents have survived in which collections and archives. In this regression analysis I selected as crisis years those years recorded in Qingdai Haihe Luanhe honglao dang'an shiliao[20] as having floods that affected 50 counties (xian ) or more. Although this compilation itself may be flawed, it is drawn from a survey of local gazetteers. In addition I selected from miscellaneous records at my

[17] This regression equation was also run with T as a variable to see if there were perhaps a nonlinear relationship between time and price. The results were not statistically significant (t < 2), and overall did not produce a better R , and so we concluded that there is not a second-order relationship between time and price.

[18] I am very grateful to Peter C. Perdue for providing the complex formula and table by which the lunar data have been converted to solar data and to Keith Head, who wrote the program that adapted this table to my data.

[19] For example, the price list submitted by the Zhili governor-general reporting prices for the third lunar month included the fourth-month prices for Baoding Prefecture, presumably because these local prices were already available to him by the time he received the third-month prices from the outlying prefectures. Consequently, if I have the third-month provincial report but am missing the fourth-month provincial report, the Baoding price is the only one I have for the fourth month and the provincial average in fact represents only Baoding. Because there is reason to think that prices behaved differently in the capital, I have thought it wise to run the regression both with and without Baoding prices.

[20] Qingdai Haihe Luanhe honglao dang'an shiliao (Beijing, 1981).


84
 

TABLE 2.1 Annual Trends and Seasonal Variation in Grain Prices in Zhili Province, 1738–1910 (regression coefficients, in taels per shi )

 

Wheat Price

Millet Price

Sorghum Price

Variable

Excluding Baoding

Including Baoding

Excluding Baoding

Including Baoding

Excluding Baoding

Including Baoding

Crisis year

0.06

0.08

0.06

0.07

0.01

0.02

Time (1738 = 0)

0.0059

0.0056

0.0061

0.0061

0.0045

0.0045

February

0.01

0.01

0.03

0.03

0.02

0.02

March

0.04

0.03

0.05

0.04

0.05

0.04

April

0.08

0.05

0.06

0.05

0.06

0.05

May

0.06

0.05

0.06

0.06

0.06

0.06

June

0.02

0.01

0.07

0.07

0.08

0.08

July

- 0.05

- 0.04

0.09

0.09

0.08

0.09

August

- 0.04

- 0.06

0.09

0.11

0.08

0.08

September

- 0.06

- 0.08

0.06

0.05

0.02

0.01

October

0.02

- 0.01

0.08

0.05

0.04

0.02

November

0.02

- 0.02

0.03

0.01

0.01

- 0.01

December

0.02

- 0.02

0.00

0.02

0.01

0.01

Constant

1.56

1.62

1.30

1.33

.87

.90

R2

.49

.44

.59

.56

.58

.55

F

40.13

37.99

61.07

61.59

57.79

58.12

disposal years in which 25 or more counties were affected by drought. This combined method yielded 54 years that were coded as crisis years.[21]

When the regression was run for 1738–1910, with Baoding prices excluded, the results (see Table 2.1) showed that the effect of time was 0.0059, 0.0061, and 0.0045 taels for wheat, millet, and sorghum respectively, meaning that for each year, the price increased by this amount for each grain. These increases confirm the impression, derived from Figure 2.1, that the inflationary trend was not very steep or significant over this long time period.

The regression results show the monthly variation of prices, using January as the base month. The results, graphed in Figure 2.5, show the effect of the multiple-crop system of Zhili. Wheat prices reached their peak in April but did not reach their lowest point until September—somewhat surprisingly, since the wheat harvest took place around July. Millet and sorghum prices, however, peaked in June, July, and August, from which point they fell until

[21] 1738, 1739, 1743, 1744, 1747, 1750, 1759, 1761, 1762, 1771, 1780, 1790, 1794, 1801, 1806, 1808, 1810, 1813, 1814, 1816, 1819, 1820, 1822, 1823, 1830, 1832, 1834, 1835, 1839, 1840, 1855, 1871–73, 1876, 1877, 1879, 1882, 1883, 1886–90, 1892–1900, and 1908. Of course, I lack price data for many of these years.


85

figure

Fig. 2.5.
Seasonal Variation of Grain Prices in Zhili Province (Excluding Baoding), 1738–1910 (difference from January
price, in taels per shi )


86

the end of the year, with a brief jump in October. The prices of all three grains rose steadily through the early months of the year. But if Baoding is included, as in Figure 2.6, there is a smoother descent for wheat in the fall, but in October, wheat, millet, and sorghum prices peak briefly. If Baoding is included and only eighteenth-century data are analyzed, the shape of the seasonal curves is smoother, with the annual low price for wheat and the high prices for millet and sorghum all occurring in August. Preliminary analysis of price data separately for each prefecture shows that the exact patterns of seasonality vary slightly from place to place but fall within a general pattern.

The regression coefficients also vary when either eighteenth-century data or nineteenth-century data are used alone. Although the greater quantity and likely higher quality of the eighteenth-century data would suggest that its regression results would be more significant, in fact the R2 —a measure of the amount of variation in price explained by the regression as a whole—is considerably higher when both centuries of data are used together.[22] When a Chow test—a measure of the extent to which two samples may affect the regression results—was run on these regressions, no significant difference was found between the two centuries of data.[23] Consequently, the results of the regression run on both centuries of data are used throughout this paper.

These sets of monthly coefficients present several puzzles. Why does the annual low price for wheat come so much later than its presumed harvest-time? Why do the prices of sorghum and millet jump around in the autumn and early winter? Why does the seasonal pattern seem to vary depending on the time period and the number of prefectures included?

One possible explanation may be that seasonal patterns do differ from one area to another. In Buck's survey, the price of wheat in the wheat-kaoliang area was highest in January–February, and lowest in May–June. Millet had its high price in May–June and its low price in November.[24] But another 1930s study of prices in Zhengding, Hebei, found that the annual high price for wheat was in April, and the low price followed immediately in May, while the high price of millet was in June–July, and the low immediately afterward, in August–September.[25]

[22] Running the regression on either eighteenth- or nineteenth-century data alone produced much lower R s than using two centuries of data together. For wheat, for example, the R is 0.4859 for both centuries together, 0.2029 for the eighteenth century, and 0.1662 for the nineteenth century. These differences are undoubtedly due to the strength of the time trend, which is by far the most significant variable in any of these regressions.

[23] The Chow test was developed by Gregory C. Chow, "Tests of Equality between Sets of Coefficients in Two Linear Regressions," Econometrica 28, no. 3 (July 1960):591–605. I am grateful to F. Michael Scherer, formerly of the Economics Department at Swarthmore College, for his help with this test. In F-ratio was insighnificant for both wheat and millet (0.83 and 0.63, respectively) and marginally significant for sorghum (1.80).

[24] Buck, Land Utilization in China , 1:335–36.

[25] "The Seasonal Variation of Prices for Farm Products and the Profitability of Storage," Economic Facts , no. 7 (October 1937):319–42.


87

figure

Fig. 2.6.
Seasonal Variation of Grain Prices in Zhili Province (Including Baoding), 1738–1910 (difference from January price,
in taels per shi )


88

The irregularities in the autumn prices could also be explained by deliveries of grain tribute to the province, which occurred before winter, or alternatively by the restocking of granaries, which was also done in the autumn. Since it was mostly unhusked millet that was stored (because wheat spoils too quickly), post-harvest prices may have risen a bit before falling to their annual low at the end of the year.[26]

Although the precise effects of seasonality seem to differ according to the way the regression is run, the overall pattern is clear. Moreover, the regression analysis reveals that the effect of seasonality was less important than the effect of crises.[27] The spread of prices within each year was less than 0.14 tael for wheat and less than 0.11 tael for millet and sorghum, no matter which way the regression is run.

Although regression analysis is a sophisticated tool for measuring the separate effects of different variables on price, it has its limitations. For the purposes of this grain price analysis, it can give us a good measure of the factors in price variability, but it is sensitive to differences in time and space and therefore cannot be absolutely accurate. It must be used as an approximate tool, not a precise measure. In future work, more accurate specification of crisis years, more complete data, and work with disaggregated, prefectural data may produce more satisfactory results. Still, these preliminary results do inspire some confidence. Although the t -value for each of the monthly coefficients tends to be under 2, in using descriptive data, as opposed to sampling, t -values are not relevant. Moreover, the R2 s achieved here are not poor, and the F -values are so large that the overall strength of the regression variables can be seen to be very substantial.[28]

Crises

One purpose of the regression analysis is to try to measure the effect of crises on price levels. The regression analysis for 1738–1910 suggests that the effect of a crisis year was, on the whole, not very dramatic. The inclusion of Baoding data raises the effect slightly (see Table 2.1). A separate regression run solely on Baoding data results in higher crisis coefficients. Moreover, if the

[26] Pierre-Etienne Will and R. Bin Wong, Nourish the People: The State Civilian Granary System in China, 1650–1850 (Ann Arbor, 1991), manuscript pp. 5, 132. Thirty percent of the granary stock was supposed to be sold off each year and replaced.

[27] This impression was confirmed when the regression was run with only the monthly dummies as variables. The resulting R s were astonishingly low: 0.0099, 0.0129, and 0.0189 for the three grains, respectively. Running the regression with interaction variables (C * M , C *T , etc.) also produced very low R s showing that a nonlinear relationship was not a better explanation for the behavior of prices.

[28] Concerning these and other statistical problems, I am grateful to Gudmund Iversen, professor of statistics at Swarthmore College, for his judgment and invaluable suggestions.


89
 

TABLE 2.2 Grain Price Increases in Crisis Years in Zhili Province, 1738–1910 (regression coefficients, in taels per shi )

Period

Wheat Price

Millet Price

Sorghum Price

Year, including Baoding

0.08

0.07

0.02

Year, excluding Baoding

0.06

0.06

0.01

Year, Baoding only

0.14

0.08

0.03

Crop year, including Baoding

0.09

0.15

0.11

Crop year, excluding Baoding

0.04

0.13

0.08

Crop year, Baoding only

0.12

0.14

0.13

monthly price data are divided into a "crop year," from July to the end of June, the effect of crises is seen to be far higher for the coarse grains but slightly lower for wheat. Table 2.2 summarizes the coefficients generated in these various ways. In all cases the R2 s are very similar, as are the coefficients for time. The monthly coefficients vary, but are within two standard errors of zero.

Dividing the year at July generates a higher coefficient for the crisis variable, because a crop year more closely approximates the seasonal weather pattern in the North. Since July and August are the months of greatest annual rainfall, droughts and floods have their first impact in the second half of the year and the next winter. They affect the coarse grains first. Although sorghum is known to be flood-resistant, its price seems to be the most differentially affected by the use of this technique. Overall, however, the price of millet was the most affected in a year of crisis.

The fact that Baoding prices were more affected by crises than prices in the rest of the province raises some interesting questions. As the seat of the provincial capital, Baoding Prefecture might be expected to have higher prices than outlying prefectures because of a stronger demand for grains. Its wheat prices were much more affected by crises than wheat prices in the rest of the province, suggesting a stronger demand for the luxury grain in the capital than elsewhere.

Tables 2.3, 2.4, and 2.5 attempt to evaluate the effect of natural disasters on grain prices in three crisis periods.[29] The tables compare the actual prices of the three grains with their predicted prices (for a noncrisis year), calculated with the regression coefficients presented in Table 2.1, using data for 1738–1910 but excluding Baoding.

[29] The period 1743–44 was chosen because it is a well-studied drought, the periods 1762–63 and 1775–76 because the data were relatively complete. Although 1775–76 is identified in documentary sources as a drought period, these years are not coded as crisis years in the regression analysis (see n. 21 above) because fewer than 25 counties were apparently affected.


90

figure

Time is the year minus 1738. Each case study covers the two calendar years that include the period of crisis.

In 1743–44 Zhili experienced a drought. As Table 2.3 shows, the actual prices of all three grains at the beginning of 1743 were below the predicted prices, particularly so for millet and sorghum. However, by May and June, when wheat prices should have fallen, they instead continued to rise, reflecting a poor harvest or an impending harvest. Although the data for the key months of the crisis, from summer 1743 to summer 1744, are missing, we can see that by July 1744 the prices of wheat and millet had risen 0.20 tael above the predicted price, and the price of sorghum, 0.15 tael. By the end of the calendar year, however, the price of wheat was almost down to the predicted level, and the prices of millet and sorghum had fallen to well below the predicted prices.

Although the actual prices at their peak reflected the impact of the drought more than the crisis coefficients of 0.06, 0.06, and 0.01 predicted they would, nevertheless it can certainly be concluded that the overall impact of the drought was rather limited in magnitude and duration. This substantiates to a considerable degree the picture of this crisis drawn by Pierre-Etienne Will.[30] First, the impact of the drought, according to the historical record, was limited to 27 counties, primarily in four prefectures. Second, these counties were the recipients of massive amounts of government relief, deployed from several sources, most notably the state granaries in Tongzhou. The grain prices in these prefectures taken separately show no difference from the provincial trends. The famine relief campaign mounted by the government was truly a model effort, and the price history seems to show that the efforts of the Qing officials were well rewarded.

Table 2.4 presents the predicted and actual prices during and after a flood in 1775, which had been preceded by a drought in 1774, to show what the conditions were in a crisis where the government may have played a less active role. In this case, it appears that wheat prices were the most seriously affected. By September 1775 wheat prices were 0.20 tael above the predicted price, and prices stayed high until June, when an apparently successful harvest sent prices tumbling down to well under their predicted or normal levels. Millet prices were close to their normal levels in the spring and summer of 1775, while sorghum prices rose 0.10 tael or slightly more. But after the fall 1775 harvest, which does not seem to have been much affected by the flood, both millet and sorghum prices fell and stayed below their predicted levels in 1776. So the main impact of the crisis fell on wheat, and as in 1743–44, the impact was limited in magnitude and duration.

[30] See Will, Bureaucratie et Famine .


91
 

TABLE 2.3 Grain Prices during the 1743-1744 Drought in Zhili Province (Excluding Baoding) (in taels per shi )

 

Wheat Price

Millet Price

Sorghum Price

Year and Month a

Predicted

Actual

Residualb

Predicted

Actual

Residualb

Predicted

Actual

Residualb

1743

                 

January

1.59

1.55

- .04

1.33

1.19

- .14

0.90

0.76

- .14

February

1.60

1.56

- .04

1.36

1.20

- .16

0.92

0.78

- .14

March

1.62

1.57

- .05

1.38

1.22

- .16

0.94

0.82

- .12

April

1.66

1.61

- .05

1.39

1.25

- .14

0.95

0.84

- .11

May

1.65

1.65

.00

1.39

1.28

- .11

0.96

0.86

- .10

June

1.61

1.68

+ .07

1.40

1.31

- .09

0.97

0.88

- .09

July

1.54

1.42

0.98

August

1.55

1.42

0.98

September

1.53

1.39

0.92

October

1.61

1.41

0.94

November

1.60

1.36

0.91

December

1.61

1.34

0.91

1744

                 

January

1.59

1.34

0.90

February

1.61

1.37

0.92

March

1.63

1.39

0.95

April

1.67

1.40

0.96

May

1.65

1.40

0.96

June

1.61

1.41

0.98

July

1.54

1.74

+ .20

1.43

1.62

+ .19

0.99

1.14

+ .15

August

1.55

1.43

0.98

September

1.53

1.71

+ .18

1.40

1.32

- .08

0.92

0.91

- .01

October

1.62

1.42

0.94

November

1.61

1.68

+ .07

1.37

1.09

- .28

0.91

0.77

- .14

December

1.62

1.65

+ .03

1.34

1.09

- .25

0.91

0.77

- .14

a Months are solar. b Actual minus predicted price.


92
 

TABLE 2.4 Grain Prices during the 1775–1776 Drought and Flood in Zhili Province (Excluding Baoding) (in taels per shi )

 

Wheat Price

Millet Price

Sorghum Price

Year and Month a

Predicted

Actual

Residualb

Predicted

Actual

Residualb

Predicted

Actual

Residualb

1775

                 

January

1.77

1.80

+ .03

1.53

1.51

- .02

1.04

1.08

+ .04

February

1.79

1.80

+ .01

1.56

1.51

- .05

1.06

1.08

+ .02

March

1.81

1.87

+ .06

1.58

1.55

- .03

1.09

1.13

+ .04

April

1.85

1.91

+ .06

1.59

1.60

+ .01

1.10

1.18

+ .08

May

1.84

1.94

+ .10

1.59

1.62

+ .03

1.11

1.21

+ .10

June

1.80

1.92

+ .12

1.60

1.60

.00

1.12

1.21

+ .09

July

1.72

1.88

+ .16

1.62

1.61

- .01

1.13

1.23

+ .10

August

1.73

1.89

+ .16

1.62

1.60

- .02

1.12

1.24

+ .12

September

1.72

1.92

+ .20

1.59

1.58

- .01

1.06

1.20

+ .14

October

1.80

1.93

+ .13

1.60

1.46

- .14

1.08

1.08

.00

November

1.79

1.96

+ .17

1.56

1.40

- .16

1.05

1.02

- .03

December

1.80

1.96

+ .16

1.53

1.39

- .14

1.05

0.98

- .07

1776

                 

January

1.78

1.97

+ .19

1.53

1.40

- .13

1.05

0.99

- .06

February

1.79

1.97

+ .18

1.56

1.40

- .16

1.07

1.00

- .07

March

1.81

1.99

+ .18

1.58

1.41

- .17

1.09

0.99

- .10

April

1.86

2.02

+ .16

1.59

1.43

- .16

1.10

1.02

- .08

May

1.84

2.02

+ .18

1.59

1.44

- .15

1.11

1.03

- .08

June

1.80

1.81

+ .01

1.60

1.44

- .16

1.12

1.02

- .10

July

1.73

1.52

- .21

1.63

1.44

- .19

1.13

1.00

- .13

August

1.74

1.41

- .33

1.63

1.42

- .21

1.13

0.99

- .14

September

1.72

1.38

- .34

1.60

1.39

- .21

1.07

0.96

- .11

October

1.80

1.61

1.09

November

1.80

1.56

1.06

December

1.80

1.44

- .36

1.54

1.34

- .20

1.06

0.91

- .15

a Months are solar. b Actual minus predicted price.


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In Table 2.5 the data for the latter part of the 1761–63 flood crisis are presented. According to documentary evidence, this flood affected 53 counties in the Hai River Basin. By the beginning of 1762, prices were considerably higher than normal for all three grains. With the exception of a brief dip in June 1762, wheat prices kept climbing, reaching a level about 0.21 tael above normal in March–April 1763, after which prices began to come down, reaching their normal levels by the end of the year. Millet was the most severely affected of all the grains. Its price kept climbing until winter 1762–63, when it was more than 0.60 above normal. Its prices remained high during the year. Sorghum prices also reached a level of about 0.40–0.46 above their predicted prices in January–April 1763.

As both Table 2.5 and Figure 2.3 show, this flood marked one of the few times in the Qing period when the price of millet actually exceeded the price of wheat. In an extensive flood, property is damaged and recovery may take a longer time than after a drought. The data in this case certainly show a greater impact and a much slower recovery than do the data in the two cases involving drought. These results suggest a hypothesis for future investigation, namely, that floods in the Qing period had a more pronounced effect on prices than droughts did.

On the whole, however, these three cases show that the impact of crises in the eighteenth century was relatively moderate, especially in comparison with the staggering price increases of other well-documented world famines, or even late-nineteenth- or twentieth-century famines in China. According to Andrew B. Appleby, grain prices in French subsistence crises of the seventeenth and early eighteenth centuries rose to three or four times their normal levels.[31] In Zhili, in the eighteenth century at least, prices were not generally affected more than 10–20 percent; prices rose just over 40 percent for millet in 1762–63, the worst case seen so far.

Appleby also argues that the English mixed farming system, with animal husbandry and multiple grains, worked to minimize the effects of shortages because people could choose to eat inferior grains, usually reserved for livestock, instead of wheat, the preferred grain—eating down the food chain, so to speak. In Zhili the grain prices maintained a separation from each other, except in the 1761–63 flood, when millet prices reached and then exceeded those of wheat. Separation of prices suggests either that the markets were indeed separate, and there was little substitutability in crisis times, or else that there really was not a crisis, because there was no need for substitution. In a real crisis people become unable, or unwilling, to pay the exorbitant price of an expensive grain and therefore substitute an inferior grain, which in turn drives up the price of the second grain. Consequently, the separation

[31] Andrew B. Appleby, "Grain Prices and Subsistence Crises in England and France, 1590–1740," Journal of Economic History 39, no. 4 (Dec. 1979):865–86.


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TABLE 2.5. Grain Prices during the Last Years of the 1761–1763 Flood in Zhili Province (Excluding Baoding) (in taels per shi )

 

Wheat Price

Millet Price

Sorghum Price

Year and Month a

Predicted

Actual

Residualb

Predicted

Actual

Residualb

Predicted

Actual

Residualb

1762

                 

January

1.70

1.89

+.19

1.45

1.85

+.40

0.98

1.18

+.20

February

1.71

1.89

+.18

1.48

1.85

+.37

1.00

1.18

+.18

March

1.73

1.89

+.16

1.50

1.87

+.37

1.03

1.22

+.19

April

1.77

1.91

+.14

1.51

1.85

+.34

1.04

1.22

+.18

May

1.76

1.86

+.10

1.51

1.83

+.32

1.05

1.23

+.18

June

1.72

1.75

+.03

1.52

1.84

+.32

1.06

1.24

+.18

July

1.65

1.54

1.07

August

1.66

1.54

1.06

September

1.64

1.75

+.11

1.51

1.89

+.38

1.01

1.22

+.21

October

1.72

1.80

+.08

1.52

1.92

+.40

1.02

1.26

+.24

November

1.72

1.85

+.13

1.48

2.01

+.53

0.99

1.33

+.34

December

1.72

1.88

+.16

1.45

2.06

+.61

0.99

1.35

+.36

1763

                 

January

1.70

1.89

+.19

1.45

2.08

+.63

0.99

1.39

+.40

February

1.72

1.92

+.20

1.48

2.10

+.62

1.01

1.44

+.43

March

1.74

1.95

+.21

1.50

2.12

+.62

1.04

1.47

+.43

April

1.78

1.99

+.21

1.51

2.14

+.63

1.05

1.51

+.46

May

1.77

1.52

1.05

June

1.73

1.52

1.06

July

1.65

1.76

+.11

1.55

2.09

+.54

1.07

1.43

+.36

August

1.66

1.75

+.09

1.55

2.04

+.49

1.07

1.40

+.33

September

1.65

1.74

+.09

1.52

1.97

+.45

1.01

1.30

+.29

October

1.73

1.74

+.01

1.53

1.90

+.37

1.03

1.24

+.21

November

1.72

1.74

+.02

1.48

1.86

+.38

1.00

1.22

+.22

December

1.73

1.75

+.02

1.46

1.84

+.38

1.00

1.20

+.20

a Months are solar. b Actual minus predicted price.


95

of prices (their nonconvergence) in 1743–44 and 1775–76 suggests that there was no real crisis in those two instances, while the convergence of millet and wheat prices in 1762–63 suggests that there was a real crisis in that situation. Again, this idea, like others in this paper, is advanced as a hypothesis that must be tested, particularly with prefectural data that will focus on the particular parts of the province affected in a particular crisis.

Regional Variation

A central question in the study of these grain prices is the extent to which they varied within the province. In a well-developed market system, the correlation of prefectural prices ought to have been high, and price variation ought to have been low. Price variation within the province should also tell us something about the impact of crises. In a well-developed market system, the impact of natural disasters ought to have been cushioned, since grain would have been able to flow from unaffected regions to the affected ones. Of course, the same effects—strong price correlations and low price variation—might also have been achieved if the granary system was highly effective in its functions of price stabilization and famine relief or if the entire province had identical weather and other environmental conditions.

In approaching the question of market integration, we first employed the statistical measure called the coefficient of variation. The coefficient of variation is the standard deviation divided by the mean, multiplied by 100. It is a measure of the extent to which prices varied among prefectures during a given period. If market integration increased over time, then the coefficient of variation should decline. If, on the other hand, markets deteriorated, then the coefficient of variation should increase. We calculated the coefficients of variation of prefectural prices for each year from 1738 to 1910 for which we had data, omitting Xuanhua and Chengde, which were in the northern sections of the province. Then we did a regression analysis of the coefficients of variation with year (T ) and crisis year (C ) as variables. The resulting regression coefficients for year were 0.018, 0.041, and 0.013 for wheat, millet, and sorghum respectively (with t -values of 3.8, 7.8, and 2.6). In other words, the coefficients of variation for all three grains increased over time, and millet prices experienced significantly greater increases in regional variation than the other two grains.

These results are contrary to the expectation that over time markets should have become more integrated; if so, the coefficient of variation should have decreased. They also draw attention to, and invite explanation for, the different price behavior of millet, which was the staple grain for most people and which might perhaps have been more sensitive to population growth and to crises.

In the same regression analysis, however, the regression coefficients for


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TABLE 2.6 Grain Price Variation in Crisis and Noncrisis Years in Zhili Province (Excluding Xuanhua and Chengde), 1738–1910

Crop

Price in Noncrisis Years (mean of values)

Price in Crisis Years (mean of values)

Difference in Mean Price

Wheat

     

Standard deviation (taels)

0.24

0.25

 

Mean price (taels)

1.97

2.10

+ .13

Coefficient of variationa

12.11

11.55

 

Millet

     

Standard deviation (taels)

0.22

0.24

 

Mean price (taels)

1.77

1.93

+ .16

Coefficient of variationa

11.97

11.88

 

Sorghum

     

Standard deviation (taels)

0.16

0.17

 

Mean price (taels)

1.22

1.32

+ .10

Coefficient of variationa

12.93

12.71

 

a (Standard deviation / mean) * 100. Computed from standard deviations and means having more than two decimal places.

crisis years were all negative, with t -values under –2.0. That crisis years would cause variation to decline seems counterintuitive. When the various components of the coefficient of variation are separated out, however, a more plausible picture emerges. As Table 2.6 shows, the coefficient of variation declines for crisis years only because the standard deviation does not increase as much as the mean price. In other words, the lower coefficient of variation for crisis years is more a reflection of a higher mean price, totally to be expected, than diminished variation among prefectural prices. The table not only shows that the standard deviation did not increase very significantly in crisis years but also shows that the increase in the mean price was greater for millet—0.16 taels on the average—than it was for wheat or sorghum—0.13 and 0.10 taels respectively.[32] On the whole, this analysis confirms the impressions derived from the earlier case studies in supporting the view that the impact of crises, both on the level of prices and on their variation across regions, was relatively limited.

As a second step, we used Pearson correlations to study regional integration. The correlation coefficients measure the relationship between grain

[32] The careful reader may note that the differences between the mean prices of crisis and noncrisis years differ somewhat from the crisis variables presented in Table 2.2. They are, however, roughly of the same order of magnitude as the last set, representing "crop year, Baoding only." Once again, it should be emphasized that regression analysis cannot produce precise results.


97

prices in pairs of prefectures, with 1.0 being a perfect correlation. The comparison of annual prices for the 17 prefectures of the province revealed astonishingly high degrees of correlation for most pairs of prefectures, with the unsurprising exceptions of Xuanhua and Chengde prefectures. Pearson correlations for wheat prices were mostly over 0.70, sometimes over 0.90, often above 0.80. Similar generalizations can be made for millet and sorghum.

When correlations are calculated separately for eighteenth- and nineteenth-century prices, no clear trend can be confirmed. While wheat correlations between most paris of prefectures seem to rise, for millet the picture is more mixed. Excluding Chengde and Xuanhua, correlations for the remaining 15 prefectures show that in 59 of 105 cases, correlations declined from the eighteenth to the nineteenth centuries, while in 46 cases they increased. This partly supports the picture presented by the study of coefficients of variation, and continues to make interesting the hypothesis that millet prices experienced greater spatial variation in the nineteenth century than the eighteenth century. Clearly, the next step in pursuing this question is to study the province on a region by region basis to see which regions shared this experience and which did not.

Since the correlation of prices themselves incorporates a similar time trend for all of the prefectures, it exaggerates the extent to which prices were actually correlated. Studying the first difference of these prices (the difference between the price in a given year and the price in the previous year) omits the common time element and provides a better measure of correlation. A preliminary analysis of annual price differences for the eighteenth century reveals that for wheat and millet about two thirds of the possible correlations between prefectures were 0.60 or over; for sorghum over 55 percent of the correlations were over 0.60. These results also suggest a reasonably high degree of correlation, but understanding their significance must await further work, and comparing them to nineteenth-century correlations must await a more complete set of nineteenth-century data.

Taken together, these preliminary attempts to study price variation in Zhili suggest a relatively high degree of integration within Zhili Province south of the Great Wall, but an integration that may have declined in some sections of the province, particularly in the case of millet, from the eighteenth to the nineteenth centuries. It should not be assumed, however, that integration was necessarily the result of a well-developed market system. The relative behavior of wheat and millet prices suggests that the market for wheat, a commercial product, may have been well developed and may have become more integrated in the nineteenth century. Millet, on the other hand, was the staple grain for ordinary people. In the eighteenth century, its apparent high degree of price correlation may have been due to the fact that the Zhili granaries predominantly stocked millet. With the decline of the granary system in the nineteenth century, price correlation may also have declined, because


98

the eighteenth-century integration of prices was probably more a function of the granary system than of the market system. Until some estimate can be made of the output and supply of each of these grains in the Qing period, and until the nineteenth-century data are more complete, these hypotheses must await testing.

Conclusions

While indicating directions for future work, this preliminary study of grain prices in Zhili Province during the Qing period also permits us to draw certain tentative conclusions. In the most general terms, this study suggests, first, that there was a relatively low inflationary trend for the Qianlong through the Xuantong reign periods (1736–1911), particularly so when the last two decades are omitted. Second, although there were distinct seasonal patterns in grain prices, they were not great in magnitude, and were offset by the multicrop system, particularly by the planting of winter wheat.

Third, crises were seen in several contexts to have been moderate in their impact, at least in the eighteenth century. Regression analysis showed the impact of crises to have been relatively contained. Crises caused the mean price of grain to rise somewhat but did not cause a very great increase in regional variation. The three eighteenth-century case studies confirm these general impressions. In fact, while these periods were deemed crises in administrative terms, and while they were triggered by natural crises, perhaps they were not really food crises if food crises are defined as periods of abnormally high food prices.[33]

Fourth, the multicrop system appears to have been a significant factor in mitigating the effect of crises, particularly because of the different seasonal patterns of the crops. Multicropping certainly helped to offset the disadvantages presented by weather and geography, and has perhaps been overlooked in previous discussions. Appleby's study concludes that in England and France "the evidence suggests that a symmetrical price structure and subsistence crises went hand in hand. When all grains were costly at the same time, food shortage had an impact on both mortality and fertility; when one or another grain remained cheap, the demographic aftereffects were absent."[34] It is too soon to conclude that such a generalization could be made for Zhili, but certainly this suggests a framework for future investigation. For North China, Buck observed in the 1920s and 1930s that farmers sold the higher-priced grain and ate inferior grain. Farmers in the wheat

[33] I have explored these issues more fully in "Using Grain Prices to Measure Food Crises: Chihli Province in the Mid-Ch'ing Period," The Second Conference on Modern Chinese Economic History (Taibei, 1989), II, pp. 467–509.

[34] Appleby, "Grain Prices," p. 882.


99

region, he said, sold half the wheat they grew, and purchased inferior grain, an estimated one quarter of their food needs, from the market.[35]

The long-term behavior of millet prices seems to be the outcome of either deteriorating economic conditions (population pressure, etc.) or the diminished role of government intervention in the grain market or, more likely, both. The greater volatility of millet in some crises, its increasing coefficients of variation over time, and its lower price correlations in some regions in the nineteenth century all suggest certain long-term changes in its role in the food supply of Zhili. On the other hand, price analysis suggests that the role of sorghum did not experience a similar change. Francesca Bray has suggested that sorghum probably became more important in the nineteenth century with increasing population pressure.[36] However, the long-term trends shown in Figure 2.4 do not support this view, since the price of sorghum continued to remain a constant percentage of the price of wheat and always remained separate from that of the higher-priced millet.

Finally, future work may confirm that prices throughout the region, with the exception of the two outlying prefectures, were remarkably well correlated, suggesting the powerful interaction of granaries and markets.

To understand better the nature of these secular changes, we need to have more complete price data for the nineteenth century and better weather data. This will permit us to understand more about long-term trends over both centuries. We also need to know more about the interaction between the grain tribute system, the granaries, and the grain markets in the determination of prices. Only then will it be possible to grasp why a region so indifferently and curiously endowed by nature could play such a large political role over centuries of history.

[35] Buck, Land Utilization in China , 1:416.

[36] Francesca Bray, Agriculture , vol. 6, pt. 2, of Joseph Needham, ed., Science and Civilization in China (Cambridge, 1984), pp. 434, 464, 451–52.


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Three
The Qing State and the Gansu Grain Market, 1739–1864

Peter C. Perdue

Most Chinese celebrate the eighteenth-century Qing empire for two achievements: the expansion by conquest of China's territory to unprecedented size and the growth of its population to become the largest in the world. Two major institutions of the Qing state made these achievements possible: the military supply system and the granary and famine relief systems. Both of these institutions depended heavily on extensive private grain markets. New data from the price memorials in the Qing archives allow us to examine the degree of integration of these grain markets in Gansu Province in the eighteenth and nineteenth centuries. Scholars have recognized that market exchange of basic commodities was spreading widely in the Chinese empire in the eighteenth century, especially in the densely populated rice paddy belts of the Lower, Middle, and Upper Yangzi, the Pearl River Basin in South China, and along the Grand Canal leading to the North. Nevertheless, few have yet studied the substantial progress of trade in the far northwestern periphery of Han China. I shall argue that Gansu had achieved a considerable degree of integration of its grain markets by the eighteenth century and that the Qing state, through its military-provisioning and granary systems, indirectly promoted this process of commercialization.[1]

The impact of the military was much greater in Gansu than in Coastal, Central, South, and Southwest China, because of Gansu's strategic location on the supply route to the garrisons occupying Central Asia. Continual military demands placed great stress on a fragile agrarian regime. But the efficient transport system developed by Qing governors of the province for military

[1] I would like to acknowledge the invaluable help of my research assistants, Jiang Xiaohong and Ren Jingzhen, in preparing the statistics for this paper. The M.I.T. Provost Fund and Metcalfe Fellowship supported the data collection and analysis.


