FRONTIER EFFICIENCY METHODOLOGIES TO MEASURE PER- FORMANCE IN THE INSURANCE INDUSTRY: OVERVIEW AND NEW EMPIRICAL EVIDENCE

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1 FRONTIER EFFICIENCY METHODOLOGIES TO MEASURE PER- FORMANCE IN THE INSURANCE INDUSTRY: OVERVIEW AND NEW EMPIRICAL EVIDENCE Martin Eling, Michael Luhnen JEL Classification: D23, G22, L11 ABSTRACT The purpose of this paper is to provide an overview and new empirical evidence on frontier efficiency measurement in the insurance industry, a topic of great interest in the academic literature during the last several years. In the first step, we review 83 studies and put them into a joint evaluation of efficiency measurement in the field of insurance. In the second step, an efficiency comparison of 3,710 insurers from 37 countries is conducted using a dataset that has not yet been subject to efficiency analysis (the AM Best Non US database). We compare different methodologies, countries, organizational forms, distribution systems, lines of business, and company sizes. We find a steady efficiency growth in international insurance markets from 2002 to 2006 with large differences across countries. Furthermore, we find evidence that stock companies are more efficient than mutuals as well as for economies of scale, but no evidence for economies of scope in the insurance industry. Only minor variations are found when comparing different frontier efficiency methodologies (data envelopment analysis, stochastic frontier analysis) and different distribution types (agency-based vs. direct writers). Our results give valuable insight into the competitiveness of insurers in various countries. (At the end of the paper there is an outline of further research that we would like to integrate in a revised version of the paper to be presented at the conference on Performance Measurement in the Financial Services Sector. ) 1. INTRODUCTION In recent years, efficiency measurement has captured a great deal of attention. The insurance sector in particular has seen rapid growth in the number of studies applying frontier efficiency methods. Berger/Humphrey (1997) and Cummins/Weiss (2000) surveyed eight and 21 studies, respectively; now, less than ten years after the Cummins/Weiss survey, there are 83 studies on efficiency measurement in the insurance industry. Recent work in the field has refined methodologies, addressed new topics (e.g., market structure and risk management), and extended geographic coverage from a previously US-focused view to a broad set of countries around the world, including emerging markets such as China and Taiwan. Martin Eling is with the University of Wisconsin, School of Business, 4279 Grainger Hall, 975 University Avenue, Madison, WI 53706, USA. Michael Luhnen is with the University of St. Gallen, Institute of Insurance Economics, Kirchlistrasse 2, 9010 St. Gallen, Switzerland. We are grateful to Mareike Bodderas, Thomas Parnitzke, Hato Schmeiser, and Denis Toplek for valuable suggestions and comments.

2 The first aim of this paper is to survey these 83 studies. We provide a comprehensive categorization of this rapidly growing body of literature and point out new developments. Frontier efficiency methodologies have been used to analyze many important economic questions, such as comparison of countries (see, e.g., Fenn et al., 2008), organizational forms (see, e.g., Cummins/Weiss/Zi, 1998), and scale economies (see, e.g., Fecher/Perelman/Pestieau, 1991). Other research questions have involved the efficiency effects of mergers and acquisitions (see, e.g., Cummins/Tennyson/Weiss, 1999) and the comparison of different frontier efficiency methodologies (see, e.g., Cummins/Zi, 1998). In our overview, we will review these fields of application, summarize the methodologies, and highlight recent developments in the field. Existing cross-country comparisons of efficiency in the insurance industry provide valuable insights into the competitiveness of insurers in different countries. However, the geographic coverage of these studies is limited to certain countries or regions. Weiss (1991b) compares the United States, Germany, France, Switzerland, and Japan; Donni/ Fecher (1996) analyze 15 OECD countries. Both authors were restricted to using aggregated economic information instead of individual company data. Diacon/Starkey/ O Brien (2002) and Fenn et al. (2008) do use individual company data, but concentrate on European countries (15 and 14, respectively). Rai (1996) takes a look at nine European countries, Japan, and the United States, but considers a relatively small dataset of 106 companies. What is missing is a broad comparison of efficiency at the international level that incorporates a large number of countries and companies. The second aim of this paper is thus to contribute to the growing body of literature on frontier efficiency at the international level by answering key research questions based on a large number of countries and companies. We therefore consider a broad international dataset that has not yet been the subject of efficiency study the AM Best Non US database. Our cross-country analysis uses data on 3,710 insurance companies from 37 countries, which gives our study to our knowledge the largest sample ever analyzed for the insurance industry. We consider six main aspects: (1) methodologies, (2) countries, (3) organizational forms, (4) distribution systems, (5) lines of business, and (6) company size. These six aspects allow us to address many of the important economic questions set out in the first survey part of the paper. Another important contribution of this paper is that we determine and compare efficiency for a large number of countries that have not been considered, at least to our knowledge, in the literature to date: the Bahamas, Barbados, Bermuda, Brazil, the Czech Republic, Hong Kong, Hungary, Indonesia, Lithuania, Mexico, Norway, Poland, Russia, Singapore, and South Africa. Thus, the main contribution of this paper is to provide a broad evaluation 2

