ABSTRACT

We study how changes in the effectiveness of targeted digital advertising shape firm behavior and product-market outcomes. We leverage the introduction of Apple’s App Tracking Transparency (ATT) framework, which limited online advertisers’ access to user data and thus substantially reduced their advertising effectiveness. Using proprietary advertiser-level data from Meta and industry-level outcomes from the Bureau of Labor Statistics, we show that greater exposure to the ATT shock led to lower ad spend, fewer establishments, and higher product prices. The findings suggest that digital advertising primarily expands markets and that privacy policies can have unintended consequences on product market competition. JEL classifications: D40, L10, L51, M37

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

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

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