101

supply and the injection of cash into the economy, combined with efforts to prevent military demands from excessively burdening the local population, stimulated market exchange. By linking Gansu to its neighbors, Shaanxi to the east and Sichuan to the south, the Qing could both maintain the local population and send support through the Gansu corridor to the large military establishment on the frontiers. A full analysis of the Northwest should include its core area in Shaanxi Province, but lacking the price data for Shaanxi at present, I shall only discuss Gansu itself.

Gansu's land area in the Qing was over 600,000 square kilometers, half again as large as California or Japan today. It was one of the largest provinces in the empire, smaller only than Yunnan and Sichuan among the 18 provinces of interior China. (During the Qing dynasty Gansu also included within its borders present-day Ningxia Autonomous Region and Xining Prefecture in present-day Qinghai.) Its population density, however, only 25 per square kilometer in 1957, makes it the most sparsely populated province of Han China.[2]

Although the population was sparse, the cultivated acreage was also low, giving Gansu a ratio of cultivated land per capita close to the national average. In 1887 less than 3 percent of the total area of the province was cultivated land, the lowest in the empire next to Yunnan, Guizhou, and Guangxi in the Southwest.[3] Wheat and millet were the major crops in the province, with acreages of 16.4 million and 14.9 million mu respectively in 1931–37. Miscellaneous crops (field peas, broad beans, oats, buckwheat) accounted for 6.2 million mu , corn for 2.2 million mu , barley for 1.9 million mu , and sorghum for 1.1 million mu . Only very small amounts of rice were grown. This was a very stable mix of crops, typical of regions with very low rainfall, little irrigation, and frequent droughts.[4]

Dwight H. Perkins estimates Gansu's grain yields as the lowest of the 18 provinces, an estimate confirmed by Governor Huang Tinggui, who complained in 1744 that harvests were poor in Gansu because the local people failed to use manure properly or to plow deeply.[5] Private grain storage was so low, claimed Governor Wu Dashan, that the people obtained half of their

[2] Population figures for Gansu are highly suspect: if the population was only 12.8 million in 1957, it is hard to believe that it could have attained the official figure of 15.2 million given for 1787. On the other hand, the 1749 figure of 5.71 million is a clear underestimate. (Dwight H. Perkins, Agricultural Development in China, 1368–1968 (Chicago, 1969), pp. 207–8; Yeh-chien Wang, Land Taxation in Imperial China, 1750–1911 (Cambridge, 1973), p. 87; Qingchao wenxian tongkao (hereafter WXTK ) 36/5195, 37/5205-6.

[3] Perkins, Agricultural Development , pp. 223, 236; Ping-ti Ho, Studies on the Population of China, 1368–1953 (Cambridge, 1959), pp. 124–25. The reported area was, of course, much less than the actual cultivated area, but the relative standing of the provinces is roughly accurate.

[4] Perkins, Agricultural Development , pp. 249–58.

[5] Ibid., p. 19; Zhupi zouzhe (hereafter ZPZZ ), tunken gengzuo 1744.3.25.


102

seed for planting each year from government granary loans.[6] Within the province, the districts west of the Yellow River suffered colder weather and later harvests than those to the east. Not only frequent floods and droughts but also hail, wind, sandstorms, insect plagues, frost, and snow could easily ruin a crop.[7]

Besides its low productivity, Gansu is noteworthy for its high percentage of tuntian , or former military garrison lands, most of which were cultivated by civilians in the Qing. Tuntian formed over 37 percent of the registered land area in 1753.[8] By 1772, the Qing had assigned 27 percent of the registered population, or perhaps three million people, to cultivate these fields. The Qing rulers relied heavily on the agricultural output of Gansu to feed not just local military forces but also the armies stationed in Xinjiang.[9]

The military presence both in Gansu and in Xinjiang strongly affected state demands on Gansu's resources. Regular garrisons in the northern, eastern, and southern military districts of Xinjiang numbered at least 25,000 men by the early nineteenth century. Including their dependents, this meant an army of at least 125,000 people to be supported by a combination of garrison lands, cash stipends, and grain shipments from the interior. Joseph Fletcher estimates that of the annual military pay in Xinjiang of 3 million taels in silver, Han China supplied 1.2 million taels.[10] Gansu was not the only source of subventions for Xinjiang, but it was the route through which all cash, grain, horses, and clothing reached the frontier.

Given the heavy demands by the state on Gansu and its low level of agrarian output, it is not surprising that the tax accounts of the province were almost always in arrears. Even though its tax quota was only 250,000 taels in 1725, it still owed the central government an unpaid deficit of 290,000 taels.[11] The tax reforms of the Yongzheng reign (1723–35) imposed on Gansu additional annual demands of over 75,000 taels to provide for the "nourishing-virtue" supplements to magistrates' salaries, for which the ordinary source used in other provinces—meltage fees on silver collection—was insufficient. Gansu used 20,000 taels from surplus collections on frontier trade duties and 11,900 taels in customary fees from the sale of merchant licenses, but still had to request a special transfer of surpluses from Shaanxi.[12] Gansu's inability to

[6] ZPZZ, zhenji 1759.3.10, Wu Dashan.

[7] ZPZZ, tunken gengzuo , 1742.2.2, Huang Tinggui.

[8] Yeh-chien Wang, An Estimate of the Land-Tax Collection in China, 1753 and 1908 (Cambridge, 1973), table 24.

[9] Jiaqing chongxiu Daqing yitongzhi, juan 251–73. Although Xinjiang was not established as a province until 1884, for convenience I use this term to refer to Chinese Central Asia in the eighteenth century.

[10] Joseph Fletcher, "Ch'ing Inner Asia," in John K. Fairbank, ed., The Cambridge History of China , vol. 10, Late Ch'ing, 1800–1911 , pt. 1 (Cambridge, 1978), p. 61.

[11] Madeleine Zelin, The Magistrate's Tael: Rationalizing Fiscal Reform in Eighteenth-Century Ch'ing China (Berkeley and Los Angeles, 1984), p. 312n.13.


103

extract sufficient tax income from agriculture led it to rely on more imaginative and less orthodox methods, especially taxes and contributions by merchants for official degrees. The central government, primarily interested in grain supplies for the military, seems to have been slow to realize Gansu's limitations. The state lowered its demands for cash by decreasing the surcharge for salary supplements from 30 percent to 15 percent, but it maintained high demands for grain. Gansu's tax quota in cash in 1745, 299,000 taels, was the lowest in the empire except for Yunnan and Guizhou, but its quota in grain (508,000 shi ) was the sixth highest.[13] In fact, Gansu paid less than its quota in grain and more in cash, relying on an annual income of 32,000 taels from the tea and horse trades.[14]

Gansu, like the other peripheral western and southwestern provinces (Shaanxi, Sichuan, Yunnan, Guizhou) was on the whole a low-revenue, deficit tax collection area, heavily dependent on subsidies from interior provinces.[15] Within the province, however, rates of collection varied widely. In 1908 the average collection per county (xian ) was 3,600 taels of silver and 5,300 shi of grain, but Zhangye (the Ganzhou prefectural capital) and Wuwei (the Liangzhou prefectural capital) paid the enormous sums of 101,800 and 94,400 taels respectively.[16] Such regions relied heavily on merchant taxes, especially on salt shipments, to meet these demands.

Gansu, then, was one of the poorest of the Han-dominated regions of the empire, comparable in remoteness, sparseness of population, and low productivity to the recently settled Southwest. Unlike the Southwest, however, Gansu occupied a strategic military position guarding the corridor leading to Central Asia, where the Qing rulers conducted their most expansive military campaigns. Unlike the Southwest, too, it had no major mining resources, and its native non-Han peoples—Muslims, Tibetans, Mongols—were assimilated far less willingly to Chinese culture than the native peoples of the Southwest. Persistent tension, sometimes leading toward accommodation, sometimes toward violent revolt, characterized Gansu's social fabric throughout the nineteenth and twentieth centuries.[17] Economically, however, Gansu increased its ties to interior China from the eighteenth century on. Shaanxi merchants controlled much of the province's internal trade. Goods from Hebei, Shanxi, Sichuan, and Henan supplied the civilian and military needs of the population. Gansu merchants, in turn, sold furs as far south as Hunan.[18] Private markets had much to do with drawing the regions together,

[12] Ibid., p. 140.

[13] Yeh-chien Wang, Estimate , tables 26, 27.

[14] Yeh-chien Wang, Land Taxation , p. 71; Yongzheng zhupi yuzhi (hereafter ZPYZ ) 4.4.92b–93 (Gansu governor's report of 1725).

[15] Yeh-chien Wang, Land Taxation , p. 101.

[16] Ibid., p. 59.

[17] Jonathan Lipman, "The Border World of Gansu" (Ph.D. diss., Stanford University, 1981).

[18] Ningxiang xianzhi , 1816/8/8.


104

but the institutions established by the Qing founders and perfected in the eighteenth century, interacting with the market economy, played an important role. The institution with the greatest influence on the agrarian sector was the national granary system.

Granary Reserves and Food Supply

The nationwide granary system of the Qing stored large amounts of grain for leveling annual price fluctuations. "Ever-normal granaries" (changpingcang ) in each county built up their reserves during the eighteenth century using funds and grain obtained from a combination of regular state revenues, contributions for degrees, and transfers from surplus provinces and areas along the Grand Canal.[19] Local elites in many provinces also supplied and managed community granaries (shecang ) and charity granaries (yicang ). This extraordinary grain storage system, whose total reserves far surpassed the holdings of any other premodern state, did succeed for a while in amassing large amounts of grain and in using these reserves to level price fluctuations. Reserve holdings rose to a peak near the end of the eighteenth century, rising from 30 million to 45 million shi . This was a volume of 31 to 46 million hectoliters, equivalent to a weight of milled rice of 261 to 391 million metric tons.[20] Along with this growth in holdings, however, appeared many signs of corrupt management, rotting of grain, and ineffective use of grain for price relief. Although the official level of grain stores dropped back to 30 million shi in the nineteenth century, the real level declined even more rapidly. Furthermore, granary reserves were increasingly diverted to other uses. By the mid-nineteenth century, the use of granary reserves for military supplies had become a very common cause of depletion of the system.

The system functioned well in the eighteenth century as long as officials maintained adequate supervision over granary accounts, took care to prevent spoilage by turning over the stocks, and used grain only for price leveling. Gansu is an example of a province where latent destructive forces of the granary system appeared very early. Supplying military demands, in particular, was explicitly recognized as one of the functions of Gansu reserves. This made it all the more difficult to maintain high levels of reserves, despite the very great demands placed on Gansu by the center.

Since other studies have described the general functioning of the granary

[19] This discussion relies on references provided in Pierre-Etienne Will and R. Bin Wong, Nourish the People: The State Civilian Granary System in China, 1650–1850 (Ann Arbor, 1991).

[20] The shi was a volume measure of grain, equivalent in the Qing dynasty to 2.94 U.S. bushels, or 103.5 liters. Its weight varied by type of grain, but one shi of milled rice, the most common granary holding in south China, roughly equaled 185 pounds, or 84.1 kilograms. See Han-sheng Chuan and Richard A. Kraus, Mid-Ch'ing Rice Markets and Trade: An Essay in Price History (Cambridge, 1975), pp. 79–98.


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system, here I shall only discuss certain aspects of the system that are peculiar to Gansu. The Qing empire demanded extraordinarily high amounts of grain storage from this poor province. Nearly all grain stored in Gansu was either wheat or millet. The Yongzheng emperor (1723–1735) set no fixed quotas for Gansu granaries, but in 1735 Gansu reported reserves of 750,000 shi .[21] In 1748, however, when the Qianlong emperor assigned new targets to all the provinces, he gave Gansu the very high total of 2.29 million shi .[22] By 1763 it had actually achieved only 1.28 million shi . After 1748 the emperor allowed most provinces to reduce their target levels, but he increased Gansu's targets because frontier military garrisons needed provisions.[23] By 1789, he had raised Gansu's target to 3.31 million shi , but it actually reported only 2.2 million shi (all figures here are given in Qing imperial units, or cangshi ). Gansu, a poor province whose population at best amounted to a mere 2 to 5 percent of the national total, was expected to store about 10 percent of the national aggregate in both 1748 and 1789. In fact, it accumulated stockpiles that accounted for 3.9 percent of national reserves in 1767 and 7.4 percent in 1789. This substantial rise in Gansu grain holdings raised it from thirteenth to fourth in size of holdings among the nineteen provinces storing grain.[24]

The target figures for 1748 and 1789, however, did not genuinely represent the required amount of stored grain in each province. At best, they revealed the expected role of each province in the granary system. It is unlikely that any Qing official seriously believed that Gansu would be able to collect over 3 million shi in 1789. Gansu's "deficit" of 1.1 million shi in this year did not necessarily signify severe inadequacies in its granary administration, but only the great limitations on the province's ability to extract large amounts of grain from a poor population. Since the northwestern provinces always maintained above-average levels of per capita grain reserves, they may well have stored enough grain to carry out their primary function of price leveling.[25]

Building up and maintaining such large reserves was always a difficult problem. Gansu had three important sources for grain besides the local agri-

[21] ZPYZ 53.44a, 45b.

[22] The grain measure used in Gansu, the jingshi , was equal to 0.7 cangshi , the standard granary measure used in the rest of the empire. Memorials in Gongzhongdang archive, Palace Museum, Taibei, Qianlong reign (hereafter GZD-QL ), 2.25, 1751/11/22. All the grain figures given here have been converted to cangshi . Prices, however, are given here in taels per jingshi . For comparison with other provinces, these prices should be raised by 14 percent.

[23] Daqing gaozong chunhuangdi shilu (Qianlong) (hereafter QSL-QL ), j.330.33-35 (1749/1/30).

[24] For 1748 figures, see Qingchao wenxian tongkao , 36.5195, 37.5205–5206, cited in Pierre-Etienne Will, Bureaucracy and Famine in Eighteenth-Century China , translated by Elborg Foster (Stanford, 1990), pp. 193, 196. For 1766 and 1789, see the tables in Will and Wong, Nourish the People .

[25] Maps in Will and Wong, Nourish the People .


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cultural population: special allocations from the central government, transfers from Shaanxi, and merchant contributions for degrees. Central government officials, when they realized that especially large deficits and restocking problems plagued the Northwest, allocated large amounts of money to the region. They gave Gansu 3 million taels to restock its granaries in 1766, a year of good harvests, and 8,000 more taels in 1767.[26] These large transfers, however, were extraordinary, one-time measures. Shaanxi, a much more productive province because of the fertile land along the Wei River, could also supplement Gansu's supply in good years. In 1756, a bumper crop year, the emperor allowed the Shaanxi governor to buy more grain than his normal quota and send it to Gansu.[27] The governor could not, however, transfer grain regularly to Gansu, for fear of straining Shaanxi's supplies. Other Shaanxi governors blamed excess government demand from Gansu for driving up prices on the Wei River by causing competition between official and merchant purchasers.[28] "Contributions" (juan )—fees paid by local merchants and others to obtain lower-level examination degrees, bypassing the first level of the examination system—became the most common regular source of grain for Gansu. From 1741 to 1745 Gansu reported collection of over 1 million shi of grain from contributions. On the other hand, this controversial policy did not always work. In 1747 Governor Huang Tinggui reported collection of only 43,900 shi from contributions, despite his exhortations.[29] A 1766 edict prohibited reliance on merchant grain contributions, because officials feared embezzlement and the difficulty that grain purchases created for the local population.[30] Gansu, however, soon resumed the practice, once local officials realized that the province had no other way to maintain its grain reserves.

Through merchant contributions for granary restocking, the Qing rulers used their monopoly authority over literati status to extract resources from the merchant community for the benefit of the rural population. The Qing state, in principle, derived most of its revenues from the land tax, levying very small taxes on internal trade. In practice, however, ties between the state and merchants were much closer than the formal fiscal structure suggests. For example, even though licensing fees for brokers in local markets formed a very small fraction of total revenue, the Qing rulers used these fees effectively to supervise local markets.[31] The use of merchant contributions for

[26] Ibid., manuscript version, p. 72.

[27] GZD-QL 21/9/9.

[28] GZD-QL , Zhongyin, 1753/6/16, 1752/8/10, 1752/8/21; Yang Yingju 1763/6/11.

[29] ZPZZ, caizheng cangchu , 1747/4/11, cited in Will and Wong, Nourish the People , manuscript version, p. 84n.72.

[30] GZD-QL 13303; WXTK 37.5205, cited in Will and Wong, Nourish the People , manuscript version, p. 63n.23.

[31] Susan Mann, Local Merchants and the Chinese Bureaucracy, 1750–1950 (Stanford, 1987).


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granary reserves shows that Qing officials not only accepted the presence of competitive markets but also recirculated commercial revenues back into agricultural subsidies. Price-leveling sales, loans to farmers, and relief distribution both stimulated agricultural production and facilitated the operations of the private grain market.

Allowing merchant contributions for granaries, however, led Gansu into one of the Qing dynasty's worst political scandals.[32] Wang Danwang, taking over as provincial treasurer in 1774, illegally commuted contributions from grain into cash, siphoned a sizable fraction off into his own pockets, and wrote fraudulent reports to the central authorities about the actual reserves in Gansu. He demanded that subordinate officials submit false reports of disasters to obtain famine relief funds from the central government. Those officials who cooperated donated part of these fraudulently obtained funds to Wang himself and kept the rest as a reward for collusion. The vast dimensions of this scandal were only discovered in 1781, after Wang had left the province, taking several hundred donkey loads of loot with him.[33] Grand Secretary Agui, sent to repress a Muslim rebellion there, exposed the huge deficits in granary accounts and set in motion an impeachment process that led to the execution of 56 officials and the banishment or flogging of more than 46. This scandal vividly illustrates the dangers of reliance on contributions and unorthodox methods to fill granary reserves. The early Qing suspicions of this method were justified: merchant contributions, in cash or grain, proved too tempting for wily and unscrupulous officials.[34] Wang's scheme devastated Gansu's granary reserves from 1774 to 1781, but they recovered under close supervision in the following years. Whatever the bureaucratic repercussions of the scandal, the measurable effects on prices, grain supply, and the local economy were slight.

Memorial reports on actual granary reserves in Gansu, as opposed to official quotas, confirm the great difficulty of maintaining stable grain reserves in the province (see Figure 3.1). In the late fall of each year, after restocking from the fall harvest, every provincial governor reported the total reserves held in his granaries. As the graph shows, Gansu's annual holdings fluctuated greatly, more, in fact, than almost every other province in the empire.

[32] Sources are from Shangyudang, QSL-QL, GZD-QL , 1774–1781; Qinding Lanzhou jilue . A brief discussion is in Will and Wong, Nourish the People . The most complete discussion in English is now Muhammad Usiar Yang Huaizhong, "The Eighteenth Century Gansu Relief Fraud Scandal" (Paper presented to the conference "The Legacy of Islam in China: An International Symposium in Memory of Joseph F. Fletcher," Harvard University, Cambridge, Mass., April, 1989).

[33] Shangyudang , 1781/7/12, p. 139.

[34] Officials, however, disagreed on whether grain or cash was easier to steal. For discussion, see R. Bin Wong and Peter C. Perdue, "Famine's Foes in Ch'ing China," Harvard Journal of Asiatic Studies 43, no. 1 (June 1983):313–14.


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figure

Fig. 3.1.
Reported Grain Reserves in Gansu Province, 1740–1860 (millions of shi )
Note: "1748 Target" and "1789 Target" refer to quotas assigned to the province in these years. The bars give the actual holdings.
(See text.)


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Although reserves built up over the long term, from the 1740s to their peak in the 1790s, the military campaigns of the 1750s and 1760s reduced granary holdings drastically by siphoning off much of the ever-normal granary reserves to feed the troops. By 1769 the granaries appear to have recovered their level of 1753, but Wang Danwang's reign of fraudulent reporting, beginning in 1774, makes the figures for the 1770s suspect. The exposure of the relief scandal revealed that in 1781 true reserves had dropped to less than 1.5 million shi . Gansu experienced a genuine recovery in the 1780s and 1790s, when its granary accounts were under close scrutiny. Figures for the nineteenth century, by contrast, show that Gansu's grain holdings plummeted to a stabler but much lower level.

Gansu's granary reserves follow, in exaggerated form, the pattern of the empire as a whole. Year-to-year fluctuations were higher in the eighteenth than in the nineteenth century. Nineteenth-century granary officials undertook sporadic, short-term rebuilding campaigns, as in Gansu during the 1830s, but these campaigns did not offset a longer-term trend toward declining reserves. The figures for the nineteenth century are also more suspect, because the institutional controls over corruption, spoilage, and false reporting were looser. Rather than interpreting this drop as evidence of general decline in Qing administration, we can also view it as a shift in Qing policies away from the difficult methods of reliance on storage in kind toward greater reliance on the private market. Gansu, in this sense, pioneered moves by the Qing state toward injection of money into the regional economy. Governor Nayancheng's campaign of 1810–11 to relieve a drought that struck 30 counties exhibited further moves toward money and away from grain. He distributed 571,900 taels of silver and only 95,600 shi of grain to feed over 2,777,000 people.[35]

Any assessment of the Qing ever-normal granary system must distinguish between what the Qing officials expected it to do and how it really functioned. The Qing rulers designed the ever-normal granary system to serve only one goal: price leveling. In principle, each granary should have sustained itself. After an initial build-up period, during which reserves were increased through official purchases and grants, granary managers were expected to keep the granaries at a stable level without outside support. By selling at high prices in the spring and repurchasing at lower prices after the fall harvest, officials should have been able both to maintain reserves at constant levels and to use the profits to pay salaries and maintenance costs. Emperors frequently reminded local officials of their duties, required annual reports of them, and sometimes punished them for very small discrepancies in granary accounts. They expected regular and full restocking every fall. Of

[35] The principal source for this relief operation is Nayancheng, Zhenji (1813). Brief discussion given in Wong and Perdue, "Famine's Foes", pp. 304–9.


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course, only perfect prediction of future market conditions could have maintained absolutely stable reserves, but regions with regular harvests could much more easily keep their granaries stocked than regions suffering from frequent, unpredictable disasters. Stability also required that grain stocks be used predominantly for price-leveling sales. If reserves were diverted to other uses, either as official levies or for sales below market prices, extra funds would be required to restock in the fall.

Despite these difficulties, many provinces did succeed in keeping reserve levels stable for considerable periods of time. This stability, however, reflected a variety of relationships between regional grain markets and official purchasing activity. In the Southwest, for example, extremely stable reserves reflected a very low rate of grain turnover in regions of relatively localized markets. In the Lower Yangzi, on the other hand, stability resulted from highly commercialized, well-integrated grain markets and low per capita reserve levels.

In this paper, I stress the very wide variability of Gansu's reserves and what it reveals about Gansu's grain market. Substantial diversion of grain for military use combined with frequent poor harvests produced widely varying annual reserve levels. Still, even though Gansu's granaries fell short of the ideal design of the Qing system, they had significant economic effects. In fact, high annual fluctuations could indicate that reserves were being used effectively to relieve harvest shortages. In a region of frequent disasters, the best way to use granaries would be to accept deficits in bad years and make them up in good years, balancing the reserves over a multiyear cycle. If we could be certain that most of Gansu's reserves were used in this manner, the fluctuation in reserves would indicate highly effective management.

They could, on the other hand, indicate widespread diversion and peculation. Some provinces reported the total amount of grain purchased and sold during the year, allowing us to calculate the turnover rate, equal to the total purchases and sales divided by the end-of-year stocks. Gansu, unfortunately, is not one of those provinces, so it is difficult to determine exactly how much grain was bought and sold on the market. A more detailed examination of famine relief distributions would help resolve this question. For now, we may say that price stability after the 1760s provides at least some evidence that granary reserves were used effectively during the late eighteenth century to relieve the impact of harvest disasters. Although Gansu did not meet its targets, its high per capita grain holdings meant that grain distributions did have a relatively strong effect on the local grain market.

Military Demands on the Grain Supply: the Campaigns of 1758–1761

The three great military campaigns of the early to middle years of the long Qianlong reign (1736–95) consolidated the Chinese hold on Central Asia,


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eliminated the centuries-old Mongol military threat, and expanded China's territory to unprecedented size. Only enormous logistic support from the interior made these campaigns possible. All of China's northern and northwestern provinces—Zhili, Shanxi, Shaanxi, and Gansu—bore the brunt of supplying the troops on the frontier with animals, wagons, porters, food, straw, uniforms, and weapons, but Gansu suffered the most. During the campaign against the Eleuth Mongols, from 1758 to 1761, large numbers of soldiers marched through Gansu. Even though they carried part of their rations with them, their demands on local grain markets drove up prices to spectacular heights. Grain prices doubled or tripled, but the prices of other goods also increased. Officials in Gansu had to raise the price they paid for horses from 8 to 10 taels, for cattle from 4.4 to 8 taels, in order to meet the local market price.[36]

Long pack trains marched through the province: 6,000 camels were sent from Zhili and Shanxi, many of which died of disease, requiring replacements; 12,000 horses were sent to Barkul (Balikun), of which the Eleuths stole 300.[37] Neighboring provinces received allotments of 3 million taels for grain transport to Gansu, in addition to 3 million taels given to Governor Huang Tinggui for military supplies.[38] The garrison in Hami, which increased from 10,000 to 20,000 in 1758, required at least 40,000 shi of grain per year.[39] Surprisingly, only 20 percent of these supplies were in kind and 80 percent in cash.

The vast distances—850 kilometers in a straight line from Lanzhou to Anxi, 300 more kilometers from there to Hami—and the high cost of transport across the steppes made it impossible to provision the troops through military pack trains alone. Expansion of garrison lands in Hami and Turfan provided valuable supplements, but only enough for 9,000 men for seven months.[40] Although Huang Tinggui instructed commanders to avoid purchases in Gansu by ordering soldiers to carry their own rations and even considered feeding the entire 20,000-man Hami garrison from Sichuan, he inevitably concluded that much of the grain had to be bought locally.[41] Of necessity, the private grain market in Gansu supplied a large share of the rations for the troops fighting on the frontier. The increase in military demand, combined with the influx of silver from government purchases, drove up local prices relentlessly.

This campaign unfortunately coincided with several years of widespread drought throughout the Northwest. Ningxia, with its irrigated fields, did send Liangzhou and Ganzhou its surplus of 112,000 shi , which was soon

[36] QSL-QL 554.2b (1758/1).

[37] Ibid., 576.36a (1758/12), 554.21b (1758/1), 557.31b (1758/2), 556.15 (1758/2).

[38] Ibid., 512.25b (1756/5), 575.17b (1758/11).

[39] Ibid., 564.19a, 564.17 (1758/6).

[40] Ibid., 573.23a (1758/10).

[41] Ibid., 567.27a (1758/7), 565.13a (1758/6).


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exhausted. Relief needs alone were estimated at 500,000 shi of grain and 300,000 taels of silver.[42] Once again, the private grain market supplied much of the relief grain. In 1759 the emperor relaxed the usual rule of 50-50 distributions of relief in cash and kind to allow full cash relief where needed.[43] Where prices were low, especially east of the Yellow River in the ninth and tenth months of the year, relief in silver allowed the poor to buy food. As winter shortages exhausted market supplies, relief shifted to grain alone.[44] Besides direct distribution, the officials sold grain to reduce prices, but these sales only reduced prices by several tenths of a tael per shi .[45] Sales of millet at 2.4 taels per shi did not prevent the price from rising to 3.5 taels in Lanzhou by the end of 1759. It peaked at 4.4 taels in mid-1760 before dropping in 1761. Refugees flocked to sheds built for them in the cities, and Shaanxi shipped in 1.02 million shi of grain simply to provide seed loans for spring planting.[46] These are only a few signs of the major disaster inflicted on the province by the combination of the frontier military campaign and widespread drought.

In the short term, the campaigns of 1758–61 inflicted great suffering on Gansu's people, but in the long term they may have promoted Gansu's integration into the rest of the empire. Military salaries and relief allocations in cash poured large amounts of silver into the local economy. Despite its remoteness from both the copper mines of the Southwest and the silver imports on the coast, in 1761 Gansu's silver-copper ratio of 890 cash per tael was comparable with the ratio in the rest of the country.[47] Gansu officials set up government moneychanging bureaus in 1761 to help currency exchange, and they tried to standardize currency within the province.[48] We need more data on currency flows to confirm this hypothesis, but these military campaigns may well have contributed decisively to the monetization of the Northwest's economy.

The defeat of the Eleuths obviated the need for additional great military expeditions and brought relative peace to the Northwest. Of course, local unrest did not disappear. Tensions between Chinese Muslims and Han Chinese broke out in a short-lived revolt in 1781, but this had no effect on

[42] Ibid., 565.20b (1758/6); ZPZZ, zhenji , 1759/6/3, Yang Yingju.

[43] QSL-QL 567.12b (1758/7), 581.2a (1759/2).

[44] ZPZZ, zhenji , 1758/10/17, Huang Tinggui.

[45] QSL-QL 578.2a (1759/1).

[46] ZPZZ, zhenji , 1760/9/9, Wu Dashan.

[47] Hans-Ulrich Vogel, "Chinese Central Monetary Policy and Yunnan Copper Mining in the Early Qing (1644–1800)" (Ph.D. diss., University of Zurich, 1983); Hans-Ulrich Vogel, "Chinese Central Monetary Policy, 1644–1800," Late Imperial China 8, no.2 (December 1987): 27; Chen Chao-nan, Yongzheng Qianlong nianjian de yinqian bijia biandong (1723–1795) (Taibei, 1966).

[48] Proposals to standardize currency and increase the copper supply are in Gongzhongdang Yongzhengchao zouzhe (Taibei, 1977–79), vol. 5, p. 230 (1725/10/1); vol. 11, p. 782 (1728/11/16); QSL-QL 580.13a (1759/2).


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price levels. Whatever the underlying ethnic tensions in the region, Gansu's markets functioned much more stably after 1761 than they had before.

Major Qing institutions affected the grain supply in Gansu, then. In feeding armies, collecting taxes, and stocking granaries, the Qing officials had to adapt to the limitations of agricultural output in a poor peripheral province. At the same time, they stimulated market exchange by injecting cash into the economy through grain purchases and soldiers' salaries. The collection of taxes in cash also stimulated market exchange by forcing peasants to sell their surplus crops for cash.[49] The eighteenth-century conquests in Central Asia not only brought vast new territories under Chinese control; they also contributed to knitting together the interior regions of China by linking internal markets to the military supply route. The analysis of price data that follows examines the extent to which Gansu's prefectures were linked together by a system of grain markets extending from the core regions of the Southeast through the corridor into Central Asia.

Price Data and Market Integration

The 351 Gansu price memorials available to me cover the years 1739 to 1864. The eighteenth century is much more fully covered than the nineteenth: there are at least some data for 38 of the years 1739–99 but for only 21 of the years 1800–64. Monthly coverage is also much fuller for the eighteenth century than for the nineteenth. Some of the reports from the nineteenth century are too suspiciously constant, giving the same price three or four months in a row. My suspicions about some of the nineteenth-century data make me reluctant to draw conclusions about differences between the two centuries until more information becomes available. The Gansu price memorials report high and low prices for five major crops: millet (two varieties), wheat, beans, and barley. Here we shall analyze only the data for the primary millet crop (sumi ). The reports cover the 13 prefectural divisions of Gansu Province, plus Hami, located in Xinjiang, and, for some years, the Jingni garrison, 100 kilometers northwest of Anxi.

Although the memorials report the highest and lowest prices within each prefecture every month, they do not tell us the locations of each high and low price. The yearly average curves of high prices and low prices within each prefecture parallel each other quite closely, but the high prices in Gongchang, for example, show occasional sharp peaks not found in the low prices (Figure 3.2). The most likely explanation for this pattern is that low prices

[49] Compare with similar processes in England and France described by Rudolf Braun, "Taxation, Sociopolitical Structure, and State-Building: Great Britain and Brandenburg-Prussia," p. 318, and Charles Tilly, "Food Supply and Public Order in Modern Europe," pp. 380–455, both in Charles Tilly, ed., The Formation of National States in Western Europe (Princeton, 1975).


114

figure

Fig. 3.2
Millet Prices in Gansu Province and Gongchang Prefecture, 1739–1864 (adjusted annual
averages, in taels per shi )


115

represent the stabler patterns found in prefectural and county capitals, which were more commercialized than remote parts of the prefecture, where sporadic periods of dearth caused the sharp peaks in the series of high prices. The limited amplitude of seasonal variation in low prices compared to high prices supports our interpretation of low prices as coming from commercialized capitals (see Figure 3.3). For these reasons, analysis of the correlation between low prices is the most appropriate way, I believe, to measure market integration in this region.

Figure 3.2 shows the trends of the adjusted annual averages of millet prices for the province and one prefecture. (On the adjustment of data here and in other figures, tables, and the map, see the appendix at the end of this chapter.) As Map 3.1 demonstrates, there was a clear gradient of average prices across the province, because high transport costs raised price levels as one moved from east to west. Millet in Hami, where the mean of annual prices averaged 2.40 taels per shi , cost nearly twice as much as in Lanzhou, and Anxi, at 1.50 to 2.19 taels, was over 40 percent higher than Lanzhou. Low-price prefectures included Qinzhou and Jiezhou, Qingyang, and Pingliang, in the eastern end of the province, with access to Sichuan and Shaanxi supplies and the irrigated fields of Ningxia.

Several factors combined to produce the variation in grain prices among prefectures. Long-term changes in the balance of population and agricultural production, or changes in monetary supply, affected the long-term trend of prices. The cropping cycles of each region determined seasonal fluctuations from month to month. The impact of the major drought and military campaigns of 1758–61 produced drastic price changes, which swamped the impact of seasonal and annual trends. I used a multiple regression equation to analyze the relative effect, on average, of these three factors on the price for any given month.

