Date of Award


Document Type


Degree Name

Doctor of Philosophy (PhD)

Legacy Department


Committee Chair/Advisor

Wilson, Paul W

Committee Member

Fleck , Robert K


This dissertation encompasses three papers. The first paper explores health outcomes in the United States, measured by the obesity rate and the prevalence of Type 2 diabetes, which have been worsening over calendar time. I extend the model by Grossman (1972a, 1972b) to derive how the demand for preventive and reactive medical care is changing over calendar time and the impacts these changes will have on the health of an individual. Assuming that reactive medical technology's effectiveness in curing individuals of illness has increased over time, I find results that are consistent with observed health trends in America. Based on improvements in medical technology, I find that the consumption of reactive care increases while time spent on preventive care decreases. The result of higher reactive care and lower preventive care means consumers may choose higher obesity and diabetes rates than what identical individuals chose in previous time periods, which explains the higher prevalence of diseases that are largely preventable. This health stock decrease is more pronounced for individuals who already spend large amounts of their budget on reactive care, who are typically lower-income or already in ill health. Numerical illustrations support the findings of an increase in the consumption of reactive care, a decrease in time spent on preventive care, and a potential decrease in the health stock for an individual over calendar time, using a wide range of values based on plausible assumptions. The numerical illustrations also show that my model supports the well-established fact that richer individuals have better health and spend more on health care. In the second paper, I examine cross-country health care efficiency rankings using modern non-parametric estimators. This paper re-examines the original analysis on cross country health care efficiency by the WHO (2000) and Evans et al. (2000), extending the dataset to include 10 new years and using non-parametric estimators to estimate efficiency rankings and Malmquist indices to determine productivity change over the panel. This paper finds that cross-country heterogeneity leads to different efficiency rankings across OECD countries when using different non-parametric estimators from those used in earlier studies. Similarly, efficiency rankings are highly dependent on the choice of input and output bundles, which may be heterogeneous across countries. This paper finds that cross-country comparisons of health care efficiency are biased by choice of estimator and input-output bundle and may lead to faulty policy conclusions. It also finds that there has been productivity regression in all countries except for the United States, whose productivity improvement is not statistically different from no productivity change. Some of the factors leading to productivity regression may be due to age demographics, lack of a recent exogenous technological shock in the health care field, the costs of reactive (instead of preventive) medicine, and increased spending on end-of-life care. In the third paper, I examine cross-state health care efficiency rankings using modern non-parametric estimators. This paper examines potential concerns raised in Gearhart (2013) about the high variability in efficiency rankings from numerous cross-country health care efficiency rankings. This paper finds the cross-state efficiency rankings are strongly positively correlated with each other with minor modifications in the input-output combinations used for estimation. This means that researchers have limited freedom to implement preferred theoretical or empirical input-output combinations. Wholesale changes in input-output combinations or in the datasets used, however, lead to highly variable efficiency rankings across states, similar to the cross-country results. This paper finds that there is no general correlation between better efficiency rankings and per capita health care costs, making reforms that target health care costs perhaps ineffective. It also finds that Massachusetts, in one dataset, has shown significant productivity improvement over the time period 2005 to 2008, the time period during which its health care reform was launched. In a second dataset, from 2002 to 2007, productivity regressed in Massachusetts. This highlights the need for more data and further study of Massachusetts, given the decision to reform the American health care system under the Affordable Care Act of 2010.

Included in

Economics Commons



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