87 between 2002 and 2009. There was a significant difference between the means of the net
assets ratio t=-3.597, p=0.000 and expenses per capita t=5.236, p=0.000. The impact of modified approach is not significant for the operating ratio t=-0.3024, p=0.7625 and
surplus per capita t=0.3426, p=0.7320. The mean net assets ratio is higher for states using the modified approach, while the mean expenses per capita is lower. By testing the
means of the long-run and service-level solvency indices for states that use the modified and traditional approach, we find that the two groups are significantly different. States
using the modified approach have a higher mean long-run solvency index value t=-4.14, p0.000 and a higher service-level solvency index value t=-4.81, p0.000. Dropping
these indicators is an option for eliminating these systematic differences. However, the expenses per capita indicator provides information on the cost of providing services,
without which the revenues and taxes per capita are biased. The net assets ratio captures a state’s ability to pay off long-term liabilities. In order to keep these indicators and protect
against bias, the approach to recording state infrastructure asset depreciation will either be added as a dummy variable to regression analysis or fixed effects will be used to
capture systematic differences.
4.4.3 Measurement Validity
Measurement validity presumes that the measure is actually assessing the intended concept, in this case, fiscal stress. Wang et al 2007 lists three criteria for
assessing the validity of these measures. First, the measures should have face validity; they should intuitively make sense as measures of fiscal stress. The reasoning behind
using financial indicators as measures of fiscal stress is detailed in Chapter 3. Second, the measures should apply to the concept comprehensively. That is, the measures should
apply to the entire state government. Third, the measures should have predictive validity. Since financial condition and by extension, fiscal stress is related to socioeconomic
88 variables, then we would expect to see a relationship between these fiscal stress measures
and certain socio-economic variables. Total state population and state personal income per capita provide insight into
the differences between larger and smaller states, as well as among higher and lower income states. In previous research, Wang et al 2007 found that larger states and higher
income states tend to have poorer financial condition. Viewing states over eight years, we find a similar relationship. All four indices had a significant correlation with population
size. Cash r=-0.1956, p=0.000, budgetr=-0.1586, p=0.002, and long-run r=-0.2795, p=0.000 indices had a negative relationship with state population size. Service-level
index had a positive significant relationship with population size r=0.1309, p=0.009, indicating that larger states have higher service-level solvency. States with increasing
populations tend to have higher cash r=0.109, p=0.029, long-run r=0.1530, p=0.002, and service-level r=0.3255, p=0.000 solvency. Three of the indices had a significant
negative relationship with income per capita. The budget index did not have a significant relationship with income per capita; however, it did have a significant positive
relationship with the percentage change in income per capita r=0.4238, p=0.000. This relationship suggests that higher income states tend to have lower cash, long-run, and
service-level solvency, but states with growing income per capita tend to have higher budget solvency.
To assess the relationship between the indices and changes in economic condition, the State Coincident index described in the data section above is used. The State
Coincident index is available for each month for over 30 years. Using the month-to- month percentage changes, we can calculate the yearly change in the State Coincident
index for each state. When the individual indices are used, only the budget index has a significant positive correlation with the economic change measure r=0.2565, p=0.000.
These results show that the budget index is the only one that is sensitive to changes in economic conditions.
89 As a final check on the validity of these indices as measures of fiscal stress, we
correlate each index with the unreserved budget balance as a percent of total expenditures. This variable is one of the most common measures of fiscal stress Rubin
and Willoughby 2009, Chaney et al 2002a and as such if no correlation exists this would throw doubt on these indices and their ability to measure fiscal stress. Table 4.4 shows
the Pearson’s correlations and significance levels between the unreserved budget balance as a percent of total expenditures UUB and each index. The table highlights the
statistically significant relationship between the UUB and the budget index. Only marginally significant correlations are present
Table 4.4: Correlation between Indices and Unreserved Budget Balance
Cash Budget
Long-run Service-level Unreserved Budget Balance
as a of Total Expenditures
-0.0491 0.3282
0.1895 0.0010
0.0898 0.0732
-0.0381 0.4482
Source: NASBO Fiscal Survey of the States and State CAFRs
between the UUB and the long-run index. These findings support the statements made in Chapter 3 that the UUB only measures one aspect of fiscal stress – budget solvency.
4.5 Conclusion
