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: 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.
Abstract: Incomplete network data are common in empirical research due to the high cost of data collection. We propose a new method to impute the network under the assumption of dyadic link formation. The method avoids parametric assumptions, does not rely on low-rank conditions, and flexibly incorporates observed node covariates and heterogeneity. The method separately estimates the part of the network explained by covariates and the residual structure: the former via projection onto the conditional expectation and the latter via localized principal component analysis. We establish entrywise convergence rates for the imputed adjacency matrix that achieve Stone's minimax optimal rate for nonparametric regression. Simulation studies demonstrate strong performance relative to existing methods, and we illustrate how the imputed network can be used in empirical applications.
University of Notre Dame
Peking University
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