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Local governments’ efficiency and its heterogeneity – empirical evidence from a Stochastic Frontier Analysis of Italian municipalities 2010-2015

Tommaso Agasisti () and Francesco Porcelli ()
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Tommaso Agasisti: Politecnico di Milano School of management

Working papers from Società Italiana di Economia Pubblica

Abstract: This paper investigates the efficiency of Italian local governments using a novel dataset collected by SOSE (an agency of the Italian Ministry of Finance specialized in econometric analysis) and by means of Stochastic Frontier Analysis, for the period 2010-2015. As dependent variable of the cost function, we consider the total current expenditure for producing six “essential” local public services: education (ancillary services), waste management, general administration, local police, urbanization and road conditions, and social services. As outputs, we employ indicators for outputs for each of the six services, although they enter simultaneously in the multioutput cost function. In an alternative specification of the model, we use a composite indicator for the output volume. Thus, we estimate a global efficiency score for each local government in each year. The findings reveal that the total expenditure declined over time, less than proportionally when compared with reduction of outputs produced. As a consequence, the global efficiency of Italian municipalities is estimated to be lower in 2015 than it was in 2010. Moreover, there is evidence of substantial scale economies as well as congestion effects, with municipalities with around 10,000 inhabitants being more efficient than their smaller and larger counterparts. Lastly, on average local governments are more efficient in providing public services in northern Regions than in southern ones.

Keywords: Efficiency; local governments; panel Stochastic Frontier Analysis (search for similar items in EconPapers)
JEL-codes: D78 H11 H50 H72 H75 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-eff, nep-eur and nep-ure
Date: 2019-10
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