The equation at the foot of Table 3.1 describes the monthly price as a function of a constant, the annual trend, seasonal variation due to monthly fluctuations, the disaster years of 1759–60, and an error term indicating random variation. A dummy variable (Y ), whose value is 1 for the major drought years and 0 for all other years, measures how widely prices in these two years diverged from the long-term trend. The value of the coefficient (T) of this dummy variable given in the table shows that the combined stress of widespread drought and military campaigns in 1759 and 1760 raised the average high price by 2.10 taels, or 135 percent of the average price for the period. Prices more than doubled during these two years in nearly all prefectures, notably excepting Qinzhou and Jiezhou, in the southeast. The coefficients for both high and low prices of the dummy variable for disaster influence in Qinzhou range from 0.42 to 1.08, much less than the effect on the average of prices for the province. Jiezhou's disaster coefficient ranges from 0.22 to –0.04, showing that this prefecture, which belongs to the Sichuan


116

figure

Map 3.1
Correlations and Variation of Low Millet Prices in Gansu Province, 1739–1864.

Note: Bivariate correlations of differences in adjusted annual averages are indicated by solid and dashed lines; solid lines represent r greater
than 0.8, while broken lines represent r greater than 0.7 and less than 0.8. Means of high and low prices are shown for each prefecture
(high prices first) in taels per shi .


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watershed, was completely unaffected by the drought in Gansu. Drought and disaster hit hardest at the core prefecture of Lanzhou (G = 2.82) along with Liangzhou (G = 3.64) and Anxi (G = 2.55) on the corridor leading to Central Asia.

If we exclude the effect of these two disaster years, prices hardly rose at all over the long term. The value of a , the coefficient of the variable T for the year, gives the annual trend for each prefecture excluding the effect of the disaster years. This coefficient is negative for both high and low prices in Anxi, Ganzhou, Hami, Suzhou, and Xining and only slightly positive for the rest. (Jingni, the one exception, only provides data for a limited time period.) The ratio of a to K , the constant term, for the average for all low prices in the province, yields a long-term annual rate of increase of less than 0.1 percent per year. Gansu did not share in the rise of prices from 1780 to 1830 described by Yeh-chien Wang and Kuo-shu Hwang for the rest of the empire.[50] Its prices followed a different rhythm, determined more by military operations than by flows of silver. The high annual fluctuations up to 1762 contrast markedly with great stability after the 1770s. The price data confirm that once the early Qianlong campaigns had brought peace to the turbulent Northwest, Gansu's grain markets proved comparatively immune to sporadic attacks of rebellion, famine, and drought.

Seasonal variation from month to month was also remarkably low. Each month in the equation, except for the first month, is represented by a dummy variable, whose value is 1 for the price in that month and 0 for all other months. For example, the value of M2 is 1 for February prices and 0 for all other prices, the value of M3 is 1 for March prices and 0 for all other prices, and so forth. The coefficients of these variables indicate how much, on average, each month contributed to a change in price from the first month of the year (see Table 3.1). Month-to-month variation rarely exceeds 10 percent of the average price in each prefecture. The especially small variation of the low prices implies that grain storage in the commercialized capital cities damped out nearly all monthly fluctuations.

The monthly variations suggest that most prefectures had two major annual harvests, with a spring crop bringing low prices in April, May, or June (sometimes February or March), while the main fall crop forced prices to their low point in October (see Figure 3.3). The shapes of the curves, however, are not uniform over the region, because cropping regimes varied widely. Gansu's mix of crops and weather differed markedly from the much more uniform seasonal patterns of a rice paddy region like Hunan (see the chapter by Wong and Perdue in this volume). In Ganzhou, Suzhou, and Anxi, at the western end of the province, the fall harvest came in later than

[50] Hwang Kuo-shu and Wang Yeh-chien, "Qingdai liangjia di changji biandong, 1763–1910," Jingji lunwen 9, no. 1 (1981).


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TABLE 3.1 Annual Trends and Seasonal Variation in Millet Prices in Gansu Prefectures, 1739–1864 (coefficients of linear regression model in taels per shi )

 

Monthly Dummy Variables

Prefecture

K

a *100

G

B 2

B 3

B 4

B 5

B 6

B 7

B 8

B 9

B 10

B 11

B 12

R2

N

Mean

HIGH PRICES

                                 

Anxi

2.96

-2.10

2.55

0.03

0.02

0.12

0.05

0.00

-0.02

0.06

0.04

-0.01

-0.03

-0.13

0.50

324

2.19

Ganzhou

1.35

-0.02

1.75

-0.04

-0.05

-0.03

0.00

0.03

0.01

0.07

0.06

0.01

-0.02

0.00

0.54

326

1.44

Gongchang

1.43

0.20

2.15

-0.03

0.05

0.11

0.08

0.00

0.03

0.09

0.11

-0.06

-0.09

-0.09

0.52

326

1.68

Hami

3.20

-1.10

0.00

-0.02

-0.01

0.06

0.05

-0.01

0.01

-0.03

-0.04

-0.02

0.00

0.55

136

2.40

Jiezhou

1.17

0.07

0.22

0.03

0.03

0.03

0.05

0.05

0.07

0.05

0.04

0.03

-0.01

-0.04

0.07

325

1.25

Jingni

1.59

10.60

1.72

0.11

0.12

0.11

0.20

0.01

0.17

0.20

0.19

-0.09

-0.04

0.04

0.54

112

3.33

Jingzhou

1.19

0.30

0.03

0.09

0.12

0.05

0.09

0.06

0.05

0.07

-0.04

-0.03

0.00

0.16

163

1.50

Lanzhou

1.00

0.70

2.82

-0.01

0.07

0.10

0.10

0.06

0.07

0.08

0.10

-0.06

0.01

0.05

0.72

326

1.52

Liangzhou

1.44

0.10

3.64

-0.03

0.05

0.12

0.14

0.08

0.06

0.11

0.17

0.03

0.06

0.02

0.79

326

1.76

Ningxia

0.96

0.55

1.65

-0.04

0.06

0.04

0.10

0.06

0.07

0.10

0.11

-0.04

0.00

0.05

0.53

325

1.34

Pingliang

1.11

0.40

2.23

0.07

0.13

0.17

0.17

0.18

0.13

0.13

0.11

0.02

0.04

0.06

0.65

320

1.52

Qingyang

0.89

0.48

1.85

0.04

0.13

0.16

0.12

0.09

0.08

0.11

0.10

0.02

-0.01

0.00

0.52

325

1.28

Qinzhou

1.04

0.30

1.08

0.02

0.06

0.09

0.07

0.05

0.03

0.05

0.08

-0.01

-0.02

-0.07

0.38

324

1.28

Suzhou

1.40

-0.08

1.16

0.03

-0.07

-0.03

0.03

-0.02

-0.03

0.04

0.08

-0.03

-0.06

-0.07

0.28

323

1.42

Xining

1.79

-0.50

2.23

-0.03

0.02

-0.01

0.03

-0.02

-0.06

-0.04

0.02

-0.09

-0.06

-0.07

0.68

325

1.65

Combined

1.40

0.00

2.10

0.00

0.04

0.08

0.08

0.04

0.03

0.07

0.09

-0.02

-0.02

-0.03

0.70

314

1.56

(Table continued on next page)


119

(Table continued from previous page)

 

TABLE 3.1 Annual Trends and Seasonal Variation in Millet Prices in Gansu Prefectures, 1739–1864 (coefficients of linear regression model in taels per shi )

 

Monthly Dummy Variables

Prefecture

K

a *100

G

b 2

b 3

b 4

b 5

b 6

b 7

b 8

b 9

b 10

b 11

b 12

R2

N

Mean

LOW PRICES

                                 

Anxi

2.03

-1.37

1.86

-0.02

-0.11

0.00

0.02

-0.06

-0.02

0.05

-0.01

-0.05

-0.04

-0.11

0.62

326

1.50

Ganzhou

1.20

-0.25

1.30

-0.06

-0.10

-0.08

-0.04

-0.04

-0.03

-0.01

0.03

-0.01

0.01

0.02

0.57

327

1.13

Gongchang

0.60

0.65

0.51

0.01

0.00

-0.01

0.02

0.01

-0.02

-0.02

-0.01

-0.01

-0.02

0.00

0.48

327

0.90

Hami

3.20

-1.05

-0.01

-0.02

-0.02

0.05

0.06

-0.01

0.01

-0.03

-0.04

-0.01

0.00

0.55

137

2.40

Jiezhou

0.76

0.39

-0.04

0.00

-0.02

-0.04

-0.01

-0.01

0.00

0.00

0.02

0.01

-0.01

-0.01

0.45

326

0.92

Jingni

1.22

11.01

1.75

0.07

0.14

0.24

0.27

0.10

0.24

0.34

0.38

0.12

0.11

0.17

0.58

112

3.11

Jingzhou

1.28

-0.07

-0.01

-0.04

-0.03

0.00

0.02

0.02

0.00

0.00

-0.04

-0.02

0.00

-0.02

164

1.22

Lanzhou

0.79

0.40

1.69

-0.03

-0.02

-0.02

-0.01

-0.06

-0.05

-0.02

-0.03

-0.07

-0.03

-0.01

0.59

327

1.02

Liangzhou

1.16

0.01

2.28

-0.06

-0.06

-0.09

-0.06

-0.05

-0.08

-0.03

0.00

-0.04

0.03

0.03

0.76

326

0.25

Ningxia

0.81

0.23

1.06

-0.05

-0.03

-0.06

0.02

-0.02

-0.01

-0.03

-0.02

-0.02

0.01

0.04

0.45

326

0.95

Pingliang

0.59

0.77

0.69

0.02

0.03

0.02

0.00

0.04

0.00

0.01

0.01

-0.01

0.01

0.02

0.66

319

0.97

Qingyang

0.66

0.51

0.35

0.03

0.02

0.02

0.01

0.01

0.00

-0.01

-0.02

0.01

0.02

-0.02

0.33

326

0.90

Qinzhou

0.67

0.43

0.42

-0.01

-0.02

-0.04

-0.02

0.00

-0.01

-0.02

-0.01

0.00

-0.02

-0.03

0.34

325

0.88

Suzhou

1.20

-0.14

1.14

-0.02

-0.06

-0.04

-0.02

0.01

-0.03

-0.03

-0.01

-0.04

-0.04

-0.04

0.43

324

1.17

Xining

1.24

-0.11

1.77

-0.03

0.05

0.04

0.06

0.04

0.04

0.04

0.10

0.00

0.03

0.05

0.66

326

1.33

Combined

1.01

0.09

1.19

-0.02

-0.04

-0.03

-0.01

-0.02

-0.03

0.00

0.00

-0.03

-0.01

-0.01

0.71

312

1.09

NOTES: The data for Hami include only the years 1789–1864; for Jingni, only 1739–61; for Jingzhou, only 1778–1864. The data for all other prefectures include 1739–1864.

"Combined" evaluates coefficients for the average of each monthly price for all prefectures excluding Hami, Jingni, Jingzhou, and Jiezhou.

The coefficients are for the linear regression model P = K + a T + b 2M2 + b 3M3 + . . . b 12M12 + G Y + e , where P is the monthly price (solar months): K is a constant; T is the year (T = 0 for 1739); M2, . . . M12 are dummy variables for each month excluding the first month (M2 = 1 for second month, 0 for all other months, etc.); Y is a dummy variable for years of great disaster (Y = 1 for 1759–60, otherwise Y = 0); a is the coefficient of T (it gives the annual trend); b 2, etc. are coefficients of the monthly dummy variables; G is the coefficient of Y; e is the error term; N is the number of months for which there are price data; R2 measures the amount of variance explained by the equation; and the mean is the average of the monthly price data.


120

figure

Fig. 3.3.
Seasonal Variation in Millet Prices in Gansu Province and Gongchang Prefecture, 1739–1864
(difference from January price, in taels per shi )


121

elsewhere and did not produce its full effect until November and December, while the spring crop seems to have arrived as early as March. Jiezhou, Jingzhou, and Qinzhou, on Gansu's eastern periphery, had much lower variation than other prefectures and do not demonstrate the effect of double-cropping at all.

Clearly a combination of government price-leveling policies and private storage contributed to the low level of variability. Although officials thought that private storage in Gansu was too low, we cannot easily distinguish the relative contributions of private and government grain storage to price leveling. Unlike in some other provinces, grain memorials in Gansu do not report actual disbursements of grain year by year. Still, Gansu's storage costs may have been lower than other provinces' costs because there was little danger of grain rotting in the dry, cold climate.

The price data also allow us to assess the degree to which Gansu's grain markets were interconnected. Computing bivariate correlation coefficients (Pearson's r ) between price series of different regions is a common method of analyzing the degree of regional market integration.[51] The possible values of this coefficient range from +1.0 to -1.0. Positive values mean some degree of synchronization between two given price series. Map 3.1 and Table 3.2 display the results of these calculations for the millet price reports from Gansu. The map uses solid lines to show correlations of greater than 0.8 and broken lines to show correlations of between 0.7 and 0.8. It uses prefectural reports of low millet prices to portray the correlations between the price differences of consecutive years from 1739 to 1864. Price differences are used to eliminate partially the influence of the annual trend: that is, the series consists of the price for 1739 subtracted from the price of 1740, the price for 1740 subtracted from the price of 1741, and so forth. (The annual average was derived from the months for which data were available after adjusting for seasonal variation. See the appendix.) Maps of correlations of monthly prices, which show short-term influences, display a similar pattern.[52]

The severe disaster years of 1759–60, however, did strongly affect all the prefectures of the province, except for Jiezhou. Removing these years from

[51] See, for example, William O. Jones, Marketing Staple Food Crops in Tropical Africa (Ithaca, N.Y., 1972).

[52] Barbara Harris has recently criticized the use of bivariate correlation coefficients to measure market integration. Recently, Martin Ravallion and Paul J. Heytens have developed more sophisticated statistical techniques to compensate for the influence of common trends and to measure more precisely the integrating effect of market exchange. Right now, missing data in the Chinese price series limit the usefulness of these techniques, but I intend to apply them to the Chinese data after obtaining more data from the archives. See Barbara Harris, "There is Method in My Madness: Or Is It Vice Versa? Measuring Agricultural Market Performance," Food Research Institute Studies 17, no. 2 (1979); Martin Ravallion, Markets and Famines (New York, 1987); Paul J. Heytens, "Testing Market Integration," Food Research Institute Studies 20, no. 1 (1986).


122
 

TABLE 3.2 Differences in Annual Averages of Low Millet Prices in Gansu Prefectures, 1739–1864 (Pearson's r)

Prefecture

Xining

Suzhou

Qinzhou

Qing-
yang

Ping-
liang

Ningxia

Liang-
zhou

Lanzhou

Jingni

Jingzhou

Jiezhou

Hami

Gong-
chang

Ganzhou

Anxi

0.810

0.602

0.607

0.540

0.694

0.772

0.792

0.819

0.420

-0.101

0.101

0.375

0.756

0.772

Ganzhou

0.788

0.801

0.442

0.363

0.667

0.792

0.856

0.700

0.491

0.351

0.124

0.509

0.549

 

Gongchang

0.846

0.357

0.808

0.725

0.836

0.763

0.772

0.900

-0.126

0.322

0.378

-0.042

   

Hami

-0.060

0.832

0.370

0.128

0.031

-0.077

0.175

-0.281

0.210

0.310

     

Jiezhou

0.253

0.347

0.404

0.155

0.221

0.135

0.166

0.237

-0.038

0.018

       

Jingzhou

0.395

0.350

0.242

0.332

0.707

0.599

0.274

0.277

         

Jingni

0.081

0.593

-0.115

-0.231

-0.273

0.239

0.095

0.113

           

Lanzhou

0.948

0.535

0.658

0.689

0.917

0.877

0.895

             

Liangzhou

0.940

0.628

0.608

0.564

0.908

0.896

               

Ningxia

0.865

0.643

0.657

0.515

0.823

                 

Pingliang

0.913

0.424

0.668

0.701

                   

Qingyang

0.607

0.175

0.702

                     

Qinzhou

0.635

0.345

                       

Suzhou

0.606

                         

NOTES: Prices are adjusted on the basis of the first month (see Appendix). Coefficients > 0.700 in boldface.


123

the data sharply reduces the strength of interprefectural correlations, so that few coefficients exceed 0.8. At a weaker level, however, the same patterns remain. Coefficients at the level of 0.6 are still statistically significant, and they demonstrate the existence of the same network of exchange relationships.

The correlation coefficients indicate that a web of market relationships tied together the core triangle of prefectural capitals, Lanzhou, Gongchang, and Pingliang. Demands by these prefectures on the surplus production of the irrigated fields of Ningxia, to the north, tied Ningxia's prices to each. Weaker links connected Qingyang, Qinzhou, Xining, and Liangzhou to one or more of the core prefectures. A chain of trading posts linked Ganzhou, Suzhou, and Anxi to each other along the old Silk Road extending into the steppe.[53] Hami, in Xinjiang, was only weakly linked, if at all, to the Gansu markets. (Map 3.1 exaggerates its links to Liangzhou and Suzhou, probably because all of the data come from the nineteenth century.) Much of the grain supply for the Hami garrison came from tuntian lands in Central Asia, but a substantial portion of its supplies came through the corridor via Anxi. Conspicuously omitted from this network is Jiezhou, in the far south. Jiezhou lies on the upper reaches of the Jialing River, one of the major tributaries of the Yangzi flowing into the Sichuan Basin. If G. William Skinner is right to stress the importance of physiographic boundaries over administrative boundaries in defining economic macroregions, Jiezhou's markets should have little connection with the rest of Gansu but be linked by river transport to the Sichuan Basin. Jiezhou's isolation from the rest of Gansu confirms Skinner's theory of macroregions, attests to the overwhelming influence of river transport routes in determining Chinese grain flows, and shows that price correlations accurately define the boundaries of macroregions.[54]

Did market integration increase in Gansu from the eighteenth to the nineteenth century? An examination of graphs of average annual prices of millet from 1739 to 1864, smoothing out year-to-year fluctuations by five-year moving averages, seems to reveal some convergence between the price series of different prefectures. As expected, Hami and Jiezhou, which belong to different marketing systems, do not conform. More precisely, when we calculate the coefficient of variation (standard deviation divided by the mean) annually for 11 prefectures (excluding Jiezhou, Jingzhou, Jingni, and Hami), we find that this coefficient declines from the eighteenth to the nineteenth century (see Table 3.3). The rapid drop in the coefficient after

[53] Gansu had 331 government post stations, more than any other province, stationed twenty-five miles apart along the major roads. Kono Michihiro, "Shindai no baekiro," Jinbun chiri 2, no. 1 (1950): 13–24, cited in Gilbert Rozman, Urban Networks in Ch'ing China and Tokugawa Japan (Princeton, 1973), p. 94.

[54] Skinner puts Jiezhou prefecture in the Upper Yangtze macroregion, with Sichuan. G. William Skinner, ed., The City in Late Imperial China (Stanford, 1977), map 2, p. 214.


124
 

TABLE 3.3 Variation in Low Millet Prices in Gansu Province, 1739–1864

Year

Mean

Coefficient of Variationa

Year

Mean

Coefficient of Variationa

Year

Mean

Coefficient of Variationa

1739

1.13

37.0

1767

1.16

19.2

1809

1.10

11.6

1740

1.13

31.5

1768

1.00

23.1

1810

1.17

12.2

1741

1.27

28.7

   

1811

1.28

15.0

1742

1.03

41.9

1774

1.06

17.6

   

1743

0.85

47.8

1775

0.94

16.6

1816

1.19

7.0

   

   

1817

1.15

6.1

1747

0.63

53.5

1778

0.99

12.8

1818

1.15

3.7

1748

0.89

33.4

1779

0.94

15.5

   

1749

0.80

36.5

1780

0.90

18.3

1820

1.17

3.3

1750

0.85

50.6

1781

0.90

16.6

1821

1.16

3.6

1751

0.74

48.3

1782

0.85

14.7

   

   

1783

0.81

16.8

1829

1.11

10.6

1753

0.68

46.7

   

1830

1.11

10.6

1754

0.75

52.1

1789

1.03

32.1

1831

1.13

14.3

1755

0.92

62.6

1790

1.06

12.2

1832

1.04

13.4

1756

1.01

53.3

   

1833

1.08

9.4

1757

1.13

59.4

1796

1.03

16.9

1834

1.12

10.4

1758

1.31

47.0

1797

1.05

16.4

1835

1.13

10.5

1759

2.09

42.6

1798

1.03

16.2

1836

1.24

6.6

1760

2.35

37.5

1799

1.05

13.2

   

1761

0.99

53.4

   

1838

1.23

5.1

1762

1.01

52.4

1804

1.05

15.0

   

   

   

1849

1.21

3.1

1764

1.16

34.2

1806

1.04

12.2

1850

1.20

2.5

1765

1.39

29.7

1807

1.06

12.5

   

   

1808

1.08

12.6

1864

0.70

39.1

a (Standard deviation/mean) * 100.

NOTE: The data are for 11 prefectures, excluding Hami, Jingzhou, Jingni, and Jiezhou. Hami, Jingni, and Jiezhou are omitted because they belong to a different economic region. Jingzhou is omitted because data are missing for half of the period.

1762 supports our argument that the end of the famine and military campaign led to greater economic integration. The figure remains fairly stable for the rest of the eighteenth century but declines to a lower level from 1816 to 1850. We can tentatively conclude that the process of economic integration, although initiated by military conquest, continued under its own steam through the late eighteenth and at least into the first half of the nineteenth century.

The study of Gansu's grain markets has just begun. The archives contain much more price data; gazetteers hold information on market structure and grain flows; the markets of other major crops await analysis. Nevertheless, the information available now shows that even in the remote Northwest, Qing officials—generals, governors, and granary managers—conducted a


125

fascinating and intricate dance with private traders—grain merchants, peasant producers, and money changers—all participating in a flourishing market economy.

Appendix: Adjustment of Data

The price memorials report the monthly high and low prices for each prefecture according to the Chinese lunar calendar. To estimate correctly the monthly variation, we first converted lunar to solar months by using a simple formula that computed each solar month's price as a weighted average of the prices in the lunar months it overlapped. For example, solar month 3 in 1739 had 9 days in lunar month 1 and 22 days in lunar month 2. The price for solar month 3 is [(9 * Price in lunar month 1) + (22 * Price in lunar month 2)] / 31.[55]

The regression equation in Table 3.1 computes each solar monthly price as a function of the year, of 11 dummy variables for the months (excluding the first month), and of a dummy variable whose value is 1 for the years 1759 to 1760. The coefficients of the 11 monthly dummy variables indicate the amount by which the price for a given month changes in relation to the first month of the solar year. Figure 3.3 displays these coefficients graphically. Subtracting these coefficients from the monthly prices for each prefecture and then averaging the months of each year for which data are available yields the seasonally adjusted annual averages displayed in Figure 3.2. These adjusted annual averages are the source for the computation of correlation coefficients in Table 3.2 and Map 3.1. To test whether the choice of base month affects the calculation of correlation coefficients, we also adjusted the monthly data using regression coefficients based on the tenth month of the year. This alternate procedure yielded few significant differences from the results discussed in the paper.

[55] The source for conversion of solar to lunar months is Zheng Hesheng, Jinshi Zhongshi shiri duizhaobiao (Taibei, 1978).


126

Four
Grain Markets and Food Supplies in Eighteenth-Century Hunan

R. Bin Wong and Peter C. Perdue

By the sixteenth century, recent immigrants to Hunan had begun to open new lands, from which rice surpluses were shipped to the growing metropolis of Hankou and the handicraft centers of the Lower Yangzi.[1] The late Ming proverb "When Huguang [Hunan and Hubei] harvests are plentiful, all under Heaven are fed" demonstrates that Hunan had become an important producer of rice. During the Qing dynasty, the province's rice exports grew. The importance of the Hunan rice trade was stressed some 30 years ago by Abe Takeo in his classic study of food supplies in the Yongzheng period (1723–35).[2] More recent studies of empirewide grain movements have confirmed the importance of the province's exports, the volume of which totaled 8 million (plus or minus 2 million) shi in normal harvest years.[3] The strong demand for Hunan rice in eighteenth-century China forged important commercial ties between the province and other parts of the empire. But what about the impact of Hunan's rice export trade on the province itself? Earlier studies of Hunan's rice export trade offer some answers by focusing princi-

R. Bin Wong wrote this essay on the basis of qualitative data he assembled over the past several years and quantitative data he collected in Beijing supplemented by data collected by James Lee and Peter Perdue. Perdue kindly supplied the technical expertise and helped prepare the data and do some of the initial calculations; he also edited an earlier draft.

[1] For a broad analysis of Hunan's settlement and agricultural expansion, as well as a nuanced portrait of the state's role in shaping these developments, see Peter C. Perdue, Exhausting the Earth: State and Peasant in Hunan, 1500–1850 (Cambridge, 1987).

[2] Abe Takeo, "Beikoku jukyu no kenkyu: Yoseishi no issho to shite mita," Toyoshi kenkyu 15, no. 4 (1957): 484–577.

[3] See Guo Songyi, "Qingdai de liangshi maoyi," Pingzhun xuekan , 1985, no. 1:289–314; Hansheng Chuan and Richard Kraus, Mid-Ch'ing Rice Markets and Trade (Cambridge, 1975); and Wu Chengming, Zhongguo ziben zhuyi yu guonei shichang (Beijing, 1985), p. 257.


127

pally on two topics: (1) the institutions of the export trade and (2) the distribution of benefits from the trade among different groups of merchants, landlords, and peasants.[4] From this work we know that it was often outside merchants who bought Hunan's surplus rice, that landlords could be major suppliers to local markets, and that all producers of surpluses could benefit from the expansion of the trade. We need, however, a sharper picture of the spatial dimensions of the trade in order to push forward our understanding of this trade's impact on the province more generally.

This paper reconstructs the spatial structure of rice marketing within Hunan Province. Which parts of the province were linked together by the rice export trade? How large were these areas—did they form narrow bands along trade routes or were the hinterlands of the trade routes also part of the market? What about rice commerce and markets in areas outside the export zone? Answers to these questions help determine the significance of market integration to an agrarian economy. Our approach combines qualitative and quantitative analyses of market integration. In the first section of this paper we present qualitative evidence on commercial rice circulation in Hunan. In the second, our discussion shifts to an analysis of rice prices. Finally, in the third section we conclude with some thoughts on market integration. We shall discover that analysis of either high prices or low prices by themselves provides an incomplete guide to market integration, since there are no a priori reasons to argue that either set should represent market integration better than the other. For Hunan, separate analyses of high and low prices reveal roughly similar pictures of market integration. We shall show that analysis must include price relationships that are not addressed in other chapters in this volume; even if some of the price relationships are not intuitively obvious, we must consider relationships among high and low prices of different prefectures in order to demonstrate that the prices reflect not just similar but related pictures of the rice export market.

[4] On Hunan's rice markets specifically, three Japanese historians published works in the 1950s that examined institutional features of market structures: Kitamura Hironao, "Shindai no shohin shijo ni tsuite," in Shindai shakai keizaishi kenkyu (Kyoto, 1978); Nakamura Jihei, "Shindai Koko kome ryutsu no ichi men," Shakai keizaishi gaku 18, no. 3:269–81; Shigeta Atsushi, "Shinsho ni okeru Konan komeshijo no ichi kosatsu," in Shindai shakai keizaishi kenkyu (Tokyo, 1975). Evelyn Sakakida Rawski's 1972 work on sixteenth-century Fujian and eighteenth-century Hunan, Agricultural Change and the Peasant Economy of South China (Cambridge, 1972), partially builds on the earlier Japanese work but presents a distinct picture. Rawski links differential economic prosperity within each province to the economic opportunities created by trade in some counties but not others. Rawski and Shigeta Atsushi sharply part company on how the benefits of expanding trade were distributed among small owner cultivators, tenants, and landlords. Rawski stresses the gains made by tenants, whereas Shigeta claims landlord control of markets. The viewpoints of the two scholars are not necessarily contradictory since tenants could reap larger benefits with landlords remaining the more important suppliers.


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Spatial Structures of the Rice Trade: Qualitative Evidence

Hunan's eighteenth-century commerce largely followed the province's river systems. Each of the four major rivers—Xiang, Zi, Yuan, and Li—flowed into Dongting Lake, located in the northeastern part of the province. The Xiang River was by far the most important river, draining nearly half the province's land area. Not surprisingly, merchants along this river collected and shipped considerable amounts of rice destined for export out of the province. A 1753 investigation of specialized rice markets, covering 49 of Hunan's 56 counties, found 16 counties in which major rice markets existed and three additional counties where minor rice markets also served the export trade.[5] These 19 counties—Anxiang, Baling, Chaling, Changsha, Hengshan, Hengyang, Huarong, Linxiang, Liuyang, Longyang, Shanhua, Taoyuan, Wuling, Xiangtan, Xiangxiang, Xiangyin, Yiyang, Youxian, and Yuanjiang—were all located in five of Hunan's 13 prefectures; three of the prefectures (Changde, Lizhou, and Yuezhou) bordered Dongting Lake, while the remaining two (Changsha and Hengzhou) were south of Dongting Lake on the Xiang River. Major markets contained the facilities to accommodate boats, store grain, and arrange transactions; minor markets sent rice to these major markets for export. Map 4.1 displays the 16 counties with specialized rice markets and the three additional counties with export surpluses.

By adding information from other sources, we can piece together commercial rice flows in other parts of the province. Small quantities sometimes moved along Hunan's other three major rivers and their tributaries. Along the Zi River, rice moved from Wugang to Shaoyang and from this point to Xinhua; other shipments of rice reached the tea-producing county of Anhua further downstream. These shipments along the Zi River probably did not reach Dongting Lake. Protests against the trade in the early nineteenth century make clear, however, that even these small, short-distance, nonexport shipments were significant sources of food to the people who depended on them.[6] In contrast, gazetteers provide no evidence of trade crossing county borders anywhere along the Li River.[7]

[5] Hunan shengli cheng'an hulü (1820), 23.2a–23b. For a discussion of the report, see Shigeta, "Shinsho ni okeru Konan komeshijo," 17–21. Rawski, Agricultural Change , p. 105, puts in tabular form information presented by Shigeta from the original.

[6] Deng Xianhe, "Lun huangzheng," Hunan wenzheng: guochao wen , 29.23a–24b, Xinning xianzhi (1893) 20.19a–21a. Repeated struggles over this flow of grain demonstrate the social and political importance of what in economic terms may be quite minor. See Kojima Shinji, Taihei tenkoku kakumei no rekishi to shiso (Tokyo, 1978), pp. 117–26, and R. Bin Wong, "Food Riots in the Qing Dynasty," Journal of Asian Studies 41, no. 4:774–79.

[7] Rice was grown in each of three counties along the Li River but was not a major food crop in any of them; more important was the combination of winter wheat, corn, and millet. Cotton and hemp cloth, like grain, circulated within county borders, while seed oils and mining products made their way downstream to the lake region. Sangzhi xianzhi (1873), 2.25–37; Yongshun fuzhi (1763), 10.5a; Shimen xianzhi (1818), 18.72a–73a, 52.49a–53a; Shimen xianzhi (1873), 3.48–49.


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figure

Map 4.2.
Correlations of Annual Price Differences for High-Grade Rice in Qing Dynasty Hunan.
(See p. 138 for Map 4.1.)


130

Along the Yuan River available sources allow us to reconstruct a more complex situation. Although they inform us that people in Chenzhou depended on imports from other counties, they do not tell us where the boats came from. The neighboring upstream counties of Luxi, Chenxi, and Xupu were unlikely suppliers of rice because of their own food supply limitations.[8] It appears, therefore, that the boats came upstream from the fertile paddy areas near the lake, apparently carrying rice that would otherwise flow out of the province with the export trade. Additional trade, including sales to Huitong, flourished along the upper reaches of the Yuan and its tributaries in Yuanzhou and Jianyang. These movements in southwestern Hunan, which also carried grain across the Guizhou border, were physically separate from the movements in downstream areas between the lake and Chenzhou.[9]

In southern Hunan small amounts of rice appear to have moved across prefectural boundaries. For instance, rice grown in Lanshan and Xintian fed miners in Guiyang. Sources also provide early eighteenth-century evidence of shipments of rice from Yongzhou to Hengzhou.[10] The southern mountain region within which rice was sold to feed miners also, at least for a while, sent rice into the Xiang River export trade. Map 4.1 also shows those counties outside the export zone in which rice trade is noted in gazetteer sources.

In summary, the export trade dominated rice movements in Hunan above the local level but coexisted with spatially separate movements along the Zi and Yuan rivers and in the southern mountains. Qualitative data indicate some rice trade crossing county borders in ten of Hunan's 13 prefectures, but only half of those ten participated directly in the province's Yangzi River export trade. The evidence we have just reviewed establishes the outlines of separate patterns of grain trade within the province. But this information cannot answer two important questions. Were the physically distinct movements of rice outside the export zone economically independent of the interprovincial trade? What degrees of market integration were achieved within the export zone and in areas beyond it? Our analysis of grain prices provides some answers.

[8] On Chenzhou, see Yuanling xianzhi (1873), 8.1a; on the counties upstream, see Fukuda Setsuo, "Shinmatsu Konan no noson shakai," Fukuoka joshi tandai kiyo 8:33, 34, 51.

[9] Shigeta, "Shinsho ni okeru Konan komeshijo," pp. 24–25; Hunan shengli cheng'an hulü , 23.2a–23b. On the route out of Hunan into the Southwest, see James Lee, State and Economy in Southwest China, 1250 to 1850 (Cambridge, forthcoming), chap. 3.

[10] Guiyang zhouzhi (1868), 6.18–19; Lanshan xianzhi (1933), 11.53. For occasional entry of Yongzhou rice into the Xiang River export trade, see Qiyang xianzhi (1765), 4.7a.


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Price Data and the Dimensions of Market Integration

From qualitative evidence we have shown a kind of market integration defined by physical movements of rice. We do not have much sense of the size of these rice shipments; we know only that some amounts of rice moved between various points within the province and to places beyond Hunan. The size of shipments, however, is not crucial to the reasoning about market integration we now develop on the basis of price data. When prices from two areas move in related ways over time, we believe the markets of these areas are integrated. There need not be very much trade between two points to cause related price movements. As long as grain merchants have enough information about prices in another area to cause them to adjust the volumes and prices of their purchases and sales accordingly, the two regions are economically integrated, regardless of the absolute size of the trade. Conversely, evidence of physical movements of grain may not signify market integration if changes in the prices do not reflect those movements. This would indicate that even though grain flows between two regions, the trade is too sporadic or localized to have a significant impact on price movements. Qualitative evidence of physical movements and quantitative price information generally complement each other, but they do not always agree completely.

