I am a first-year Economics Ph.D. student at UC Berkeley. My research interests are applied microeconomics, with most of my ongoing research in labor and innovation.
Previously I was a research associate at Harvard where I worked on projects related to political economy and econometrics. I hold a B.A. from Middlebury College.
We study how grading policies shape employers’ interpretations of labor market signals embedded in academic credentials. In our experiment, hiring managers observe letter grades assigned to math tests taken by job candidates and make wage offers to match their beliefs about each candidate’s underlying ability. We exogenously vary the coarseness of the grading scheme while holding candidate performance constant. As predicted, coarser grading leads managers to place less weight on grade signals and more on prior beliefs, reducing match efficiency. Departing from predictions, managers extract systematically higher signals from inflated grades, behaving as if candidates with As represent a positively selected pool. Furthermore, managers place greater decision weight on inflated As than on compressed Bs, creating a compounding wage advantage for candidates even though grade inflation is common knowledge. Considering the broader implications of our results, the shift toward prior-based evaluation under coarser grading falls disproportionately on female candidates, contributing to a wider gender wage gap among managers with gendered priors.
@techreport{Pu26,category={Working Papers},author={Pu, Zhizhong and Abel, Martin and Carpenter, Jeffrey P.},title={Grade Inflation and the Interpretation of Labor Market Signals},year={2026},month=may,institution={Institute of Labor Economics (IZA)},address={Bonn},type={IZA Discussion Paper},number={18654},keywords={grade inflation;signaling;hiring experiment;gender wage gap;statistical discrimination},}
