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Opening the Black Box: Task and Skill Mix and Productivity Dispersion
September 2022
Authors:
John Haltiwanger,
Lucia Foster,
Cheryl Grim,
Zoltan Wolf,
Cindy Cunningham,
Sabrina Wulff Pabilonia,
Jay Stewart,
Cody Tuttle,
G. Jacob Blackwood,
Matthew Dey,
Rachel Nesbit
Working Paper Number:
CES-22-44
An important gap in most empirical studies of establishment-level productivity is the limited information about workers' characteristics and their tasks. Skill-adjusted labor input measures have been shown to be important for aggregate productivity measurement. Moreover, the theoretical literature on differences in production technologies across businesses increasingly emphasizes the task content of production. Our ultimate objective is to open this black box of tasks and skills at the establishment-level by combining establishment-level data on occupations from the Bureau of Labor Statistics (BLS) with a restricted-access establishment-level productivity dataset created by the BLS-Census Bureau Collaborative Micro-productivity Project. We take a first step toward this objective by exploring the conceptual, specification, and measurement issues to be confronted. We provide suggestive empirical analysis of the relationship between within-industry dispersion in productivity and tasks and skills. We find that within-industry productivity dispersion is strongly positively related to within-industry task/skill dispersion.
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Shareholder Power and the Decline of Labor
May 2022
Working Paper Number:
CES-22-17
Shareholder power in the US grew over recent decades due to a steep rise in concentrated
institutional ownership. Using establishment-level data from the US Census Bureau's Longitudinal Business Database for 1982-2015, this paper examines the impact of increases in concentrated institutional ownership on employment, wages, shareholder returns, and labor productivity. Consistent with theory of the firm based on conflicts of interests between shareholders and stakeholders, we find that establishments of firms that experience an increase in ownership by larger and more concentrated institutional shareholders have lower employment and wages. This result holds in both panel regressions with establishment fixed effects and a difference-in-differences design that exploits large increases in concentrated institutional ownership, and is robust to controls for industry and local shocks. The result is more pronounced in industries where labor is relatively less unionized, in more monopsonistic local labor markets, and for dedicated and activist institutional shareholders. The labor losses are accompanied by higher shareholder returns but no improvements in labor productivity, suggesting that shareholder power mainly reallocates rents away from workers. Our results imply that the rise in concentrated institutional ownership could explain about a quarter of the secular decline in the aggregate labor share.
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Can Displaced Labor Be Retrained? Evidence from Quasi-Random Assignment to Trade Adjustment Assistance
February 2022
Working Paper Number:
CES-22-05
The extent to which workers adjust to labor market disruptions in light of increasing pressure from trade and automation commands widespread concern. Yet little is known about efforts that deliberately target the adjustment process. This project studies 20 years of worker-level earnings and re-employment responses to Trade Adjustment Assistance (TAA)'a large social insurance program that couples retraining incentives with extended unemployment insurance (UI) for displaced workers. I estimate causal effects from the quasi-random assignment of TAA cases to investigators of varying approval leniencies. Using employer-employee matched Census data on 300,000 workers, I find TAA approved workers have $50,000 greater cumulative earnings ten years out'driven by both higher incomes and greater labor force participation. Yet annual returns fully depreciate over the same period. In the most disrupted regions, workers are more likely to switch industries and move to labor markets with better opportunities in response to TAA. Combined with evidence that sustained returns are delivered by training rather than UI transfers, the results imply a potentially important role for human capital in overcoming adjustment frictions.
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Twisting the Demand Curve: Digitalization and the Older Workforce
November 2020
Working Paper Number:
CES-20-37
This paper uses U.S. Census Bureau panel data that link firm software investment to worker earnings. We regress the log of earnings of workers by age group on the software investment by their employing firm. To unpack the potential causal factors for differential software effects by age group we extend the AKM framework by including job-spell fixed effects that allow for a correlation between the worker-firm match and age and by including time-varying firm effects that allow for a correlation between wage-enhancing productivity shocks and software investments. Within job-spell, software capital raises earnings at a rate that declines post age 50 to about zero after age 65. By contrast, the effects of non-IT equipment investment on earnings increase for workers post age 50. The difference between the software and non-IT equipment effects suggests that our results are attributable to the technology rather than to age-related bargaining power. Our data further show that software capital increases the earnings of high-wage workers relative to low-wage workers and the earnings in high-wage firms relative to low-wage firms, and may thus widen earnings inequality within and across firms.
