Chaos Before Order: Productivity Patterns in U.S. Manufacturing

Within-industry productivity dispersion is pervasive and exhibits substantial variation across countries, industries, and time. We build on prior research that explores the hypothesis that periods of innovation are initially associated with a surge in business start-ups, followed by increased experimentation that leads to rising dispersion potentially with declining aggregate productivity growth, and then a shakeout process that results in higher productivity growth and declining productivity dispersion. Using novel detailed industrylevel data on total factor productivity and labour productivity dispersion from the Dispersion Statistics on Productivity dataset along with novel measures of entry rates from the Business Dynamics Statistics and productivity growth data from the Bureau of Labor Statistics for U.S. manufacturing industries, we find support for this hypothesis, especially for the high-tech industries. An increase in entry rates in a two-year period t is associated with an increase in dispersion and decrease in aggregate productivity growth in two-year 1 Cindy Cunningham and Sabrina Pabilonia are Research Economists at the U.S. Bureau of Labor Statistics, Jay Stewart is a Senior Research Economist at the U.S. Bureau of Labor Statistics, Lucia Foster is the Chief Economist and Chief of the Center for Economic Studies at the U.S. Census Bureau, Cheryl Grim is the Assistant Center Chief for Interdisciplinary Research of the Center for Economic Studies at the U.S. Census Bureau, John Haltiwanger is the Dudley and Louisa Dillard Professor of Economics at the University of Maryland and a Schedule A part-time employee at the U.S. Census Bureau, and Zoltan Wolf is a Senior Data Scientist at New Light Technologies. The authors would like to thank two anonymous referees, Lucy Eldridge, Andrew Sharpe, T. Kirk White, and participants at the Sixth World KLEMS Conference in March 2021 for comments. Any opinions and conclusions expressed herein are those of the authors and do not necessarily represent the views of the U.S. Census Bureau or the U.S. Bureau of Labor Statistics. The Census Bureau has reviewed this data product for unauthorized disclosure of confidential information and has approved the disclosure avoidance practices applied to this release (Approval ID: CBDRB-FY21-261). Corresponding author: Pabilonia.Sabrina@bls.gov. 138 NUMBER 41, Fall 2021 period t+1 and a decrease in dispersion and increase in aggregate productivity growth in two-year period t+2. Within-industry productivity dispersion is large and exhibits substantial variation across countries, industries, and time (Bartelsman and Doms, 2000; Syverson, 2011). Many factors have been shown to be related to this dispersion, including frictions and distortions that vary across these same dimensions (e.g., Decker, Haltiwanger, Jarmin, and Miranda, 2020). These frictions and distortions, such as barriers to entry, costs of adjusting factors of production, establishment-specific markups, and regulations preventing the equalization of marginal products, may inhibit productivity-enhancing reallocation. This would suggest that increasing withinindustry dispersion is associated with slower productivity growth. An alternative hypothesis is that periods of rising within-industry dispersion may reflect innovation and experimentation. This hypothesis is based on seminal research by Gort and Klepper (1982) and Jovanovic (1982). These papers hypothesize that periods of innovation are initially associated with a surge in firm entry, followed by increased experimentation that yields rising dispersion potentially with declining aggregate productivity growth and then a shakeout process, where successful businesses grow and unsuccessful ones exit, which eventually results in higher productivity growth and declining productivity dispersion. To explore this latter hypothesis, Foster, Grim, Haltiwanger, and Wolf (2021) looked at the dynamic relationship between entry rates (an indirect measure of innovation), within-industry labour productivity (LP) dispersion, and LP growth using firm-level data for the entire U.S. private sector, where LP is defined as output per job. They find that a surge in firm entry in a four-digit NAICS industry during a three-year period is followed by an increase in within-industry dispersion and a temporary slowdown in industry-level LP growth in the next period. In the subsequent period, there is a fall in dispersion and a rise in LP growth. These relationships are stronger in high-tech industries, where the pace of innovation is presumably faster. In this article, we build on Foster et al. (2021) by exploiting novel, detailed industry-level data on within-industry total factor productivity (TFP) and LP dispersion from the Dispersion Statistics on Productivity (DiSP) data, along with new measures of establishment and firm entry rates from the Business Dynamics Statistics (BDS) data for U.S. manufacturing