A Task-based Approach to Constructing Occupational Categories with Implications for Empirical Research in Labor Economics
September 2019
Working Paper Number:
CES-19-27
Abstract
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.
Document Tags and Keywords
Keywords
Keywords are automatically generated using KeyBERT, a powerful and innovative
keyword extraction tool that utilizes BERT embeddings to ensure high-quality and contextually relevant
keywords.
By analyzing the content of working papers, KeyBERT identifies terms and phrases that capture the essence of the
text, highlighting the most significant topics and trends. This approach not only enhances searchability but
provides connections that go beyond potentially domain-specific author-defined keywords.
:
survey,
agency,
employ,
employed,
labor,
job,
classified,
worker,
occupation,
union
Tags
Tags are automatically generated using a pretrained language model from spaCy, which excels at
several tasks, including entity tagging.
The model is able to label words and phrases by part-of-speech,
including "organizations." By filtering for frequent words and phrases labeled as "organizations", papers are
identified to contain references to specific institutions, datasets, and other organizations.
:
Internal Revenue Service,
Social Security Administration,
Stern School of Business,
Current Population Survey,
Survey of Income and Program Participation,
Federal Register,
Journal of Economic Literature,
Standard Occupational Classification,
PSID,
Census Bureau Disclosure Review Board
Similar Working Papers
Similarity between working papers are determined by an unsupervised neural
network model
know as Doc2Vec.
Doc2Vec is a model that represents entire documents as fixed-length vectors, allowing for the
capture of semantic meaning in a way that relates to the context of words within the document. The model learns to
associate a unique vector with each document while simultaneously learning word vectors, enabling tasks such as
document classification, clustering, and similarity detection by preserving the order and structure of words. The
document vectors are compared using cosine similarity/distance to determine the most similar working papers.
Papers identified with 🔥 are in the top 20% of similarity.
The 10 most similar working papers to the working paper 'A Task-based Approach to Constructing Occupational Categories with Implications for Empirical Research in Labor Economics' are listed below in order of similarity.
-
Working PaperAn 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.View Full Paper PDF
-
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
-
Working PaperOccupation 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.View Full Paper PDF
-
Working PaperWage 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.View Full Paper PDF
-
Working PaperOpening the Black Box: Task and Skill Mix and Productivity Dispersion
September 2022
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.View Full Paper PDF
-
Working PaperFurther 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.View Full Paper PDF
-
Working PaperUnderstanding 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.View Full Paper PDF
-
Working PaperThe Role of Establishments and the Concentration of Occupations in Wage Inequality
September 2015
Working Paper Number:
CES-15-26
This paper uses the microdata of the Occupational Employment Statistics (OES) Survey to assess the contribution of occupational concentration to wage inequality between establishments and its growth over time. We show that occupational concentration plays an important role in wage determination for workers, in a wide variety of occupations, and can explain some establishmentlevel wage variation. Occupational concentration is increasing during the 2000-2011 time period, although much of this change is explained by other observable establishment characteristics. Overall, occupational concentration can help explain a small amount of wage inequality growth between establishments during this time period.View Full Paper PDF
-
Working PaperTask Trade and the Wage Effects of Import Competition
January 2016
Working Paper Number:
CES-16-03
Do job characteristics modulate the relationship between import competition and the wages of workers who perform those jobs? This paper tests the claim that workers in occupations featuring highly routine tasks will be more vulnerable to low-wage country import competition. Using data from the US Census Bureau, we construct a pooled cross-section (1990, 2000, and 2007) of more than 1.6 million individuals linked to the establishment in which they work. Occupational measures of vulnerability to trade competition ' routineness, analytic complexity, and interpersonal interaction on the job ' are constructed using O*NET data. The linked employer-employee data allow us to model the effect of low-wage import competition on the wages of workers with different occupational characteristics. Our results show that low-wage country import competition is associated with lower wages for US workers holding jobs that are highly routine and less complex. For workers holding nonroutine and highly complex jobs, increased import competition is associated with higher wages. Finally, workers in occupations with the highest and lowest levels of interpersonal interaction see higher wages, while workers with medium-low levels of interpersonal interaction suffer lower wages with increased low-wage import competition. These findings demonstrate the importance of accounting for occupational characteristics to more fully understand the relationship between trade and wages, and suggest ways in which task trade vulnerable occupations can disadvantage workers even when their jobs remain onshore.View Full Paper PDF
-
Working PaperHow 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.View Full Paper PDF