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An Evaluation of the Gender Wage Gap Using Linked Survey and Administrative Data
November 2020
Working Paper Number:
CES-20-34
The narrowing of the gender wage gap has slowed in recent decades. However, current estimates show that, among full-time year-round workers, women earn approximately 18 to 20 percent less than men at the median. Women's human capital and labor force characteristics that drive wages increasingly resemble men's, so remaining differences in these characteristics explain less of the gender wage gap now than in the past. As these factors wane in importance, studies show that others like occupational and industrial segregation explain larger portions of the gender wage gap. However, a major limitation of these studies is that the large datasets required to analyze occupation and industry effectively lack measures of labor force experience. This study combines survey and administrative data to analyze and improve estimates of the gender wage gap within detailed occupations, while also accounting for gender differences in work experience. We find a gender wage gap of 18 percent among full-time, year-round workers across 316 detailed occupation categories. We show the wage gap varies significantly by occupation: while wages are at parity in some occupations, gaps are as large as 45 percent in others. More competitive and hazardous occupations, occupations that reward longer hours of work, and those that have a larger proportion of women workers have larger gender wage gaps. The models explain less of the wage gap in occupations with these attributes. Occupational characteristics shape the conditions under which men and women work and we show these characteristics can make for environments that are more or less conducive to gender parity in earnings.
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Family-Leave Mandates and Female Labor at U.S. Firms: Evidence from a Trade Shock
September 2020
Working Paper Number:
CES-20-25
We study the role of family-leave mandates in shaping the gender composition at U.S. firms that experience a negative demand shock. In a regression discontinuity framework, we compare firms mandated to provide job-protected leave under the Family and Medical Leave Act (FMLA) and firms that are exempt from the law (non-FMLA) following the post-2001 surge in Chinese imports. Using confidential microdata on matched employers and employees in the U.S. non-farm private sector, we find that between 2000 and 2003, an increase in import competition decreases the share of female workers at FMLA compared to non-FMLA firms. The negative differential effect is driven by female workers in prime childbearing years, with less than college education, and is strongest at firms with all male managers. We find similar patterns in changes in the female share of earnings and promotions. These results suggest that, when traditional gender norms prevail, adverse shocks may exacerbate gender inequalities in the presence of job-protected leave mandates.
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Measuring the Effect of COVID-19 on U.S. Small Businesses: The Small Business Pulse Survey
May 2020
Working Paper Number:
CES-20-16
In response to the novel coronavirus (COVID-19) pandemic, the Census Bureau developed and fielded an entirely new survey intended to measure the effect on small businesses. The Small Business Pulse Survey (SBPS) will run weekly from April 26 to June 27, 2020. Results from the SBPS will be published weekly through a visualization tool with downloadable data. We describe the motivation for SBPS, summarize how the content for the survey was developed, and discuss some of the initial results from the survey. We also describe future plans for the SBPS collections and for our research using the SBPS data. Estimates from the first week of the SBPS indicate large to moderate negative effects of COVID-19 on small businesses, and yet the majority expect to return to usual level of operations within the next six months. Reflecting the Census Bureau's commitment to scientific inquiry and transparency, the micro data from the SBPS will be available to qualified researchers on approved projects in the Federal Statistical Research Data Center network.
