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Bank Productivity: A Meta-Regression Analysis

Year 2024, , 639 - 650, 31.10.2024
https://doi.org/10.51551/verimlilik.1430048

Abstract

Purpose: This study aims at examining studies employing the Malmquist Productivity Index (MPI) in calculating banks’ productivity. It also seeks to determine the factors affecting the total factor productivity change of banks through meta-regression analysis.
Methodology: On December, 2023, relevant works were systematically reviewed using Web of Science (WoS), Scopus, and Google Scholar. The literature review employed a comprehensive search involving all files with the keywords such as ‘‘productivity” and “bank’’. The research process adhered to the PRISMA guidelines.
Findings: Key features of the 35 studies incorporated in the analysis are presented. The samples of 65.71% of the studies are Asian countries. The bank productivity of 45.71% was calculated through the DEA-MPI method. The studies under consideration were sourced from diverse populations. These studies share key similarities in terms of subject and methodology. Random Effects Model was used to test heterogeneity across studies. The common effect size is 19.361 (z= 4.23, 95% CI: [10.384, 28.338]). Inter-study heterogeneity was determined through Cochran Q test and I^2 index (I^2= % 100, df=32.000, Q=141163533.762, p<0.001).
Originality: No meta analysis of studies calculating productivity with the Malmquist Productivity Index (MPI) has been found in the relevant literature. This study provides robust, valid and reliable parameter estimates for future studies that will use the Malmquist Productivity Index in evaluating banks' productivity.

References

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Banka Verimliliği: Bir Meta-Regresyon Analizi

Year 2024, , 639 - 650, 31.10.2024
https://doi.org/10.51551/verimlilik.1430048

Abstract

Amaç: Bu çalışmanın amacı, bankalarda verimliliğin hesaplanmasında Malmquist Verimlilik Indeksi (MPI) kullanan çalışmaları incelemektir. Bankaların Toplam faktör verimlilik değişimini etkileyen faktörleri meta-regresyon analizi ile belirlemektir.
Yöntem: Aralık 2023'te “verimlilik” ve “banka” anahtar kelimelerinin yer aldığı tüm çalışmaları kapsayan bir arama Web of Science (WoS), Scopus ve Google Akademik’ te yapılmıştır. Araştırma sürecinde PRISMA yönergelerine bağlı kalınmıştır.
Bulgular: Analize 35 çalışma dahil edilmiştir. Çalışmaların %65,71' inin örneklemi Asya ülkeleridir. Banka verimliliğinin %45,71'i DEA-MPI yöntemiyle hesaplanmıştır. Söz konusu çalışmalar farklı popülasyonlardan alınmıştır. Bu çalışmalar konu ve metodoloji açısından temel benzerlikleri paylaşmaktadır. Çalışmalar arasındaki heterojenliği test etmek için Rastgele Etkiler Modeli kullanılmıştır. Ortak etki büyüklüğü 19,361'dir (z= 4,23, %95 GA: [10,384, 28,338]). Çalışmalar arası heterojenlik Cochran Q testi ve I^2 indeksi ile belirlenmiştir. (I^2= % 100, df=32.000, Q=141163533.762, p<0.001).
Özgünlük: Yazında verimliliği Malmquist Productivity Index ile hesaplayan çalışmaların meta analizine rastlanmamıştır. Çalışma banka verimliliğinin hesaplanmasında Malmquist Verimlilik İndeksini kullanacak çalışmalar için etkili, geçerli ve güvenilir parametre tahminleri sunmaktadır.

