nep-mic New Economics Papers
on Microeconomics
Issue of 2023‒05‒08
twelve papers chosen by
Jing-Yuan Chiou
National Taipei University

  1. Data, Competition, and Digital Platforms By Dirk Bergemann; Alessandro Bonatti
  2. Influence or Advertise: The Role of Social Learning in Influencer Marketing By Ron Berman; Aniko Oery; Xudong Zheng
  3. Waiting for Fake News By Raphael Boleslavsky
  4. Interoperability between Ad-Financed Platforms with Endogenous Multi-Homing By Marc Bourreau; Adrien Raizonville; Guillaume Thébaudin
  5. Eliciting Awareness By Evan Piermont
  6. Managed Campaigns and Data-Augmented Auctions for Digital Advertising By Dirk Bergemann; Alessandro Bonatti; Nicholas Wu
  7. Screening while Controlling an Externality By Franz Ostrizek; Elia Sartori
  8. Diversity Fosters Learning in Environments with Experimentation and Social Learning By Cunha, Douglas; Monte, Daniel
  9. The Devil You Know: Rational Inattention to Discrete Choices when Prior Information Matters By Bruno Pellegrino
  10. Redeeming Falsifiability? By Mark Whitmeyer; Kun Zhang
  11. On the state-space model of unawareness By Alex A. T. Rathke
  12. Politicians, Bureaucrats, and the Battle for Credit By Awad, Emiel; Karekurve-Ramachandra, Varun; Rothenberg, Lawrence

  1. By: Dirk Bergemann (Cowles Foundation, Yale University); Alessandro Bonatti
    Abstract: We analyze digital markets where a monopolist platform uses data to match multiproduct sellers with heterogeneous consumers who can purchase both on and off the platform. The platform sells targeted ads to sellers that recommend their products to consumers and reveals information to consumers about their values. The revenueoptimal mechanism is a managed advertising campaign that matches products and preferences efficiently. In equilibrium, sellers offer higher qualities at lower unit prices on than off the platform. Privacy-respecting data-governance rules such as organic search results or federated learning can lead to welfare gains for consumers.
    Keywords: Data, Data, Privacy, Data Governance, Digital Advertising, Competition, Digital Platforms, Digital Intermediaries, Personal Data, Matching, Price Discrimination, Automated Bidding, Algorithmic Bidding, Managed Advertising Campaigns, Showrooming
    JEL: D18 D44 D82 D83
    Date: 2023–04
  2. By: Ron Berman (University of Pennsylvania - The Wharton School); Aniko Oery (Cowles Foundation, Yale University); Xudong Zheng (Johns Hopkins University, Department of Economics)
    Abstract: We compare influencer marketing to targeted advertising from information aggregation and product awareness perspectives. Influencer marketing leverages network effects by allowing consumers to socially learn from each other about their experienced content utility, but consumers may not know whether to attribute promotional post popularity to high content or high product quality. If the quality of a product is uncertain (e.g., it belongs to an unknown brand), then a mega influencer with consistent content quality fosters more information aggregation than a targeted ad and thereby yields higher profits. When we compare influencer marketing to untargeted ad campaigns or if the product has low quality uncertainty (e.g., belongs to an established brand), then many micro influencers with inconsistent content quality create more consumer awareness and yield higher profits. For products with low quality uncertainty, the firm wants to avoid information aggregation as it disperses posterior beliefs of consumers and leads to fewer purchases at the optimal price. Our model can also explain why influencer campaigns either "go viral" or "go bust, " and how for niche products, micro-influencers with consistent content quality can be a valuable marketing tool.
    Date: 2023–01
  3. By: Raphael Boleslavsky
    Abstract: This paper studies a dynamic model of information acquisition, in which information might be secretly manipulated. A principal must choose between a safe action with known payoff and a risky action with uncertain payoff, favoring the safe action under the prior belief. She may delay her decision to acquire additional news that reveals the risky action's payoff, without knowing exactly when such news will arrive. An uninformed \aaa{} with a misaligned preference may have the capability to generate a false arrival of news, which is indistinguishable from a real one, distorting the information content of news and the principal's search. The analysis characterizes the positive and normative distortions in the search for news arising from such manipulation, and it considers three remedies that increase the principal's payoff: a commitment to naive search, transfer of authority to the agent, and delegation to an intermediary who is biased in the agent's favor.
