In addition to overall dispersion, the distributional shape of economic status has attracted growing attention in the inequality literature. Economic polarization is a specific form of distributional change, characterized by a shrinking middle of the distribution and a growing top and bottom, with potentially important and unique social consequences. Building on relative distribution methods and drawing from the literature on job polarization, the authors develop an approach for analyzing economic polarization at the individual level. The method has three useful features. First, it offers intuitive and flexible measurement of economic polarization both between and within categories. Second, it helps disentangle two potential sources of economic polarization: compositional change, which involves changes to the allocation of workers across categories, and relative economic status change, which involves changes to the allocation of economic rewards between individuals. Third, it enables researchers to uncover and examine potential heterogeneity in economic polarization, for example, across occupations, geographic units, demographic and educational groups, and firms. The authors demonstrate the utility of this approach through two empirical applications: (1) an analysis of trends in wage polarization between and within occupations and (2) an examination of geographic variation in income polarization.
Occupations have long been central to the study of inequality and mobility. However, the occupational categories typical in most U.S. survey data conceal potentially important patterns within occupations. This project uses a novel data source that has not previously been released for analysis: the verbatim text responses provided by respondents to the General Social Survey from 1972 to 2018 when asked about their occupation. These text data allow for an investigation of variation within occupations, in terms of job titles and task descriptions, and the occupation-level factors associated with this variation. I construct an index of occupational similarity based on the average pairwise cosine similarity between job titles and between task descriptions within occupations. Findings indicate substantial variation in the level of similarity across occupations. Occupational prestige, education, and income are associated with less heterogeneity in terms of job titles but slightly more heterogeneity in terms of task descriptions. Gender diversity is associated with more internal heterogeneity in terms of both job titles and task descriptions. In addition, I use the case of gender segregation to demonstrate how occupational categories can conceal the depth and form of stratification.
In this paper, we describe and analyze a new dataset consisting of matched ACS and IRS 1040 occupation reports. This dataset allows validation and quality analysis of the IRS’s large Form 1040 occupational write-in database by comparing it with the high-quality ACS write-in and coding process. We analyze the similarity between the two datasets both along the token and semantic dimensions. We find a bimodal distribution of response quality in the token dimension, with over 50 percent of the ACS sample a high-quality token match with its IRS counterpart, but also a significant set of seeming no-matches.
Occupations are defined as categories of work containing similar jobs and tasks. But how similar are these jobs and tasks? To what extent do people in the same occupation describe their work in cohesive ways on household surveys? A limited body of prior research has found that occupational categories are highly internally varied and becoming more so over time, with implications for how we understand the meaning of occupations and how occupations relate to inequality. However, these findings are based on relatively small samples, with limited coverage of detailed occupational categories. To address this limitation, I construct an index of occupational similarity based on write-in textual data on job names and tasks from the 2011 to 2021 American Community Survey, which has over a million observations per year. Findings indicate differential levels of occupational similarity cross-sectionally, but little change in similarity over the period studied. Similarity is highly sensitive to changes in the survey questionnaire, reinforcing the importance of data collection methods for occupation. Replicating prior research, this paper finds that occupational similarity is highly correlated with income, education, and demographic composition. It also finds that similarity predicts within-occupation income inequality. These results show that the similarity index is a useful tool for measurement and interpretation of occupations.