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Evaluating the efficiency of municipalities in the presence of unobserved heterogeneity

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Abstract

The paper applies some of the latest advances of probabilistic approach to account directly for unobserved heterogeneity in the estimation of efficiency measures of performance for a sample of 154 Spanish municipalities over the period 2005–2012. Specifically, we develop time-dependent nonparametric conditional measures considering unobserved factors like local government strategies or institutional arrangements that might affect local governments performance. Moreover, we apply a second-stage approach to estimate measures of pure or managerial efficiency, which are an approximation of the unexplained part of the conditional measures after removing the effect of those latent factors. The empirical results suggest that the dynamic effect of those unobserved conditions play an important role as influencing efficiency distribution and the production set. Likewise, we identify that this influence has been more emphatic towards the core of the economic and financial crisis period. In terms of policy implications, our empirical findings suggest that Spanish municipalities could not cope and adapt their administration strategies properly and in time with the ongoing volatile crisis environment.

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Notes

  1. A detailed analysis of this line of research is beyond the scope of this study. In this regard, two very recent papers (Narbón and De Witte 2018a, 2018b) provide excellent up-to-date systematic literature reviews of this issue at the international level.

  2. In their original paper, Simar et al. (2016) denotes Ω as an instrumental variable. However, this denomination might lead to certain confusion with the concept of instrumental variable that is commonly used in the field of econometrics, so we have decided to use the term “auxiliary variable” as well as in the recent work by Daraio et al. (2019).

  3. We need to assume monotonicity of ϕ with respect to the second argument E and an uniform distribution on [0,1] for E, thus it can be interpreted in terms of quantile function and the cumulative distribution funcion (cdf).

  4. For technical details for bandwidth selection (ht,he,hω) and kernel estimation (Kht,Khe,Khω) see Bădin et al. (2010, 2019) and the references therein.

  5. See Daraio and Simar (2007b) for a detailed description of those approaches

  6. In addition, this selection is also guided by the non-availability of data about some of the outputs for municipalities with a population over 50,000 inhabitants as indicated also by Pérez-López et al. (2018).

  7. Table 6 included in the Appendix summarizes the competences assumed by municipalities according to their population size.

  8. See Dyson et al. (2001), Daraio and Simar (2007b) or Wilson (2018) for details.

  9. Several empricial studies conducted at the local government level have also applied this procedure for the outputs (Nijkamp and Suzuki 2009; Bosch et al. 2012; Nakazawa 2014; Yusfany 2015; Cordero et al. 2017b).

  10. Data about population was gathered from the Spanish National Statistics Institute.

  11. We have selected the DEA estimator after performing a test of convexity (see Kneip et al., 2016 for details). The statistic yields a value \({\hat \tau} \cong\) 1.4063 and the corresponding p-value after 1000 bootstrap replications is 0.9202, thus the convexity assumption cannot be rejected.

  12. We select the value of 20,000 to split the sample because this is the threshold established in the current legislation (Law 7/1985, regulating the bases of local government) to attribute additional competences to Spanish municipalities (See Table 6 in the Appendix).

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Acknowledgements

We would like to thank the Associate Editor and three anonymous reviewers for their helpful comments. Any remaining errors are solely the authors’ responsibility. Finally, Jose M. Cordero and Cristina Polo would like to express their gratitude to the Junta de Extremadura for the financial support through grant IB16171.

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Authors and Affiliations

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

    Jose M. Cordero & Cristina Polo

  2. Department of Economics, University of Thessaly, 28th October Street, 78, 38333, Volos, Greece

    Nickolaos G. Tzeremes

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  1. Jose M. Cordero
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  3. Nickolaos G. Tzeremes
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Correspondence to Jose M. Cordero.

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Appendix

Appendix

Table 6

Table 6 Competencies of Spanish local governments by population size and selected outputs
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Cordero, J.M., Polo, C. & Tzeremes, N.G. Evaluating the efficiency of municipalities in the presence of unobserved heterogeneity. J Prod Anal 53, 377–390 (2020). https://doi.org/10.1007/s11123-020-00579-5

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