The Great Recession produced rising debt, deficits, and exposed vulnerabilities for municipalities in a globalist economy. The two-month COVID recession in 2020 accelerated these burdens; a lagging downturn recently added pressures of reduced economic activity, record inflation, and rising costs in 2022. This dissertation studies how local financial sustainability (FS) and financial condition (FC) approaches can work in concert towards a set of indicators with internal and external categorization to explain municipal financial health (MFH). Unassigned fund balance plus select formal stabilizations measure MFH, are conceptually supported in having retrospective (FC) and prospective (FS) value as an intergenerational resource and are theoretically supported by common-pool resource theory. The resource-based view supports 51 unique predictor variables within MFH elements—demographics, economics, organizational structure, fiscal management, and politics/fiscal policy. This exploratory-predictive research uses partial least squares structural equation modeling, 2017 data, and a final sample of 391 Florida cities to predict variations in MFH using three primary models: FC, FS, and Hybrid. The study found the models have valid measurement assessment. The Hybrid model was the best in structural assessment. Advanced testing of Hybrid modeling found politics/fiscal policy to have the strongest relationship with MFH. Higher order modeling found the internal construct (fiscal management and politics/fiscal policy) outperformed external (demographics and economics). Multigroup testing of binary organizational structure attributes found cities with utility-enterprise revenue different than those without. The residential stock equity measure offered can improve resident understanding of MFH and (inter)intragovernmental analysis for researchers and public agencies in any economic climate.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2020-2216 |
| Date | 01 January 2022 |
| Creators | Henley, Terry |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations, 2020- |
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