IDEAS home Printed from https://ideas.repec.org/a/jed/journl/v40y2015i2p105-130.html

Technical Efficiency Determinants Of The Tunisian Manufacturing Industry: Stochastic Production Frontiers Estimates On Panel Data

Author

Listed:
  • KAMEL HELALI

    (University of Sfax, Tunisia)

  • MAHA KALAI

    (University of Sfax, Tunisia)

Abstract

In this paper, we analyze the development of technical efficiency in the Tunisian manufacturing sector using advanced analysis methods. The technical efficiency of the industrial sectors is measured on the basis of panel data through the bias of a classical approach and a Bayesian one, which makes the inefficiency terms change over time. This exercise helps to assess the robustness of the estimated technical efficiency compared to the choice of the estimation technique. The mean efficiency score is found to be of 77 percent and there is no evidence of a continuous increase in efficiency.

Suggested Citation

  • Kamel Helali & Maha Kalai, 2015. "Technical Efficiency Determinants Of The Tunisian Manufacturing Industry: Stochastic Production Frontiers Estimates On Panel Data," Journal of Economic Development, Chung-Ang Unviersity, Department of Economics, vol. 40(2), pages 105-130, June.
    • Handle: RePEc:jed:journl:v:40:y:2015:i:2:p:105-130
    as