The price data we use come from the monthly provincial reports to the central government of the highest and lowest county-level prices within each prefecture.[11] We have created annual series of the high prices and low prices for the most commercialized grain, high-grade rice, which we have adjusted for missing monthly data, in each of 13 prefectures reporting this information in the 63 of 68 years between 1738 and 1805 for which we have at least some observations.[12] The quantitative indicator we have chosen to guide our discussion of market integration is the bivariate correlation (Pearson's r ) between differences of annual price averages. We consider any correlation between two sets of price differences that exceeds 0.65 to indicate a market relationship and thus integration of markets.[13]

[11] Chen Jinling, "Qingchao de liangjia zoubao yu qi shengshuai," Zhongguo shehui jingji shi yanjiu , no. 3 (1985): 63–68.

[12] Price data for a total of 693 lunar months for the years between 1738 and 1858 have been located, transcribed, and converted into solar monthly data. For the analysis in this essay we consider only high-grade rice for the years between 1738 and 1805; the coverage after 1805 is too thin to merit inclusion. Analysis of other grades of rice and other grains will be made in the future to complement this work on high-grade rice; such work will show the degree of substitution among grains, which forms yet another kind of "integration."

[13] Annual averages have been created by assigning monthly weights according to the coefficients for dummy monthly variables in a regression equation for annual price by prefecture. Other studies on the Chinese Southwest (Lee, State and Economy , chap. 6) and on France during the eighteenth century (David Weir, "Markets and Mortality in France, 1600–1784," in Essays in Honor of Andrew Appleby [Cambridge, forthcoming]) have employed comparable statistical measures and established the same kind of standard; our minimum of 0.65 is a bit higher than that used in Lee's Southwest Chinese study (0.60), but a lower standard includes many correlations that represent linkages for which we have little or no reason, judging from qualitative evidence, to expect market relationships.


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With the prefecture as the unit of observation, two kinds of market integration are easily conceptualized: (1) inter prefectural integration, indicated by much of our qualitative information and by analyses of high prices and low prices separately, and (2) intra prefectural integration, indicated by the correlation of annual price differences of the high prices and low prices for each prefecture.[14] Obviously, market integration within each of two prefectures says nothing about integration between them. Likewise, if less obviously, measures of integration between two prefectures also say nothing by themselves about integration within each prefecture. Two separate indicators of market relationships, the correlation of differences in annual high prices and the correlation of differences in annual low prices, need not themselves be necessarily related. After examining interprefectural market integration indicated by high prices and by low prices, we shall explore the relationships between highs and lows across prefectures to demonstrate a market integration more complex than that suggested by evaluations of interprefectural correlations of highs and of lows and by intraprefectural correlations. These two independent measures are unable to capture the reality of market integration. Indeed, evaluated in light of qualitative evidence, these measures present us, in the Hunan case at least, with a serious puzzle.

Spatial Patterns of High and Low Prices

The proposition that related changes in annual price differences reflect market factors assumes that other forces are not driving the observed movements of prices. If, for instance, there was a strong and sharp trend in annual prices, due perhaps to inflation, the changes in annual price differences could not reasonably be taken as indicators of market integration. We therefore first look at an adjusted annual series of high and low prices to see what kind of long-term trend there might be. The provincial averages are displayed in Figure 4.1. Despite some fluctuations in individual years, for most of the eighteenth century prices remained between 1.1 and 1.3 taels; prices rose only modestly. In most prefectures, therefore, the rates of increase cannot help to explain the relationships among annual price changes to be analyzed below.[15]

Rice prices throughout Hunan also displayed remarkably similar seasonal patterns, compared, for instance, to those for Gansu millet examined by

[14] We are grateful to James Lee for our discussions about intraprefectural price comparisons. See his essay in this volume for an example of a much fuller analysis of intraprefectural prices.

[15] The annual trends are derived from the regression equation discussed in note 17.


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figure

Fig. 4.1.
Adjusted Annual Averages of High and Low Prices for High-Grade Rice in Hunan Province, 1738–1858 (taels per shi )

Note: There is a substantial amount of missing data after 1798.


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figure

Fig. 4.2.
Seasonal Variation in Prices of High-Grade Rice in Selected Hunan Prefectures, 1738–1858
(difference from January price, in taels per shi )


135

figure


136

Peter C. Perdue in his essay in this volume. Figure 4.2 displays seasonal variations for four of Hunan's 19 prefectures, two representing exporting regions and two selected from the 15 non-exporting prefectures.[16] The lowest prices of the year come in December and the highest in June and July. Prices are generally low in the winter months, rise steeply in the spring, and plummet between August and September. Since these roughly symmetrical curves are similar in areas linked by rice trade as well as those that are not, trade alone cannot explain the similarities. Similar schedules of planting and harvesting are the more general reasons for the seasonal price patterns.

To use our price data to study market integration, we must remove the shared price behavior due to common annual trends and monthly variations. We obtained coefficients for each of 11 monthly dummy variables by fitting a regression equation to the price series for each prefecture, then recalculated annual averages after subtracting the coefficients from each monthly price.[17] Because the correlations discussed are calculated from the annual differences, the effect of the annual trend is nearly completely removed. The remaining correlations are a minimal set representing those price connections that are most likely caused by trade relations and not by common annual or seasonal patterns.

For the high prices in the years for which we have data between 1738 and 1805, 14 relationships show correlations of annual price differences exceeding 0.65, of which the 13 displayed in Map 4.2 confirm the basic outlines of the rice export network in eighteenth-century Hunan.[18] Nine of these links con-

[16] The seasonal variations are calculated from the coefficients of dummy variables representing 11 months in a regression equation discussed in note 17.

[17] The coefficients are for the linear regression model P = K + a T + b2 M2 + b3 M3 + . . . + b12 M12 + e , where P is the (solar) monthly price; K is a constant; T is the year (T = 0 for 1739); M2 , M3 , . . ., M12 are dummy variables for months 2 through 12 (M2 = 1 for the second month and zero for all other months; no separate dummy variable is needed for the first month, during which M2 = M3 = . . . = M12 = 0); a , the coefficient of T, gives the annual price trend. b2 , b3 , . . ., b12 are the coefficients of the monthly dummy variables; they indicate patterns of seasonal variation. e is the error term.

[18] In addition to these fourteen relationships, there are six other high price correlations present in the data from 1738 to 1805 that challenge the picture of the rice trade sketched on the basis of qualitative information. These correlations often show a link between distant areas or between remote areas and an exporting prefecture (Yongshun-Yongzhou, Baoqing-Yongzhou, Baoqing-Yongshun, Yongzhou-Yuezhou, Baoqing-Yuezhou, Chen-Lizhou). Leaving out the years after 1777, when prices become volatile because of a combination of poor harvests and political intervention, we find that five of the six dubious relationships disappear while all fourteen of the realistic ones remain. For the shorter period 1738–77, four new relationships emerge, none of which neatly fits our expectations, based on qualitative indicators, of how rice marketing worked. Links between Baoqing and the prefectures to its east (Hengzhou) and west (Yuanzhou) seem at least possible, since they are close to each other. Very difficult to consider even plausible are links between Yuanzhou and the distant export prefectures of Yuezhou and Hengzhou. Since none of these four prefectural pairs had correlations of at least 0.65 in the longer series, we reject them as likely market relationships. While harvest fluctuations or political intervention might cause price changes in some years, sustained patterns of related price changes make the presence of a market factor more plausible. We therefore consider prefectural pairs for which the correlations of annual price differences are at least 0.65 for both the 1738–1805 and the 1738–77 periods to be the strongest candidates for market influenced price behavior, and display all but one of them in Map 4.2. The one not included is the only one of the sixty-one remaining linkages theoretically possible among Hunan's thirteen prefectures for which we find a correlation above 0.65 that cannot be explained in terms of what we know about rice marketing. Some combination of similar harvest results and political intervention presumably created this unlikely outcome. To have but one perplexing case is in fact reassuring. In general there is a good fit between our qualitative and quantitative data at the prefectural level.


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nected the major rice-exporting prefectures near Dongting Lake (Changde, Lizhou, and Yuezhou) and along the Xiang River (Changsha and Hengzhou). Four of these links (Changsha-Hengzhou, Changsha-Yuezhou, Hengzhou-Yuezhou, Changde-Yuezhou) lay along paths on which rice physically moved, while the other five links represent indirect price relationships without direct physical movements of rice between them. Changde's prices, for example, were connected indirectly with Hengzhou's via links to Changsha, even though no grain flowed directly from Changde to Hengzhou. Together, the nine links outline the integrated export market.

Four other links connect Chenzhou to three of the five rice-exporting prefectures and to Yuanzhou. The price data confirm the market relationship with Changde described by gazetteers. Since prices were higher in Chenzhou than in Changde, rice must have moved upstream, allowing Chenzhou to tap the export trade. Chenzhou's links to Changsha and Hengzhou further support the notion that it had ties to the export zone by showing market relationships without the connection of direct physical trade. In addition, Chenzhou prices are related to Yuanzhou prices, but Yuanzhou prices are lower, suggesting that Yuanzhou rice supplemented shipments from Changde to Chenzhou.

Completely separate from the export network is the price relationship between Guiyang and Chen. The generally higher prices in Chen suggest some small-scale trade going from Guiyang to Chen. The scanty qualitative information on this region for the eighteenth century provides no clear indication of this trade. In fact, this price relationship may not be a genuine economic link but may only represent changes due to harvest fluctuations in adjacent areas subject to similar weather conditions.

The failure of quantitative data to reveal movements of grain along the upper reaches of the Xiang, Zi, and Yuan rivers suggests that small amounts of grain can cross prefectural borders without clearly influencing price relationships. As we said above, small amounts of physical trade need not create strong economic relationships. Also, as is likely in the case of the Xiang River trade flows between Yongzhou and Hengzhou, the trade that existed in the early 1700s may have nearly disappeared by the second half of the century.


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figure

Map 4.1.
Qing Dynasty Hunan. (See p. 129 for Map 4.2.)


139

Data on low prices again show a concentration of relationships among prefectures within the rice export zone. Map 4.2 also displays nine significant correlations of annual price differences for the low prices of high-grade rice.[19] Six of the links connect the five export prefectures, while two others connect Chenzhou to the export zone. Only the one between Yongzhou and Chen is completely separate from the export prefectures; here again, as in the Guiyang-Chen high-price relationship, a combination of trade and common weather conditions may have caused the observed connection.

A comparison of high-price correlations and low-price correlations reveals strong similarities. Both high and low prices connect the export prefectures. Six of the nine low-price relationships parallel high-price links (Changde-Lizhou, Changde-Yuezhou, Lizhou-Changsha, Changde-Changsha, Yuezhou-Changsha, Changde-Chenzhou). The Hengzhou high-price links that are lacking for low prices account for most of the differences between the high-price and the low-price relationships.

Why is Hengzhou different from the other export prefectures? Before finding an answer to this question we must first confirm that interprefectural highs and lows both represent the export market. We can generally attribute the differences between highs and lows to transport costs in the same way as we treat differences between prefectures as the product of transport cost differences. But this is not enough. For the highs and lows to be related, we must find correlations between them. The most obvious place to look for such relations with prefectural-level data is in intraprefectural correlations.

Our sources provide the highest and lowest prices within each prefecture without giving the counties these prices come from, which can in principle vary each month. Still, if the highs and lows are closely correlated, all the counties in the prefecture likely follow similar patterns. In other words, a relationship between the high and low prices in the same prefecture indicates market integration within the area, including counties reporting prices between the high and the low. The precise counties reporting the high and the low each month need not therefore necessarily be the same; we can still observe more general features of the rice market in the prefectures. We consider first the correlations of annual price changes for high and low prices

[19] In addition, there are correlations over 0.65 for ten pairs of prefectures in only one of the two data samples (1738–1805, 1738–1777). In five of these cases prices might be related because of geographical proximity (Changsha-Hengzhou, Baoqing-Yuanzhou, Hengzhou-Baoqing, Changsha-Baoqing, Yuanzhou-Chenzhou), if low prices come from adjacent counties in different prefectures. In five other instances, however, there is no obvious explanation for the high correlations (Baoqing-Yongshun, Yuezhou-Yuanzhou, Baoqing-Yuezhou, Yuezhou-Chen, Changsha-Chen). Since market relationships are very unlikely according to our qualitative sources, we suspect that similar harvest conditions or political intervention combined to create the correlations. We consider only those links affirmed by both data samples to be ones with a strong market component; these are presented in Map 4.2.


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within each prefecture (with the number of counties under its jurisdiction shown in parentheses):

Baoqing (5)

0.18

Jingzhou (4)

0.64

Changde (4)

0.75

Lizhou (6)

0.74

Changsha (12)

0.39

Yongshun (5)

0.14

Chen (6)

0.19

Yuanzhou (3)

0.08

Chenzhou (4)

0.34

Yuezhou (4)

0.53

Guiyang (4)

0.89

Yongzhou (8)

0.23

Hengzhou (7)

-0.01

   

Seven of the 13 prefectures—Hengzhou, Yongzhou, Baoqing, Chenzhou, Yongshun, Yuanzhou, Chen—have no statistically significant correlations. Two of the six with significant correlations are small southern prefectures (Jingzhou and Guiyang), where short distances and low production levels allow greater impact from common weather patterns. Four export prefectures (Changsha, Yuezhou, Changde, and Lizhou) are the other four cases with statistically significant correlations, confirming the 1753 report's identification of major rice markets in these prefectures.[20] But the correlations for Changsha and Yuezhou are well below the 0.65 cutoff we use to identify strong indications of market integration. We therefore have a problem. Interprefectural highs and interprefectural lows each outline market integration for the rice export trade in a roughly similar manner (Hengzhou being the important difference), but we cannot establish convincing relations among the highs and lows by looking simply at intraprefectural relations. Further analysis of the price data is necessary.

Thus far we have considered correlations of annual price differences for high prices and low prices between and within prefectures. In other words, for prefectures A and B, we have considered those correlations of highs and lows depicted in Figures 4.3a and b. Together our measures represent the combination of intraprefectural and interprefectural market integration depicted in Figure 4.3c.

The organization of data by prefecture leads analysts to concentrate on the relations making the boxlike Figure 4.3c. But market relationships do not map neatly onto politically defined space. In economic terms, there is no reason to expect the market(s) providing the low prices in one prefecture to be more related to the market(s) providing the high prices in that prefecture than to the market(s) providing the high prices in some nearby prefecture. Market placements and transportation networks could easily link the highs of one prefecture with the lows of another. Unfortunately, since we do not know the county-level locations of the prefectural highs and lows, we can

[20] Changsha's correlation is significant at the 0.01 level; each of the correlations for the other three is significant at the 0.001 level.


141

figure

Fig. 4.3.
Ways to Measure Correlations of High (H) and Low (L) Prices in Prefectures A and B

only propose this scenario as a reasonable explanation of the correlations we shall examine in a moment. When related "cross" prices, shown in Figure 4.3d, are combined with the relationships between highs and lows of two prefectures, we can argue that the two interprefectural relationships are themselves related (Fig. 4.3e); in other words, we can establish market integration between highs and lows, even in the absence of strong intraprefectural correlations.

For Changsha and Yuezhou, the two export prefectures with low intraprefectural correlations, we can demonstrate the integration of their high and low prices into a common rice export network by observing in each case their cross-price relationships with nearby Lizhou shown in Figures 4.4a and 4.4b. The correlations of high Lizhou prices with low Changsha prices (0.70) and with low Yuezhou prices (0.68) reveal a market integration by cross prices that is much broader and stronger than an examination of high and of low prices between and within prefectures would have mistakenly suggested.

The number of strong relationships between rice-exporting prefectures varies. The extreme case is Changde and Lizhou (Fig. 4.4c), where all possible relationships among high and low prices are strong, thus suggesting widespread market integration within and between prefectures. For other pairs of export prefectures, some combination of strong and weak correlations can be found. The larger the number of strong correlations, the broader


142

figure

Fig. 4.4.
Correlations of High (H) and Low (L) Rice Prices in Selected Rice-Exporting Prefectures in Hunan,
1739–1805

the likely degree of market integration spanning the two prefectures. This kind of analysis confirms the integration of highs and lows in the export zone and asserts that the political boundaries of prefectures are not an important guide to the possible lines of economic integration represented by cross prices. The export market encompassed more than a narrow string of places along a single major transportation route; it spanned large portions of all but one of the export prefectures.

Hengzhou is the exception. The absence of low-price correlations between Hengzhou and other prefectures is shown most clearly for the Hengzhou price relationships displayed in Figure 4.4d. We first observe, in dramatic contrast to the high-price relationships, that the low prices have no relationship. Second, in contrast to the strong connection between high Hengzhou prices and low Changde prices, low Hengzhou prices have no link to high Changde prices. Finally, low and high Hengzhou prices have no relation to each other. Figure 4.4d displays this right triangle of strong


143

relationships (high Hengzhou prices–high Changde prices–low Changde prices), showing how high cross-price correlations strengthen our sense of highly integrated markets. The low and high prices from Hengzhou together suggest that the prefecture embraces integrated zones along the Xiang River and isolated zones toward the province's hilly eastern border. The examination of high and low prices together reveals more completely the degree to which rice-exporting prefectures are integrated into a larger marketing network than does examination of either highs or lows by themselves. Prefectures outside this network have fewer strong relationships among their prices.

Market Integration

We have analyzed qualitative and quantitative data on Hunan's rice trade. Our findings delineate Hunan's rice export trade and distinguish it from rice trade patterns in other parts of the province. First, price data generally confirm the outlines of the export trade based on qualitative information. The rice market within the export zone, with the important exception of Hengzhou, appears highly integrated both between prefectures and within prefectures. Second, price data reveal very little market integration outside the export zone—only a few thin connections in southern Hunan. The price data clarify movements along the Yuan River by suggesting amounts of some size going upstream from Changde and downstream from Yuanzhou. For the Zi River trade, however, Baoqing prices correlate poorly with other prefectures; this suggests that the trade identified by qualititative evidence may have been largely limited to movements within Baoqing Prefecture.

In Hunan's periphery, no rice markets, with the important exception of those in Chenzhou, were tied to the export trade. The case of Chenzhou, which tapped part of the Changde trade, demonstrates how an export market can include prefectural exports inside as well as outside the province. As is true for interprefectural integration, intraprefectural integration, except for two small isolated prefectures (Guiyang and Jingzhou), is high only in commercialized prefectures connected to interregional trade (Changde and Lizhou).

In sum, our analysis confirms the importance of commerce to eighteenth-century China's agrarian economy by combining qualitative and quantitative analyses of the spatial reach of China's grain markets within one province. The two approaches complement and correct each other: gazetteer evidence of small amounts of trade outside Hunan's export zone need not mean clear-cut market integration, while prices in export prefectures could be tied together without direct trading links. Persistent images of rural isolation and "natural" rather than commercial economy in Qing China will ultimately have to yield before the mounting evidence of active, integrated mar-


144

kets. But the presence of areas outside the export zone unaffected by rice market integration reminds us that any picture of China as a market society is also incomplete.

Let us not, therefore, jump to the conclusion that the kind of market activity we find in this agrarian Chinese setting implies everything that market activity does in other times and places. True, eighteenth-century China displayed some features of modern economies, but which ones? Even though additional research is clearly needed, we can provisionally propose that market integration in eighteenth-century China, as in modern economies, promoted regional specialization. Price signals informing people of the most profitable production strategy to pursue stimulated specialization according to comparative advantage then as now. But the presence of economically "rational" behavior does not necessarily mean that the resulting economic activity as a whole was "modern." Lacking were the forces that promote modern economic expansion—big capital investments, major technological advances, and new expanding markets. These forces work through an integrated market economy. Integrated markets may be highly desirable for economic advance, but they hardly create such developments by themselves. The Hunan rice case, like other Qing dynasty grain price examples, may represent a kind of market integration achieved without the familiar dynamics of modern economic change.


145

Five
Infanticide and Family Planning in Late Imperial China: the Price and Population History of Rural Liaoning, 1774–1873

James Lee, Cameron Campbell, and Guofu Tan

Between 1700 and 1900 China's population more than tripled, increasing from 150 million to almost 500 million. This dramatic rise in population is probably the most frequently noted achievement of Qing society. It is also one of the most important elements in any explanation of Qing economic performance.[1] Nevertheless, despite considerable research on the economic and demographic history of late imperial China, we have yet to devise precise demographic or economic measures for much of this period. In consequence, we have very little detailed quantitative knowledge about either the economy or the population; we also have little understanding about how population, as a variable, actually interacted with the economy during the eighteenth and nineteenth centuries.

Detailed population and price records do, however, survive in the historical archives of Taiwan and the People's Republic of China.[2] This paper is

We presented a preliminary version of this paper at the conference on Economic Methods for Chinese Historical Research organized by Thomas G. Rawski and Lillian M. Li and held in Oracle, Arizona, in January 1988. We would like to thank the participants and especially the organizers for their comments as well as George Alter, Francesca Bray, Peter H. Lindert, Donald N. McClosky, Susan Naquin, Jean-Laurent Rosenthal, Wang Shaowu, and Susan Cotts Watkins. We would also like to thank the following institutions for their financial support: California Institute of Technology, Liaoning Population Research Institute, National Academy of Sciences, National Endowment for the Humanities, and Wang Institute of Graduate Studies.

[1] See Dwight H. Perkins, Agricultural Development in China, 1368–1968 (Chicago, 1969); Mark Elvin, The Pattern of the Chinese Past (Stanford, 1973); and Ramon H. Myers, The Chinese Economy Past and Present (Belmont, 1980) for three recent influential books where population plays a key role in the analysis of Qing economic performance.

[2] For a description of some of these materials and their location in China, see Michael Finegan and Ted Telford, "Chinese Archival Holdings at the Genealogical Society of Utah,"Late Imperial China 9.2 (Dec. 1988): 86–114; and James Lee and Bin Wong, "New Research Sources for the Study of Late Imperial China," China Exchange News 15.3–4 (1987): 6–8. Until the discovery of these materials most historians have had to rely largely on genealogical data. See also four articles by Ts'ui-jung Liu: "Chinese Genealogies as a Source for the Study of Historical Demography" in Studies and Essays in Commemoration of the Golden Jubilee of the Academia Sinica (Taipei, 1978), pp. 849–70; "The Demographic Dynamics of Some Clans in the Lower Yangtze Area, ca. 1400–1900," Academia Economica Papers 9.1 (Mar. 1981): 115–60; "Ming Qing renkou zhi zengzhi yu qianyi," in Zhongguo shehui jingji shi yantao hui lunwenji (Taibei, 1983), pp. 283–316; and "The Demography of Two Chinese Clans in Hsiao-shan, Chekiang, 1650–1850," in Family and Population in East Asian History (Stanford, 1985), pp. 13–61; also see three articles by Ted Telford: "Marriage and Fertility in Tongcheng County, 1520–1661" (Manuscript presented to the Workshop on Qing Population History, Pasadena, 1985); "Survey of Social Demographic Data in Chinese Genealogies," Late Imperial China 7.2 (1986): 80–117; and "Fertility and Population Growth in the Lineages of Tongcheng County, 1520–1661" (Manuscript presented to the Conference on Chinese Lineage Demography, Asilomar, Calif., 1987); and Stevan Harrell, "The Rich Get Children: Segmentation, Stratification, and Population in Three Chekiang Lineages, 1550–1850," Family and Population in East Asian History (Stanford, 1985), pp. 81–109. See too, however, the important cautionary article by Harrell, "On the Holes in Chinese Genealogies," Late Imperial China 9.1 (Dec. 1987): 52–87.


146

a preliminary attempt to use such materials to reconstruct the price and population history of Daoyi District, a rural suburb of Shenyang in Liaoning Province, for approximately 100 years, from 1774 to 1873. First, we summarize the results of an ongoing study of the population history of Daoyi and demonstrate that mortality and fertility differed sharply by sex. Second, we reconstruct the price history of five food grains (rice, millet, sorghum, wheat, and soybeans) and determine the degree of price integration in the prefectural market. Finally, we analyze the relationship between grain prices and demographic rates in order to prove that the differential rates by sex were the product of a systematic pattern of infanticide according to household situation as well as economic conditions. We conclude that in Liaoning both mortality and fertility were highly responsive to changes in economic circumstances.

The study of food prices and population has, of course, long been a central topic both in the historical demography and in the economic history of the preindustrial world. This is only natural, for population was everywhere one of the most dramatic and dynamic economic variables and fluctuations in population were at least in part a function of harvest variations. Numerous studies in European history have repeatedly discovered relatively strong positive correlations between food prices and mortality and even weak negative correlations between food prices and fertility.[3] As we shall see, in Liao-

[3] Indeed Goubert has gone so far as to claim that "the price of wheat almost always constitutes a true demographic barometer. The range and frequency of the fluctuations in grain prices control the size and the frequency of the demographic crises." Pierre Goubert, "En Beauvaisis: problèmes demographiques de XVIIe siècle," Annales ESC 7.4 (1952): 453–68. For several recent examples of the state of this field, see Tommy Bengtsson, Gunnar Fridlizius, and Rolf Ohlsson, eds., Preindustrial Population Change: The Mortality Decline and Short-Term Population Movements (Stockholm, 1981). See too the detailed analysis by Ronald Lee and others in E. A. Wrigley and R. S. Schofield, eds., The Population History of England, 1541–1871 (Cambridge, Mass., 1981).


147

ning correlations between vital rates and prices were just as strong, but the patterns of population behavior were fundamentally different from European ones. Indeed, because of the widespread use of infanticide as a method of family planning, the strongest correlations link prices with fertility, not mortality. The Chinese apparently regarded infanticide as a form of postnatal abortion through which they could choose the number, spacing, and sex of their children in response to short-term economic conditions as well as their long-term family-planning goals.

Our demographic data for Liaoning come from an ongoing study of over 12,000 Chinese peasants who lived between 1774 and 1873. So far as we can tell, these peasants were direct descendants of an earlier Ming garrison.[4] The Qing government certainly classified the population as Han Chinese and in the early seventeenth century organized them as members of the Han banner armies.[5] Two thirds were farmers who lived in three villages (Baodao tun , Daoyi tun , and Dingjia fangshen ) in Daoyi District, a northern suburb of Shenyang. The rest were farmers originally from Daoyi, who had since moved to nearby villages. Because almost all of these villages were located near the provincial capital, the vast majority of these farmers undoubtedly produced food for the city market as well as for their own consumption. We have, however, almost no information on the specific structure of the village economy or the nature of these market relations during the eighteenth and nineteenth centuries.

What we do have are 85,000 individual records and 12,000 household records on the demography of this population throughout this period. This information is preserved in 25 triennial registers. These registers provide a nominative list of the families that received state banner land and in turn were liable for special corvée and military banner service. Specifically, they record for each person his or her name, age, occupation, family and lineage

[4] The 1566 edition of the Quan liao zhi , 4.3, includes a Daoyi tun in a list of Ming garrison villages.

[5] Our registers are entitled Zheng huangqi Daoyi tun Hanjun rending hukou ce ("Daoyi Village population registers from the Han army of the Plain Yellow Standard"). According to Zhou Yuanlian, Qingchao kaiguo shi yanjiu (Shenyang, 1981), and "Guanyu baqi zhidu de jige wenti," Qingshi luncong 3 (1982): 140–54, these Han banners date from the Manchu conquest of Shenyang in 1625. We discuss the changing social organization and ethnicity of this population in some detail in James Lee and Cameron Campbell, "Happy Families: Household Hierarchy and Differential Vital Rates in Rural Liaoning, 1774–1873" (Forthcoming). In 1982, of the 17,792 people who lived in Daoyi District (qu ), a suburb of present-day Shenyang, 60 were Muslim, 62 were Mongol, 343 were Xibo, 563 were Manchu, 618 were Korean, and 16,146 were Han Chinese.


148
 

TABLE 5.1 Summary of the Population Registers of Daoyi, Liaoning, 1774–1873

 

Population

Entrances

Exits

Unannotated Disappearances

Year of Register

Total

Males

Females

Births

Marriages

Immigration

Deaths

Marriages

Emigration

Total

Males

Females

1774

2,192

1,234

958

135

31

1780

2,548

1,467

1,081

366

115

266

118

38

2

242

109

133

1786

2,748

1,578

1,170

479

170

226

188

47

32

417

217

200

1792

2,772

1,568

1,204

357

203

60

252

60

8

283

136

147

1795

2,902

1,629

1,273

260

112

24

174

50

29

14

5

9

1798

2,951

1,642

1,309

226

95

42

213

46

7

45

19

26

1801

3,014

1,697

1,317

198

73

14

162

38

14

13

7

6

1804

3,155

1,768

1,387

317

120

22

223

72

17

8

6

2

1810

3,144

1,776

1,368

354

176

37

251

56

16

255

95

160

1813

3,181

1,782

1,399

182

97

33

207

55

8

4

3

1

1816

3,131

1,758

1,373

131

88

4

209

50

3

7

4

3

1819

3,154

1,781

1,373

124

65

11

128

39

11

6

4

2

1822

3,151

1,781

1,370

236

150

24

285

106

2

21

11

10

1828

3,270

1,869

1,401

530

215

38

196

60

15

395

185

210

1831

3,270

1,865

1,405

197

85

6

233

46

0

11

6

5

1837

3,291

1,929

1,362

400

187

17

204

65

6

309

128

181

1840

3,214

1,912

1,302

154

84

2

236

64

1

17

7

10

(Table continued on next page)


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(Table continued from previous page)

 

TABLE 5.1 Summary of the Population Registers of Daoyi, Liaoning, 1774–1873

 

Population

Entrances

Exits

Unannotated Disappearances

Year of Register

Total

Males

Females

Births

Marriages

Immigration

Deaths

Marriages

Emigration

Total

Males

Females

1843

3,125

1,889

1,236

114

54

2

195

35

2

27

6

21

1846

3,094

1,869

1,225

118

93

1

173

39

3

29

12

17

1855

3,187

1,953

1,234

393

283

28

190

29

6

386

172

214

1858

3,162

1,962

1,200

126

95

7

174

36

14

32

12

20

1861

3,199

1,997

1,202

156

99

3

177

30

3

12

6

6

1864

3,132

1,997

1,155

173

98

32

334

15

1

22

13

9

1867

3,156

2,012

1,144

188

109

31

244

21

8

30

17

13

1873

3,271

2,067

1,204

316

225

35

249

10

4

204

108

96

Total

12,466a

6,326a

6,140a

6,095

3,091

965

5,150

1,138

212

2,789

1,288

1,501

NOTES: The columns under "Population" record the number of people alive at the end of each register period. Exits include all the people who are recorded as having departed during the intercensal period through death, marriage, or emigration. Entrances list all the people who appear in the registers for the first time through birth, marriage, or immigration. People who disappeared without annotation between the previous and current register are listed under "Unannotated Disappearances." The number alive in a register is equal to the number alive in the previous register plus the entrances and minus the exits recorded in the current register. The totals under "Population" are counts of the number of individuals by sex who make at least one appearance in the data set.

Registers were compiled every three years. Most numbers, therefore, represent the cumulated events over a three-year intercensal period. Because of missing registers, some numbers, however, represent the cumulated events over six years, and in one case, over nine years. These numbers are in boldface.

a The number of individuals who make at least one appearance in the data set.


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relationships, birth date, recent demographic events, and village of residence. The registers survive in the Liaoning Provincial Archives and were coded into machine-readable form at the Liaoning Population Research Institute and the California Institute of Technology.[6] To the best of our knowledge, no other material records a Chinese peasant population before 1900 with such continuity and detail. We present a crude summary profile of all 25 registers in Table 5.1.

The sources are a product of the Eight Banner registration system.[7] In Liaoning, Qing officials relied heavily on such records for civilian and military administration. They accordingly devised a remarkable system of internal cross-checks to ensure consistency and accuracy. First, they assigned every person in the banner population to a residential household (linghu ) and registered them on a household certificate (menpai ). Then they organized these households into clans (zu ) and compiled annually updated clan genealogies (zupu ). Every three years local authorities compared these genealogies with the household certificates to compile the population registers. Thanks to such efforts, the banner registers provide far more comprehensive and accurate data than the population registration system (baojia ) common elsewhere in China.

These registers do not, however, record the entire population. They have two related defects. On the one hand, nine of the registers from the century under observation are damaged or lost. We therefore have incomplete information on deaths and to a lesser extent births for 27 years.[8] On the other hand, even when registers do exist, registration is still incomplete in the very early age groups. Almost no one below 2 sui is registered. Indeed, the mean age at first appearance for both sexes is 6 sui , which is slightly less than five Western years of age.[9] Many children who died before 6 sui simply do not

[6] The process has been very labor-intensive, requiring several thousand hours over five years, and would not have been completed without the assistance of many people. Registers were transcribed by James Lee (1774, 1792), Robert Eng (1780, 1786), Julie Sun (1798, 1819), Liu Guiping (1855), Wang Yuanqing (1861), He Ti (1864), Anna Chi (1795, 1843, 1846, 1858, 1873), and Alice Suen (1801, 1804, 1810, 1813, 1816, 1822, 1828, 1831, 1837, 1840). Lawrence Anthony, Cameron Campbell, and Martin Hunt wrote machine programs to identify transcription errors. Anna Chi and James Lee repeatedly "cleaned" the entire data set. We would like to thank all the participants for their help, in particular Anna Chi and Alice Suen.

[7] See Fu Kedong, "Baqi zhidu huji chutan," Minzu yanjiu 6 (Dec. 1983): 34–43, for a more complete description of the banner registration system. We would like to thank Pamela Crossley for bringing this article to our attention.

[8] We are missing nine registers, dated 1777, 1783, 1789, 1807, 1825, 1834, 1849, 1852, and 1870.

