The Life Insurance Industry in India

Parida, Tapas Kumar; Acharya, Debashis

doi:10.1007/978-981-10-2233-3

Cited by 6 publications

(9 citation statements)

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“…Studies on the contemporary Indian insurance sector have outlined the history of the insurance business in India (Parida and Acharya, 2017;Mandal and Dastidar, 2014). Also, an overview of recent regulatory developments in the general insurance sector is reported in Sinha (2017a, b).…”

Section: Overview Of the Indian Insurance Sectormentioning

confidence: 99%

“…To the authors' best knowledge, there are 22 studies on the specific case of the Indian insurance sector. Among these 22 studies, Tone and Sahoo (2005), Sinha (2007aSinha ( , 2012Sinha ( , 2015, Sinha and Chatterjee (2009), Dutta and Sengupta (2011), Chakraborty et al (2013), Chakraborty (2016a), Parida and Acharya (2017), Anandarao et al (2018), Shetty and Basri (2018) and Savitha et al (2019) focussed on the life insurance sector. Sinha (2007bSinha ( , 2017a Mandal and Dastidar (2014), Bawa and Kaur (2014) and Chakraborty (2016bChakraborty ( , 2018 zeroed in on the nonlife insurance sector.…”

Section: Related Literaturementioning

confidence: 99%

“…In addition, they used Tobit regression in the second stage and found that claims ratio, distribution ration and input cost ratio have a similar effect on the productivity of life and non-life insurers. Parida and Acharya (2017) assessed efficiency and productivity growth of the 13 life insures during the period 2002-2014 using DEA and Malmquist index. They suggested that the life insurance market is moderately efficient and the mean productivity over the sample period has improved by 1 per cent.…”

Section: Related Literaturementioning

confidence: 99%

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An empirical investigation of efficiency and productivity in the Indian non-life insurance market

Ilyas

Rajasekaran

2019

BIJ

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Purpose 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. Design/methodology/approach 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. Findings 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. Practical implications The results hold practical implications for the regulators, policy makers, practitioners and decision makers of the Indian non-life insurance companies. Originality/value 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.

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Section: Overview Of the Indian Insurance Sectormentioning

confidence: 99%

Section: Related Literaturementioning

confidence: 99%

Section: Related Literaturementioning

confidence: 99%

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An empirical investigation of efficiency and productivity in the Indian non-life insurance market

Ilyas

Rajasekaran

2019

BIJ

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“…In India, the literature on productive efficiency evaluation of life insurance firms is limited (see, e.g., Tone & Sahoo [57,60], Sengupta & Sahoo [53], Dutta & Sengupta [18], Dutta [17], Ghosh [35], Sinha [54], Parida & Acharya [44], Anandarao et al [2], among others). To the best of our knowledge, there is no study addressing the issue of whether competition arising from the insurance reforms has improved the productive efficiency of the industry.…”

Section: Introductionmentioning

confidence: 99%

Testing the Impact of Input and Output Substitutability in Nonparametric Production Analysis: Evidence From Life Insurance Industry in India

Sahoo¹,

Mohanty²

2022

JORSJ

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In this paper, two different variants (constrained and unconstrained) of a data envelopment analysis (DEA) model are used to compare inferences about production correspondence of the life insurance industry in India. While the unconstrained model implicitly assumes input substitutions and output transformations without their empirical verification, the constrained model explicitly incorporates weight restrictions on those inputs and outputs that are not substitutable and transformable, respectively. Our key findings are as follows. First, although the constrained model seems closely related to the notion of industry (structural) efficiency model, this link remains unexplored: we suggest a link between both models. Second, though the constrained model generates efficiency scores that are no more than those of its unconstrained counterpart, both models are in broad agreement in revealing that the life insurance industry experiences a sustained surge in its efficiency due to competition arising from insurance reforms adopted by the government over years, thus supporting the competition and X-efficiency hypothesis. Third, when the economic requirement of input substitutions is absent, both the models are found to give statistically significant results on efficiency ratings, returns to scale possibilities, and total factor productivity growth. In such a case, the unconstrained model generates benchmarking results based on an incorrect production frontier, which are potentially misleading and can hardly be used in managerial contexts. This finding, therefore, cautions the researchers not to blindly use any unconstrained model to evaluate the efficiency, productivity growth and returns to scale characterizations of firms without empirically verifying the presence/absence of input substitutions and output transformations.

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“…4 4 Note that the literature on the efficiency evaluation of life insurers in India is very limited (see, e.g., Anandarao et al, 2019; Dutta, 2013; Dutta & Sengupta, 2011; Parida & Acharya, 2016; Sengupta & Sahoo, 2006; Sinha, 2015; Tone & Sahoo, 2005, 2017; among others), and none of these studies deal with the evaluation of potential efficiency gains due to mergers.…”

mentioning

confidence: 99%

Evaluating the potential efficiency gains from optimal industry configuration: A case of life insurance industry of India

Sahoo

Tone

2022

Manage Decis Econ

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Using data envelopment analysis, this paper presents a scheme to decompose industry efficiency into two components: learning efficiency and harmony efficiency. This decomposition scheme is empirically illustrated on 23 life insurance companies in India for 8 years (2011–2012–2018–2019). Our two broad empirical findings reveal that first, as per the year‐wise results, the Indian life insurance industry has the potential to increase its efficiency by 28% by improving its learning efficiency by 20% and harmony efficiency by 10%; and second, based on the results on the top 75 promising mergers, the potential gains stem from learning rather than harmony.

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The Life Insurance Industry in India

Cited by 6 publications

References 0 publications

An empirical investigation of efficiency and productivity in the Indian non-life insurance market

An empirical investigation of efficiency and productivity in the Indian non-life insurance market

Testing the Impact of Input and Output Substitutability in Nonparametric Production Analysis: Evidence From Life Insurance Industry in India

Evaluating the potential efficiency gains from optimal industry configuration: A case of life insurance industry of India

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