“Product Quality and Entering through Tying: Experimental Evidence” (with Michael Luca). Management Science 65(2), February 2019: 596-603.
Dominant platform businesses often develop products in adjacent markets to complement their core business. One common approach used to gain traction in these adjacent markets has been to pursue a tying strategy. For example, Microsoft preinstalled Internet Explorer into Windows, and Apple set Apple Maps as the iOS default. Policymakers have raised concerns that dominant platforms may be leveraging their market power to gain traction for lower quality products when they use a tying strategy. In this paper, we empirically explore this question by examining Google’s decision to tie its new reviews product to its search engine. We experimentally vary the reviews displayed above Google’s organic search results to show either exclusively Google reviews (Google’s current tying strategy) or reviews from multiple platforms determined to be the best-performing by Google’s own organic search algorithm. We find that users prefer the version that does not exclude competitor reviews. Furthermore, looking at observational data on user traffic to Yelp from search engines, we find that Google’s exclusion of downstream competitors may have been effective. The share of Yelp’s traffic coming from Google has declined over this period, relative to traffic from Bing and Yahoo (which do not exclude other companies’ reviews), and Google reviews has grown more quickly than Yelp and TripAdvisor during the period in which they excluded these (and other) reviews providers. Overall, these results suggest that tying has the potential to facilitate entry in complementary markets even when the tied product is of worse quality than competitors.
“Nowcasting Gentrification: Using Yelp Data to Quantify Neighborhood Change” (with Edward L. Glaeser and Michael Luca). American Economic Review Papers and Proceedings 108 (May 2018): 77–82.
Data from digital platforms have the potential to improve our understanding of gentrification, both by predicting gentrification and by characterizing the local economy of gentrifying neighborhoods. To explore, we identify gentrifying neighborhoods using government data, and then use Yelp data to analyze local business activity. We find that gentrifying neighborhoods tend to have growing numbers of local groceries, cafes, restaurants, and bars, with little evidence of crowd-out of other types of businesses. Moreover, local economic activity, as measured by Yelp data, is a leading indicator for housing price changes and can help to predict which neighborhoods are gentrifying.
“Nowcasting the Local Economy: Using Yelp Data to Measure Economic Activity” (with Edward L. Glaeser and Michael Luca). Big Data for 21st Century Economic Statistics (NBER Vol. 79). University of Chicago Press, February 2022.
Can new data sources from online platforms help to measure local economic activity? Government datasets from agencies such as the U.S. Census Bureau provide the standard measures of local economic activity at the local level. However, these statistics typically appear only after multi-year lags, and the public-facing versions are aggregated to the county or ZIP code level. In contrast, crowdsourced data from online platforms such as Yelp are often contemporaneous and geographically finer than official government statistics. In this paper, we present evidence that Yelp data can complement government surveys by measuring economic activity in close to real time, at a granular level, and at almost any geographic scale. Changes in the number of businesses and restaurants reviewed on Yelp can predict changes in the number of overall establishments and restaurants in County Business Patterns. An algorithm using contemporaneous and lagged Yelp data can explain 29.2 percent of the residual variance after accounting for lagged CBP data, in a testing sample not used to generate the algorithm. The algorithm is more accurate for denser, wealthier, and more educated ZIP codes.
“Which Firms Gain from Digital Advertising?” (with Daisy Dai and Michael Luca). January 2023. Accepted at Marketing Science.
To what extent are firms knowledgeable of available information on key competitor decisions, and how does competitor information change their own strategic choices? These questions are fundamental to understanding how firms compete and make strategic decisions, yet systematic evidence on them remains limited. I designed a field experiment across 3,218 firms in the personal care industry, where firms randomly assigned to treatment received easily accessible information on competitor prices. At baseline, nearly half of treatment firms were unable to specify competitor prices. However, once treatment firms received competitor information, they were more likely to change their prices, aligning their decisions with competitors rather than differentiating from them. These changes were driven by firms that were more misaligned in their price and quality decisions, and treatment firms subsequently observed higher measures of performance. If competitor information was both easily accessible and decision-relevant, why did firms not use this information on their own? Results from a follow-up experiment suggest that their lack of knowledge may have been driven by managerial inattention. These findings highlight the role that attention may play over information access in improving firm decisions, and suggest that the growing availability of competitor data across many markets may lead firms to become increasingly similar.
"Decision Authority and the Returns to Algorithms” (with Edward L. Glaeser, Andrew Hillis, Scott Duke Kominers, and Michael Luca). December 2022.
"The Impact of Communicating Multiple Goals." January 2023.
“Measuring Gentrification: Using Yelp Data to Quantify Neighborhood Change” (with Edward L. Glaeser and Michael Luca). NBER Working Paper Series, No. 24952.
We demonstrate that data from digital platforms such as Yelp have the potential to improve our understanding of gentrification, both by providing data in close to real time (i.e. nowcasting and forecasting) and by providing additional context about how the local economy is changing. Combining Yelp and Census data, we find that gentrification, as measured by changes in the educational, age, and racial composition within a ZIP code, is strongly associated with increases in the numbers of grocery stores, cafes, restaurants, and bars, with little evidence of crowd-out of other categories of businesses. We also find that changes in the local business landscape is a leading indicator of housing price changes, and that the entry of Starbucks (and coffee shops more generally) into a neighborhood predicts gentrification. Each additional Starbucks that enters a zip code is associated with a 0.5% increase in housing prices.