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The first is that leaders may be adopting policies that increase Medicaid expenditures including
those that reduce eligibility requirements to receive Medicaid benefits. It is also possible that
innovators in a policy area may actually experiment with policies that ultimately prove to be
more costly and inefficient than other approaches. Scholars have found that states that are
slower to adopt programs – or “laggards” - often have more comprehensive programs as a result
of learning from the experience of earlier states (Mooney and Lee 1995; Hays 1996). The final
variable found to be significant was liberal citizen ideology, which was positive and significant
in model 2 (Column 2, Table 1). A more liberal electorate may be more willing to support
expenditures on public assistance programs such as Medicaid. This could result in higher
expenditures and increased the risks of shortfalls.
Factors Underlying Structural Deficit Risks
From our identification of state structural deficits as a predictor of Medicaid shortfall, we
turn to an analysis of the factors that lay behind the risk of structural deficit itself. Since the
dependent variable in Table 2 is continuous, OLS regression coefficients are reported.
Table 2 about here
Three variables were found to be significantly related to structural deficit risks. The first
is Midwest which was found to be negatively related to structural deficit risks. This indicates
that states in the Midwest are less likely then those in the South or West “Sunbelt” to have risks
for structural deficits. The Sunbelt has, since the 1950s, experienced not only a tremendous
growth in population but in economic prosperity. The economic prosperity is in part because
these states now have greater representation in the U. S. House of Representatives. However,
because the federal government has significantly cut its funding to the states, the cost of
Convention