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Do we estimate an input or an output distance function? An application of the mixture approach to European railways

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  • Subal Kumbhakar
  • Luis Orea
  • Ana Rodríguez-Álvarez
  • Efthymios Tsionas

Abstract

In this paper, we estimate parametric input and output distance functions and discuss how to estimate a mixture/latent class model (LCM) involving the output and input distance functions in the context of multi-input and multi-output production technology. The proposed technique is applied to a panel data on European Railways (1971–1994). This model allows us to identify determinants of the efficiency orientation, thereby providing useful information that can help researchers to choose between the input and the output-oriented approaches. In addition, we develop cross-indices that can be used to compute input (output) technical inefficiency from the estimates of output (input) distance function. Copyright Springer Science+Business Media, LLC 2007

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  • Subal Kumbhakar & Luis Orea & Ana Rodríguez-Álvarez & Efthymios Tsionas, 2007. "Do we estimate an input or an output distance function? An application of the mixture approach to European railways," Journal of Productivity Analysis, Springer, vol. 27(2), pages 87-100, April.
    • Handle: RePEc:kap:jproda:v:27:y:2007:i:2:p:87-100
    DOI: 10.1007/s11123-006-0031-5
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    5. Marchetti, Dalmo & Wanke, Peter F., 2019. "Efficiency in rail transport: Evaluation of the main drivers through meta-analysis with resampling," Transportation Research Part A: Policy and Practice, Elsevier, vol. 120(C), pages 83-100.
    6. Heike Wetzel, 2008. "Productivity Growth in European Railways: Technological Progress,Efficiency Change and Scale Effects," Working Paper Series in Economics 101, University of Lüneburg, Institute of Economics.
    7. Sanzidur Rahman & Basanta Kumar Barmon, 2018. "Total Factor Energy Productivity and Efficiency Changes of the Gher (Prawn-Carp-Rice) Farming System in Bangladesh: A Stochastic Input Distance Function Approach," Energies, MDPI, vol. 11(12), pages 1-17, December.
    8. Adwoa Asantewaa & Tooraj Jamasb & Manuel Llorca, 2022. "Electricity Sector Reform Performance in Sub-Saharan Africa: A Parametric Distance Function Approach," Energies, MDPI, vol. 15(6), pages 1-29, March.
    9. Latruffe, Laure & Bravo-Ureta, Boris E. & Moreira, Victor H. & Desjeux, Yann & Dupraz, Pierre, 2011. "Productivity and Subsidies in European Union Countries: An Analysis for Dairy Farms Using Input Distance Frontiers," 2011 International Congress, August 30-September 2, 2011, Zurich, Switzerland 114396, European Association of Agricultural Economists.
    10. Roberto Mosheim & Robin C. Sickles, 2021. "Spatial effects of nutrient pollution on drinking water production," Empirical Economics, Springer, vol. 60(6), pages 2741-2764, June.
    11. Zdeňka Žáková Kroupová & Gabriela Trnková & Monika Roman, 2022. "Is Market Power or Efficiency behind Economic Performance? The Case of the Czech Food Processing Industry," Economies, MDPI, vol. 10(9), pages 1-22, September.
    12. Orea, Luis, 2019. "The Econometric Measurement of Firms’ Efficiency," Efficiency Series Papers 2019/02, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).
    13. Ogundari, K. & Brümmer, Bernhard, 2011. "Estimating Technical Efficiency, Input substitution and complementary effects using Output Distance Function: A study of Cassava production in Nigeria," Agricultural Economics Review, Greek Association of Agricultural Economists, vol. 12(2).
    14. Nguyen, Huy, 2014. "Crop diversification, economic performance and household’s behaviours Evidence from Vietnam," MPRA Paper 59090, University Library of Munich, Germany.
    15. Rahman, Sanzidur, 2009. "Whether crop diversification is a desired strategy for agricultural growth in Bangladesh?," Food Policy, Elsevier, vol. 34(4), pages 340-349, August.
    16. Marchetti, Dalmo & Wanke, Peter, 2017. "Brazil's rail freight transport: Efficiency analysis using two-stage DEA and cluster-driven public policies," Socio-Economic Planning Sciences, Elsevier, vol. 59(C), pages 26-42.
    17. Wang, Sun Ling & Newton, Doris J., 2015. "Productivity and Efficiency of U.S. Field Crop Farms: A Look at Farm Size and Operator’s Gender," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205344, Agricultural and Applied Economics Association.
    18. Régina D.C. Bonou-zin & Khalil Allali & Aziz Fadlaoui, 2019. "Environmental Efficiency of Organic and Conventional Cotton in Benin," Sustainability, MDPI, vol. 11(11), pages 1-17, May.
    19. Rahman, Sanzidur & Kazal, Mohammad Mizanul Haque, 2015. "Whether crop diversification is energy efficient: An empirical analysis from Bangladesh," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 745-754.
    20. Inmaculada C. Álvarez & Luis Orea & Alan Wall, 2023. "Estimating the propagation of both reported and undocumented COVID-19 cases in Spain: a panel data frontier approximation of epidemiological models," Journal of Productivity Analysis, Springer, vol. 59(3), pages 259-279, June.
    21. Latruffe, Laure & Bravo-Ureta, Boris E. & Moreira, Victor H. & Desjeux, Yann & Dupraz, Pierre, 2012. "Productivity and Subsidies in the European Union: An Analysis for Dairy Farms Using Input Distance Frontiers," 2012 Conference, August 18-24, 2012, Foz do Iguacu, Brazil 126846, International Association of Agricultural Economists.
    22. Rahman, Sanzidur & Barmon, Basanta K., 2012. "Energy productivity and efficiency of the ‘gher’ (prawn-fish-rice) farming system in Bangladesh," Energy, Elsevier, vol. 43(1), pages 293-300.
    23. Collier, Trevor & Johnson, Andrew L. & Ruggiero, John, 2011. "Technical efficiency estimation with multiple inputs and multiple outputs using regression analysis," European Journal of Operational Research, Elsevier, vol. 208(2), pages 153-160, January.

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