    Date: 2023–04
  4. By: Marc Bourreau; Adrien Raizonville; Guillaume Thébaudin
    Abstract: Platform interoperability is considered a powerful tool to promote competition in digital markets when network effects are at play. We study the effect of interoperability on competition between two ad-financed platforms, allowing for endogenous multi-homing of consumers. When the platforms are symmetric and decide non-cooperatively on their level of interoperability, interoperability emerges in equilibrium if the value of multi-homers relative to single-homers is sufficiently low for advertisers. From a welfare perspective, the equilibrium level of interoperability can be either too low or too high. When one (“large”) platform has an installed base of customers, its incentive to make its services interoperable is lower than for the other, smaller platform. However, mandating interoperability between the asymmetric platforms is not always socially optimal.
    Keywords: interoperability, platform competition, multi-homing, advertising
    JEL: L13 L86 L15
    Date: 2023
  5. By: Evan Piermont
    Abstract: This paper examines how a decision maker might incentive an expert, who is more aware than himself, to reveal novel contingencies. In particular, the decision maker will take an action that determines, along with the resolution of uncertainty, the payoffs to both players. I delineate the exact level of commitment needed to ensure full revelation: any fully revealing incentive scheme is equivalent to a dynamic game wherein each round the decision maker proposes a plan of action and the expert chooses to reveal some novel contingencies. The game ends when nothing novel is revealed and the expert chooses which of the proposed plans gets implemented. That is, the decision maker must be able to commit that once a plan of action is proposed, it cannot be taken off the table. I then consider the set of robust strategies -- those strategies that maximize the worst case outcome across any possible awareness type of the expert -- and show they are characterized by a principle of myopic optimality: at each round, the decision maker maximizes his payoff as if the expert had nothing further to reveal. It turns out, this myopic principle also solves the mechanism designer's problem: the set of outcomes that are achievable by any incentive compatible and Pareto efficient mechanism are delineated by the set of robust strategies in the above dynamic game.
    Date: 2023–04
  6. By: Dirk Bergemann (Cowles Foundation, Yale University); Alessandro Bonatti (MIT Sloan School of Management); Nicholas Wu (Cowles Foundation, Yale University)
    Abstract: We develop an auction model for digital advertising. A monopoly platform has access to data on the value of the match between advertisers and consumers. The platform support bidding with additional information and increase the feasible surplus for on-platform matches. Advertisers jointly determine their pricing strategy both on and off the platform, as well as their bidding for digital advertising on the platform. We compare a data-augmented second-price auction and a managed campaign mechanism. In the data-augmented auction, the bids by the advertisers are informed by the data of the platform regarding the value of the match. This results in a socially efficient allocation on the platform, but the advertisers increase their product prices off the platform to be more competitive on the platform. In consequence, the allocation off the platform is inefficient due to excessively high product prices. The managed campaign mechanism allows advertisers to submit budgets that are then transformed into matches and prices through an autobidding algorithm. Compared to the data-augmented second-price auction, the optimal managed campaign mechanism increases the revenue of the digital platform. The product prices off the platform increase and the consumer surplus decreases.
