Understanding financial resilience from a resource-based view: Evidence from US state governments

ABSTRACT

To sustain government services in times of disruptions, it is important to enhance the financial resilience of the government. This study explains the relationship between an organization’s financial resilience and its financial, human, and political resources using the framework of the resource-based view theory (RBV). The results show that the effect of government resources on financial resilience differs depending on the type of resources. Overall, while it is found that an increase in financial and human resources positively relates to financial resilience, an abundance of political resources negatively relates to resilience; and these findings are evident in the short-run model.

KEYWORDS:

• Organizational resources
• financial resilience
• financial sensitivity analysis of public spending
• resource-based view theory

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1. The OLS model with two-way fixed effects enables us to better control for time-invariant or unobservable factors across all studies. The fixed effects model has an assumption that each state has specific time-invariant characteristics that influence the dependent variable, whereas the random effects model assumes such effect to be random. Therefore, this study considers both random and fixed effects models and performs the Hausman test to find which model yields a more consistent estimator. Nevertheless, when the error terms have heteroscedasticity and autocorrelation, estimators of the fixed and random effects are biased and inefficient. To overcome this problem, we use the Prais–Winsten model that corrects for heteroscedasticity and autocorrelation. The GMM model includes lags of endogenous variables as instruments and differentiates the equation. Through this process, it can address possible biases created by other endogenous explanatory variables and the correlation between lagged dependent variables and the error terms. Thus, this study uses these multiple regression models to test the hypotheses.

Additional information

Notes on contributors

Sungyoon Lee

Sungyoon Lee is a PhD candidate in Public Administration and Policy at the Rockefeller College of Public Affairs and Policy, University at Albany, SUNY. She studies public financial management with particular focus on the fiscal impacts of disasters on governments, fiscal reserves, and fiscal stability.

Gang Chen

Gang Chen is an Associate Professor in Public Administration and Policy at the Rockefeller College of Public Affairs and Policy, University at Albany, SUNY. His research focuses on state and local budgeting, public financial management, and public pension governance.