One result of the 50-state model was the estimation of unexplained variance in spending across different states. These state effects were estimated intercepts or constant terms for each of the states in the econometric models. They may be interpreted as general dispositions of states-averaged across the entire period, 1977-2000-to support certain types of spending after controlling for the linear effects of fiscal capacity, social needs, federal grants, and population density.29
In their original form, the state intercepts, or effects, were difficult to interpret. To make them easier to understand, we standardized them with respect to the mean and standard deviation of the state effects. That is, for each set of estimated state effects-one set of 50 for each dependent variable, such as cash assistance or Medicaid-the mean of the 50 state effects was set at zero and the standard deviation was set at 100. Thus, if a state's effect for Medicaid was 2 standard deviations above the mean of the 50 state effects that particular effect was scored as 200. If the state's effect for cash assistance was 1/4 of a standard deviation below the mean for all states, then that effect was scored -25.
Exhibit III-13 shows the standardized versions of the estimated state effects for all of the states. The larger and more positive the number, the greater the tendency of the state to spend on that particular category of public function over the entire time period, 1977-2000, after controlling for the linear effects of the other independent variables, including per capita personal income, per capita grants, and the various need variables. For example, even after controlling for these factors, Alaska shows a strong additional propensity to spend on cash assistance and non-social welfare and a tendency to spend less on Medicaid and public hospitals relative to other states. New York and Minnesota, however, show additional propensities (again, compared to other states) to spend more than predicted by the econometric model on all social programs.
Despite the good fit of the models to the data, these state effects show variation in their spending on different types of social programs. For example, the difference between the 25th and 75th percentiles in the state effect estimates for cash assistance is $49 per capita, a large amount compared to the mean per capita spending for all states (averaged over all years, 1977-2000) of $82.
When these estimated state effects are analyzed, they show that state fiscal capacity interacts with program area (i.e., the relationship with fiscal capacity varies with program area). Exhibit III-14 shows these variations by displaying the average state effects, in their standardized versions, for states of different fiscal capacities, using our basic four quartiles. The relationship between state effects and fiscal capacity are compared across four different program areas: cash assistance, Medicaid, non-health social services, and public hospitals. We should note that the differences in state effects are most important, not the absolute values (e.g., whether they are negative or positive, that is, above or below the average state effects across all states).
Exhibit III-14 indicates that states of different fiscal capacities still vary in their long-run spending patterns even after controlling for the linear effects of annual changes in states' per capita personal income, as the 50-state model does. For example, the wealthiest states (Quartile 1) spent on average about $180 more per capita per year on cash assistance than did the poorest quartile (Quartile 4). A consistent and positive, albeit weaker, relationship between fiscal capacity and average state effects is also evident in spending on non-health social services.
Average State Effects for Different Types of Social Welfare Spending, by State Fiscal Capacity, Based on Data From 1977-2000
Health-related expenditures show a different pattern. With respect to Medicaid, the average state effects for the richest states were higher than for states in the other quartiles, but the differences among the three less wealthy quartiles were small. The relationship between fiscal capacity and spending on public hospitals was actually reversed. Per capita spending was lowest among the richest states and highest among the poorest states. After controlling for the linear effects of annual changes in fiscal capacity and other variables, as the 50-state model does, poor states still spent less on cash assistance and other social welfare, while their spending on health-related programs was not much lower and sometimes higher than the amount wealthier states spent.
Poor states, on average, thus revealed greater support for spending on health-related programs than for spending on non-health programs. One possible consequence of this pattern was a weaker statistical relationship among poor states in their support across different program areas. Among non-poor states (i.e., states in the first three quartiles for fiscal capacity), tendencies to spend on different social welfare functions were, for the most part, either positively correlated with each other or not correlated at all, suggesting that no major tradeoff existed among these states between their financial support for one type of social welfare and their support for another.
We can see these relationships in the first column of Exhibit III-15, which shows the bivariate correlation coefficients between the state effect estimates for cash assistance, Medicaid, non-health social services, and public hospitals. For the 38 states in the first three quartiles of fiscal capacity, the correlations were generally either positive or especially small. The strongest correlation was between Medicaid and non-health social services, though a moderate relationship also existed between cash assistance and non-health social services. Only the state effects for public hospitals showed a slight negative relationship to state effects for other types of spending.
By contrast, among the 12 poorest states, the correlations among these spending tendencies were more likely to be negative. Cash assistance was negatively correlated with both types of health-related functions, Medicaid and public hospitals. Non-health social services was also negatively related to spending on public hospitals and, albeit weakly, cash assistance. On the other hand, the poor states showed a slightly stronger relationship between the two types of health program areas. We can see an example of the contrasting structure of these relationships in Exhibit III-16, which shows the scatterplots between the state effects for cash assistance and Medicaid-separately for poor and non-poor states. No correlation existed between the estimated state effects for non-poor states, but a clear negative relationship existed among the poor states.
Scatterplots Between State Effects for Payments to Medicaid and Cash Assistance, Based on Model Estimated for Years 1977-2000
Scatterplot between state effects estimated for Medicaid and cash assistance, only states in Quartiles 1, 2, and 3 in fiscal capacity (i.e., wealthier 75%)
Scatterplot between state effects estimated for Medicaid and cash assistance, only states in Quartile 4 in fiscal capacity (plus Arizona, because it is one of the study states and is near the cutoff point between Quartiles 3 and 4)
More generally, low fiscal capacity states divided between those that put money into health programs and little else and those that put money into other programs, especially cash assistance. As Exhibit III-16 shows, the former were southern and border states, including Mississippi, Arkansas, West Virginia, and South Carolina. Poor western states, including Utah and New Mexico, showed greater levels of support for cash assistance. Whatever the reasons for these differences among poor states, such states clearly showed divisions in their spending patterns across different functions. Poor states, unlike wealthy states, seemed to choose or specialize in one or another type of social program area. Their packages of social programs were, thus, more particularized as well as smaller. Although knowing how much a wealthy state spent in one social program area often helped us know how much it spent in another area, the same was untrue for poor states.