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Controlling for spatial heterogeneity in nonparametric efficiency models: An empirical proposal

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  • Vidoli, Francesco
  • Canello, Jacopo

Abstract

This paper introduces an original methodology, derived by the robust order-m model, to estimate technical efficiency with spatial autocorrelated data using a nonparametric approach. The methodology is aimed to identify potential competitors on a subset of productive units that are identified through spatial dependence, thus focusing on peers located in close proximity of the productive unit. The proposed method is illustrated in a simulation setting that verifies the territorial differences between the nonparametric unconditioned and the conditioned estimates. A firm-level application to the Italian industrial districts is proposed in order to highlight the ability of the new method to separate the global intangible spatial effect from the efficiency term on real data.

Suggested Citation

  • Vidoli, Francesco & Canello, Jacopo, 2016. "Controlling for spatial heterogeneity in nonparametric efficiency models: An empirical proposal," European Journal of Operational Research, Elsevier, vol. 249(2), pages 771-783.
  • Handle: RePEc:eee:ejores:v:249:y:2016:i:2:p:771-783
    DOI: 10.1016/j.ejor.2015.10.050
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    1. Bădin, Luiza & Daraio, Cinzia & Simar, Léopold, 2012. "How to measure the impact of environmental factors in a nonparametric production model," European Journal of Operational Research, Elsevier, vol. 223(3), pages 818-833.
    2. Elisa Fusco & Francesco Vidoli, 2013. "Spatial stochastic frontier models: controlling spatial global and local heterogeneity," International Review of Applied Economics, Taylor & Francis Journals, vol. 27(5), pages 679-694, September.
    3. Duranton, Gilles & Puga, Diego, 2004. "Micro-foundations of urban agglomeration economies," Handbook of Regional and Urban Economics, in: J. V. Henderson & J. F. Thisse (ed.), Handbook of Regional and Urban Economics, edition 1, volume 4, chapter 48, pages 2063-2117, Elsevier.
    4. Chunfeng Huang & Tailen Hsing & Noel Cressie, 2011. "Nonparametric estimation of the variogram and its spectrum," Biometrika, Biometrika Trust, vol. 98(4), pages 775-789.
    5. Hughes, Neal & Lawson, Kenton & Davidson, Alistair & Jackson, Tom & Sheng, Yu, 2011. "Productivity pathways: climate-adjusted production frontiers for the Australian broadacre cropping industry," 2011 Conference (55th), February 8-11, 2011, Melbourne, Australia 100563, Australian Agricultural and Resource Economics Society.
    6. Cinzia Daraio & Léopold Simar, 2005. "Introducing Environmental Variables in Nonparametric Frontier Models: a Probabilistic Approach," Journal of Productivity Analysis, Springer, vol. 24(1), pages 93-121, September.
    7. Andrew Johnson & Timo Kuosmanen, 2011. "One-stage estimation of the effects of operational conditions and practices on productive performance: asymptotically normal and efficient, root-n consistent StoNEZD method," Journal of Productivity Analysis, Springer, vol. 36(2), pages 219-230, October.
    8. Francisco José Areal & Kelvin Balcombe & Richard Tiffin, 2012. "Integrating spatial dependence into Stochastic Frontier Analysis," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 56(4), pages 521-541, October.
    9. Erniel B. Barrios & Rouselle F. Lavado, 2010. "Spatial Stochastic Frontier Models," Microeconomics Working Papers 23091, East Asian Bureau of Economic Research.
    10. Simar, Leopold & Wilson, Paul W., 2007. "Estimation and inference in two-stage, semi-parametric models of production processes," Journal of Econometrics, Elsevier, vol. 136(1), pages 31-64, January.
    11. Léopold Simar & Paul Wilson, 2011. "Two-stage DEA: caveat emptor," Journal of Productivity Analysis, Springer, vol. 36(2), pages 205-218, October.
    12. Seok-Oh Jeong & Byeong Park & Léopold Simar, 2010. "Nonparametric conditional efficiency measures: asymptotic properties," Annals of Operations Research, Springer, vol. 173(1), pages 105-122, January.
    13. Cinzia Daraio & Léopold Simar, 2007. "Conditional nonparametric frontier models for convex and nonconvex technologies: a unifying approach," Journal of Productivity Analysis, Springer, vol. 28(1), pages 13-32, October.
    14. Vidoli, Francesco, 2011. "Evaluating the water sector in Italy through a two stage method using the conditional robust nonparametric frontier and multivariate adaptive regression splines," European Journal of Operational Research, Elsevier, vol. 212(3), pages 583-595, August.
    15. Florens, Jean-Pierre & Simar, Léopold & Van Keilegom, Ingrid, 2014. "Frontier estimation in nonparametric location-scale models," Journal of Econometrics, Elsevier, vol. 178(P3), pages 456-470.
    16. Rajiv D. Banker & Ram Natarajan, 2008. "Evaluating Contextual Variables Affecting Productivity Using Data Envelopment Analysis," Operations Research, INFORMS, vol. 56(1), pages 48-58, February.