References

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  • Akhtar, M.H. (2010b). ‘‘Technical Efficiency and Productivity Growth of Saudi Banks: A Data Envelopment Analysis Approach’’, Global Business Review, 11(2), 119-133. DOI: 10.1177/097215091001100201
  • Alam, I.M.S. (2001). ‘‘A Nonparametric Approach for Assessing Productivity Dynamics of Large US Banks’’, Journal of Money, Credit and Banking, 121-139. DOI: 10.2307/2673875
  • Alhassan, A.L. and Asare, N. (2016). ‘‘Intellectual capital and bank productivity in emerging markets: evidence from Ghana’’, Management Decision, 54(3), 589-609, DOI: 10,1108/md-01-2015-0025
  • Alhassan, A.L. and Biekpe, N. (2016). ‘‘Explaining Bank Productivity in Ghana’’, Managerial and Decision Economics, 37(8), 563-573, DOI: 10,1002/mde,2748
  • Arjomandi, A., Valadkhani, A. and Harvie, C. (2011). ‘‘Analysing Productivity Changes Using the Bootstrapped Malmquist Approach: The Case of the Iranian Banking Industry’’, Australasian Accounting, Business and Finance Journal, 5(3), 35-56.
  • Baral, R. and Patnaik, D. (2023). ‘‘Bank Efficiency and Governance: Evidence from Indian Banking’’, Journal of Management and Governance, 27(3), 957-985. DOI: 10.1007/s10997-021-09610-9
  • Chen, T.Y. and Yeh, T.L. (2000). ‘‘A measurement of bank efficiency, ownership and productivity changes in Taiwan’’, Service Industries Journal, 20(1), 95-109, DOI: 10.1080/02642060000000006
  • Cho, T.Y. and Chen, Y.S. (2021). ‘‘The Impact of Financial Technology on China’s Banking Industry: An application of the Metafrontier Cost Malmquist Productivity Index’’, The North American Journal of Economics and Finance, 57, 101414. DOI: 10.1016/j.najef.2021.101414
  • Chortareas, G.E., Girardone, C. and Ventouri, A. (2009). ‘‘Efficiency and Productivity of Greek Banks in the EMU Era’’, Applied Financial Economics, 19(16), 1317-1328, DOI: 10,1080/09603100802599506
  • Core Team, R. (2013). R: A Language and Environment for Statistical Computing (Version 4.1), https://cran.r-project.org, (Accessed:08.12.2023).
  • Daştan, H. and Çalmaşur, G. (2015). ‘‘Productivity Change in the Turkish Banking Industry’’, Mediterranean Journal of Social Sciences, 6(5), 148-159, DOI: 10.5901/mjss.2015.v6n5p148
  • De, N., De, S. and Chakraborty, D. (2021), ‘‘Productivity of Indian Banks since 2000s: Impact of Technology’’, Nmims Management Review, 39(1), 106-116.
  • Defung, F. (2020). ‘‘Factors Determining the Productivity of Indonesian Banks during Restructuring Period’’, Journal of Critical Reviews, 7(12), 1212-1218, DOI: 10,31838/jcr,07,12,211
  • Downs, S.H. and Black, N. (1998). ‘‘The Feasibility of Creating a Checklist for the Assessment of the Methodological Quality Both of Randomised and Non-Randomised Studies of Health Care Interventions’’, Journal of Epidemiology & Community Health, 52(6), 377-384. DOI: 10.1136/jech.52.6.377
  • Du, M., Wang, B., Chen, Z. and Liao, L. (2023). ‘‘Productivity Evaluation of Urban Water Supply Industry in China: A Metafrontier-Biennial Cost Malmquist Productivity Index Approach’’, Annals of Operations Research, 1-21. DOI: 10.1007/s10479-023-05294-6
  • Fang, Z., Gui, W., Han, Z. and Lan, L. (2023). ‘‘The Efficiency Evaluation and Influencing Factor Analysis of Regional Green Innovation: A Refined Dynamic Network Slacks-Based Measure Approach’’, Kybernetes, 53(6), 2153-2193. DOI: 10.1108/K-03-2022-0420
