In principle, the Longitudinal Research Database ( LRD ) which links the establishments in the Annual Survey of Manufactures (ASM) is ideal for examining the dynamics of firm and aggregate behavior. However, the published ASM aggregates are not simply the appropriately weighted sums of establishment data in the LRD . Instead, the published data equal the sum of LRD-based sample estimates and nonsample estimates. The latter reflect adjustments related to sampling error and the imputation of small-establishment data. Differences between the LRD and the ASM raise questions for users of both data sets. For ASM users, time-series variation in the difference indicates potential problems in consistently and reliably estimating the nonsample portion of the ASM. For LRD users, potential sample selection problems arise due to the systematic exclusion of data from small establishments. Microeconomic studies based on the LRD can yield misleading inferences to the extent that small establishments behave differently. Similarly, new economic aggregates constructed from the LRD can yield incorrect estimates of levels and growth rates. This paper documents cross-sectional and time-series differences between ASM and LRD estimates of levels and growth rates of total employment, and compares them with employment estimates provided by Bureau of Labor Statistics and County Business Patterns data. In addition, this paper explores potential adjustments to economic aggregates constructed from the LRD. In particular, the paper reports the results of adjusting LRD-based estimates of gross job creation and destruction to be consistent with net job changes implied by the published ASM figures.
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Manufacturing Establishments Reclassified Into New Industries: The Effect Of Survey Design Rules
November 1992
Working Paper Number:
CES-92-14
Establishment reclassification occurs when an establishment classified in one industry in one year is reclassified into another industry in another year. Because of survey design rules at the Census Bureau these reclassifications occur systematically over time, and affect the industry-level time series of output and employment. The evidence shows that reclassified establishments occur most often in two distinct years over the life of a sample panel. Switches are not only numerous in these years, they also contribute significantly to measured industry change in industry output and employment. The problem is that reclassifications are not necessarily processed in the year that they occur. The survey rules restrict most change to certain years. The effect of these rules is evidenced by looking at the variance across industry growth rates which increases greatly in these two years. Whatever the reason for reclassifying an establishment, the way the switches are processed raises the possibility of measurement errors in the industry level statistics. Researchers and policymakers relying upon observations in annual changes in industry statistics should be aware of these systematic discontinuities, discrepancies and potential data distortions.
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Gross Job Creation, Gross Job Destruction and Employment Reallocation
June 1990
Working Paper Number:
CES-90-04
This paper measures the heterogeneity of establishment-level employment changes in the U.S. manufacturing sector over the 1972 to 1986 period. Our empirical work exploits a rich data set with approximately 860,000 annual observations on 160,000 manufacturing establishments to calculate rates of gross job creation, gross job destruction, and their sum, gross job reallocation. The central empirical findings are as follows: (1) Based on March-to-March establishment-level employment changes, gross job reallocation averages more than 20% of employment per year. (2) For the manufacturing sector as a whole, March-to-March gross job reallocation varies over time from 17% to 23% of employment per year. (3) Time variation in gross job reallocation is countercyclic-gross job reallocation rates covary negatively with own-sector and manufacturing net employment growth rates. (4) Virtually all of the time variation in gross job reallocation is accounted for by idiosyncratic effects on the establishment growth rate density. Changes in the shape and location of the growth rate density due to aggregate-year effects and sector-year effects cannot explain the observed variation in gross job reallocation. (5) The part of gross job reallocation attributable to idiosyncratic effects fluctuates countercyclically. Combining (3) ' (5), we conclude that the intensity of shifts in the pattern of employment opportunities across establishments exhibits significant countercyclic variation. In preparing the data for this study, we have greatly benefited from the assistance of Robert Bechtold, Timothy Dunne, Cyr Linonis, James Monahan, Al Nucci and other Census Bureau employees at the Center for Economic Studies. We have also benefited from helpful comments by Katherine Abraham, Martin Baily, Fischer Black, Timothy Dunne, David Lilien, Robert McGuckin, Kevin M. Murphy, Larrty Katz, John Wallis, workshop participants at the University of Maryland, the Resource Mobility Session of the Econometric society (Winter 1988 meetings), an NBER conference on Alternative Explanations of Employment Fluctuations, and the NBER's Economic Fluctuations Program Meeting (Summer 1989). Scott Schuh provided excellent research assistance. We gratefully acknowledge the financial assistance of the National Science Foundation (SES-8721031 and SES-8720931), the Hoover Institution, and the Office of Graduate Studies and Research at the University of Maryland. Davis also thanks the National Science Foundation for it's support through a grant to the National Fellows Program at the Hoover Institution. Most of the research for this paper was conducted while Davis was a National Fellow at the Hoover Institution.
