Published Papers

We examine the consequences of an intrusive debt-collection tactic that targets delinquent borrowers' social circles. Our identification strategy relies on the fact that some of the delinquent loans are not worked on because of collection agents' excessive workload. Using two approaches to estimate the local treatment effect, we show that this social-shaming tactic backfires and substantially increases the borrowers' default rate. Borrowers with better outside options for credit access and male borrowers respond more strongly after they are shamed socially. These findings are in general consistent with the negative reciprocity interpretation; angered borrowers retaliate by defaulting on their loans.

This paper quantifies the effects of online review platforms on restaurant revenue and consumer welfare. Using a novel data set containing revenues and information from major online review platforms in Texas, I show that online review platforms help consumers learn about restaurant quality more quickly. The effects on learning show up in restaurant revenues. Specifically, doubling the review activity increases the revenue of a high-quality independent restaurant by 5%-19% and decreases that of a low-quality restaurant by a similar amount. These effects vary widely across restaurants' locations. Restaurants around highway exits are affected twice as much as those in nonhighway areas, implying that reviews are more useful to travelers and tourists than locals. The effects also decline as restaurants age, consistent with the diminishing value of information in learning. In contrast, chain restaurants are affected to a much lesser degree than independent restaurants. Building on this evidence, I develop a structural demand model with aggregate social learning. Counterfactual analyses indicate that online review platforms raise consumer welfare much more for tourists than for locals. By encouraging consumers to eat out more often at high-quality independent restaurants, online review platforms increased the total industry revenue by 3.0% over the period from 2011-2015.

We examine how freemium platforms can design social referral programs to encourage growth and engagement without sacrificing revenue. On the one hand, social referral programs generate new referrals from users who would not have paid for the premium features. On the other hand, they also attract new referrals from users who would have paid but prefer to invite others, resulting in more referrals but fewer paying users. We use data from a large-scale randomized field experiment in an online dating platform to assess the effects of adding referrals programs to freemium platforms and changing the referral requirements on users' behavior, namely, on their decisions to invite, pay, and engage with the platform. We find that introducing referral programs in freemium platforms can significantly contribute to increasing the number of referrals at the expense of revenue. Platforms can avoid the loss in revenue by reserving some premium features exclusively for paying users. We also find that increasing referral requirements in social referral programs can work as a double-edged sword. Increasing the referral threshold results in more referrals and higher total revenue. Yet these benefits appear to come at a cost. Users become less engaged, decreasing the value of the platform for all users. We explore two mechanisms that help to explain the differences in users' social engagement. Finally, and contrary to prior findings, we find that the quality of the referrals is not affected by the referral requirements. We discuss the theoretical and practical implications of our research.

In a finitely repeated game with asymmetric information, we experimentally study how individuals adapt the nature of their lies when settings allow for reputation building. Although some lies can be detected ex post by the uninformed party, others remain deniable. We find that traditional market mechanisms, such as reputation, generate strong changes in the way people lie and lead to strategies in which individuals can maintain plausible deniability; people simply hide their lies better by substituting deniable lies for detectable lies. Our results highlight the limitations of reputation to root out fraud when a deniable lie strategy is available.

We experimentally study information transmission by experts motivated by their reputation for being well-informed. In our game of reputational cheap talk, a reporter privately observes information about a state of the world and sends a message to an evaluator; the evaluator uses the message and the realized state of the world to assess the reporter's informativeness. We manipulate the key driver of misreporting incentives: the uncertainty about the phenomenon to forecast. We highlight three findings. First, misreporting information is pervasive even when truthful information transmission can be an equilibrium strategy. Second, consistent with the theory, reporters are more likely to transmit information truthfully when there is more uncertainty on the state. Third, evaluators have difficulty learning reporters' strategies and, contrary to the theory, assessments react more strongly to message accuracy when reporters are more likely to misreport. In a simpler environment with computerized evaluators, reporters learn to best reply to evaluators' behavior and, when the state is highly uncertain and evaluators are credulous, to transmit information truthfully.

Content-sharing social network platforms rely heavily on user-generated content to attract users and advertisers, but they have limited authority over content provision. We develop an intervention that leverages social interactions between users to stimulate content production. We study social nudges, whereby users connected with a content provider on a platform encourage that provider to supply more content. We conducted a randomized field experiment (N=993,676) on a video-sharing social network platform where treatment providers could receive messages from other users encouraging them to produce more, but control providers could not. We find that social nudges not only immediately boosted video supply by 13.21% without changing video quality but also, increased the number of nudges providers sent to others by 15.57%. Such production-boosting and diffusion effects, although declining over time, lasted beyond the day of receiving nudges and were amplified when nudge senders and recipients had stronger ties. We replicate these results in a second experiment. To estimate the overall production boost over the entire network and guide platforms to utilize social nudges, we combine the experimental data with a social network model that captures the diffusion and over-time effects of social nudges. We showcase the importance of considering the network effects when estimating the impact of social nudges and optimizing platform operations regarding social nudges. Our research highlights the value of leveraging co-user influence for platforms and provides guidance for future research to incorporate the diffusion of an intervention into the estimation of its impacts within a social network.

