Essays on strategic communication and advertising

This dissertation consists of three chapters, all presented as self-contained papers. The chapters are given in a natural order from broad to narrow in terms of their substantive focus. The common underlying theme in all three chapters is strategic communication, with specific applications to political advertising. In "Persuasion with Coarse Communication", we focus on a key aspect of persuasion games: the complexity of the communication between agents. Frequently, the communication capacity of an advertiser is limited due to constraints on the amount of time they have or the natural limitations of the medium they use, which limits their persuasive abilities. We begin with a motivating example in political advertising, where we demonstrate a counter-intuitive result: a customer who prefers more informative ads could prefer to limit the targeting ability of the advertiser. In general settings, we characterize optimal ways to send information, show that the sender's optimization problem can be solved by searching within a finite set, and describe the set of highest attainable payoffs for the sender using a concavification-based approach, which is a useful method to analyze how the value of precise communication depends on prior beliefs. We leverage the relationship between lower and higher dimensional solutions to the sender's problem to prove an upper bound on the marginal value of a signal. Under specific preference structures where the sender's utility is independent from the state, we show that additional signals are more valuable when it is more difficult for the sender to induce beneficial actions. Finally, we provide extensions of our model and show that the tools we develop can be applied to settings with cheap talk and heterogeneous priors. In the second chapter titled "Electoral Campaigns as Dynamic Contests", we turn to the dynamic aspects of advertising and changing opinions. We build a theoretical model of electoral campaigning, where two competing candidates make decisions on how much to spend on advertising every period leading up to an election. Our model is a general one that can be applied to any two-player dynamic contest in which the players influence a state variable which is modeled as stochastic process. We characterize the path of equilibrium spending and extend the model to allow for (i) early voting, (ii) candidates who value money left over at the end of the race, (iii) multi-district competition, and (iv) endogenous budget processes that react to short-term fluctuations in popularity. A key policy-relevant insight derived from our approach is that time-dependent dynamic regulations—for example, those that prohibit spending in the final stages of a campaign—can be welfare-enhancing and outperform static regulations—specifically, aggregate spending caps. The tractability of our setup allows us to estimate key parameters of our model using spending data from US elections. We use our model and our empirical estimates to examine the effects of dynamic campaign spending regulations. The third chapter focuses on a specific policy making problem, and is titled "Regulating Online Political Advertising". In the United States, regulations have been established in the past to oversee political advertising in TV and radio. The laws governing these marketplaces were enacted with the fundamental premise that important political information is provided to voters through advertising, and politicians should be able to easily inform the public. Today, online advertising constitutes a major part of all political ad spending, but lawmakers have not been able to keep up with this rapid change. In the online advertising marketplace, ads are typically allocated to the highest bidder through an auction. Auction mechanisms provide benefits to platforms in terms of revenue maximization and automation, but they operate very differently to offline advertising, and existing approaches to regulation cannot be easily implemented in auction-based environments. This chapter aims to provide a theoretical model and deliver key insights that can be used to regulate online ad auctions for political ads, and analyzes the implications of the proposed interventions empirically. We characterize the optimal auction mechanisms where the decision maker takes into account both the ad revenues collected and societal objectives (such as the share of ads allocated to politicians, or the prices paid by them). We use bid data generated from Twitter's political advertising database to analyze the implications of implementing these changes. The results suggest that achieving favorable outcomes for political advertisers at a small revenue cost is possible through easily implementable, simple regulatory interventions

Keywords: Business.
Advisor: Callander Steven
Acharya Avidit
Martin Gregory (Gregory J.)