Your browser does not support the PubReader view.
Go to this page to see a list of supporting browsers.
Seoul Journal of Economics
[ Article ]
Seoul Journal of Economics - Vol. 32, No. 4, pp.421-465
ISSN: 1225-0279 (Print)
Print publication date 30 Nov 2019
Received 21 Mar 2019 Revised 24 Sep 2019 Accepted 25 Sep 2019

Short- and Long-run Technical Efficiency Analysis: Application to Ethiopian Manufacturing Firms

Yismaw Ayelign ; Lakhwinder Singh
Yismaw Ayelign, Debre Tabor University, PO box 272, Debre Tabor, Ethiopia, Tel: +251918815156 yismaway@gmail.comyismayw@dtu.edu.et
Lakhwinder Singh, Professor, Department of Economics and Centre for Development Economics and Innovation Studies (CDEIS), PO box 147002, Punjabi University, Patiala, India, Tel: +919888755642 lakhwindergill@pbi.ac.in

JEL Classification: O50, O47, O39, O14, L25, C23

Abstract

This study attempts to investigate the level of transient and persistent technical efficiencies of large- and medium-scale manufacturing establishments in Ethiopia. A stochastic frontier approach was used for Cobb–Douglas production technology and a panel data set (1996–2015) was developed to obtain the coefficients of technical efficiency. The determinants of both components of efficiency were obtained while using the Tobit model. Results show that labor and real capital input coefficients are statistically significant, with positive input elasticities of 0.54% and 0.19%, respectively. The coefficient of the time trend variable, which captures the effect of exogenous technical progress on real value added by shifting the production frontier, is 0.019 (1.9%). Thus, as a year passes, the production frontier shifts outward due to technical change, which results in the increase of real value by 1.9%. The mean time-varying (short run), persistent (long run), and overall technical effciency effects are 64.2%, 57.2%, and 36.7%, respectively. Thus, firms can increase their output by 63.3% by removing transient and structural factors without increasing their input usage nor changing their technology. Particularly, trade variables have positive effects on transient efficiency but negative effects on persistent efficiency. Capital intensity has a negative coefficient in both cases, whereas average wage has a positive coefficient in both cases. Hence, policymakers, such as managers and public regulatory bodies, should give due attention to transient and structural problems. This study suggests that labor quality should be improved, which requires high average wage and participation in the global market. Such an improvement can be achieved by solving structural rigidities related to customs, promoting capital productivity updating and renovating the existing one, and importing capital goods that contain new technology.

Keywords:

Transient efficiency, Persistent efficiency, Technical efficiency, Manufacturing

Acknowledgments

The authors are grateful to two anonymous referees of the journal and Professor Keun Lee for their helpful suggestions on the previous version of the paper. We wish to thank the Central Statistical Agency of Ethiopia for providing us raw data free of charge. We have received the Stata code from Gudbrand Lien, with an initial request to Professor S.C. Kumbhakar for disentangling the short- and long-run technical efficiency components. We appreciate this precious gift.

