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Efficiency assessment of Portuguese municipalities using a conditional nonparametric approach

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Abstract

Measuring local government efficiency is a complex task that has to take into account that they usually operate in a heterogeneous context. Therefore, the estimation of relative efficiency measures of their performance needs to account for the effect of contextual and exogenous variables. This should assure that the respective measures adequately reflect the portion of inefficiency that may be attributable to local authorities. In this paper, we apply time-dependent conditional frontier estimators to assess the performance of the 278 Portuguese mainland municipalities for the 2009–2014 period. By applying this nonparametric approach, we can avoid the strong assumptions on the specification of the estimated production function required by traditional two-stage methods. Furthermore, we examine the effect of contextual variables and time on the shape of the frontier and the distribution of inefficiencies. The results reveal that the local reforms do not seem to have achieved the pursued objective of improving the efficiency of local government performance. However, we can observe a small improvement in the last year of the period after the amalgamation of some civil parishes, thus it seems that this specific process might have had a certain impact in the short term. Moreover, we find that the economic and demographic indicators included as contextual variables in our model play an important role as influencing the production set, although those effects do not seem to vary much over time. This evidence is corroborated after conducting a second-stage nonparametric regression of the conditional efficiency measures over those variables.

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Notes

  1. In 2011, private sector and public debt represented 326% and 108% of the GPD, respectively.

  2. The 2013 Portuguese local election took place on September and the previous one was in October 2009.

  3. Some previous studies have analyzed the influence of regulation changes using panel data (e.g. Simões and Marques 2012b), but they refer to specific services.

  4. There are some exceptions using the nonparametric Malmquist productivity index (e.g. Sung 2007; Kutlar and Bakirci 2012; Doumpos and Cohen 2014) or the parametric time-variant SFA approach (e.g. Stastna and Gregor 2015).

  5. Narbón-Perpiñá and De Witte (2016a, b) provide a recent and detailed review of those empirical contributions.

  6. These studies also provide systematic reviews of the existing literature related to this topic of research. Afonso and Fernandes (2008) covered the most representatives studies published until 2006, while Cruz and Marques (2014) extended the review until 2012.

  7. Nevertheless, various studies (e.g. Worthington 2000; Geys and Moesen 2009; Kalb et al. 2012; Otsuka et al. 2014) apply stochastic frontier methods (SFA).

  8. Although this variable is not a direct output, most empirical studies include it in their models (e.g. Afonso and Fernandes (2006); Balaguer-Coll et al. 2007; Balaguer-Coll and Prior 2009; De Borger and Kerstens 1996a; Geys et al 2010; Giménez and Prior 2007; Worthington and Dollery 2000).

  9. As robustness checks we have also estimated the ratios for other alternative values (e.g. α = 0.25 and α = 0.75). Those results have not been included in the text to save space, but they are available upon request.

  10. We use nonparametric local constant models and bandwidths determined by the usual LSCV (Pagan and Ullah 1999).

  11. The recent Law 75/2013 defined two types of administrative regions (metropolitan areas and intermunicipal communities) and specified their powers and duties.

  12. The nomenclature of territorial units for statistics (NUTS) is the classification used by Eurostat for sub-national spatial units.

  13. Afonso and Fernandes (2008) provide similar arguments to support their decision to exclude the island municipalities from their analysis of Portuguese local governments.

  14. This variable is the output indicator most frequently used in local government efficiency analysis as a proxy of general services (e.g. De Borger and Kerstens 1996a; Worthington and Dollery 2000; Balaguer-Coll et al. 2007; Giménez and Prior 2007; Balaguer-Coll and Prior 2009; Geys et al 2010; Cruz and Marques 2014).

  15. This problem arises because the convergence rate of frontier estimators is sensitive to the number of variables (outputs and inputs) included in the model. See Simar and Wilson (2013) for a detailed discussion about this question.

  16. The use of a global output indicator has been widely used in previous literature (e.g. Afonso and Fernandes 2006, 2008; Nijkamp and Suzuki 2009 or Bosch et al. 2012).

  17. The values of the component have been rescaled by adding the maximum negative value to all of them, thus all of them are positive.

  18. Several authors suggest that the demand for local public services may vary with income (De Borger et al. 1994; Hayes et al. 1998).

  19. This strategy has also been used in some recent empirical studies using panel data (e.g. Tzeremes 2014; Halkos and Managi 2016; Mallick et al. 2016; Matousek and Tzeremes 2016).

  20. In the case of the conditional model, many units have an average score of 1, thus we present the efficiency measures obtained with the robust quantile partial frontier considering α = 0.95. This approach report values above one (super-efficient units), thus we can better distinguish among best performers.

  21. Small municipalities are more dependent on government funding, while the larger ones are better able to increase their resources.

  22. We have also calculated the ratios for the order-α frontiers with α = 0.95 as a robustness check for analyzing the effects on technological change based on the estimates for full frontiers and to inspect if some extreme observations would hide a potential effect. The 3-dimensional pictures built from those ratios provide a robust visualization of the examined effect that is very similar to the one obtained with full frontiers, thus they are not reproduced here to save space.

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Authors and Affiliations

  1. Department of Economics, University of Extremadura, Av. Elvas s/n, Badajoz, 06006, Spain

    Jose M. Cordero, Francisco Pedraja-Chaparro & Cristina Polo

  2. Câmara Municipal de Estremoz, Rossio Marquês de Pombal 105, Estremoz, Portugal

    Elsa C. Pisaflores

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  1. Jose M. Cordero
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  2. Francisco Pedraja-Chaparro
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  3. Elsa C. Pisaflores
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  4. Cristina Polo
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Correspondence to Jose M. Cordero.

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Cordero, J.M., Pedraja-Chaparro, F., Pisaflores, E.C. et al. Efficiency assessment of Portuguese municipalities using a conditional nonparametric approach. J Prod Anal 48, 1–24 (2017). https://doi.org/10.1007/s11123-017-0500-z

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