  • George, K. (2015). ‘‘Productivity Efficiency of the Systemic Banks: Evidence from Greece’’, Corporate Ownership and Control, 13, 362-369, DOI: 10.22495/cocv13i1c3p4
  • Guzman, I. and Reverte, C. (2008). ‘‘Productivity and Efficiency Change and Shareholder Value: Evidence from the Spanish Banking Sector’’, Applied Economics, 40(15), 2033-2040, DOI: 10.1080/00036840600949413
  • Huang, M.Y., Juo, J.C. and Fu, T.T. (2015). ‘‘Metafrontier Cost Malmquist Productivity Index: An Application to Taiwanese and Chinese Commercial Banks’’, Journal of Productivity Analysis, 44, 321-335. DOI: 10.1007/s11123-014-0411-1
  • Isik, I. and Hassan, M.K. (2003). ‘‘Financial Disruption and Bank Productivity: The 1994 Experience of Turkish Banks’’, The Quarterly Review of Economics and Finance, 43(2), 291-320.
  • Kasman, A. and Mekenbayeva, K. (2016). ‘‘Technical Efficiency and Total Factor Productivity in the Kazakh Banking Industry’’, Acta Oeconomica, 66(4), 685-709, DOI: 10.1556/032.2016.66.4.6
  • Kasman, S. and Kasman, A. (2011). ‘‘Efficiency, Productivity and Stock Performance: Evidence from the Turkish Banking Sector’’, Panoeconomicus, 58(3), 355-372, DOI: 10,2298/PAN1103355K
  • Kaya, N., and Algın, A. (2022). ‘‘Kamu Hastanelerinde Teknik Etkinlik: Bir Meta-Regresyon Analizi’’, Eskişehir Osmangazi Üniversitesi İktisadi ve İdari Bilimler Dergisi, 17(3), 810-821. DOI: 10.17153/oguiibf.1094736
  • Keskin Benli, Y. and Degirmen, S. (2013). ‘‘The Application of Data Envelopment Analysis Based Malmquist Total Factor Productivity Index: Empirical Evidence in Turkish Banking Sector’’, Panoeconomicus, 60(2), 139-159, DOI: 10.2298/PAN1302139K
  • Koutsomanoli-Filippaki, A., Margaritis, D. and Staikouras, C. (2009). ‘‘Efficiency and Productivity Growth in the Banking Industry of Central and Eastern Europe’’, Journal of Banking & Finance, 33(3), 557-567. DOI: 10.1016/j.jbankfin.2008.09.009
  • Lakens, D. (2017). ‘‘Equivalence Tests: A Practical Primer for t Tests, Correlations, and Meta-Analyses’’, Social Psychological and Personality Science, 8(4), 355-362. DOI: 10.1177/1948550617697177
  • Li, J. F., Xu, H.C., Liu, W.W., Wang, D.F. and Zheng, W.L. (2021). ‘‘Influence of Collaborative Agglomeration between Logistics Industry and Manufacturing on Green Total Factor Productivity Based on Panel Data of China’s 284 Cities’’, IEEE Access, 9, 109196-109213. DOI:10.1109/ACCESS.2021.3101233
  • Majid, M.S.A., Azhari, A., Faisal, F. and Fahlevi, H. (2022). ‘‘What Determines Co-Operatives’ Productivity in Indonesia? A-Two Stage Analysis’’, Economics and Sociology, 15(1), 56-77, DOI: 10,14254/2071-789X,2022/15-1/4
  • Maniadakis, N. and Thanassoulis, E. (2004). ‘‘A Cost Malmquist Productivity Index’’, European Journal of Operational Research, 154(2), 396-409. DOI: 10.1016/S0377-2217(03)00177-2
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Details

Primary Language English
Subjects Business Administration
Journal Section Araştırma Makalesi
Authors

Neylan Kaya 0000-0003-2645-3246

Publication Date October 31, 2024
Submission Date February 1, 2024
Acceptance Date August 5, 2024
Published in Issue Year 2024

Cite

APA Kaya, N. (2024). Bank Productivity: A Meta-Regression Analysis. Verimlilik Dergisi, 58(4), 639-650. https://doi.org/10.51551/verimlilik.1430048

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