Social media influencers are category enthusiasts who often post product recommendations. Firms sometimes pay influencers to skew their product reviews in favor of the firm. We ask the following research questions. First, what is the optimal level of affiliation (if any) from the firm's perspective? Affiliation introduces positive bias to the influencer's review but also decreases the persuasiveness of the review. Second, because affiliated reviews are often biased in favor of the firm, what is the impact of affiliation on consumer welfare? We find that the affiliation decision depends on the cost of information acquisition, the consumer's prior and awareness, and the disclosure regime. When the consumer's prior belief is low, the firm needs to affiliate less closely or not at all in order to preserve the influencer's persuasiveness, the change in the consumer's belief following the influencer's review. In contrast, when the consumer's prior belief is high, the firm fully affiliates with the influencer to both maximize awareness and prevent a negative review. We also show that the firm's involvement can be Pareto improving if the information acquisition cost is relatively high, and a partial disclosure rule may increase consumer welfare.

Brands increasingly face pressure from consumers to take a stance on political issues, but there is limited empirical evidence on the effect of political consumerism on sales. In this paper, we quantify the consequences of a brand taking a political stance. In July 2020, the chief executive officer of Goya, a large Latin food brand, praised then president Donald Trump, triggering a boycott and a counter "buycott" movement supporting the brand. Using consumer-level purchase data, we measure the net effect of the boycott/buycott movements on sales. Boycott-related social media posts and media coverage dominated buycott ones, but the sales impact was the opposite: Goya sales temporarily increased by 22%. However, this net sales boost fully dissipated within three weeks. We then explore heterogeneity in the sales response with the goal of understanding which households are most likely to engage in political consumerism and what factors serve as frictions to participation. We document large sales increases (56.4%) in heavily Republican counties but do not find a strong countervailing boycott effect in heavily Democratic counties or among Goya's core customer base-Latino consumers. Finally, we show that brand loyalty and switching costs are potential explanations for the limited evidence of boycotting among experienced Goya customers.

We study how an online marketplace's personalized product recommendations and its consumer profiling accuracy affect third-party sellers' competition and the market outcomes. Sellers strategically adjust prices to compete for the marketplace's recommendations. As the marketplace more accurately predicts consumers' preferences, the equilibrium price first decreases and then increases, and both the marketplace's and the sellers' profits may decrease despite the improved recommendation accuracy. Moreover, recommending the most profitable product for each recommendation may reduce profits of the marketplace and the sellers, and the marketplace can benefit from excluding pricing information in its recommendation decisions to prevent sellers' recommendation competition. Counterintuitively, regulations that bar recommendations from considering profit margin information can lead to higher prices and thus harm consumers. These results are driven by competing sellers' three distinctive incentives: competing for recommendations, exploiting targeted consumers, and undercutting rivals' prices. We also find that our key insights remain qualitatively unchanged if the marketplace recommends products based on consumer surplus, and the equilibrium price will be lower in comparison. Finally, various extensions demonstrate the robustness of these results.

We use posts on the investor-focused StockTwits social media network to generate new insights regarding investor disagreement, disclosure processing costs, and trading volume around earnings announcements. Using social media-based measures of disagreement, we find that both preannouncement disagreement and increases in disagreement around an earnings announcement are positively associated with trading volume. Drawing upon the disclosure processing costs literature, we provide evidence that the effects of disagreement increase when disclosure processing costs are lower. Our social media measures of disagreement remain significant after including traditional analyst earnings estimate measures of disagreement in the model. Our study provides new evidence on the importance of disclosure processing costs and is consistent with lower disclosure processing costs amplifying both the resolution of preannouncement disagreement and new disagreement about earnings information.

Performance ranking can trigger multiple social incentives for workers. On one hand, it offers status rewards to induce them to increase effort. On the other, better-ranked workers may reduce effort to conform to coworkers' productivity in fear of social retribution. This paper uses a field experiment in a sweater factory to disentangle the incentives underlying performance ranks. Treated workers receive ranks either privately or publicly. I find that private ranks do not have any effect on average but that public ranks reduce worker productivity. Additional evidence confirms that productivity declines because of workers' social concerns and their desire to conform to the productivity of their friends. Cooperation between workers decreases too but with limited effect on productivity. The paper illustrates how inducing worker competition may be counterproductive for firms.

This study examines the informational value of local news outlets and how they affect insider trading. We hypothesize that local news coverage is a critical channel through which outsiders acquire local information, which restricts insiders' ability to profit from their information advantage. We argue that a loss of local news coverage increases information opacity faced by outsiders, while making it easier for insiders to seize profitable trading opportunities. Exploring the staggered shutdown of local newspapers, our difference-in-differences estimation presents novel evidence that insiders from closure counties trade more profitably after local newspaper closures, particularly in small firms that lack alternative news sources. Further analyses reaffirm that the post-closure increase in trading profits is unlikely to be wholly driven by regional economic conditions and is likely driven by increased information costs. Our results highlight that local newspapers play a meaningful role in mitigating information asymmetry between insiders and outsiders.