industries. We combine these data with the official U.S. TFP and LP growth measures 2 The DiSP (developed jointly by BLS and the Census Bureau) is public-use data available at https: //www.bls.gov/lpc/productivity-dispersion.htm and https://www.census. gov/disp. Restricteduse microdata is available for qualified researchers on approved projects in the Federal Statistical Research Data Centers (FSRDCs) (http://www.census.gov/f srdc). The BDS is available at https: //www.census. gov/programs-surveys/bds.html. Industry productivity growth data are available at https://www.bls.gov/lpc/tables_by_sector_and_industry. htm. The public-use data and STATA code to INTERNATIONAL PRODUCTIVITY MONITOR 139 from the Bureau of Labor Statistics (BLS) to examine the relationships between entry, dispersion, and productivity.2. To abstract from business cycle dynamics and to focus on the hypothesis, we examine lowfrequency variation (average annual growth rates over two-year periods) and include industry and period effects. Relative to Foster et al. (2021), a primary contribution of this article is the use of dispersion and growth measures of TFP, which are better metrics for examining the innovation hypothesis. We find support for the hypothesis that innovation is an important driver of withinindustry TFP dispersion and aggregate TFP growth, especially for high-tech industries, using entry rates as a proxy for innovation. A surge in entry in a hightech industry over a two-year period results in an increase in within-industry TFP dispersion in the next two-year period, followed by an increase in TFP growth in the two subsequent two-year periods. We also find evidence that the increase in dispersion in the first two-year period following a surge in entry is accompanied by negative TFP growth. Relatedly, we find evidence of the reverse, declining TFP dispersion and faster TFP growth in the second two-year period. In addition, we find the relationships between entry and TFP dispersion are stronger when we focus on hightech industries. For non-tech industries, we find a small decrease in TFP growth, but with an additional lag and no subsequent increase in the following period. We find broadly similar results for LP measures of dispersion and growth. The article proceeds as follows. In the first main section, we describe the data and present descriptive statistics. The main results are in section two. Concluding remarks are in section three. Data and Descriptive Statistics This article uses detailed industry-level data on productivity growth, establishment and firm entry rates, and establishment level productivity dispersion from three public-use data sources: BLS Industry Productivity Statistics, Business Dynamics Statistics (BDS), and Dispersion Statistics on Productivity (DiSP). In addition, we construct additional dispersion measures from the restricted-use data underlying DiSP.3 Throughout the article, we use industry-level measures for all 86 fourdigit NAICS industries in the manufacturing sector. To mitigate business cycle influences, we construct our measures for non-overlapping two-year periods to examine the longer-term relationships between entry, productivity dispersion growth, and productivity growth. BLS produces the official U.S. measures of LP and TFP growth for four-digit NAICS manufacturing industries (Bureau of Labor Statistics, 2020). The industry LP measures are defined as the ratio of the growth in real sectoral output—the total value of goods and services sold outside replicate the analyses based on the public-use data are available at https://doi.org/10.5281/zenodo.5770628 3 The experimental data product DiSP was first released in September 2019. Industry-level BDS data were first released in September 2020. 140 NUMBER 41, Fall 2021 the four-digit NAICS industry — to the growth in hours worked by all persons in the industry.4 For most industries, real output is derived by deflating sales revenue using industry-level BLS implicit price indexes. Output is also adjusted to remove resales and to account for changes in finished goods and work-in-process inventories. Data for industry output measures are primarily from economic censuses and annual surveys of the U.S. Census Bureau. Data on hours worked come from BLS surveys.5 The industry TFP measures are defined as the ratio of the growth in real sectoral output to the growth in the costweighted combined inputs utilized in producing that output. Inputs include capital, labour hours, energy, materials, and purchased business services. Although the BLS productivity data for detailed industries in the manufacturing sector are available annually beginning in 1987, we restrict our main analyses to growth in productivity and dispersion over the 1997–2017 period, because the DiSP data start in 1997.6 The BLS productivity growth rates exhibit considerable yearover-year variation for many manufacturing industries (see Online Appendix Table A1 for four-digit NAICS industry productivity means and coefficients of