3 of efficiency measurement in the insurance industry with a special emphasis on an international comparison of efficiency. Our main empirical findings can be summarized as follows: (1) We find a steady efficiency growth in international insurance markets during the sample period ( ), with large efficiency differences between the 37 countries. The highest efficiency is found for Japan; the lowest for the Phillipines. (2) We find evidence for the expense preference hypothesis (i.e., stock companies are more efficient than mutuals) and for economies of scale (i.e., larger companies are more efficient than smaller companies). (3) We find only minor difference between distribution types (agency-based vs. direct writers) and very little evidence for economies of scope (i.e., multi-line insurers are not necessarily more efficient than mono-line insurers). (4) We find that there is not much difference between the two frontier efficiency methodologies data envelopment analysis and stochastic frontier analysis. The remainder of the paper is organized as follows. Section 2 contains an overview of 83 studies on efficiency measurement in the insurance industry. The empirical examination is performed in Section 3. The results of the study are summarized in Section OVERVIEW OF EFFICIENCY MEASUREMENT IN THE INSURANCE INDUSTRY In this section we provide an overview of 83 studies on efficiency measurement in the insurance industry. Our overview builds upon and significantly extends two earlier surveys of efficiency measurement in the financial services industry: one by Berger/Humphrey (1997), which focuses on banks and provides a valuable criteria framework that we use to analyze the studies in the field of insurance, the second by Cummins/Weiss (2000), which focuses on the insurance industry and covers 21 studies that had been published by the year Table 1 summarizes the main features of the 83 studies. Although many of the studies make contributions to more than one topic, in order to categorize them, we have arranged them according to their primary field of application (first column). The ten application categories were chosen and refined based on the earlier overview by Berger/Humphrey (1997). Columns 2 and 3 show the country(ies) under investigation and the efficient frontier method used; the fourth column lists the authors and year of publication of the study. More detailed information, such as input and output factors, types of efficiency analyzed, sample periods, lines of business covered, and main findings, can be found in the Appendix. In the following we discuss these studies, particularly focusing on recent developments. 3

4 Table 1: Studies on Efficiency in the Insurance Industry Application Country Method Author (date) Deregulation, regulation change Distribution systems Financial and risk management, capital utilization General level of efficiency and productivity, evolution of efficiency over Intercountry comparisons Market structure Mergers Ukraine DEA Badunenko/Grechanyuk/Talavera (2006) Korea, Philippines, Taiwan, DEA Boonyasai/Grace/Skipper (2002) Thailand Spain DEA Cummins/Rubio-Misas (2006) Germany, UK DEA, DFA Hussels/Ward (2006) Germany DEA Mahlberg (2000) Germany DEA Mahlberg/Url (2000) Austria DEA Mahlberg/Url (2003) Germany, UK DEA Rees et al. (1999) US DFA Ryan/Schellhorn (2000) US DFA Berger/Cummins/Weiss (1997) US DEA Brockett et al. (1998) US DEA Carr/Cummins/Regan (1999) UK SFA Klumpes (2004) UK SFA Ward (2002) US DEA Brockett et al. (2004) US SFA Cummins et al. (2006) US DEA Cummins/Nini (2002) Portugal DEA Barros/Barroso/Borges (2005) Nigeria DEA Barros/Obijiaku (2007) Canada Index Bernstein (1999) Netherlands SFA Bikker/van Leuvensteijn (2008) US DEA Cummins (1999) Tunisia DFA, SFA Chaffai/Ouertani (2002) Italy DEA Cummins/Turchetti/Weiss (1996) US SFA Cummins/Weiss (1993) France DEA, SFA Fecher et al. (1993) US DFA Gardner/Grace (1993) Taiwan DFA Hao (2007) Taiwan DFA Hao/Chou (2005) UK SFA Hardwick (1997) China SFA Huang (2007) Germany DEA Kessner/Polborn (1999) China DEA Leverty/Lin/Zhou (2004) Malaysia DEA Mansor/Radam (2000) Greece DEA Noulas et al. (2001) China DEA Qiu/Chen (2006) India DEA Tone/Sahoo (2005) US SFA Weiss (1991a) Australia DEA Worthington/Hurley (2002) China DEA Yao/Han/Feng (2007) France, Belgium DEA, SFA Delhausse/Fecher/Pestieau (1995) 6 European countries DEA Diacon (2001) 15 European countries DEA Diacon/Starkey/O Brien (2002) 15 OECD countries DEA Donni/Fecher (1997) Germany, UK DEA Kessner (2001a) Austria, Germany DEA Mahlberg (1999) 11 countries DFA, SFA Rai (1996) Japan, France, Germany, Index Weiss (1991b) Switzerland, US US SFA Choi/Weiss (2005) Austria SFA Ennsfellner/Lewis/Anderson (2004) 14 European countries SFA Fenn et al. (2008) US DEA Cummins/Tennyson/Weiss (1999) US DEA Cummins/Xie (2008) US DFA Kim/Grace (1995) 8 European countries DEA Klumpes (2007) 4

5 Table 1: Studies on Efficiency in the Insurance Industry (continued) Application Country Method Author (date) Methodology issues, comparing US DEA, DFA, Cummins/Zi (1998) different techniques or FDH, SFA assumptions Spain SFA Fuentes/Grifell-Tatjé/Perelman (2001) Japan DEA Fukuyama/Weber (2001) Taiwan DEA Hwang/Kao (2008) US Index Weiss (1986) Canada DEA Wu et al. (2007) Organizational form, corporate governance issues Scale and scope economies Canada DEA Yang (2006) US DEA Brockett et al. (2005) Spain DEA Cummins/Rubio-Misas/Zi (2004) US DEA Cummins/Weiss/Zi (1999) Germany DEA Diboky/Ubl (2007) Belgium FDH Donni/Hamende (1993) US DEA Erhemjamts/Leverty (2007) Japan DEA Fukuyama (1997) US SFA Greene/Segal (2004) UK DEA Hardwick/Adams/Zou (2004) Japan DEA Jeng/Lai (2005) US DEA Jeng/Lai/McNamara (2007) US SFA Berger et al. (2000) US DEA Cummins/Weiss/Zi (2003) France SFA Fecher/Perelman/Pestieau (1991) Spain SFA Fuentes/Grifell-Tatjé/Perelman (2005) Japan SFA Hirao/Inoue (2004) Ireland DFA Hwang/Gao (2005) Germany DEA Kessner (2001b) US DFA Meador/Ryan/Schellhorn (2000) Finland SFA Toivanen (1997) US SFA, TFA Yuengert (1993) Abbreviations: DEA: data envelopment analysis; DFA: distribution-free approach; FDH: free-disposal hull; Index: index numbers; SFA: stochastic frontier approach; TFA: thick frontier approach FRONTIER EFFICIENCY METHODOLOGIES The two main methodologies in efficient frontier analysis are the econometric (parametric) and the mathematical programming (nonparametric) approach, which we discuss only briefly here. For a more detailed overview, the reader is referred to Banker et al. (1989), Greene (1993), Charnes et al. (1994), Berger/Humphrey (1997), Cummins/Weiss (2000), and Cooper/Seiford/Tone (2006). Econometric (parametric) approach The econometric approach specifies a production, cost, revenue, or profit function with a specific shape and makes assumptions about the distributions of the inefficiency and error terms. There are three principal types of econometric frontier approaches. Although they all specify an efficient frontier form (usually translog but, increasingly, alternative forms such as generalized translog, Fourier flexible, or composite cost), they differ in their distributional assumptions of the inefficiency and random components (see Cummins/Weiss, 2000). The stochastic frontier approach (SFA) assumes a composed error model where inefficiencies follow an asymmetric distribution (e.g., half-normal, exponential, or gamma) and the error term follows a symmetric distribu- 5

