The purpose of this article is to investigate the efficiency and productivity growth of Indian life insurance industry and to assess the effect of branch office locations on efficiency. This study has analysed the efficiency and productivity performance of all the 24 life insurance companies during the period from 2016 to 2019, using slack-based measures (SBM) of data envelopment analysis. SBM super-efficiency model is used to rank the fully efficient life insurers. Malmquist index is used to assess the productivity of life insurance companies. To assess the effect of branch office geographical locations on efficiency, double bootstrap regression has been used. The findings indicate that Indian life insurance industry experienced significant fluctuations in mean technical efficiency during the study period. Almost 50% of life insurers operated efficiently in one or more years during the study period. Only 3 out of 24 life insurers were found scale efficient. Interestingly, 50% of life insurers experienced growth during our study period. Double bootstrap regression analysis indicates that semi-urban and rural branch offices have positive effect on the efficiency of the life insurers. This study is first of its kind that has assessed the effect of branch office locations on the efficiency of life insurers. The study brings to light the operating characteristics, efficiencies and productivity of the Indian life insurance companies for the period from 2016 to 2019.
BankerR., CharnesA., & CooperW. W. (1984). Some models for estimating technical and scale efficiency. Management Science, 30(9), 1078–1092.
2.
BannaH., AlamM. D., AhmadR., & SariN. M. (2020). Does financial inclusion drive the Islamic banking efficiency? A post financial crises analysis. The Singapore Economic Review. https://doi.org/10.1142/S0217590819420050
3.
BergerA. N., & HumphreyD. B. (1992). Measurement and efficiency issues in commercial banking. In GrilchesZ. (Ed.), Output measurement in the service sectors, 56 (pp. 245–279). National Bureau of Economics Research, Studies in Income and Wealth. University of Chicago Press.
4.
BergerA. N., CumminsJ. D., WeissM. A., & ZiH. (2000). Conglomeration versus strategic focus: Evidence from insurance industry. Journal of Financial Intermediation, 9(4), 323–362.
5.
BienerC., & ElingM. (2012). Organization and efficiency in the international insurance industry: A cross frontier analysis. European Journal of Operational Research, 221, 454–468.
6.
BrockettP. L., CooperW. W., GoldenL. L., RousseauJ. J., & WangY. (2004). Evaluating solvency versus efficiency performance and different forms of organization and marketing in US property-liability insurance companies. European Journal of Operational Research, 154, 492–514.
7.
BrockettP. L., CooperW. W., GoldenL. L., RousseauJ. J., & WangY. (2005). Financial intermediary versus production approach to efficiency of marketing distribution system and organizational structure of insurance companies. Journal of Risk and Insurance, 72(3), 393–412.
8.
ChakrabortyK., DuttaA., & SenguptaP. P. (2013). Efficiency and productivity of Indian life insurance industry. Asia-Pacific Journal of Risk and Insurance, 7(1), 1–28.
9.
CharnesA., CooperW. W., & RhodesE. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444.
10.
CumminsJ. D., & DanzonP. M. (1997). Price, financial quality and capital flows in insurance markets. Journal of Financial Intermediation, 6, 3–38.
11.
CumminsJ. D., & MariaR. (2006). Deregulation, consolidation and efficiency: Evidence from the Spanish insurance industry. Journal of Money, Credit and Banking, 38(2), 323–355.
12.
CumminsJ. D., & XieX. (2008). Mergers and acquisitions in the US property-liability insurance industry: Productivity and efficiency effects. Journal of Banking & Finance, 32, 30–55.
13.
CumminsJ. D., MarioR., & ZiH. (2004). The effect of organizational structure on efficiency: Evidence from the Spanish insurance industry. Journal of Banking & Finance, 28(12), 3113–3150.
14.
CumminsJ. D., MisasM. R., & VencappaD. (2017). Competition, efficiency and soundness in European life insurance markets. Journal of Financial Stability, 28(1), 66–78.
15.
CumminsJ. D., TurchettiG., & WeissM. A. (1996). Productivity and technical efficiency in the Italian insurance industry(Working Paper No. 96-10). Wharton School.
16.
CumminsJ. D., WeissM. A., & ZiH. (1999). Organizational form and efficiency: The coexistence of stock and mutual property-liability insurers. Management Science, 45(9), 1254–1269.
17.
