Papers Containing Keywords(s): 'wage data'
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Viewing papers 1 through 10 of 15
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Working PaperIndustry Wage Differentials: A Firm-Based Approach
August 2023
Working Paper Number:
CES-23-40
We revisit the estimation of industry wage differentials using linked employer-employee data from the U.S. LEHD program. Building on recent advances in the measurement of employer wage premiums, we define the industry wage effect as the employment-weighted average workplace premium in that industry. We show that cross-sectional estimates of industry differentials overstate the pay premiums due to unmeasured worker heterogeneity. Conversely, estimates based on industry movers understate the true premiums, due to unmeasured heterogeneity in pay premiums within industries. Industry movers who switch to higher-premium industries tend to leave firms in the origin sector that pay above-average premiums and move to firms in the destination sector with below-average premiums (and vice versa), attenuating the measured industry effects. Our preferred estimates reveal substantial heterogeneity in narrowly-defined industry premiums, with a standard deviation of 12%. On average, workers in higher-paying industries have higher observed and unobserved skills, widening between-industry wage inequality. There are also small but systematic differences in industry premiums across cities, with a wider distribution of pay premiums and more worker sorting in cities with more highpremium firms and high-skilled workers.View Full Paper PDF
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Working PaperMarket Power And Wage Inequality
September 2022
Working Paper Number:
CES-22-37
We propose a theory of how market power affects wage inequality. We ask how goods and labor market power jointly affect the level of wages, the Skill Premium, and wage inequality. We then use detailed microdata from the US Census between 1997 and 2016 to estimate the parameters of labor supply, technology and the market structure. We find that a less competitive market structure lowers the wage level, contributes 7% to the rise in the Skill Premium and accounts for half of the increase in between-establishment wage variance.View Full Paper PDF
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Working PaperOccupational 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.View Full Paper PDF
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Working PaperThe Distributional Effects of Minimum Wages: Evidence from Linked Survey and Administrative Data
March 2018
Working Paper Number:
carra-2018-02
States and localities are increasingly experimenting with higher minimum wages in response to rising income inequality and stagnant economic mobility, but commonly used public datasets offer limited opportunities to evaluate the extent to which such changes affect earnings growth. We use administrative earnings data from the Social Security Administration linked to the Current Population Survey to overcome important limitations of public data and estimate effects of the minimum wage on growth incidence curves and income mobility profiles, providing insight into how cross-sectional effects of the minimum wage on earnings persist over time. Under both approaches, we find that raising the minimum wage increases earnings growth at the bottom of the distribution, and those effects persist and indeed grow in magnitude over several years. This finding is robust to a variety of specifications, including alternatives commonly used in the literature on employment effects of the minimum wage. Instrumental variables and subsample analyses indicate that geographic mobility likely contributes to the effects we identify. Extrapolating from our estimates suggests that a minimum wage increase comparable in magnitude to the increase experienced in Seattle between 2013 and 2016 would have blunted some, but not nearly all, of the worst income losses suffered at the bottom of the income distribution during the Great Recession.View Full Paper PDF
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Working PaperSorting Between and Within Industries: A Testable Model of Assortative Matching
January 2017
Working Paper Number:
CES-17-43
We test Shimer's (2005) theory of the sorting of workers between and within industrial sectors based on directed search with coordination frictions, deliberately maintaining its static general equilibrium framework. We fit the model to sector-specific wage, vacancy and output data, including publicly-available statistics that characterize the distribution of worker and employer wage heterogeneity across sectors. Our empirical method is general and can be applied to a broad class of assignment models. The results indicate that industries are the loci of sorting-more productive workers are employed in more productive industries. The evidence confirm that strong assortative matching can be present even when worker and employer components of wage heterogeneity are weakly correlated.View Full Paper PDF
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Working PaperMeasuring the Effects of the Tipped Minimum Wage Using W-2 Data
June 2016
Working Paper Number:
carra-2016-03
While an extensive literature exists on the effects of federal and state minimum wages, the minimum wage received by tipped workers has received less attention. Researchers have found it difficult to capture the hourly wages of tipped workers and thus assess the economic effects of the tipped minimum wage. In this paper, I present a new measure of hourly wages for tipped servers (wait staff and bartenders) using linked W-2 and survey data. I estimate the effect of tipped minimum wages on the wages and hourly tips of servers, as well as server employment and hours worked. I find that higher mandatory tipped minimum wages increase that portion of wages paid by employers, but decrease tip income by a similar percentage. I also find evidence that employment increases over lower values of the tipped minimum wage and then decreases at higher values. These results are consistent with a monopsony model of server employment. The wide variance of tipped minimum wages compared to non-tipped minimums provide insight into monopsony effects that may not be discernible over a smaller range of minimum wage values.View Full Paper PDF
