Impatience, Incentives, and Obesity

This paper explores the relationship between time preferences, economic incentives, and body mass index (BMI). Using data from the 1979 cohort of the National Longitudinal Survey of Youth, we first show that greater impatience increases BMI even after controlling for demographic, human capital, and occupational characteristics as well as income and risk preference. Next, we provide evidence of an interaction effect between time preference and food prices, with cheaper food leading to the largest weight gains among those exhibiting the most impatience. The interaction of changing economic incentives with heterogeneous discounting may help explain why increases in BMI have been concentrated amongst the right tail of the distribution, where the health consequences are especially severe. Lastly, we model time-inconsistent preferences by computing individualsquasi-hyperbolic discounting parameters (β and δ). Both long-run patience (δ) and present-bias (β) predict BMI, suggesting obesity is partly attributable to rational intertemporal tradeoffs but also partly to time inconsistency.

Income-Based Disparities in Health Care Utilization under Universal Coverage in Brazil

Since Brazil's adoption of universal health care in 1988, the country's health care system has consisted of a mix of private providers and free public providers. We test whether income-based disparities in medical visits and medications remain in Brazil despite universal coverage using a nationally representative sample of over 48,000 households. Additional income is associated with less public sector utilization and more private sector utilization, both using simple correlations and regressions controlling for household characteristics and local area fixed effects. Importantly, the increase in private care use is greater than the drop in public care use. Also, income and unmet medical needs are negatively associated. These results suggest that access limitations remain for low-income households despite the availability of free public care.

Competing with Costco and Sam's Club: Warehouse Club Entry and Grocery Prices

Prior research shows grocery stores reduce prices to compete with Walmart Supercenters. This study finds evidence that the competitive effects of two other big box retailers – Costco and Walmart-owned Sam's Club – are quite different. Using city-level panel grocery price data matched with a unique data set on Walmart and warehouse club locations, we find that Costco entry is associated with higher grocery prices at incumbent retailers, and that the effect is strongest in cities with small populations and high grocery store densities. This could be explained by a segmented-market model, or by incumbents competing with Costco along non-price dimensions such as product quality or quality of the shopping experience. We find no evidence that Sam’s Club entry affects grocery stores’ prices, consistent with Sam’s Club’s focus on small businesses instead of consumers.

Racial and Ethnic Disparities in COVID-19: Evidence from Six Large Cities

As of June 2020, the coronavirus pandemic has led to more than 2.3 million confirmed infections and 121 thousand fatalities in the United States, with starkly different incidence by race and ethnicity. Our study examines racial and ethnic disparities in confirmed COVID-19 cases across six diverse cities – Atlanta, Baltimore, Chicago, New York City, San Diego, and St. Louis – at the ZIP code level (covering 436 “neighborhoods” with a population of 17.7 million). Our analysis links these outcomes to six separate data sources to control for demographics; housing; socioeconomic status; occupation; transportation modes; health care access; long-run opportunity, as measured by income mobility and incarceration rates; human mobility; and underlying population health. We find that the proportions of black and Hispanic residents in a ZIP code are both positively and statistically significantly associated with COVID-19 cases per capita. The magnitudes are sizeable for both black and Hispanic, but even larger for Hispanic. Although some of these disparities can be explained by differences in long-run opportunity, human mobility, and demographics, most of the disparities remain unexplained even after including an extensive list of covariates related to possible mechanisms. For two cities – Chicago and New York – we also examine COVID-19 fatalities, finding that differences in confirmed COVID-19 cases explain the majority of the observed disparities in fatalities. In other words, the higher death toll of COVID-19 in predominantly black and Hispanic communities mostly reflects higher case rates, rather than higher fatality rates for confirmed cases