Frictions in a Competitive, Regulated Market Evidence from Taxis

Abstract

an editor and four referees offered very helpful feedback. Milena Almagro Garcia provided excellent research assistantship. We gratefully acknowledge financial support from the National Science Foundation via grant SES-1558857. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

Spatial Distribution of Supply and the Role of Market Thickness: Theory and Evidence from Ride Sharing

“…Variants of this method are being developed and leveraged by papers that study questions related to ours (Brancaccio et al, 2019a,b,c;Buchholz, 2018;Frechette et al, 2019).…”

“…Papers that study spatial demand-supply mismatches (e.g., Buchholz (2018); Lagos (2000); Afèche et al (2018); Banerjee et al (2018)) focus on other mechanisms, mainly search frictions. Papers that study the consequences of market thickness (such as Frechette et al (2019); Nikzad (2018)) have not looked at its implications for the geographical distribution of supply. We attempt to bring these two pieces together both empirically and theoretically.…”

Linear IV Regression Estimators for Structural Dynamic Discrete Choice Models

“…A primary goal of labor economics is to understand how agents make labor supply decisions. Recent studies have used the DDC framework to investigate the daily labor supply decisions of taxi drivers, taking advantage of available rich data on taxi trips in New York City (Buchholz, 2019;Fréchette, Lizzeri, and Review, 2019;Schmidt, 2019). In our empirical application, we make use of this data to estimate taxi drivers' optimal stopping decisions.…”

“…Expected market hourly earnings w mt is defined as the total earnings (including fare and tips) averaged across drivers in that hour t of that day m. We restrict our sample to consider only drivers who were working during the day shifts, and that worked for at least one hour and at most 12 hours. Following the literature, we also restrict the sample to the weekdays Monday-Thursday because supply patterns across these days are very similar (Buchholz, 2019;Fréchette, Lizzeri, and Review, 2019). The final data set used for estimation is an unbalanced panel with N = 3, 937 drivers observed in M = 209 days during the T = 12 hours between 5 AM and 5PM.…”

Battle of Sharing Economy: The Contingent Effects of Ride-Sharing on Taxi Industry

“…Ride-sharing platforms facilitate to seamlessly connect customers to the nearest driver when they request a ride. Using a simulation approach in estimating a dynamic general equilibrium model of the taxi market in NYC in 2011-2012, Frechette et al [19] argue that it was estimated to reduce the search time for taxis by 9.3 percent if assuming that drivers knew the location of the closest passengers, as the case of ridesharing platforms. Reduced drivers' search costs increases expected payoffs from driving in the suburbs of the city, leading to a broader services provision.…”