The article analyses the operational efficiency of Indian scheduled commercial banks (SCBs) using data from a sample of 35 Indian SCBs, comprising 19 public sector banks (PSBs) and 16 private sector banks (PVBs), in the period between 2010 and 2019. The research uses data envelopment analysis (DEA) to estimate the efficiency of PSBs in comparison with PVBs. The impact of the drivers of efficiency measures is analysed using the Tobit regression, while the Malmquist DEA is used to calculate the overall change in efficiency and productivity over the same time period. Finally, the rate of change in efficiency/productivity is examined with seemingly unrelated regression models. Findings suggest that while the total factor productivity of PSBs’ improved over the last decade, their performance, when compared to PVBs, is poor. This research indicates the relative inefficiency of PSBs and emphasizes the need for attention to this sector. It empirically discovers that PSBs that are larger and older are relatively more efficient, thereby supporting the government policy of PSB mergers.
AhnH., & LeM. H. (2014). An insight into the specification of the input-output set for DEA-based bank efficiency measurement. Management Review Quarterly, 64(1), 3–37. https://doi.org/10.1007/s11301-013-0098-9
2.
AsmildM., ParadiJ. C., AggarwallV., & SchaffnitC. (2004). Combining DEA window analysis with the Malmquist index approach in a study of the Canadian banking industry. Journal of Productivity Analysis, 21(1), 67–89. https://doi.org/10.1023/B:PROD.0000012453.91326.ec
3.
AvkiranN. K. (2006). Developing foreign bank efficiency models for DEA grounded in finance theory. Socio-Economic Planning Sciences, 40(4), 275–296. https://doi.org/10.1016/j.seps.2004.10.006
4.
BaidyaM. K., & MitraD. (2012). An analysis of the technical efficiency of Indian public sector banks through DEA approach. International Journal of Business Performance Management, 13(3–4), 341–365. https://doi.org/10.1504/IJBPM.2012.047300
5.
BankerR. D., CharnesA., & CooperW. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9), 1078–1092. https://doi.org/10.1287/mnsc.30.9.1078
6.
BawaJ. K., GoyalV., MitraS. K., & BasuS. (2019). An analysis of NPAs of Indian banks: Using a comprehensive framework of 31 financial ratios. IIMB Management Review, 31(1), 51–62. https://doi.org/10.1016/j.iimb.2018.08.004
7.
BenstonG. J. (1965). Branch banking and economies of scale. The Journal of Finance, 20(2), 312–331. https://doi.org/10.2307/2977238
8.
BergerA. N., & HumphreyD. B. (1992). Measurement and efficiency issues in commercial banking. In GrilichesZ. (Ed.), Output measurement in the service sectors (pp. 245–300). University of Chicago Press. https://doi.org/10.7208/9780226308890
9.
BergerA. N., & HumphreyD. B. (1997). Efficiency of financial institutions: International survey and directions for future research. European Journal of Operational Research, 98(2), 175–212. https://doi.org/10.1016/S0377-2217(96)00342-6
10.
ChakrabartyK. C. (2013). Indian banking sector: Pushing the boundaries. RBI Bulletin, 3, 17–22. https://rbidocs.rbi.org.in/rdocs/Bulletin/PDFs/03SPB080313F.pdf
11.
ChenT. Y. (2001). An estimation of X-inefficiency in Taiwan’s banks. Applied Financial Economics, 11(3), 237–242. https://doi.org/10.1080/096031001300138627
12.
ChitnisA., & VaidyaO. S. (2016). Efficiency ranking method using DEA and TOPSIS (ERM-DT): Case of an Indian bank. Benchmarking: An International Journal, 23(1), 165–182. https://doi.org/10.1108/BIJ-09-2013-0093
13.
DarA. H., MathurS. K., & MishraS. (2021). The efficiency of Indian banks: A DEA, Malmquist and SFA analysis with bad output. Journal of Quantitative Economics, 19(4), 653–701. https://doi.org/10.1007/s40953-021-00247-x
14.
DasA., & GhoshS. (2006). Financial deregulation and efficiency: An empirical analysis of Indian banks during the post reform period. Review of Financial Economics, 15(3), 193–221. https://doi.org/10.1016/j.rfe.2005.06.002
15.
