A Check on Gig Driver Background Checks: Make Sure or Detour
Abstract:
The fast expansion of ridesharing has buoyed political debates on running background checks on drivers. Though these checks protect the public by reducing contact with criminals in these services, they also eliminate an accessible income source for marginalized people, which clouds their overall effectiveness. Exploiting a ragged difference-in-difference design on monthly crime data, corroborated by our analytical prediction based on a discrete choice model, we settle the doubt on its policy effectiveness. Although we find the passage of BCL to have a negative impact on the total reported TNC-related incidents, its effect on the total number of criminal cases, especially property crimes, is surprisingly positive. Our paper highlights the importance of overall social welfare assessment beyond a specific realm in public policymaking.
Bio:
Yanzhen Chen is an Assistant Professor at HKUST, ISOM Department. She received her Ph.D. from the University of Texas at Austin. Her research focuses on artificial intelligence in transforming financial analytics, the future of labor markets, and the sharing economy. She has been invited to present her work to the Federal Reserve Bank at Philadelphia and the Office of the Comptroller of the Currency, U.S. Department of Treasury. She also has work published in journals of IS and statistics, such as MISQ and Biometrika.