The general aim of this report is to develop a methodology to describe the relative financial condition or risk of local government authorities in general and that of the 134 Queensland shires in particular. The successful development of models has practical application in the Queensland Department of Local Government. It would allow the Department to flag shires that were potentially risky and likely to fail. Remedial assistance could be provided early, saving the costs and distresses associated with shire failures.
The data analysed involved the standardized Local Government Financial Statistics for 1981-7 collected by the Australian Bureau of Statistics.
Univariate and multivariate analysis is applied to examine relations between financial and non-financial variables. Multivariate models are developed to determine fitted values of shire sizes, funding, expenditure and debt levels. Deviations or residual amounts are examined in relation to risk scores.
Factors that contributed to financial risk in shires are gleaned from published accounts and from investigations of "failed" shires. Twelve financial ratios are then constructed which conceptually "measure or capture" components of risk. The statistical behaviours of each ratio are tested prior to analysis. Each ratio is "mean centered" annually and a new value calculated each year for each shire. The slope, 1987 intercept and variability of these values are determined for each ratio for each shire by the least squares method. These values are then standardized and applied in a principal component analysis to calculate Mahalanobis Distance for each shire from the populations average performance. Shires that have a large Mahalanobis Distance are flagged as "deviants".
A method based on the area under the fitted slope and variability of the standardized values of each ratio is devised to score each ratio for each shire. This value scores a ratio as either more or less risky than the average performance. These scores are examined to determine if shires flagged by Mahalanobis distance are in a low or high risk class.
The models correctly flagged all those shires known from other information to be risky.
Relations between risk scores and revenue residuals, debt residuals, and growth rates are examined. Few correlations are discovered. Under-funding andover-borrowing are related to risk levels in deviant shires but not in average shires.
The nature of financial risk in shires appears to be a multi-dimensional phenomenon expressed as a continuous rather than discrete variable. Individual shires may be outliers and risky for different combinations of causes.