Explanators of Local Government Distress

Stewart Jones and R. G. Walker

Abacus, 2007, vol. 43, issue 3, 396-418

Abstract: This article develops a statistical model to explain sources of distress in local government. Whereas ‘financial distress’ in the private sector has been equated with a failure to meet financial commitments, here ‘distress’ is interpreted as an inability to maintain pre‐existing levels of services to the community. Since the late 1990s local councils in an Australian state (New South Wales) have been required to estimate the cost of restoring infrastructure assets to a satisfactory condition (a requirement which predates that form of reporting on infrastructure condition introduced as an option in U.S. GASB 34). Information regarding the cost of restoring infrastructure is used in this study as a proxy for levels of distress (in contrast to the binary classification that characterizes much of prior private sector financial distress research). Data regarding service levels for a sample of 161 councils for 2001 and 2002 were used and a multiple regression model was estimated and interpreted. The main findings were that the degree of distress in local councils is positively associated with the size of the population they serve and the size and composition of their revenues. Road maintenance costs featured prominently in results, as higher road program costs were associated with higher levels of distress (particularly when interacted with other variables). However, the revenue generating capacity of councils had the strongest statistical impact on local government distress. Councils with lower percentages of rates revenue to total revenue and lower ordinary revenue levels to total assets were typically identified as more distressed. However, no systematic evidence was found that rural councils have higher distress levels than urban councils (i.e., both rural and urban councils serving larger populations were relatively more distressed than councils serving smaller populations). It is suggested that the model (or modifications thereof) may serve as an early warning system for those monitoring the circumstances and performance of local governments.

Date: 2007
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