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Are Customs Records Consistent Across Countries? Evidence from the U.S. and Colombia
March 2020
Working Paper Number:
CES-20-11
In many countries, official customs records include identifying information on the exporting and importing firms involved in each shipment. This information allows researchers to study international business networks, offshoring patterns, and the micro-foundations of aggregate trade flows. It also provides the government with a basis for tariff assessments at the border. However, there are no mechanisms in place to ensure that the shipment-level information recorded by the exporting country is consistent with the shipment-level information recorded by the importing country. And to the extent that there are discrepancies, it is not clear how prevalent they are or what form they take. In this paper we explore these issues, both to enhance our understanding of the limitations of customs records, and to inform future discussions of possible revisions in the way they are collected.
Specifically, we match U.S.-bound export shipments that appear in Colombian Customs records (DIAN) with their counterparts in the US Customs records (LFTTD): U.S. import shipments from Colombia. Several patterns emerge. First, differences in the coverage of the two countries customs records lead to significant discrepancies in the official bilateral trade flow statistics of these two countries: the DIAN database records 8 percent fewer transactions than the LFTTD database over the sample period, and the average export shipment size in the DIAN is roughly 4 percent smaller than the corresponding import shipment size in the LFTTD. These discrepancies are not due to difference in minimum shipment sizes and they are not particular to a few sectors, though they are more common among small shipments and they evolve over time.
Second, if we rely exclusively on firms' names and addresses, ignoring other shipment characteristics (value, product code, etc.), we are able to match 85 percent of the value of U.S. imports from Colombia in our LFTTD sample with particular Colombian suppliers in the DIAN. Further, fully 97 percent of the value of Colombian exports to the U.S. can be mapped onto particular importers in the U.S. LFTTD.
Third, however, match rates at the shipment level within buyer-seller pairs are low. That is, while buyers and sellers can be paired up fairly accurately, only 25-30 percent of the individual transactions in the customs records of the two countries can be matched using fuzzy algorithms at reasonable tolerance levels.
Fourth, the manufacturer ID (MANUF_ID) that appears in the LFTTD implies there are roughly twice as many Colombian exporters as actually appear in the DIAN. And similar comments apply to an analogous MANUF_ID variable constructed from importer name and address information in the DIAN. Hence studies that treat each MANUF_ID value as a distinct firm are almost surely overstating the number of foreign firms that engage in trade with the U.S. by a substantial amount.
Finally, we conclude that if countries were to require that exporters report standardized shipment identifiers'either invoice numbers or bill of lading/air waybill numbers'it would be far easier to track individual transactions and to identify international discrepancies in reporting.
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Why are employer-sponsored health insurance premiums higher in the public sector than in the private sector?
February 2019
Working Paper Number:
CES-19-03
In this article, we examine the factors explaining differences in public and private sector health insurance premiums for enrollees with single coverage. We use data from the 2000 and 2014 Medical Expenditure Panel Survey-Insurance Component, along with decomposition methods, to explore the relative explanatory importance of plan features and benefit generosity, such as deductibles and other forms of cost sharing, basic employee characteristics (e.g., age, gender, and education), and unionization. While there was little difference in public and private sector premiums in 2000, by 2014, public premiums had exceeded private premiums by 14 to 19 percent. We find that differences in plan characteristics played a substantial role in explaining premium differences in 2014, but they were not the only, or even the most important, factor. Differences in worker age, gender, marital status, and educational attainment were also important factors, as was workforce unionization.
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Occupational Classifications: A Machine Learning Approach
August 2018
Working Paper Number:
CES-18-37
Characterizing the work that people do on their jobs is a longstanding and core issue in labor economics. Traditionally, classification has been done manually. If it were possible to combine new computational tools and administrative wage records to generate an automated crosswalk between job titles and occupations, millions of dollars could be saved in labor costs, data processing could be sped up, data could become more consistent, and it might be possible to generate, without a lag, current information about the changing occupational composition of the labor market. This paper examines the potential to assign occupations to job titles contained in administrative data using automated, machine-learning approaches. We use a new extraordinarily rich and detailed set of data on transactional HR records of large firms (universities) in a relatively narrowly defined industry (public institutions of higher education) to identify the potential for machine-learning approaches to classify occupations.
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