6 tion, usually normal. The distribution-free approach (DFA) makes fewer specific assumptions, but requires several years of data; efficiency of each company is assumed to be stable over, and the random noise averages out to zero. Finally, the thick frontier approach (TFA) does not make any distributional assumptions for the random error and inefficiency terms, but assumes that inefficiencies differ between the highest and lowest quartile firms (see Berger/Humphrey, 1997). Mathematical programming (nonparametric) approach Compared with the econometric approach, the mathematical programming approach puts significantly less structure on the specification of the efficient frontier and does not decompose the inefficiency and error terms. The most widespread mathematical programming approach is data envelopment analysis (DEA), which uses linear programming to measure the relationship of produced goods and services (outputs) to assigned resources (inputs). As an optimization result, DEA determines an efficiency score, which can be interpreted as a performance measure. DEA was introduced by Charnes/Cooper/Rhodes (1978) and builds on the method suggested by Farell (1957) for computation of technical efficiency. DEA models can be specified under the assumption of constant (CRS) or variable returns to scale (VRS) and can be used to decompose cost efficiency into its single components technical, pure technical, allocative, and scale efficiency (see Cummins/Weiss, 2000). The free-disposal hull (FDH) approach is a special configuration of DEA. Under this approach, the points on the lines connecting the DEA vertices are excluded from the frontier and the convexity assumption on the efficient frontier is relaxed (see Berger/Humphrey, 1997). Although it is not the focus of our overview, we will briefly mention total factor productivity, since many of the reviewed studies calculate this measure. The Malmquist productivity index is the most common measure of total factor productivity. It is efficient-frontier based (mostly DEA) and able to split total factor productivity growth into a technical efficiency and a technological change component (see Grosskopf, 1993). Another approach to total factor productivity measurement is the calculation of index numbers (IN), which are, however, not frontier based, and have to do with the difference between input and output growth (see Cummins/Weiss, 2000). Both these approaches econometric and mathematical programming have their supporters and there is no consensus in the field as to which method is superior (see, e.g., Cummins/Zi, 1998; Hussels/Ward, 2006), most likely because both approaches have their advantages and disadvantages. While the econometric approach uses strong assumptions regarding the form of the efficient frontier, and therefore implies a certain economic behavior, the mathematical programming approach has the advantage of assuming less structure for the frontier; however, it does not take into account a ran- 6

7 dom error and therefore runs the risk of mistaking a true random error for inefficiency (see Berger/Humphrey, 1997). Looking at the overview of methods used in frontier efficiency studies as set out in Table 1, it becomes obvious that the nonparametric DEA approach has been most frequently used: out of the 83 surveyed studies, 46 use DEA, 18 SFA, eight DFA, one FDH, and three use index numbers. Additionally, total factor productivity using the Malmquist productivity index has been calculated by 22 studies. For DEA, the most widely used specification has been under the assumption of VRS. Only seven studies follow the advice given by Cummins/Zi (1998) to consider multiple approaches, ideally from both the econometric and mathematical programming sides. However, most of those seven studies find highly correlated results across different approaches (see, e.g., Hussels/Ward, 2006; Fecher et al., 1993; Delhausse/Fecher/Pestieau, 1995). Often, the choice of methods is determined by the available data, e.g., if the available data are known to be noisy, the econometric approach, featuring an error term, may lead to more accurate results than the mathematical programming approach (see Cummins/Weiss, 2000). In recent years, there have been a number of proposals for the improvement of efficient frontier methodology in the insurance field. For the econometric approach, a major direction has been to find more flexible specifications of the functional form, such as the Fourier flexible distribution. This form approximates the real underlying distribution more closely than do other forms, such as the translog, and thus addresses the main drawback of the econometric approach (see, e.g., Berger/Cummins/Weiss, 1997; Fenn et al., 2008; Klumpes, 2004), which is the possible misspecification of the shape of the efficient frontier due to strong distributional assumptions. A further proposal has been made regarding the incorporation of firm-specific variables into the estimation process. Instead of using a two-stage approach, which first estimates inefficiency of sample firms and then examines the association of inefficiency with firm-specific variables (such as organizational form), a one-stage approach is suggested. The estimated frontier directly takes into account firm-specific variables and allows a joint modeling of inefficiency effects (see, e.g., Greene/Segal, 2004; Fenn et al., 2008). Another contribution has been made with regard to the Malmquist index of total factor productivity. Although this index is usually applied to nonparametric DEA for insurance companies, Fuentes/Grifell-Tatjé/Perelman (2001) develop a parametric distance function approach for the Malmquist productivity index calculation. The mathematical programming approach has traditionally been viewed as a strictly nonparametric approach; however, it has been shown that DEA can be interpreted as a maximum likelihood estimator, providing a statistical base to DEA (see, e.g., Banker, 7

