Forecasting fiscal stability in municipalities is increasingly critical as public entities face unprecedented financial challenges. Recent research has adapted Altman's renowned Z-Score model — originally designed to predict corporate bankruptcy — to assess municipal financial health. By recalibrating the model's metrics to reflect government priorities, the approach now evaluates liquidity, operating efficiency, and solvency in a framework that serves as an early warning system for fiscal distress.
Edward Altman is a distinguished finance professor best known for developing the Altman Z-Score, a model introduced in 1968 that predicts corporate bankruptcy by analyzing various financial ratios. His work has had a lasting impact on risk assessment in both corporate finance and, more recently, in adapting financial health models for municipal use.
Altman's original model combined five financial ratios to predict corporate failures. For government entities, certain ratios have been modified. For instance, while both corporations and municipalities use the working capital-to-total assets ratio to gauge liquidity, municipalities substitute retained earnings with unrestricted fund balances to better represent available fiscal resources. Similarly, operating efficiency is measured by replacing EBIT with operating surplus, and the market value of equity is adjusted by comparing unrestricted general fund reserves with general fund expenditures. Finally, asset turnover is assessed by examining the ratio of revenue to total assets rather than traditional sales figures. These modifications ensure the model captures the unique fiscal dynamics of government operations while retaining its predictive strength.
The adapted model categorizes municipal financial health into three distinct zones. Municipalities scoring above 2.99 are considered to have robust fiscal profiles, with strong liquidity, efficiency and solvency. Scores between 1.81 and 2.99 indicate moderate risk, suggesting that while these entities are not yet in crisis, they warrant closer scrutiny and potential preemptive intervention. Scores below 1.81 signal significant fiscal vulnerability, emphasizing the need for immediate corrective measures to prevent deeper financial deterioration.
Historical case studies lend credence to this approach. The experiences of Detroit, Vallejo and Stockton illustrate how persistent low Z-Scores — often accompanied by declining liquidity and operational inefficiencies — preceded fiscal collapse. Vallejo's steady decline to a Z-Score of 0.97 and Detroit's dramatic plunge into negative territory were early indicators of underlying financial problems. Even Stockton, with moderately low scores, demonstrated that even slight deviations from the benchmark could forewarn a fiscal crisis. These examples highlight the model's value in foreseeing distress well before it reaches a tipping point.
Recent evaluations of six municipalities from fiscal years 2020 to 2024 offer a contemporary perspective. In Connecticut, Bristol exhibits a declining trend in its Z-Scores, signaling emerging risks despite still hovering above the critical threshold. In contrast, Bridgeport and Milford display persistently negative scores, suggesting that both face chronic liquidity challenges and operational inefficiencies reminiscent of the pre-bankruptcy conditions seen in earlier case studies. Meanwhile, cities like Raleigh, Ogden and Gainesville have maintained scores above the distress threshold. Although Raleigh experienced a minor decline in recent reporting periods, its overall financial management remains solid, while Ogden and Gainesville have shown consistent, if cautious, improvement.
This analysis is not merely retrospective. It offers valuable lessons for current municipal financial management. Continuous monitoring of liquidity and solvency is essential, as a gradual decline in these indicators can be an early sign of trouble. The adapted Z-Score model provides a clear, quantifiable method for comparing fiscal health across municipalities. By benchmarking against historical cases, public officials can identify which cities are on a precarious path and require timely intervention.
The implications for policy are significant. Municipalities must prioritize regular and rigorous financial monitoring. Enhancing revenue diversification is also crucial, as overreliance on volatile income streams can exacerbate fiscal instability during economic downturns. Prudent debt management, including reevaluating existing debt structures and exploring refinancing options, can further reduce long-term pressures. Finally, integrating this model into existing financial reporting platforms — such as those mandated by the Government Data Transparency Act — could streamline the process of identifying potential distress, allowing for more agile responses.
Adapting the Z-Score model for municipal finance represents a promising step forward in managing public fiscal health. By recalibrating traditional corporate metrics to better fit the context of government financial management, this approach offers a proactive, data-driven tool for early intervention. As municipalities continue to grapple with complex fiscal challenges, embracing such innovative analytical frameworks will be vital to safeguarding public resources and ensuring long-term stability.
