Lih-Ru Chen National Chengchi University. Gene C. Lai Washington State University. Jennifer L. Wang National Chengchi University

Transcription

1 Nonspecialized Strategy versus Specialized Strategy:Evidence from the Property-Liability Insurance Industry Lih-Ru Chen National Chengchi University Gene C. Lai Washington State University Jennifer L. Wang National Chengchi University

2 Nonspecialized Strategy versus Specialized Strategy: Evidence from the Property-Liability Insurance Industry Abstract We use the data envelopment analysis and the stochastic frontier approach to examine the efficiency performance and scope economy for nonspecialists and specialists in the U.S. property and liability insurance industry. The results of the data envelopment approach suggest that nonspecialists (specialists) dominate specialists (nonspecialists) in producing nonspecialists (specialists) input output vectors. These results are robust with respect to different size quartiles. The results of the scope economy analyses suggest that small nonspecialists are more likely to realize cost scope economies, whereas large nonspecialists are more likely to suffer scope diseconomies. We also find small nonspecialist suffer revenue and profit scope diseconomies, whereas large nonspecialists enjoy revenue and profit scope economies. Specifically, the regression evidence indicates that insurers with the following characteristics are more likely to realize profit scope economies. That is, insurers that (1) are large, (2) specialize in personal lines, (3) are less cost efficient, (4) have lower risk, (5) have high leverage, (6) in a less competitive market environment, or (7) are mutuals are more likely to realized profit scope economy. The above-mentioned results suggest that nonspecialists are more efficient for some types of insurers, whereas specialists are more efficient for other types of insurers. The overall empirical evidence supports that both the nonspecialist hypothesis and the specialist hypothesis hold for different type of property-liability insurers.

3 Nonspecialized Strategy versus Specialized Strategy: Evidence from the Property-Liability Insurance Industry 1. INTRODUCTION We observe some insurers write many lines of insurance, including personal lines and commercial lines in the U.S. property-liability insurance industry. Apparently, these insurers follow the diversified or nonspecialized strategy. In contrast, we also observe some insurers focus their business on very few personal lines or very few commercial lines. These insurers follow a focused (undiversified) or specialized strategy. Which strategy is better? This is an important question for the company and its stakeholders. The literature provides some guidance but not direct answers to this question, because most literature has not specifically examined the issue from the perspective of a property-liability insurer. Some studies have investigated the conglomerate strategy and focus strategy outside the financial services industry (e.g., Mester, 1987; Land and Stulz, 1994; Berger and Ofek, 1995; Comment and Jarrell, 1995; Servaes, 1996). These studies usually find evidence that supports the focused strategy. For example, Comment and Jarrell (1995) find that the greater corporate focus is consistent with shareholder wealth maximization. Some studies have examined the issue within the financial services industry. For example, Berger, Humphrey and Pulley (1996) discover that the cost economies of scope between bank deposits and loans is small and revenue economies of scope are insignificant. 1 The studies of bank mergers and acquisitions (e.g., Houston and Ryngaert, 1994; Houston, James, and Ryngaert, 1999) find some mixed results. These studies mainly investigate whether the firm should operate in different industries not in different lines within the industry. Five studies are most relevant to our article. Yuengert (1993) finds that there is no evidence of cost scope of economies by using the U.S. life cross-sectional data. 2 Using U.S. life 1 Panzar and Willing (1981) are among the first to introduce the concept of economies of scope. 2 Berger et al. (2000) provide the literature on scope economies in insurance and other financial services.

4 and property-liability data, Meador, Ryan, and Shellhorm (1998) find that diversification hypothesis dominates focused hypothesis in terms of X-efficiency in the U.S. life insurance industry. 3 Using Japanese property-liability insurance data, Vivian and Lai (2005) find that Keiretsu firms seem to be more cost-efficient than nonspecialized independent firms. On the contrary, Hirao and Inoue (2004) find the cost scope economies exist in the Japanese property-liability insurance industry. Cummins, Weiss, and Zi (2003) employ the DEA bootstrapping method to analyze the economies of scope in the U.S. insurance industry. Berger, Cummins, Weiss, and Zi (2000) find that the conglomeration hypothesis dominates for some types financial services, whereas the strategic focus hypothesis dominates for other types. Berger et al. (2000) define the specialist as either a life insurer or a property-liability insurer, and the joint producer as an insurer issues both life and property-liability insurance. None of the literature, though, investigates nonspecialized strategy versus specialized strategy within the U.S. property-liability insurance industry. We defined a nonspecialist as a property-liability insurer that issues both personal and commercial policies, while a specialist as a property-liability insurer that issues either personal policies or commercial policies, but not both. We use the terms of the nonspecialized strategy and the specialized strategy instead of the conglomeration strategy and the strategic focus strategy to avoid confusion. The first purpose of this article is to examine whether nonspecialized strategy or specialized strategy is more efficient for property-liability insurers. Specifically, this paper develops the nonspecialized hypothesis and the specialized hypothesis and then examines the two hypotheses by using both the data envelopment approach (DEA) and the stochastic frontier approach. The DEA approach allows us to examine whether the efficiency of specialists dominate nonspecialists. We then use the stochastic frontier approach to examine whether there exists a scope economy for the nonspecialists. 3 Other studies examine cost scope economies in the insurance industry include Kellner and Mathewson (1983), Grace and Timme (1992), Toivanen (1997). 2

