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The purpose of this paper is to analyse the performance of the Indian non-life (general) insurance sector in terms of efficiency, productivity and returns-to-scale economies. In addition to this, it identifies the determinants of efficiency.

This study employs a two-stage data envelopment analysis (DEA) bootstrap approach to estimate the level and determinants of efficiency. In the first stage, the DEA bootstrap approach is employed to estimate bias-corrected efficiency scores. In the second stage, the truncated bootstrapped regression is used to identify the effect of firm-level characteristics on the efficiency of insurers. Moreover, the bootstrapped Malmquist index is used to examine the productivity growth over the observation period 2005–2016.

The bootstrapped DEA results show that the Indian non-life insurance sector is moderately technical, scale, cost and allocative efficient, and there is a large opportunity for improvement. Moreover, the results reveal that the public insurers are more cost efficient than the private insurers. It is also evident that all the insurers irrespective of size and ownership type are operating under increasing returns to scale. Malmquist index results divulge an improvement in productivity of insurers, which is attributable to the employment of the best available technology. Bootstrapped DEA and bootstrapped Malmquist index results also show that the global financial crisis of 2008 has not severely affected the efficiency and productivity of the Indian non-life insurance sector. The truncated regression results spell that size and reinsurance have a statistically significant negative relationship with efficiency. It also shows a statistically significant positive age–efficiency relationship.

The results hold practical implications for the regulators, policy makers, practitioners and decision makers of the Indian non-life insurance companies.

This study is the first of its kind that comprehensively investigates different types of robust efficiency measures, determinants of efficiency, productivity growth and returns-to-scale economies in the Indian non-life insurance market for an extended time period.

The present study aims to provide an overview of the life insurance industry in India and scrutinise various dimensions impacting life insurance uptake in accordance with the views of the management representatives. An exploratory study was assumed by conducting in-depth face-to-face/telephonic interviews with six employees and one agent affiliated to the most prominent life insurance companies operating in India. The interviews focused on operation of life insurance companies in general, their work culture, approach towards individuals/customers, steps taken to attract and retain their human capital (agents), the overall impact of reforms on the life insurance industry and their tactics which make them unique in the market. The study observed that life insurance uptake in India is discernibly affected by an individual's financial knowledge, needs and the level of trust they have on the company apart from brand of the life insurance company and grievance redressal system. It was also found that reforms (Foreign Direct Investment (FDI) Policy, entry of private players) did bring about a difference in work culture, improved employment opportunities and increased the reach of the insurance industry in the country. The study highlights dimensions that life insurance companies constantly work/need to work upon to remain at the zenith of success, broadens the horizons of the life insurance industry in an emerging nation like India as it is one of the few studies to have probed the management's outlook of the Indian life insurance industry and holds scope for future theoretical investigation and development of a comprehensive model as well.

The purpose of this article is to evaluate revenue efficiency performance of life insurance companies in India. The study also compares if private or public insurance sector is more “revenue efficient”. Furthermore, the study determines the nature of return to scale (RTS) and identifies the leaders and laggards amongst insurance companies operating in India.

Revenue efficiency is calculated by employing data envelopment analysis – a non-parametric approach, on a data set of 24 insurance companies over the period 2013–2014 to 2017–2018.

The empirical results suggest that life insurance companies in India could generate only 34.4% of revenue, which is very less than what these are expected to generate from the same inputs. Majority of life insurance companies operating in India are operating at decreasing return to scale (DRS). There is a reduction in leaders and the highest proportion of companies is falling in the category of laggards.

As per the best knowledge of researchers, no empirical work has been carried out with respect to measuring the revenue efficiency of Indian insurance companies. The current study appropriately fills the gap by not only calculating the revenue efficiency scores of insurance companies in India but also provides insights into the causes of revenue inefficiencies. It also gives implications for efficient and effective management of insurance companies.

This paper aims to compare the emergence and evolution of organizational fields through an analysis of the life insurance industries in two large emerging markets. Using institutional theory as a conceptual framework, we compare the regulatory, cognitive and normative dimensions of the life insurance industry in China and India.