References

  • Abegaz, M. T. “Total factor productivity and technical efficiency in the Ethiopian manufacturing sector” (No.010 2013).
  • Agasisti, T., & Gralka, S. “The transient and persistent efficiency of Italian and German universities: A stochastic frontier analysis.” Applied Economics (2019): 1-19. [https://doi.org/10.1080/00036846.2019.1606409]
  • Albalate, D. & Rosell,J. “Persistent and transient efficiency on the stochastic production and cost frontiers–an application to the motorway sector.” XREAP2016-04 (2016). [https://doi.org/10.2139/ssrn.2852114]
  • Badunenko, O., & Kumbhakar, S. C. “When, where and how to estimate persistent and transient efficiency in stochastic frontier panel data models.” European Journal of Operational Research 255 (No.1 2016): 272-287. [https://doi.org/10.1016/j.ejor.2016.04.049]
  • Badunenko, O., & Kumbhakar, S. C. “Economies of scale, technical change and persistent and time-varying cost efficiency in Indian banking: Do ownership, regulation and heterogeneity matter?” European Journal of Operational Research 260 (No.2 2017): 789-803. [https://doi.org/10.1016/j.ejor.2017.01.025]
  • Bartels, B. “Beyond” fixed versus random effects”: a framework for improving substantive and statistical analysis of panel, time-series cross-sectional, and multilevel data.” The Society for Political Methodology 9 (2008): 1-43.
  • Battese, G. E., & Coelli, T. J. “Frontier production functions, technical efficiency and panel data: with application to paddy farmers in India.” Journal of productivity analysis 3 (Nos.1-2 1992): 153-169. [https://doi.org/10.1007/BF00158774]
  • Battese, G. E., & Coelli, T. J. “A model for technical inefficiency effects in a stochastic frontier production function for panel data.” Empirical economics 20 (No.2 1995): 325-332. [https://doi.org/10.1007/BF01205442]
  • Bauer, P. W. “Recent developments in the econometric estimation of frontiers.” Journal of econometrics 46 (Nos.1-2 1990): 39-56. [https://doi.org/10.1016/0304-4076(90)90046-V]
  • Becheikh, N., Landry, R., & Amara, N. “Lessons from innovation empirical studies in the manufacturing sector: A systematic review of the literature from 1993–2003.” Technovation 26 (Nos.5-6 2006): 644-664. [https://doi.org/10.1016/j.technovation.2005.06.016]
  • Belotti, F., Daidone, S., Ilardi, G., & Atella, V. “Stochastic frontier analysis using Stata.” The Stata Journal 13 (No.4 2013): 719-758. [https://doi.org/10.1177/1536867X1301300404]
  • Belotti, F., & Ilardi, G. “Consistent Estimation of the’True’Fixed-Effects Stochastic Frontier Model.” 2012. [https://doi.org/10.2139/ssrn.2045474]
  • Bigsten, A., &Gebreeyesus, M. “The small, the young, and the productive: Determinants of manufacturing firm growth in Ethiopia.” Economic Development and Cultural Change 55 (No.4 2007): 813-840. [https://doi.org/10.1086/516767]
  • Bigsten, A., & Gebreeyesus, M. “Firm productivity and exports: Evidence from Ethiopian manufacturing.” The Journal of Development Studies 45 (No.10 2009): 1594-1614. [https://doi.org/10.1080/00220380902953058]
  • Blasch, J., Boogen, N., Filippini, M., & Kumar, N. “Transient and Persistent efficiency in residential electricity consumption in Switzerland and the role of energy literacy and energy saving behaviour.” In 7th Atlantic Workshop on Energy and Environmental Economics (AWEEE). AWEEE (2016)
  • Blundell, R., & Bond, S. “GMM estimation with persistent panel data: an application to production functions.” Econometric reviews 19 (No.3 2000): 321-340. [https://doi.org/10.1080/07474930008800475]
  • CSA. A. “Report on large and medium scale manufacturing and electricity industries survey.” Statistical bulletin 580 (2015).