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Who Creates Jobs? Small vs. Large vs. Young
August 2010
Working Paper Number:
CES-10-17
There's been a long, sometimes heated, debate on the role of firm size in employment growth. Despite skepticism in the academic community, the notion that growth is negatively related to firm size remains appealing to policymakers and small business advocates. The widespread and repeated claim from this community is that most new jobs are created by small businesses. Using data from the Census Bureau Business Dynamics Statistics and Longitudinal Business Database, we explore the many issues regarding the role of firm size and growth that have been at the core of this ongoing debate (such as the role of regression to the mean). We find that the relationship between firm size and employment growth is sensitive to these issues. However, our main finding is that once we control for firm age there is no systematic relationship between firm size and growth. Our findings highlight the important role of business startups and young businesses in U.S. job creation. Business startups contribute substantially to both gross and net job creation. In addition, we find an 'up or out' dynamic of young firms. These findings imply that it is critical to control for and understand the role of firm age in explaining U.S. job creation.
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Private Equity and Employment
March 2008
Working Paper Number:
CES-08-07R
Private equity critics claim that leveraged buyouts bring huge job losses. To investigate this claim, we construct and analyze a new dataset that covers U.S. private equity transactions from 1980 to 2005. We track 3,200 target firms and their 150,000 establishments before and after acquisition, comparing outcomes to controls similar in terms of industry, size, age, and prior growth. Relative to controls, employment at target establishments declines 3 percent over two years post buyout and 6 percent over five years. The job losses are concentrated among public-to-private buyouts, and transactions involving firms in the service and retail sectors. But target firms also create more new jobs at new establishments, and they acquire and divest establishments more rapidly. When we consider these additional adjustment margins, net relative job losses at target firms are less than 1 percent of initial employment. In contrast, the sum of gross job creation and destruction at target firms exceeds that of controls by 13 percent of employment over two years. In short, private equity buyouts catalyze the creative destruction process in the labor market, with only a modest net impact on employment. The creative destruction response mainly involves a more rapid reallocation of jobs across establishments within target firms.
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Gross Job Flows for the U.S. Manufacturing Sector: Measurement from the Longitudinal Research Database
December 2006
Working Paper Number:
CES-06-30
Measures of job creation and destruction are now produced regularly by the U.S. statistical agencies. The Bureau of Labor Statistics releases via the Business Employment Dynamics (BED) on a quarterly basis measures of job creation and destruction for the U.S. nonfarm business sector and related disaggregation by industrial sector and size class. The U.S. Census Bureau has developed the Longitudinal Business Database (LBD) covering the nonfarm business sector that has been used to produce research analysis and special tabulations including tabulations of job creation and destruction. Both of these data programs build upon the measurement methods and data analysis of job creation and destruction measures from the Longitudinal Research Database (LRD) developed and published by Davis, Haltiwanger and Schuh (1996). In this paper, the LRD based estimates of job creation and destruction are updated and made available for consistent annual and quarterly series from 1972-1998. While the BED and LBD programs are more comprehensive in scope than the LRD, the extensive development of the LRD permits the construction of measures of job creation and destruction for a rich array of employer characteristics including industry, size, business age, ownership structure, location and wage structure. The updated series that are released with this working paper provide measures along each of these dimensions. The paper describes in detail the changes in the processing of the Annual Survey of Manufactures over the 1972-1998 period that are important to incorporate by users of the LRD at Census Research Data Centers as well as users of products from the LRD such as job creation and destruction.