    17. Areal, Francisco Jose & Balcombe, Kelvin & Tiffin, Richard, 2012. "Integrated spatial dependence into Stochastic Frontier Analysis," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 56(4), pages 1-21, December.
    18. Jacopo Canello & Paolo Pavone, 2016. "Mapping the Multifaceted Patterns of Industrial Districts: A New Empirical Procedure with Application to Italian Data," Regional Studies, Taylor & Francis Journals, vol. 50(8), pages 1374-1387, August.
    19. Cazals, Catherine & Florens, Jean-Pierre & Simar, Leopold, 2002. "Nonparametric frontier estimation: a robust approach," Journal of Econometrics, Elsevier, vol. 106(1), pages 1-25, January.
    20. Battese, G E & Coelli, T J, 1995. "A Model for Technical Inefficiency Effects in a Stochastic Frontier Production Function for Panel Data," Empirical Economics, Springer, vol. 20(2), pages 325-332.
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    2. Hou, Zhezhi & Jin, Man & Kumbhakar, Subal C., 2020. "Productivity spillovers and human capital: A semiparametric varying coefficient approach," European Journal of Operational Research, Elsevier, vol. 287(1), pages 317-330.
    3. Tran, Kien C. & Tsionas, Mike G. & Prokhorov, Artem B., 2023. "Semiparametric estimation of spatial autoregressive smooth-coefficient panel stochastic frontier models," European Journal of Operational Research, Elsevier, vol. 304(3), pages 1189-1199.
    4. Levent Kutlu & Robin C. Sickles & Mike G. Tsionas & Emmanuel Mamatzakis, 2022. "Heterogeneous decision-making and market power: an application to Eurozone banks," Empirical Economics, Springer, vol. 63(6), pages 3061-3092, December.
    5. Álvarez, Inmaculada C. & Gude, Alberto & Orea, Luis, 2019. "Effects of inter-industry and spatial spillovers on regional productivity: Evidence from Spanish panel data," Efficiency Series Papers 2019/01, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).
    6. Shunan Zhao & Man Jin & Subal C. Kumbhakar, 2021. "Estimation of firm productivity in the presence of spillovers and common shocks," Empirical Economics, Springer, vol. 60(6), pages 3135-3170, June.
    7. Kien C. Tran & Mike G. Tsionas, 2023. "Semiparametric estimation of a spatial autoregressive nonparametric stochastic frontier model," Journal of Spatial Econometrics, Springer, vol. 4(1), pages 1-28, December.
    8. Kassoum Ayouba, 2023. "Spatial dependence in production frontier models," Journal of Productivity Analysis, Springer, vol. 60(1), pages 21-36, August.
    9. Bergantino, Angela Stefania & Intini, Mario & Volta, Nicola, 2020. "Spatial competition and efficiency: an investigation in the airport sector," The Warwick Economics Research Paper Series (TWERPS) 1287, University of Warwick, Department of Economics.
    10. Ioannis Skevas & Alfons Oude Lansink & Theodoros Skevas, 2023. "Analysing inefficiency in a non‐parametric spatial‐dynamic by‐production framework: A k‐nearest neighbour proposal," Journal of Agricultural Economics, Wiley Blackwell, vol. 74(2), pages 591-607, June.
    11. Marina Cavalieri & Calogero Guccio & Domenico Lisi & Ilde Rizzo, 2020. "Does Institutional Quality Matter for Infrastructure Provision? A Non-parametric Analysis for Italian Municipalities," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 6(3), pages 521-562, November.
    12. Julián Ramajo & José Manuel Cordero & Miguel Ángel Márquez, 2017. "European regional efficiency and geographical externalities: a spatial nonparametric frontier analysis," Journal of Geographical Systems, Springer, vol. 19(4), pages 319-348, October.
    13. Fusco, Elisa & Vidoli, Francesco & Sahoo, Biresh K., 2018. "Spatial heterogeneity in composite indicator: A methodological proposal," Omega, Elsevier, vol. 77(C), pages 1-14.
    14. Kevin Schneider & Ioannis Skevas & Alfons Oude Lansink, 2021. "Spatial Spillovers on Input‐specific Inefficiency of Dutch Arable Farms," Journal of Agricultural Economics, Wiley Blackwell, vol. 72(1), pages 224-243, February.
    15. Yiorgos Gadanakis & Francisco José Areal, 2020. "Accounting for rainfall and the length of growing season in technical efficiency analysis," Operational Research, Springer, vol. 20(4), pages 2583-2608, December.
    16. Vidoli, Francesco & Cardillo, Concetta & Fusco, Elisa & Canello, Jacopo, 2016. "Spatial nonstationarity in the stochastic frontier model: An application to the Italian wine industry," Regional Science and Urban Economics, Elsevier, vol. 61(C), pages 153-164.
    17. Julián Ramajo & Miguel A. Márquez & Geoffrey J. D. Hewings, 2024. "Addressing spatial dependence when estimating technical efficiency: A spatialized data envelopment analysis of regional productive performance in the European Union," Growth and Change, Wiley Blackwell, vol. 55(1), March.
    18. Emir Malikov & Jingfang Zhang & Shunan Zhao & Subal C. Kumbhakar, 2023. "Accounting for Cross-Location Technological Heterogeneity in the Measurement of Operations Efficiency and Productivity," Papers 2302.13430, arXiv.org.

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