[9] By sui the Chinese meant to indicate the number of calendar years during which a person had lived. People are accordingly 1 sui at birth and 2 sui at the next New Year. Sui are, therefore, on average one and a half years higher than Western years of age. The mean age at first appearance for all 25 registers was 6.1 sui for females and 5.8 sui for males. Age at first appearance by family relationship was similarly consistent: 6 sui for children in the generation below the head and 5 sui for grandchildren.


151

appear in our records. Girls are more likely than boys not to be registered, especially after 1840. Our estimations of vital rates accordingly fall considerably short of the actual levels of fertility and mortality.

Fortunately, with the exception of female underregistration, these omissions, at least before 1840, do not appear to follow any selective bias. Such bias as there is appears to be uniform over time and household position. Thus, mean age at first appearance is quite consistent by sex, time, and household relationship, so that the data, although incomplete, are nevertheless sufficient to document a variety of distinct patterns of mortality and fertility behavior in Daoyi.[10] These patterns by sex, age, household type, and family relationship, as well as economic condition, are far too consistent to be the product of underregistration. We summarize three particularly pertinent examples.

First, although the level of mortality during this century was moderate, deaths were distributed highly unevenly by sex. We contrast the overall mortality experiences of males and females in Table 5.2. We then present the experiences of the two sexes in periods of high and low overall mortality in Tables 5.3 and 5.4. The contrasts reveal significantly higher levels of mortality among females than among males but also far greater fluctuations in mortality among males than among females. The pattern of mortality, in other words, suggests a system of resource allocation wherein the female share, although relatively constant, was smaller than the male share. Males consumed more resources; but because they relied on the harvest surplus for their larger share, they were more vulnerable to harvest fluctuations than females. In Daoyi, in other words, the price of privilege was economic insecurity.

Second, the patterns of birth spacing and birth stopping strongly suggest that most married couples controlled their fertility to a considerable extent. Two very useful indications of such conscious limitation are the length of time between births and the age of women at the birth of their last child. Studies of many historical European populations show that in the absence of fertility control, birth intervals beginning with the second birth are rarely

[10] These findings derive from a number of conference papers that elaborate on their significance. See Lawrence Anthony, James Lee, and Alice Suen, "Adult Mortality in Rural Liaoning 1795 to 1819," California Institute of Technology Working Paper 115 (1985); James Lee and Robert Eng, "Population and Family History in Eighteenth Century Manchuria: Preliminary Results from Daoyi, 1774–1798," Ch'ing-shih wen-t'i 5.1 (June 1984): 1–55; James Lee and John Gjerde, "Comparative Household Morphology of Stem, Joint and Nuclear Household Systems: Norway, China, and the United States," Continuity and Change 1.1 (May 1986): 89–112; John Gjerde, Anita Tien, and James Lee, "Comparative Household Processes of Stem, Joint, and Nuclear Household Systems: Scandinavia, China, and the United States" (Manuscript presented to the annual meeting of the Social Science History Association, 1987); James Lee, Cameron Campbell, and Lawrence Anthony, "A Century of Mortality in Rural Liaoning, 1774–1873," Le peuplement du monde avant 1850 , ed. Antoinette Fauve-Chamoux (Paris, 1990).


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TABLE 5.2 Male and Female Life Expectancy in Daoyi, 1792–1867

Age Group

Male Life Expectancy

Female Life Expectancy

Difference in Life Expectancy

Standard Deviation of Difference in Life Expectancy

1–5

35.2

28.0

-7.2

0.8

6–10

43.3

35.8

-7.5

0.7

11–15

42.9

35.0

-7.9

0.6

16–20

39.7

33.2

-6.5

0.6

21–25

36.4

33.2

-3.2

0.5

26–30

32.7

31.1

-1.6

0.4

31–35

28.9

28.6

-0.3

0.4

36–40

25.2

25.9

0.7

0.4

41–45

21.8

23.4

1.6

0.4

46–50

18.3

20.7

2.4

0.4

51–55

15.4

17.5

2.1

0.3

56–60

12.6

14.6

2.0

0.2

61–65

10.4

11.5

1.1

0.2

66–70

8.1

8.6

0.5

0.2

71–75

5.7

6.2

0.5

0.2

76+

3.5

3.7

0.2

0.2

SOURCE: Computed from intercensal life tables for 1792–1795–1798–1801–1804, 1810–1813–1816–1819–1822, 1828–1831, 1837–1840–1843–1846, 1855–1858–1861–1864–1867. See James Lee, Cameron Campbell, and Guofu Tan, "A Century of Mortality in Rural Liaoning, 1774–1873" in Antoinette Fauve-Chamoux, ed. Le peuplement du monde avant 1850 (Paris, 1990).

NOTE: All ages are in sui , on average one and a half years higher than Western years of age.

much more than two years long.[11] Moreover, the mean age at last birth is almost always within one year of age 40. In our population, by contrast, the mean age at last birth was only 34 (35.5 sui ). Furthermore, the mean birth interval beginning with the second birth was almost five years. As we can see in Figure 5.1, in striking contrast with European populations, where birth intervals increase by order of child, birth intervals in Daoyi actually decrease. According to these data, most couples decided to have fewer children than their natural limit and tended to space these children far apart.

Third, people in Daoyi appear to have used some form of sex-selective fertility control.[12] Table 5.5 analyzes the birth histories of almost 1,000 com-

[11] Most of our information for Europe comes from Michael W. Flinn, The European Demographic System, 1500–1820 (Baltimore, 1981); and Ansley J. Coale and Susan Cotts Watkins, The Decline of Fertility in Europe (Princeton, 1986).

[12] Apparently the Chinese did not consider children during the first year of life to be fully human. This was a traditional concept. An imperial edict recorded in the Tang huiyao (Important documents of the Tang; Beijing, 1955) for the year 623 says: "People when they are first born are just young animals [huang ]. At four sui they become minors [xiao ]. At 16 sui they become youths [zhong ]. At 21 sui they become adults [ding ]. At 60 sui they become old [lao ]." Tang huiyao , 85.1555. According to a famous passage from the Rites of Zhou , a compendium of statements on early political institutions and policies probably completed in the second century B.C. , "people should be registered after they have grown their teeth." In a well-known commentary on this passage, Qiu Jun, a fifteenth-century statesman, explained: "The human body is not fully developed until teeth are grown. Boys grow their first set of teeth in their eighth month and their second set in their eighth year. Girls grow their first set of teeth in their seventh month and their second set in their seventh year. They should then all be recorded in the population register." See Qiu Jun, Daxue yanyi bu (The Supplement to the exposition of the Great Learning, 1792), 13.14.


153
 

TABLE 5.3 Female Life Expectancy in Daoyi during Good and Bad Periods, 1792–1867

Age Group

Life Expectancy in Good Periods

Life Expectancy in Bad Periods

Difference in Life Expectancy

Standard Deviation of Difference in Life Expectancy

1–5

30.2

18.4

11.8

2.2

6–10

42.8

23.8

19.0

2.0

11–15

42.1

24.9

17.2

1.9

16–20

39.6

23.3

16.3

1.8

21–25

36.4

28.4

8.0

1.4

26–30

33.7

26.9

6.8

1.2

31–35

30.6

24.6

6.0

1.1

36–40

28.0

22.4

5.6

1.1

41–45

26.2

20.2

6.0

1.0

46–50

23.7

18.3

5.4

0.9

51–55

20.6

15.5

5.1

0.9

56–60

16.8

13.6

3.2

0.8

61–65

12.8

11.0

1.8

0.7

66–70

9.3

8.3

1.0

0.6

71–75

7.1

6.0

1.1

0.5

76+

4.2

3.2

1.0

0.3

NOTE: Good and bad periods are those intercensal periods when life expectancy was at least one standard deviation higher or lower than average life expectancy during the century under consideration. For females 1816–19 and 1828–31 were good periods and 1795–98, 1861–64, and 1864–67 were bad periods. See James Lee, Cameron Campbell, and Guofu Tan, "A Century of Mortality in Rural Liaoning, 1774–1873" in Antoinette Fauve-Chamoux, ed. Le peuplement du monde avant 1850 (Paris, 1990).

pleted marriages and computes the sex ratios by birth order and completed family size, that is, the total number of births registered to the parents by the time the mother reached age 45. The numbers are certainly exaggerated because of the underregistration of females, but it is the unusual pattern that is important. In single-child families there were 576 boys for every 100 girls. For families with two children ever born there were 211 boys per 100 girls at


154
 

TABLE 5.4 Male Life Expectancy in Daoyi during Good and Bad Periods, 1792–1867

Age Group

Life Expectancy in Good Periods

Life Expectancy in Bad Periods

Difference in Life Expectancy

Standard Deviation of Difference in Life Expectancy

1–5

45.0

28.9

16.1

1.8

6–10

53.0

36.4

16.6

1.3

11–15

51.0

36.4

14.6

1.2

16–20

47.6

33.2

14.4

1.1

21–25

43.7

30.4

13.3

1.1

26–30

40.6

27.2

13.4

1.0

31–35

36.4

23.8

12.6

0.9

36–40

32.8

20.0

12.8

0.9

41–45

28.8

17.3

11.5

0.8

46–50

24.8

13.8

11.0

0.8

51–55

21.3

11.4

9.9

0.7

56–60

17.3

9.7

7.6

0.7

61–65

13.3

8.3

5.0

0.6

66–70

9.6

6.3

3.3

0.6

71–75

6.9

4.6

2.3

0.5

76+

4.1

3.1

1.0

0.3

NOTE: For males 1798–1801 and 1816–19 were good periods and 1795–98, 1819–22, and 1861–64 were bad periods. See James Lee, Cameron Campbell, and Guofu Tan, "A Century of Mortality in Rural Liaoning, 1774–1873" in Antoinette Fauve-Chamoux, ed. Le peuplement du monde avant 1850 (Paris, 1990).

 

TABLE 5.5 Sex Ratios by Birth Order and Completed Family Size (male births per 100 female births)

 

Completed Family Size

Birth Order

1

2

3

4

5+

1

576

211

156

158

88

2

450

294

229

139

3

324

278

149

4

422

138

5+

162

N

115

328

428

401

599

NOTE: These calculations include only children born between 1792 and 1840 to the 883 completed first marriages that began before 1840. Births after 1840 are included in the completed family size, but are not included in the computations of sex ratios because of the decline in female registration after 1840. Inclusion would show even more lopsided sex ratios in later parities.


155

figure

Fig. 5.1.
Preceding Birth Interval by Order of Child in Daoyi and France, 1774–1873

Note: Based on 1774–1873 data for Daoyi and 1500–1820 data for France.
Data for France are from Michael W. Flinn, The European Demographic System (Baltimore, 1981), p. 330.


156

the first birth and 450 boys to 100 girls at the second birth. For families with three children ever born the ratio was 156 boys to 100 girls at the first birth, 294 boys to 100 girls at the second birth, and 324 boys for every 100 girls at the last birth. This highly unnatural pattern continues through all other completed family sizes. The closer a girl's birth order was to the completed family size, the less likely she was to survive to registration. The pattern is too systematic to be explained by random underregistation.

Daoyi peasants, in other words, used sex-selective methods of fertility control to determine the number and sex of their children. Generally there was a strong preference for boys, since sons had a higher utility, especially during their parents' old age. Couples targeted the number of boys they wanted and stopped having children after the desired number had been reached. This behavior produced both the low mean age at last birth and the unusual pattern in Table 5.5, where sex ratios increase steadily with birth order. Whether or not a girl born before the cutoff would be allowed to live depended on many factors, one of the most important of which was wealth. Poorer couples who only planned on one or two boys would be less willing to be burdened with girls. Wealthier couples, however, would want more children and would be more likely to allow early girls to live. As a result, the sex ratios in Table 5.5 for couples who had few children are comparatively high, while the ratios for couples who had more children are comparatively moderate.[13]

A study of age-specific fertility by household position underlines the strong relationship between wealth and fertility. Most peasants in Daoyi lived in highly hierarchical, complex households, where resources depended to a large extent on their position within the household.[14] We would therefore expect fertility patterns to reflect two well-known Confucian principles of household organization, whereby household heads took precedence over other family members and senior relatives took precedence over junior relatives. Indeed, the calculation of fertility by household position, presented in Figure 5.2, confirms that, at least in Daoyi, most Chinese families obeyed such principles. The hierarchy of fertility begins with the head at the top, followed by the head's brothers and sons, then his uncles, brothers' sons, cousins, and cousins' sons. The marital fertility rate of household heads is

[13] We would like to thank George Alter for his illuminating analysis of Table 5.5 Stevan Harrell, "The Rich Get Children," pp. 81–109, shows clearly that in late imperial China fertility was tied to wealth. He studied three lineages in Xiaoshan County, Zhejiang, from 1550 to 1850 and found that members of the richer branches of the lineages had more children than members of the poorer branches. This behavior can be attributed to a long natalist tradition in Chinese society.

[14] We discuss the structure of household hierarchy as well as the patterns of household progression and calculate differential vital rates by household structure and household position in James Lee and Cameron Campbell, Happy Families .


157

figure

Fig. 5.2.
Male Fertility in Daoyi by Family Relationship, 1792–1873 (births per 1,000 person-years)

Note: General male marital fertility equals male births over married men 15–35. General male fertility equals male births over
all men aged 15–35.


158

twice that of cousins' sons. The marital fertility of sons is not only higher than that of brothers' sons, it is almost as high as that of brothers and far higher than that of cousins. Removing the control for marriage accentuates the differences between the privileged and the dispossessed. Because chances of marrying depended on household position as well, not only does the gap between the head and cousins' sons widen considerably: so do the gaps between the head and his sons and brothers. In both cases as distance from the household head increases, fertility decreases.

Given the technology of birth control available in late imperial China, such distinctive sex-selective patterns of family planning could only arise if parents consciously chose the number and sex of their children at least in part through infanticide. The patterns according to birth order, completed family size, and household position are too rational and systematic to be explained by underregistration. Parents' decisions to use infanticide were, of course, not based solely on their long-term goals and household position. Couples must have been influenced by short-term conditions. Indeed, as we shall see, couples were influenced in the short term by the price of food.

The price data come from an empirewide system to monitor food conditions that began elsewhere in China as early as the late seventeenth century but did not extend to Liaoning until the late eighteenth century. Thereafter until well into the twentieth century, magistrates in each of Liaoning's two prefectures, Fengtian and Jinzhou, reported every ten days to the provincial government on food supply conditions, including the price of all major food grains, the state of the weather, and harvest yields when appropriate. The provincial governor in turn prepared for the emperor a brief summary each lunar month of the lowest and highest prices reported in each prefecture for five food grains (rice, husked and unhusked millet, sorghum, wheat, and soybeans). It is these monthly summaries that provide our price data for Fengtian prefecture.

The principal virtue of these price summaries is the systematic spatial and temporal coverage they provide across the entire empire for over 200 years. To date, we have collected over 1,500 of these monthly price reports for the prefecture, almost two thirds of which are from the century under consideration in this paper.[15] Even after converting these lunar data to solar-month equivalents, we have complete or almost complete information (nine months' worth or more) for 65 years and no information for only five years (1791,

[15] James Lee collected 1,200 of these memorials from the First Historical Archives in Beijing in 1985, 1986, and 1987. Yeh-chien Wang kindly provided the 300 remaining memorials from his own research in the National Palace Museum in Taibei. We would like to thank Yehchien Wang for his gracious assistance to us here and elsewhere. Anna Chi transcribed the material into machine-readable form. We gratefully acknowledge her assistance.


159

1815, 1822, 1823, and 1825).[16] Figures 5.3 and 5.4 illustrate the price curves of the monthly lowest reported price and the monthly highest reported price for all five grains from 1774 to 1873 in taels of silver per shi of grain. These price data provide a systematic measure of food availability in Daoyi and, by extension, of economic conditions, which we can correlate with vital rates. They also enable us to identify which grains were most closely tied to specific changes in population and which grains may therefore have loomed largest in individual decision making on fertility and mortality.

It is important to remember, however, that these price materials also have a number of deficiencies, especially for microanalysis at the subprefectural level.[17] They are denominated in silver and accordingly do not accurately reflect the retail market, which commonly used copper cash. They only provide us with the highest and lowest prices in each month and tell us nothing about the overall distribution of prices within each prefecture. Finally, they do not tell us the location of the reported prices. We consequently cannot calculate an average prefectural price. We cannot even assume that the monthly low and high prices were necessarily related. We can only assume that the data represent general price trends over time. For the purposes of this analysis we shall therefore separately analyze all ten types of price at our disposal.

Correlation coefficients between annual averages of different grain prices, summarized in Table 5.6, reveal a high degree of substitutability among most grains. This was especially true for the monthly low and high prices of husked and unhusked millet, as well as the monthly low and high prices of millet, rice, and sorghum. Monthly low and high prices for the same grain, however, are less strongly correlated than low-priced and high-priced grains by themselves. We suspect that this pattern occurred because these two sets of prices may represent two different regions within the prefecture.[18] Our analysis, in other words, confirms that monthly low-priced and high-priced grains were not always substitutable.

[16] Peter C. Perdue produced the programs that converted monthly prices from lunar to solar dates. We gratefully acknowledge his repeated assistance and encouragement as we converted our data. We would also like to note that Anna Chi helped supplement this program to cover fully the period from 1736 to 1911.

[17] Although subprefectural data for Fengtian exist in the Liaoning Provincial Archives, all the weekly and monthly reports that we have found to date are from the very early twentieth century. We would like to thank the Liaoning Provincial Archives for making these data available.

[18] For the moment we have no direct information on where the monthly low and high prices come from. Unless we can find the subprefectural price reports for the eighteenth and early nineteenth centuries, our analysis will always be incomplete. We do not know if these price data consistently originate from the same region. We consequently cannot guarantee that our results always accurately reflect the historical realities.


160

figure

Fig. 5.3.
Low Grain Prices in Fengtian Prefecture, 1774–1873 (annual averages in taels per shi )


161

figure

Fig. 5.4.
High Grain Prices in Fengtian Prefecture, 1774–1873 (annual averages in tales per shi )


162
 

TABLE 5.6 Correlations of High and Low Grain Prices in Fengtian Prefecture, 1774–1873

 

High Prices

Low Prices

 

Mean Price

Standard Deviation

Rice

Husked Millet

Unhusked Millet

Sorghum

Wheat

Rice

Husked Millet

Unhusked Millet

Sorghum

Wheat

Soybeans

Low Prices

                         

Soybeans

0.625

.194

.60

.66

.66

.48

.72

.80

.83

.83

.89

.65

 

Wheat

1.465

.341

.48

.67

.66

.53

.69

.63

.69

.69

.68

   

Sorghum

0.565

.234

.62

.76

.77

.62

.75

.81

.91

.91

     

Unhusked Millet

0.536

.232

.56

.74

.75

.63

.74

.84

1.00

       

Husked Millet

1.073

.463

.56

.74

.75

.62

.74

.84

         

Rice

1.858

.605

.66

.69

.69

.54

.75

           

High Prices

                         

Soybeans

1.809

.591

.60

.74

.75

.75

.73

           

Wheat

3.047

.712

.82

.87

.87

.75

             

Sorghum

1.568

.564

.76

.89

.90

               

Unhusked Millet

1.208

.424

.81

1.00

                 

Husked Millet

2.406

.846

.81

                   

Rice

3.785

.958

                     

163

Nevertheless, virtually all prices rose and fell usually in tandem and occasionally by as much as a factor of two or three within the space of just one or two years. A recent study by Wang Shaowu of the climate and harvest history of Manchuria indicates that fluctuations in summer temperature were responsible for most of these sharp price variations through their impact on crop yields.[19] Even as recently as 1954, 1957, 1969, 1972, and 1976, low summer temperatures reduced harvest yields by as much as one third. According to Wang, there were at least 26 similarly cold summers between 1774 and 1873, concentrated in the 1780s, 1810s, and 1830s.[20] Given the low level of agricultural technology in the late eighteenth and early nineteenth centuries, the impact of these low temperatures on harvests may have been even more severe than in the twentieth century.[21] Indeed, an examination of Figures 5.3 and 5.4 reveals that these three decades were characterized by rapid price increases. Prices, in other words, appear to accurately reflect the availability of food within Daoyi and should have had great impact on population behavior.

To what extent, then, did grain prices affect vital rates? Figures 5.5 and 5.6 summarize what information we have on annual crude birth and death rates for the century under observation. Again these data suffer from two limitations. On the one hand, we cannot calculate reliable birth or death rates for several years due to missing registers. On the other hand, because of the limitations of the registration system, we can only compute average death rates over three-year periods.[22] We cannot compute actual annual death rates for either sex. Our mortality statistics therefore appear deceptively stable. Given the incompleteness of the vital data and the volatility of the price data, we should anticipate that the correlations of prices to population will be weaker on paper than they were in reality.

[19] Wang Shaowu, "Jin sibainian dongya de lengxia" (Unpublished manuscript, 1988), reconstructs the temperature history of Manchuria by using a time series from the Japanese island of Hokkaido, which, he explains, was dominated by the same pressure system and therefore subject to the same large-scale fluctuations in temperature.

[20] According to Wang, these unusually cold summers were 1774, 1776, 1778, 1782, 1783, 1785, 1786, 1789, 1793, 1813, 1815, 1825, 1830–38, 1835–38, 1841, 1846, 1856, 1857, 1866, and 1869.

[21] For example, again according to Wang, a mean summer temperature just 1.5 to 2 degrees centigrade below normal in 1902 and 1913 resulted in 50 percent and 80 percent reductions in harvest yields respectively, compared in each case to the average yield of the five previous years.

[22] Female death rates cover only 25 intercensal periods and therefore include only 75 years of the century under observation. Male death rates, by contrast, include 30 intercensal periods and cover virtually the entire century. It is important to remember that these crude rates are unadjusted for deaths missing because of late registration. We estimate elsewhere that as many as one fourth of all deaths are missing in every population register (Lee, Campbell, and Anthony, "A Century of Mortality"). Since the proportion of underregistration appears to have been consistent, the temporal pattern illustrated in Figure 5.6 should be substantively correct.


164

figure

Fig. 5.5.
Crude Birth Rates in Daoyi, 1774–1864 (per 1,000 married women aged 15–45)


165

figure

Fig. 5.6.
Crude Death Rates in Daoyi, 1771–1873 (per 1,000 population, by intercensal period)


166
 

TABLE 5.7 Correlations of Grain Prices and Death and Birth Rates in Daoyi, 1774–1873

 

Household Death Rate

Household Birth Rate

 

All

All

Complexa

Simpleb

Grain Price

Female

Male

Female

Male

Female

Male

Female

Male

Rice

               

High

- 0.62

- 0.46*

- 0.46*

- 0.36

Low

- 0.60

- 0.37*

- 0.48

- 0.54

- 0.46

Millet

               

High

- 0.65

- 0.37

- 0.55

- 0.33*

- 0.50*

- 0.56

Low

0.32

- 0.49

- 0.42*

- 0.45

Sorghum

               

High

- 0.57

- 0.46*

- 0.33*

- 0.39*

- 0.39

Low

0.26

- 0.58

- 0.40*

- 0.54

- 0.46*

- 0.49

Wheat

               

High

- 0.68

- 0.36*

- 0.54

- 0.34

Low

0.43

- 0.44

- 0.38*

- 0.48

- 0.39

Soybean

               

High

- 0.45*

- 0.63

- 0.51

Low

0.39

- 0.57

- 0.40*

- 0.36

- 0.40*

- 0.47

NOTE: All correlations have a significance of 0.001 unless marked with an asterisk, in which case the significance is 0.01. Correlations with a significance of less than 0.01 have been omitted. Our calculations begin from 1774 for all households and from 1789 for the breakdown by simple and complex households, and end in 1840 for female births and in 1873 for male births. The prices are adjusted annual averages from Fengtian prefecture; the birth and death rates are annual rates from Daoyi.

a Households with two or more conjugal units.

b Households with only one conjugal family unit.

Nevertheless, in spite of these limitations, the comparison of annual birth and death rates with annual average grain prices yields a number of truly significant results. In keeping with our previous analysis of mortality, the correlations between food prices and death rates are far stronger for men than for women. Indeed, as we can see from Table 5.7, there are no significant correlations between food prices and female death rates. The only meaningful relationships we can find are between male death rates and the annual average of monthly low prices. There are no correlations between the annual averages of monthly high prices with mortality. From these findings we can infer that the monthly low prices for the prefecture somehow reflect the availability of food in Daoyi during subsistence crises. We can also identify which of the five reported grains were the most important subsistence


167

crops for our population. Rice prices, for example, were uncorrelated with mortality and can be considered relatively unimportant. All other grains had significant if weak correlations with mortality. Wheat was the most important of these food crops.

However, in keeping with our analysis of fertility and family planning, correlations between grain prices and birth rates are not only far stronger than correlations between grain prices and death rates, they are also stronger for females than for males, especially in complex households. Strong negative correlations exist for virtually all grain prices regardless of the type of price (monthly low or monthly high) or the variety of grain. Figure 5.7, which plots birth rates by sex against low millet prices—millet being a common food staple—graphically illustrates the relationship between prices and fertility. When food prices were high, people had fewer children, especially fewer girls.

Fetal wastage and standard methods of family planning would have produced dramatically different results. Spontaneous abortions would have affected males as well as females.[23] Contraception would have produced strong correlations with lagged prices as well as current prices. In fact, though, correlations between prices and fertility are not only stronger for females than for males, they are stronger for current prices than for lagged prices.[24] Parents, in other words, made their fertility decisions in response to conditions at time of birth rather than conditions at time of conception. The unnatural response of birth rates by sex to immediate economic conditions, therefore, strongly suggests that in Daoyi many peasants limited their fertility through sex-selective neglect or infanticide.

Parents, of course, were most likely to make such drastic decisions in response to extreme economic conditions, only adjusting their fertility when prices were exceedingly high or exceedingly low. As a result, the correlations between birth rates and food prices in Table 5.7 may not accurately reflect the full responsiveness of fertility to economic conditions. We therefore calculate the percent changes in birth rates between years of medium prices (within one standard deviation of the mean) to years of unusually high or low prices (above or below one standard deviation) in order to measure the sensitivity of birth rates to high and low price extremes. According to these calculations, summarized in Figures 5.8 through 5.11, virtually all households

[23] According to Henri Leridon, Human Fertility (Chicago, 1977), p. 15, the natural sex ratio for stillbirths is 120–30 dead boys per 100 dead girls.

[24] If the peasants were limiting their fertility though sexual restraint rather than infanticide, we would expect even more pronounced correlations between births and prices, with a lag of one year or more. In fact we discovered that the relationship remains strong, although the values for both sexes decrease rather than increase. Therefore, although some couples may have practiced restraint, others preferred to rely on infanticide as a means of fertility control in times of economic pressure.


168

figure

Fig. 5.7.
Birth Rates by Sex in Daoyi as a Function of Low Millet Prices, 1775–1840 (per 1,000 population)
Note: Male correlation coefficient = -0.59. Female correlation coefficient = -0.69


169

responded to high prices by reducing fertility and to low prices by increasing fertility.

Household wealth, and, by extension, household type, played a key role in the decision to let a daughter survive. Wealthy households, which were generally complex, were less affected by economic conditions than were poorer households, which were usually simple. Indeed, as we can see in Figure 5.8, when prices were high, parents in complex households reduced their female birth rate by one quarter, while parents in simple households cut theirs in half. In contrast, when prices were low, parents in complex households increased their female birth rate by one third, while parents in simple households did not change theirs at all. Simple households, therefore, were so impoverished relative to complex households that they not only kept far fewer girls than normal when times were bad but they allowed no extra ones to live when times were good.

The decision to keep a daughter depended, of course, on whether or not the couple already had a child. Indeed as we saw in Table 5.5, a girl was most likely to be kept if she was the first of several children. According to Figure 5.8, however, this was especially true in complex households. When times were bad, the birth rate for girls without older siblings dropped by less than one tenth while the rates for girls with older siblings dropped by four-tenths. In contrast, when times were good, the rate for girls without older siblings almost doubled while the rates for girls with older siblings went up only by one tenth. That complex-household parents were willing in bad times as well as good times to support a daughter so long as she was their firstborn reflects a desire to keep first children regardless of their sex. In spite of the widespread practice of infanticide, in other words, primordial affection could triumph over material concerns.

A girl's chances of surviving in a complex household, however, depended on household position as well as birth order. Figure 5.9 reveals that the less incentive parents had to produce a male heir, the more girls they allowed to live when prices were low. In good years, therefore, brothers and nephews, who were the furthest from the line of inheritance, had 50 percent more girls than usual. Heads, however, had only as many girls as they did in normal times. In contrast, all parents, whatever their relationship to the head, had at least one-third fewer girls in bad times than normal. Even in complex households, while some parents were willing to support more female children than normal in times of plenty, girls were still enough of a luxury that few parents were willing to save them when times were hard.

But baby girls were by no means the only victims of infanticide. Some parents also neglected or perhaps even killed their sons as well as their daughters when times were bad. According to the correlations for male births in Table 5.7, this was especially true in the simple, that is, poorer, households. Male birth rates had uniformly strong negative correlations with all


170

figure

Fig. 5.8.
Female Births by Household Type in Daoyi in Periods of High and Low Grain Prices, 1792–1840 (% change from normal rate)

Notes: Marital fertility only. For boys, periods of high and low grain prices are years where the price is more than one standard deviation
above or below the mean for the period 1792–1873. For girls, periods of high and low grain prices are years where the price is more than one
standard deviation above or below the mean for the period 1792–1840. Different years were selected for boys and girls because the mean
and standard deviation of grain prices for the period 1792–1873 were different from the mean and standard deviation for the period
1792–1840. The years of high prices were therefore 1812–1816 for girls and 1807–1817, 1823–1827, 1829, 1833, and 1836–1838
for boys. The years of low prices were 1795–1802 and 1805 for girls and 1797–1801 and 1854–1862 for boys.


171

figure

Fig. 5.9.
Female Births by Family Relationship in Daoyi in Periods of High and Low Grain Prices, 1792–1840 (% change from normal rate)

Note: See notes in Fig. 5.8.


172

food prices. Indeed, the correlations with male fertility were almost as strong as the correlations with female fertility. When prices were high, in other words, parents in these households neglected their sons as well as their daughters. In Fengtian, at least, the common assumption that Chinese parents neglected only their daughters may be untrue.[25]

Even in simple households, of course, parents only made the decision not to keep a son under extreme economic pressure. As we can see in Figure 5.10, when prices were high, the male birth rate in simple households declined by one third, but when prices were low, it increased by only one sixteenth. Parents, in other words, did not regard their boys as luxury goods. They thought of them as necessities. As a result, male birth rates remained relatively constant so long as there was no crisis. Parents in simple households only gave up their sons when they did not have the resources to support them.

Male infanticide, however, was not restricted to simple households. Figure 5.11 indicates that in complex households parents at the very top and very bottom of the household hierarchy also reduced their male birth rate in response to economic pressure. Indeed, when prices were high, cousins, who were at the bottom of the hierarchy, only allowed half as many boys to live as when prices were normal. Similarly, heads who were at the top of the household hierarchy had one quarter fewer boys. Evidently, just as men paid for their privileged position relative to women with greater increases in mortality in times of crisis, so household heads paid for their privileged position by lowering their fertility to the levels of other relations. Here too the price of privilege was greater vulnerability to economic fluctuation.

Just as with girls, birth order was an important consideration for boys in complex households, but the response to economic conditions differed considerably. The birth rates for firstborn sons, unlike those for their female counterparts, were more responsive to high prices than the birth rates for later sons. Thus when prices were high, the rate for firstborn sons declined by one fifth while the rate for later-born boys changed hardly at all (see Figure 5.10). This was especially true for the children of co-resident sons, brothers, and nephews, who were neither at the top nor at the bottom of the household hierarchy. Mid-ranking couples apparently had extra boys only if they were wealthy enough to be relatively immune to economic pressure. Heads, on the other hand, were under constant pressure to produce more sons regardless of wealth. It was poorer heads who responded to economic crisis by delaying or canceling plans for extra sons.

[25] According to Hanley and Wolf, eds., Family and Population , p. 5: "Although female infanticide was common in some parts of China in difficult times, there is no evidence that the Chinese ever tried to limit the number of sons. . . . In Japan, in sharp contrast, infanticide and abortion were commonplace, not only as a response to natural and social catastrophes, but as a kind of family planning with long-range objectives."


173

figure

Fig. 5.10.
Male Births by Household Type in Daoyi in Periods of High and Low Grain Prices, 1792–1873 (% change from normal rate)

Note: See notes in Fig. 5.8.


174

figure

Figure. 5.11.
Male Births by Family Relationship in Daoyi in Periods of High and Low Grain Prices, 1792–1873 (% change from normal rate)

Note: See notes in Fig. 5.8.


175

But while male birth rates were not as responsive as female birth rates to changes in food prices, we should remember that male death rates were more strongly correlated with prices than female death rates. Girls, in other words, were apparently considered such luxuries that, like wives, they were permitted only when parents were confident that they could support them, albeit at a very low level. Boys, on the other hand, were valued so highly that in even the most marginal of household circumstances they were often allowed to live. Thus while females who survived infancy were unaffected by price fluctuations, males were vulnerable even as adults precisely because they were allowed to survive in poor as well as wealthy households.

Until recently virtually the only attempt at precise demographic measures of a "traditional" rural Chinese population has been the reexamination by G. W. Barclay, A. J. Coale, M. A. Stoto, and T. J. Trussell of the data collected by John Lossing Buck in his 1929–31 survey of Chinese agriculture.[26] Relying very heavily on a body of indirect techniques of demographic estimation, these distinguished demographers concluded that in China, although marriage was both early and universal, fertility was nevertheless extremely low. They furthermore suggested that such low fertility may well have been characteristic of the late imperial period. The problem that then confronted historians of late imperial China was how to explain the sustained rapid rise of Qing population in the face of such low "natural" fertility. The present study, in relying on direct analysis of eighteenth and nineteenth century materials, suggests that such low fertility may well have been the result of fertility control and that the level of "natural" fertility in eighteenth- and nineteenth-century China may in fact have been higher.

