Long-term efficiency of public service provision in a context of budget restrictions. An application to the education sector

Laura López-Torres and Diego Prior

Socio-Economic Planning Sciences, 2022, vol. 81, issue C

Abstract: This paper proposes an extension of the non-parametric long-term evaluation of efficiency, the conditional panel data DEA model, which takes into account the panel structure of the data and, at the same time, incorporates the role of contextual factors in the estimations. Its application to the education sector for the period analyzed (2009–2014) shows the utility of this method, since it obtains more representative efficiency scores for the complete time-period, is more robust to external shocks, and allows improvements to the decision-making process in the allocation of the budget available for the public education sector. The results are clear and present an evolution towards the convergence of the efficiency scores, precisely in a time period when hard budget constraints severely reduced the resources available for public schools.

Keywords: Public education; Efficiency; DEA; Panel data assessment; Budget constraints; Convergence (search for similar items in EconPapers)
JEL-codes: C14 C33 C61 C67 I21 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0038012120307837
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:soceps:v:81:y:2022:i:c:s0038012120307837

DOI: 10.1016/j.seps.2020.100946

Access Statistics for this article

Socio-Economic Planning Sciences is currently edited by Barnett R. Parker

More articles in Socio-Economic Planning Sciences from Elsevier
Bibliographic data for series maintained by Catherine Liu ().