| State and local governments are substantial economic entities that provide services not offered by for-profit organizations, such as education, welfare, and public safety. Therefore, the financial health of these entities is vital to society and the economy. Yet, state and local governments have been experiencing increasing financial difficulties.; Municipal bankruptcies and debt have increased substantially during the past 10 years. More significantly, state and local governments are reporting continuous deficit fund balances. Continuous deficit fund balances are leading indicators of fiscal stress. Fiscal stress and poor financial health significantly detract from the performance of governments.; The primary purpose of this study was to provide evidence of the usefulness of financial ratios in predicting fiscal stress. Thus, the objective of this study was to develop a model specified with financial ratios to measure their ability in predicting governmental deficit fund balances. In pursuing this goal, financial ratios were identified and measured for governments experiencing deficits and surplus general fund balances.; Financial data were collected on the 50 states from 1992 through 1996 and for an average of 327 counties and 1,190 cities from 1991 through 1995. General fund balances is the dependent variable, defined by the ratio of general revenue to general expenditures. Financial ratios, computed from the data gathered, served as independent variables. Initially, 19 ratios for state governments and 18 ratios for local governments were included in the analysis.; The independent variables were used in logistic regression to construct a prediction model for fiscal stress, as defined by the dependent variable. Eleven variables were found to be significant in predicting state and county general fund balances, and 12 variables were found to be significant in predicting city general fund balances. Overall classification accuracy rates for the models were 87.20 percent, 86.06 percent, and 81.95 percent for states, counties, and cities, respectively. Outliers were removed, which increased overall accuracy rates to 89.75 percent, 89.07 percent, and 85.08 percent for states, counties, and cities, respectively. These overall accuracy rates are greater than that due to chance and comparable to prior studies on for-profit bankruptcy and municipal bond rating prediction models. |
