Thesis: Ph. D., Massachusetts Institute of Technology, Department of Economics, 2018. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 147-156). / This thesis consists of three chapters on the economics of health and healthcare. The first and third chapters explore geographic variation in health outcomes within the United States. The second chapter focuses on empirical methods for obtaining causal estimates of treatment effects with an application to healthcare settings. In the first chapter I study geographic variation in health care utilization under two different insurance systems: traditional Medicare and employer-provided private insurance. For each system, I use patient migration as a source of identification combined with empirical Bayes methods to construct optimal linear forecasts for the causal effects of place on utilization. These place effects measure the causal differences in treatment intensity across areas. I find similar levels of variation in the causal place effects for the publicly and privately insured patients, with a correlation of .39 across the two systems. These findings emphasize that insurance systems are affecting the forces that drive the causal component of geographic variation in utilization. In the second chapter, Liyang Sun and I explore event studies, a model for estimating treatment effects using variation in the timing of treatment. Researchers often run fixed effects regressions for event studies that implicitly assume treatment effects are constant across cohorts first treated at different times. In this paper we show that these regressions produce causally uninterpretable estimands when treatment effects vary across cohorts. We propose alternative estimators that identify convex averages of the cohort-specific treatment effects, hence allowing for causal interpretation even under heterogeneous treatment effects. We illustrate the shortcomings of fixed effects estimators in comparison to our proposed estimators through an empirical application on the economic consequences of hospitalization. In the third chapter, Raj Chetty, Michael Stepner, Shelby Lin, Benjamin Scuderi, Nicholas Turner, Augustin Begeron, David Cutler and I use newly available administrative data to quantify the relationship between income and mortality in the United States. Although it is well known that there are significant differences in health and longevity between income groups, debate remains about the magnitudes and determinants of these differences. We use new data from 1.4 billion anonymous earnings and mortality records to construct more precise estimates of the relationship between income and life expectancy at the national level than was feasible in prior work. We then construct new local area (county and metro area) estimates of life expectancy by income group and identify factors that are associated with higher levels of life expectancy for low-income individuals. Our study yields four sets of results. First, higher income was associated with greater longevity throughout the income distribution. The gap in life expectancy between the richest 1% and poorest 1% of individuals was 14.6 years for men and 10.1 years for women. Second, inequality in life expectancy increased over time. Between 2001 and 2014, life expectancy increased by 2.34 years for men and 2.91 years for women in the top 5% of the income distribution, but increased by only 0.32 years for men and 0.04 years for women in the bottom 5%. Third, life expectancy varied substantially across local areas. For individuals in the bottom income quartile, life expectancy differed by approximately 4.5 years between areas with the highest and lowest longevity. Changes in life expectancy between 2001 and 2014 ranged from gains of more than 4 years to losses of more than 2 years across areas. Fourth, geographic differences in life expectancy for individuals in the lowest income quartile were significantly correlated with health behaviors such as smoking, but were not significantly correlated with access to medical care, physical environmental factors, income inequality, or labor market conditions. Life expectancy for low income individuals was positively correlated with the local area fraction of immigrants, fraction of college graduates, and local government expenditures. Additional information on this project is available at https: //healthinequality. org/. / by Sarah Marie Abraham. / Ph. D.
| Identifer | oai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/120447 |
| Date | January 2018 |
| Creators | Abraham, Sarah Marie |
| Contributors | Amy Finkelstein and Heidi Williams., Massachusetts Institute of Technology. Department of Economics., Massachusetts Institute of Technology. Department of Economics. |
| Publisher | Massachusetts Institute of Technology |
| Source Sets | M.I.T. Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | 156 pages, application/pdf |
| Rights | MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission., http://dspace.mit.edu/handle/1721.1/7582 |
Page generated in 0.0026 seconds