Assessing and ranking the financial risk of municipal governments

Abstract

Purpose The purpose of this paper is to develop a model to assess and rank the financial risk of a municipal government (“municipality”). Financial risk is the likelihood that a municipality will experience financial distress. Design/methodology/approach Logistic regression is used with financial indicators to assess the level of financial risk. Then, the municipalities are ranked according to their financial risk. As predictor variables for the regression model, indicators are used that were developed by a Pennsylvania state agency to monitor the financial condition of municipalities. Findings Financial risk is positively associated with debt service, population, tax effort, and public service on roadways, while negatively correlated with intergovernmental revenues, operating position, user charges, capital outlays, fund balances, and tax revenue concentration. The financial risk model is able to correctly classify up to 99 percent of municipalities as either at risk or not at risk of financial distress. Research limitations/implications The financial risk model was developed using data from one state in the USA. Further research is needed to test the model’s application to other states and countries. Practical implications Financial risk is on the rise since the Great Recession. This study may be used by municipal managers, citizens, creditors, and regulators to assess and rank the financial risk of a municipality. Originality/value This study provides a method of classifying municipalities as either at risk or not at risk of financial distress. Previous models of the financial condition of municipalities do not provide a method of assessing and ranking financial risk.

Testing the Reliability of Financial Sustainability. The Case of Spanish Local Governments

“…This means that there is a powerful relationship between Spanish indicators and these indicators of ICMA, which could be pooled into three main groups of indicators: that link revenues (Indicator 1 and Indicator 9), that link expenditures (Indicator 10 and Indicator 15) and that link debt (Indicator 19 and Indicator 20), which means that Hypothesis 1, 2 and 3 are accepted. This result is consistent with the previous reviewing of the comparison of the meaning of both kinds of indicators as Cabaleiro et al [22], Kioko [19], Navarro-Galera [27,28], Trussel and Patrick [24] and Gorina et al [21] support in their analysis. ICMA indicators with a higher power of discrimination are those whose definition is in line with Spanish indicators, which supports the idea that the default or non-default concept of Spanish indicators is supported by the ICMA indicator system, i.e., the definition of financial condition by the Spanish legislation is consistent with the empirical evidence and with the financial condition standards at the international level.…”

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Family Control, Biaya Keagenan Dan Risiko Keuangan Terhadap Nilai Perusahaan

“…Untuk itu perusahaan diharapkan mempersiapkan strategi dengan mempertimbangkan risiko yang akan terjadi. Menurut (Trussel & Patrick, 2018) untuk menilai tingkat risiko keuangan dapat menggunakan regresi logistik dengan indikator keuangan.…”

Assessing financial risk management in local governments

“…The results showed that the municipality of Karibib has been performing poorly in terms of financial management. In another study by Trussel and Patrick (2018) aimed to developed to be a model to assess and rank the financial risk of a municipal government (Municipality of Pennsylvania). Logistic regression was used with financial indicators to assess the level of financial risk and the municipalities were ranked according to their financial risk.…”