Do temporary labor supply programs cause physicians to move to and stay in undesirable areas? To what extent do these programs improve the health of the elderly and non-elderly population in those areas? I investigate these questions by studying state and local loan repayment programs for new eligible physicians which were rolled out over the last four decades in hundreds of counties across US states. Leveraging a new longitudinal dataset that tracks all physicians from medical school to mid-career, and exploiting both space and time variation, I find that these policies increase the number of physicians by 5% in treated counties relative to untreated counties in the state. The inflows of physicians are driven by higher paying eligible specialities. The programs continue to influence physicians' location decisions even after they end -- effects persist for at least ten years after the minimum obligation period. This is driven by employer learning effects. Furthermore, the programs modestly spur trainees to enter eligible specialities in treated states by substituting away from ineligible specialities. Treated counties also see the elderly and non-elderly increase their visits to physicians while reducing those to the emergency rooms. Using patient level data from California, I demonstrate these results are not driven by selective admission of patients to treated hospitals. My findings underline the relevance of policies that decrease financial frictions for highly skilled individuals in changing community health outcomes.
I investigate how allowing lower tuition for undocumented students at public colleges improves education outcomes, and changes institutional pricing patterns. I use administrative data and a residual method to quantify the actual number of undocumented students at school level in the pre-reform period. Exploiting the reform's staggered adoption across states and time, as well as variation in the intensity of exposure to the reform across institutions -- I find a higher enrollment of undocumented students at the treated states' 'more exposed' community colleges. Transfer, technical and vocational colleges drive the enrollment outcomes. In contrast to enrollment, there is strong evidence of higher graduation of undocumented students at both 2-year and 4-year colleges in the treated states. I also observe that students at these `more exposed' institutions experience modest tuition reductions. There is negligible displacement of Americans in treated public colleges. My findings indicate that the education benefits to undocumented students come with no significant unintended costs to other students. I estimate that the reform costs around \$16.4 million per year on average.
Clots-Figueras(2012) studies how politicians' gender affects primary educational attainment in India. Exploiting close races to identify the causal effect of female politicians, they find higher educational achievement of primary school students in urban districts with more female politicians. I successfully reproduce their results, using primarily NSS data linked to data on electoral outcomes. However, I provide three observations on their paper. First, after correcting for multiple outcomes, female politicians do not seem to build more primary schools in urban areas -- an important mechanism explaining their result. Second, in their regression discontinuity design, continuity of the distribution of baseline covariates is violated at the cutoff for majority of the covariates in urban areas -- when the more powerful Kamat(2017) permutation test is applied. Third, the positive effects in urban areas become weaker and largely insignificant when Rehavi (2007)'s identification strategy is used.
Work stoppage in the health care sector is a serious concern. It disrupts the timely delivery of health care services and thereby harms patients' health. During the period 2005-2017, there were 161 strikes in the California health care sector. These strikes were of much shorter duration (lasting on average for less than 10 days), relative to the nurses' strikes in New York state considered by Gruber and Kleiner(2012). Additionally, unlike Gruber and Kleiner, I consider variation in the types of health care workers (i.e. nurses, engineers, technicians) covered by these brief strikes. Utilizing daily patient level data from all hospitals in California over the period 2004-2017 and exploiting the variation in strike timing across hospitals in a difference-in-differences and event study design, I analyze the spillover effects of these short strikes on the nearby, non-striking hospitals. Specifically, I consider admission, readmission, mortality, emergency room use and intensity of care outcomes. Based on the residential locations of the patients, I also explore changes in patients' admissions to a neighboring hospital if the striking hospital is closer to their home vs if it is farther from their home. I also examine whether these short strikes benefit the striking hospitals in the long run in terms of productivity, and how these benefits(if any) in turn affect the productivity of non-striking hospitals in close proximity. Overall, this project aims to determine the winners and losers of short-term negative shocks to the healthcare sector in the US, a relatively understudied area.
Doctors and medical trainees of certain racial backgrounds - mainly African Americans, Hispanic, and American Indians - are under-represented in medicine compared to the larger population. This lack of racial equity is an important issue, given that, similar race between doctors and patients leads to improved communication and higher demand for preventive care. Moreover, doctors from underrepresented minorities have a higher likelihood of serving underserved and uninsured populations. It has been suggested in the medical literature that a lack of minority faculty on resident selection committees is one of several factors contributing to lower racial equity among trainees. To test this claim, I leverage institution level variation in a policy that mandated change in the evaluation criteria for trainees. I build a dataset of medical trainees with their training institutions and specialities, along with information on the members of the recruitment committees. Using a query based method, I extract details on trainees' work schedules, employment policies and benefits, compensation and leave structures from the official records. This analysis can shed light on the unstudied role of recruitment committees - considering the degree of their human bias and private information - in advancing racial equity among medical trainees.