Assessing fiscal distress in small county governments

Purpose - 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. Design/methodology/approach - 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., Klohaet al., 2005; Gorinaet al., 2018). The authors test the validity of this approach using correlation analysis and regression modeling, similar to Wanget al.(2007). Findings - 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