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Punctuated Entrepreneurship (Among Women)
May 2018
Working Paper Number:
CES-18-26
The gender gap in entrepreneurship may be explained in part by employee non-compete agreements. Exploiting exogenous state-level variation in non-compete policy, I find that women more strictly subject to non-competes are 11-17% more likely to start companies after their employers dissolve. This result is not explained by the incidence of non-competes or lawsuits; however, women face higher relative costs in defending against potential litigation and in returning to paid employment after abandoning their ventures. Thus entrepreneurship among women may be 'punctuated' in that would-be female founders are throttled by non-competes, their potential unleashed only by the failure of their employers.
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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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Micro and Macro Data Integration: The Case of Capital
May 2005
Working Paper Number:
CES-05-02
Micro and macro data integration should be an objective of economic measurement as it is clearly advantageous to have internally consistent measurement at all levels of aggregation ' firm, industry and aggregate. In spite of the apparently compelling arguments, there are few measures of business activity that achieve anything close to micro/macro data internal consistency. The measures of business activity that are arguably the worst on this dimension are capital stocks and flows. In this paper, we document, quantify and analyze the widely different approaches to the measurement of capital from the aggregate (top down) and micro (bottom up) perspectives. We find that recent developments in data collection permit improved integration of the top down and bottom up approaches. We develop a prototype hybrid method that exploits these data to improve micro/macro data internal consistency in a manner that could potentially lead to substantially improved measures of capital stocks and flows at the industry level. We also explore the properties of the micro distribution of investment. In spite of substantial data and associated measurement limitations, we show that the micro distributions of investment exhibit properties that are of interest to both micro and macro analysts of investment behavior. These findings help highlight some of the potential benefits of micro/macro data integration.
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Aggregate Productivity Growth: Lessons From Microeconomic Evidence
September 1998
Working Paper Number:
CES-98-12
In this study we focus on the role of the reallocation of activity across individual producers for aggregate productivity growth. A growing body of empirical analysis yields striking patterns in the behavior of establishment-level reallocation and productivity. Nevertheless, a review of existing studies yields a wide range of findings regarding the contribution of reallocation to aggregate productivity growth. Through our review of existing studies and our own sensitivity analysis, we find that reallocation plays a significant role in the changes in productivity growth at the industry level and that the impact of net entry is disproportionate since entering plants tend to displace less productive exiting plants, even after controlling for overall average growth in productivity. However, an important conclusion of our sensitivity analysis is that the quantitative contribution of reallocation to the aggregate change in productivity is sensitive to the decomposition methodology employed. Our findings also confirm and extend others in the literature that indicate that both learning and selection effects are important in this context. A novel aspect of our analysis is that we have examined the role of reallocation for aggregate productivity growth to a selected set of service sector industries. Our analysis considers the 4-digit industries that form the 3-digit industry automobile repair shops. We found tremendous churning in this industry with extremely large rates of entry and exit. Moreover, we found that productivity growth in the industry is dominated establishment data at Census, the results are quite striking and clearly call for further analysis.
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Explaining Cyclical Movements in Employment: Creative-Destruction or Changes in Utilization?
November 2006
Working Paper Number:
CES-06-25
An important step in understanding why employment fluctuates cyclically is determining the relative importance of cyclical movements in permanent and temporary plant-level employment changes. If movements in permanent employment changes are important, then recessions are times when the destruction of job specific capital picks up and/or investment in new job capital slows. If movements in temporary employment changes are important, then employment fluctuations are related to the temporary movement of workers across activities (e.g. from work to home production or search and back again) as the relative costs/benefits of these activities change. I estimate that in the manufacturing sector temporary employment changes account for approximately 60 percent of the change in employment growth over the cycle. However, if permanent employment changes create and destroy more capital than temporary employment changes, then their economic consequences would be relatively greater. The correlation between gross permanent employment changes and capital intensity across industries supports the hypothesis that permanent employment changes do create and destroy more capital than temporary employment changes.
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