Analyzing firm performance in non-life insurance industry--parametric and non-parametric approaches.

Author:Chakraborty, Kalyan
Position::Abstract
 
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Abstract

This study investigates productive efficiency and total factor productivity growth for Indian non-life insurance industry after deregulation. The empirical analysis uses seven years panel data from twelve leading non-life insurers that accounts for above ninety-five per cent of the business in the industry. Productive efficiency estimates are based on Battese and Collie (1995) Stochastic Frontier inefficiency-effect model and Fixed Effect Stochastic Frontier model. Dynamic productivity index (Malmquist index) is estimated using data envelopment analysis (DEA). The study found differences in efficiency scores obtained from parametric (regression) and nonparametric (DEA) methods. Regression analysis found net claims, operating expenditure, and total investment are positively related to net premiums earned. Dynamic productivity analysis found eight out of twelve firms achieved gain in total factor productivity growth.

Keywords: Stochastic, Malmquist, Productivity, Frontier, Nonlife

JEL Code: G22; C14; G14

INTRODUCTION

Over the last decade insurance industry is moving toward the developing countries where the governments are actively pursuing deregulation and liberalization policies. Financial liberalization acts as incentive and draws foreign domestic investment (FDI) leading to a free flow of insurance services across the national boundaries. Between 1997 and 2004 insurance sector in emerging markets grew by 52 per cent compared to 27 per cent in the industrialized nations (UNCTD, 2007). Although the global financial crisis in 2007-09 made a dent on the flow of FDI worldwide, according to a recent report by the United Nations survey of transnational corporation (TNC) (WIPS, 2010) the FDI flow will reach USD 1.5 trillion in 2011 and USD 2.0 trillion in 2012 which is close to pre-crisis level. Surprisingly, the survey found that in the post-crisis recovery era for the first time the emerging countries like India, China, and Russian Federation are the top recipients of FDI investor countries in 2012. The growth of non-life insurance premium in 2010 in emerging markets is 22 per cent compared to 1.0 per cent for the industrialized countries and 2.1 per cent for the world (Swiss Re, 2010). A major part of the FDI flow in India is expected in the financial services sector, the non-life insurance industry in particular. India's insurance industry is one of the fastest growing markets in the global insurance industry.

The non-life insurance (property/casualty and health) premium in India for the year ended 31st March 2011 grew 28 per cent over the previous year (Towers, 2011). Unfortunately for India the insurance density (ratio of premiums to total population) and insurance penetration (ratio of premiums to GDP) numbers for non-life insurance are among the lowest in Asia. This implies there is a significant room for growth potential (Table 1). As India's GDP is expected to grow by 8.0-8.5 per cent for 2011 and 8.3-8.8 per cent for 2012, the role of insurance services as provider of risk transfer and indemnification and a promoter of growth will increase in the future (S & P, 2011). Studies have found that with the growth in national income both insurance density and penetration increase, more-so for the life than for nonlife since life insurance is more income elastic (Beck et al. 2010). With changing population demographics such as, increasing income and fast urbanization the demand for vehicles, increased awareness for health care, and customized sophisticated risk products would increase the demand for non-life insurance significantly in the near future. The deregulation and liberalization of insurance industry in 2000 and de-tariffing of the general insurance sector in 2007 is assumed to have improved the operating efficiency of the existing domestic companies through increased competition and by bringing in new techniques, skills, training procedures, and product innovations The number of non-life insures increased from 11 (2000) to 26 (2011), 4 being public and 22 private. Considering the fast growth of the Indian non-life insurance sector in recent years there is a lack of systematic research studies analyzing the efficiency and productivity of the non-life insurers. Table 1 reports a snap-shot for insurance density and penetration for life and non-life insurance in India, Japan, and selected South-Asian Nations: Insurance density is measured as a ratio of premiums to total population and expressed in US$. The insurance penetration is measured as a ratio of premiums to GDP.

