ABSTRACT

Why do establishments exhibit wide variation in their productivity and profitability? Can variation in returns to advertising help answer this question? We present results from a large field experiment on Facebook and Instagram that documents variance in advertisers’ ability to generate returns to advertising. We focus on campaigns aimed at boosting sales and tie advertising expenses to revenues for each advertiser. We find that spending on advertising led to significant increases in revenues, number of purchases, number of purchasers, and number of conversions. The heterogeneity in these results by expenditure, age, and engagement documents patterns consistent with learning by doing and variance in how sophisticated advertisers are. Advertisers who engage in more learning activities and more sophisticated data collection exhibit the highest returns and are more likely to continue their activities over time, suggesting that differences in advertising effectiveness may account for some of the variance in productivity across firms.

ABSTRACT

Bargaining breakdown is common in bargaining in environments with incomplete information. We study whether, in these environments, permitting communication impacts bargaining outcomes. On May 23, 2016, eBay Germany’s Best Offer platform introduced unstructured communication allowing desktop users, but not the mobile users, to accompany offers with a message. Using this natural experiment, our difference- in-differences approach documents a 14% decrease in the the rate of breakdown among compliers. Though adoption is immediate, the effect is not. We show, using text analysis, that the dynamics are consistent with repeat players learning how to use communication in bargaining. Finally, tying the two results together, we show that messages that emulate the text content of experienced sellers are more likely to be accepted. JEL classifications: C78, D82, D83, M21. 

ABSTRACT

Concerns have recently been raised that rapid technological development in artificial intelligence and ecommerce would help facilitate collusive behavior and threaten competition. We build on several strands of research in economics to assess whether algorithmic pricing poses new threats to competition, and, more importantly, whether new enforcement tools are needed to respond to these potential threats. We argue that the concerns raised are overly aggressive because of a basic and fundamental difficulty of achieving coordination in real-world pricing games, which cannot be easily overcome by algorithms. We further argue that current policy and enforcement tools seem broadly adequate to address the threats of collusion that can be expected to be present when pricing algorithms are used, at least until more rigorous research shows otherwise. JEL classifications: D43, K21, L13, L41.

ABSTRACT

Reputation mechanisms used by platform markets suffer from two problems. First, buyers may draw conclusions about the quality of the platform from single transactions, causing a reputational externality. Second, reputation measures may be coarse or biased, preventing buyers from making proper inferences. We document these problems using eBay data and claim that platforms can benefit from identifying and promoting higher quality sellers. Using an unobservable measure of seller quality we demonstrate the benefits of our approach through a large-scale controlled experiment. Highlighting the importance of reputational externalities, we chart an agenda that aims to create more realistic models of platform markets. JEL classifications: D47, D82, L15, L21, L86 

ABSTRACT

A mounting number of studies suggest that individuals are not selfish, which perhaps explains the prevalence of trust among strangers. Models of players who care about their opponents' payoffs have been used to rationalize these facts. An alternative motive is that players care directly about how they are perceived by others. I propose and implement an experimental design that distinguishes perception motives from payoff motives. Participants not only exhibit concerns for perception, but they seem strategically rational by anticipating the change in behavior of their opponents. The approach can explain previously documented behaviors, both in the lab and in the field, and can shed light on some determinants of trust. JEL classifications C72, C91, D03, D82