This paper investigates technical efficiency and productivity growth in Indian life insurance industry in the era of deregulation. The empirical study uses DEA method and Malmquist productivity index to measure and decompose technical efficiency and productivity growth, respectively. The results suggest that the growth in overall productivity is mainly attributed to improvement in efficiency. Higher pure technical efficiency and lower scale efficiency indicate the insurance firms have generally, moved away from the optimal scale over the study period. The truncated regression exploring the main drivers of efficiency in the long run found claims ratio, distribution ratio, and firm-size influence technical efficiency positively. The study also found firms that had both life and non-life businesses are more efficient than firms that has only life insurance business.
India initiated reforms in the insurance sector with the passage of the Insurance Regulatory and Development Authority Bill by the Parliament in December 1999 and by opening up its insurance market to private competitors by the year 2000. However, the public sector company, Life Insurance Corporation of India, has overwhelmingly dominated the domestic life insurance market. As the market is still underdeveloped and the demand for life insurance is rising exponentially, there exists huge potential, opportunities as well as challenges (insurers have to focus on underwriting discipline and reduction in costs so as to remain profitable) for the managers of the insurance companies. Efficiency is the key concern of policymakers to encourage further development of the insurance industry as well as for the managers of the insurance companies to exist profitably in the business in the long run. This article focuses on this important issue. It uses a panel dataset of 14 life insurance companies over the period 2004-09, to evaluate their efficiency scores by applying Data Envelopment Analysis and calculating the scale efficiency. The results render light on policy design and implementations for future development of the life insurance industry in India.
The insurance sector was opened up for private participation on the ground that the interests of the consumers would be better served if there is competition among the insurers, which will ultimately increase productivity of the sector as a whole. The main objective of this article is to analyze the productivity changes of Indian life and non-life insurance business with the application of non-parametric Malmquist indices. Therefore, this study uses 'expenses related to labour' (x 1 ), 'expenses related to business service and materials' (x 2 ) and 'total investment' (x 3 ) as major three inputs; and following the 'intermediation approach' and the 'value-added approach', this study uses 'premiums earned' (y 1 ) as the output for risk bearing/risk pooling service following the value-added approach and 'income from investment' (y 2 ) as the intermediation output. From the productivity analysis we found that, during the study period (2005-2006 to 2009-2010) total factor productivity decreased in life insurance business but it increased in case of non-life insurance business with different reason behind the change. We can also conclude in overall factor productivity analysis excluding public sector insurers; though the public sector insurers are significantly older in the market but it has an unfavourable impact on the insurance market in terms of factor productivity and technological progress.
The presence of seasonal effects in monthly returns has been reported in several developed and emerging stock markets. The objective of this study is to explore the interplay between the month-of-the-year effect and market crash effects on monthly returns in Indian stock markets. The study uses dummy variable multiple linear regression to assess the seasonality of stock market returns and the impact of market crashes on the same. The results of the study provide evidence for a month-of-the-year effect in Indian stock markets, particularly positive November, August, and December effects, and a negative March effect. Further, the study suggests that the incidence of market crashes reduces the seasonal effects.
A new method for synthesizing non-intrusive concurrent error detection (CED) circuitry is presented. The idea is to use single-bit parity to detect all errors affecting an odd number of bits and then synthesize a circuit to detect the even errors. A novel statistical sampling and expanding methodology is proposed for constructing the even error detection circuitry. A major feature of the proposed methodology is that it allows very efficient tradeoffs between error coverage and overhead. While CED schemes that use a fixed checker based on a particular error detecting code are not amenable to simplification without a major impact on coverage, the proposed scheme can easily facilitate significant reductions in overhead with only a small loss in coverage. Experimental results show that the proposed scheme can provide very high levels of soft error protection at a fraction of the cost of duplication.
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