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A “true” random effects stochastic frontier analysis for technical efficiency and heterogeneity: Evidence from manufacturing firms in Ethiopia

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  • Hailu, Kidanemariam Berhe
  • Tanaka, Makoto

Abstract

This study examines the technical efficiency of the Ethiopian manufacturing sector using establishment-level census panel data over the period of 2000 to 2009. The “true” random effects stochastic frontier model (Greene, 2005a,b), which can disentangle time-varying technical inefficiency from time-invariant unobserved heterogeneity, and the conventional fixed and random effects models are used to estimate efficiency for the aggregated and individual industry groups. The results indicate that efficiency estimates are sensitive to model specifications of firm-specific unobserved heterogeneity. We find a significant gap in efficiency estimates between the “true” random effects model and the fixed and random effects models, which would imply considerable heterogeneity of manufacturing firms in Ethiopia. Our results suggest that firm-specific heterogeneity would be particularly significant in the food and beverages, non-metals, and furniture industries. We also show that the production of the Ethiopian manufacturing sector is largely responsive to changes in intermediate inputs compared to labor and capital inputs. The estimated technical efficiency considerably varies across firms within an industry suggesting a significant potential for improving efficiency in the sector. We discuss that the major problem for the variation in efficiency is the inability of firms to operate at their full production capacity, which was mainly caused by shortage of raw material supply. Generally, it is important to differentiate between inefficiency and unobserved heterogeneity in a stochastic frontier framework when firms operate under diverse social, industrial and environmental conditions.

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  • Hailu, Kidanemariam Berhe & Tanaka, Makoto, 2015. "A “true” random effects stochastic frontier analysis for technical efficiency and heterogeneity: Evidence from manufacturing firms in Ethiopia," Economic Modelling, Elsevier, vol. 50(C), pages 179-192.
  • Handle: RePEc:eee:ecmode:v:50:y:2015:i:c:p:179-192
    DOI: 10.1016/j.econmod.2015.06.015
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    1. Tetsushi Sonobe & John E. Akoten & Keijiro Otsuka, 2009. "An Exploration into the Successful Development of the Leather‐Shoe Industry in Ethiopia," Review of Development Economics, Wiley Blackwell, vol. 13(4), pages 719-736, November.
    2. Dianah Ngui & Joseph Muniu, 2012. "Firm Efficiency Differences and Distribution in the Kenyan Manufacturing Sector," African Development Review, African Development Bank, vol. 24(1), pages 52-66.
    3. Abid, Anis Bou & Drine, Imed, 2011. "Efficiency frontier and matching process on the labour market: Evidence from Tunisia," Economic Modelling, Elsevier, vol. 28(3), pages 1131-1139, May.
    4. Willam Greene, 2005. "Fixed and Random Effects in Stochastic Frontier Models," Journal of Productivity Analysis, Springer, vol. 23(1), pages 7-32, January.
    5. Berta, Paolo & Callea, Giuditta & Martini, Gianmaria & Vittadini, Giorgio, 2010. "The effects of upcoding, cream skimming and readmissions on the Italian hospitals efficiency: A population-based investigation," Economic Modelling, Elsevier, vol. 27(4), pages 812-821, July.
    6. Federico Belotti & Silvio Daidone & Giuseppe Ilardi & Vincenzo Atella, 2013. "Stochastic frontier analysis using Stata," Stata Journal, StataCorp LP, vol. 13(4), pages 718-758, December.
    7. Drine, Imed & Nabi, M. Sami, 2010. "Public external debt, informality and production efficiency in developing countries," Economic Modelling, Elsevier, vol. 27(2), pages 487-495, March.
    8. Soderbom, Mans & Teal, Francis, 2004. "Size and efficiency in African manufacturing firms: evidence from firm-level panel data," Journal of Development Economics, Elsevier, vol. 73(1), pages 369-394, February.
    9. Kinda, Tidiane & Plane, Patrick & Veganzones-Varoudakis, Marie-Ange, 2009. "Firms'productive performance and the investment climate in developing economies : an application to MENA manufacturing," Policy Research Working Paper Series 4869, The World Bank.
    10. Jondrow, James & Knox Lovell, C. A. & Materov, Ivan S. & Schmidt, Peter, 1982. "On the estimation of technical inefficiency in the stochastic frontier production function model," Journal of Econometrics, Elsevier, vol. 19(2-3), pages 233-238, August.
    11. Sangho Kim, 2003. "Identifying And Estimating Sources Of Technical Inefficiency In Korean Manufacturing Industries," Contemporary Economic Policy, Western Economic Association International, vol. 21(1), pages 132-144, January.
    12. Kumbhakar, Subal C., 1990. "Production frontiers, panel data, and time-varying technical inefficiency," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 201-211.
    13. MA Hossain & ND Karunaratne, 2004. "Trade Liberalisation and Technical Efficiency: Evidence from Bangladesh Manufacturing Industries," Journal of Development Studies, Taylor & Francis Journals, vol. 40(3), pages 87-114.
    14. Meeusen, Wim & van den Broeck, Julien, 1977. "Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(2), pages 435-444, June.
    15. Berndt, Ernst R & Wood, David O, 1975. "Technology, Prices, and the Derived Demand for Energy," The Review of Economics and Statistics, MIT Press, vol. 57(3), pages 259-268, August.
    16. Subal Kumbhakar & Gudbrand Lien & J. Hardaker, 2014. "Technical efficiency in competing panel data models: a study of Norwegian grain farming," Journal of Productivity Analysis, Springer, vol. 41(2), pages 321-337, April.
    17. Pitt, Mark M. & Lee, Lung-Fei, 1981. "The measurement and sources of technical inefficiency in the Indonesian weaving industry," Journal of Development Economics, Elsevier, vol. 9(1), pages 43-64, August.
    18. Schmidt, Peter & Sickles, Robin C, 1984. "Production Frontiers and Panel Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(4), pages 367-374, October.
    19. Mehdi Farsi & Massimo Filippini & Michael Kuenzle, 2005. "Unobserved heterogeneity in stochastic cost frontier models: an application to Swiss nursing homes," Applied Economics, Taylor & Francis Journals, vol. 37(18), pages 2127-2141.
    20. Melaku T. Abegaz, 2013. "Total Factor Productivity and Technical Efficiency in the Ethiopian Manufacturing Sector," Working Papers 010, Policy Studies Institute.
    21. Cornwell, Christopher & Schmidt, Peter & Sickles, Robin C., 1990. "Production frontiers with cross-sectional and time-series variation in efficiency levels," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 185-200.
    22. Hinh T. Dinh & Vincent Palmade & Vandana Chandra & Frances Cossar, 2012. "Light Manufacturing in Africa : Targeted Policies to Enhance Private Investment and Create Jobs [L’industrie légère en Afrique : Politiques ciblées pour susciter l’investissement privé et créer des," World Bank Publications - Books, The World Bank Group, number 2245, December.
    23. 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.
    24. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
    25. Greene, William, 2005. "Reconsidering heterogeneity in panel data estimators of the stochastic frontier model," Journal of Econometrics, Elsevier, vol. 126(2), pages 269-303, June.
    26. Karl Lundvall & George Battese, 2000. "Firm size, age and efficiency: Evidence from Kenyan manufacturing firms," Journal of Development Studies, Taylor & Francis Journals, vol. 36(3), pages 146-163.
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