8 1993). Nevertheless, efficiency estimates are biased upward in finite examples and therefore need to be corrected. That is why the bootstrapping procedure proposed by Simar/Wilson (1998) has been applied to and extended for the insurance industry to account for various kinds of efficiency (cost, technical, revenue) as well as CRS and VRS models (see, e.g., Cummins/Weiss/Zi, 2003; Erhemjamts/Leverty, 2007; Diboky/Ubl, 2007). A further innovation is the introduction of cross-frontier efficiency analysis, which estimates efficiency of one particular technology relative to a best practice frontier of an alternative technology (see Cummins/Weiss/Zi, 1999, 2003; Cummins/Rubio-Misas/Zi, 2004). Cross-frontier analysis has been mainly used to examine the efficiency of different organizational forms. Finally, Brocket et al. (2004, 2005) apply a RAM (range-adjusted measure) version of DEA to the insurance industry, addressing theoretical problems regarding the comparison set of each firm for which efficiency has been calculated INPUT AND OUTPUT FACTORS USED IN EFFICIENCY MEASUREMENT An important decision when employing frontier efficiency models involves the choice of inputs, outputs, and their prices, since the definition of these factors can significantly impact the results of a study (see Cummins/Weiss, 2000). Choice of input factors There are three main insurance inputs: labor, business service and materials, and capital. Labor can be further divided into agent and home-office labor. The category of business service and materials is usually not further subdivided, but includes items like travel, communications, and advertising. At least three categories of capital can be distinguished: physical, debt, and equity (see Cummins/Tennyson/Weiss, 1999; Cummins/Weiss, 2000). Since data on the number of employees or hours worked are not publicly available for the insurance industry, to proxy labor and business service input, input quantities are derived by dividing the expenditures for these inputs by publicly available wage variables or price indices, e.g., the US Department of Labor data on average weekly wages for SIC Class 6311 (home-office life insurance labor), in the case of a US study (see Berger/Cummins/Weiss, 1997; Cummins/Zi, 1998). Physical capital is often included in the business service and materials category, but debt and equity capital are important inputs for which adequate cost measures have to be found (see Cummins/Weiss, 2000). One example of how to derive equity cost is the three-tier approach to measuring cost of equity capital based on AM Best ratings (see Cummins/Tennyson/Weiss, 1999). There appears to be widespread agreement among researchers with regard to the choice of inputs (see Appendix): 55 out of 83 studies use at least labor and capital as inputs and most of them also add a third category (miscellaneous, mostly business ser- 8

9 vices). Out of those 55 studies, 15 differentiate between agent and nonagent labor. Also, the number of studies differentiating between equity and debt capital is quite low; only ten papers do so. Looking at the remaining 28 contributions that do not follow the standard input categories, it becomes obvious that 15 of them incorporate broader expenditure categories as inputs (e.g., total operating expenses) without decomposing them into quantities and prices (see, e.g., Rees et al., 1999; Mahlberg/Url, 2003). There are a further nine studies that do not cover capital explicitly, i.e., they consider labor only or labor and an additional composite category. Finally, four studies that focus on financial intermediation consider only capital-related inputs (see, e.g., Brocket et al., 1998). The choice of input prices is mainly determined by the data that are publicly available in the countries under investigation. Choice of output factors There are three principal approaches to measuring outputs in the financial services industry: the asset or intermediation approach, the user-cost approach, and the value approach (see Berger/Humphrey, 1992). The intermediation approach views the insurance company as a financial intermediary that manages a reservoir of assets, borrowing funds from policyholders, investing them on capital markets, and paying out claims, taxes, and other costs (see Brocket et al., 1998). The user-cost method differentiates between inputs and outputs based on the net contribution to revenues: if a financial product yields a return that exceeds the opportunity cost of funds or if the financial costs of a liability are less than the opportunity costs, it is deemed a financial input; otherwise, it is considered a financial output (see Hancock, 1985; Cummins/Weiss, 2000). The value- approach counts outputs as important if they contribute a significant value based on operating cost allocations (see Berger et al., 2000). Usually, several types of outputs are defined, representing the single lines of business under review. In line with the theoretical literature on insurance pricing, the price of insurance outputs is calculated as the sum of premiums and investment income minus output divided by output (see Cummins/Weiss, 2000). The value- approach to output measurement is clearly dominant in the insurance industry: 69 out of 83 studies apply this approach (see Appendix). However, there is an intense debate among those using the value- approach as to whether claims/benefits or premiums are the most appropriate proxy for value (see, e.g., Cummins/Weiss, 2000). Out of the 69 articles, 32 specify output as either claims/present value of claims (property-liability) or benefits/net incurred benefits (life), following the arguments of Cummins/Weiss (2000). However, 35 studies specify output as premiums/sum insured, most likely because these measures are more readily available for most countries. Two of the 69 studies applying the value- approach use neither of the two main proxies (e.g., Yuengert (1993) uses re- 9

10 serves/additions to reserves to proxy value ). There is no recognizable trend over as to whether either of the two main proxies is gaining more of a following among researchers. Regarding the other two approaches for output measurement, five studies employ the intermediation approach, e.g., taking ROI, liquid assets to liability, and solvency scores as outputs (see Brockett et al., 2004, 2005). As argued by Cummins/Weiss (2000), this approach is not optimal because insurers provide many services in addition to financial intermediation. None of the studies reviewed uses the user-cost approach, because this approach requires precise data on product revenues and opportunity costs, which are not available in the insurance industry (see Klumpes, 2007). Four studies use both the value- and intermediation approaches (see, e.g., Jeng/Lai, 2005). One study proposes a new relational two-stage approach to output measurement (see Hwang/Kao, 2008): the authors construct a series relationship between the first output process (premium acquisition), providing the inputs for the second output process (profit generation). Four studies apply physical outputs, e.g., Weiss (1986) and Bernstein (1999) use number of policies as insurance output FIELDS OF APPLICATION IN INSURANCE EFFICIENCY MEASUREMENT AND SE- LECTED RESULTS Frontier efficiency methods have been applied to a wide range of countries (33 countries according to our survey) as well as to all major lines of business. Furthermore, frontier efficiency methods have been used to investigate various economic issues, including risk management (see, e.g., Cummins et al., 2006), market structure (see, e.g., Choi/Weiss, 2005), organizational forms (see, e.g., Jeng/Lai, 2005), and mergers (see, e.g., Cummins/Xie, 2008). However, it should be noted that findings regarding the same economic issues often vary depending on country, line of business, horizon, and method considered in the different studies. In the following, we analyze the 83 studies of our survey according to their field of application and selected main results. For this purpose, we consider ten application categories (see Table 1). Deregulation and regulation change There has been significant market deregulation of financial services in many countries (e.g., European Union Third Generation Insurance Directive, 1994). The aim of deregulation in the financial services sector is to improve market efficiency and enhance consumer choice through more competition. Consolidation, in particular, has the potential to improve efficiency by efficient firms taking over less efficient ones and realizing economies of scale (see Cummins/Rubio-Misas, 2006). However, the evidence on efficiency gains due to deregulation have been mixed. Rees et al. (1999) find modest efficiency gains from deregulation for the United Kingdom and Germany for the period from ; however, Hussels/Ward (2006) do not find clear evidence for 10

