Assistant Professor, Department of Economics
Sam M. Walton College of Business
University of Arkansas
Fayetteville, AR 72701, USA
Office: Room 419, Walton College of Business (WCOB)
Research Interests: Macroeconomics, Labor Economics, Development
Email: gsun@uark.edu | Curriculum Vitae (PDF)
Pronunciation: “Ge” /gə/ is pronounced like “guh” (soft g, as in “get”)
Abstract: This paper studies how employers' expectations about women's future fertility contribute to the gender wage gap. I argue that this expectations-based channel is particularly important in labor markets with persistent employment relationships, where wages are set in advance based on the expected present value of future productivity rather than contemporaneous output. If employers expect women's productivity growth to slow after childbirth, they may discount women's wages before children are born. I test this mechanism using China's 2013 selective relaxation of the One-Child Policy as a natural experiment. Exploiting cross-provincial variation in pre-reform policy enforcement in a difference-in-differences framework, I find that young women's wages declined by 15.3% immediately after the reform, despite no short-run increase in actual fertility. The wage response is concentrated in sectors with more persistent employment relationships, consistent with employers pricing expected future productivity rather than replacement costs. To interpret these findings, I develop a search-and-matching model with wage posting and on-the-job human-capital accumulation, integrated with a household model in which fertility influences future productivity through household time allocation and non-contractible effort. Estimating the model using Chinese labor-market data, I find that gender differences in expected future productivity, arising from the unequal division of household labor, explain nearly the entire pre-reform gender wage gap and approximately 80% of the post-reform widening. These findings suggest that persistent employment relationships can shift part of the child penalty from the post-childbirth period to the wage-setting stage, implying that policies preserving employment alone may be insufficient to eliminate gender wage gaps.
Abstract: Work experience is an important source of human-capital accumulation and economic growth, especially in advanced economies. Yet poorer countries exhibit lower measured returns to experience. Understanding this pattern is important for explaining why some countries remain poor. This paper proposes a new mechanism that contributes to limited wage growth over the life cycle in developing countries: rapid technological progress can accelerate the obsolescence of previously accumulated human capital, lowering the relative earnings of older workers. Using repeated cross-sectional data across countries and decades, we show that periods of faster GDP per capita or TFP growth are associated with lower relative earnings for older workers, especially among more educated workers. We then develop and estimate an overlapping-generations model in which stochastic technological innovations raise productivity and skill prices but also increase human-capital depreciation. The estimates imply sizable technology-driven depreciation: a 1.5 percent increase in the technological frontier raises the depreciation rate by about 4 percentage points in the skilled sector and 2.78 percentage points in the unskilled sector. Counterfactual simulations show that replacing the U.S. technology process with a faster process calibrated from Indian TFP data flattens the experience profile. The results suggest that rapid development can have an overlooked distributional cost: technology-induced skill obsolescence reduces the relative earnings of older workers and increases inequality across cohorts over the life cycle.
Abstract: Sampled network data are common in empirical research because collecting complete network information is costly, but analyses based directly on sampled networks can produce biased estimates. We propose a flexible method for imputing missing links in sampled networks and show that downstream empirical analyses based on the imputed networks yield consistent parameter estimates. Our nonparametric procedure combines a projection onto observed covariates with a local two-way fixed-effects regression. It 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 adjacency matrix and prove the consistency of GMM estimators based on the imputed network. We also derive the convergence rate of the corresponding estimator in a linear-in-means peer-effects model. Simulations demonstrate strong performance in both imputation accuracy and downstream empirical analysis. We illustrate the method using the microfinance network data of Banerjee et al. (2013).
Abstract: A student’s choice of major is consequential for academic progress and subsequent labor-market outcomes. This paper studies an overlooked determinant of major choice: unexpected grade shocks during the early years of college. Using administrative records from Purdue University, we combine course-evaluation data reporting students’ expected letter grades shortly before final exams with their realized grades to construct grade shocks, defined as actual minus expected performance. We find that unexpected negative grade shocks significantly increase the probability that students leave their current major in the following term. The response is stronger among women: a one-standard-deviation negative shock raises the probability that a female student leaves her major by approximately 2.5 percentage points relative to female students who do not experience such a shock. These findings highlight the role of early academic signals in major sorting and reveal substantial gender heterogeneity in responses to adverse performance feedback.
University of Arkansas
University of Notre Dame
Peking University
Last updated: