Assessing fiscal distress in small county governments

Identifying tools for predicting fiscally distressed local governments has received heightened attention following the Great Recession of 2007–2009. Despite the recent expansion of research, measuring fiscal distress is challenging because of the operational complexity associated with the term. Furthermore, many local governments are too small to produce a Comprehensive Annual Financial Report (CAFR), upon which many empirical studies of fiscal condition or fiscal distress are based. This study designs a parsimonious tool for identifying fiscally distressed entities based on existing literature. The authors examine Nebraska's 93 counties over a nine-year period (from 2010 to 2018). In order to ensure the validity of our tool, we replicate two well-known empirical approaches of assessing local fiscal condition and compare the results with ours. The authors find nearly all counties in Nebraska to be free from fiscal distress in the past decade. However, since most counties in Nebraska have small populations and are far from urban centers, they may still be vulnerable to future fiscal shocks and may need to closely monitor their fiscal condition.

The authors offer a parsimonious method for assessing the existence of fiscally distressed counties. They select predictors of fiscal distress based on two criteria. First, for the purpose of this study, the authors use financial information that is uniform, easily accessible and does not rely on CAFRs. In order to make their model parsimonious and replicable, the authors only consider factors that have the most decisive effects on local fiscal conditions. Second, the authors draw on indicators that have been consistently supported by previous studies (e.g., Kloha et al., 2005; Gorina et al., 2018). The authors test the validity of this approach using correlation analysis and regression modeling, similar to Wang et al. (2007).

The authors’ fiscal distress measure shows encouraging signs. Results show that all but Brown's model are highly correlated. The decile and standard deviation models have the strongest correlation (r = 0.955, p < 0.01). These two models are also significantly associated with Kloha et al.'s model. Their correlation coefficients are 0.812 and 0.830, respectively. Consistent with Wang et al. (2007), the authors find modest associations between our fiscal measures and socioeconomic measures.

Limitations include questions of generalizability – we are only studying Nebraska counties. The extent to which the findings are generalizable to counties in other states remains to be seen. We advise readers and policymakers to bear in mind that at this point, there is no perfect way to measure local fiscal condition or fiscal distress. Specifically, with our model, the foremost advantages of parsimony are data accessibility and replicability. However, unlike other existing tools that consider dozens of indicators, our tool bears the cost of not employing a more comprehensive perspective that may be required to capture a full picture of local fiscal condition.

The purpose of this research was to construct and present a parsimonious way of identifying local fiscal distress that is easily replicated and applied in practice. The challenges were operational – both in terms of definition and measurement. Fiscal distress is a nebulous concept that can vary based on the researcher's intent. Our chosen set of indicators have two characteristics: accessibility of financial information and consistency with past studies. Thus, we assess two of the four dimensions of solvency: budgetary solvency and long-run solvency. The authors suggest that this effort should not be used as a tool by state lawmakers to accuse and judge local governments. Instead, it should be used to assist local governments as Iowa and Colorado do. The findings could be the beginning of a conversation between the state and local governments to determine the best course(s) of action. As previously mentioned, there are many causes of fiscal distress and poor decision-making is not very common. Looking into the future, the authors expect more local governments to become fiscally distressed and the primary cause would be economic/demographic change. Since many local governments in Nebraska have very small populations and are far from the urban centers of Omaha and Lincoln, they might be vulnerable to future fiscal shocks. Thus, state lawmakers need to begin considering strategies to deal with local fiscal distress. The authors do have limitations in measurement. However, if used appropriately, this research can add value to the discussion of managing local government fiscal distress in Nebraska and other similar states.

While the analysis finds little fiscal distress currently in Nebraska, there is concern that with population migration to the urban areas and the “graying” of the state, local governments in rural areas (the vast majority in Nebraska) could face more serious issues in future years. A recent study showed that local fiscal condition is negatively associated with the distance from the municipality to the urban centers of Omaha and Lincoln (Maher et al., 2019). These spatial effects could be further exacerbated in a state that ranks near the bottom in financial support of local governments and policy makers are committed to “controlling” property taxes.

This study, while building on prior work, is unique in that it focuses on counties as opposed to municipalities, which are the most common units of analysis. The authors also offer a model for assessing fiscal distress in a state that currently does not have state-level systems to monitor local finances. Finally, rather than relying on audited annual financial reports which would disqualify many smaller local governments, the authors offer a parsimonious tool that is easily replicated and can be used by all local governments that submit uniform financial reports to their states.