The authors introduce a qualitative variation of the country institutional profile (CIP) that has been traditionally implemented as a quantitative analytical tool used to describe differences in national environments. This newly proposed methodology captures the socially embedded aspects of the phenomenon more completely than commonly employed survey-based methodology.

This analysis leads to a three-dimensional typology of constructs and themes within each national environment. These themes include the importance of regulation and protectionism, the domestic savings culture, family support structures and human capital development within the industry. The authors conclude by comparing these typologies to consider the implications for studying change in organizational fields across contexts.

As the authors reflect on the evolution of organizational fields, they demonstrate how the interplay of historical factors and new global norms results in a negotiated stance between compliance with new norms and allegiance to local interests. In terms of methodological contribution, we show how the socially embedded aspects of the examined phenomenon are explored more completely by the proposed qualitative CIP than through its quantitative variation. This approach and the analysis illustrate a complex interplay of local and global norms within a selected industry that may be missed by other research methods.

The purpose of this paper is to analyse the performance of the Indian non-life (general) insurance sector in terms of total factor productivity (TFP) over the period 2005–2016.

This study utilises Färe‒Primont index (FPI) to access the change in TFP and its components: technical change, technical efficiency and mix and scale efficiency over the observation period. Moreover, it employs the Mann–Whitney U-test to scrutinise the difference between the public and the private insurers in terms of growth in productivity.

The results reveal that the insurance sector possesses a very low level of TFP. Also, the results divulge an improvement of 11.98 per cent in TFP of the insurance sector at an annual average rate of 12.41 per cent over the observation period. The growth in productivity is mainly attributable to the improvement of 10.81 per cent in the scale‒mix efficiency. The progress in scale‒mix efficiency is mainly the result of improvements in residual scale and residual mix efficiency. The results also show that the privately owned insurers have experienced a high productivity growth rate than the state-owned insurers.

The results hold practical implications for the regulators, policymakers and decision makers of the Indian non-life insurance companies.

This study is the first of its kind to use FPI, which satisfies all economically relevant axioms and tests defined by the index number theory to comprehensively access the change in TFP of the Indian non-life insurance sector.

Introduction: Foreign direct investment (FDI) is a deciding factor in the insurance industry’s growth in any nation. Besides, similar socioeconomic conditions, some countries tend to attract more FDI inflows. This chapter focuses on exploring the FDI in the insurance industry in Brazil, Russia, India, China, and South Africa (BRICS).

Purpose: The chapter aims to explore the current situation of FDI in the insurance industry in BRICS member nations and uncover the factors that have led to higher foreign investments in some countries.

Methodology: Using descriptive and comparative approaches, this chapter explains the FDI scenario in the insurance sector of BRICS nations.

Findings: Based on a comparative analysis, the authors observed that deregulation, increased foreign engagement, and adoption of innovative technology and distribution methods are some avenues that could be worked upon to improve FDIs in the Indian insurance sector.

The role of financial institutions and financial intermediaries in fostering the economic growth by improving the efficiency of capital accumulation, encouraging savings and ultimately improving the productivity of the economy has been well accepted by now. Recent studies show that the insurance industry can improve the economic growth through financial intermediation, risk aversion and generating employment. This study aims to find the relationship between life insurance industry and economic development in India.

The study uses the VAR‐VECM model to find out the long run and short run relationship (if any) between life insurance growth and economic growth along with Granger causality test to suggest any causal relationship.

This study finds that there is long term relationship between life insurance industry and economic development in India. And the Granger causality test suggests that life insurance sector improves the overall economic development in India and the reverse is not significant.

The only limitation to study the relationship between life insurance sector development and economic development is the data set which has been used is annual data as the quarterly data were not available for insurance industry.

The study documented the long run relationship between life insurance industry and economic development in India and finds that the life insurance sector improves the overall economic development in India. This would help us to understand the implications of the life insurance market development in the post reform era.

There is a dearth of literature on the Indian economy in relation to the insurance sector, specifically the life insurance sector. This is the first attempt to study the impact of life insurance development on Indian economy after the reforms initiated in the insurance sector.

The purpose of this paper is to benchmark the solvency status of Indian general insurance firms.