This study further suggests that if all Chinese peasants controlled their fertility in response to economic conditions, then the rise in population during the eighteenth and nineteenth centuries may well have been a direct response to significant advances in economic growth. Our findings, in other words, seem to corroborate recent claims by Western as well as Chinese historians of unprecedented agrarian expansion beginning in the eighteenth century and increasingly rapid commercialization beginning in the nineteenth century.[27]

[26] See Buck; G. W. Barclay, A. J. Coale, M. A. Stoto, and T. J. Trussell, "A Reassessment of the Demography of Traditional Rural China," Population Index 42.4 (1976): 606–35.

[27] In English see, for example, Yen-p'ing Hao, The Commercial Revolution in Nineteenth-Century China , (Berkeley and Los Angeles, 1986); Peter Perdue, Exhausting the Earth: State and Peasant in Hunan, 1500–1800 (Cambridge, Mass., 1987); William T. Rowe, Hankow: Commerce and Society in a Chinese City, 1796–1889 (Stanford, 1984). In Chinese see Guo Songyi, "Qingchu fengjian guojia kenhuang zhengce fenxi," Qingshi luncong 2 (1980): 111–38; Guo Songyi, "Qingdai de renkou zengzhang he renkou liuqian," Qingshi luncong 5 (1984): 103–38; Wu Chengming, Zhongguo ziben zhuyi yu guonei shichang (Beijing, 1985); and Xu Dixin and Wu Chengming, Zhongguo ziben zhuyi de mengya (Beijing, 1985).


176

Finally, this study provides some insights into the complexity of household decision making in eighteenth- and nineteenth-century Daoyi and perhaps by extension (a very wide one to be sure) in China at large. Peasants based their decisions about infanticide on a complex combination of interrelated factors, including economic conditions, household type, position within the household, number and sex of previous children, sex of child, and long-term goals for family size and composition. Food prices were an important factor in this decision-making process, but the equation differed considerably for each individual depending on the other variables. The relative importance of these factors has yet to be determined. We can already appreciate that the study of prices and population ultimately leads to a better understanding of Qing society as well as the Qing economy.


177

PART TWO
MARKET RESPONSE


179

Six
Land Concentration and Income Distribution in Republican China

Loren Brandt and Barbara Sands

There is reason to think that in the (last) twenty years . . . occupying ownership has lost ground. . . . The fact of concentration can hardly be questioned. . . . Over a large part of China, tenancy of one kind or another undoubtedly predominates and it appears everywhere to be increasing .
RICHARD TAWNEY, AGRARIAN CHINA (1938)


These remarks by the British historian Richard Tawney typify much of the thinking both in China and in the West about the growing concentration of landholdings in late nineteenth- and early twentieth-century rural China. Perceptios of widening disparities in land ownership in turn underlie a nearly endless list of critical assessments of the changing distribution of incomes and economic welfare during the same period. Carl Riskin, for example, argues: "Chinese rural society was hardly egalitarian with respect to the distribution of income and wealth: and although characterized by substantial social mobility, the predominant direction for the latter was downward, at least in relative terms."[1] Harry Harding similarly notes: "Though still prone to drought, flood, and plague, China's rural economy could, in most years, also produce the surplus to support one of the world's most advanced urban civilizations and most highly bureaucratic states. But the distribution of that output was increasingly unequal."[2]

Quite often these concerns of widening inequality are accompanied by observations of increasing population pressure and the lack of technological change; indeed, the received economic story of late nineteenth- and early twentieth-century agrarian China is one of increasing immiseration for the populace in general and a relative worsening for the rural poor. Political events in the first half of the twentieth century involving two major regime

[1] Carl Riskin, China's Political Economy (New York, 1986), p. 32.

[2] See Harry Harding, China's Second Revolution: Reform after Mao (Washington, D.C., 1987), p. 13.


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changes have only strengthened these views of China's recent troubled past, if not fired them directly.[3]

Empirical support for this history is, however, quite limited. This is particularly true for questions concerning the distribution of land ownership and economic welfare. Despite the keen interest in the development literature concerning questions of distribution and their obvious historical relevancy to the case of China, they have been largely neglected.[4] Moreover, China's distributive record prior to 1949 has rarely been put in any kind of comparative perspective.

The purpose of our paper is to reexamine a number of issues relating to the distribution of land ownership and economic welfare in the pre-1949 economy. Because the historical data on distribution are spotty at best and China itself is so diverse, this paper is largely exploratory, and some of its findings preliminary in nature. Despite these limitations, based on data presently at our disposal, we make the following points.

First, we have failed to find convincing evidence that land ownership became more concentrated during the late nineteenth and early twentieth centuries. Second, even if concentration increased, it cannot be inferred that the increase necessarily heralded increasing immiseration or welfare inequality. A number of alternative processes can underlie increasing concentration in land ownership with opposing interpretations for the behavior of economic welfare. Third, drawing on survey materials for the 1930s, some of them covering all of China and others relating only to a select number of North China villages, we have calculated measures of income distribution that suggest income inequality was much lower in rural early twentieth-century China than has been previously inferred on the basis of data on land distribution alone; in fact, when compared with other low-income countries, China actually appears to be on the moderate side. Moreover, the estimates of income inequality that we have obtained fall within the limits some Chinese officials have recently recommended for their rural sector.

These observations suggest that the current view of China's modern economic history is uncertain at best and yet to be firmly established by contem-

[3] Philip C. C. Huang, in fact, argues that these imbalances underlay the social and political upheavals of the nineteenth and twentieth centuries. See his Peasant Economy and Social Change in North China (Stanford, 1985), p. 293. Exceptions to this conventional wisdom include two books: Thomas G. Rawski, Economic Growth in Prewar China (Berkeley and Los Angeles, 1989), and Loren Brandt, Commercialization and Agricultural Development: Central and Eastern China, 1870–1937 (Cambridge, 1989).

[4] C. Robert Roll's unpublished Ph.D. dissertation (Harvard University, 1974) represents the only empirical investigation of income distribution in pre-1949 rural China that we are aware of. Ramon H. Myers, in The Chinese Peasant Economy: Agricultural Development in Hopei and Shantung, 1890–1949 (Cambridge, Mass., 1970), on the other hand, examines changes in land distribution in North China from the 1890s to the 1940s.


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porary quantitative evidence. In the sections below, we take each of the above points in turn and discuss our findings.

Increasing Concentration of Landholdings?

Concerns over a rise in the concentration of landholdings and growing tenancy were voiced in China prior to the early twentieth century. During the late Ming and Qing, for example, government officials regularly expressed alarm over what appeared to be an increasing accumulation of land by the wealthy and a decline in the holdings of smaller farm households. These developments are frequently linked to the commercialization and market development of the period.[5] Are data consistent with views of a secular rise in land concentration?

Reasonably good data on land ownership exist for the early twentieth century. On the basis of data compiled by the National Land Commission in 1934 on almost 1.75 million rural households residing in 16 provinces, Table 6.1 presents an estimate of the size distribution of landholdings for rural China.[6] These aggregate data are complemented by many village-level surveys undertaken during the 1920s and 1930s.[7] Both kinds of data confirm what others have long argued—the distribution of land ownership in rural China was highly unequal—and on the surface are consistent with the view that distribution may have been worsening. For the distribution in Table 6.1, the Gini coefficient, a frequently used measure of inequality, is 0.72.[8] The top 1 percent of rural households owned approximately 18 percent of the land;

[5] See, for example, Yang Yi, "Qingchao qianqi de tudi zhidu," Shehui yuekan 7 (1958): 21–26, and Fu Yiling, "Guanyu Mingmo Qingchu Zhongguo nongcun shehui guanxi de xin guji," Xiamen daxue xuebao 6 (1959): 57–70.

[6] These data suffer from a number of omissions that have been overlooked. These problems are discussed in more detail in Brandt, Commercialization and Agricultural Development , chap. 6.

[7] The results of 47 surveys carried out between 1931 and 1941 are reported in Chao Kang [Zhao Gang] and Chen Chung-yi [Chen Zhongyi], Zhongguo tudi zhidu shi (Taibei, 1982), pp. 234–38. A majority of these are the product of the research efforts of the South Manchurian Railway Company and can be found in Minami Manshu Tetsudo Kabushiki Kaisha, Mantetsu chosa geppo .

[8] The Gini coefficient is equal to 1 minus 2 times the area under the Lorenz curve. The Lorenz curve, based on an arrangement of households in ascending order of income, relates the cumulative proportion of households to the cumulative proportion of income received. The Gini coefficient can assume a value between 0 and 1 with a measure of 0 implying a uniform distribution of incomes (or assets) across the population and the measure 1 implying extreme inequality. It can also be interpreted as the expected gain—measured as a percentage of the mean for the population—of a lottery in which each person is allowed to draw an income at random from the population and compare it with his or her own. Thus, when incomes are uniformly distributed, the expected gain is 0, as would be the Gini coefficient. The more concentrated incomes are, the larger the expected gain will be. See Graham Pyatt, "On the Interpretation and Disaggregation of Gini Coefficients," Economic Journal 86 (June 1976): 243–55.


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TABLE 6.1 Size Distribution of Land Owned by 1.75 Million Rural Households in China, 1930s

Size of Holding (mu)

Average Size (mu)

Percentage of Households (mu)

Percentage of Land Owned (mu)

Landless

0.00

25.80

0.00

0–5

2.65

26.42

6.21

6–10

7.23

17.80

11.42

11–15

12.25

9.77

10.63

16–20

17.42

5.93

9.17

21–30

24.33

6.10

13.17

31–50

38.01

4.60

15.54

51–70

58.59

1.61

8.38

71–100

82.61

0.98

7.16

101–150

120.21

0.54

5.71

151–200

171.97

0.18

2.76

201–300

240.95

0.14

3.17

301–500

378.40

0.08

2.63

501–1,000

671.87

0.01

2.30

1,001+

1,752.60

0.01

1.75

Total

11.04

100.00

100.00

SOURCE: Tudi Weiyuanhui, Quanguo tudi diaocha baogao gangyao (Nanjing, 1937), tables 21 and 23.

the top 5 percent owned nearly 39 percent; and the top 10 percent owned around 53 percent. By contrast, slightly more than a quarter of all rural households were landless, and an additional quarter owned less than 5 mu . Similar percentages are suggested by the village-level data.

The problem is that comparable information on the previous distribution of landholdings, be it 25 years earlier or 200, presently does not exist. For the Qing we are limited to a handful of observations at the county and village level that simply are too few to generalize about. Table 6.2, nonetheless, documents one such case: the distribution of landholdings in the early eighteenth century for several subdivisions in Huailu County, Hebei, and village-level data in the same county 200 years later.[9] At least for this North China village the distributions are very similar, though, not unexpectedly, average farm size in 1939 was slightly lower than in either 1706 or 1736.

While data on landholdings are also seriously deficient for the late nineteenth century, additional information on such things as the percentage of land that was rented out and the hiring of long-term labor, either or both of which typically accompany an increasing concentration of landholdings,

[9] Additional pre-twentieth-century data can be found in Kang Chao, Man and Land in Chinese History (Stanford, 1986), chap. 6.


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TABLE 6.2 Size Distribution of Land Owned by Households in Huailu County, Hebei, 1706, 1736, and 1939

 

1706

1736

1939

Size of
Holding (mu)

Households
(%)

Land
(%)

Households
(%)

Land
(%)

Households
(%)

Land
(%)

Landless

18.4

0.0

25.5

0.0

19.5

0.0

0–10

37.6

12.4

35.3

11.3

42.2

13.8

11–20

22.7

22.0

18.4

18.4

19.2

18.8

21–30

10.8

17.6

8.3

14.3

7.8

14.2

31–40

4.5

10.5

4.4

10.3

4.9

12.3

41–50

1.8

5.4

2.2

6.8

1.9

6.4

51–100

2.9

12.1

4.2

19.0

3.2

17.8

101–150

0.5

3.7

0.5

5.3

0.3

3.0

151+

0.8

16.4

1.2

15.6

1.0

13.7

Total

100.0

100.0

100.0

100.0

100.0

100.0

SOURCES: The data for 1706 and 1736 are taken from Jiang Taixin, "Qingchu kenhuang zhengce ji diquan fenpei qingkuang de kaocha," Lishi Yanjiu 5 (1982), pp. 167–82; for 1939, Minami Manshu tetsudo kabushiki kaisha, Kito noson jittai chosahen, Noka keizai chosa hokoku, Hokushi keizai shiryo , no. 32, p. 85.

NOTE: The calculations for 1706 are based on data for 29 jia , 7,652 households; those for 1736, on data for 4 jia , 1,094 households; and those for 1939, on data for 308 households in Macun, Huailu County.

suggests no long-term rise in the half-century between the 1880s and 1930s. Estimates of land rental, for example, reveal that in both periods approximately a third of cultivated acreage was rented, two thirds of which was owned by absentee landlords.[10] We have not uncovered estimates of the number of long-term agricultural laborers for the 1880s—though qualitative evidence suggests their numbers were substantial—but of the 1.75 million rural households surveyed by the National Land Commission in the 1930s, only 1.57 percent were classified as landless, laboring households, that is, households earning a living as long-term contract agricultural laborers.[11] Finally, over the 25-year period between 1912 and 1937, the percentage of households classified as owner-operators failed to decrease, as we might expect had tenancy been on the rise.[12]

On the basis of these very limited data we do not want to push any view very far, especially one suggesting that the degree of concentration of landholdings remained constant for several hundred years. As we will explain

[10] Chung-li Chang has estimated that about a third of all land was rented in the 1880s, or roughly the same percentage as in the 1930s. See Chung-li Chang, The Income of the Chinese Gentry (Seattle, 1962), p. 145.

[11] Tudi Weiyuanhui, Quanguo tudi diaocha baogao gangyao (Nanjing, 1937), p. 34.

[12] Nongqing baogao 5, no. 12 (Dec. 15, 1937): 330.


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below, over the long run there were a number of alternative processes simultaneously influencing land concentration; and indeed, it would be odd if these processes offset one another exactly. Nonetheless, the point needs to be made that data presently at out disposal simply cannot support the popularly held view that the high degree of concentration of landholdings observed in the 1930s was the product of several decades (or centuries) of increasing inequality.

Finally, although in the 1930s 10 percent of the population owned over half the farmland in China, can this be considered extreme compared to landholding patterns in other countries? Interestingly, data compiled by Peter H. Lindert suggest that it was not extreme. Concentration in China is markedly less than in Mexico in the 1920s, where 10 percent of the households owned an estimated 64–99 percent of the land and 79 percent were landless, or in Victorian England (excluding London), where 85 percent of all households owned no land and 10 percent owned 82 percent of the land; and it is actually slightly better than that estimated separately for farming households and the entire rural population (whites only) in the United States—certainly not noted for its extreme concentration—in both 1798 and 1860.[13] In many respects, land distribution in China in the 1930s is very similar to that estimated for post-independence India.[14]

Historical Processes

From the perspective of many observers, land sales by indebted smallholders were the major underlying cause for the increasing concentration of landholdings. Farm households borrowed for a variety of reasons: to obtain working capital, to meet consumption needs in times of emergency, and in some cases to help cover major ceremonial expenses, such as weddings and funerals. To secure these loans, land was frequently pledged as collateral. In still other cases, the rights of land use were ceded to the lender, with the income generated from the land serving as the interest. Once the loan was repaid, rights of land use reverted back to the original owner. This land was commonly referred to as diandi .

In many cases, borrowers were unable to service these loans. Han-seng Chen's description is typical: "When a poor peasant in China mortgages his bit of land, he has practically no hope of ever getting it back. Everything conspires against him in his frantic effort to meet the interest charges, and eventually he loses not only the land but also this additional fruit of his

[13] Peter Lindert, "Who Owned Victorian England? The Debate over Landed Wealth and Inequality," Agricultural History 61, no. 4 (1987): 25–51.

[14] I. Z. Bhatty, "Inequality and Poverty in Rural India," in Poverty and Income Distribution in India , ed. T. N. Srinivasan and P. K. Bardhan (Calcutta, 1974).


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labor."[15] These difficulties were invariably attributed to declining economic fortunes caused by high taxes and land rents, local disaster, depressed agricultural conditions, and so forth. If land was used as collateral, it might simply be forfeited; if an asset other than land secured the loan, land still might have to be sold to make repayment. If land-use rights were ceded to the lender, the possibility of redeeming the land in the near future became even more remote.[16] With the loss of their land, small cultivators were invariably forced either to rent back the same land from the new owners or possibly to hire out as wage laborers. This explains the link implicit in the opening quote from Tawney between rising tenancy (or perhaps wage labor) and a growing concentration of landholdings.

Although some small farm households were unquestionably forced to relinquish ownership of their land during times of economic duress, this analysis suffers from several weaknesses; moreover, there are a number of equally compelling explanations for increasing concentration of landholdings and a rise in tenancy with alternative implications for the secular behavior of incomes.[17]

For example, in asserting that the loss of land through debt-sales gives rise to an increasing concentration of land ownership, there is an implicit assumption of who the borrowers and lenders were, the lenders typically being larger landowners/landlords, frequently absentee, the borrowers, on the other hand, being primarily poorer, small farm households that had earlier exhausted all personal financial resources. Although larger landowners were usually heavily involved in the credit market, a detailed examination of land and credit arrangements in three diverse villages in North China reveals that lending activity was more diversified among households than previously believed; in other words, lending activity was not limited to a class of big landowners. In fact, in a majority of those cases involving diandi , farm households with less than 30 mu were the creditors. The borrowers were frequently larger landowners themselves in need of capital.[18] Data compiled by John Lossing Buck on lending activity in 151 localities in 16 provinces are very

[15] Han-seng Chen, Landlord and Peasant in China (New York, 1936), p. 95.

[16] In some cases, the rights of land use were actually rented back to the original owner. According to Madeleine Zelin, failure to fulfill the rental agreement could mean the total alienation of the original owner from the land. See Zelin, "The Rights of Tenants in Mid-Qing Sichuan," Journal of Asian Studies 45, no. 3 (May 1986): 499–526.

[17] Please recall, however, our earlier remarks regarding the uncertainty over the inter-temporal behavior of land distribution.

[18] Based on an examination of contracts involving diandi in the surveys cited in n. 33. In the villages we examined, the amount of land that villagers had obtained the farming rights to through diandi exceeded the amount of land that they had ceded the rights to . The likely explanation for this is that some of the former was owned by absentee landlords or perhaps village members who had migrated yet wanted to retain some claim to village resources should they decide to return.


186

consistent with the village-level data for North China: for only 8 percent of all loans were landlords and other wealthy households cited as the source of credit. Much more important were relatives and neighbors.[19]

Observations such as these make generalizations about the relationship between forced land sales and increasing concentration difficult without much more careful examination of credit and land markets in rural China. Moreover, even if it is true that land ownership was increasingly concentrated, this does not necessarily mean that income became more concentrated as well. John R. Shepherd has recently pointed out that there are alternative historical processes capable of simultaneously generating an increasing concentration of landholdings and a rise in tenancy which do not necessarily imply increasing immiseration of the rural poor.[20] These include an expansion in cultivated area through investment by wealthy households in land reclamation, migration of landless households into such areas because of better economic opportunities, and shifts in forms of landlord farm management. These processes could operate during periods of economic growth and expansion just as well as in times of economic decline. More generally, a rise in real wages and an increase in labor's share of national income could offset the influence on income distribution of any increased concentration of landholdings.

Each process can be adduced for many parts of China. Between the seventeenth and nineteenth centuries, for example, the increase in the demand for rice in the Lower Yangzi drew the capital of wealthy landowners into land reclamation projects in the Dongting Lake area of Hunan. Immigrant households supplied much of the labor for reclaiming and later for tenanting this land.[21] This process contributed to a relatively high degree of land concentration and tenancy in the area, which persisted into the 1930s.[22] Similar kinds of investments occurred in the Jiangnan area and in some of the more commercialized areas of the North China Plain. During the last half of the Qing, cultivated area increased by roughly half, much of it through investments such as these.[23]

[19] John L. Buck, Land Utilization in China: Statistical Volume (Chicago, 1937), p. 404. If the loans extended by landlords and other wealthy households were larger than average, they may have been more important than their percentage of the total number of loans otherwise suggests.

[20] John R. Shepherd, "Rethinking Tenancy: Spatial and Temporal Variation in Land Ownership Concentration in Late Imperial and Republican China," Comparative Studies in Society and History 30.3 (July 1988): 403–31.

[21] See Peter C. Perdue, Exhausting the Earth (Cambridge, Mass., 1987).

[22] According to the National Land Commission survey, 47.8 percent of cultivated area in Hunan was rented. The high degree of land rental and estimates of the concentration of cultivated holdings provided in Table 6.3 suggest a highly concentrated land ownership.

[23] By the early twentieth century, if not before, much of the potential for expanding cultivated area in China proper (excluding Manchuria) had been exhausted. In eastern Jiangsu, however, an estimated 5 million to 6 million mu of land in abandoned salt farms (or a 25 percent increase over existing cultivated area in eastern Jiangsu) was reclaimed for agricultural use. In these counties we typically find a relatively high degree of tenancy. Information on tenancy and the landholdings of land reclamation companies in the area can be found in the Ministry of Industry survey of Jiangsu for 1933. See Zhongguo Shiyebu, Guoji Maoyiju, Zhongguo shiye zhi: Jiangsu sheng (Shanghai, 1933), pp. 231–32.


187

There was also migration, most notably in the eighteenth century into Sichuan, in the mid-to-late nineteenth century into highly fertile areas of the Lower Yangzi depopulated by the Taiping Rebellion, and in the twentieth century into Manchuria.[24] In each case, individuals or entire households moved into these areas to take advantage of better economic opportunities. Even without land exchanging hands, migration under these conditions can lead to an increasing concentration of landholdings (because of an increase in the number of landless households) and a rise in tenancy in the area experiencing the in-migration.[25] In fact, one observes a higher degree of concentration of land ownership in Manchuria for the early twentieth century than in other parts of China.[26]

The historical role of these alternative processes throughout much of China points to the shortcomings in many earlier interpretations of land concentration and tenancy in the early twentieth century. The concentration of landholdings cannot simply be viewed as the cumulative product of a single process. Rather, the degree of concentration observed in a locality at any given time needs to be seen as the legacy of several, possibly interrelated, economic/historical processes including migration, reclamation, and commercialization as well as forced land sales that may or may not have been weakened over time by other forces operating on the rural economy.[27]

[24] On the migration to Manchuria, see Thomas R. Gottschang, "Economic Change, Disasters, and Migration: The Historical Case of Manchuria," Economic Development and Cultural Change 35.3 (Apr. 1987): 461–90.

[25] A possible reduction in concentration in the area experiencing the out-migration may have kept inequality, in the aggregate, from worsening.

[26] For a sample of villages settled after the turn of the century, Myers has found an average Gini coefficient for landholdings equal to 0.86. This is 15–20 percent higher than the average for the 47 villages cited in n. 7. See Ramon H. Myers, "Socioeconomic Change in Villages of Manchuria during the Qing and Republican Periods: Some Preliminary Findings," Modern Asian Studies 10, no. 4 (1976): 591–620. Interestingly, the Gini coefficient for villages settled before the turn of the century is not markedly less than for those settled afterward, thus suggesting the legacy of the initial pattern of settlement.

[27] One of these other forces we have in mind here is family division. Since Adam Smith, there has been a presumption that over the course of several generations family division helps to reduce the inequality of landholdings. Lavely and Wong argue that this need not be the case and demonstrate how the impact of partible succession on land concentration is influenced by both inter- and intra-landclass differences in reproductivity. See William Lavely and R. Bin Wong, "Family Division, Reproductivity, and Landholding in North China," Research Report no. 84–65, Population Studies Center, University of Michigan, Nov. 1984.


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Land and Income Distribution in Rural China

Data on land distribution have long served as the key indicator of the degree of economic welfare inequality in the rural sector. Yet how accurate are landholdings as such a measure? There exist data sources from the 1930s that allow estimates of the degree of dispersion of both land and income in the rural sector. These include the survey of the National Land Commission used earlier that provides provincial-level data, and village-level surveys carried out by the South Manchurian Railway Company in North China. Although these data sources differ in a number of key respects and the latter are confined to North China, together they provide new insights into the relationship between the degree of concentration of incomes and land ownership in China's rural sector.

In estimating the degree of inequality in an economy, several important methodological questions immediately arise. These are related to the selection of the measure of economic well-being and the unit of observation (individual, family, income earner, etc.).[28] We have selected income as our measure of well-being, although other indicators of welfare, such as consumption or expenditures, could also be used and might actually be preferable. All the income data are provided at the household level, but rather than rank households by total household income as has frequently been done, we have elected to rank them by per capita household income. In this regard, we are following Simon Kuznets, who concludes in a comparison of the two methods:

It makes little sense to talk about inequality in the distribution of income among families or households by income per family or household when the underlying units differ so much. . . . Before any analysis can be undertaken, size distributions of families or households by income per family must be converted to distributions of persons (or consumer equivalents) by size of family or household income per person (or per consumer).[29]

Kuznets's remarks would seem to have a particular relevancy for rural China, where the range of household sizes was enormous. With many households consisting of one or two people and others including up to 20, the potential for differences in measures of inequality based on household rather than individual income or wealth is obviously large.

Before reporting provincial-level estimates of the degree of income in-

[28] These issues are discussed in R. Albert Berry, "Evidence on Relationships among Alternative Measures of Concentration: A Tool for Analysis of LDC Inequality," Review of Income and Wealth 33.4 (Dec. 1987): 417–30.

[29] Simon Kuznets, "Demographic Aspects of the Size Distribution of Income: An Exploratory Essay," Economic Development and Cultural Change 25, no. 1 (Oct. 1976): 87. See also Gautam Datta and Jacob Meerman, "Household Income or Household Income per Capita in Welfare Comparisons," Review of Income and Wealth 26, no. 4 (Dec. 1980): 401–18.


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TABLE 6.3 Distribution of Cultivated Holdings and Incomes per Household in China, 1930s

 

Gini Coefficient

Percentage of Land Rented (mu)

Province

Cultivated Holdings

Incomes

Jiangsu

0.570

0.430

42.23

Zhejiang

0.569

0.416

51.31

Anhui

0.538

0.473

52.64

Jiangxi

0.530

0.339

45.10

Hunan

0.601

0.428

47.70

Hubei

0.525

0.393

27.79

Hebei

0.596

0.458

12.89

Shandong

0.541

0.454

12.63

Henan

0.598

0.437

27.27

Shanxi

0.528

0.517

Shaanxi

0.562

0.415

16.64

Chahar

0.437

0.648

10.20

Suiyuan

0.641

0.497

8.75

Fujian

0.557

0.345

39.33

Guangdong

0.423

0.335

76.95

Guangxi

0.542

0.445

21.20

Arithmetic average

0.547

0.439

32.84

Entire sample

0.615

0.458

30.73

SOURCE: Tudi weiyuanhui, Quanguo tudi diaocha baogao gangyao (Nanjing, 1937), tables 15 and 33.

equality, we first look at the concentration of landholdings reported in the National Land Commission survey. Unfortunately, the summary report of the National Land Commission only provides a distribution of landholdings for the country as a whole (see Table 6.1). Provincial-level information, however, is provided on the distribution of operational holdings, the sum of owned and rented land that a household cultivates. The survey reports the number of households in each of 13 size categories. We have modified these data slightly to include households that did not farm, that is, had no operational holdings, and then calculated Gini coefficients for the new distributions.[30] These estimates are provided in Table 6.3.

It is widely agreed that land rental increased access to land; thus, the distribution of operational holdings should be more equal than the distribu-

[30] Most computational formulas for the Gini coefficient based on grouped data such as these underestimate the degree of inequality because they ignore intragroup inequality. We have followed Kakwani in correcting for this, using the midpoint of each interval as an estimate of its mean. See Nanak Kakwani, "On the Estimation of Income Inequality from Grouped Observations," Review of Economic Studies 43, no. 3 (Oct. 1976): 483–92.


190

tion of land ownership. This is borne out by the data: the Gini coefficient for landholdings is 0.72, while that for cultivated holdings is 0.62. For a majority of provinces the Gini coefficient for operational holdings is between 0.53 and 0.60.

In general, we expect the difference between the Gini coefficient for owned and cultivated holdings to be positively correlated with the percentage of land rented; in other words, the more land that is rented out to tenants, the lower the concentration of operational holdings associated with any given level of inequality of land ownership. Since the Gini coefficient for operational holdings for the provinces of North China and the Yangzi Valley are almost the same, the greater prevalence of tenancy in the Yangzi Valley than in North China would suggest that land ownership was more widely distributed in North China than in the Yangzi Valley.[31] The Gini coefficient for landholdings in the Yangzi was probably in excess of 0.75, while that for North China was nearer to 0.65.

But what about incomes? Were they just as concentrated as land ownership? Again drawing on data compiled by the National Land Commission, we have calculated Gini coefficients for household incomes in each of the 16 provinces. These estimates also appear in Table 6.3. Without exception, they reveal a degree of income inequality much lower than that suggested by the land data alone. For a majority of the provinces the Gini coefficient for household income was in the vicinity of 0.40–0.45, while the Gini coefficient for the entire sample is 0.46.[32] These estimates are 35 percent to 40 percent lower than what the provincial Gini coefficients for landholdings probably are. Geographically, the degree of income dispersion appears to be slightly below the national average in the more densely populated areas of the Yangzi and South China and modestly higher in the North and Northwest.

Data from North China

In a number of respects these estimates are not entirely satisfactory. On the one hand, it is not exactly clear from the summary report which measure of income was used: net or gross, cash or total (which includes cash income plus income in kind). C. Robert Roll has argued that households probably re-

[31] Approximately 45 percent of land was rented out in the Yangzi region, as compared to 15–20 percent in North China. These estimates are based on Tudi Weiyuanhui, Quanguo tudi diaocha , pp. 26–27.

[32] In calculating the Gini coefficient for the entire sample, we have pooled the data by taking a weighted average of the percentage of households in each province in each size category, using as weights the percentage of the total rural population in each province. Rural population estimates are based on provincial population estimates contained in Dwight H. Perkins, Agricultural Development in China, 1368–1968 (Chicago, 1969), p. 212, and estimates of the percentage of population classified as rural are provided in Wu Baosan, Zhongguo guomin suode, 1933 nian (Shanghai, 1947), 1:151.


191

ported their total net income, yet the difficulty of accurately estimating all in-kind income and production expenditure cannot be underestimated. On the other hand, if household incomes and family size are systematically related, the distribution of household income may provide a misleading indicator of income distribution on a per capita basis. From a welfare perspective, it is the latter distribution with which we are concerned. Again, we need to know how the distributions for household income and household income on a per capital basis are related to each other.

Three village surveys undertaken by the South Manchurian Railway Company in 1936 in Hebei Province provide important information on each of these questions.[33] These surveys are unique because they enumerate every household in the village and give information on both in-kind and cash incomes and expenditures, thus allowing comprehensive estimates of household incomes.[34] The village data allow us to compare the degree of inequality in the distribution of assets and income by households and in per capita terms. The difference between these two measures of village inequality can be combined with the information in Table 6.3 to make a tentative estimate of the degree of income inequality in rural China at the household level on a per capita basis, which can then be compared with estimates for other low-income countries. These surveys also provide important clues as to why the concentration of incomes observed in Table 6.3 was so much lower than that suggested by the data on land ownership alone.

The three villages selected by the South Manchurian Railway Company for detailed examination include Michang Village in Fengrun County, Dabeiguan in Pinggu County, and Qianlianggezhuang (Lianggezhuang for short) in Changli County. All three were located in northeastern Hebei within 100 kilometers of each other and were less than several hundred kilometers from Tianjin. Despite some obvious similarities, there were marked differences between the villages, some of which are captured in Table 6.4.

Dabeiguan was a relatively poor, only moderately commercialized farming village that was first settled in the early Ming dynasty (1368–1644). At the time of the survey it was made up of 98 households and had a total population of 601. Much of its acreage was devoted to food crop production

[33] See Minami Manshu Tetsudo Kabushiki Kaisha, Kito Noson Jittai Chosahan, Dainiji kito noson jittai chosa hokokuso: tokeihen ; Dai ichiban: Heikoku ken ; Dai sanban: Hojun ; Dai yonban: Shorei ken (Dairen, 1937). These surveys are part of an enormous collection of materials on social and economic life in China compiled by the South Manchurian Railway Company during the late 1930s and early 1940s. For an introduction to these materials, see Philip C. C. Huang, Peasant Economy .

[34] An earlier estimate of the degree of income inequality in a North China village made by Marc Blecher suffers in both regards because it is based on a stratified sample in which larger farms are overrepresented and because household income is measured simply by the gross agricultural output of each household. See Marc Blecher, "Income Distribution in Small Rural Chinese Communities," China Quarterly 68 (Dec. 1976): 795–816.


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TABLE 6.4 Market Exchange in Three Hebei Villages, 1936

Market Exchange

Michang

Lianggezhuang

Dabeiguan

Percentage of land rented

32.07

49.08

5.09

Hiring-in/out of labor

     

Annual basis (no. of laborers)

34/17

8/0

18/11

Monthly basis (no. of months of labor)

10/10

1/0

12/19

Daily basis (no. of days of labor)

807/2,959

561/4,096

342/2,610

Net labor importer (-) or exporter (+) (expressed in terms of labor hired annually)

- 8.04

+ 9.65

+ 3.03

Labor hired as a percentage of total village labor

14.80

5.67

10.12

Number of village members who migrated

37

19

4

Number of households borrowing/lending

49/27

65/47

44/43

Net borrowing (+) or lending (-) (yuan per year)

+ 4,185.00

+ 1,267.30

- 3,510.00

Draft animal rental (days)

132

0

10

Percentage of agricultural output marketed

47.00

11.00

13.00

Percentage of self-sufficiency in grain production

69.00

28.00

90.00

Percentage of gross income earned in cash

76.00

69.00

35.00

SOURCE: Based on data contained in Minami Manshu Tetsudo Kabushiki Kaisha, Kito Noson Jittai Chosahan, Dainiji kito noson jittai chosa hokokusho: tokeihen. Dai ichiban: Heikoku ken; Dai sanban: Hojun; Dai yonban: Shorei ken (Dairen, 1937).

