Photo by Vanessa Coleman
Photo by Vanessa Coleman
I am a PhD candidate in Economics at Stanford University, specializing in behavioral, experimental, and labor economics. My research spans two main areas: one focuses on inequality and discrimination, and the other explores how time influences preferences and behavior.
You can find my CV here.
Contact: cmeyer20@stanford.edu
with Nina Buchmann and Colin Sullivan
Revise and Resubmit, Econometrica
We combine two field experiments in Bangladesh with a structural labor model to identify paternalistic discrimination, the differential treatment of two groups to protect one group, even against its will, from harmful or unpleasant situations. We observe hiring and application decisions for a night-shift job that provides worker transport at the end of the shift. In the first experiment, we use information about the transport to vary employers' perceptions of job costs to female workers while holding taste-based and statistical discrimination constant: Not informing employers about the transport decreases demand for female labor by 21%. Employers respond more to transport information than cash payments to female workers that enable workers to purchase transport themselves. In the second experiment, not informing applicants about the transport reduces female labor supply by 15%. In structural simulations, paternalistic discrimination has a larger effect on gender employment and wage gaps than taste-based and statistical discrimination.
Media: IDEAS FOR INDIA, VoxDev, Econimate
with Zach Freitas-Groff and Trevor Woolley
Kilts Center at Chicago Booth Marketing Data Center Paper
Revise and Resubmit, European Economic Review
Over the past two decades, U.S. meat consumption has remained high while purchases of plant-based alternatives have grown, raising questions about whether consumer behavior is shifting. Answering this question is difficult: surveys overstate meat avoidance due to social-desirability bias, and aggregated data obscure heterogeneity across households and regions. To address these challenges, we use a nationally representative household panel (2004-2020) linked with ingredient-level product data and develop a machine-learning-based classification of grocery purchases. We show that, despite modest growth in aggregate meat purchases, the share of households buying no meat rises by about 10% and the share buying no animal products nearly doubles, revealing growing polarization in dietary behavior. These patterns predate the introduction of modern plant-based meat alternatives, whose limited market share cannot explain the observed changes. Demographic analyses indicate that growing meat- and animal product-avoidance is driven largely by population turnover rather than behavioral change within existing consumers. Our findings reconcile persistently high aggregate meat consumption with the increasing visibility of meat avoidance.
Media: Vox