5 The second purpose is to examine what types of insurers are likely to realize economies of scope. Specifically, we are interested in whether large insurers (or small insurers), insurers emphasize personal lines of business, insurers use the independent agency system are more likely to realize scope economies. Moreover, the results also can shed insight on why we observe the long-run coexistence of nonspecialized and specialized property-liability insurers. 4 The data set are obtained from property-liability insurance annual statement of National Association of Insurance Commissioner for the sample period from 1997 to We summarized the empirical results below. The results of the DEA approach suggest nonspecialists and specialists have different technologies. In addition, we find that nonspecialists (specialists) dominate specialists (nonspecialists) in producing nonspecialists (specialists) input output vectors. These results are robust with respect to different size quartiles. The results of the scope economy analyses suggest that small nonspecialists are more likely to realize cost scope economies, whereas large nonspecialists are more likely to suffer scope diseconomies. We also find small nonspecialists suffer revenue and profit scope diseconomies, whereas large nonspecialists enjoy revenue and profit scope economies. Specifically, the regression evidence indicates that insurers with the following characteristics are more likely to realize profit scope economies. That is, insurers that (1) are large, (2) specialize in personal lines, (3) are less cost efficient, (4) have lower risk, (5) have high leverage, (6) in a less competitive market environment, or (7) are mutuals are more likely to realized profit scope economies. The above-mentioned results suggest that nonspecialists are more efficient for some types of insurers, whereas specialists are more efficient for other types of insurers. The overall empirical evidence supports that both the nonspecialist hypothesis and the specialist hypothesis hold for different type of property-liability insurers. The major contributions of this study are discussed below. First, this is the first study to 4 The research questions are inspired by Berger et al. (2000). 3

6 develop the nonspecialized hypothesis and the specialized hypothesis, and examine whether nonspecialized strategy or specialized strategy is more efficient for the U.S. property-liability insurers. Second, most literature only addresses cost scope economies, while we follow Berger et al. (2000) and examine cost scope economies, revenue scope economies, and profit scope economies. Finally, this study uses both the DEA approach and the scope economy approach. The two approaches provide additional and complementary insight on the issue. 2. HYPOTHESIS DEVELOPMENT To investigate whether the nonspecialized strategy or the specialized strategy is better in terms of efficiency performance, we first need to examine the pros and cons of the two strategies. The literature (e.g., Meador, 1997; Berger et al., 2000; Vivian and Lai, 2005) provides some guidance for the discussion, even though no literature addresses the issues that are directly related property-liability insurers. The five main hypotheses are proposed and discussed below. H1: The Nonspecialized Hypothesis The nonspecialized hypothesis suggests that nonspecialists issue many different lines of property-liability insurance and may exploit cost scope economies by sharing administrative expenses, marketing costs, and fixed costs (e.g., depreciation expenses for computers and buildings). Nonspecialists may take advantages of revenue of scope economies in providing various commercial lines of business. Specifically, business owners may prefer the convenience of purchasing all commercial insurance from one insurer for the convenience reason. Moreover, a nonspecialist may diversify risk through different line of insurance. Risk-sensitive policyholders may be willing to pay more for insurance policies from nonspecialists. H2: The Specialized Hypothesis The specialized hypothesis suggests that specialists may perform better than nonspecialists because they can focus on few lines of insurance. The reasons are stated below. Managers of specialists may be able to increase efficiency performance because they can develop 4

