Local governments’ responses to the fiscal stress label: the case of New York

ABSTRACT

Fiscal stress has been a source of significant concern for local governments and has led to the introduction of a variety of approaches for dealing with such a situation. One emerging practise is to adopt early-warning systems which identify fiscal stress, assign a fiscal stress label, and assist with local governments’ financial management. Although there is a growing body of research focusing on fiscal stress indicators, there is a lack of studies examining whether localities given a fiscal stress label by the fiscal stress monitoring system improve their fiscal health. With a regression discontinuity design, we have found that less stressed localities improve their financial condition as compared with more stressed ones in the short run. This study has the potential to inform discussions about the role and strategies of states in strengthening localities’ financial situations and designing a better fiscal monitoring system.

KEYWORDS:

• Fiscal stress
• regression discontinuity
• fiscal stress monitoring

Disclosure statement

No potential conflict of interest was reported by the authors.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Notes

1. We use stress to refer to a broad range of conditions, some literature may refer to the more extreme forms of stress as distress.

2. We analysed population and property per capita non-filing localities and found little evidence that out-of-sample localities differ from in-sample localities.

3. See Lee and Lemieux (2010) and Jacob et al. (2012)

4. The Appendix Tables describe the results of internal validity checks at each cut-off level. The main findings are similar to results of Table 5.

5. Randomisation inference (or so-called permutation test) is a non-parametric approach where the p-values of treatment effect are obtained with the use of a sampling distribution created by estimates from all possible randomisations (Fisher 1935). These approaches have been widely used in experiment settings with a small sample case in the regression discontinuity design. Results are presented in appendix A.7, which are similar to our main results.

6. The results of model selection processes are presented in the Appendix Tables.

7. We also estimated the models with pretreatment variables (e.g., median age, poverty rate, property value per capita, and population change) as control variables and checked whether the findings are consistent. Findings are similar to the baseline main results (See the appendix).

Additional information

Notes on contributors

Il Hwan Chung

Il Hwan Chung is an associate professor in the Department of Public Administration and Graduate School of Governance at Sungkyunkwan University, Republic of Korea. His primary research interests include public finance, budgeting and education policy.

Daniel Williams

Daniel Williams’ teaching focuses on budgeting, performance measurement and the conceptual understanding of public administration. His research interests include public finance, forecasting, participatory budgeting, performance measurement methods, and the intellectual history of public administration. Recent publications include, with Thad Calabrese (eds), the Palgrave Handbook of Government Budget Forecasting, Springer, 2019, and with Thad Calabrese and Anubhav Gupta, ‘Does participatory budgeting alter public spending? Evidence from New York City’, Administration & Society, https://doi.org/10.1177/0095399720912548.