Ph.D. Candidate in Economics, University of Notre Dame (Expected May 2026)
Research Interests: Macroeconomics, Labor Economics, Development
Email: gsun4@nd.edu | Curriculum Vitae (PDF)
Department of Economics, University of Notre Dame • 3060 Jenkins Nanovic Hall • Notre Dame, IN 46556, USA
Pronunciation: “Ge” /gə/ is pronounced like “guh” (soft g, as in “get”).
Title: Expected Fertility, Labor Market Contracts, and the Gender Wage Gap (2025)
Abstract: This paper examines how employers’ expectations about women’s future fertility increase the gender wage gap in contract-based labor markets—standard settings in many occupations that involve long-horizon, complex tasks. In such environments, salaries are set in advance based on expected match productivity rather than contemporaneous output; if employers expect women’s productivity to decline more than men’s after childbirth, they offer lower wages today. Exploiting China’s relaxation of the One-Child Policy as a quasi-experiment, I implement a difference-in-differences design and find that women’s wages declined by 15.3% immediately after the reform, despite no short-term increase in actual births. To interpret these findings, I develop a search-and-matching model with on-the-job human capital accumulation, integrated with a household framework in which non-contractable fertility-driven effort choices are made. Effort links the two components by governing human capital growth and, in turn, long-run productivity in the labor market. Estimating the model on Chinese data, I find that gender differences in expected productivity—rooted in the unbalanced division of household labor—explain nearly the entire pre-reform wage gap and approximately 80% of the post-reform widening. The policy implication is stark: women-protective rules that preserve employment through legislative contract provisions may not reduce the gap; by reinforcing employers’ present-value pricing, they can be offset by ex ante wage markdowns applied to all women.
Abstract: Work experience is an important source of human capital and growth, especially in advanced economies. However, poor countries exhibit lower measured returns to experience. The reasons for these patterns could have important implications for growth and development. In this paper, we propose a new mechanism that contributes to the limited wage growth over the life cycle in developing countries with very different implications for growth. Specifically, higher growth rates could lower the relative wage for senior workers because rapid technological updates make earlier vintages of human capital obsolete. This technology-induced skill obsolescence leads to lower measured returns to experience, resulting in lower relative wages for older cohorts and exacerbating inter-generational inequality.
Abstract: Sampled network data are common in empirical research because collecting full network information is costly, but using sampled networks can lead to biased estimates. We propose a flexible imputation method for sampled networks and show that downstream empirical analysis based on imputed networks yields consistent parameter estimates. We propose a nonparametric method that imputes missing network links by combining a projection onto covariates with a local two-way fixed-effects regression. The imputation method avoids parametric assumptions, does not rely on low-rank restrictions, and flexibly accommodates both observed covariates and unobserved heterogeneity. We establish entrywise convergence rates for the imputed matrix and prove the consistency of GMM estimators based on the imputed network. We further derive the convergence rate of the corresponding estimator in the linear-in-means peer-effects model. Simulations show strong performance of our method both in terms of imputation accuracy and in downstream empirical analysis. We illustrate our method with an application to the microfinance dataset of Banerjee et al. (2013).
Abstract: A student’s major choice is pivotal for academic progress and later labor-market outcomes. This paper examines an overlooked driver of major selection: unexpected grade shocks in early years in college. Using administrative records from Purdue University, we combine course evaluation data that elicit students’ expected letter grades just before final exams with realized grades to construct grade shocks (actual minus expected). We find that unexpected negative shocks significantly increase the likelihood that students switch out of their current major in the subsequent term. The response is stronger for women: a one standard-deviation negative shock raises the probability that a female student leaves her major by about 2.5 percentage points relative to female peers without such a shock. These results highlight the role of early performance signals in major sorting and reveal meaningful gender heterogeneity in responsiveness to adverse academic feedback.
University of Notre Dame
Peking University
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