We propose a tractable framework to introduce externalities in a screening model. Agents differ in both payoff-type and influence (how strongly their actions affect 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 produce the externality. 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 payoff-types and influence. If observable, influence is used as a signal of the payoff-type. We provide sufficient conditions for rents from influence to emerge even in a setting featuring atomistic agents.

We study a decision-framing design problem: a principal faces an agent with frame-dependent preferences and designs an extensive form with a frame at each stage. This allows the principal to circumvent incentive compatibility constraints by inducing dynamically inconsistent choices of the sophisticated agent. We show that a vector of contracts can be implemented if and only if it can be implemented using a canonical extensive form, which has a simple high-low-high structure using only three stages and the two highest frames, and employs unchosen decoy contracts to deter deviations.
We then turn to the study of optimal contracts in the context of the classic monopolistic screening problem and establish the existence of a canonical optimal mechanism, even though our implementability result does not directly apply. In the presence of naive types, the principal can perfectly screen by cognitive type and extract full surplus from naifs.

We analyze how the seller adjusts reserve prices in infinitely repeated auctions using information from previous bids. Our model is a stylized representation of the display-ad market, where publishers sell impressions through real-time first- or second-price auctions. Buyers have constant valuations and bid myopically. Losers are replaced by new bidders, while winners participate in subsequent auctions with an exogenous probability, which measures persistence. The optimal reserve price is either equal to the latest winner’s value or, if this value is high, strictly lower than it. In the latter case, the reserve price decreases with the winner’s value in first-price auctions, while it remains constant — and is typically lower — in second-price auctions, where winners substitute for the reserve price. Second-price auctions yield higher revenue unless persistence is very high. Because higher persistence increases the reserve prices, it can reduce the frequency of trade.

We examine tax audit policies when the Tax Authority directs audits leveraging the predictions of a statistical model. Taxpayers are informed about the use of model-based audit rules, which can therefore influence both their declaration incentives and the targeting of tax audits. When the model’s precision is sufficiently high, the Authority achieves near-optimal tax collection. However, audit targeting itself yields minimal revenue, as optimal reliance on the model is primarily directed toward creating firm-specific incentives to declare truthfully. In general, model-based rules allow to screen high-incomes, tolerating evasion from low-incomes with high propensity to evade. The prediction power is therefore used to deter evasion rather than to direct audits and, by reducing the evasion of high incomes, it effectively mitigates the inequality inherent in optimal enforcement. We corroborate and extend our theoretical findings with numerical simulations calibrated on aggregate moments from administrative audit data. Enhanced model precision reduces tax evasion, particularly among higher incomes. If taxpayers did not adjust their declarations, at the optimum the Authority would reduce the reliance on the model, confirming that deterrence drives the desirability of using information in audits. While plausible enhancements in model precision yield modest revenue gains, these gains are substantial compared to the audit budget increase required to achieve similar tax revenues without a statistical model.

We show that tax authorities can stimulate tax compliance by strategically releasing audit-relevant information. We focus on audit policies that disclose to taxpayers that audit risk discretely drops above a threshold determined by their predicted revenues. In a theoretical framework, we derive conditions for the existence of improvements over flat undisclosed audit rules, and we build a test for such improvements that relies on a change in the probability jump at the threshold. Our empirical analysis relies on the Sector Studies, an Italian policy with a disclosed threshold-based design. We leverage

more than 26 million Sector Study files submitted between 2007 and 2016. First, we show that taxpayers bunch at the threshold to a great extent, and that this behavior is related to evasion proxies, availability of evasion technologies, and tax incentives. Then, we exploit a staggered Sector Studies reform that widens the initial audit risk discontinuity. In line with our theory, taxpayers who benefit from audit exemptions above the threshold reduce their relative compliance, while those below the threshold improve it. However, mean reported profits increase by 16.2% in treated sectors over

six years, suggesting – in light of our test – that a disclosed rule performs better than an undisclosed one.

We study the use of simplistic arguments in political communication, developing a model of mobilization through rhetoric with naive and sophisticated voters. Politicians sometimes choose simplistic arguments to appear more competent, exploiting what we call Poe’s Law, i.e., the uncertainty on whether the argument used by the politician reflects her competence or is ‘degraded’ to meet naive voters’ preferences. We compare the Bayesian game with one where sophisticated voters conceptualize Poe’s Law assuming that the politician communicates to a fully naive crowd, effectively dismissing their fellow citizens’ cognitive abilities. Dismissal induces an overly simplistic political debate.

We investigate the behavioral foundations of informed trade. We extend the canonical (Kyle, 1989) model to allow for wide range of misperception about the information environment (e.g. overconfidence and correlation delusion) as well as the market clearing condition (e.g. understatement of individual impact) and ask when a trading equilibrium can exist. We show that existence requires either i) the market clearing rule being perceived with (cognitive) noise of arbitrary size, or ii) sufficiently strong misperceptions that lead traders to overestimate the precision of their private information (relative to that of others) or underestimate their market impact. Following i) provides a cognitive foundation for the noise trader approach, while ii) yields a highly tractable linear model of (sufficiently) biased traders. Fixing the bias, a higher number of traders is beneficial for existence, though the economy is typically discontinuous in the countable-trader limit. In the latter case, equilibrium is characterized by limit uncertainty, a property which is satisfied if and only if traders perceive some correlation in their competitors’ information.