CumminsJ. D., WeissM. A., XieX., & ZiH. (2010). Economies of scope in financial services: A DEA efficiency analysis of the US insurance industry. Journal of Banking & Finance, 34(7), 1525–1539.
18.
DiaconS. R. (2001). The efficiency of UK general insurance companies(Working Paper). Center of Risk and Insurance Studies, University of Nottingham.
19.
DiaconS. R., StarkeyK., & O’BrienC. O. (2002). Size and efficiency in European long term insurance companies: An international comparison. Geneva Papers on Risk and Insurance, 27(3), 444–466.
20.
DuttaA., & SenguptaP. P. (2011). Efficiency measurement of Indian Life Insurance Industry in Post-Reforms Era. Global Business Review, 12(3), 415–430.
21.
ElingM., & HuangW. (2011). An efficiency comparison on the non life insurance industry in the BRIC countries(Working Papers on Risk and Insurance). University of St. Gallen.
22.
ElingM., & JiaR. (2018). Business failure, efficiency and volatility: Evidence from the European insurance industry. International Review of Financial Analysis, 59(5), 58–76.
23.
ElingM., & SchaperP. (2017). Under pressure: How the business environment affects productivity and efficiency of European life insurance companies. European Journal of Operational Research, 258, 1082–1094.
24.
FareR., GrosskopfS., NorrisM., & ZhangZ. (1994). Productivity growth, technical progress and efficiency change in industrialized countries. American Economic Review, 84(1), 66–83.
25.
HusselsS., & WardD. R. (2007). The impact of deregulation on the German and the UK insurance markets—An analysis of efficiency and productivity between 1991–2002. Research Paper Series, 4(07), 1–24.
26.
Insurance Regulatory & Development Authority. (2018–2019). Annual reports, 2015–2016, 2016–2017, 2017–2018 and 2018–2019. https://www.irdai.gov.in
27.
LépineA., VassallA., ChandrashekarS., BlancE., & Le NestourA. (2015). Estimating unbiased economies of scale of HIV prevention projects: A case study of Avahan. Social Science & Medicine, 131, 164–172.
28.
SavithaB., BanerjeeS., & ShettyA. (2019). Product diversification versus technical efficiency of conglomerate life micro insurance companies: Evidence from India. The Geneva Papers on Risk and Insurance—Issues and Practice. https://doi.org/10.1057/s41288-019-00128-8
29.
SerranoV. C., BenitezR., & BolosV. J. (2018). Data envelopment analysis with deaR. R package version1.0. http://CRAN.RProject.org/package=deaR
30.
ShettyA., & SavithaB. (2018). Assessing the technical efficiency of traditional and corporate agents in Indian life insurance industry: Slack based data envelopment analysis approach. Global Business Review, 21(1), 1–17.
31.
SiddiquiS. A. (2020). Evaluating the efficiency of Indian life insurance sector. Indian Journal of Economics and Development, 16(1), 72–80.
32.
SiddiquiS. A. (2021). Total factor productivity growth of Indian life insurance companies: A Malmquist Approach. Indian Journal of Economics and Development, 17(1), 232–236.
33.
SimarL., & WilsonP. W. (2007). Estimation and inference in two-stage, semi-parametric models of production processes. Journal of Econometrics, 136, 31–64.
34.
SinhaR. P. (2015). A dynamic DEA model for Indian life insurance companies. Global Business Review, 16(2), 1–12.
35.
Swiss Re Institute. (2019). Annual report Sigma No. 03/2017. https://www.swissre.com
36.
ToneK. (2001). A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research, 130, 498–509.
37.
ToneK. (2002). A slacks-based measure of super-efficiency in data envelopment analysis. European Journal of Operational Research, 143, 32–41.
38.
ToneK. & SahooB. K. (2005). Evaluating cost efficiency and returns to scale in the Life Insurance Corporation of India using data envelopment analysis. Socio-Economic Planning Sciences, 39(4), 261–285.
39.
WankeP., & BarrosC. P. (2016). Efficiency drivers in Brazilian insurance: A two-stage DEA meta frontier-data mining approach. Economic Modelling, 53, 8–22.
40.
WilsonP. W. (2008). FEAR: A software package for frontier efficiency analysis with R. Socio-Economic Planning Sciences, 42, 247–254.
41.
WiseW. (2017). A survey of life insurance efficiency papers: Methods, pros & cons, trends. Accounting, 3(1), 137–170.
