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

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.

Learning during the COVID-19 pandemic: It is not who you teach, but how you teach.

We use unique data from seven intermediate economics courses taught at four R1 institutions to examine the effects of the COVID-19 pandemic on student learning. Because the same assessments of course knowledge mastery were administered across semesters, we can cleanly infer the impact of the unanticipated switch to remote teaching in Spring 2020. During the pandemic, total assessment scores declined by 0.2 standard deviations on average. However, we find substantial heterogeneity in learning outcomes across courses. Course instructors were surveyed about their pedagogy practices and our analysis suggests that prior online teaching experience and teaching methods that encouraged active engagement, such as the use of small group activities and projects, played an important role in mitigating this negative effect. In contrast, we find that student characteristics, including gender, race, and first-generation status, had no significant association with the decline in student performance in the pandemic semester.

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.

Can Marginal Rates of Substitution Be Inferred From Happiness Data? Evidence from Residency Choices.

We survey 561 students from U.S. medical schools shortly after they submit choice rankings over residencies to the National Resident Matching Program. We elicit (a) these choice rankings, (b) anticipated subjective well-being (SWB) rankings, and (c) expected features of the residencies (such as prestige). We find substantial differences between choice and anticipated-SWB rankings in the implied tradeoffs between residency features. In our data, evaluative SWB measures (life satisfaction and Cantril's ladder) imply tradeoffs closer to choice than does affective happiness (even time-integrated), and as close as do multi-measure SWB indices. We discuss implications for using SWB data in applied work.

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's 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.