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Working PaperJOB-TO-JOB (J2J) Flows: New Labor Market Statistics From Linked Employer-Employee Data
September 2014
Working Paper Number:
CES-14-34
Flows of workers across jobs are a principal mechanism by which labor markets allocate workers to optimize productivity. While these job flows are both large and economically important, they represent a significant gap in available economic statistics. A soon to be released data product from the U.S. Census Bureau will fill this gap. The Job-to-Job (J2J) flow statistics provide estimates of worker flows across jobs, across different geographic labor markets, by worker and firm characteristics, including direct job-to-job flows as well as job changes with intervening nonemployment. In this paper, we describe the creation of the public-use data product on job-to-job flows. The data underlying the statistics are the matched employer-employee data from the U.S. Census Bureau's Longitudinal Employer-Household Dynamics program. We describe definitional issues and the identification strategy for tracing worker movements between employers in administrative data. We then compare our data with related series and discuss similarities and differences. Lastly, we describe disclosure avoidance techniques for the public use file, and our methodology for estimating national statistics when there is partially missing geography.View Full Paper PDF
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Working PaperEstimating the "True" Cost of Job Loss: Evidence Using Matched Data from Califormia 1991-2000
June 2009
Working Paper Number:
CES-09-14
Estimates of the cost of job displacement from survey and administrative data differ markedly. This paper uses a unique match of data between the Displaced Worker Survey (DWS) and administrative wage records from California to examine the sources of this discrepancy. When we use similar estimation methods and account for measurement error in survey wages correlated with worker demographics, estimates of earnings losses at displacement are similar from both datasets and significantly larger than those based on the DWS alone. Also correcting for measurement errors in reported displacements suggests both sources of such estimates may yield lower bounds for the true cost of displacement.View Full Paper PDF
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Working PaperWage Dispersion, Compensation Policy and the Role of Firms
November 2005
Working Paper Number:
tp-2005-04
Empirical work in economics stresses the importance of unobserved firm- and person-level characteristics in the determination of wages, finding that these unobserved components account for the overwhelming majority of variation in wages. However, little is known about the mechanisms sustaining these wage di'er- entials. This paper attempts to demystify the firm-side of the puzzle by developing a statistical model that enriches the role that firms play in wage determination, allowing firms to influence both average wages as well as the returns to observable worker characteristics. I exploit the hierarchical nature of a unique employer-employee linked dataset for the United States, estimating a multilevel statistical model of earnings that accounts for firm-specific deviations in average wages as well as the returns to components of human capital - race, gender, education, and experience - while also controlling for person-level heterogeneity in earnings. These idiosyncratic prices reflect one aspect of firm compensation policy; another, and more novel aspect, is the unstructured characterization of the covariance of these prices across firms. I estimate the model's variance parameters using Restricted (or Residual) Maximum Likelihood tech- niques. Results suggest that there is significant variation in the returns to worker characteristics across firms. First, estimates of the parameters of the covariance matrix of firm-specific returns are statistically significant. Firms that tend to pay higher average wages also tend to pay higher than average returns to worker characteristics; firms that tend to reward highly the human capital of men also highly reward the human capital of women. For instance, the correlation between the firm-specific returns to education for men and women is 0.57. Second, the firm-specific returns account for roughly 9% of the variation in wages - approximately 50% of the variation in wages explained by firm-specific intercepts alone. The inclusion of firm-specific returns ties variation in wages, otherwise attributable to firm-specific intercepts, to observable components of human capital.View Full Paper PDF
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Working PaperProduction Function and Wage Equation Estimation with Heterogenous Labor: Evidence from a New Matched Employer-Employee Dataset
April 2004
Working Paper Number:
CES-04-05
In this paper, we first describe the 1990 DEED, the most recently constructed matched employeremployee data set for the United States that contains detailed demographic information on workers (most notably, information on education). We then use the data from manufacturing establishments in the 1990 DEED to update and expand on previous findings, using a more limited data set, regarding the measurement of the labor input and theories of wage determination (Hellerstein, et al., 1999). We find that the productivity of women is less than that of men, but not by enough to fully explain the gap in wages, a result that is consistent with wage discrimination against women. In contrast, we find no evidence of wage discrimination against blacks. We estimate that both the wage and productivity profiles are rising but concave to the origin (consistent with profiles quadratic in age), but the estimated relative wage profile is steeper than the relative productivity profile, consistent with models of deferred wages. We find a productivity premium for marriage equal to that of the wage premium, and a productivity premium for education that somewhat exceeds the wage premium. Exploring the sensitivity of these results, we also find that different specifications of production functions do not have any qualitative effects on the these results. Finally, the results indicate that the returns to productive inputs (capital, materials, labor quality) as well as the residual variance are virtually unaffected by the choice of the construction of the labor quality input.View Full Paper PDF