11 a link between deregulation and efficiency for the same countries during the period Mahlberg (2000) finds decreasing efficiency for Germany for the period of , but an increase in productivity. For Spain, Cummins/Rubio-Misas (2006) find clear evidence for total factor productivity growth for the period of , with consolidation reducing the number of firms in the market. Boonyasai/ Grace/Skipper (2002) find evidence for productivity increases in Korea and the Philippines due to deregulation. On the issue of changing regulation in the United States, Ryan/Schellhorn (2000) find unchanged efficiency levels from the start of the 1990s to the middle of that decade, a period during which risk-based capital requirements (RBC) became effective. Distribution systems The effect of different distribution systems on the efficiency of insurance companies has been another field of investigation but in this case the results have been more consistent. Brockett et al. (1998, 2004), studying the United States, and Klumpes (2004), studying the United Kingdom, both find that independent agent distribution systems are more efficient than direct systems involving company representatives or employed agents. However, Berger/Cummins/Weiss (1997) find in their study of the United States that agent systems are less cost efficient, but equally profit efficient, as direct systems due to differences in service intensity that are offset by higher revenues. On a more general level, Ward (2002), in his study of the United Kingdom, finds that insurers focusing on one distribution system are more efficient than those employing more than one mode of distribution. Financial and risk management, capital utilization A relatively new field of application for frontier efficiency methods is that of financial and risk management. Cummins et al. (2006) were the first to explicitly investigate the relationship between risk management, financial intermediation, and economic efficiency of insurance companies. In their application to the US property-liability industry, they find that both activities significantly increase efficiency. In a similar vein, Brockett et al. (2004) find that solvency scores used as an output have only limited impact on efficiency scores and Cummins/Nini (2002) state that capital is used suboptimally in US property-liability insurance. General level of evolution of efficiency over This category is comprised of all those studies whose primary research objective is to show a country s levels of efficiency and productivity and, in most cases, also to investigate the evolution of those measures over. Many of these studies also look at other issues, such as scale or scope economies. Naturally, this category contains a large number of studies that represent a first application of efficiency frontier methods 11

12 to countries such as Nigeria (see Barros/Obijiaku, 2007), Tunisia (see Chaffai/ Ouertani, 2002), Malaysia (see Mansor/Radam, 2000), or Australia (see Worthington/ Hurley, 2002). Given the broad range of countries and horizons employed, findings regarding efficiency and productivity are obviously mixed; however, nearly all studies note that there are significant levels of inefficiency in most countries with corresponding room for improvement, e.g., the Netherlands with 75% cost efficiency on average (see Bikker/van Leuvensteijn, 2008), average efficiency of 77% (non-life) and 70% (life) in China (see Yao/Han/Feng, 2007), and average efficiency of 65% for Greece (see Noulas et al., 2001). Intercountry comparisons Cross-country comparisons in the insurance industry can provide valuable insight into the competitiveness and efficiencies of insurers in different countries. This sort of study is especially interesting for the European market, where implementation of the single European Union (EU) insurance license in 1994 raised concerns about international competitiveness among insurers (see Diacon/Starkey/O Brien, 2002). Consequently, there have been quite a few efficiency studies on this topic. For a sample of 450 companies from 15 European countries and for the period , Diacon/ Starkey/O Brien (2002) find striking international differences in average efficiency. According to their study, the United Kingdom, Spain, Sweden, and Denmark have the highest levels of technical efficiency. Additionally, in a recent study involving 14 European countries for the period , Fenn et al. (2008) find increasing returns to scale for the majority of EU insurance companies, indicating that mergers and acquisitions, facilitated by the liberalized EU market, have led to efficiency gains. There are also cross-country efficiency studies looking at international country samples, e.g., Weiss (1991b), covering the United States, Germany, Switzerland, France, and Japan; Donni/Fecher (1997), comparing 15 OECD countries; and Rai (1996), analyzing 11 countries. Market structure Several contributions to the efficient frontier literature study the effects of deregulation and consolidation in general (see above), but only very few explicitly analyze the relationship between market structure and performance. In a recent contribution looking at the US property-liability market, Choi/Weiss (2005) introduce the efficiency measure into market structure analysis. They formalize the efficient structure hypothesis, which claims that more efficient firms charge lower prices than their competitors, allowing them to capture larger market shares as well as economic rents, leading to increased market concentration, and incorporate x-efficiency and scale-efficiency into their analysis. Results suggest that regulators should be more concerned with efficiency rather than market power arising from industry consolidation. Further contributions to 12

