Most applied research in labor economics that examines returns to worker skills or differences in earnings across subgroups of workers typically accounts for the role of occupations by controlling for occupational categories. Researchers often aggregate detailed occupations into categories based on the Standard Occupation Classification (SOC) coding scheme, which is based largely on narratives or qualitative measures of workers' tasks. Alternatively, we propose two quantitative task-based approaches to constructing occupational categories by using factor analysis with O*NET job descriptors that provide a rich set of continuous measures of job tasks across all occupations. We find that our task-based approach outperforms the SOC-based approach in terms of lower occupation distance measures. We show that our task-based approach provides an intuitive, nuanced interpretation for grouping occupations and permits quantitative assessments of similarities in task compositions across occupations. We also replicate a recent analysis and find that our task-based occupational categories explain more of the gender wage gap than the SOC-based approaches explain. Our study enhances the Federal Statistical System's understanding of the SOC codes, investigates ways to use third-party data to construct useful research variables that can potentially be added to Census Bureau data products to improve their quality and versatility, and sheds light on how the use of alternative occupational categories in economics research may lead to different empirical results and deeper understanding in the analysis of labor market outcomes.
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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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Occupation Inflation in the Current Population Survey
September 2012
Working Paper Number:
CES-12-26
A common caveat often accompanying results relying on household surveys regards respondent error. There is research using independent, presumably error-free administrative data, to estimate the extent of error in the data, the correlates of error, and potential corrections for the error. We investigate measurement error in occupation in the Current Population Survey (CPS) using the panel component of the CPS to identify those that incorrectly report changing occupation. We find evidence that individuals are inflating their occupation to higher skilled and higher paying occupations than the ones they actually perform. Occupation inflation biases the education and race coefficients in standard Mincer equation results within occupations.
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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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Wage Determination in Social Occupations: the Role of Individual Social Capital
January 2016
Working Paper Number:
CES-16-46
We make use of predicted social and civic activities (social capital) to account for selection into "social" occupations. Individual selection accounts for more than the total difference in wages observed between social and non-social occupations. The role that individual social capital plays in selecting into these occupations and the importance of selection in explaining wage differences across occupations is similar for both men and women. We make use of restricted 2000 Decennial Census and 2000 Social Capital Community Benchmark Survey. Individual social capital is instrumented by distance weighted surrounding census tract characteristics.
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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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Further Evidence from Census 2000 About Earnings by Detailed Occupation for Men and Women: The Role of Race and Hispanic Origin
November 2011
Working Paper Number:
CES-11-37
A 2004 report by the author reviewed data from Census 2000 and concluded "There is a substantial gap in median earnings between men and women that is unexplained, even after controlling for work experience (to the extent it can be represented by age and presence of children), education, and occupation." This paper extends the analysis and concludes that once those characteristics are controlled for, no further explanatory power is attributable to race or Hispanic origin.
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Understanding Selection Processes: Organization Determinants and Performance Outcomes
October 1997
Working Paper Number:
CES-97-14
We use an establishment-level survey to examine the predictors of different types of selection practices as well as the relationship of different selection practices to organizational performance. We find that a wide range of contingencies in the organization, including job requirements, organizational size, union status, salary, and training, predict the intensity and the types of selection practices used. Further, we find that selection intensity has a significant and negative relationship with organizational sales, other things equal, that is driven by the use of less valid selection techniques.
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Gender Segregation Small Firms
October 1992
Working Paper Number:
CES-92-13
This paper studies interfirm gender segregation in a unique sample of small employers. We focus on small firms because previous research on interfirm segregation has studied only large firms and because it is easier to link the demographic characteristics of employers and employees in small firms. This latter feature permits an assessment of the role of employer discrimination in creating gender segregation. Our first finding is that interfirm segregation is prevalent among small employers. Indeed men and women rarely work in fully integrated firms. Our second finding is that the education and gender of the business owner strongly influence the gender composition of a firm's workforce. This suggests that employer discrimination may be an important cause of workplace gender segregation. Finally, we estimate that interfirm segregation can account for up to 50% of the gender gap in annual earnings.
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NOISE INFUSION AS A CONFIDENTIALITY PROTECTION MEASURE FOR GRAPH-BASED STATISTICS
September 2014
Working Paper Number:
CES-14-30
We use the bipartite graph representation of longitudinally linked em-ployer-employee data, and the associated projections onto the employer and em-ployee nodes, respectively, to characterize the set of potential statistical summar-ies that the trusted custodian might produce. We consider noise infusion as the primary confidentiality protection method. We show that a relatively straightfor-ward extension of the dynamic noise-infusion method used in the U.S. Census Bureau's Quarterly Workforce Indicators can be adapted to provide the same confidentiality guarantees for the graph-based statistics: all inputs have been modified by a minimum percentage deviation (i.e., no actual respondent data are used) and, as the number of entities contributing to a particular statistic increases, the accuracy of that statistic approaches the unprotected value. Our method also ensures that the protected statistics will be identical in all releases based on the same inputs.
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How long do early career decisions follow women? The impact of industry and firm size history on the gender and motherhood wage gaps
January 2018
Working Paper Number:
CES-18-05
We add to the gender wage gap literature by considering how characteristics of past employers are correlated with current wages and whether differences between the work histories of men and women are related to the persistent gender wage gap. Our hypothesis is that women have spent less time over the course of their careers in higher paying industries and have less job- and industry-specific human capital and that these characteristics are correlated with male-female earnings differences. Additionally, we expect that difference in the work histories between women with children and childless women might help explain the observed motherhood wage gap. We use unique administrative employer history data to conduct a standard decomposition exercise to determine the impact of differences in observable job history characteristics on the gender and motherhood wage gaps. We find that industry work history has two opposing effects on both these wage gaps. The distribution of work experience across industries contributes to increasing the wage gaps, but the share of experience spent in the industry sector of the current job works to decrease earnings differences.
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