Research
You can also find my articles on my Google Scholar profile.
Peer-reviewed publications
Zheng, Haowen. “Diverging Trajectories: Gendered Income Dynamics Pre- and Post-Family Migration” (Accepted) Demographic Research
Click for summary: Gender differences in the returns to migration on labor market outcomes remain substantial, yet we know little about how they unfold over the life course.
Using NLSY79 linked-couple data and event-study methods that track income changes before and long after a move, this study shows that men experience cumulative income gains for up to 15 years, while women face sharp short-term penalties that peak around five years after moving and decline gradually, with no long-term advantage over stayers. The findings highlight family migration as a persistent source of gender inequality.- ASA Sociology of Population Student Paper Award Honorable Mention
- Kerckhoff Award (RC28)
- Robin M. Williams, Jr. Best Paper Award (Cornell Sociology)
Zheng, Haowen, Anders Holm, Robert Andersen, and Kristian B. Karlson. 2026. “Is College Really ‘the’ Equalizer: New Evidence Addressing Unobserved Selection” Sociological Science, 13: 242-272 Open access link
Click for summary: One of the most fundamental stratification findings is that college graduates achieve similar labor market outcomes regardless of socioeconomic origin, leading to the view that a college degree is a “great equalizer.” Recent causal analysis studies suggest that college’s equalizing effect may largely reflect the characteristics of those who pursue higher education. Still, the role of unobserved selection into college has rarely been examined.
After formally illustrating how this unobserved selection can bias estimates of the college effect, we present new analyses that correct for this bias using an instrumental-variable approach on white male respondents in the 1979 cohort of the National Longitudinal Survey of Youth. The selection-corrected results suggest that intergenerational mobility is similar among college graduates and nongraduates. Although college yields substantial returns for all, these returns do not differ by family background. We conclude that for higher education to serve as a true equalizer, it must become both less selective and more accessible to students from disadvantaged backgrounds.Zheng, Haowen, Siwei Cheng. 2025. “Social Rigidity Across and Within Generations: A Predictive Approach” Sociological Methods & Research, 54(4), 1683-1725 Online first
Click for summary: How well can individuals’ parental background and previous life experiences predict their mid-life socioeconomic status (SES) attainment (using flexible machine learning methods)?
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, 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 summary: What is the relationship between gender segregation across fields of study and gender segregation across occupations?
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.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