The objective of the current study is to measure the productive efficiency and dynamic productivity of the nonlife insurers using data envelopment and stochastic frontier models and explore the causes of inefficiency. By comparing the efficiency scores across firms and over time this study will provide beneficial impact of deregulation in a highly competitive market. The study will also provide an understanding of the dynamic behavior of the insurance firms by analyzing the changes in total factor productivity and its various components using seven years panel data. Several studies in the insurance literature suggest that both parametric and non-parametric approaches should be employed for efficiency measurement because each uses different set of underlying assumptions on the construction of the frontier (Weill, 2004; Cummins and Zi, 1998; Hussels et al. 2006). In conformity with the suggestions the efficiency measure in this study follows both parametric (DEA) and non-parametric methods (fixed effect stochastic frontier model and Battese and Coellie, 1995 inefficiency effects model).

The empirical analysis uses seven years panel data (2004-10) from twelve leading non-life insurers which accounts for ninety-five per cent of the business in the industry. The productive efficiencies are measured using (i) data envelopment analysis (DEA); (ii) Fixed Effect Stochastic Frontier model (SFM); and (iii) Battese and Coellie (1995) Stochastic Frontier inefficiency-effect model (BC-95). Further, dynamic productivity index (Malmquist index), also known as total factor productivity and its various components are also measured. The study found that efficiency scores from the DEA model are higher than the econometric models. There are slight differences in efficiency score rankings for individual firms obtained from two econometric models, whoever they are consistent and is mainly due to the differences in the structure of the models. Among others, regression analysis found that net claims, operating expenditure, and total investment are positively related to net premium earned. The results from dynamic productivity analysis found eight out of twelve firms achieved gain in total factor productivity growth, ICICI Lombard leading the group with a 6.9 per cent annual growth during the study period (2004-2010).

The remainder of this paper is organized as follows. In section two a brief overview of the literature on efficiency measure in insurance industry is provided. Section three discusses the choice of input and output variables followed by section four on the methodology. Section five discusses the dataset and empirical results are discussed in section six. The summary and conclusions are in section seven.

  1. BRIEF OVERVIEW OF THE LITERATURE

    Efficiency measurement using frontier methodology is a fast growing research field in the insurance literature. Eling and Luhnen (2009) surveyed of 95 studies on efficiency measurement in the insurance industry and found that the recent studies used refined methodologies, addressed new topics, and extended the geographic coverage from Europe and U.S to Southeast and East Asia. The most common technique for efficiency measurement in the insurance literature is to assume the insurance firms as production units that produce 'value added' outputs using a set of inputs. In that respect the efficiency of an insurer is its ability to produce a given set of outputs (i.e., premium and/or investment income) using a set of inputs (i.e., commission and sales expenses, capital, and labor). (Diacon, 2001; Cummins and Weiss, 2001; and Cummins and Zi, 1999)

    Contrary to this line of research, Brockett et al. (2004, 2005) argue that a production approach that uses premiums, investment income, supplied capital, and labor costs as inputs and probably uses losses paid as an output ignores the fact that loss maximization is not the primary objective of a firm. The authors argue, for example, when these losses increase abnormally due to hurricane, earth quake, tsunami, or from a terrorist attack a firm making this loss payments without corresponding increase in inputs, would become insolvent, not efficient. For property liability insurance companies the authors contend that efficiency measure should use 'intermediary approach' rather that 'production approach' (Berger and Humphrey, 1997; Staking and Babble, 1995; Lai and Witt, 1992). In a financial intermediary approach an insurance company provides a basket of services to the stake holders, and each has its own concern for the overall success of the company. For example, solvency can be a main concern for the regulators for the insurance companies, claims paying ability can be a primary concern for the policy holders, and return on investment can be a primary concern for the investors. Hence solvency, financial returns, and claims paying ability can be considered as outputs for nonlife insurance firms for efficiency analysis. Jeng et al. (2007) used both 'value added' approach and 'financial intermediary' approach and measured efficiency for life insurers before and after demutualization using data from Taiwan. The study found no efficiency improvement after demutualization relative to stock control insurers. However, use of financial intermediary approach improved efficiency related to mutual control insurers.

    Efficiency measurement using frontier methodology is fast expanding in...

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