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Dispersion in Dispersion: Measuring Establishment-Level Differences in Productivity
April 2018
Working Paper Number:
CES-18-25RR
We describe new experimental productivity statistics, Dispersion Statistics on Productivity (DiSP), jointly developed and published by the Bureau of Labor Statistics (BLS) and the Census Bureau. Productivity measures are critical for understanding economic performance. Official BLS productivity statistics, which are available for major sectors and detailed industries, provide information on the sources of aggregate productivity growth. A large body of research shows that within-industry variation in productivity provides important insights into productivity dynamics. This research reveals large and persistent productivity differences across businesses even within narrowly defined industries. These differences vary across industries and over time and are related to productivity-enhancing reallocation. Dispersion in productivity across businesses can provide information about the nature of competition and frictions within sectors, and about the sources of rising wage inequality across businesses. Because there were no official statistics providing this level of detail, BLS and the Census Bureau partnered to create measures of within-industry productivity dispersion. These measures complement official BLS aggregate and industry-level productivity growth statistics and thereby improve our understanding of the rich productivity dynamics in the U.S. economy. The underlying microdata for these measures are available for use by qualified researchers on approved projects in the Federal Statistical Research Data Center (FSRDC) network. These new statistics confirm the presence of large productivity differences and we hope that these new data products will encourage further research into understanding these differences.
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Innovation, Productivity Dispersion, and Productivity Growth
February 2018
Working Paper Number:
CES-18-08
We examine whether underlying industry innovation dynamics are an important driver of the large dispersion in productivity across firms within narrowly defined sectors. Our hypothesis is that periods of rapid innovation are accompanied by high rates of entry, significant experimentation and, in turn, a high degree of productivity dispersion. Following this experimentation phase, successful innovators and adopters grow while unsuccessful innovators contract and exit yielding productivity growth. We examine the dynamic relationship between entry, productivity dispersion, and productivity growth using a new comprehensive firm-level dataset for the U.S. We find a surge of entry within an industry yields an immediate increase in productivity dispersion and a lagged increase in productivity growth. These patterns are more pronounced for the High Tech sector where we expect there to be more innovative activities. These patterns change over time suggesting other forces are at work during the post-2000 slowdown in aggregate productivity.
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High Growth Young Firms: Contribution to Job, Output and Productivity Growth
February 2017
Working Paper Number:
carra-2017-03
Recent research shows that the job creating prowess of small firms in the U.S. is better attributed to startups and young firms that are small. But most startups and young firms either fail or don't create jobs. A small proportion of young firms grow rapidly and they account for the long lasting contribution of startups to job growth. High growth firms are not well understood in terms of either theory or evidence. Although the evidence of their role in job creation is mounting, little is known about their life cycle dynamics, or their contribution to other key outcomes such as real output growth and productivity. In this paper, we enhance the Longitudinal Business Database with gross output (real revenue) measures. We find that the patterns for high output growth firms largely mimic those for high employment growth firms. High growth output firms are disproportionately young and make disproportionate contributions to output and productivity growth. The share of activity accounted for by high growth output and employment firms varies substantially across industries - in the post 2000 period the share of activity accounted for by high growth firms is significantly higher in the High Tech and Energy related industries. A firm in a small business intensive industry is less likely to be a high output growth firm but small business intensive industries don't have significantly smaller shares of either employment or output activity accounted for by high growth firms.
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Who Moves Up the Job Ladder?*
January 2017
Working Paper Number:
CES-17-63
In this paper, we use linked employer-employee data to study the reallocation of heterogeneous workers between heterogeneous firms. We build on recent evidence of a cyclical job ladder that reallocates workers from low productivity to high productivity firms through job-to-job moves. In this paper we turn to the question of who moves up this job ladder, and the implications for worker sorting across firms. Not surprisingly, we find that job-to-job moves reallocate younger workers disproportionately from less productive to more productive firms. More surprisingly, especially in the context of the recent literature on assortative matching with on-the-job search, we find that job-to-
job moves disproportionately reallocate less-educated workers up the job ladder. This finding holds even though we find that more educated workers are more likely to work with more productive firms. We find that while highly educated workers are less likely to match to low productivity firms, they are also less likely to separate from them, with less-educated workers both more likely to separate to a better employer in expansions and to be shaken off the ladder (separate to nonemployment) in contractions. Our findings underscore the cyclical role job-to-job moves play in matching workers to
better paying employers.
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Macro and Micro Dynamics of Productivity: From Devilish Details to Insights
January 2017
Working Paper Number:
CES-17-41R
Researchers use a variety of methods to estimate total factor productivity (TFP) at the firm level and, while these may seem broadly equivalent, how the resulting measures relate to the TFP concept in theoretical models depends on the assumptions about the environment in which firms operate. Interpreting these measures and drawing insights based upon their characteristics thus must take into account these conceptual differences. Absent data on prices and quantities, most methods yield 'revenue productivity' measures. We focus on two broad classes of revenue productivity measures in our examination of the relationship between measured and conceptual TFP (TFPQ). The first measure has been increasingly used as a measure of idiosyncratic distortions and to assess the degree of misallocation. The second measure is, under standard assumptions, a function of funda-
mentals (e.g., TFPQ). Using plant-level U.S. manufacturing data, we find these alternative
measures are (i) highly correlated; (ii) exhibit similar dispersion; and (iii) have similar relationships with growth and survival. These findings raise questions about interpreting the first measure as a measure of idiosyncratic distortions. We also explore the sensitivity of estimates of the contribution of reallocation to aggregate productivity growth to these alternative approaches. We use recently developed structural decompositions of aggregate productivity growth that depend critically on estimates of output versus revenue elasticities. We find alternative approaches all yield a significant contribution of reallocation to
productivity growth (although the quantitative contribution varies across approaches).
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Declining Dynamism, Allocative Efficiency, and the Productivity Slowdown
January 2017
Working Paper Number:
CES-17-17
A large literature documents declining measures of business dynamism including high-growth young firm activity and job reallocation. A distinct literature describes a slowdown in the pace of aggregate labor productivity growth. We relate these patterns by studying changes in productivity growth from the late 1990s to the mid 2000s using firm-level data. We find that diminished allocative efficiency gains can account for the productivity slowdown in a manner that interacts with the within firm productivity growth distribution. The evidence suggests that the decline in dynamism is reason for concern and sheds light on debates about the causes of slowing productivity growth.
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High Growth Young Firms: Contribution to Job, Output and Productivity Growth
January 2016
Working Paper Number:
CES-16-49
Recent research shows that the job creating prowess of small firms in the U.S. is better attributed to startups and young firms that are small. But most startups and young firms either fail or don't create jobs. A small proportion of young firms grow rapidly and they account for the long lasting contribution of startups to job growth. High growth firms are not well understood in terms of either theory or evidence. Although the evidence of their role in job creation is mounting, little is known about their life cycle dynamics, or their contribution to other key outcomes such as real output growth and productivity. In this paper, we enhance the Longitudinal Business Database with gross output (real revenue) measures. We find that the patterns for high output growth firms largely mimic those for high employment growth firms. High growth output firms are disproportionately young and make disproportionate contributions to output and productivity growth. The share of activity accounted for by high growth output and employment firms varies substantially across industries ' in the post 2000 period the share of activity accounted for by high growth firms is significantly higher in the High Tech and Energy related industries. A firm in a small business intensive industry is less likely to be a high output growth firm but small business intensive industries don't have significantly smaller shares of either employment or output activity accounted for by high growth firms.
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The Consequences of Long Term Unemployment:
Evidence from Matched Employer-Employee Data*
January 2016
Working Paper Number:
CES-16-40
It is well known that the long-term unemployed fare worse in the labor market than the short-term unemployed, but less clear why this is so. One potential explanation is that the long-term unemployed are 'bad apples' who had poorer prospects from the outset of their spells (heterogeneity). Another is that their bad outcomes are a consequence of the extended unemployment they have experienced (state dependence). We use Current Population Survey (CPS) data on unemployed individuals linked to wage records for the same people to distinguish between these competing explanations. For each person in our sample, we have wage record data that cover the period from 20 quarters before to 11 quarters after the quarter in which the person is observed in the CPS. This gives us rich information about prior and subsequent work histories not available to previous researchers that we use to control for individual heterogeneity that might be affecting subsequent labor market outcomes. Even with these controls in place, we find that unemployment duration has a strongly negative effect on the likelihood of subsequent employment. This finding is inconsistent with the heterogeneity ('bad apple') explanation for why the long-term unemployed fare worse than the short-term unemployed. We also find that longer unemployment durations are associated with lower subsequent earnings, though this is mainly attributable to the long-term unemployed having a lower likelihood of subsequent employment rather than to their having lower earnings once a job is found.
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Where Has All the Skewness Gone? The Decline in High-Growth (Young) Firms in the U.S.
November 2015
Working Paper Number:
CES-15-43
The pace of business dynamism and entrepreneurship in the U.S. has declined over recent decades. We show that the character of that decline changed around 2000. Since 2000 the decline in dynamism and entrepreneurship has been accompanied by a decline in high-growth young firms. Prior research has shown that the sustained contribution of business startups to job creation stems from a relatively small fraction of high-growth young firms. The presence of these high-growth young firms contributes to a highly (positively) skewed firm growth rate distribution. In 1999, a firm at the 90th percentile of the employment growth rate distribution grew about 31 percent faster than the median firm. Moreover, the 90-50 differential was 16 percent larger than the 50-10 differential reflecting the positive skewness of the employment growth rate distribution. We show that the shape of the firm employment growth distribution changes substantially in the post-2000 period. By 2007, the 90-50 differential was only 4 percent larger than the 50-10, and it continued to exhibit a trend decline through 2011. The reflects a sharp drop in the 90th percentile of the growth rate distribution accounted for by the declining share of young firms and the declining propensity for young firms to be high-growth firms.
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Cyclical Reallocation of Workers Across Employers by Firm Size and Firm Wage
June 2015
Working Paper Number:
CES-15-13
Do the job-to-job moves of workers contribute to the cyclicality of employment growth at different types of firms? In this paper, we use linked employer-employee data to provide direct evidence on the role of job-to-job flows in job reallocation in the U.S. economy. To guide our analysis, we look to the theoretical literature on on-the-job search, which predicts that job-to-job flows should reallocate workers from small to large firms. While this prediction is not supported by the data, we do find that job-to-job moves generally reallocate workers from lower paying to higher paying firms, and this reallocation of workers is highly procyclical. During the Great Recession, this firm wage job ladder collapsed, with net worker reallocation to higher wage firms falling to zero. We also find that differential responses of net hires from non-employment play an important role in the patterns of the cyclicality of employment dynamics across firms classified by size and wage. For example, we find that small and low wage firms experience greater reductions in net hires from non-employment during periods of economic contractions.
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