Evidence on economies of scale in local public service provision: A meta-analysis

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Abstract

The standard theory of optimal jurisdictional size hinges on the existence of economies of scale in the provision of local public goods and services. However, despite its relevance for forced local amalgamation programs and related policies, the empirical evidence on the existence of such economies of scale remains elusive. The main goal of this paper is to produce an updated and comprehensive quantitative review of the existence of economies of scale in the provision of local public goods using a meta-analysis approach to systematize the wide range of empirical approaches and modeling frameworks found in the previous literature. Our analysis confirms the presence of moderately increasing to constant returns to scale in the provision of local services with no reduction in the average costs of production in the delivery of most local public services beyond a certain, modest jurisdictional size, which many studies have estimated at 10,000 residents. Also, the potential for economies of scale differs at least across three traditional services: education, water and sanitation, and garbage collection, being highest for education and lowest for garbage collection. Our analysis also offers guidelines for future empirical research in this area. Physical output and production cost data should be used, together with translog specifications for the modeling of cost functions. Last, we find evidence that the determinants of output cost elasticity include bidirectional publication bias and population density but do not include the presence or absence of modern “lean” production technologies or the (perceived) capital intensity of the sector, contrary to conventional wisdom. These findings have significant policy implications for countries considering jurisdictional consolidation programs.

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Gómez-Reino, J. L., Lago-Peñas, S., & Martinez-Vazquez, J. (2023). Evidence on economies of scale in local public service provision: A meta-analysis. Journal of Regional Science, 63(4), 793–819. https://doi.org/10.1111/jors.12640

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