An Analysis on the Efficiency and Productivity for Major Mutual Financing Cooperatives in Korea

우리나라 상호금융조합의 효율성 및 생산성 분석

  • Received : 2020.01.13
  • Accepted : 2020.02.20
  • Published : 2020.02.28


The Mutual Financial Cooperatives(MFCs) in Korea need to make efforts to increase efficiency and productivity in order to secure stable and sustainable growth and competitiveness. Therefore, this study analyzes the efficiency and productivity of MFCs from 2012 to 2018 and suggests some implications. The methodology employed is a Dynamic-Network Slacks-Based Measure(DNSBM) Model. The findings from an empirical study include that first, on average efficiency scores of the institutions, NH(0.225) showed the highest overall efficiency, and followed by SH(0.128) and MG(0.126). After 2015, most of the MFCs' efficiency scores had risen until to 2018. Second, in divisional analysis, the inefficiency in creating the high profitability-stage had been greater than establishing-funds-stage. Third, in projection analysis of Division 2, the inefficiency of the output factors such as interest income and operating income was severe. Fourth, the results from the Malmquist Productivity Index analysis of Division 1 of the fist-stage illustrate that all three MFCs showed minus catch-up effects. Also, a soundness from reducing bad loans and expansion of loans in combination with generating various ways of creating profits besides the interest income is urgently needed for Korean MFCs.


  1. A. Charnes, W. Cooper & E. Rhodes. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429-444.
  2. R. Banker, A. Charnes & W. Cooper. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9), 1078-1092.
  3. J. S. Hwang. (2003). The Analysis on Management Efficiency for the Regional Agricultural Cooperatives. Journal of Industrial Economics and Business, 16(4), 29-43.
  4. H. A. Kang & C. H. Park. (2016). A comparative study of management efficiency in credit business of unit Nonghyup and Unit SuHyup. Korean Journal of Cooperative Studies, 34(3), 45-71.
  5. J. H. Jung & S. M. Lim. (2014). Investigation of efficiency and productivity of Saemaul Credit Cooperatives by Using DEA-Malmquist Productivity Measure. Korean Journal of Financial Engineering, 13(1), 79-100.
  6. B. C. Kim. (2013). Union efficiency change by using non-radial SBM Model and DEA Window Model. Journal of Professional Management, 16(3), 39-60.
  7. D. H. Rho & S. W. Hahn. (2001). A Study on the Management Efficiency of Gunsan Credit Cooperatives by Data Envelopment Analysis. Regional Development Study, 2(1), 71-89.
  8. M. S. Lee & U. S. Beon. (2003). A Study on Improvements of Efficiency of Community Credit Cooperatives. Korean Academic Society of Accounting, 8(2), 79-98.
  9. B. Y. Hong & J. O. Gu. A Data Envelopment Analysis of the Efficiency of Credit Unions. Management Study, 17(4), 121-138.
  10. S. S. Koh. (2003). Management efficiency measurement of credit union in Jeonrabukdo district. Accounting Information Review, 19, 221-236.
  11. E. K. Kang. A measurement of credit union efficiency by slack-based measure. Journal of Business Research, 2015, 30(4), 1-26.
  12. J. D. Kim, Y. S. Cho and S. C. Park. (2014). Managerial efficiency of local credit unions using bootstrap DEA. Korean Journal of Financial Engineering, 13(1), 101-127.
  13. J. U. Baek. (2001). An efficiency analysis of Korean thrift institutions through DEA. Journal of Industrial Economics and Business, 24(3), 1363-1378.
  14. Y. H. Hahn & Y. S. Kim. Analysis of efficiency for community credit cooperative by DEA. Korea International Accounting Review, 66, 99-120.
  15. B. Amoah, K. Ohene-Asare, G. Bokpin & A. Aboagye. (2018). Technical efficiency: the pathway to credit union cost efficiency in Ghana. Managerial Finance, 44(11), 1292-1310.
  16. H. Fukuyama & W. L. Weber. (2012). Estimating twostage network technology inefficiency, an application to cooperative Shinkin Banks in Japan. International Journal of Operations Research and Information Systems, 3(2), 2012, pp. 1-23.
  17. H. Fukuyama & W. L. Weber. (2015). Measuring Japanese bank performance: a dynamic network DEA approach. Journal of Productivity Analysis, 44, 240-264.
  18. P. Wanke & C. Barros. (2014). Two-stage DEA: an application to major Brazilian banks. Expert Systems With Applications, 41(5), 2337-2344.
  19. S. Akther, H. Fukuyama & W. Weber. (2013). Estimating two-stage network slacks-based inefficiency: an application to Bangladesh banking. Omega, 41, 88-96.
  20. S. Malmquist. (1953). Index Numbers and Indifference Surfaces. Trabajos De Estatistica, 4, 209-242.
  21. R. S. Fare, S. Grosskopf & C. A. Lovell. (1994). Production Frontiers. Cambridge University Press.
  22. J. K. An. (2001). Analysis on the Efficiency of Domestic Banks in the Process of Restructuring: DEA and Malmquist Index Approaches. Journal of Korean Economic Studies, 6, 5-39.
  23. F. Pasiouras & E. Sifodaskalakis. (2007). Total Factor Productivity Change of Greek Cooperative Banks. University of Bath Working Paper. 2007.
  24. A. Bonfiglio. (2006). Efficiency and Productivity Changes of the Italian Agrifood Cooperatives: A Malmquist Index Analysis. Working Paper 250, Universita' Politecnica delle Marche(I), Dipartimento di Scienze Economiche e Sociali,
  25. B. S. Bassem. (2014). Total Factor Productivity Change of MENA Microfinance Institutions: A Malmquist Productivity Index Approach. Economic Modelling, 39, 182-189.
  26. B. Z. Gebremichae & D. I. Rani. (2012). Total Factor Productivity Change of Ethiopian Microfinance Institutions (MFIs): A Malmquist Productivity Index Approach (MPI). European Journal of Business and Management, 4(3), 105-114.
  27. G. Krishnasamy. (2004). A. Ridzwa, and P. Vignesan, "Malaysian Post-merger Banks' Productivity: Application of Malmquist Productivity Index. Management and Finance, 30, 63-74.
  28. F. Sufian. (2007). Total Factor Productivity Change in Non-bank Financial Institutions: Evidence from Malaysia Applying A Malmquist Productivity Index (MPI). Applied Econometrics and International Development, 7(1), 177-186.
  29. H. C. Kim. (2017). An analysis of efficiency for credit union in Daejeon and Chungcheong by employing dynamic and network SBM. Journal of Industrial Economics and Business, 30(1), 189-215.
  30. H. Fukuyama & W. L. Weber. (2017). Measuring bank performance with A dynamic network Luenberger indicator. Annals of Operations Research, 250(1), 85-104.
  31. F. Serrano, L. Guerrero, G. Waeselynck, G. Cheng and P. Zervopoulos. (2014). A dynamic network DEA model: an application to Mexican commercial banks before and after the 2008 global financial crisis. Proceedings of the 12th International Conference of DEA.
  32. K. Tone & M. Tsutsui, (2014). Dynamic DEA with network structure: a slacks-based measure approach. Omega, 42, 124-131.
  33. W. W. Cooper, L. M. Seiford & K. Tone. (2007). Some models measures for evaluating performances with DEA : past accomplishments and future prospects. Journal of Productivity Analysis, 28(3), 151-163.
  34. A. Berger & D. Humphrey. (1997). Measurement and Efficiency Issues in Commercial Banking. in Output Measurement in the Service Sectors, edited by Z. Griliches, University of Chicago Press.