  • Chen, Y. Y., Schmidt, P., & Wang, H. J. “Consistent estimation of the fixed effects stochastic frontier model.” Journal of Econometrics 181 (No.2 2014): 65-76. [https://doi.org/10.1016/j.jeconom.2013.05.009]
  • Chu, S. N., & Kalirajan, K. “Impact of trade liberalisation on technical efficiency of Vietnamese manufacturing firms.” Science, Technology and Society 16 (No.3 2011): 265-284. [https://doi.org/10.1177/097172181101600302]
  • Colombi, R., Martini, G., & Vittadini, G. “Determinants of transient and persistent hospital efficiency: The case of Italy.” Health economics 26 (S-2 2017): 5-22. [https://doi.org/10.1002/hec.3557]
  • Colombi, R., Martini, G., & Vittadini, G. “A stochastic frontier model with short-run and long-run inefficiency random effects.” (2011).
  • Constantin, P. D. et al. “Cobb-Douglas, translog stochastic production function and data envelopment analysis in total factor productivity in Brazilian agribusiness.” Journal of Operations and Supply Chain Management (JOSCM) (No.2 2009): 20-33. [https://doi.org/10.12660/joscmv2n2p20-33]
  • Cornwell, C., Schmidt, P., & Sickles, R. C. “Production frontiers with cross-sectional and time-series variation in efficiency levels.” Journal of econometrics 46 (Nos.1-2 1990): 185-200. [https://doi.org/10.1016/0304-4076(90)90054-W]
  • Diewert, W. E. “Decompositions of productivity growth into sectoral effects: some puzzles explained.” Productivity and Efficiency Analysis. Springer, Cham (2016): 1-13. [https://doi.org/10.1007/978-3-319-23228-7_1]
  • Färe, R., Grosskopf, S., & Lee, W. F. “Productivity and technical change: the case of Taiwan.” Applied Economics 33 (No.15 2001): 1911-1925. [https://doi.org/10.1080/00036840010018711]
  • Filippini, M., & Greene, W. “Persistent and transient productive inefficiency: a maximum simulated likelihood approach.” Journal of Productivity Analysis 45 (No.2 2016): 187-196. [https://doi.org/10.1007/s11123-015-0446-y]
  • Filippini, M., Greene, W., & Masiero, G. “Persistent and transient productive inefficiency in a regulated industry: electricity distribution.” Energy Economics 69 (2018): 325-334. [https://doi.org/10.1016/j.eneco.2017.11.016]
  • Filippini, M., & Hunt, L. C. “Measuring persistent and transient energy efficiency in the US.” Energy Efficiency 9 (No.3 2016): 663-675. [https://doi.org/10.1007/s12053-015-9388-5]
  • Fungáčová, Z., Weill, L., & Klein, P. O. “Persistent and transient inefficiency: Explaining the low efficiency of Chinese big banks.” (2018).
  • Førsund, F. R., Lovell, C. K., & Schmidt, P. “A survey of frontier production functions and of their relationship to efficiency measurement.” Journal of econometrics 13 (No.1 1980): 5-25. [https://doi.org/10.1016/0304-4076(80)90040-8]
  • Gebreeyesus, M. “Firm turnover and productivity differentials in Ethiopian manufacturing.” Journal of Productivity Analysis 29 (No.2 2008): 113-129. [https://doi.org/10.1007/s11123-007-0076-0]
  • Gralka, S. “Persistent inefficiency in the higher education sector: Evidence from Germany.” Education Economics (2018): 1-20. [https://doi.org/10.1080/09645292.2017.1420754]
  • Greene, W. “Reconsidering heterogeneity in panel data estimators of the stochastic frontier model.” Journal of econometrics 126 (No.2 2005): 269-303. [https://doi.org/10.1016/j.jeconom.2004.05.003]
  • Greene, W. “Fixed and random effects in stochastic frontier models.” Journal of productivity analysis 23 (No.1 2005): 7-32. [https://doi.org/10.1007/s11123-004-8545-1]
  • Greene, W. “The econometric approach to efficiency analysis. The measurement of productive efficiency and productivity growth.” The Measurement of Productive Efficiency: Techniques and Applications. Oxford University Press, New York (2008): 68-119. [https://doi.org/10.1093/acprof:oso/9780195183528.003.0002]