    Download full text from publisher

    File URL: http://www.jed.or.kr/full-text/40-2/5.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jean-Paul Chavas & Ragan Petrie & Michael Roth, 2005. "Farm Household Production Efficiency: Evidence from The Gambia," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 87(1), pages 160-179.
    2. Kamel Helali & Maha Kalai, 2015. "Estimate of the elasticities of substitution of the CES and translog production functions in Tunisia," International Journal of Economics and Business Research, Inderscience Enterprises Ltd, vol. 9(3), pages 245-253.
    3. Margarita Grazhdaninova & Zvi Lerman, 2005. "Allocative and Technical Efficiency of Corporate Farms in Russia," Comparative Economic Studies, Palgrave Macmillan;Association for Comparative Economic Studies, vol. 47(1), pages 200-213, March.
    4. M. Grazdaninova & Z. Lerman., 2005. "Estimating Allocative and Technical Efficiency of Corporate Farms," VOPROSY ECONOMIKI, N.P. Redaktsiya zhurnala "Voprosy Economiki", vol. 6.
    5. 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.
    6. Griffin, J. E. & Steel, M. F. J., 2004. "Semiparametric Bayesian inference for stochastic frontier models," Journal of Econometrics, Elsevier, vol. 123(1), pages 121-152, November.
    7. Kumbhakar, Subal C., 1990. "Production frontiers, panel data, and time-varying technical inefficiency," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 201-211.
    8. Stevenson, Rodney E., 1980. "Likelihood functions for generalized stochastic frontier estimation," Journal of Econometrics, Elsevier, vol. 13(1), pages 57-66, May.
    9. Kumbhakar, Subal C. & Tsionas, Efthymios G., 2005. "Measuring technical and allocative inefficiency in the translog cost system: a Bayesian approach," Journal of Econometrics, Elsevier, vol. 126(2), pages 355-384, June.
    10. Ho-chuan Huang, 2004. "Estimation of Technical Inefficiencies with Heterogeneous Technologies," Journal of Productivity Analysis, Springer, vol. 21(3), pages 277-296, May.
    11. van den Broeck, Julien & Koop, Gary & Osiewalski, Jacek & Steel, Mark F. J., 1994. "Stochastic frontier models : A Bayesian perspective," Journal of Econometrics, Elsevier, vol. 61(2), pages 273-303, April.
    12. Yangseon Kim & Peter Schmidt, 2000. "A Review and Empirical Comparison of Bayesian and Classical Approaches to Inference on Efficiency Levels in Stochastic Frontier Models with Panel Data," Journal of Productivity Analysis, Springer, vol. 14(2), pages 91-118, September.
    13. Lyubov A. Kurkalova & Alicia Carriquiry, 2002. "An Analysis of Grain Production Decline During the Early Transition in Ukraine: A Bayesian Inference," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 84(5), pages 1256-1263.
    14. 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.
    15. Reifschneider, David & Stevenson, Rodney, 1991. "Systematic Departures from the Frontier: A Framework for the Analysis of Firm Inefficiency," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 32(3), pages 715-723, August.
    16. Kumbhakar, Subal C & Ghosh, Soumendra & McGuckin, J Thomas, 1991. "A Generalized Production Frontier Approach for Estimating Determinants of Inefficiency in U.S. Dairy Farms," Journal of Business & Economic Statistics, American Statistical Association, vol. 9(3), pages 279-286, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Deng, Yaguo & Wiper, Michael Peter & Lopes Moreira da Veiga, María Helena, 2024. "A Bayesian semi-parametric approach to stochastic frontier models with inefficiency heterogeneity," DES - Working Papers. Statistics and Econometrics. WS 43837, Universidad Carlos III de Madrid. Departamento de Estadística.
    2. Jorge Galán & Helena Veiga & Michael Wiper, 2014. "Bayesian estimation of inefficiency heterogeneity in stochastic frontier models," Journal of Productivity Analysis, Springer, vol. 42(1), pages 85-101, August.
    3. Shirong Zhao & Jeremy Losak, 2024. "Two-tiered stochastic frontier models: a Bayesian perspective," Journal of Productivity Analysis, Springer, vol. 61(2), pages 85-106, April.
    4. 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.
    5. Yan, Jia & Sun, Xinyu & Liu, John J., 2009. "Assessing container operator efficiency with heterogeneous and time-varying production frontiers," Transportation Research Part B: Methodological, Elsevier, vol. 43(1), pages 172-185, January.
    6. Luis R. Murillo‐Zamorano, 2004. "Economic Efficiency and Frontier Techniques," Journal of Economic Surveys, Wiley Blackwell, vol. 18(1), pages 33-77, February.
    7. Jim Griffin & Mark Steel, 2007. "Bayesian stochastic frontier analysis using WinBUGS," Journal of Productivity Analysis, Springer, vol. 27(3), pages 163-176, June.
    8. Tim J. Coelli, 1995. "Recent Developments In Frontier Modelling And Efficiency Measurement," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 39(3), pages 219-245, December.
    9. Michael Danquah and Bazoumana Ouattara, 2018. "Comparison of Stochastic Frontier Approaches for Estimating National Efficiency: An Application to Sub-Saharan African Countries," Journal of Economic Development, Chung-Ang Unviersity, Department of Economics, vol. 43(3), pages 119-142, September.
    10. Larson, Donald F. & Plessmann, Frank, 2009. "Do farmers choose to be inefficient? Evidence from Bicol," Journal of Development Economics, Elsevier, vol. 90(1), pages 24-32, September.
    11. Julio Peña & Julio Aguirre & René Cerca D'amico, 2004. "Pesca demersal en Chile: eficiencia técnica y escalas de operación," Revista de Analisis Economico – Economic Analysis Review, Universidad Alberto Hurtado/School of Economics and Business, vol. 19(1), pages 119-160, June.
    12. Massimiliano Piacenza, 2002. "Regulatory Constraints and Cost Efficiency of the Italian Public Transit Systems: An Exploratory Stochastic Frontier Model," CERIS Working Paper 200202, CNR-IRCrES Research Institute on Sustainable Economic Growth - Torino (TO) ITALY - former Institute for Economic Research on Firms and Growth - Moncalieri (TO) ITALY.
    13. Martín Rossi, 2015. "The Econometrics Approach to the Measurement of Efficiency: A Survey," Working Papers 117, Universidad de San Andres, Departamento de Economia, revised Feb 2015.
    14. Manish Roy & Ritwik Mazumder, 2016. "Technical Efficiency of Fish Catch in Traditional Fishing: A Study in Southern Assam," Journal of Regional Development and Planning, Rajarshi Majumder, vol. 5(1), pages 55-68.
    15. William Griffiths & Xiaohui Zhang & Xueyan Zhao, 2010. "A Stochastic Frontier Model for Discrete Ordinal Outcomes: A Health Production Function," Department of Economics - Working Papers Series 1092, The University of Melbourne.
    16. Henderson, Benjamin B. & Kingwell, Ross S., 2001. "An Investigation of the Technical and Allocative Efficiency of Broadacre Farmers," 2002 Conference (46th), February 13-15, 2002, Canberra, Australia 125109, Australian Agricultural and Resource Economics Society.
    17. Karagiannis, Giannis & Tzouvelekas, Vangelis, 2009. "Parametric Measurement of Time-Varying Technical Inefficiency: Results from Competing Models," Agricultural Economics Review, Greek Association of Agricultural Economists, vol. 10(01), pages 1-30.
    18. Gholamreza Hajargasht & William E. Griffiths, 2018. "Estimation and testing of stochastic frontier models using variational Bayes," Journal of Productivity Analysis, Springer, vol. 50(1), pages 1-24, October.
    19. Fernandez, Carmen & Osiewalski, Jacek & Steel, Mark F. J., 1997. "On the use of panel data in stochastic frontier models with improper priors," Journal of Econometrics, Elsevier, vol. 79(1), pages 169-193, July.
    20. Willam Greene, 2005. "Fixed and Random Effects in Stochastic Frontier Models," Journal of Productivity Analysis, Springer, vol. 23(1), pages 7-32, January.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • L6 - Industrial Organization - - Industry Studies: Manufacturing

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:jed:journl:v:40:y:2015:i:2:p:105-130. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sung Y. Park (email available below). General contact details of provider: https://edirc.repec.org/data/eccaukr.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.