The paper collects, compiles and analyses the key financial, operational and business data of eight Indian insurance firms. The authors first decide on initial firm‐specific economic variables and use data of last five years from IRDA Reports and Company Annual Reports. The NAIC IRIS ratios method was used to obtain an initial risk classification. This was used as a proxy of insolvency risk. Linear regression and logit techniques were thereafter applied to estimate the significant factors (direction‐wise and magnitude‐wise) which influence insurer solvency.

The results suggest that the factors that most significantly influence Indian non‐life insurers are lines of business, the firm's market share, the premium growth rate, the underwriting performance and the claims incurred. Further, the factors which have the strongest effect are market share, change in inflation rate, firm size, lines of business and claims incurred.

The sample of Indian general insurers used is limited with regard to the time span. No holdout sample was used and the entire data set was subjected to statistical analysis. These somewhat limit the findings and implications.

The paper provides insurers with easy‐to‐use operational and marketing indicators to benchmark their solvency risk. It will lead to competitive goal setting for continuous improvement. Estimation of appropriate market/economic parameters can be a useful input for regulators. A few suggested indicators are new.

Previous studies of insurance companies have focused on developed economies (USA, Europe) or the Asian Markets (China and Japan). This paper determines a set of marketing, financial and operational variables to predict benchmark financial strength of general insurance firms in India. It incorporates qualitative inputs from practising managers and industry experts before carrying out quantitative modeling and analysis.

Traditional statistical methods to study the financial performance of any industry have many barriers and limitations in terms of the statistical distribution of the financial ratios, and, in particular, it considers only its positive values of it. The purpose of this paper is to estimate the financial performance of 24 Indian life insurance companies for the period from 2013 to 2016 using Grey relational analysis (GRA) proposed by Deng (1982) that accommodates the negative values in the analysis.

Financial performance of 24 Indian life insurance companies for the years from 2013–2014 to 2015–2016 is examined using a total of 14 indicators from capital adequacy ratios, liquidity ratios, operating ratios and profitability ratios (PR). The methodology used is GRA to obtain the Grey grades to rank the performance indicators, where higher relational grade shows better financial performance, and a lower score depicts the scope for improving the performance.

The results rank the insurance companies according to their financial performance in which Shriram insurance stands first with higher relational grade score, followed by the companies like IDBI Insurance, Sahara Insurance and Life Insurance Corporation of India. The main finding is that PR which have negative values are playing a crucial role in determining the financial performance of Indian life insurance companies.

This study has far-reaching practical implications in twofold: first, for the Indian life insurance industry, they have to concentrate more on PR for better financial health and, second, for any financial performance analysis, ignoring negative value ratios produce biased inference and GRA can be used for better inference.

This study is the first attempt to evaluate the financial performance of Indian life insurance using the GRA methodology. The advantage of GRA is that there is no restrictions on the statistical distribution of the data and it also accommodates the negative values, whereas all the other traditional methods insist on the statistical distribution of data, and, more importantly, they cannot handle negative values in the performance analysis.

This paper aims to measure the change and the sources of change in total factor productivity (TFP) of the Indian non-life insurance sector over the period 2005–2016.

This study employs the bootstrapped Malmquist index (MI) to assess the changes in the TFP and adopts a decomposition approach proposed by Balk and Zofío (2018). Moreover, it utilises truncated regression to identify the determinants of the TFP. In addition, it employs Wilcoxon-W test and t-test to scrutinise the difference between the state-owned and the private insurers in terms of variations in TFP and its various components.

The results divulge a miniature improvement in TFP of the insurance sector, which is primarily attributable to the improvement in scale efficiency (economies of scale). The results also reveal that there are no significant TFP differences across the ownership. However, private insurers have better scale efficiency and lower input-mix efficiency than state-owned insurers. In addition, the results unveil that size, diversification and reinsurance have a negative impact on the TFP, while age has a positive impact on it.

The results may help the policymakers to frame new consolidation policies. Moreover, the findings may guide the decision-makers of the Indian non-life insurance companies to abate inefficiency and improve TFP.

This study estimates bias-corrected changes in TFP and efficiency in the non-life insurance sector. Moreover, it adopts an elaborated decomposition of the MI to identify the true sources of change in the TFP.