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Statistics on the Small Business Administration's Scale-Up America Program
April 2019
Working Paper Number:
CES-19-11
This paper attempts to quantify the difference in performance, of 'treated' (program participant) and 'non-treated' (non-participant) firms in SBA's Scale-Up initiative. I combine data from the SBA with administrative data housed at Census using a combination of numeric and name and address matching techniques. My results show that after controlling for available observable characteristics, a positive correlation exists between participation in the Scale-Up initiative and firm growth. However, publicly available survey results have shown that entrepreneurs have a variety of goals in-mind when they start their businesses. Two prominent, and potentially contradictory ones are work-life balance and greater income. That means that not all firms may want to grow and I am unable to completely control for owner motivations. Finally, I do not find a statistically significant relationship between participation in Scale-Up and firm survival once other business characteristics are accounted for.
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Fraudulent Financial Reporting and the Consequences for Employees
March 2019
Working Paper Number:
CES-19-12
We examine employment effects, such as wages and employee turnover, before, during, and after periods of fraudulent financial reporting. To analyze these effects, we combine U.S. Census data with SEC enforcement actions against firms with serious misreporting ('fraud'). We find compared to a matched sample that fraud firms' employee wages decline by 9% and the separation rate is higher by 12% during and after fraud periods while employment growth at fraud firms is positive during fraud periods and negative afterward. We discuss several reasons that plausibly drive these findings. (i) Frauds cause informational opacity, misleading employees to still join or continue to work at the firm. (ii) During fraud, managers overinvest in labor changing employee mix, and after fraud the overemployment is unwound causing effects from displacement. (iii) Fraud is misconduct; association with misconduct can affect workers in the labor market. We explore the heterogeneous effects of fraudulent financial reporting, including thin and thick labor markets, bankruptcy and non-bankruptcy firms, worker movements, pre-fraud wage levels, and period of hire. Negative wage effects are prevalent across these sample cuts, indicating that fraudulent financial reporting appears to create meaningful and negative consequences for employees possibly through channels such as labor market disruptions, punishment, and stigma.
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Predictive Analytics and Organizational Architecture:
Plant-Level Evidence from Census Data
January 2019
Working Paper Number:
CES-19-02
We examine trends in the use of predictive analytics for a sample of more than 25,000 manufacturing plants using proprietary data from the US Census Bureau. Comparing 2010 and 2015, we find that use of predictive analytics has increased markedly, with the greatest use in younger plants, professionally-managed firms, more educated workforces, and stable industries. Decisions on data to be gathered originate from headquarters and are associated with less delegation of decision-making and more widespread awareness of quantitative targets among plant employees. Performance targets become more accurate, long-term oriented, and linked to company-wide performance, and management incentives strengthen, both in terms of monetary bonuses and career outcomes. Plants increasing predictive analytics become more efficient, with lower inventory, increased volume of shipments, narrower product mix, reduced management payroll and increased use of flexible and temporary employees. Results are robust to a specification based on increased government demand for data.
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The Management and Organizational Practices Survey (MOPS): Collection and Processing
December 2018
Working Paper Number:
CES-18-51
The U.S. Census Bureau partnered with a team of external researchers to conduct the first-ever large-scale survey of management practices in the United States, the Management and Organizational Practices Survey (MOPS), for reference year 2010. With the help of the research team, the Census Bureau expanded and improved the survey for a second wave for reference year 2015. The MOPS is a supplement to the Annual Survey of Manufacturing (ASM), and so the collection and processing strategy for the MOPS built on the methodology for the ASM, while differing on key dimensions to address the unique nature of management relative to other business data. This paper provides detail on the mail strategy pursued for the MOPS, the collection methods for paper and electronic responses, the processing and estimation procedures, and the official Census Bureau data releases. This detail is useful for all those who have interest in using the MOPS for research purposes, those wishing to understand the MOPS data more deeply, and those with an interest in survey methodology.
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Firm Leverage, Labor Market Size, and Employee Pay
August 2018
Working Paper Number:
CES-18-36
We provide new estimates of the wage costs of firms' debt using an empirical approach that exploits within-firm geographical variation in workers' expected unemployment costs due to variation in local labor market in a large sample of public firms. We find that, following an increase in firm leverage, workers with higher unemployment costs experience higher wage growth relative to workers at the same firm with lower unemployment costs. Overall, our estimates suggest wage costs are an important component in the overall cost of debt, but are not as large as implied by estimates based on ex post employee wage losses due to bankruptcy; we estimate that a 10 percentage point increase in firm leverage increases wage compensation for the median worker by 1.9% and total firm wage costs by 17 basis points of firm value.
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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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Small and Large Firms Over the Business Cycle
February 2018
Working Paper Number:
CES-18-09
Drawing on a new, con dential Census Bureau dataset of financial statements of a representative sample of 80000 manufacturing firms from 1977 to 2014, we provide new evidence on the link between size, cyclicality, and financial frictions. First, we only find evidence of lower cyclicality among the very largest firms (the top 1% by size). Second, due to high and rising concentration of sales and investment, the lower sensitivity of the top 1% firms dominates the behavior of aggregate fluctuations. Third, we show that this differential sensitivity does not appear to be driven by financial frictions. The higher sensitivity of the bottom 99% does not disappear after controlling for measures of financial strength, is not statistically significant after
identified monetary policy shocks, and does not appear in debt financing flows. Evidence from 3-digit industries suggests a non-financial explanation: the largest 1% of firms are less sensitive due to a more diversified customer base.
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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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Total Error and Variability Measures with Integrated Disclosure Limitation for Quarterly Workforce Indicators and LEHD Origin Destination Employment Statistics in On The Map
January 2017
Working Paper Number:
CES-17-71
We report results from the rst comprehensive total quality evaluation of five major indicators in the U.S. Census Bureau's Longitudinal Employer-Household Dynamics (LEHD) Program Quarterly Workforce Indicators (QWI): total employment, beginning-of-quarter employment, full-quarter employment, total payroll, and average monthly earnings of full-quarter employees. Beginning-of-quarter employment is also the main tabulation variable in the LEHD Origin-Destination Employment Statistics (LODES) workplace reports as displayed in OnTheMap (OTM). The evaluation is conducted by generating multiple threads of the edit and imputation models used in the LEHD Infrastructure File System. These threads conform to the Rubin (1987) multiple imputation model, with each thread or implicate being the output of formal probability models that address coverage, edit, and imputation errors. Design-based sampling variability and nite population corrections are also included in the evaluation. We derive special formulas for the Rubin total variability and its components that are consistent with the disclosure avoidance system used for QWI and LODES/OTM workplace reports. These formulas allow us to publish the complete set of detailed total quality measures for QWI and LODES. The analysis reveals that the five publication variables under study are estimated very accurately for tabulations involving at least 10 jobs. Tabulations involving three to nine jobs have quality in the range generally deemed acceptable. Tabulations involving zero, one or two jobs, which are generally suppressed in the QWI and synthesized in LODES, have substantial total variability but their publication in LODES allows the formation of larger custom aggregations, which will in general have the accuracy estimated for tabulations in the QWI based on a similar number of workers.
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