Welcome! I am a PhD Candidate in Economics at Yale University. My research lies at the intersection of Labor Economics and Macroeconomics, with a focus on:

1. How spatial, informational, and institutional frictions shape worker mobility and gender inequality, and their policy implications
2. The effect of technology (such as AI) on the labor market

I am on the 2025-2026 job market.

Here is my Curriculum Vitae . You can reach me via email at: ziqing.yan@yale.edu.

Pronunciation of my name: Zi-ching.

Research

Working Papers

1. Households in Motion: Co-location Frictions and Gender Inequality

   Job Market Paper. Draft coming soon!

   Abstract

   This paper studies how co-location frictions—constraints that arise when accepting a job in another location induces job interruptions for the spouse—shape migration patterns and gender inequality in the labor market. Using data on displaced workers, I show that households are more than twice as likely to relocate after a husband’s job loss than after a wife’s. While displaced movers suffer smaller earnings losses than stayers, the gains accrue disproportionately to men, widening gender gaps. To interpret these patterns, I develop and estimate a two-location household job search model that incorporates gender-specific offer distributions, offer arrival rates, migration costs, and unequal weighting of spousal earnings. The model implies that co-location frictions account for roughly half of the gender employment gap and 8.6 percent of the wage gap. Counterfactual simulations highlight that expanding access to remote work substantially relaxes these frictions, raising women’s employment and narrowing gender disparities in post-displacement outcomes.

2. The Effect of Occupational Licensing on The Gender Wage Gap
   Under Review, [Paper], [SSRN].
   Abstract

   Occupational licensing covers one-fifth of the U.S. workforce and a quarter of female employment. This paper provides new causal evidence on its impact on the gender wage gap. Using individual-level data from the Current Population Survey and exploiting cross-state variation in licensing regulations within a two-way fixed effects framework, I find that licensing raises women’s wages by 3.7 percentage points more than men’s, narrowing the gender wage gap by 26 percent. To validate identification, I construct a novel dataset on the timing of state-occupation licensing reforms, estimate dynamic difference-in-difference models, and obtain similar results. The gap reducing effect of licensing is strongest among unionized workers, college graduates, mothers, and workers at the top and bottom of the wage distribution, for whom asymmetric information between employers and employees is particularly costly. Guided by a model of statistical discrimination, I show that licensing can mitigate the gap by signaling ability when productivity is imperfectly observed. Additional requirements bundled with licenses, such as courses, exams, and continuing education, further reduce the gap through both signaling and human capital channels, with particularly pronounced effects in states with Paid Family and Medical Leave policies, where temporary labor force interruptions for women are more common.

3. AI and Returns to Experience in Entrepreneurship (with Irisa Zhou)
   [Paper]
   Abstract

   This paper studies how advances in Artificial Intelligence (AI) have altered the value of skills accumulated through different types of work experience in entrepreneurship. Using employment histories from public LinkedIn profiles (2007-2019), we exploit industry-level variation in AI exposure following the diffusion of neural networks and ImageNet after 2012. We find that among U.S. LinkedIn users, the share of founders and researchers both increased, but entry gains were concentrated among more-experienced workers, especially those with research backgrounds. To understand the mechanism behind AI’s impact on the labor market, we develop a directed search model with occupational choice, multi-dimensional skills, and stochastic human capital investment. The model shows that AI shocks increase the productivity premium for researchers, shifting entrepreneurship toward more experienced individuals with research expertise.