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Abstract
The restructuring of the allocation of governmental competencies in France has increased the importance of subnational governments by transferring additional tasks. We analyse the efficiency of public spending on the intermediate government level for the 96 départements in metropolitan France in 2008. Spending efficiency is measured using Data Envelopment Analysis. Results indicate significant room for improvement and we detect an average spending inefficiency of 12%. To explain efficiency, a bootstrapped truncated regression is applied. The second-stage regression shows that efficiency is also determined by exogenous factors and identifies the distance to the national capital, inhabitants’ income and the share of inhabitants older than 65 as significant determinants of efficiency.
Acknowledgements
This article was produced as part of the project Growth and Sustainability Policies for Europe (GRASP), funded by the European Commission’s Seventh Framework Programme, contract number 244725. We furthermore thank Kristiaan Kerstens, David Saal, Christian von Hirschhausen, Astrid Cullmann, Petra Zloczysti, Michael Zschille, Anne Neumann, Ronny Freier and the participants of the EWEPA 2011 young researchers session for helpful comments. Further, we thank Adam Lederer for excellent editing.
Notes
1. The European Commission (Citation2010, p. 66) mentions the falling share of working-age people in the population, lower (potential) economic growth and higher costs associated with providing services for the aging population.
2. These include expenditures by central, state and local government plus social security.
3. This argumentation is in line with public choice theory (effectiveness and knowledge about needs), for example, Mueller (Citation2003), Balaguer-Coll et al. (Citation2010).
4. It is common consensus that public sector production exhibits inefficiencies that arise from numerous sources, for example, organisational settings and personnel, procurement and budgeting restrictions. Therefore, the private sector serves as the standard of comparison. Alternatively, inefficiencies can be identified by comparing economic activities of government bodies among a homogenous group.
5. For a comparison and discussion of alternative efficiency analysis methods, see, among others, Coelli et al. (Citation2005), and Hjalmarsson et al. (Citation1996).
6. France is included in cross-country analyses considering OECD countries, for example, Afonso et al. (Citation2005) and Maudos et al. (Citation2003).
7. This excludes expenditures contributed by the large publicly owned enterprises.
8. The communal sector includes single communes and groupings of communes that may cooperate in providing general public services such as waste management or water supply, but can also jointly levy taxes.
9. This approach is along the same lines as Stiglitz (Citation2000), who refers to the governmental management as a public good itself, where everybody benefits from a better, more efficient and responsive management.
10. Based on Monte Carlo simulation, Banker and Chang (Citation2006) propose to define observations that exceed an efficiency level of 1.2 as outliers.
11. Contrary to the pure number of the elderly, that is, the population over 65, this variable contains more information on the number of dependent elderly.
12. This variable is chosen to measure the services regarding the provision of education infrastructure. In our opinion this is a more appropriate measure then the number of schools, since it also takes different school sizes into account.
13. In 2008 the expenditures for these four sectors was about 78% of total current expenditures.
14. Loikkanen et al. (Citation2011) point out that resident income might also be an indicator of regional input price differentials. They argue that capital cost and especially land prices will be higher in areas with higher income.
15. Overseas départements are excluded in order to maintain comparability of observations. They differ strongly from the départements located in Europe in not just socio-economic but also geographic terms. Furthermore, input prices may diverge.
16. However, one cannot rule out the possibility of measurement errors.
17. Note that regression results may vary when bootstrapping is applied. Nevertheless, our results are robust.
18. We also test a variable that contains information on the topography, that is, the highest elevation in the départements. Such a variable is highly correlated with DISTANCE. Therefore, our estimator might also include effects of the land form on efficiency.
19. A higher share of the elderly population is also related to a rural structure of a département. Therefore our estimator might also include negative effects from this factor, for example, by allowing for agglomeration and scale economies.
Additional information
Notes on contributors
Stefan Seifert
Stefan Seifert is currently a doctoral student in the Department of Innovation, Manufacturing, Service at the German Institute for Economic Research (DIW Berlin). His research interests are performance measurement in public service provision and efficiency analysis of municipal infrastructure.
Maria Nieswand
Maria Nieswand is currently Jean-Monnet-Fellow at the European University Institute in Florence and researcher at the German Institute for Economic Research (DIW Berlin). Her research interests include performance measurement of public service provision and regulated and non-regulated network industries.
