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Income Inequality Health Insurance Ridge Regression Graduate MACRO INTRO intro lecture notes email me |
For example, the theme of my income inequality model is that if you can encourage low income people to save more, you can bring the economy to a higher level of economic development and reduce the degree of income inequality. I want to know how to encourage low income people to invest in their own human capital. I want to know who moves up the income ladder, what their characteristics are and if there's a way to "give" those characteristics to people who otherwise would not move up the income ladder. The next paper that I write will address these questions. I also want to know how economic development and income inequality affect people's standard of living. If we use average health status as a proxy for average standard of living, then one should be concerned about the rising degree of income inequality in America because countries that have greater degrees of income inequality tend to have lower average health status. Babones (2008), for example, finds that higher income inequality is associated with lower life expectancy, higher infant mortality and higher murder rates. In fact, coupling my income inequality model with the positive correlation between health and schooling (Grossman, 1975) might explain the correlation between inequality and health status. If we interpret capital in my income inequality model as human capital (acquired through education), then the model implies that countries which favor educational investment by low-income people should have lower income inequality and better health status. Although years of formal schooling is highly correlated with good health, schooling is no substitute for a doctor when a person becomes ill. At those times, it is important to have access to medical care. As in any market, price is the mechanism by which medical services are rationed. In the United States, an individual's ability to afford medical services depends on his/her health insurance coverage. But -- from a social policy perspective -- what price should older and sicker workers pay for health insurance coverage? If health insurance premiums were actuarially fair, the people who need medical care the most would not be able to afford it. In an attempt to make health insurance more accessible and affordable, many states have restricted insurers' ability to set premium rates on the basis of health status and other factors which predict a group's future medical needs. The risk associated wth such a public policy however is that younger and healthier individuals will reduce their coverage, thus reducing health insurance coverage rates and pushing premium rates back upward. My paper on health insurance examines the effect of health insurance rating restrictions on employment-based coverage rates and market concentration in the insurance industry. Unfortunately, those were the only data available to me. A better analysis of the subject would use individual data on health insurance coverage, health status, place of employment and the state in which the individual lives. One can link the household and insurance components of the Medical Expenditure Panel Survey, but to do so you must first obtain approval and then travel to the AHRQ Data Center in Rockville, Maryland to do the research on-site. Finally, the public policy implications of my research are always on the front of my mind, so I carefully examine the effect of multicollinearity among regulatory and tax variables in my work. When two explanatory variables in a regression model are positively correlated, their regression coefficients will be negatively correlated and one of the OLS coefficients might have the "wrong" sign. A strict interpretation of the OLS coefficients would therefore lead the researcher to conclude that a particular tax or regulatory variable has no effect or the effect opposite of the true effect on the outcome of interest. Ridge regression helps diagnose such problems by tracing the paths of the coefficients as they are shrunk towards zero. References S. J. Babones. "Income inequality and population health: Correlation and causality" Social Science and Medicine, 66(7):1614-1626, Apr. 2008. M. Grossman. "The Correlation between Health and Schooling." in Household Production and Consumption, ed. N.E. Terleckyj. 147-211, 1975. |