Abstract
We estimate a model of consumer choices over restaurants using data from several thousand anonymous mobile phone users. Restaurants have latent characteristics (whose distribution may depend on restaurant observables) that affect consumers' mean utility as well as willingness to travel to the restaurant, while each user has distinct preferences for these latent characteristics. We analyze how consumers reallocate their demand after a restaurant closes to nearby restaurants versus more distant restaurants, comparing our predictions to actual outcomes. We also address counterfactual questions such as what type of restaurant would attract the most consumers in a given location.
Highlights
This Online Appendix begins by providing details of the data and dataset creation
From Yelp, we obtained a list of restaurants, locations, ratings, price ranges, and categories, and we infer dates of openings and closings from the dates on which consumers created a listing on Yelp or marked a location as closed, respectively
We begin with the set of restaurants known to Yelp in the San Francisco Bay Area, which we reduce through the following restrictions:
Summary
Our dataset is constructed using data from SafeGraph, a company which aggregates locational information from anonymous consumers who have opted in to. The data consists of “pings” from consumer phones; each observation includes a unique device id that we associate with a single consumer; the time and date of the ping; and the latitude and longitude and horizontal accuracy of the ping, all for smartphones in use during the sample period from January through October 2017. From Yelp, we obtained a list of restaurants, locations, ratings, price ranges, and categories, and we infer dates of openings and closings from the dates on which consumers created a listing on Yelp or marked a location as closed, respectively
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