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Decomposing regional efficiency

Author

Listed:
  • Schaffer, Axel
  • Simar, Leopold
  • Rauland, Jan

Abstract

Applying an outlier robust extension of the data envelopment analysis (DEA) followed by a geoadditive regression analysis, this study identifies and decomposes the efficiency of 439 German regions in using infrastructure and human capital. The findings show that the regions' efficiency is driven by a spatial and a non-spatial, arguably structural factor. As a consequence, concrete regional funding schemes, shaped by best practice results, might not be appropriate for all regions. Instead, a more differentiated funding scheme that accounts for both spatial and structural factors seems more promising.
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Suggested Citation

  • Schaffer, Axel & Simar, Leopold & Rauland, Jan, 2010. "Decomposing regional efficiency," LIDAM Discussion Papers ISBA 2010042, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    • Handle: RePEc:aiz:louvad:2010042
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    Citations

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    Cited by:

    1. Alicja Olejnik & Agata Zoltaszek & Jakub Olejnik, 2019. "Spatial Solution to Measure Regional Efficiency - Introducing Spatial Data Analysis (SDEA)," Lodz Economics Working Papers 4/2019, University of Lodz, Faculty of Economics and Sociology.
    2. Manevska-Tasevska, Gordana & Hansson, Helena & Asmild, Mette & Surry, Yves, 2018. "Assessing the regional efficiency of Swedish agriculture under the CAP ‒ a multidirectional efficiency approach," 162nd Seminar, April 26-27, 2018, Budapest, Hungary 271971, European Association of Agricultural Economists.
    3. Cristina Polo & Julián Ramajo & Alejandro Ricci‐Risquete, 2021. "A stochastic semi‐non‐parametric analysis of regional efficiency in the European Union," Regional Science Policy & Practice, Wiley Blackwell, vol. 13(1), pages 7-24, February.
    4. Guliak Roman, 2017. "New Resonance Approach to Competitiveness Interventions in Lagging Regions: The Case of Ukraine before the Armed Conflict," Review of Economic Perspectives, Sciendo, vol. 17(1), pages 25-56, March.
    5. Carlos Mendez, 2020. "Regional efficiency convergence and efficiency clusters," Asia-Pacific Journal of Regional Science, Springer, vol. 4(2), pages 391-411, June.
    6. Yang, Guo-liang & Fukuyama, Hirofumi, 2018. "Measuring the Chinese regional production potential using a generalized capacity utilization indicator," Omega, Elsevier, vol. 76(C), pages 112-127.
    7. Elena Toma, 2014. "Regional scale efficiency evaluation by input-oriented Data Envelopment Analysis of tourism sector," International Journal of Academic Research in Environment and Geography, Human Resource Management Academic Research Society, International Journal of Academic Research in Environment and Geography, vol. 1(1), pages 15-20, June.
    8. Schaffer, Axel, 2011. "Appropriate policy measures to attract private capital in consideration of regional efficiency in using infrastructure and human capital," Working Paper Series in Economics 31, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    9. Mendez, Carlos, 2019. "Regional Efficiency Dispersion, Convergence, and Efficiency Clusters: Evidence from the Provinces of Indonesia 1990-2010," MPRA Paper 95972, University Library of Munich, Germany.
    10. Manevska-Tasevska, Gordana & Hansson, Helena & Asmild, Mette & Surry, Yves, 2021. "Exploring the regional efficiency of the Swedish agricultural sector during the CAP reforms ‒ multi-directional efficiency analysis approach," Land Use Policy, Elsevier, vol. 100(C).
    11. Carlucci, Fabio & Corcione, Carlo & Mazzocchi, Paolo & Trincone, Barbara, 2021. "The role of logistics in promoting Italian agribusiness: The Belt and Road Initiative case study," Land Use Policy, Elsevier, vol. 108(C).
    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. Xia, X.H. & Chen, Y.B. & Li, J.S. & Tasawar, H. & Alsaedi, A. & Chen, G.Q., 2014. "Energy regulation in China: Objective selection, potential assessment and responsibility sharing by partial frontier analysis," Energy Policy, Elsevier, vol. 66(C), pages 292-302.

    More about this item

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)
    • R15 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Econometric and Input-Output Models; Other Methods

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