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The efficiency of private pension companies using dynamic data envelopment analysis

Quantitative Methods Department, School of Business, Istanbul University, Avcilar, Istanbul, Turkey

Saving plays an important role in economics. Private Pension System (PPS) helps individuals make savings and contribute to the nation’s economy. Therefore, it is important to know the performance of Private Pension Companies (PPCs) that manage the pension funds. Data Envelopment Analysis (DEA) is one of the useful nonparametric techniques to measure the relative efficiency of Decisions Making Units (DMUs) for a specific time or process. In this study, relative efficiency scores of PPCs are investigated for a time interval with Dynamic DEA and compared with the traditional DEA. Inputs of the model are considered as the number of workers, total assets; and the outputs of the model are the number of contracts, total contribution and the market share of each PPC. Also, to ensure the dynamic procedure, Shareholders’ Equity is used as a quasi-fixed input data to link consecutive periods in the time interval. Our results demonstrate that the efficiency score can be improved by considering the effects of the inter-relations of the consecutive periods. The implications arising from the results of the study are important for the companies’ policies.
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Keywords network DEA; dynamic DEA; pension companies; efficiency analysis; quasi-fixed inputs

Citation: Yonca Erdem Demirtaş, Neslihan Fidan Keçeci. The efficiency of private pension companies using dynamic data envelopment analysis. Quantitative Finance and Economics, 2020, 4(2): 204-219. doi: 10.3934/QFE.2020009


