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Declining Business Dynamism

Paper Session

Saturday, Jan. 4, 2020 2:30 PM - 4:30 PM (PDT)

Marriott Marquis, Catalina
Hosted By: Econometric Society
  • Chair: Ufuk Akcigit, University of Chicago

Allocating Misallocation: Decomposing Measures of Aggregate Allocative Efficiency

G. Jacob Blackwood
,
Amherst College
John Haltiwanger
,
University of Maryland
Zoltan Wolf
,
U.S. Census Bureau

Abstract

The pace of output and input reallocation has declined in the U.S. with an acceleration of the decline in the post 2000 period. Since 2000, the decline in the pace of within sector reallocation has been ubiquitous for all broad sectors including manufacturing. The causes and consequences of this decline remain open questions. We explore the implications for productivity that uses a structural decomposition of the contribution of reallocation aggregate productivity growth (APG). The structural decomposition we use builds on that developed by Levinsohn and Petrin (2012) (LP) but extends it to make the incorporation of imperfect competition readily transparent. In the LP decomposition, the contribution of contribution of reallocation depends on the gaps between marginal revenue products and marginal costs of these factors. One of the challenges of implementing this decomposition is that it requires estimation of the revenue, output and demand elasticities. Our empirical strategy relies on semiparametric identification of revenue elasticities. The elasticities of freely variable inputs are identified using the share of respective costs in revenue. This strategy circumvents non-identification arguments raised by previous studies. The elasticities of quasi-fixed inputs are estimated using a proxy to control for unobserved productivity differences. The demand elasticity is estimated within this framework thus permitting also identification of the output elasticity (as the revenue elasticity of an input is the product of output elasticity and the inverse of the markup). The empirical results show that the contribution of reallocation to APG declined between the 1970s and the 2000s in the U.S. manufacturing sector.

What Happened to United States Business Dynamism?

Ufuk Akcigit
,
University of Chicago
Sina Ates
,
Federal Reserve Board

Abstract

In the past several decades, the U.S. economy has witnessed a number of striking trends that indicate a rising market concentration and a slowdown in business dynamism. In this paper, we make an attempt to understand potential common forces behind these empirical regularities through the lens of a micro-founded general equilibrium model of endogenous firm dynamics. Importantly, the theoretical model captures the strategic behavior between competing firms, its effect on their innovation decisions, and the resulting ``best vs. the rest' dynamics. We focus on four potential mechanisms that can potentially drive the observed changes and use the calibrated model to assess the relative importance of these channels. One particular exercise replicates the transitional dynamics of the U.S. economy through joint moves in all four channels and decomposes the contribution of each channel to the resulting trends. Our results highlight the dominant role of a decline in the intensity of knowledge diffusion from the frontier firms to the laggard ones in explaining the observed shifts. We conclude by presenting new evidence that corroborates a declining knowledge diffusion in the economy. We document higher concentration of patenting in the hands of firms with the largest stock and a changing nature of patents, especially in the post-2000 period, which suggests a heavy use of intellectual property protection by market leaders to limit the dissemination of knowledge. These findings present a potential avenue for future research on the drivers of declining knowledge diffusion.

The Productivity J-Curve: How Intangibles Complement General Purpose Technologies

Chad Syverson
,
University of Chicago

Abstract

General purpose technologies (GPTs) such as AI enable and require significant complementary investments, including business process redesign, co-invention of new products and business models, and investments in human capital. These complementary investments are often intangible and poorly measured in the national accounts, even if they create valuable assets for the firm. We develop a model that shows how this leads to an underestimation of output and productivity in the early years of a new GPT, and how later, when the benefits of intangible investments are harvested, productivity will be overestimated. Our model generates a Productivity J-Curve that can explain the productivity slowdowns often accompanying the advent of GPTs, as well as the follow-on increase in productivity later. We use our model to assess how AI-related intangible capital is currently affecting measured total factor productivity (TFP) and output. We also conduct a historical analysis of the roles of intangibles tied to R&D, software, and computer hardware, finding substantial and ongoing effects of software in particular and hardware to a lesser extent.

Slower Productivity and Higher Inequality: Are They Related?

Jason Furman
,
Harvard University
Peter Orszag
,
Brookings Institution

Abstract

Income growth for typical American families has slowed dramatically since 1973. Slower productivity growth and an increase in income inequality have both contributed to this trend. This paper addresses whether there is a relationship between the productivity slowdown and the increase in inequality, specifically exploring the extent to which reduced competition and dynamism can explain both of these phenomena. Productivity growth has been uneven across the economy, with top firms earning increasingly skewed returns. At the same time, the between-firm disparities have been important in explaining the increase in labor income inequality. Both these findings are consistent with the observed reductions in competition, as evidenced by increasing concentration and economic rents, and business dynamism. The authors also explore the scenarios under which government policies can help mitigate, or contribute to, declining competition and dynamism.
JEL Classifications
  • O4 - Economic Growth and Aggregate Productivity
  • L5 - Regulation and Industrial Policy