Federal legislation passed in the late 1980s greatly expanded the potential coverage of the Medicaid
program to include children in families with incomes at and slightly above the poverty threshold,
including families with two parents and working parents. Prior to these expansions, the
distribution of health insurance coverage in the population of children was distinctly U-shaped,
with children in the second and third income deciles having the lowest levels of coverage. In this
paper I evaluate the impact of the expansions on the distribution of coverage both by income class
and by region. I find that the expansions served to reduce the variation in insurance coverage,
raising coverage levels substantially for low-income children and children in historically low-
coverage regions. Using the fact that the impact of the legislation varied regionally and by income
decile, I explore whether the fall in private coverage that occurred in the late 1980s and early
1990s could be attributed to the expansions. I conclude that the decline in private coverage was
unlikely to have arisen as a result of the expansions.

This paper is an examination of a potential problem inherent in instrumental variables
estimation in samples drawn from populations with a grouped structure. When data used in a
regression model are drawn from such a population, the regression errors may not satisfy the
assumption that they not be correlated. While the consequences of this correlation have been
recognized previously in the context of ordinary least squares estimation where the values of the
exogenous variables do not vary within group, little attention has been paid to the consequences of
such correlation for instrumental variables estimation. In this paper I examine the consequences of
intra-group correlation for instrumental variables estimation where the instruments (rather than the
exogenous variables) have repeated values within groups.
I first briefly summarize analytical results which demonstrate that ignoring the problem of
the grouped structure will yield estimated standard errors which are understated. While the
magnitude of the understatement depends on the size of the within-group variance relative to the
total variance, even small amounts of within-group correlation result in understatement. I then
perform simulations using different magnitudes of within-group correlation and various sample
sizes and calculate the standard errors with and without accounting for the correlation. I find that
with a data set comparable in size to many cross-sectional data sets used by empirical economists.
even within-group variance only one-tenth the size of the total variance yields estimated standard
errors that are as much as eight times too small relative to the correctly estimated standard errors.
Finally, I describe two methods for estimating standard errors which account for the within-group
correlation.

Federal legislation passed in the late 1980s greatly expanded the potential coverage of the
Medicaid program. Whereas in 1985 Medicaid was essentially limited to mothers and children
on AFDC, by the early 1990s eligibility was expanded to include all children born after 1983 in
poor families, regardless of family structure or income sources. In this paper I evaluate the
effects of these expansions on Medicaid coverage and overall health insurance coverage of low-
income children. Growth in Medicaid enrollment between 1988 and 1993 is decomposed into
three underlying sources: changes in the eligibility rules of the program; changes in the eligibility
characteristics of the population; and changes in takeup among the eligible. l find that about 68
percent of the 6.7 percentage point rise in coverage rates is attributable to the expanded eligibility
rules. While the expansion of Medicaid eligibility may have increased Medicaid enrollment, an
important question is whether the increase represented a net gain in health insurance coverage, or
a substitution from private to publicly-provided coverage. I employ between-state variation in the
impact of the federally-mandated expansions to measure the potential "crowding out" of private
health insurance by public insurance. I find little evidence of crowding out: instead, the
Medicaid expansions seem to have maintained overall health insurance coverage rates against a
backdrop of declining private coverage.