  • Gralka, S. “Persistent inefficiency in the higher education sector: evidence from Germany.” Education Economics 26 (No.4 2018): 373-392. [https://doi.org/10.1080/09645292.2017.1420754]
  • Hailu, K. B., & Tanaka, M. A. ““true” random effects stochastic frontier analysis for technical efficiency and heterogeneity: Evidence from manufacturing firms in Ethiopia.” Economic Modelling 50 (2015): 179-192. [https://doi.org/10.1016/j.econmod.2015.06.015]
  • Herrendorf, B., Rogerson, R., & Valentinyi, Á. “Growth and structural transformation, in Handbook of Economic Growth.” Elsevier Vol.2 (2014): 855-941. [https://doi.org/10.1016/B978-0-444-53540-5.00006-9]
  • Heshmati, A., Kumbhakar, S. C., & Kim, J. “Persistent and transient efficiency of international airlines 444.” Royal Institute of Technology, CESIS-Centre of Excellence for Science and Innovation Studies.
  • Heshmati, A., Kumbhakar, S. C., & Kim, J. “Persistent and transient efficiency of international airlines.” European Journal of Transport and Infrastructure Research 18 (No.2 2018).
  • Hossain, M. A., & Karunaratne, N. D. “Trade liberalisation and technical efficiency: evidence from Bangladesh manufacturing industries.” Journal of Development Studies 40 (No.3 2004): 87-114. [https://doi.org/10.1080/0022038042000213210]
  • Ismail, R., & Abidin, S. Z. “Determinant of Technical Efficiency of Small and Medium Enterprises in Malaysian Manufacturing Firms.” International Business Management 11 (No.2 2017): 299-307.
  • Kabeta, Z. E., & Sidhu, I. S. “Service sector: the source of output and employment growth in Ethiopia.” Academic Journal of Economic Studies 2 (No.4 2016): 139-156.
  • Kabeta, Z. E. “Structure and Growth of Service Sector in Ethiopia.” Doctoral dissertation, Punjabi University, Patiala. (2017).
  • Khalaf, L., & Saunders, C. J. “Dynamic Technical Efficiency.” Productivity and Efficiency Analysis. Springer, Cham. (2016): 99-107. [https://doi.org/10.1007/978-3-319-23228-7_6]
  • Kumbhakar, S. C. “Production frontiers, panel data, and time-varying technical inefficiency.” Journal of econometrics 46 (Nos.1-2 1990): 201-211. [https://doi.org/10.1016/0304-4076(90)90055-X]
  • Kumbhakar, S. C., Lien, G., & Hardaker, J. B. “Technical efficiency in competing panel data models: a study of Norwegian grain farming.” Journal of Productivity Analysis 41 (No.2 2014): 321-337. [https://doi.org/10.1007/s11123-012-0303-1]
  • Kumbhakar, S. C., & Lovell, C. K. “Stochastic production frontier.” Cambridge University Press (2000).
  • Kumbhakar, S. C., & Zelenyuk, V. “Stochastic frontier analysis: Foundations and advances.” Handbook of production economics. New York, NY: Springer (2017).
  • Kumbhakar, S. C., & Sarkar, S. “Deregulation, ownership, and productivity growth in the banking industry: evidence from India.” Journal of Money, Credit and Banking (2003): 403-424. [https://doi.org/10.1353/mcb.2003.0020]
  • Kumbhakar, S. C., Wang, H. J., & Horncastle, A. P. “A practitioner’s guide to stochastic frontier analysis using Stata.” Cambridge University Press (2015). [https://doi.org/10.1017/CBO9781139342070]
  • Kutlu, L. “Battese-Coelli estimator with endogenous regressors.” Economics Letters 109 (No.2 2010): 79-81. [https://doi.org/10.1016/j.econlet.2010.08.008]
  • Lai, H. P., & Kumbhakar, S. C. “Panel data stochastic frontier model with determinants of persistent and transient inefficiency.” European Journal of Operational Research (2018). [https://doi.org/10.1016/j.econlet.2017.10.003]