13 the topic of market structure, but with a focus on the EU, have been made by Fenn et al. (2008) and Ennsfellner/Lewis/ Anderson (2004). Mergers Another relatively new field for the application of frontier efficiency methods is that of mergers and acquisitions. The few studies that have been done aim at understanding the motivations of merger activity from the angle of efficiency gains. An early study by Kim/Grace (1995) simulating efficiency gains from hypothetical horizontal mergers in the US life insurance industry indicates that most mergers would improve cost efficiencies, with the exception of mergers between large firms. Two other studies looking at the US market are Cummins/Tennyson/Weiss (1999) for life insurance and Cummins/Xie (2008) for property-liability insurance. Both studies conclude that mergers are beneficial for efficiency, for both the acquiring and the target firm. Also, financially vulnerable firms are found more likely to be acquired. In his application to the European insurance market, Klumpes (2007) tests the same hypothesis as did Cummins/Tennyson/Weiss (1999) and Cummins/Xie (2008) and finds that acquiring firms are more likely to be efficient than nonacquiring firms. However, he finds no strong evidence that target firms achieve greater efficiency gains than nontarget firms. He also implies that merger activity in the major European insurance markets seems to be mainly driven by solvency objectives (i.e., financially weak insurers are bought by financially sound companies) and less by value maximization, as found in the US. Methodology issues, comparing different techniques or assumptions There are a few studies whose primary objective is to solve methodological issues or compare different techniques or assumptions over. Among the most important of these are Cummins/Zi (1998), comparing different frontier efficiency methods (DEA, DFA, FDH, SFA), and Fuentes/Grifell-Tatjé/Perelman (2001), introducing a parametric frontier approach for the application of the Malmquist index. For a discussion of these and other studies having methodological issues as their primary or secondary research objective, the reader is referred to Section 2.1 of this paper, covering different efficiency frontier techniques, and Section 2.2, covering different ways of treating inputs and outputs. Organizational form, corporate governance issues A very important and well-developed field of frontier efficiency analysis deals with the effect of organizational form on performance. The two principal hypotheses in this area are the expense preference hypothesis (see Mester, 1991) and the managerial discretion hypotheses (see Mayers/Smith, 1988). While the expense preference hypothesis states that mutual insurers are less efficient than stock companies due to higher perquisite consumption of mutual managers, the managerial discretion hypotheses pos- 13

14 its that the two organizational forms use different technologies and that mutual companies are more efficient in lines of business with relatively low managerial discretion (see Cummins/Weiss, 2000). Frontier efficiency studies have confirmed these hypotheses with the general finding that stock companies are more efficient than mutual companies in most cases (see, e.g., Cummins/Weiss/Zi (1999) and Erhemjamts/Leverty (2007) for the United States and Diboky/Ubl (2007) for Germany). However, in some areas, mutuals have been found to be more efficient. For example, in the study by Cummins/Rubio-Misas/Zi (2004) for Spain smaller mutuals dominate stock companies in the production of mutual output vectors. In another study covering Japan, Fukuyama (1997) rejects the managerial discretion hypothesis, finding that mutual and stock companies possess identical technologies. Other studies investigate efficiency improvements after demutualization (see, e.g., Jeng/Lai/McNamara, 2007) and, looking at corporate governance issues, the relation between cost efficiency and the size of the corporate board of directors (see Hardwick/Adams/Zou, 2004). Scale and scope economies Scale and scope economies are classic areas of research in frontier efficiency literature. So far, scale economies has been the more extensively researched of the two and is particularly important in the context of consolidation and the justification of mergers (see Cummins/Weiss, 2000). Although detailed results vary across studies, depending on countries, methods, and horizons employed, many contributions have found, on average, evidence for increasing returns to scale, meaning that unit costs of production decline as firm size increases (see, e.g., Hardwick (1997) for UK, Hwang/Gao (2005) for Ireland, Qiu/Chen (2006) for China, and Fecher/Perelman/Pestieau (1991) for France). However, the differentiation between size clusters must be considered to achieve more specific results. For example, Yuengert (1993), in his application to the US life insurance market, finds increasing returns to scale for firms with up to US$15 billion in assets and constant returns to scale for bigger firms. In contrast, Cummins/Zi (1998), for the same market, find increasing returns to scale for firms having up to US$1 billion in assets, and decreasing returns to scale for all others except for a few firms with constant returns to scale. The topic of scope economies is also of importance, since there is an increasing number of cross-industry mergers involving insurers from different lines of business (see Cummins/Weiss, 2000). In general, researchers have found evidence for the existence of economies of scope, meaning that multiproduct/-branch firms are more efficient than specialized firms (see, e.g., Meador/ Ryan/Schellhorn, 2000; Cummins/Weiss/Zi, 2003; Fuentes/Grifell-Tatjé/Perelman, 2005). Again, however, looking at the study results in more detail is revealing. For example, in Berger et al. (2000), a US application, it is shown that profit scope economies are more likely to be realized by larger firms. 14

