Alex Rees-Jones

Suboptimal Behavior in Strategy-Proof Mechanisms: Evidence from the Residency Match

Strategy-proof mechanisms eliminate the possibility for gain from strategic misrepresentation of preferences. If market participants respond optimally, these mechanisms permit the observation of true preferences and avoid the implicit punishment of market participants who do not try to “game the system.” Using new data from a flagship application of the matching literature—the medical residency match—I study if these potential benefits are fully realized. I present evidence that some students pursue futile attempts at strategic misrepresentation, and I examine the causes and correlates of this behavior. These results inform the assessment of the costs and benefits of strategy-proof mechanisms and demonstrate broad challenges in mechanism design.

Attention Variation and Welfare: Theory and Evidence from a Tax Salience Experiment

This article shows that accounting for variation in mistakes can be crucial for welfare analysis. Focusing on consumer under-reaction to not-fully-salient sales taxes, we show theoretically that the efficiency costs of taxation are amplified by differences in under-reaction across individuals and across tax rates. To empirically assess the importance of these issues, we implement an online shopping experiment in which 2,998 consumers purchase common household products, facing tax rates that vary in size and salience. We replicate prior findings that, on average, consumers under-react to non-salient sales taxes—consumers in our study react to existing sales taxes as if they were only 25% of their size. However, we find significant individual differences in this under-reaction, and accounting for this heterogeneity increases the efficiency cost of taxation estimates by at least 200%. Tripling existing sales tax rates nearly doubles consumers’ attention to taxes, and accounting for this endogeneity increases efficiency cost estimates by 336%. Our results provide new insights into the mechanisms and determinants of boundedly rational processing of not-fully-salient incentives, and our general approach provides a framework for robust behavioural welfare analysis.

Taxing Humans: Pitfalls of the Mechanism Design Approach and Potential Resolutions

A growing body of evidence suggests that psychological biases can lead different implementations of otherwise equivalent tax incentives to result in meaningfully different behaviors. We argue that in the presence of such failures of “implementation invariance,” decoupling the question of optimal feasible allocations from the tax system used to induce them—the “mechanism design approach” to tax analysis—cannot be the right approach to analyzing optimal tax systems. After reviewing the diverse psychologies that lead to failures of implementation invariance, we illustrate our argument by formally deriving three basic lessons that arise in the presence of these biases. First, the mechanism design approach neither estimates nor bounds the welfare computed under psychologically realistic assumptions about individuals’ responses to the tax instruments used in practice. Second, the optimal allocations from abstract mechanisms may not be implementable with concrete tax policies, and vice versa. Third, the integration of these biases may mitigate the importance of information asymmetries, resulting in optimal tax formulas more closely approximated by classical Ramsey results. We conclude by proposing that a “behavioral” extension of the “sufficient statistics” approach is a more fruitful way forward in the presence of such psychological biases.

An Experimental Investigation of Preference Misrepresentation in the Residency Match

Significance Policymakers increasingly rely on matching algorithms to assign students to schools. Common algorithms can be “gamed” by students misrepresenting their preferences for schools, resulting in assignments that are unduly influenced by application strategies. In strategy-proof algorithms that incentivize students to tell the truth, this undesirable influence of strategic sophistication is argued to be eliminated. We conduct an online experiment among participants in a leading exemplar of strategy-proof market design: the assignment of new doctors to medical residencies. Our results suggest that many market participants do not understand that telling the truth is optimal. This illustrates that strategy-proof environments are not immune to the influence of strategic sophistication, and that practical tensions arise when using complex means to implement simple incentives. The development and deployment of matching procedures that incentivize truthful preference reporting is considered one of the major successes of market design research. In this study, we test the degree to which these procedures succeed in eliminating preference misrepresentation. We administered an online experiment to 1,714 medical students immediately after their participation in the medical residency match—a leading field application of strategy-proof market design. When placed in an analogous, incentivized matching task, we find that 23% of participants misrepresent their preferences. We explore the factors that predict preference misrepresentation, including cognitive ability, strategic positioning, overconfidence, expectations, advice, and trust. We discuss the implications of this behavior for the design of allocation mechanisms and the social welfare in markets that use them.