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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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Does Federally-Funded Job Training Work? Nonexperimental Estimates of WIA Training Impacts Using Longitudinal Data on Workers and Firms
January 2018
Working Paper Number:
CES-18-02
We study the job training provided under the US Workforce Investment Act (WIA) to adults and dislocated workers in two states. Our substantive contributions center on impacts estimated non-experimentally using administrative data. These impacts compare WIA participants who do and do not receive training. In addition to the usual impacts on earnings and employment, we link our state data to the Longitudinal Employer-Household Dynamics (LEHD) data at the US Census Bureau, which allows us to estimate impacts on the characteristics of the firms at which participants find employment. We find moderate positive impacts on employment, earnings and desirable firm characteristics for adults, but not for dislocated workers. Our primary methodological contribution consists of assessing the value of the additional conditioning information provided by the LEHD relative to the data available in state Unemployment Insurance (UI) earnings records. We find that value to be zero.
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Hours Off the Clock
January 2017
Working Paper Number:
CES-17-44
To what extent do workers work more hours than they are paid for? The relationship
between hours worked and hours paid, and the conditions under which employers can demand more hours 'off the clock,' is not well understood. The answer to this question impacts worker welfare, as well as wage and hour regulation. In addition, work off the clock has important implications for the measurement and cyclical movement of productivity and wages. In this paper, I construct a unique administrative dataset of hours paid by employers linked to a survey of workers on their reported hours worked to measure work off the clock. Using cross-sectional variation in local labor markets, I find only a small cyclical component to work off the clock. The results point to labor hoarding rather than efficiency wage theory, indicating work off the clock cannot explain the counter-cyclical movement of productivity. I find workers employed by small firms, and in industries with a high rate of wage and hour violations are associated with larger differences in hours worked than hours paid. These findings suggest the importance of tracking hours of work for enforcement of labor regulations.
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Food and Agricultural Industries: Opportunities
for Improving Measurement and Reporting
January 2016
Working Paper Number:
CES-16-58
We measure one component of off-farm food and agricultural industries using establishment
level microdata in the federal statistical system. We focus on services for crop production, and compare measures of firm and employment dynamics in this sector during the period 1992-2012 with county-level publicly available data for the same measures. Based on differences across data sources, we establish new facts regarding the evolution of food and agricultural industries, and demonstrate the value of working with confidential microdata. In addition to the data and results we present, we highlight possibilities for collaboration across universities and federal agencies to improve reporting in other segments of food and agricultural industries.
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