Academic Journal

Local government size and service level provision. Evidence from conditional non-parametric analysis

التفاصيل البيبلوغرافية
العنوان: Local government size and service level provision. Evidence from conditional non-parametric analysis
المؤلفون: D'Inverno G, Moesen W, De Witte K
المساهمون: D'Inverno, G, Moesen, W, De Witte, K
سنة النشر: 2022
المجموعة: ARPI - Archivio della Ricerca dell'Università di Pisa
مصطلحات موضوعية: Efficiency analysis, Municipal mergers, Municipal expenditure, Local governments, Composite indicator
الوصف: Although the local provision of public goods accommodates better to the heterogeneous local preferences and mitigates the fiscal illusion problem, it comes at the cost of potential diseconomies of scale. This paper examines the relationship between municipal size and local service level provision by applying state-of-the-art non-parametric techniques to a unique panel dataset of Flemish data. We measure the service provision level by using an innovative robust conditional ‘Benefit of the Doubt’ model and we estimate its efficiency in relationship with the local expenditures by means of a robust conditional Data Envelopment Analysis model. Overall, the main findings suggest the presence of diseconomies of scale, and provide weak evidence on an optimal size of local public good provision of around 10,000 citizens.
نوع الوثيقة: article in journal/newspaper
اللغة: English
Relation: info:eu-repo/semantics/altIdentifier/wos/WOS:000797380200016; volume:81; numberofpages:15; journal:SOCIO-ECONOMIC PLANNING SCIENCES; https://hdl.handle.net/11568/1139678; info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85089729498; https://www.sciencedirect.com/science/article/pii/S0038012119306597
DOI: 10.1016/j.seps.2020.100917
الاتاحة: https://hdl.handle.net/11568/1139678
https://doi.org/10.1016/j.seps.2020.100917
https://www.sciencedirect.com/science/article/pii/S0038012119306597
Rights: info:eu-repo/semantics/openAccess
رقم الانضمام: edsbas.8BB5B230
قاعدة البيانات: BASE
الوصف
DOI:10.1016/j.seps.2020.100917