Information Technology and the Productivity Puzzle
The argument in favor of the wholesale adoption of the new information technology (IT) in universities, publishing houses, libraries, and scholarly communication
rests on the hope-indeed the dogma-that IT will substantially raise productivity. It behooves us to take a step back to discuss briefly the general relationship between IT, on the one hand, and economic growth and productivity increases on the other.
There seems to be solid agreement among experts that the trend growth rate of real GDP in the United States has been between 2.0 and 2.5% per annum during the past 20 years, with 2.2% being perhaps the best point estimate. About 1.1% of this increase is accounted for by the growth in the labor force, leaving 1.1% for annual productivity growth. This figure is very unremarkable in light of the miracles that are supposed to have occurred in the past 20 years in IT. Technology communications and information gathering have grown tremendously: for example, some steel factories now have hardly any workers in them, and banking is done by computers. Yet the productivity figures do not seem to reflect these savings. Admittedly, there are measurement problems: to the extent that we overestimate the rate of inflation (and there is some evidence that that we do), we also underestimate the rate of growth of GDP and productivity. It may also be true that our measurement of inflation and hence productivity does not correctly measure the quality improvements caused by IT: perhaps an argument in support of that view is that the worst productivity performance is seen to be in industries in which measurement of output is chronically very difficult (such as in financial intermediaries). But it is difficult to escape the conclusion that IT has not delivered what the hype surrounding it has claimed.
What can we say about the effects of IT in universities, libraries, and publishing houses, or in teaching, research, and administration? Productivity increases are clearly a sine qua non for improvement in the economic situation of universities and libraries, but labor productivity increases are not enough. If every worker in a university produces a greater output than before and the university chooses to produce the greater output without diminishing the number of workers employed (who now need more and more expensive equipment to do their work), its economic situation will not improve. As a minimum, we must secure increases in "total factor productivity"; that is, real output per real composite input ought to rise. But will this improvement be forthcoming? And how do we measure labor or total factor productivity in an institution with highly varied products and inputs, most of which cannot be measured routinely by the piece or by weight or volume? What is the "output contribution" of students being able to write papers with left and right margins aligned and without-thanks to spell checkers-too many spelling errors? What is the output contribution (that is, the contribution to producing "truth" in particle physics) of Ginsparg's preprint server in Los Alamos?
Scott Bennett's important contribution to this volume (chapter 4) gives a specific example of how one might tackle the question of the effect of IT on productivity in teaching and shows that the answer depends very much on the time horizon one has in mind. This conclusion is important (and extremely reasonable). The investments that are necessary to introduce IT in teaching need to be amortized, and that takes time. One need look only as far as the eighteenth and nineteenth centuries to recognize that the inventions that fueled the Industrial Revolu-
tion did not achieve their full effects overnight; on the contrary, it took many decades for the steam engine, railroads, and later, electricity to diffuse throughout the economy. Hence, even the most productive IT breakthroughs in an isolated course will not show up in overall university productivity figures: the total investment in IT technology is too small a fraction of aggregate capital to make much difference.
In the papers by Malcom Getz (chapter 6) and Robert Shirrell (chapter 10), we have examples of the potential impact on research productivity. Getz illustrates why libraries will prefer to buy large packages of electronic journals, and Shirrell stresses that productivity is likely to be higher (and costs lower) over longer horizons. By coincidence, both authors happened to choose as their specific illustration the American Economic Review.
Bennett's, Getz's, and Shirrell's papers, among others, suggest that much could be learned by studying academic productivity more systematically and in more detail. We need to study particular examples of innovation in teaching and to analyze the productivity changes that occur over suitably long horizons and with full awareness that a complete understanding of productivity in teaching must cope with the problem of how to measure whether students have learned faster or better or more. We also need to pay more explicit attention to research productivity, mindful of the possibility that research productivity may mean different things in different disciplines.
But these considerations raise particularly murky questions. When journals are electronic and access to information is much faster and requires less effort, do scholars in the sciences and social sciences write more papers and do humanists write more books? Or do they write the same number of articles and books, but these writings are better than they would have been without the IT aids? What measures do we have for scholarly productivity? Obviously, every self-respecting tenure-and-promotion committee will shudder at the thought that productivity is appropriately measured by the quantity of publications; but how do we measure quality? And what is the relationship between the quality of access to information and the quality of ideas? While we agree with Hal Varian's view (chapter 25) that journals tend to have an agreed-upon pecking order and we find his suggestions for a new model of electronic publishing fascinating and promising, we still believe that in the short run, much could be learned by studying the impact of particular IT advances on scholarly productivity in specific fields.
It is possible that in the short run our views about productivity enhancements from IT in universities, libraries, and publishing houses must be expressions of faith. But unlike previous eras, when inventions and innovations did not always lead to self-conscious and subsequently documented examinations of the productivity effects, we have remarkable opportunities to measure the productivity effects in the discrete applications of IT by universities, libraries, and scholarly presses, and thus provide earlier feedback on the innovation process than would otherwise occur.