15 3. NEW EMPIRICAL EVIDENCE As mentioned above, the geographic coverage of efficiency studies in the insurance industry has to date been limited to certain countries or regions. The contribution of this section is to give new insights into efficiency at the international level by analyzing a large number of countries and insurance companies. We compare different (1) methodologies, (2) countries, (3) organizational forms, (4) distribution systems, (5) lines of business, and (6) company sizes, which allows us to address many of the research questions surveyed in Section 2. In each case, the results are presented at different levels of aggregation, which enables us to identify the pure effect of methodologies, countries, organization, distribution, lines of business, and size on efficiency. Additional cross-sectional insights including a Tobit regression analysis are provided at the end of this section. In our analysis, we determine and compare efficiency for 15 countries that have not been considered in the literature to date: the Bahamas, Barbados, Bermuda, Brazil, the Czech Republic, Hong Kong, Hungary, Indonesia, Lithuania, Mexico, Norway, Poland, Russia, Singapore, and South Africa DATA AND METHODOLOGY Our main data source is the 2007 edition of the AM Best Non US database (Version ). It contains information on 5,031 life and non-life insurance companies from 98 countries. The database has five years of data, covering the period Companies were included in our analysis if they had positive values for all the inputs and outputs described in Table 2. This reduces our sample to 4,103 companies from 90 countries. Furthermore, in order to appropriately compare the different countries we require each country to have at least a total of 30 firm years and to have data for each of the five years that we analyze. This reduces our sample to 3,710 companies from 37 countries. The remaining 393 companies from 53 countries were included in the analysis as other countries. 1/2 1 These countries are : Antigua and Barbuda (1 company/3 firm years), Argentina (4/15), Bahrain (4/18), Bolivia (14/37), British Virgin Islands (3/8), Bulgaria (5/14), Cayman Islands (14/57), Chile (50/144), China (8/19), Croatia (4/12), Cyprus (5/17), the Dominican Republic (1/4), Ecuador (40/107), Egypt (6/27), El Salvador (8/17), Estonia (12/47), Greece (3/6), Guernsey (2/6), Iceland (7/21), India (12/52), the Isle of Man (3/9), Israel (10/28), Jamaica (3/12), Jordan (2/6), Kazakhstan (1/5), Kenya (4/14), Kuwait (4/17), Latvia (8/29), Lebanon (1/5), Macau (4/19), Malta (2/8), Monaco (1/1), Montserrat (1/2), Nigeria (2/9), the Northern Mariana Islands (1/4), Oman (3/8), Pakistan (4/14), Panama (3/13), Peru (9/27), Qatar (3/14), Romania (3/6), Saudi Arabia (1/5), Slovakia (10/23), Slovenia (4/15), South Korea (9/45), Tanzania (5/16), Thailand (17/45), Trinidad and Tobago (6/27), Tunisia (2/10), the Ukraine (3/9), the United Arab Emirates (3/7), Uruguay (12/34), and Venezuela (46/192). Unfortunately, for many South American countries (e.g., Venezuela), there are no data available for 2006, which is why we excluded these from the country-specific analysis even though the number of companies is relatively large. However, detailed results for all these countries are available upon request. 2 We did not include Canadian insurers, which are covered in the database, but presented in a separate section with different data fields. 15

16 As discussed above, there is widespread agreement in literature with regard to the choice of inputs. We thus use labor, business services and material, debt capital, and equity capital as inputs. Due to data availability, it was necessary to simplify this scheme by combining labor and business services; only operating expenses (including commissions) are available in the AM Best data. This simplification is common in many other international comparisons (see Diacon/Starkey/O Brien, 2002; Fenn et al., 2008) for much the same reason it is made here. Furthermore, Ennsfellner/Lewis/Anderson (2004) argue that the operating expenses should be treated as a single input in order to reduce the number of parameters that will need to be estimated. We thus use operating expenses to proxy both labor and business services and handle these as a single input in the following analysis. Cummins/Weiss (2000) showed in their analysis of operating expenses in the US insurance market that these are mostly labor related, e.g., in both life and non-life insurance, the largest expenses are employee salaries and commissions. We therefore concentrate on labor to determine the price of the operating-expenses-related input factor. The price of labor is determined using the ILO October Inquiry, a worldwide survey of wages and hours of work published by the International Labour Organization (ILO; see and used in a variety of efficiency applications (see, e.g., Fenn et al., 2008). The price of debt capital is determined using country-specific oneyear treasury bill rates for each year of the sample period; the price of equity capital is determined using the yearly rate of total return of the country-specific MSCI stock market indices (all data were obtained from the Datastream database; see Cummins/ Rubio-Misas (2006) for a comparable selection and a discussion on selection depending on the insurer s capital structure and portfolio risk). To ensure that all monetary values are directly comparable, we deflate each year s value by the consumer price index to the base year 2002 (see Weiss, 1991b; Cummins/Zi, 1998). Country-specific consumer price indices were obtained from the International Labour Organization. As is usual in most studies on efficiency in the insurance industry, we use the value approach to determine the outputs. We thus distinguish between the three main services provided by insurance companies risk-pooling/-bearing, financial services, and intermediation. According to Yuengert (1993), a good proxy for the amount of risk-pooling/-bearing and financial services is the value of real incurred losses, defined as current losses paid plus additions to reserves. As different types of services are provided by life and non-life insurance firms, we need separate output measures for each type of firm (see Choi/Weiss, 2005). We use the present value of claims plus additions to reserves as a proxy for the output volume for non-life insurance and the present value of net incurred benefits plus additions to reserves for life insurance. As done, for example, in Berger/Cummins/Weiss (1997), the price of risk-pooling/-bearing and 16

17 financial services is calculated as net premiums minus the volume proxy, expressed as a ratio to the volume proxy. The output variable, which proxies the intermediation function, is the real value of invested assets. To obtain present values we again deflate each year s value using the consumer price index. The price of the intermediation output is the expected rate of return on assets (see Berger/Cummins/Weiss, 1997); we therefore again use the yearly rate of total return of the country-specific MSCI stock market indices. Panel A of Table 2 presents an overview of the inputs and outputs used in this analysis. Each input and output is calculated using a volume proxy that we obtained from AM Best. This volume proxy is then divided by a proxy for price. Panel B of Table 2 contains summary statistics on the variables employed. All numbers were converted into US dollars for comparative purposes using the exchange rates published in the AM Best database. Table 2: Inputs and Outputs Panel A: Overview Inputs Proxy for volume Proxy for price Labor and business service AM Best operating expenses ILO October Inquiry wages per year Debt capital AM Best total liabilities Long-term government bonds rates Equity capital AM Best capital & surplus MSCI stock market indices returns Outputs Proxy for volume Proxy for price Non-life losses AM Best claims + addition to reserves (Net premium income - Proxy for volume) / Proxy for volume Life benefits AM Best net incurred benefits + addition to reserves (Net premium income - Proxy for volume) / Proxy for volume Assets AM Best total investments MSCI stock market indices returns Panel B: Summary statistics for variables used Variable Unit Mean St. Dev. Min Max. AM Best operating expenses Million $ AM Best total liabilities Million $ AM Best capital & surplus Million $ AM Best losses + changes in reserves Million $ AM Best benefits paid + changes in reserves Million $ AM Best total investments Million $ ILO consumer price index % ILO October Inquiry wages per year % Long-term government bonds rates % MSCI stock market indices returns $ (Net premium income - Proxy for non-life $ volume) / Proxy for non-life volume (Net premium income - Proxy for life volume) / $ Proxy for life volume Labor and business service Quantity Debt capital Quantity Equity capital Quantity Non-life losses Quantity Life benefits Quantity Assets Quantity