NOTE: In calculating the net import or export of labor by each village, we have converted daily labor into monthly at the rate of 24 days equal 1 month, and monthly to annual at the rate of 10 months equal 1 year.

for village consumption, although approximately 15 percent of the total cultivated area was used to grow cotton and sesame primarily for market sale. Slightly less than 15 percent of agricultural output was marketed. Animal husbandry represented an important, but perhaps declining, sideline for some households.[35]

Every household in the village was engaged in agriculture, and all but five actually owned some land. Farms averaged almost 25 mu , or 1.67 hectares (15 mu equals 1 hectare). Only five percent of the land in the village, however, exchanged hands through the rental market. In contrast, the labor market

[35] According to the preface to the survey, population pressure had reportedly begun to crowd out animal husbandry. Some households (the poorer ones) were forced to abandon animal husbandry altogether, whereas others switched to rearing smaller animals.


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was very active. Villagers employed substantial amounts of labor from inside and outside the village on an annual and seasonal basis. Villagers also worked outside the village primarily on a daily basis. In this regard, the labor market appears to have been the primary mechanism through which households offset imbalances in resource endowments. On the whole, Dabeiguan was a small net exporter of labor. In all likelihood because of more lucrative opportunities outside the village, Daibeguan was, at the same time, a net lender of capital.

Michang, in contrast, typifies the richer, more highly commercialized villages of North China. It was made up of 114 households and had a total population of 753. Because of its rail links to the region's major urban centers, agriculture had become highly specialized in the course of the early twentieth century. In the process, farm households replaced the cultivation of basic foodgrains, like sorghum and millet, with cash crops, such as cotton, and purchased large amounts of grain from outside sources. By the mid-1930s more than a third of the cultivated area was in cotton, and almost half of agricultural output was marketed. Commercial fertilizers also began to be widely used and represented a significant expenditure item for almost all farming households.

Although agriculture was the primary source of income in Michang, 15 households were engaged solely in nonagricultural activities. An additional 22 households were engaged in nonfarm sidelines. These activities included peddling, carpentry, food processing, and local government service. Three households reported no income and were classified by surveyors as "beggar households." A total of 19 village members had also migrated (primarily to Tangshan and Tianjin, and mostly as shopkeepers) and, through remittances, represented a key source of income for some households.

Cash-cropping and the commercialization of the local economy substantially increased productivity and the demand for labor.[36] Only a portion of this increase, however, was accommodated by an increase in labor supply from within the village, and Michang looked increasingly outside its boundaries for labor. In the 1930s, Michang was a net importer of labor. This was complemented by a very active land rental market, in which almost a third of all land exchanged hands. Yet most of this exchange was due to villagers cultivating land owned by village outsiders (absentee landlords). Within Michang, households cultivated most of the land they owned. Unlike Debeiguan, Michang was a net borrower of capital.

Situated on the Bei-Ning Railroad, Lianggezhuang was also a highly commercialized village, although more closely tied to Manchuria than to North

[36] The annual labor requirement for cotton, for example, was 12 days per mu , or double the requirement for grain crops. The demand for marketing and auxiliary services also increased labor demand.


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China. With 101 families and a total population of 574, it was slightly smaller than either Michang or Dabeiguan. Through the first three decades of the twentieth century, Manchuria had provided a rapidly expanding market for farm products and employment for some village members. Lianggezhuang's chief agricultural export was pears. Much of the profit from these exports was reportedly reinvested in land, and by the 1930s almost one third of total acreage was in pear orchards.

With the establishment of the puppet state of Manchoukuo in 1932, however, exports and seasonal migration were sharply curtailed. These difficulties were further compounded by a series of poor harvests. In 1936 pear output contributed only one-seventh to the gross value of agricultural output despite representing almost a third of cultivated area. This had profound ramifications for the labor market, for this one crop had formerly absorbed over one half of agricultural household labor and had drawn in much labor from outside the village.[37] As these opportunities dried up, many villagers now sought short-term work in the nearby prefectural capital, and Lianggezhuang actually became a net exporter of labor. Reflecting these difficulties, in 1936 agriculture contributed only 58 percent of total gross village income.

What can we say about the distribution of land and income in these three villages, and how does it compare to the aggregate estimates for Hebei? Table 6.5 reports Gini coefficients for asset holdings (land, draft animals, and labor power) and incomes, both on a household and household per capita basis for each of the three villages. Mean values of each variable are reported in parentheses.[38] In Michang and Lianggezhuang average landholdings were smaller, and land ownership much more concentrated, than in Dabeiguan. The same is also true of household ownership of draft animals. In the two more highly commercialized villages the degree of concentration of landholdings was 20–25 percent higher than in Dabeiguan. The average Gini coefficient for the three villages was 0.70, only slightly higher than the average (0.65) for 32 villages also surveyed in Hebei in 1936.[39] In all three villages, however, land rental helped to equalize access to land among households; this effect is most pronounced in Michang and Lianggezhuang, where a third and a half of the land were leased to tenants, respectively. In Michang, for example, the Gini coefficient for landholdings was 0.75, while that for operational holdings was 0.55; in Lianggezhuang the coefficients were 0.75 and 0.53, respectively. The mean operational holding in Michang was also much larger than the mean landholding because of the substantial

[37] This was the opinion of the surveyors as expressed in the preface to the survey.

[38] Here, and throughout this paper, we apply the term "per capita" to data on landholdings, incomes and other variables that we obtain by dividing household figures for these variables by the number of persons per household.

[39] See Mantetsu chosa geppo 18, no. 1 (1939): 39–73 and 18, no. 4 (1939): 21–31.


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TABLE 6.5 Asset and Income Distribution in Three Hebei Villages, 1936 (Gini coefficients)

Components of Asset and Income Distribution

Michang

Lianggezhuang

Dabeiguan

Household size

0.30 (6.44)

0.27 (5.29)

0.26 (6.09)

Labor powera

0.33 (2.86)

0.32 (2.09)

0.24 (4.11)

Male laborers

0.29 (2.20)

0.33 (1.73)

0.25 (2.13)

Female laborers

0.64 (1.07)

0.64 (0.55)

0.32 (3.22)

Draft animals

0.72 (0.50)

0.81 (0.30)

0.49 (0.80)

Land owned

0.75 (15.30)

0.75 (19.47)

0.58 (23.57)

Land cultivated

0.55 (21.40)

0.53 (15.49)

0.51 (24.87)

Capital borrowed

0.82 (93.91)

0.76(151.84)

0.76 (49.50)

Capital lent

0.90 (57.20)

0.85(164.39)

0.77 (85.32)

Net cash income

0.60(153.81)

0.52(101.07)

0.60 (37.38)

Net in-kind income

0.46 (98.84)

0.56 (61.40)

0.45(123.21)

Net income

0.49(252.65)

0.41(162.47)

0.44(160.59)

Net income per capitab

0.39 (37.28)

0.35 (32.81)

0.35 (26.36)

Consumption expenditure

0.44(178.42)

0.35(139.80)

0.39(129.91)

Consumption expenditure per capita

0.30 (27.02)

0.29 (29.59)

0.33 (24.62)

NOTES: Village averages are in parentheses. Land is measured in mu and incomes and expenditures in yuan . Unless otherwise noted, the Gini coefficients are for distributions at the household level.

a The number of laboring males plus 0.65 times the number of laboring females in the household.

b The reported figures are the average net incomes per capita for all households. Net income per capita for each village (in the order they appear in the table) is 39.23, 30.71, and 26.50.

holdings of absentee landlords. In Lianggezhuang the situation was the exact opposite, because a quarter of the land was rented out to nonvillagers, who were not included in the survey.

Table 6.5 also shows Gini coefficients for household income and household income on a per capita basis.[40] The Gini coefficients for household income are very consistent with the estimate suggested by the National Land Commission data for Hebei. As reported in Table 6.3, the Gini coefficient for the distribution of rural incomes in Hebei was 0.46. We also observe that for two of the three villages net household incomes were much more evenly distributed than cash incomes.

Yet for reasons spelled out above, it is the distribution of household income on a per capita basis that concerns us. And here we observe in every case a much lower degree of inequality on a per capita basis than on a household basis: Michang, 0.488 versus 0.391; Lianggezhuang, 0.409 versus 0.346;

[40] The details of the income calculations are included in a lengthy appendix available from either author on request.


196

and Dabeiguan, 0.436 versus 0.349. The average difference between the Gini coefficients is 0.08. If this relationship held throughout the province, it would suggest a Gini coefficient for per capita rural household incomes in Hebei of 0.38, that is, 0.46 – 0.08. We also note that when measured on a per capita basis, the differences in the degree of income inequality between the villages are very small. Moreover, although overall inequality was slightly higher in Michang, average per capita income was also more than a fifth higher than in either of the other villages. With differences in consumption expenditure per capita even smaller, much of the higher income in Michang obviously took the form of higher savings.

Several questions immediately arise: (1) why is the inequality of incomes on a per capita basis much lower than the inequality at the household level? (2) why are household incomes distributed so much more equitably than landholdings? and (3) what diluted the influence of the much higher inequality in landholdings in Michang and Lianggezhuang and produced a dispersion in incomes similar to that observed for Dabeiguan?

Until the recent work of Kuznets, the influence of the size distribution of households on income disparities was neglected. Most estimates of income inequality were calculated using income per household. Since households differ in size, this can provide a very misleading indicator of the degree of dispersion of incomes on a per capita basis. In general, family size and family incomes are highly positively correlated. In the three villages we examined, the households with the highest incomes are usually those with ten or more members. The most obvious reason for this is that larger families typically have more members of working age who can add to the household's income. In China these households were typically extended, multigenerational households that had not recently divided.

If incomes increase more than proportionally with household size, the dispersion of incomes on a per capita basis will actually exceed their dispersion on a per household basis. But this is typically not the case; rather, incomes increase less than proportionally, and family size and per capita incomes are negatively associated. Indeed, some of the poorest households on a per capita basis in these villages are among those identified as having the highest total household incomes. Although it need not always be the case, under such conditions the dispersion of household incomes on a per capita basis can be less than that for total household incomes. This is in fact what we observe for all three villages.[41]

[41] The tendency for the dispersion of household incomes on a per capita basis to be lower than their dispersion on a household basis appears to be much more common for developing than for developed countries. For a sample of six developing countries (Colombia, India, Malaysia, Nepal, Sri Lanka, and Taiwan), the average difference between the Gini coefficient for household incomes or expenditures and the same measure on a per capita basis was 0.05. The largest difference was observed for Malaysia, where the Gini coefficient for households ranked by incomes was 0.52, whereas that for households ranked on a per capita basis was 0.43. The data also suggest a slightly larger difference between the two measures in rural than in urban areas. These estimates are taken from Pravin Visaria, "Demographic Factors and the Distribution of Income: Some Issues," in International Union for the Scientific Study of Population, Economic and Demographic Change: Issue for the 1980's (Helsinki, 1979), 1:289–320; and Berry, "Evidence on Relationships." On the relationship more generally between household size and income distribution, see Simon Kuznets, "Size of Households and Income Disparities," Research in Population Economics 3 (1981): 1–40.


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The second and third questions concerning the link between land ownership and income earnings benefit from a brief examination of what we mean by income. Conceptually, income can be thought of as a stream of earnings generated by all factors of production owned by an individual or household (current technology defining what is and is not a factor of production). In a primarily agrarian economy, the key factors of production are land, labor, and capital (the last including such things as farm implements and draft animals). Letting H, L , and K represent these three factors, and r, w , and t their returns, for household i , income, Y , is figured by the equation

figure

where Hi , Ki , and Li denote household i 's ownership of land, labor, and capital. A household's income, therefore, is positively related to how much of each factor it owns and to the return to each factor.

Yet where do r, w , and t come from? The returns to the various factors of production depend on the nature of the rural economy—most importantly, existing production technology, factor proportions, and the workings of markets or exchange. If all households had access to the same production technology, but each household simply used the resources that it possessed, ri , wi , and ti would differ among households, with the return to each factor negatively correlated with its relative availability within the household. Exchange alters all this. Allowing households to buy and sell the services of the factors of production frees them from their particular endowments and allows them access to all factors of production within the village. Exchange effectively coordinates all households' demands for and supplies of all factors of production, so that the return to each factor is determined by its relative availability within the village rather than within the household.[42] That is, r, w , and t would be the same across households.

Looking at incomes from this perspective, it becomes clear why many observers thought income distribution within China was highly unequal. For a majority of rural households agriculture was the primary source of income, only modestly supplemented by income from subsidiary activities, such as

[42] If factors are mobile, their returns will depend on relative availability within an even wider marketing area, not just availability within the village.


198

handicrafts. Thus, household incomes in the rural sector were systematically tied to income from agriculture and land ownership. At the same time, farming in China was highly labor-intensive; the land-labor ratio, in fact, was one of the lowest in the world. Modern inputs into agriculture had yet to be introduced in any quantity. As a result, the marginal productivity of labor, the level of rural wages, and the percentage of output captured by labor tended to be low relative to the return to land. A direct result of the limited off-farm opportunities, low relative returns to labor, and high concentration of land ownership was an unequal distribution of rural incomes.[43]

Yet perceptions were that income inequality was becoming more severe. Why? On the one hand, both Chinese and Western observers believed that land ownership had become much more concentrated; on the other hand, they observed a falling land-labor ratio and associated with that a marked decline in wages relative to land rents.[44] This fall was only exacerbated by the imperfectly working factor markets, which prevented households from rationally redistributing factors among themselves.[45] A concurrent decline in off-farm employment opportunities, which small farm households disproportionately relied on, was also perceived to have occurred.[46] What all this seemed to mean was that landowners, who were becoming an even smaller minority, appeared to be capturing an increasing percentage of the net product of the rural sector.

[44] Estimates by Dwight H. Perkins suggest that the land-labor ratio in China proper declined by roughly 15 percent between the 1890s and the 1930s. See Agricultural Development in China , pp. 212 and 236.

[45] See Philip C. C. Huang, Peasant Economy , esp. chaps. 4–12.

[46] See, for example, Fan Baichuan, "Zhongguo shougongye zai waiguo zibenzhuyi qinruhou de zaoyu he mingyun," Lishi yanjiu 3 (1962): 88–115.


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The evidence on land and income distribution presented earlier belies much of this story. To see why, recall that income distribution depends not only on the distribution of land ownership but also on the distribution of the other factors of production in the rural economy, most importantly, labor power and, to a lesser extent, draft animals and other forms of capital. It also depends on the returns to these factors and on the correlation of factor ownership across households. A high correlation of landholdings with ownership of other factors of production would only reinforce the influence of a high concentration of landholdings on incomes. The distributive impact of a high degree of concentration in land ownership could, however, be partly or entirely offset by the effects of more even distribution of ownership for other factors of production, by a rise in the share of total income earned by other factors, or by a weak or inverse correlation between ownership of different productive factors by different households.

In Table 6.5 we observe that labor power (and therefore income from labor) was indeed much more evenly distributed among households than was land. The Gini coefficients for labor power per household were 0.33, 0.32, and 0.24, for Michang, Lianggezhuang, and Dabeiguan, respectively. Draft animal ownership, on the other hand, was about as unequally distributed as landholdings in the three villages.

Yet the strength of this influence clearly depends on the portion of output that labor captures. If labor power is evenly distributed among households, but the portion of output going to labor is relatively small, the equalizing effect would itself be modest. In rural North China, land captured about half of agricultural output, labor a third, and the remainder represented the return to draft animals and other forms of farm capital.[47] These estimates are misleading, however, because agriculture was not the only source of income. In all three villages there were a variety of sidelines that offered subsidiary sources of incomes that primarily represented returns to labor. Our calculations, based on the three village surveys, show that land rents (including rents attributed to owner-operated farm land as well as cash or in-kind payments by tenants to landlords) amounted to about a third of total village income, while labor incomes accounted for more than half of all income.

Intervillage differences along these same lines helped to diminish the influence of the much higher concentration of landholdings on incomes in Michang and Lianggezhuang. In Michang, land rents averaged between 4 and 5 yuan per mu , while a long-term laborer could expect to earn 45–50 yuan in cash, plus an in-kind component of perhaps equal value. In Dabeiguan, by contrast, land rents were modestly less, at 3–3.50 yuan per mu , but wages paid long-term agricultural laborers were only about half the rate in Michang. In

[47] These estimates are taken from Loren Brandt, "Farm Household Behavior, Factor Markets, and the Distributive Consequences of Commercialization in Early Twentieth-Century China," Journal of Economic History 47.3 (September 1987): 731.


200

Lianggezhuang, land rents averaged even less, between 2 and 2.50 yuan per mu , while wages (cash component only) paid long-term agricultural laborers were 30–35 yuan . The same relationship is reflected in output shares; the share of agricultural output going to land in Michang and Lianggezhuang was roughly 40 percent, while in Dabeiguan it exceeded 50 percent.[48] The significantly higher return to labor relative to land in both Michang and Lianggezhuang helped to reduce the influence of a more concentrated land ownership in these villages and produce a degree of income inequality not much different from that in Dabeiguan.

Nonagricultural activities and remittances played a similar role. In both Michang and Lianggezhuang, 40 households were engaged in nonagricultural activities either as a sideline or on a full-time basis.[49] From both villages there was also substantial out-migration—37 household members from Michang, and 19 from Lianggezhuang had migrated—and the out-migrants in turn typically remitted a healthy portion of their earnings back to the villages. The higher the percentage of income generated from nonagricultural sources and received in the form of remittances, the weaker the influence of land concentration on the overall dispersion of incomes. In Lianggezhuang, nonagricultural activities and remittances produced 42 percent of gross household income, while in Michang they amounted to 17.4 percent; in Dabeiguan they totaled only 6.1 percent.

Finally, Table 6.6 reveals that the correlation between land ownership and the other factors of production was also much weaker in Michang and Lianggezhuang than in Dabeiguan. The same is also true for the relationship between labor power and draft animal ownership. While factor ownership was positively correlated, the probability that a household with substantial landholdings simultaneously had more than average labor power and draft animals was significantly lower in either Michang or Lianggezhuang than in Dabeiguan. This partially reflects the higher degree of specialization in the local economy of the first two. As a result, even though land was distributed more unequally in Michang and Lianggezhuang than in Dabeiguan, the

[48] Average land rents were computed from detailed land contract information provided by the survey. Land's share was then calculated by dividing average land rent by average output per unit of land.

[49] The percentage of households in Michang and Lianggezhuang engaged in nonagricultural activity on a full-time basis (3.5 percent and 7.4 percent, respectively) was low compared to that in other North China villages. The average for 30 villages examined by the South Manchurian Railway Company was 13.7 percent. In a number of localities, 20 percent or more of households was classified solely as nonagricultural. These include Macun in Huailu County, 21.8 percent; Zhongliangshan in Changli County, 26.8 percent; Huzhang in Ninghe County, 31.2 percent; and Dongjiao, located just outside Shijiazhuang, 46.8 percent (see Huang, Peasant Economy , pp. 314–20). In a sample of 22 villages in southern Manchuria, 16 percent of all labor (not households) was classified as nonagricultural; see Kokumuin Jitsugyobu Rinji Sangyo Chosakyoku, Kotoku gannendo noson jittai chosa , 4 vols. (Changchun, 1936).


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TABLE 6.6 Correlations of Factor Holdings by Households in Three Hebei Villages, 1936 (Pearson's r )

Factors of Production

Draft Animal Ownership

Landholdings

Female Agricultural Labor

Landholdings

     

Lianggezhuang

0.10

   

Debeiguan

0.80a

   

Michang

0.55a

   

Labor power

     

Lianggezhuang

0.32a

- 0.03

 

Debeiguan

0.57a

0.61a

 

Michang

0.38a

0.49b

 

Male agricultural labor

     

Lianggezhuang

   

0.32a

Debeiguan

   

0.49a

Michang

   

0.54a

NOTE: Labor power is measured in adult male equivalents.

a Statistically significant at 1 percent.

b Statistically significant at 10 percent.

superior market access and non-farm employment opportunities available to residents of Michang and Lianggezhuang produced distributive outcomes that displayed no more inequality than we observe in Dabeiguan.

These calculations show, then, that the distribution of landholdings can be a very misleading indicator of the degree of income inequality in the rural economy. There is no direct line of causality running from higher concentration of landholdings to a higher concentration of income. What is true for several villages at a single time is probably true for a single village over a long period of time. Unfortunately, we do not have earlier data on distribution for these villages, and so the origins of the higher concentration of landholdings in Michang and Lianggezhuang remain a bit of a mystery. Nevertheless, even if landownership in the latter two villages became more concentrated, no widening of income differentials can be inferred.

Income Distribution in China in a Comparative Perspective

If household-level data for our small sample of North China villages are an accurate indication of the difference between the Gini coefficient for household incomes and household incomes on a per capita basis in other parts of


202

the country—the Gini coefficient for the rural sector as a whole was probably in the vicinity of 0.38.[50] What tentative conclusions can we draw about the degree of inequality in the rural sector and China more generally on the basis of these data? In other words, how does the distribution of incomes in China compare with that in other low-income countries? Are there reasons to believe that incomes in rural China were exceptionally unequal? Given recent suggestions in the People's Republic of China that the degree of rural inequality, as measured by the Gini coefficient, be controlled within 0.30 to 0.40, perhaps not.[51]

Although much more work is required before these questions can be answered exhaustively, some comments are in order. Ideally, we would like to compare the distribution of income observed for China with that estimated for other low-income countries in the 1930s. Short of this, we would like to compare our estimate with contemporary estimates for low-income countries similar in key respects to the China of the 1930s. India is an obvious candidate. Comparisons such as these are hampered by data limitations, however.

On the one hand, estimates of the degree of dispersion of rural incomes are almost always for households rather than households on a per capita basis. As we argued earlier, the latter is the preferred basis for measuring the distribution of economic welfare. Unless one is willing to assume that the influence of the size distribution of households on income distribution is constant over time and space, comparisons on a household basis can be misleading.

With this caveat in mind, in Table 6.7 we report Gini coefficients for rural household incomes in several Asian countries. And here we observe a degree of dispersion very similar to that estimated for China. Only in the case of Taiwan, which after World War II benefited from an income-leveling land reform, is it consistently markedly lower. Moreover, for nine of the 16 provinces that we provide estimates for (see Table 6.3), the degree of concentration compares even more favorably. In light of this finding, it seems highly unlikely that inequality was rising over time; if it was, extrapolation would imply a degree of concentration of incomes before the turn of the century that would be considered exceptionally low by international standards.

[50] This estimate was obtained by subtracting the average difference between the Gini coefficient for household incomes and household incomes on a per capita basis for the three Hebei villages from the Gini coefficient for the pooled sample in Table 6.3. One indication that in the aggregate household incomes on a per capita basis were more evenly distributed than household incomes is that cultivated area per household on a per capita basis was more evenly distributed than cultivated area per household. This is reflected in data provided by Buck on the size distribution of farms. The fact that Buck oversampled larger farms is not a point here. While the largest farms were almost 20 times larger than the smallest, on a per capita basis the difference was only one third of this. The same is true for data on land ownership. These relationships also hold separately for each of the eight regions Buck surveyed. See Buck, Land Utilization in China: Statistical Volume , pp. 289, 291, 300.

[51] "Nongcun you meiyou liangji fenbu?" Banyuetan 22 (1987): 20–21.


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TABLE 6.7 Distribution of Household Incomes in Selected Low-Income Countries (Gini coefficients)

 

Rural Sector

Urban and Rural Sectors

Country

Income per
Household as a Whole

Income for
Household per Capita

Income for
Household per Capita

Brazil

     

1968

   

0.424

Colombia

     

1974

   

0.461

India

     

1961–62

0.410

   

1964–65

0.350

   

1967–68

0.460

   

1968–69

0.430

0.397

 

Malaysia

     

1957

0.421

   

1970

0.464

 

0.428

Philippines

     

1961

0.397

   

1965

0.423

   

1971

0.462

   

Singapore

     

1966

0.447

   

1975

0.436

   

Taiwan

     

1972

0.344

   

1975

0.363

   

Thailand

     

1962–63

0.361

   

1968–69

0.381

   

1971–73

0.466

   

SOURCES: For all countries except India, estimates of rural sector inequality are taken from Income Distribution by Sectors and Overtime in East and Southeast Asian Countries , Harry Oshima and Toshiyuki Mizoguchi, eds. (Quezon and Tokyo, 1978). The estimates for India are taken from I. Z. Bhatty, "Inequality and Poverty in Rural India," in Poverty and Income Distribution in India , T. N. Srinivasan and P. K. Bardhan, eds. (Calcutta, 1974). Remaining estimates are taken from R. Albert Berry, "Evidence on Relationships Among Alternative Measures on Concentration: A Tool for Analysis of LDC Inequality," Review of Income and Wealth 43.3 (October 1987), pp. 483–92.


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On the other hand, the few estimates that we have for the dispersion of household incomes on a per capita basis are for the rural and urban sectors combined, while our estimate for China is for the rural sector alone. This is not as great a problem as at first it might seem. In the 1930s, the rural sector—the focus of the National Land Commission survey—constituted between 85 percent and 90 percent of the entire population. The urban sector, although growing at a rate of 2–3 percent per annum, was still very small. The critical question is, how does the addition of data for household incomes in the expanding urban sector influence our overall assessment of income distribution in the Chinese economy? Is there any reason to believe that the distribution for the entire economy differs markedly from that for the rural sector alone?

It was once widely believed that income distribution tends to worsen initially with the growth of the modern/urban sector. Kuznets cites two primary reasons for this belief: (1) a tendency to pay substantially higher wages in the modern/urban sector than in the rural sector and (2) an increasing concentration of capital in the modern/urban sector.[52] More recent work on a host of countries casts some doubt on this relationship. Nevertheless, for China Kuzent's hypothesis would seem to imply that incomes were more concentrated nationally than they were in the rural sector.

Data on the distribution of incomes in the urban sector presently do not exist. Nonetheless, it seems to us that any upward revision to the estimates obtained separately for the rural sector would actually be relatively modest. Despite the absolute doubling of China's urban population in the early twentieth century, it still amounted to no more than 10–15 percent of the total. As for incomes, it is important to remember here the small size of China's modern sector, almost all of which was located in the cities. Estimates by Ta-chung Liu and Kung-chia Yeh suggest that even as late as the mid-1930s the modern sector constituted no more than 6–8 percent of the gross domestic product.[53] Because agriculture's contribution to total GDP was two thirds and rural households earned an additional 20–25 percent of their income from nonfarm sources, the percentage of national income going to the urban population could not have been much more than 20 percent and was probably slightly less.[54] Moreover, an examination of budgetary surveys for

[52] Simon Kuznets, "Economic Growth and Income Inequality," American Economic Review 45 (Mar. 1955): 1–28.

[53] Ta-chung Liu and Kung-chia Yeh, The Economy of the Chinese Mainland (Princeton, 1965), p. 66.

[54] Assuming that 10 percent of income from agriculture went to absentee landlords residing in urban areas (based on the assumptions that 35 percent of land was rented, 60 percent by absentee landlords, and that rental payments constituted 45 percent of output on rented land), then 60 percent of total gross domestic product would have been going to rural households in the form of income from farming. If we add to this the income earned by farming households from nonagricultural activities—estimated by Buck to have been approximately 15 percent of total household income—plus the income earned by the 10–15 percent of the rural population classified as nonagricultural (under the assumption that their average incomes equaled that of the farming population), the percentage of total income going to the urban sector would have been roughly 1 - {((0.90*0.67) / 0.85) / 0.125} = 0.165, or 16.5 percent. With slightly in excess of 10 percent of the population classified as urban, the ratio of average urban-to-rural incomes would have been approximately 1.5 to 1.


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working-class households in such cities as Shanghai, Wuxi, and Tianjin reveals a level of per capita income very similar to that observed for the rural sector.[55] This is consistent with empirical work of Thomas B. Wiens, who found that "despite considerable regional variation in the monetary wage levels of farm laborers and industrial workers in various industries . . . nationally . . . farm wages appear to be slightly below the level of wages of the least skilled urban occupations, as might be expected."[56]

Although examples can be provided of accumulation of great wealth by some Chinese families, the above considerations lead us to believe that the Gini coefficient for household incomes on a per capita basis for all of China was probably not much in excess of 0.40. How does this compare to that for other countries? In Table 6.7, we report estimates of the Gini coefficients for the distribution of household incomes on a per capita basis for several countries. The sample is small, but the degree of concentration of incomes in China does not appear to be high by comparison.

Finally, what can we say about the concentration of incomes and landholdings in the aggregate? Excluding the holdings of absentee landlords, the Gini coefficient for rural landholdings was 0.72. Making allowances for these landholdings by assuming an average holding of 100 mu suggests a Gini coefficient for rural landholdings in the vicinity of 0.80. This, of course, excludes urban landholdings, which, by all indications, were even more concentrated, implying a still higher degree of concentration. Compared to the Gini coefficient for incomes, it becomes obvious that even in a primarily agrarian economy like China's, the link between land concentration and income distribution is tenuous at best.

Summary

In this paper we have reexamined the links between land concentration and income distribution in rural China. From our perspective, much of the conventional wisdom regarding distribution in late nineteenth- and early

[55] See Bureau of Social Affairs, City Government of Shanghai, Shanghaishi gongren shenghuo chengdu (Shanghai, 1934); Bureau of Statistics, Ministry of Industry, Wuxi gongren shenghuofei ji qi zhishu (Nanjing, 1935); H. N. Feng, "An Enquiry into the Family Budget of Handicraftsmen in Tientsin, 1927–28," Quarterly Journal of Economics and Statistics 1, no. 3 (Sept. 1932).

[56] Thomas B. Wiens, "The Microeconomics of Peasant Economy: China, 1920–40" (Ph.D. diss., Harvard University, 1973), p. 169.


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twentieth-century China is badly in need of reexamination. Certainly on the basis of the exploratory examination we have carried out, the views of Tawney and many other observers now seem very difficult to sustain. The many questions that do remain can only be answered through more detailed empirical work of the kind we have attempted here.

Income distribution in rural China has always been a highly charged issue. Perhaps inevitably, views on the subject are difficult to divorce from individual perceptions of the events leading up to and including the 1949 Communist takeover. Yet to say that incomes were no more concentrated—perhaps even less so—than they were in other low-income countries is not, of course, to deny that there was extreme poverty in China or that the number of people living on the margin of subsistence might have possibly increased during the early twentieth century. Given that between the 1890s and 1930s the Chinese population expanded by almost 125 million, an increase in the absolute number of poverty-stricken people can be reconciled almost as easily with the view that incomes became less concentrated as it can be with the view that income distribution worsened. Nonetheless, once we free ourselves from the established paradigm of a secular worsening of inequality and begin to entertain notions of more complicated behavior for land and income distribution, it will be easier to analyze objectively the long-term evolution of the Chinese economy, including the post-1949 changes in the rural sector.


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Seven
Farming, Sericulture, and Peasant Rationality in Wuxi County in the Early Twentieth Century

Lynda S. Bell

Peasant decision making is a subject of interest to a wide range of social science theorists as well as policy planners. A compelling reason for this interest is that in the third world, improving the productive capacity of the rural sector is an important component of development. Not only is a larger food supply necessary to support a growing population of industrial workers, but peasants must have improved standards of living so that they can transform themselves into investors and consumers to support fledgling industry. Therefore, understanding how and why peasants decide production and resource-allocation issues has become an important theme in economic decision theory.

Within this context, there has been a debate about whether or not peasants behave rationally. For example, Samuel Popkin has argued vehemently in favor of the idea, stating that peasants constantly take risks to improve their profit-making potential. This is a purist approach to the issue of rationality, and makes no concessions to other circumstances that may affect the peasant's decision-making processes.[1] However, many economists who study peasant economies have come to accept a slightly modified view of peasant rationality. Rather than seeing peasants as pure profit maximizers, they observe that even when markets are well developed and functioning

[1] Samuel Popkin, The Rational Peasant: The Political Economy of Rural Society in Vietnam (Berkeley and Los Angeles, 1979). Popkin argues specifically against James C. Scott, The Moral Economy of the Peasant: Rebellion and Subsistence in Southeast Asia (New Haven, 1976). Scott claimed that peasants operate according to the principle of safety first and that patron-client ties figure prominently in peasants' efforts to avoid risk. Scott's work was influenced by the earlier social theorist Karl Polanyi, who made the important point that social institutions other than the marketplace greatly affect peasant decision making in precapitalist societies. See Scott; Polanyi, The Great Transformation (New York, 1944).


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smoothly, peasants often lack the education and information necessary to take full advantage of them. Moreover, peasants also face the constraints of environment, low land-labor ratios, adverse land tenure relationships, and low levels of technological development.[2]

Under these circumstances, the concerns of most peasants involve not only profit maximization but also the satisfaction of the immediate consumption demands of all household members. To accomplish this second goal, peasants often work at many sorts of supplementary tasks in addition to their principal crop-producing activity, a strategy that spreads risk and provides additional household income. Perhaps the best way to describe this behavior in the language of economics is to say that peasant families act simultaneously as both profit-maximizing producers, or firms, and income-maximizing consumers. Consequently, individual peasant households sometimes display behavior that in their role as producers could be considered irrational, since their supplementary activities often bring far fewer returns per unit of labor than prevailing market wages in agriculture. However, if there are a number of institutional and environmental constraints at work and satisfaction of basic consumption needs can be fulfilled in no other way, adopting such behavior helps to maximize the total real and future income of the family as a whole and become a consumer-specific form of economic rationality.

As described here, my analysis builds on the work of A. V. Chayanov and of Philip Huang, who uses Chayanov's arguments to elaborate a model of China's long-term development. Because Huang believes that one of the principal characteristics of China's economy in the late imperial period was diminishing returns to labor, he calls his model "economic involution," or alternatively, "involutionary growth," terms meant to convey the importance of increasing labor intensification and the propensity for per capita incomes to stagnate even as total output increased. Although I cannot fully address here all the issues raised by the concept of involution, I should note that I follow both Chayanov and Huang in my analysis of peasant decision making.[3]

[2] Michael Lipton, "The Theory of the Optimizing Peasant," Journal of Development Studies 4, no. 3 (April 1968): 327–51.

