From Exhibit III-11, we can see that when the states are separated by quartiles based on average per capita personal income, differences across quartiles in the estimated effects emerge. For example, for cash assistance and non-health social services, the effect of personal income for the richer states (Quartiles 1 and 2) is statistically significant25, while for the poorer states (Quartile 3 and 4), the effect is nearer zero for both categories and statistically insignificant for cash assistance. The reverse occurs with respect to effects on Medicaid, for which the effects of personal income is near zero for the richer states, although statistically significant and positive for Quartile 1, and larger and statistically significant for the poorer states. For public hospitals, the effect of personal income is positive and statistically significant only for the richest states.
In general, the regression analysis confirmed that personal income was an important factor in causing social welfare spending disparities among rich and poor states. These disparities based on sample means were reported, for example, in Exhibit III-2. When we controlled for the effects of non-income explanatory variables, the differences in per capita spending on social welfare across rich and poor states narrowed but did not disappear. For example, if Quartile 1 states were assigned the same average income as those in Quartiles 2, 3 and 4, the regression model for Quartile 1 predicted that Quartile 1 per capita spending on social welfare would fall by $60, $98, and $137, respectively. This amounted to reductions of 7 percent, 12 percent, and 17 percent.
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