18 In the next section, we analyze two methodologies (data envelopment analysis, stochastic frontier analysis), 37 countries (see Table 3 for listing), three organizational forms (stocks, mutual, other), three distributional forms (agency based, direct writers, both), three lines of business (life, non-life, groups), and three company sizes (large, medium, small). For data envelopment analysis, we use Deap, Version 2.1 (see Coelli, 1996a) and calculate efficiency values assuming input orientation and variable returns to scale. For stochastic frontier analysis, we calculate a translog production frontier using Frontier, Version 4.1 (see Coelli, 1996b), assuming the inefficiencies to follow a half-normal distribution. Company-specific information on domiciliary country, organization type, distribution type, and lines of business is extracted from the AM Best database. Total assets is a widespread measure of insurer size (see, e.g., Cummins/Zi, 1998; Diacon/Starkey/O Brien, 2002). For comparison of different company sizes, we thus subdivide all companies by their total assets into large (total assets larger than $816 million), medium, and small (total assets smaller than $64 million) insurers RESULTS Data envelopment analysis The results of the data envelopment analysis are set out in Table 3, presented at different levels of aggregation so as to focus on different aspects of efficiency. The first focus is on countries (Panel A), the second on organization (Panel B), the third on distribution (Panel C), the fourth on lines of business (Panel D), and the fifth on size (Panel E). For comparison purposes, the average values are presented in the last line of the table. Altogether our analysis covers 4,103 insurers, with a total of 15,306 firm years. The last line of Table 3 shows that across all 4,103 companies efficiency increased by 17.08% from 2002 to There is a steady growth in efficiency from 0.69 in 2002 to 0.75 in However, large differences can be found between countries (see Panel A). The country with the highest efficiency is Japan (average efficiency 0.89), followed by Denmark (0.87) and Switzerland (0.83). The lowest efficiency values are found for the Philippines (average efficiency 0.51), the Bahamas (0.57), and Ireland (0.60). Overall, it appears that developed countries in Asia and Europe achieve higher efficiency scores than do emerging market countries. The efficiency of the largest economies under evaluation is in the upper-middle field: Germany is in 13th place (average efficiency 0.77); France is in 15th place (0.76). Relatively low efficiency values were found for the United Kingdom, which is in 28th place (0.69). Considering the development of efficiency over the sample period, the highest efficiency gains were obtained in Sweden (+50.12%) and Turkey (+47.07%). Austria ( 1.76%) and the Czech Republic ( 3.06%) are the only countries that did not increase efficiency from 2002 to

19 Table 3: Results of the Data Envelopment Analysis Year No. of insurers No. of firm years Average Growth 2002 to 2006 (%) Panel A: Comparison of countries Australia Austria Bahamas Barbados Belgium Bermuda Brazil Czech Repub Denmark Finland France Germany Hong Kong Hungary Indonesia Ireland Italy Japan Lithuania Luxembourg Malaysia Mexico Netherlands New Zealand Norway Other Philippines Poland Portugal Russia Singapore South Africa Spain Sweden Switzerland Taiwan Turkey UK Panel B: Comparison of organizational types Stocks Mutual Other Panel C: Comparison of distributional systems Agency based Direct writers Both Panel D: Comparison of lines of business Life Non-life Groups Panel E: Comparison of company size Large Medium Small Total *: Some companies changed size during the sample period, which is why the sum of large, medium, and small insurers is larger than the total of 4,103 insurers. 19

20 The second focus of our analysis concerns different organizational forms and their effects on efficiency (see Panel B of Table 3). We find evidence for the expense preference hypothesis, as the average efficiency values of stock companies (0.81) are much higher than those of mutual insurers (0.68). This finding is in line with most research in the field, e.g., Cummins/Weiss/Zi (1999) and Erhemjamts/Leverty (2007). Other legal forms (e.g., public companies) play only a very minor role in our sample. While there is a steady growth in efficiency over for stocks and other companies, we find large variations and a decreasing efficiency for mutuals over, an effect that might be due to mergers, acquisitions, and demutualization occurring during the period of investigation. Panel C of Table 3 shows the results for different distribution systems. The DEA results do not confirm Ward s (2002) finding that insurers focused on one distribution system are more efficient than those using more than one system. Indeed, in our study, the highest efficiency values are found for insurers using different distribution systems (average efficiency 0.75), followed by agency based (0.74) and direct writers (0.73). However, the differences are minor. Furthermore, note that the number of insurers that identify as pure direct writer or agency based is relatively small in our sample (but still large in absolute terms compared to other studies covering this aspect; e.g., Ward, 2002 covers 44 companies and Klumpes, 2004, 90 companies). Interesting differences can be found when comparing different lines of business (see Panel D of Table 3), a field that has not been in focus of literature to date. On a very aggregate level (detailed results on less aggregated level are available upon request), we compare life insurers, non-life insurers, and groups (i.e., companies offering both life and non-life insurance). We find that average efficiency in life insurance is much higher than in non-life insurance (0.85 vs. 0.67). This effect might be connected with the size effects and economies of scale (see next paragraph) as, based on total assets, the average life insurer is six s larger than the average non-life insurer. We find no evidence for economies of scope; the multi-product/-branch firms are not more efficient than specialized firms. In Panel E of Table 3 the total sample is subdivided by total assets into three size groups large, medium, and small insurers. In agreement with most research on economies of scale, we find that large companies have much higher efficiency than small companies. Average efficiency for large companies is 0.84, while it is only 0.72 for medium companies, and 0.69 for small companies. However, smaller companies have the most rapid gains in efficiency: from 2002 to 2006, efficiency increased for 20

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