I
imagine that someone who does not know me would have a hard time
finding a unifying theme in the working papers listed here. One might
read my income inequality paper and wonder why I'm not extending it by
adding technological progress and human capital to the model.
Quite frankly, I'm much more interested in the microeconomic
implications of my income inequality model. The whole point of the
paper 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.