What Determines Productivity?
Economists have shown that large and persistent differences in productivity levels across businesses are ubiquitous. This nding has shaped research agendas in a number of elds, including (but not limited to) macroeconomics, industrial organization, labor, and trade. This paper surveys and evaluates recent empirical work addressing the question of why businesses differ in their measured productivity levels. The causes are manifold, and differ depending on the particular setting. They include elements sourced in production practices—and therefore over which producers have some direct control, at least in theory—as well as from producers’ external operating environments. After evaluating the current state of knowledge, I lay out what I see are the major questions that research in the area should address going forward. ( JEL D24, G31, L11, M10, O30, O47)
1. Introduction hanks to the massive infusion of detailed production activity data into economic study over the past couple of decades, researchers in many elds have learned a great deal about how rms turn inputs into outputs. Productivity, the ef ciency with which this conversion occurs, has been a topic of particular interest. The particulars of these studies have varied depending on the researchers’ speci c interests, but there is a common thread. They have documented, virtually without exception, enormous and * University of Chicago and National Bureau of Economic Research. I thank Eric Bartelsman, Nick Bloom, Roger Gordon, John Haltiwanger, Chang-Tai Hsieh, Ariel Pakes, Amil Petrin, John Van Reenen, and anonymous referees for helpful comments. This work is supported by the NSF (SES-0519062 and SES-0820307), and both the Stigler Center and the Centel Foundation/Robert P. Reuss Faculty Research Fund at the University of Chicago Booth School of Business.
persistent measured productivity differences across producers, even within narrowly de ned industries. The magnitudes involved are striking. Chad Syverson (2004b) nds that within fourdigit SIC industries in the U.S. manufacturing sector, the average difference in logged total factor productivity (TFP) between an industry’s 90th and 10th percentile plants is 0.651. This corresponds to a TFP ratio of e0.651 = 1.92. To emphasize just what this number implies, it says that the plant at the 90th percentile of the productivity distribution makes almost twice as much output with the same measured inputs as the 10th percentile plant. Note that this is the average 90–10 range. The range’s standard deviation across four-digit industries is 0.173, so several industries see much larger productivity differences among their producers. U.S. manufacturing is not exceptional in terms of productivity dispersion. Indeed, if anything, 326
Syverson: What Determines Productivity? it is small relative to the productivity variation observed elsewhere. Chang-Tai Hsieh and Peter J. Klenow (2009), for example, nd even larger productivity differences in China and India, with average 90–10 TFP ratios over 5:1.1 These productivity differences across producers are not eeting, either. Regressing a producer’s current TFP on its one-yearlagged TFP yields autoregressive coef cients on the order of 0.6 to 0.8 (see, e.g., Árpád Ábrahám and Kirk White 2006 and Foster, Haltiwanger, and Syverson 2008). Put simply, some producers seem to have gured out their business (or at least are on their way), while others are woefully lacking. Far more than bragging rights are at stake here: another robust nding in the literature—virtually invariant to country, time period, or industry—is that higher productivity producers are more likely to survive than their less ef cient industry competitors. Productivity is quite literally a matter of survival for businesses. 1.1 How Micro-Level Productivity Variation and...