  • Lee, H. T. “Causes of the changing performance of firms with diverse types of ownerships in China.” Seoul Journal of Economics 29 (2016): 95-112.
  • Lee, K., Miyagawa, T., Kim, Y., & Edamura, K. “Comparing the Management Practices and Productive Efficiency of Korean and Japanese Firms: An Interview Survey Approach.” Seoul Journal of Economics 29 (2016): 1-41.
  • Lien, G., Kumbhakar, S. C., & Alem, H. “Endogeneity, heterogeneity, and determinants of inefficiency in Norwegian crop-producing farms.” International Journal of Production Economics 201 (2018): 53-61. [https://doi.org/10.1016/j.ijpe.2018.04.023]
  • Lemi, A., & Wright, I. “Exports, Foreign Ownership and Firm-level Efficiency in Ethiopia and Kenya: An Application of Stochastic Frontier Model.” Springer-Verlag GmbH Germany (2018). [https://doi.org/10.1007/s00181-018-1521-9]
  • Linna, M. “Measuring hospital cost efficiency with panel data models.” Health economics 7 (No.5 1998): 415-427. [https://doi.org/10.1002/(SICI)1099-1050(199808)7:5<415::AID-HEC357>3.0.CO;2-9]
  • Mattsson, P., Månsson, J., & Greene, W. “TFP Change and its Components for Swedish Manufacturing Firms During the 2008-2009 Financial Crisis.” (2018). [https://doi.org/10.1007/s11123-019-00561-w]
  • O’Donnell, C. J. “Productivity and Efficiency Analysis: An Economic Approach to Measuring and Explaining Managerial Performance.” Springer (2018). [https://doi.org/10.1007/978-981-13-2984-5]
  • Paul, S., & Shankar, S. “Estimating Efficiency Effects in a Panel Data Stochastic Frontier Model.” (2018). [https://doi.org/10.2139/ssrn.3193286]
  • Ritter, C., & Simar, L. “Pitfalls of normal-gamma stochastic frontier models.” Journal of productivity analysis 8 (No.2 1997): 167-182. [https://doi.org/10.1023/A:1007751524050]
  • Schmidt, P., & Sickles, R. C. “Production frontiers and panel data.” Journal of Business & Economic Statistics 2 (No. 4 1984): 367-374. [https://doi.org/10.1080/07350015.1984.10509410]
  • Sena, V. “The frontier approach to the measurement of productivity and technical efficiency.” Economic Issues-Stoke On Trent- 8 (No.2 2003): 71-98.
  • Sinani, E., Jones, D. C., & Mygind, N. “Determinants of firm level technical efficiency: A stochastic frontier approach.” Copenhagen Business School (2007). [https://doi.org/10.22495/cocv5i3c1p7]
  • Sun, H., Hone, P., & Doucouliago, H. “Economic openness and technical efficiency: a case study of Chinese manufacturing industries.” Economics of Transition 7 (No.3 1999): 615-636. [https://doi.org/10.1111/1468-0351.00028]
  • Stevenson, R. E. “Likelihood functions for generalized stochastic frontier estimation.” Journal of econometrics 13 (No.1 1980): 57-66. [https://doi.org/10.1016/0304-4076(80)90042-1]
  • Torres-Reyna, O. “Panel data analysis fixed and random effects using Stata (v. 4.2).” Data & Statistical Services, Priceton University (2007).
  • Tran, K. C., & Tsionas, E. G. “GMM estimation of stochastic frontier model with endogenous regressors.” Economics Letters 118 (No.1 2013): 233-236. [https://doi.org/10.1016/j.econlet.2012.10.028]
  • Tsionas, E. G., & Kumbhakar, S. C. “Firm Heterogeneity, Persistent and Transient Technical Inefficiency: A Generalized True Random‐Effects model.” Journal of Applied Econometrics 29 (No.1 2014): 110-132. [https://doi.org/10.1002/jae.2300]
  • Wang, H. J., & Ho, C. W. “Estimating fixed-effect panel stochastic frontier models by model transformation.” J ournal o f Econometrics 157 (No.2 2010): 286-296. [https://doi.org/10.1016/j.jeconom.2009.12.006]
  • Wikström, D. “Consistent method of moments estimation of the true fixed effects model.” Economics Letters 137 (No.C 2015): 62-69. [https://doi.org/10.1016/j.econlet.2015.08.036]