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Nanyang Business School Forum on Risk Management and Insurance

Efficiency and Profitability in the Global Insurance Industry

by | Jan 4, 2019 | Efficiency, Market, Operations

Tags: Cost efficiency, Data envelopment analysis, Frontier efficiency, Industry dependency, Industry idiosyncrasy
More from: Martin Eling, Ruo Jia

Editor’s Note: Presented at the 2018 China International Risk Forum. Posted by Ruo Jia, Assitant Professor, Department of Risk Management and Insurance, School of Economics, Peking University. Martin Eling, Professor, School of Finance, University of St. Gallen

The measurement of firm performance is central to the business research. Previous studies have demonstrated that emphasis on purely financial measures (e.g. return on equity) may overlook a firm’s competitive advantage embedded in its efficiency in transforming resources (Chen, Delmas, and Lieberman, 2015). Frontier efficiency measures reveal this productive dimension and thus constitute an important element of overall firm performance. With the two measures of different focuses, an important question is to what extent a firm’s efficiency in converting inputs to outputs translates into its financial profit. In other words, what is the link between what firms do (efficiency) and what their shareholders get (profitability)? The intention of this paper is to analyze the alignment and differences between the frontier efficiency and financial profitability measures.

This question has been investigated in manufacturing (see e.g., Chen et al., 2015), banking (see e.g., Olsen and Zoubi, 2011), as well as life (Greene and Segal, 2004) and nonlife insurance (Leverty and Grace, 2010) industries. The extant results in general reveal a positive E-P relationship in various industries, however, do not discuss that the magnitudes of such impact differ from industry to industry. Some industries’ efficiency of transmitting resources may be more critical to their financial profitability than others. For example, Sherman and Gold (1985) and Oral and Yolalan (1990) suggest that efficiency is only a secondary determinant of profitability in the banking industry, while Greene and Segal (2004) argue that efficiency is of paramount importance to the life insurers’ profitability. Thus, the E-P relationship is industry dependent. Moreover, the E-P relationship may not be necessarily linear, that is the impact of efficiency on profitability may be contingent on the degree of efficiency. While the general positive E-P relationship is well documented, these latter two questions (potential industry dependence and nonlinearity) have not yet been answered in the literature. The insurance industry provides a unique context to investigate the industry dependency of E-P relationships with its two sub-industries—life and nonlife insurance—operated by separate legal entities in most markets. Moreover, with its large variation of efficiency degrees in different markets, the global insurance industry is also ideal to reveal the nonlinear E-P relationship, if any.

The optimization principle in microeconomics suggests that firms minimize costs and maximize profits subject to existing technologies and expertise; competition will drive firms that are not attaining the optimization out of the market in the long run (Bauer, Berger, Ferrier, and Humphrey, 1998; Cummins and Weiss, 2013). Various theories explain why inefficient firms can also survive in the long run due to insufficient competition (Motta, 2004), management motivations (Leibenstein, 1966), and behavioral reasons (Stein, 1989). In business practice, managers and regulators focus on identifying non-optimized production units by benchmarking them with peers in the industry (Kaplan and Norton, 2005).

Farrell (1957) develops the modern framework of frontier efficiency analysis following the concept of optimization in microeconomics and aims to identify firms that do not succeed in optimization and to measure how far they are from the best practice firms. The frontier efficiency analysis aggregates multiple inputs and outputs to a single efficiency measure. Since the methodological contribution of Aigner, Lovell, and Schmidt (1977) and Charnes, Cooper, and Rhodes (1978), the academic studies on the performance of financial institutions have increasingly focused on the frontier efficiency methods (Bauer et al., 1998), first in the banking industry (see Berger and Humphrey, 1997 for a review), and shortly afterwards also in the insurance industry (see Eling and Luhnen, 2010a; Cummins and Weiss, 2013 for reviews).

The E-P relationship is important to better understand the linkage between the profitability and the operational process of input-output-transformation (Kaplan and Norton, 2005). This is particularly true for those organizations focusing on non-tangible services, innovations, and learning, such as financial institutions. However, the frontier efficiency measures have not yet been prevalent in business as it is in academia. Bauer et al. (1998) discuss six conditions that may affect the application of frontier efficiency measures by regulators and managers. One of them is the consistency between frontier efficiency measures and conventional financial measures. This criterion is critical because the financial ratios are the present language of managers, investors, and regulators, which thus define the “reality”. As a relatively new performance concept, the frontier efficiency measure has to demonstrate its consistency with conventional measures, i.e. it is not just artifact of the efficiency approach assumptions (Bauer et al., 1998; Laverty and Grace, 2010). Thus, we are motivated to analyze the relationship between firm efficiency and profitability (E-P relationship) in financial services, in our case the global insurance industry.

Our analysis is novel and informative in the following aspects: (1) insurance is one of the most rapidly growing fields of frontier efficiency analyses, with over one hundred peer-review journal articles published in the past decade (see Eling and Luhnen, 2010a; Cummins and Weiss, 2013 for reviews). However, the evidence on the E-P relationship in the insurance industry is limited to the U.S. life (Cummins and Zi, 1998; Greene and Segal, 2004) and nonlife (Leverty and Grace, 2010) insurance markets. We extend the analysis to non-U.S. markets and found support for the global validity of the E-P relationship. (2) We go beyond the existing literature and contribute to the understanding of E-P relationship’s nature in terms of nonlinearity and industry dependency. The importance of a firm’s efficiency in determining its profitability depends on the level of its own efficiency and on the industry idiosyncrasies. (3) We expand the methods that Cummins and Zi (1998), Greene and Segal (2004), and Leverty and Grace (2010) use to examine the E-P relationship by using the rank-order correlation measures, Spearman’s Rho and Kendall’s Tau; another innovation on the methodological side is our attempt to also assess the relationship between profit efficiency and profitability ratios in the insurance industry. Furthermore, the strength of our analysis lies with a large sample of around 5,000 insurers and 27,000 firm-year observations over 11 years, which covers more than 50% of premium volume outside North America (Swiss Re, 2014).

By way of preview, we document a significantly positive correlation between firm efficiency and profitability. This E-P correlation is economically significant and comparable to Greene and Segal’s (2004) results for the U.S. life and Leverty and Grace’s (2010) results for the U.S. nonlife insurance industry. Moreover, we show that the E-P correlation is nonlinear and industry dependent; the positive impact of efficiency becomes smaller as the firm approaches to the best practice, and efficiency is more critical to the profitability of life insurers than to that of nonlife insurers; the latter result is probably due to higher degree of competition and more difficult general market conditions (e.g. tougher regulation, low interest rates).

The remainder of this paper is organized as follows. We first review the extant E-P relationship literature to develop our hypotheses. Then, we introduce our sample, methodology, and empirical models, followed by results and robustness tests. Finally, we conclude.

The complete paper is available at: