An Experimental Investigation of Preference Misrepresentation in the Residency Match

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

Addressing Personal-Income-Tax Manipulation with Tools from Psychology

In order to better understand the tax manipulation decision-making process—both legal uses of tax deductions and illegal tax evasion—this brief looks at the impact of gain/loss framing. Analysis of tax data confirms that tax decisions are influenced by “loss aversion.” For instance, taxpayers are more likely to pursue tax reduction activities when they make a loss smaller, as compared to when they make a gain larger. The brief looks at tools that policymakers have at their disposal for both deterring tax evasion and making exiting tax incentives maximally effective. The brief discusses instances when such gain/loss framing interventions might be deployed, and provides estimates around the size of the revenue responses they may generate. The author estimates that if tax filers who face losses experienced the lower motivation to manipulate shown by those facing gains, annual tax revenue would increase by $1.4 billion. Even attempts at marginal interventions, though smaller in predicted effects, might be financially worthwhile.https://repository.upenn.edu/pennwhartonppi/1051/thumbnail.jp

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

This paper shows that accounting for variation in mistakes can be crucial for welfare analysis. Focusing on consumer underreaction to not-fully-salient sales taxes, we show theoretically that the efficiency costs of taxation are amplified by differences in underreaction 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 underreact 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 underreaction, and accounting for this heterogeneity increases the efficiency cost of taxation estimates by at least 200%. Tripling existing sales tax rates nearly doubles consumers\u27 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 behavioral welfare analysis

Tax Psychology and the Timing of Charitable-Giving Deadlines

This brief discusses the timing of the charitable-giving tax deduction deadline, evaluated in light of recent evidence from the behavioral public finance literature. We discuss how tax salience, inattention, and aversion to taxes interact with different possible deadlines. We present several arguments in favor of moving the charitable-giving deadline to tax day

A Pilot Study of Uncertainty in Income Tax Forecasts

How confidently can taxpayers forecast the tax bill that they will face? We asked survey respondents to provide both point estimates and subjective probability distributions of items from the tax return that they will submit the following April. In a pilot study, consisting of a sample of 188 participants from Amazon Mechanical Turk, we find evidence of substantial uncertainty over both the final tax and its determinants. We discuss the implications of this uncertainty for both tax policy and economic modeling

Experience of the COVID-19 pandemic and support for safety-net expansion

Altres ajuts: Economic and Social Research Council (ES/S00713X/1) ; Bill & Melinda Gates Foundation and by grant U01AG054580 from the National Institute on Aging.Did individuals' experiences with the harms of the COVID-19 pandemic influence their attitudes towards safety-net programs? To assess this question, we combine rich information about county-level impacts and individual-level perceptions of the early pandemic, repeated measurements of attitudes towards safety-net expansion, and pre-pandemic measurements of related political attitudes. Individuals facing higher county-level impact or greater perceived risks are more likely to support long-term expansions to unemployment insurance and government-provided healthcare when surveyed in June 2020. These differences persist across time, with experiences in the early months of the pandemic remaining strongly predictive of attitudes towards safety-net expansion in early 2021

What Do You Think Would Make You Happier? What Do You Think You Would Choose?

Would people choose what they think would maximize their subjective well-being (SWB)? We present survey respondents with hypothetical scenarios and elicit both choice and predicted SWB rankings of two alternatives. While choice and predicted SWB rankings usually coincide in our data, we find systematic reversals. We identify factors—such as predicted sense of purpose, control over one\u27s life, family happiness, and social status—that help explain hypothetical choice controlling for predicted SWB. We explore how our findings vary by SWB measure and by scenario. Our results have implications regarding the use of SWB survey questions as a proxy for utility

Do People Seek to Maximize Happiness? Evidence from New Surveys

Are subjective well-being (SWB) measures a good empirical proxy for utility? We evaluate one necessary assumption: that people’s preferences coincide with what they predict will maximize their SWB. Our method is to present survey respondents with hypothetical scenarios and elicit both choice and predicted SWB rankings of two alternatives. While choice and predicted SWB rankings usually coincide, we find systematic reversals. Furthermore, we identify factors—such as predicted sense of purpose, control over one‘s life, family happiness, and social status—that help explain choice controlling for predicted SWB. We explore how our findings vary with the SWB measure and the choice situation.

In situ correlative observation of humping-induced cracking in directed energy deposition of nickel-based superalloys

Directed energy deposition (DED) is a promising additive manufacturing technique for repair; however, DED is prone to surface waviness (humping) in thin-walled sections, which increases residual stresses and crack susceptibility, and lowers fatigue performance. Currently, the crack formation mechanism in DED is not well understood due to a lack of operando monitoring methods with high spatiotemporal resolution. Here, we use inline coherent imaging (ICI) to optically monitor surface topology and detect cracking in situ, coupled with synchrotron X-ray imaging for observing sub-surface crack healing and growth. For the first time, ICI was aligned off-axis (24° relative to laser), enabling integration into a DED machine with no alterations to the laser delivery optics. We achieved accurate registration laterally (0.93), directly tracking surface roughness and waviness. We intentionally seed humping into thin-wall builds of nickel super-alloy CM247LC, locally inducing cracking in surface valleys. Crack openings as small as 7 µm were observed in situ using ICI, including sub-surface signal. By quantifying both humping and cracking, we demonstrate that ICI is a viable tool for in situ crack detection

The Astropy Problem

The Astropy Project (http://astropy.org) is, in its own words, "a community effort to develop a single core package for Astronomy in Python and foster interoperability between Python astronomy packages." For five years this project has been managed, written, and operated as a grassroots, self-organized, almost entirely volunteer effort while the software is used by the majority of the astronomical community. Despite this, the project has always been and remains to this day effectively unfunded. Further, contributors receive little or no formal recognition for creating and supporting what is now critical software. This paper explores the problem in detail, outlines possible solutions to correct this, and presents a few suggestions on how to address the sustainability of general purpose astronomical software