DasA., & GhoshS. (2009). Financial deregulation and profit efficiency: A nonparametric analysis of Indian banks. Journal of Economics and Business, 61(6), 509–528. https://doi.org/10.1016/j.jeconbus.2009.07.003
16.
DasA., & KumbhakarS. C. (2012). Productivity and efficiency dynamics in Indian banking: An input distance function approach incorporating quality of inputs and outputs. Journal of Applied Econometrics, 27(2), 205–234. https://doi.org/10.1002/jae.1183
17.
DhananjayaK. (2020) Corporate debt vulnerability and non-performing assets in India. Global Business Review. https://doi.org/10.1177/0972150920935758
18.
FäreR., GrosskopfS., NorrisM., & ZhangZ. (1994). Productivity growth, technical progress, and efficiency change in industrialized countries. The American Economic Review, 84(1), 66–83. https://www.jstor.org/stable/2117971
19.
FethiM. D., & PasiourasF. (2010). Assessing bank efficiency and performance with operational research and artificial intelligence techniques: A survey. European Journal of Operational Research, 204(2), 189–198. https://doi.org/10.1016/j.ejor.2009.08.003
20.
FujiiH., ManagiS., & MatousekR. (2014). Indian bank efficiency and productivity changes with undesirable outputs: A disaggregated approach. Journal of Banking & Finance, 38, 41–50. https://doi.org/10.1016/j.jbankfin.2013.09.022
21.
HancockD. (1986). A model of the financial firm with imperfect asset and deposit elasticities. Journal of Banking & Finance, 10(1), 37–54. https://doi.org/10.1016/0378-4266(86)90019-1
22.
JayaramanA. R., SrinivasanM. R., & JeremicV. (2013). Empirical analysis of banks in India using DBA and DEA. Management, 18(69), 25–36. https://doi.org/10.7595/management.fon.2013.0029
23.
JemricI., & VujcicB. (2002). Efficiency of banks in Croatia: A DEA approach. Comparative Economic Studies, 44(2), 169–193. https://doi.org/10.1057/ces.2002.13
24.
NidugalaG. K., & PantA. (2017). Lessons from NPAs crisis in Indian banks. Journal of Public Affairs, 17(4), e1672. https://doi.org/10.1002/pa.1672
25.
RahamanS. M., & SurD. (2021). Identifying key drivers of non-performing assets in Indian public sector banks: A panel data analysis. IIM Kozhikode Society & Management Review, 22779752211000146. https://doi.org/10.1177%2F22779752211000146
26.
Reserve Bank of India. (2021). Database on Indian economy. https://dbie.rbi.org.in/DBIE/dbie.rbi?site=home
27.
Sealey JrC. W., & LindleyJ. T. (1977). Inputs, outputs, and a theory of production and cost at depository financial institutions. The Journal of Finance, 32(4), 1251–1266. https://doi.org/10.1111/j.1540-6261.1977.tb03324.x
28.
StaubR. B., de SouzaG. D. S., & TabakB. M. (2010). Evolution of bank efficiency in Brazil: A DEA approach. European Journal of Operational Research, 202(1), 204–213. https://doi.org/10.1016/j.ejor.2009.04.025
29.
SwamiO. S., NethajiB., & SharmaJ. P. (2019). Determining risk factors that diminish asset quality of Indian commercial banks. Global Business Review, 0972150919861470. https://doi.org/10.1177/0972150919861470
30.
TarafdarM., & VaidyaS. D. (2007). Information technology adoption and the role of organisational readiness: The case of an Indian bank. Journal of Cases on Information Technology (JCIT), 9(3), 27–49. https://doi.org/10.4018/jcit.2007070103
31.
TobinJ. (1958). Estimation of relationships for limited dependent variables. Econometrica: Journal of the Econometric Society, 24–36. https://doi.org/10.2307/1907382
32.
ZellnerA. (1962). An efficient method of estimating seemingly unrelated regressions and tests for aggregation bias. Journal of the American Statistical Association, 57(298), 348–368. https://doi.org/10.1080/01621459.1962.10480664