[3] A. V. Chayanov's main works are Peasant Farm Organization and "On the Theory of Noncapitalist Economic Systems," both translated and edited by Daniel Thorner, Basile Kerblay, and R. E. F. Smith, in A. V. Chayanov on the Theory of the Peasant Economy (Homewood, Ill., 1966). For an excellent summary of Chayanov's arguments, see Mark Harrison, "Chayanov and the Economics of the Russian Peasantry," Journal of Peasant Studies 2.4 (July 1975): 389–417. Philip C. C. Huang has written two books, The Peasant Economy and Social Change in North China (Stanford, 1985) and The Peasant Family and Rural Development in the Yangzi Delta, 1350–1988 (Stanford, 1990) advancing his argument on involution. I will discuss the pros and cons of Huang's concept of involution at much greater length in a book manuscript now in preparation.


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Specifically, I use Chayanov's idea that the producer-consumer dualism of peasant households affects their approach to both labor allocation and marketing activity. In my view, however, this dualism did not prevent some Chinese peasants from introducing innovative production techniques or from achieving higher levels of consumption; for this reason, the term involution used by Huang to describe the late imperial economy may be misleading, seeming to connote an inevitable process of universal, inward-looking decline. Nevertheless, I do share Huang's view that the producer-consumer dualism of peasant households precluded substantial gains in material welfare for the vast majority of Chinese peasants by the early twentieth century. Under conditions of declining land-labor ratios coupled with little technological change, most Chinese peasants approached markets as vehicles to improve their short-term consumption needs and found no leeway to pursue the expanded circulation and accumulation of capital that could have led to innovative long-term investment.

In this chapter, I will present early twentieth-century evidence from the Lower Yangzi county of Wuxi that supports this producer-consumer view of peasant rationality. I shall show that Wuxi peasants engaged in sericulture as a form of supplementary-income-earning activity under conditions of extensive commercialization, dense population, and unfavorable land-labor ratios. Cash-cropping in mulberry has often been analyzed as a way that some rural districts increased their wealth in the early twentieth century, and I do not dispute that Wuxi was one of China's most rapidly developing counties at that time. I shall also show, however, that most peasants who produced mulberries and raised silkworms in Wuxi did so out of economic necessity, that the income per labor day earned from those activities was far smaller than for rice/wheat farming, and that it was difficult to make improvements in silk production overall as long as relatively poor peasant households were responsible for innovations in sericulture technique. For these reasons, although peasants in Wuxi behaved quite rationally, they had trouble contributing to long-term plans of silk industry leaders for the improvement of silk quality and became an important obstacle to long-term silk industry growth.

Qing Trends in Commercialization and Demographic Growth

To understand the dynamics of farming and sericulture in Wuxi in the early twentieth century, it is useful to explore trends already under way in Wuxi's local economy. Situated in the heart of the Lower Yangzi regional core, Wuxi had been experiencing a long process of commercial growth and


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population increase for at least two centuries before 1900.[4] This prior experience set the stage for the rapid spread of sericulture as a supplementary income-earning activity among Wuxi peasants.

The dynamics of Qing period commercialization in Wuxi had two parts. First of all, Wuxi became an important rice-marketing center from the Qianlong period (1736–95) onward. Second, Wuxi peasants became heavily involved in cotton cloth production and marketing to supplement the income-earning capacity of their households. I shall explore these two forms of commercial activity in turn.

During the early eighteenth century, Wuxi was well on its way to becoming a major center of rice trade and transport within the Lower Yangzi region.[5] By the early Qianlong years, areas to the west and south of Wuxi, in Anhui and Jiangxi provinces, were experiencing grain surpluses, while areas to the southeast of Wuxi, in Jiangsu and Zhejiang provinces, were evoling as a grain-deficit region because of escalating population growth, arable land becoming fully occupied, and regional urbanization.[6] Under these conditions, Wuxi's strategic position on the Grand Canal, just south of its confluence with the Yangzi, and on the northeast bank of Lake Tai, an important transport link to northern Zhejiang, made the county a place where large-scale grain shipping, storage, and marketing began to take place.[7]

At this point, state policy was also a dynamic factor, as Wuxi became an important concentration point in central China for the collection of tribute grain (caoliang ) before it was sent via the Grand Canal northward to Beijing. This had two consequences. First of all, as surcharges on tribute grain mounted and certain portions of the total tax were converted to cash equivalents, officials responsible for these levies increasingly collected them in cash.

[4] The term "Lower Yangzi regional core" is derived from G. William Skinner's influential work on economic "macroregions" in China as they developed in the late imperial period. The Lower Yangzi macroregion encompassed parts of Jiangsu, Zhejiang, and Anhui provinces and was one of China's richest agricultural areas from the tenth century onward. The "regional core" centered on the highly commercialized and densely populated area extending from the Shanghai-Ningbo coastal region inland to the area straddling the Jiangsu-Zhejiang-Anhui border. For a more detailed discussion of China's macroregions, see G. William Skinner, ed., The City in Late Imperial China (Stanford, 1977).

[5] I am indebted to Zhou Guangyuan of the Institute of Economics, Chinese Academy of Social Sciences, Beijing, for help in locating several sources dealing with eighteenth-century trends and in developing an overall picture of Wuxi's early patterns in commercialization and demographic growth.

[6] Shehui Jingji Yanjiusuo, Wuxi mishi diaocha (Shanghai, 1935), preface, pp. 1–2; Dwight H. Perkins, Agricultural Development in China, 1368–1968 (Chicago, 1969), pp. 140–51.

[7] Sun Jingzhi et al., Economic Geography of the East China Region (Shanghai, Kiangsu, Anhwei, Chekiang ), published in Chinese, Nov. 1959, citation from the English translation by JPRS (Washington, D.C., 1961), pp. 4, 98; Wuxi Difangzhi Bianji Weiyuanhui, ed., Wuxi gushixuan (Wuxi, 1959), pp. 36–40.


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They then purchased grain to meet the portion of the total tax still demanded in kind by the central government in Beijing. Thus, rice circulated ever more freely as a commodity for purchase and sale in various central China locales, and Wuxi emerged as a leading center for this activity.[8] Second, the development of an extensive regional shipping network also proceeded at a faster pace as tribute grain came toward the Grand Canal via adjacent river routes from the eight central provinces responsible for providing it. Strategically placed at a regional transport hub, Wuxi took full advantage of this situation.[9]

As commercial opportunities increased, so too did population. Estimating the precise rate of population growth in the eighteenth century is difficult because of China's methods of enumeration. In a study of Chinese population from the Ming dynasty through the mid-twentieth century, Ping-ti Ho explains that the prevailing system of population reporting in Qing times was based on the ding , a unit of taxation that originally referred to all adult males of working age. As is typical for the era, the only existing population figure for Wuxi in the early eighteenth century is a gazetteer report of 142,509 ding in 1726.[10] Ho also points out that during the eighteenth century, the ding figures no longer bore any relation to the number of adult males in a given population, but rather had evolved over the two centuries or so of their usage into pure fiscal units corresponding only to a locale's total tax liability. On the basis of extensive examination and evaluation of such figures, Ho concludes that the ding represented "substantially less than one-half" the real population.[11]

By the late eighteenth century, population reporting was beginning to correspond to reality more closely. Earlier in the century, the Kangxi emperor introduced a series of tax reforms referred to as tanding rudi or tanding rumu , literally, "spreading the ding into the land," with the intention of eliminating the ding system of tax accounting and instituting a tax based purely on land ownership at a permanent fixed rate.[12] These reforms spread slowly, however, and were only brought to fruition by the Qianlong emperor in the mid 1770s, with decrees that permanently ended the ding assessment and called for an annual accounting of true population figures and real grain

[8] Harold C. Hinton, The Grain Tribute System of China (1845–1911) (Cambridge, Mass., 1956), pp. 1–15.

[9] The eight provinces responsible for tribute grain payments were Shandong, Henan, Anhui, Jiangsu, Zhejiang, Jiangxi, Hubei, and Hunan. See Hinton, Grain Tribute System , p. 7. On Wuxi's role as a transport and marketing center in this process, see Shehui Jingji Yanjiusuo, Wuxi mishi diaocha , preface, 1–2.

[10] Wuxi-Jinkui xianzhi (A gazetteer of Wuxi and Jinkui counties), 1881 edition, 8:5.

[11] Ping-ti Ho, Studies on the Population of China, 1368–1959 (Cambridge, 1959), p. 35.

[12] I am grateful to Chen Qiguang of the Institute of Economics in Beijing for explaining Kangxi's reforms that began in 1712; Ping-ti Ho also discusses the reforms in Studies , p. 25.


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output.[13] The effect of imperial reforms on population enumeration in Wuxi was that the revised ding figures reported for 1795 totaled 566,217, a closer measure of the true number of males within the county.[14]

To derive population figures and to calculate a rate of demographic growth for Wuxi, I follow Ho's guidelines for the early eighteenth century and the suggestions of Chinese population expert Liang Fangzhong that male ding figures for Lower Yangzi region around the turn of the nineteenth century were approximately 53 percent of the total population.[15] Therefore, I consider the 1726 ding figure to have been only around 30 percent of the real population, a rough estimate to achieve Ho's description of the ding as being "substantially less than one-half" the population, to arrive at an approximate population of 475,030; and following Liang, I take the 1795 ding figure to be 53 percent of the total, for an estimated population in that year of 1,068,334. This translates into an annual rate of population growth of 1.2 percent for the period 1726 to 1795.[16] For China as a whole, Ho has estimated the rate of annual increase to have been 0.9 percent during this period.[17] It is also useful to compare my calculations with Dwight H. Perkins's estimate that population doubled in Jiangsu from the mid-eighteenth century into the early nineteenth, a rate of increase that corresponds roughly to his national-level estimate that the country reached an all-time high in population, increasing from 200–250 million in 1750 to 400 million by the early years of the nineteenth century. When translated into an annual rate of population growth, Perkins's figures produce estimates very similar to Ho's, from 0.8–0.9 percent. Because of Wuxi's special conditions of rapid commercialization and the development of rural industry, it is possible that population there grew more quickly than either Ho's or Perkins's estimate suggests. But for lack of better population data for the early eighteenth century, it seems impossible for the moment to provide any more accurate calculations than these.[18]

[13] Ping-ti Ho, Studies , pp. 47–48.

[14] Wuxi-Jinkui xianzhi , 8:5–6.

[15] Liang Fangzhong, Zhongguo lidai hukou, tiandi, tianfu tongji (China's dynastic statistics on household population, land, and land taxes) (Shanghai, 1980), pp. 440–41. Liang's figures are for Songjiang prefecture immediately to the east of Wuxi.

[16] Calculations follow George W. Barclay, Techniques of Population Analysis (New York, 1958), pp. 28–33, for continuous population growth.

[17] Ping-ti Ho, Studies , p. 64.

[18] Perkins, Agricultural Development , pp. 202–9. On trying to answer why Wuxi's annual rate of population growth may have been higher during the eighteenth century, studies of demographic growth conducted for various European locales offer intriguing hypotheses. The work of Franklin F. Mendels on Flanders in the eighteenth century, for example, argues that rapid commercialization and the development of rural industry altered nuptiality patterns—people married earlier because they could afford to—thus accelerating the regional rate of population growth (Industrialization and Population in Eighteenth-Century Flanders [New York, 1981]). Historians of Qing China have been interested in testing such theories for various regions of China, but the reconstruction of the demographic data is a formidable task due to the problems in population enumeration that I have discussed here. Ongoing work by James Lee, R. Bin Wong, and William Lavely promises to shed future light on China's unresolved demographic issues.


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In any case, the rate of growth of Wuxi's population is not as important to the argument at hand as the degree of population density that Wuxi experienced by the end of the eighteenth century. In the 1790s, when figures on population and land had become far more reliable, Wuxi had only 1.3 mu of arable land per capita, just 0.1 hectare, or slightly more than one sixth of an acre.[19] At the same time, as Perkins has demonstrated, there were no radically new developments occurring in agricultural technology during this period that would have led to substantial gains in grain output.[20] Therefore, to comprehend how peasants in densely populated regions such as Wuxi continued to support themselves, we must turn to an exploration of trends in subsidiary activities designed to raise the potential earning power of the peasant household.

Significantly, it was also during the eighteenth century that gazetteer reports for Wuxi began to note the importance of mianbu or huabu , handicraft cotton cloth, which Wuxi peasants produced in order to secure additional grain supplies. A 1752 gazetteer explains how these exchange relationships operated among Wuxi peasant households:

Of five counties in Changzhou prefecture, Wuxi is the only one that does not cultivate cotton of its own; yet cotton cloth is even more important here than in the other counties. Wuxi peasants get only enough grain from their fields for three winter months' consumption. After they pay their rents, they hull the rice that is left, put some in bins, and take the rest to pawnshops to redeem their clothing. In the early spring, entire households spin and weave, making cloth to exchange for rice to eat because by then, not a single grain of their own is left. When spring planting is underway in the fifth month, they take their winter clothing back to the pawnshops in order to get more rice to eat. . . . In the

[19] Land per capita is calculated from a population figure estimated from the ding statistics for 1795 in Wuxi-Jinkui xianzhi , 8:5–6, and land statistics for the mid 1770s in Wuxi-Jinkui xianzhi , 9:1 and 10:1.

[20] Perkins, chaps. 3, 4. Perkins's main argument is that for China as a whole during the fourteenth through the twentieth centuries, grain output rose steadily, but mainly through the better use of "traditional" technology—the opening of new lands, the use of better and more extensive irrigation techniques, the introduction of double-cropping to new areas, and the use of a few new crops such as sorghum, sweet potatoes, and corn. Twentieth-century surveys show that no double-cropping of rice was attempted in Wuxi (although another form of double-cropping was achieved through planting a winter crop of wheat, which was treated as a cash crop) and that early-ripening varieties of rice were rarely used. As I shall argue below, the preferred method of supporting a burgeoning population in Wuxi seems to have been for peasants to develop various methods to earn cash income to purchase rice imported from surrounding areas.


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autumn, with the slightest rainfall the sound of looms permeates the villages once again and the peasants take their cloth [to market] to exchange it for rice to eat. Although there are sometimes bad crop years in Wuxi, as long as other places have good cotton harvests, our peasants have no great difficulties.[21]

From this passage, we can see that by the middle of the eighteenth century, Wuxi peasants had developed a complex pattern of cotton spinning and weaving, pawning of clothing items made of cotton, selling of cotton cloth, and rice buying. As commercialization proceeded and population grew, it was important for peasant families to develop methods that would adequately support their consumption demands. Although it is difficult to document the process of peasant decision making more precisely for the eighteenth century (I shall be more exact with twentieth-century data), it is likely that household labor was used more effectively by combining cotton spinning and weaving with the regular farming enterprise. Especially important was that the labor of women and children could be tapped and also that spinning and weaving could proceed during the slack agricultural periods of late fall, winter, and early spring. Even though peasants had to use relatively scarce rice resources immediately after the harvest to redeem their winter clothing and to acquire cotton, operating in this fashion must have meant that the income-earning potential of the family as a whole, or, stated another way, the "total product" of their labor, was raised. With an active and growing rice market within the county, the possibility that peasant demand for grain could be satisfied by rice imported from other areas helped to promote this particular pattern of agrarian development.[22]

With this brief sketch of Qing period commercialization in Wuxi, I shall turn now to a discussion of changing conditions in the late nineteenth century. I shall argue that the large-scale undertaking of sericulture by Wuxi peasants at this time should be seen not only as an adaptation to the new market demand for Chinese raw silk but also as a continuation of seeking ways of supplementing household income through diversification.

Sericulture After the Taiping Rebellion

Sericulture became an attractive option for Wuxi peasants around 1870 because of two converging sets of circumstances: the "opening" of Shanghai by the British as a result of the Opium War and subsequent developments in the international silk market, and the chance for peasants to make innovative

[21] Xi-Jin shi xiaolu (A brief record of what is known about Wuxi and Jinkui counties) (Wuxi, 1752), 1:6–7.

[22] Further discussion of these developments in Xi-Jin shi xiaolu , 1:8, refers to the rice purchased by peasant households in Wuxi as kemi , literally "guest rice," meaning that this rice was coming into the county from other areas.


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adaptations in their patterns of subsidiary activity following the devastation of central China in the Taiping period. I shall begin this discussion with the international situation.

In the 1840s, Shanghai became a port open to foreign trade, and an international community of foreign businessmen and Chinese merchants began to congregate there. Among the foreigners were men interested in promoting the development of machine-based spinning of silk yarn and its export to expanding silk markets in both Europe and the United States. The first modern steam-operated silk filatures, plants where this yarn was processed, were built in Shanghai in the early 1860s, with capital and equipment provided by European investors.[23] A chronic problem plagued these early filatures, however. A marketing network to secure cocoons for filatures had not yet been established in rural areas adjacent to Shanghai. Moreover, silk filatures faced an uphill battle in establishing such a network, because they were thrown into direct competition for cocoons with well-entrenched production and marketing networks for handicraft silk products. For lack of adequate cocoon supplies, all of Shanghai's early filatures had great difficulties remaining open.[24]

Meanwhile, just to the west of Shanghai, a domestic war of great intensity was building. In 1861, the Taiping rebels made their way to Nanjing, and a four-year period of intense warfare within the immediate environs ensued. The consequences of this war for many rural areas were devastating. Peasants and landlords alike fled the region, while many tens of thousands who stayed behind died as a result of the fighting. After the Taiping defeat in 1865, the area was badly in need of resettlement and restoration to its full productive capacity.[25]

Because of these converging circumstances, the time was now ripe for peasants in Wuxi to act. As the Taiping period ended and peasants migrated to the area, they converted portions of former paddy land to mulberry fields and began to raise cocoons each spring for sale to newly developing Shanghai filatures. Although data for this period are insufficient to determine the precise start-up costs of these activities, we do know that members of elite society in Wuxi often encouraged this switch to sericulture. In some cases, gentry

[23] Yin Liangying, Zhongguo canye shi (A history of Chinese sericulture) (Nanjing, 1931), p. 12; E-tu Zen Sun, "Sericulture and Silk Textile Production in Ch'ing [Qing] China," in W. E. Willmott, ed., Economic Organization in Chinese Society (Stanford, 1972), pp. 103–4; Lillian M. Li, China's Silk Trade: Traditional Industry in the Modern World (Cambridge, 1981), p. 164.

[24] Yin Liangying, Zhongguo canye shi , p. 12; Wuxi Difangzhi Bianji Weiyuanhui, ed., Wuxi gushixuan , p. 41. For an extended discussion of the competition for cocoons between producers of handicraft silk and the new steam-powered filatures in Shanghai, see Lynda S. Bell, "Merchants, Peasants, and the State: The Organization and Politics of Chinese Silk Production, Wuxi County, 1870–1937" (Los Angeles, 1985), chap. 2.

[25] Li Wenzhi, "Lun Qingdai houqi Jiang-Zhe-Wan sansheng yuan Taiping Tianguo zhanlingqu tudi guanxi de bianhua," Lishi yanjiu 6 (Dec. 15, 1981): 82–86.


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members bore the initial costs of mulberry purchase and early dissemination of sericulture technique. Undoubtedly, they did this in the hope that they could attract new tenants to their land with the promise of substantial cash earnings.[26] From this point onward, sericulture replaced cotton cloth production as the primary subsidiary activity of peasant households, and Wuxi began to develop as the cocoon-marketing capital of the central China region.[27]

Although ecologically it was possible to raise both mulberries and cocoons in Wuxi, it also became clear as sericulture developed that there were relatively higher risks there than in other locales. Spring weather in Wuxi was not quite as warm as in older sericulture areas closer to the coast and further south, and so Wuxi peasants often experienced seasons in which they lost their silkworm crops entirely to the vagaries of rapidly changing humidity, cool snaps, and the subsequent growth of incurable bacterial infections.[28] Moreover, sericulture in Wuxi was tied directly and solely to the international market for machine-spun raw silk. None of its cocoons entered into the domestic handicraft market, making Wuxi peasants especially vulnerable to drops in the international price of raw silk and the filature closings that inevitably resulted.[29] A kind of boom-bust atmosphere prevailed within the Wuxi cocoon market, and the risks associated with cocoon production in Wuxi were likewise relatively high.[30]

[26] Yan Jinqing, ed., Yan Lianfang yigao (Wuxi, 1923), 10:9, Gao Jingyue and Yan Xuexi, "Wuxi zuizao de sangyuan,"' Wuxi xianbao (Aug. 20, 1980): 4; Zhang Kai, "Mantan lishishang Jiangsu de canye," Canye keji 3 (Oct. 1979): 53; Li, China's Silk Trade , pp. 131–38.

[27] On cocoon marketing in Wuxi, see Bell, "Merchants, Peasants, and the State," chap. 3.

[28] "Sican zhaiyao," Jiangnan shangwu bao 9 (Apr. 21, 1900), commercial raw materials section, p. 2; and Dierlishi Dang'anguan, file no. 3504, "Liangnianlai bensheng [Zhejiang] canzhong zhizao ji qudi jingguo gaikuang, Minguo ershinian—Minguo ershiernian," p. 4; Sun Guoqiao, "Wuxi zhidao yu yangcan shiye zhi yaodian," Nongye zhoubao 1, no. 25 (Oct. 16, 1931): 985; "Wuxi jianshi jianse," Minguo ribao , May 21, 1916, sec. 3:10; "Canxun yin yushui guoduo shousun," Minguo ribao , May 11, 1921, sec. 3:11; "Wuxi chunjian shoucheng jianse," Minguo ribao , June 5, 1921, sec. 2:8.

[29] The link between the Wuxi cocoon market and the central China filature industry is treated at length in Bell, "Merchants, Peasants, and the State," chaps. 1 and 3. For the problem of periodic filature closings, see Minami Manshu Tetsudo Kabushiki Kaisha, Shanhai Jimusho Chosashitsu (hereafter SMR, Shanghai Office), Mushaku kogyo jittai chosa hokokusho (Shanghai, 1940), pp. 85–87, 94; Yinhang zhoubao 13, no. 34 (Sept. 3, 1929), weekly commerce section, pp. 2–3, and 13, no. 37 (Sept. 24, 1929), weekly commerce section, p. 4; Gongshang banyuekan 2, no. 17 (Sept. 1, 1930), legislation section, pp. 4–6; and Shenbao , Jan. 19, 1937, sec. 4:14. For filature closings in Wuxi during the depression, see Gongshang banyuekan 2, no. 3 (Feb. 1, 1930), commercial news section, p. 16, and 4, no. 19 (Oct. 1, 1932), national economy section, pp. 1–2; He Bingxian, "Minguo ershiyinian Zhongguo gongshangye de huigu," Gongshang banyuekan 5, no. 1 (Jan. 1, 1933), articles section, p. 18; and Chushi Kensetsu Shiryo Seibi Jimusho, ed., Mushaku kogyo jijo (Shanghai, 1941), p. 40.

[30] This aspect of the modern silk industry in central China is often referred to in the literature as its speculative nature. See, for example, Wuxi Difangzhi Bianji Weiyuanhui, ed. Wuxi gushixuan , pp. 45–46; Chen Tingfang, "Juyou fengjian de maiban xingzhi de Zhongguo saosiye," in Chen Zhen, et al., eds., Zhongguo jindai gongyeshi ziliao (Beijing, 1959–61), 4:113; Zhuang Yaohe, interview with author, Wuxi, May 27, 1980; Chushi Kensetsu Shiryo Seibi Jimusho, ed., pp. 39–43; and SMR, Shanghai Office, Mushaku kogyo jittai chosa hokokusho , pp. 85–87.


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Despite the riskiness of the endeavor, once Wuxi peasants plunged into sericulture, they persisted with great tenacity. Throughout the decades of the late nineteenth and early twentieth centuries, the proportion of land devoted to mulberries climbed steadily in Wuxi, to reach peaks of 20–30 percent in various locales by the early 1920s.[31] And yet in many ways, such a decision on the part of Wuxi peasants remained something of an anomaly. Why did they continue to engage in sericulture, even after it became obvious that serious uncertainties existed both in weather and marketing conditions? Were the profits they reaped in good years enough to offset the possibility that, in bad years, they would lose their cocoon crop entirely? Or were there some other, even more compelling reasons for Wuxi peasants to persist in sericulture?

In the remainder of this chapter, I shall try to answer these questions using detailed survey materials on twentieth-century Wuxi agriculture. As I introduce the world of peasant-household decision making, I shall attempt to relate this discussion to the earlier trends I have described as evolving in Wuxi since the eighteenth century—a high level of commercialization, dense population, and the consequent need for peasants to develop strategies to supplement their income-earning potential through subsidiary household activity.

Land and Labor Allocation in three Wuxi Villages

Three villages in Wuxi were surveyed in 1940 by the Shanghai Office of the Japanese South Manchurian Railway Company (SMR), a research organization active in areas of China then occupied by the Japanese government.[32] These villages lay within a one kilometer radius of the market town of Rongxiang zhen , approximately eight kilometers from the west gate of Wuxi City.[33] This was a highly commercialized and densely populated area, yet the large majority of households still depended primarily upon rice/wheat agriculture and household-based subsidiary activities, with sericulture the

[31] Lu Guanying, "Jiangsu Wuxixian ershinianlai zhi siye guan," Nongshang gongbao 8, no. 1 (Aug. 15, 1921), articles and translations section, p. 45; Gongshang banyuekan 2, no. 15 (Aug. 1, 1930), investigation section, p. 3; and SMR, Shanghai Office, Kososho Mushakuken noson jittai chosa hokokusho (Shanghai, 1941), p. 11.

[32] SMR, Shanghai Office, Kososho Mushakuken . For a broader discussion of the research activities of the South Manchurian Railway Company in China, see Philip C. C. Huang, The Peasant Economy and Social Change in North China (Stanford, 1985).

[33] SMR, Shanghai Office, Kososho Mushakuken , pp. 14–15.


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most important among these, for the bulk of their yearly incomes. Of the 80 households in these villages, 75 engaged in farming; they constituted the sample with which the Japanese researchers worked.[34]

Although there is some cause for concern as to whether or not the conditions in these villages still reflected economic patterns as they had evolved before the effects of the depression and the Japanese occupation, the researchers themselves were highly sensitive to this issue. They explored available secondary sources on the percentage of land devoted to mulberries before the depression and found estimates ranging from one fifth to one third for various locales.[35] In the survey villages in 1940, 22.5 percent of arable land was devoted to mulberries, a finding within the range of predepression figures.[36] Even though mulberry acreage declined during the years when the depression affected silk prices most severely, that is, from 1930 to 1932, by 1940 peasants were restoring mulberry fields to their former position within the agrarian cropping regime.[37] On the issue of prices for agricultural goods, the researchers reported not only 1939 data but also 1933 data collected in Wuxi by the Nationalist government in an effort to determine effects of inflationary trends caused by the Japanese occupation.[38]

As elsewhere in Wuxi, most families in the Japanese-surveyed villages combined rice/wheat farming with mulberry cultivation and cocoon rearing. Rice was grown once yearly during the summer months. Then, after the fall harvest, peasants pumped their rice paddies dry and planted winter wheat on all or some of the land. Mulberries were planted in "field fashion" in Wuxi. Trees were not grown on embankments between rice fields, as in the older sericulture districts to the south of Wuxi, in Jiangsu and Zhejiang. Rather, a portion of arable land that could have been used for grain cultivation was used instead for mulberry trees. While the trees could be pruned and fertilized in off-peak periods of late fall, winter, and early spring, the first monthlong busy period for mulberries, during which the trees were stripped of their leaves for silkworm feeding, came from late April to late May, coinciding precisely with rice seedling preparation. In June, cocoons were marketed, wheat harvested, and rice seedlings transplanted, giving the peasants no time to recover from their extremely busy monthlong period of silkworm feeding and cocoon raising. Peasants raised a second cocoon crop in summer or, ideally, in early fall, when the mulberry regrowth was fuller. Fall crops

[34] Ibid., pp. 25–26.

[35] Ibid., p. 11.

[36] Ibid., table 1, following the text.

[37] Ibid., pp. 9–11, 18–19. Figures reported by the SMR research group indicate that about 378,000 mu were devoted to mulberry in 1927, 30 percent of all cultivated land in Wuxi. This figure fell steadily until 1932, when only 84,000 mu were cropped to mulberries. By the late 1930s, mulberry land was up again to 240,000 mu .

[38] Ibid., pp. 9–10. I shall also say more about the reconstruction of prices in note 43 below.


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first became possible in Wuxi in the late 1910s, with the introduction of refrigeration and delayed incubation of silkworm eggs. The fall round of silkworm raising usually came in late August and ended in mid-September, long before the late October rice harvest. Since late summer/early fall was a less busy time for grain cultivation, this round of cocoon raising was not nearly as taxing for peasant families as in the spring.[39]

To relate data from the Japanese survey to the previous discussion of peasant rationality, let me pose the question I am most interested in exploring: Precisely what did peasant families gain by engaging in this particular work regime? When considering this issue, I found it useful to construct Table 7.1, comparing income, production costs, and labor usage for rice/wheat farming and mulberry culivation combined with silkworm raising.[40] The main point I wish to make with the comparison is that mulberry cultivation with silkworm raising brought slightly higher returns per mu than rice/wheat farming (9.25 yuan for rice/wheat versus 11.96 yuan for coccons) but fewer returns per labor day (0.27 yuan for rice/wheat versus 0.19 yuan for cocoons). Moreover, earnings from wage labor in agriculture were also higher, averaging 0.25 yuan per labor day.[41] We see a situation, therefore, much like that observed by economists elsewhere—peasants who worked at certain tasks, in this case those of sericulture, for less than optimal returns to labor.[42] Should we conclude from this finding that peasants were "irra-

[39] Ibid., pp. 55, 61, 69–70, 72, 74.

[40] Table 7.1 originally appeared in Bell, "Merchants, Peasants, and the State," p. 122; most of the data are derived from the SMR survey report. For a full accounting of all the calculations, see "Merchants, Peasants, and the State," pp. 122–24. Philip C. C. Huang (Peasant Family and Rural Development , p. 127) has argued that my original calculation for labor days spent in sericulture in this table was too low because I failed to take into account a sufficient number of days for pruning the trees and also the days in the growing cycle of silkworms when they rested. He then adds 28 days to my original estimate of 52 days to come up with a total of 80 days for mulberry cultivation and silkworm raising. I agree with Huang that the days silkworms rested (an additional 11 days for the spring and fall cycles combined) should be added to the total and I have done this in my calculations here. However, I am less sanguine about his decision to add an additional 17 days for pruning the trees. Villagers in neither survey discussed here (for more on the second survey, see the next section) said that they spent a total of 30 days yearly (Huang's estimate) working the mulberry fields; by contrast, they regularly gave a number in the range of five to thirteen days (see Table 7.1 and Table 7.5, labor for mulberry cultivation). What the villagers did say very often when questioned about their work in sericulture is that they spent 30 days in the spring season raising silkworms, an estimate which I interpret to have included all the work involved, including the stripping (or "pruning") of the trees to get leaves to feed to their silkworms. What seems to have happened in Huang's method of calculating, therefore, is that he counts the days during the silkworm raising cycles when leaves were stripped from the trees as labor days for both sericulture and work in the mulberry fields, to get a total of 80 days rather than my estimate of 63.

[41] The figure of 0.25 yuan for day labor in agriculture is the average found for central China's rice/wheat region by John Lossing Buck's 1929–33 survey of agricultural conditions throughout China. It is cited in SMR, Shanghai Office, Kososho Mushakuken , p. 93.

[42] Chayanov, Peasant Farm Organization .


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TABLE 7.1 Annual Income, Production Costs, and Labor Usage per Mu for Rice/Wheat Cultivation and Silkworm Raising in Wuxi County, Jiangsu, 1939

Summer Rice/ Winter Wheat

Spring/Fall Silkworm Raising

Income (yuan )

     

Rice (1.48 shi × 7.00 yuan )

10.36

Spring silkworms (0.27 dan × 42.00 yuan )

11.34

Wheat (0.53 shi × 4.50 yuan )

2.39

Fall silkworms (0.11 dan × 42.00 yuan )

4.62

Total

12.75

Total

15.96

Production costs (yuan )

     

Fertilizer

2.00

Fertilizer

2.80

Irrigation

1.50

Irrigation

0.00

   

Silkworm egg cards

1.20

Total

3.50

Total

4.00

Net income (yuan )

     

Income minus production costs

9.25

Income minus production costs

11.96

Labor (days )

     

Rice

20

Mulberry cultivation

13

Wheat

14

Spring silkworm feeding

30

   

Fall silkworm feeding

20

Total

34

Total

63

Net income (yuan ) per labor day

0.27

 

0.19

NOTE: All labor day values are rounded to the nearest whole number.

tional"? I would argue that we should not, and at this point, take into careful account the problems of population density and scarcity of other options for earning cash income to understand why peasant families in Wuxi undertook sericulture.[43]

[43] In order to dispel doubts that price figures in Table 7.1 may have been atypical and hence relevant only for the postdepression period in Wuxi, I have also computed price figures for a sample of 146 households in Wuxi in 1928 and 1929, selected from the Guoli Zhongyang Yangiuyuan survey. This second survey is explained more fully in the next section. Prices for rice, wheat, and cocoons were all slightly higher in 1928 and 1929 than in 1939, but the crucial comparison of earnings per labor day between rice/wheat farming and cocoon rearing remains valid. In fact, it tips even more dramatically in favor of rice/wheat farming producing higher returns to labor, with the average being 0.71 yuan as opposed to 0.28 yuan for cocoons. The even larger margin arises primarily because wheat prices were higher by about 55 percent in 1928–29 than in the postdepression period, while the prices for rice and cocoons were higher by only about 20 percent. Another point worth noting concerning calculations in Table 7.1 is that I have purposely excluded land price as a production cost, because price figures for rice/wheat land and mulberry land were nearly identical. I have confirmed this fact via an analysis of variance test on land prices from the Guoli Zhongyang Yanjiuyuan survey of 1929, which showed no significant difference between prices for the two types of land. Finally, interplanting mulberry with other crops, a strategy that might have lowered land costs for mulberry culture by raising total yield, was rare in Wuxi because local farmers planted their mulberry trees close together, leaving no room for other crops. This is substantiated by the Guoli Zhongyang Yanjiuyuan survey, which demonstrates that other crops such as peas, beans, and potatoes were not grown with mulberries, but rather were raised on small supplemental plots.