Data and Code for: Matching and Network Effects in Ride-Hailing

Principal Investigator(s): View help for Principal Investigator(s) Juan Camilo Castillo, University of Pennsylvania. Department of Economics; Shreya Mathur, University of Pennsylvania. The Wharton School

Version: View help for Version V1

Name	File Type	Size	Last Modified
CodePackage			03/22/2023 03:51:PM

Project Citation:

Castillo, Juan Camilo, and Mathur, Shreya. Data and Code for: Matching and Network Effects in Ride-Hailing. Nashville, TN: American Economic Association [publisher], 2023. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2023-04-26. https://doi.org/10.3886/E186903V1

Project Description

Summary: A recent empirical literature models search and matching frictions by means of a reduced-form matching function. An alternative approach is to simulate the matching process directly. In this paper, we follow the latter approach to model matching in ride-hailing. We compute the matching function implied by the matching process. It exhibits increasing returns to scale, and it does not resemble the commonly used Cobb-Douglas functional form. We then use this matching function to quantify network externalities. A subsidy on the order of $2 per trip is needed to correct for these externalities and induce the market to operate efficiently. This repository contains the code and a subset of the data used for the paper.

Scope of Project

Subject Terms: matching functions; ride-hailing; search and matching

JEL Classification:
L91 Transportation: General
R41 Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise

Geographic Coverage: Houston, Texas, USA

Time Period(s): 3/16/2017 – 4/8/2017

Universe: Uber trips in Houston, Texas, USA

Data Type(s): administrative records data; event/transaction data

Collection Notes: Some data cannot be made publicly available. The ride-hailing data for this research is proprietary and was made available for the purposes of this project by Uber Technologies, Inc. Uber will provide access to researchers interested in obtaining access to the data under the condition that they execute a data use agreement with Uber and arrange for secure access to the relevant files. You will find in this package detailed data description as well as all the source code used to generate the data, estimations, and simulations.

Methodology

Data Source: Uber Technologies, Inc

Unit(s) of Observation: Neighborhood by Hour, Uber H3, Trip

Geographic Unit: Neighborhood

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Published Versions

V1 [2023-04-26]

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