Research
Gender inequality in labor market outcomes
Much of my research uses demographic methods to examine the sources of gender differences in occupations and pay, focusing on how these labor market outcomes are affected by dynamics within the family and the education system.
Zheng, Haowen. (Paper on life course, family migration, and gender; under journal review)
- ASA Sociology of Population Student Paper Award Honorable Mention
- Kerckhoff Award (RC28)
- Robin M. Williams, Jr. Best Paper Award (Cornell Sociology)
Zheng, Haowen, Kim A. Weeden. 2023. “How Gender Segregation in Higher Education Contributes to Gender Segregation in the U.S. Labor Market” Demography, 60(3), 761-784. Open access link
Click for Abstract: What is the relationship between gender segregation in higher education and gender segregation in the labor market?
Using Fossett's (2017) difference-of-means method for calculating segregation indices and data from the American Community Survey, we show that approximately 36% of occupational segregation among college-educated workers is associated with gender segregation across 173 fields of study, and roughly 64% reflects gender segregation within fields. A decomposition analysis shows that fields contribute to occupational segregation mainly through endowment effects (men's and women's uneven distribution across fields) than through the coefficient effects (gender differences in the likelihood of entering a male-dominated occupation from the same field). Endowment effects are highest in fields strongly linked to the labor market, suggesting that educational segregation among fields in which graduates tend to enter a limited set of occupations is particularly consequential for occupational segregation. Within-field occupational segregation is higher among heavily male-dominated fields than other fields, but it does not vary systematically by fields' STEM status or field–occupation linkage strength. Assuming the relationship between field segregation and occupational segregation is at least partly causal, these results imply that integrating higher education (e.g., by increasing women's representation in STEM majors) will reduce but not eliminate gender segregation in labor markets.The geography of stratification & spatial mobility
The second line of my research examines the geographic disparities in the distribution of occupational and educational opportunities. Chapters in my dissertation examine how evolving local labor market contexts interact with individual characteristics to shape spatial mobility rates and patterns over time. A related project studies how access to good public schools varies across metropolitan housing markets.
Rich, Peter, Haowen Zheng and Christian Sprague. “Inequality in the Competition for Access to High-Achieving and High-Growth Schools Across Metropolitan Area Housing Markets” (Working paper)
Inter- & Intra-generational social mobility
My third line of research focuses on social mobility, i.e., how individuals move between socioeconomic positions across and within generations. My projects examine how various factors at different life stages, such as education, family background, and family structure, shape the inter- and intra-generational persistence of socioeconomic status.
Zheng, Haowen, Siwei Cheng, “Social Rigidity Across and Within Generations: A Predictive Approach” Sociological Methods & Research Online first
Click for Abstract: How well can individuals’ parental background and previous life experiences predict their mid-life socioeconomic status (SES) attainment?
This question is central to stratification research, as a strong power of earlier experiences in predicting later-life outcomes signals substantial intra- or intergenerational status persistence, or put simply, social rigidity. Running machine learning models on panel data to predict outcomes that include hourly wage, total income, family income, and occupational status, we find that a large number (around 4,000) of predictors commonly used in the stratification literature improves the prediction of one’s life chances in middle to late adulthood by about 10 percent to 50 percent, compared with a null model that uses a simple mean of the outcome variable. The level of predictability depends on the specific outcome being analyzed, with labor market indicators like wages and occupational prestige being more predictable than broader socioeconomic measures such as overall personal and family income. Grouping a comprehensive list of predictors into four unique sets that cover family background, childhood and adolescence development, early labor market experiences, and early adulthood family formation, we find that including income, employment status, and occupational characteristics at early career significantly improves models’ prediction accuracy for mid-life SES attainment. We also illustrate the application of the predictive models to examine heterogeneity in predictability by race and gender and identify important variables through this data-driven exercise.Zheng, Haowen, Kristian B. Karlson, Anders Holm, and Robert Andersen, (Paper on education and social mobility, under journal review)
Zheng, Haowen. 2020. “The Only-child Premium and Moderation by Social Origin: Educational Stratification in Post-reform China.” Chinese Journal of Sociology, 6(3): 384-409 Open access link