  • 1. Ali AS (2016) Efficiency of Private Pension Companies in Turkey Using Data Envelopment Analysis (DEA), In: Chaos, Complexity and Leadership 2014, Springer, 495-505.
  • 2. Altan MS (2010) Türk Sigortacilik Sektöründe Etkinlik: Veri Zarflama Analizi Yöntemi ile Bir Uygulama. Gazi Üniversitesi Iktisadi ve Idari Bilimler Fakültesi Dergisi 12: 185-204.
  • 3. Anandarao S, Durai SRS, Goyari P (2019) Efficiency decomposition in two-stage data envelopment analysis: an application to life insurance companies in India. J Quant Econ 17: 271-285.    
  • 4. Banker RD, Charnes A, Cooper WW (1984) Some models for estimating technical and scale inefficiencies in data envelopment analysis. Manage Sci 30: 1078-1092.    
  • 5. Barrientos A, Boussofiane A (2005) How efficient are pension fund managers in Chile. Revista de Economia Contemporânea 9: 289-311.
  • 6. Barros CP, Barroso N, Borges MR (2005) Evaluating the efficiency and productivity of insurance companies with a Malmquist index: A case study for Portugal. Geneva Pap Risk Insurance-Issues Pract 30: 244-267.    
  • 7. Barros CP, Garcia MTM (2006) Performance evaluation of pension funds management companies with data envelopment analysis. Risk Manage Insur Rev 9: 165-188.    
  • 8. Berger AN, Humphrey DB (1997) Efficiency of financial institutions: International survey and directions for future research. Eur J Oper Res 98: 175-212.    
  • 9. Brockett PL, Cooper WW, Golden LL, et al. (2005) Financial intermediary versus production approach to efficiency of marketing distribution systems and organizational structure of insurance companies. J Risk Insur 72: 393-412.    
  • 10. Bulbul SE, Baykal KB (2017) Hayat dışı branşlarda faaliyet gösteren Türk sigorta şirketlerinin finansal performans analizi: Vikoryönetimi. 3rd National Insurance and Actuarial Congress, Karabük, 1-9.
  • 11. Charnes A, Cooper WW, Rhodes E (1978) Measuring the efficiency of decision making units. Eur J Oper Res 2: 429-444.    
  • 12. Cooper W, Seiford LM, Tone K (2007) A Comprehensive Text with Models, Applications, References and DEA-Solver Software. Data Envelopment Anal.
  • 13. Cummins JD, Weiss MA, Zi H (1999) Organizational form and efficiency: The coexistence of stock and mutual property-liability insurers. Manage Sci 45: 1254-1269.    
  • 14. Cummins JD, Rubio-Misas M, Zi H (2004) The effect of organizational structure on efficiency: Evidence from the Spanish insurance industry. J Bank Financ 28: 3113-3150.    
  • 15. Davis EP (2005) The role of pension funds as institutional investors in emerging markets. Brunel University Research Archive. Economics and Finance Discussion Papers, Brunel University, 05-18, London.
  • 16. Eling M, Schaper P (2017) Under pressure: how the business environment affects productivity and efficiency of European life insurance companies. Eur J Oper Res 258: 1082-1094.    
  • 17. Emrouznejad A, Thanassoulis E (2005) A mathematical model for dynamic efficiency using data envelopment analysis. Appl Math Comput 160: 363-378.
  • 18. Emrouznejad A, Yang GL (2018) A survey and analysis of the first 40 years of scholarly literature in DEA: 1978-2016. Socio-Econ Plann Sci 61: 4-8.    
  • 19. Erdemir OK, Tatlidil H (2017) Veri Zarflama Analizinde Veri İndirgeme: Sigorta Şirketlerinin Etkinliği Üzerine Bir Araştırma. J Current Res Bus Econ 7: 65-78.
  • 20. Fallah-Fini S, Triantis K, Johnson AL (2014) Reviewing the literature on non-parametric dynamic efficiency measurement: state-of-the-art. J Prod Anal 41: 51-67.    
  • 21. Fare R, Grosskopf S (1997) Intertemporal production frontiers: with Dynamic DEA. J Oper Res Society 48: 656-656.    
  • 22. Fare R, Grosskopf S, Whittaker G (2007) Network DEA, In: Modeling data irregularities and structural complexities in data envelopment analysis, Springer, 209-240.
  • 23. Ferrier GD, Lovell CK (1990) Measuring cost efficiency in banking: Econometric and linear programming evidence. J Econometrics 46: 229-245.    
  • 24. Garcia MTM (2010) Efficiency evaluation of the Portuguese pension funds management companies. J Int Financ Markets Inst Money 20: 259-266.    
  • 25. Grigorian DA, Manole V (2006) Determinants of commercial bank performance in transition: an application of data envelopment analysis. Comp Econ Stud 48: 497-522.    
  • 26. Gokgoz F (2010) Measuring the financial efficiencies and performances of Turkish funds. Acta Oeconomica 60: 295-320.    
  • 27. Gurol B, Imam M (2018) Measuring The Performance Of Private Pension Sector By Topsis Multi Criteria Decision-Making Method. J Econ Financ Accounting 5: 288-295.
  • 28. Kaffash S, Marra M (2017) Data envelopment analysis in financial services: a citations network analysis of banks, insurance companies and money market funds. Annals Oper Res 253: 307-344.    
  • 29. Kaffash S, Azizi R, Huang Y, et al. (2019) A survey of data envelopment analysis applications in the insurance industry 1993-2018. Eur J Oper Res.
  • 30. Kao C (2013) Dynamic data envelopment analysis: A relational analysis. Eur J Oper Res 227: 325-330.    
  • 31. Kao C (2014) Network data envelopment analysis: A review. Eur J Oper Res 239: 1-16.    
  • 32. Kao C, Hwang SN (2010) Efficiency measurement for network systems: IT impact on firm performance. Decis Support Syst 48: 437-446.    
  • 33. Karakaya A, Kurtaran A, Dagli H (2014) Bireysel emeklilik sirketlerinin veri zarflama analizi ile etkinlik ölçümü: Türkiye örnegi. Yönetim ve Ekonomi Arastırmaları Dergisi 12: 1-23.
  • 34. Kolia DL, Papadopoulos S (2020) The levels of bank capital, risk and efficiency in the Eurozone and the US in the aftermath of the financial crisis. Quant Financ Econ 4: 66-90.    
  • 35. Luo Y, Bi G, Liang L (2012) Input/output indicator selection for DEA efficiency evaluation: An empirical study of Chinese commercial banks. Expert Syst Appl 39: 1118-1123.    
  • 36. Mariz FB, Almeida MR, Aloise D (2018) A review of Dynamic Data Envelopment Analysis: state of the art and applications. Int Tran Oper Res 25: 469-505.    
  • 37. Nemoto J, Goto M (1999) Dynamic data envelopment analysis: modeling intertemporal behavior of a firm in the presence of productive inefficiencies. Econ Lett 64: 51-56.    
  • 38. Nemoto J, Goto M (2003) Measurement of dynamic efficiency in production: an application of data envelopment analysis to Japanese electric utilities. J Prod Anal 19: 191-210.    
  • 39. Nourani M, Ting IWK, Lu WM, et al. (2019) Capital Structure And Dynamic Performance: Evidence From Asean-5 Banks. Singapore Econ Rev 64: 495-516.    
  • 40. Pension Monitoring Center. Available from: https://www.egm.org.tr/data-center/statistics/ips-statistics/summary-ips-data/.
  • 41. Republic of Turkey Prime Ministry Undersecretariat of Treasury. Available from: https://www.treasury.gov.tr/insurance-reports.
  • 42. Ross SA, Westerfield R, Jaffe JF (2005) Corporate Finance, International Edition. McGraw-Hill/Irwin.
  • 43. Ruzgar B, Akkaya A (2009) Examining efficiencies of private pension companies in Turkey with data envelopment analysis, In IWW2009-proceeding of the IVth international workshop on applications of wavelets to real world problems, At Kocaeli, Turkey.
  • 44. Seiford LM, Zhu J (1999) An investigation of returns to scale in data envelopment analysis. Omega 27: 1-11.    
  • 45. Sengupta JK (1999) A dynamic efficiency model using data envelopment analysis. Int J Prod Econ 62: 209-218.    
  • 46. Tunca H, Deliktas E (2015) OECD Ülkelerinde Tarımsal Etkinlik Ölçümü: Dinamik Veri Zarflama Analizi. Ege Acad Rev 15.
  • 47. Ulucan A (2002) İSO 500 Şirketlerinin Etkinliklerinin Ölçülmesinde Veri Zarflama Analizi Yaklaşımı: Farklı Girdi Çıktı Bileşenleri ve Ölçeğe Göre Getiri Yaklaşımları ile Değerlendirmeler. Ankara üniversitesi SBF dergisi 57: 185-202.
  • 48. Varian HR (1987) Intermediate economics: A modern approach, W. W. Norton & Company, NewYork.
  • 49. Wanke P, Barros CP (2016) Efficiency drivers in Brazilian insurance: A two-stage DEA meta frontier-data mining approach. Econ Model 53: 8-22.    


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