    Date: 2023–04
  7. By: Franz Ostrizek (ECON - Département d'économie (Sciences Po) - Sciences Po - Sciences Po - CNRS - Centre National de la Recherche Scientifique); Elia Sartori
    Abstract: We propose a tractable framework to introduce externalities in a screening model. Agents differ in both payoff-type and influence-type (ranking how beneficial their actions are for others). Applications range from pricing network goods to regulating industries that create externalities. Inefficiencies arise only if the payoff-type is unobservable. When both dimensions are unobserved, the optimal allocation satisfies lexicographic monotonicity: increasing along the payoff-type to satisfy incentive compatibility, but tilted towards influential agents to move the externality in the socially desirable direction. In particular, the allocation depends on a private characteristic that is payoff-irrelevant for the agent. We characterize the solution through a two-step ironing procedure that addresses the nonmonotonicity in virtual values arising from the countervailing impact of payoffand influence-type. Rents from influence can emerge but only indirectly, i.e. when the observed level of influence is used as a signal of the unobserved payoff-type.
    Keywords: Multidimensional Screening, Externality, Ironing, Network Goods, Influence
    Date: 2023–05
  8. By: Cunha, Douglas; Monte, Daniel
    Abstract: We study long-lived rational agents who learn through experimentation and observing each other’s actions. Experimentation and social learning, even when combined, often lead to learning failures as agents may stop experimenting due to the Rothschild effect or social conformity. We show that when there is diversity in preferences, there will be complete learning in the limit, thereby overcoming these learning failures. Our analysis demonstrates the critical interaction between experimentation, social learning, and diversity and provides a new rationale for the increasingly held view that diversity is crucial in institutions.
    Keywords: two armed bandits, social learning, diversity
    JEL: D00 D83
    Date: 2023–04–18
  9. By: Bruno Pellegrino
    Abstract: In the seminal rational inattention model of Matĕjka and McKay (2015), logit demand arises from the discrete choice of agents who are uncertain about choice payoffs and have access to a flexible, costly information acquisition technology (RI-logit). A notable limitation of this powerful framework is the lack of known general closed-form solutions that allow the decision maker’s prior information to be asymmetric across choices. In this paper, I solve the RI-logit model analytically for a large family of priors known as multivariate Tempered Stable (TS) distributions. In my analytical formulation, decision makers can be biased, display aversion to prior uncertainty, and thus tend to select choices that are familiar (i.e. for which they hold a less disperse prior). My result extends the applicability of the RI-logit model to a new range of settings where prior information matters. I provide one such application, by showing how it can be used to model the behavior of risk-averse investors who select risky projects in an environment characterized by epistemic uncertainty (risk-adjusted expected returns are unknown, but can be learnt at a cost).
    Keywords: rational inattention, discrete choice, uncertainty
    JEL: D11 D81 D83
    Date: 2023
  10. By: Mark Whitmeyer; Kun Zhang
    Abstract: We revisit Popper's falsifiability criterion. A tester hires a potential expert to produce a theory, offering payments contingent on the observed performance of the theory. We argue that if the informed expert can acquire additional information, falsifiability does have the power to identify worthless theories.
    Date: 2023–03
  11. By: Alex A. T. Rathke
    Abstract: We show that the knowledge of an agent carrying non-trivial unawareness violates the standard property of 'necessitation', therefore necessitation cannot be used to refute the standard state-space model. A revised version of necessitation preserves non-trivial unawareness and solves the classical Dekel-Lipman-Rustichini result. We propose a generalised knowledge operator consistent with the standard state-space model of unawareness, including the model of infinite state-space.
    Date: 2023–04
  12. By: Awad, Emiel; Karekurve-Ramachandra, Varun; Rothenberg, Lawrence
    Abstract: How does blaming and crediting affect the implementation of policies and what are the constraints that reputation-concerned politicians face in commenting about bureaucrats? On one hand, politicians may want to claim credit when things go well and deflect blame when outcomes go awry. On the other, the distribution of blame and credit not only affects the politicians' reputation but also those of bureaucratic agencies and potentially their willingness to work over time. To investigate this tension, we develop and analyze a model where a bureaucrat cares about his reputation vis-a-vis an interested audience, and the politician can blame the bureaucrat for failed policies or give credit for successes via cheap talk. We show that the bureaucrat can be induced to exert more effort through blame and credit, but that the politician is constrained in communication by considerations for future effort and her own reputation concerns.
    Date: 2023–04–03

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