Is there an Optimal Size for Local Governments? A Spatial Panel Data Model Approach
Miriam Hortas-Rico () and
Vicente Rios ()
International Center for Public Policy Working Paper Series, at AYSPS, GSU from International Center for Public Policy, Andrew Young School of Policy Studies, Georgia State University
The paper presents a framework for determining the optimal size of local jurisdictions. To that aim, we first develop a theoretical model of cost efficiency that takes into account spatial interactions and spillover effects among neighbouring jurisdictions. The model solution leads to a Spatial Durbin panel data specification of local spending as a non-linear function of population size. The model is tested using local data over the 2003-2011 period for two aggregate (total and current) and four disaggregate measures of spending. The empirical findings suggest a U-shaped relationship between population size and the costs of providing public services that varies depending on (i) the public service provided and (ii) the geographical heterogeneity of the territory.
Pages: 41 pages
New Economics Papers: this item is included in nep-ure
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
Journal Article: Is there an optimal size for local governments? A spatial panel data model approach (2020)
Working Paper: Is there an optimal size for local governments? A spatial panel data model approach (2018)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:ays:ispwps:paper1807
Access Statistics for this paper
More papers in International Center for Public Policy Working Paper Series, at AYSPS, GSU from International Center for Public Policy, Andrew Young School of Policy Studies, Georgia State University Contact information at EDIRC.
Bibliographic data for series maintained by Paul Benson ().