Implicit marginal tax rates sometimes go over 100% when you consider lost subsidies as well as higher taxes. This could be trapping many people in poverty, but we don’t have a good idea of how many, because so many of the relevant subsidies operate at the state and local level. Descriptive work such as cataloguing where all these “benefits cliffs” are and how many people they effect would be hugely valuable. You could also study how people react to benefits cliffs using the data we do have (https://benefitscliffs.org).
“Evidence from the Introduction of Medicare” (Finkelstein, 2007) is a great paper built on weak data. There’s an AER waiting for anyone who could do the archival work to dig up the data to re-do it properly. Most importantly, can you get 1960s health spending data by payer at the state level or lower? Can you also get data on pre-Medicare public insurance programs like Kerr-Mills or Medical Assistance for the Aged?
Atul Gawande argues that one major driver of variation of health care spending across the US is variation in physician greed; some towns have a “culture of money” or “entrepreneurial spirit” among physicians. How could we get a good quantitative proxy for physician greed to test this? Ideas: the number of (non-medical?) businesses the average physician has a stake in, proportion of physicians with business degrees, physician spending on cars or luxury goods, proportion of physicians taking pharma money (Sunshine Act data).
States have passed over a hundred different types of mandated benefits, but the vast majority have zero papers focused on them. Many likely effects of the laws have also never been studied for any mandate or combination of mandates. Do they actually reduce uncompensated hospital care, as Summers (1989) predicts? Do mandates cause higher deductibles and copays, less coverage of non-mandated care, or narrower networks? How do mandates affect the income and employment of relevant providers? Can mandates be used as an instrument to determine the effectiveness of a treatment? On the identification side, redoing older papers using a dataset like MEPS-IC where self-insured firms can be used as a control would be a major advance.
Screening based on personalities gives job applicants incentives to misrepresent themselves. If groups misrepresent themselves in different fashions, then biases in the hiring process may arise. Using a within-subject, laboratory experiment comparing personality measures with and without incentives for misrepresentation, we find evidence of racial differences in faking behavior, but no evidence of gender differences. Faking attenuates gender differences evident in unincentivized personality measures but leads to racial differences where no differences exist in unincentivized measures. Our findings indicate that selection based on incentivized personality measures has the potential to adversely impact racial minorities in hiring.
Loneliness is increasingly recognized as an important public health issue, leading to appointments of ministers of loneliness in Japan and UK. It affects both young adults (Ellard, Dennison, and Tuomainen, 2022) and older people. For example, Stress in America 2020 survey finds that 73% of US adults aged 18-23 reported feeling lonely within the last two weeks. Do people value meeting new people and why can't they do it to deal with loneliness? Hypothesis: many people put a significant value on new connections, but establishing new connections might be hard, because any cold matching proposals are disproportionally likely to come from lemons (criminals, social outcasts). This means that most connection attempts with strangers are rejected. The potential experiment would attempt to elicit a monetary estimate of a 10-min meeting with a stranger by using the BDM approach. There are two subjects in the experiment who assume themselves to be strangers. Both receive an endowment of 10 USD. Each subject independently states their secret WTP. One subject of the pair is randomly chosen as a proposer. If the random number chosen for the proposer is above the threshold, the match is made. Matched subjects are invited to spend 10-min in a room or behind a meeting table within a larger room. Treatments: opposite sexes/same sex, online/offline meetings(?).The proposed experimental design eliminates adverse selection, because the probability of a match for a proposer depends only on their own WTP: if your WTP matters, WTP of your match does not.
This paper documents a new stylized fact about the United States labor market: internal migration rates are dramatically different across college majors. For some college majors, migration rates are even lower than those without a college degree. I relate major migration rates with majors' spatial concentration and find that a major's spatial concentration explains about one fourth of the cross-major variation in migration rates. With this descriptive evidence as a guide, I estimate a structural model of locational choice where college graduates have heterogeneous preferences---at the detailed major level---for living close to home, and for working in a location with a high concentration of their fellow majors. Using estimates of the structural model, I decompose the cross-major migration rates into supply and demand factors and find that supply factors (i.e. moving costs) explain the vast majority of differences in migration rates across majors. My findings underscore the difficulty in attracting college majors to a particular location using demand-side investments. My results also highlight the importance of place in determining the labor market outcomes of college majors.
While the severity of armed conflicts has been increasing recently, there is little evidence on the causal impacts of the end of conflicts on local labor market. This paper provides the first evidence on the local labor market effects of peace exploiting the sudden and unexpected end of the Aceh Insurgency in Indonesia. I find that the end of the Aceh Insurgency significantly reallocated short-run female labor market activity to male labor market activity within the household in two years. The effects were driven by the decrease in violence and economic hardship after the end of the Aceh Insurgency. The negative effects of peace on female labor market activity were stronger for young females and the females in households with a farm. The 2-year short-run effects were not significantly different from the 5-year and 10-year effects of peace. The evidence suggests the importance of the household-level labor market in the post-conflict economy.
This paper studies the effects of teachers’ gender biases on students’ long-term outcomes, including high school completion, college attendance, and formal sector employment. I measure teacher bias using differences in gender gaps between teacher-assigned and blindly-graded tests, and validate the assessment-based measure with novel data on teachers’ attitudes, as captured by the Implicit Association Test (IAT). I develop a large-scale online portal available to teachers and students in Peruvian public schools to collect IAT scores nationwide. This analysis provides evidence that math teachers who strongly associate males with scientific disciplines give higher scores to male students, when compared to blindly-graded test scores, while language arts teachers who strongly associate females with humanities-based disciplines award higher grades to female students. Next, using graduation, college enrollment, and matched employer-employee data on 1.7 million public high school students who were expected to graduate between 2015 and 2019, I find that female students who are assigned to more biased teachers are less likely to complete high school and apply to college than male students. Moreover, female students assigned to more biased teachers in high school are less likely to hold a job in the formal sector after graduation and have fewer paid working hours relative to their male classmates. Exposure to gender-biased teachers also leads to monthly earnings losses for women, further exacerbating the gender pay gap.
This paper studies the limitations of political affirmative action policies. In India, certain state legislature seats are restricted for the historically-discriminated lower castes (Dalits). Dalits are a large and heterogeneous group and there is little understanding of how different castes have been impacted by such enfranchisement, due to a lack of data on the individual caste of beneficiaries. Exploiting the link between names and caste membership, I create a new dataset including the caste of workers involved in a public workfare program (NREGA). Because constituencies are reserved for Dalit legislators based on a population cutoff rule, I use a regression discontinuity design to estimate the effect of having a Dalit state representative on the timing of payments to low-caste laborers in NREGA. I explore this effect on all Dalit workers and differentially by the individual caste of the worker. I find that Dalit workers represented by a Dalit state legislator experience a 12% higher probability of receiving their payments late. This effect is constant across all individual castes, except when considering constituencies won by parties that expressly favor Dalit voters. In this instance, I estimate that Dalit workers receive earlier payments in reserved constituencies and that those belonging to the state’s largest Dalit caste are even more advantaged. The deleterious effects of having a Dalit representative on Dalit workers are borne entirely by areas where the legislator has lower bargaining power over the local bureaucrat who directly manages the processing of payments. Given the high desirability of stable public employment, often these bureaucrat postings attract people from a more advantaged social background, relative to the Dalit legislators. Hence, my findings point to the importance of considering vertical power structures when designing policies aimed at empowering under-represented minorities around the world.