“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). Accepted in NBER/CRIW Volume for Big Data for 21st Century Statistics (forthcoming).
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.
"The Value of Competitor Information: Evidence from a Field Experiment." Working Paper, October 2021.
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). Working Paper, October 2021.
Algorithms have the potential to improve managerial decisions—but the returns depend on how decision-makers use them. We explore a pilot run by an Inspectional Services Department to test a pair of predictive algorithms that vary in their sophistication and inputs. We find that both algorithms provide substantial gains on the department’s measure of interest compared to human judgment. But, there is little difference in performance between the two, suggesting that in this context the greatest gains stem from integrating any data rather than from algorithmic sophistication. Despite these measurable gains, decision-makers are only half as likely to follow algorithmic recommendations compared to their own judgment. Qualitative and exploratory empirical evidence suggests that this effect is driven, at least in part, by inspectors’ differing predictions that rely on their own intuition about which features are most predictive. Our findings suggest that for algorithms to translate into improved managerial decisions, organizations must carefully manage how decision authority is allocated and used, and that simple rules based on intuition may become an impediment to effective use of algorithms for decision-making.
“Which Firms Gain from Digital Advertising?” (with Daisy Dai and Michael Luca). Working Paper, March 2020.
In recent years, digital advertising has been on the rise, enabling companies to target their advertising using granular data on customers that were not previously available through traditional channels. We empirically evaluate heterogeneity in the returns to advertising across a variety of business attributes, to understand which companies might gain more from advertising. We design and run a field experiment in collaboration with Yelp across over 18,000 firms in the restaurant industry, where we randomly assign over 7,000 restaurants to freely receive Yelp’s standard ads package over a three-month period. We find that restaurants that receive three months of free advertising on Yelp observe on average a 7-19% increase in a wide range of customer purchase intentions, as well as a 5% increase in customer reviews. We find that gains are heterogeneous across firms, with newer, independent, and higher-rated businesses observing larger returns to advertising. These results support the informative view of advertising, implying that high-quality firms that are not yet prominently known to customers gain more from digital advertising. However, these returns dissipate almost immediately once the free advertising is suspended, suggesting that advertising may only temporarily raise customer awareness.
“Aligning Employee Effort to Strategic Change: The Role of Gift Exchange” (with Michael Norton). Working Paper, September 2019.
A large literature across strategy and organizational theory suggests that aligning employee behaviors to execute strategic change is difficult. We explore whether gifting can help organizations align employee behavior to strategic change. We propose that gifts may be useful in eliciting behavior reorientation in settings like strategic change where formal incentives may be insufficient, as they are based on reciprocity rather than contingent on measurable outcomes. We examine this empirically by running a field experiment in a company that embarked on a new strategic direction requiring employees to shift from maximizing individual performance to collaborating together. To explore whether and how gifts can help firms align employee efforts to new goals, we introduce in-kind gifts from managers in two ways: one using a relational frame (“Thanks for your hard work collaborating!”) and the other using an additive incentive frame (“Thanks for your hard work collaborating! As a reminder, under the new HR reform, higher team performance will increase your bonus.”). Compared to employees who receive no gift, both gift treatments increase employees' reported willingness to help co-employees by some 30 minutes per day. We also find that the incentive frame significantly reduces the willingness to help of high-performing employees, providing suggestive evidence that gift exchange may work through developing relational ties that motivate desired employee behaviors.
“Measuring Gentrification: Using Yelp Data to Quantify Neighborhood Change” (with Edward L. Glaeser and Michael Luca). NBER Working Paper Series, No. 24952, August 2018.
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.