7 expertise for few lines of insurance rather than many lines of insurance. In addition, specialists can achieve cost savings through fewer costs of hiring and training of actuaries, underwriters, and claim adjusters. For example, an insurer who issues only personal auto and homeowner insurance policies should have lower costs in actuaries, underwriters, and claim adjusters than an insurer who issues many personal lines and commercial lines. In addition to the two main hypotheses, we propose the following null hypotheses. H3: The technology of nonspecialists is the same as that of the specialists. The above-mentioned discussions of the nonspecialized hypothesis and specialized hypothesis indicate that nonspecialists operate many lines of insurance, whereas specialists issue only very few lines, thus, it is reasonable to expect the technology used by the two types of insurers is different. In the reality, it is very likely that the nonspecialists and specialists use different technology. For example, the nonspecialists may put more emphasis on exploiting the shared resources, such as brand name and marketing system, to sell products, whereas the specialists may adopt the best technology to produce tailored products. H4: It is not feasible, on average, to replicate nonspecialists (specialist) input-output combination using the specialists (non-specialist) technology. We expect nonspecialists and specialists have different efficiency performance as discussed in hypotheses 1 and 2, because nonspecialists issue both commercial lines and personal lines, whereas specialists issue either commercial lines or personal lines. Thus, it is not feasible, on average, to replicate nonspecialists (specialist) input-output combination using the specialists (non-specialist) technology. H5: There are no scope economies for either nonspecialists or specialists. The above-mentioned discussions about the pros and cons of the nonspecialized strategy and specialized strategy suggest that there may exist revenue and cost scope economies for the nonspecialists, specialists, or both. 3. DATA AND METHODOLOGY 5

8 3.1 Data The data source is the National Association of Insurance Commissioner for property-liability insurer from 1997 to Firms that have negative net premium written and that have information less than 5 years are discarded. Firms (i.e., nonspecialists or specialists) that change strategy throughout the sample period are excluded. We use the average Herfindahl index of net premium over 5 years as a measurement to identify the specialists and nonspecialists. Insurers in the first quartile of Herfindahl index for all 5 years are defined as nonspecialists. The insurers in the bottom quartile of Herfindahl index for each of the 5 years are defined as specialists. The final sample consists of 277 property-liability insurers, that is, 133 nonspecialists and 144 specialists. With regard to the methodology, we use the DEA to evaluate the efficiency performance for nonspecialists and specialists. We also use stochastic frontier approach to estimate scope economies for the two groups of insurers. 3.2 Data Envelopment Analysis To save space, we do not discuss the methodology of the data envelopment approach and the cross-frontier method in details. Cummins and Weiss (2000) provide a detailed review of the DEA approach. To estimate the relative efficiency for the nonspecialists and specialists, we adopt the cross-frontier analysis used by Cummins, Weiss, and Zi (1999). We first investigate whether nonspecialists and specialists have the same technology (frontier) by using the DEA approach. If the nonspecialists and specialists have different frontiers, we then perform cross-frontier analyses. The outputs and inputs used in this study are discussed later. Outputs The output variables include the loss amounts for different product lines and total invested assets. Cummins and Weiss (1993) suggest that insurers provide consumers with services associated with insured losses, risk-pooling, and risk-bearing. Following Cummins and 6

9 Weiss (1993), Cummins, Weiss, and Zi (1999), and Cummins et al. (2004), we use loss incurred for different product lines as proxies for outputs. We further separate the losses into four categories: losses incurred in short-tail personal lines (y1), losses incurred in long-tail personal lines (y2), losses incurred in short-tail commercial lines (y3), and losses incurred in long-tail commercial lines (y4). Based on Berger, Cummins, and Weiss (1997), we also include invested assets (y5) as an output variable. All output numbers are deflated to the base year 1997 with the consumer price index. Inputs The inputs used in measuring the efficiency performance include labor (x1), business service (x2), equity (x3), and debt capital (x4). Labor input is the labor cost divided by average weekly employee wages. We measure the price of labor (p1) as average weekly wages for insurance agent (standard industrial classification [SIC] Class 6411) by using U.S. Department of Labor data. The second input, business services, consists of agent commissions and loss adjustment expenses. Both the business service and price of labor are deflated to the base year The price of business services (p2) is the average weekly earnings of workers in SIC The third input is the equity. We use policyholder surplus as the proxy for the equity. To avoid the problem of improper estimates, we do not take the ratio of an insurer s net income to capital (ROE) as the price of policyholder because insurers with poor performance are more likely to have negative net incomes and price cannot be negative. Consequently, we use the debt-equity ratio of the insurer as the price of equity (p3). 5 Following Cummins, Weiss, and Zi (1999) and Cummins et al. (2004), we consider debt capital as an input variable and use insurance reserves as the proxy for debt. The price of debt (p4) is ratio of the difference between total investment income and investment income attributed to equity capital to debt capital. 3.3 Scope Economy Estimation 5 Price of equity should be a function of a firm s debt-equity ratio. See Jeng and Lai (2005) for detailed discussions. 7

10 Three different analyses are performed in the scope economy analyses. We first use the stochastic frontier approach to estimate the traditional translog- cost, revenue, and profit functions for the specialists and nonspecialists. We then use the estimates from translog functions to calculate the estimated cost, revenue, and profit scope economies for each insurer. Finally, we investigate the relation between scope economies and firm characteristics by regressing scope economy values on other firm characteristics. The traditional approach for estimating scope economies is to use single continuous cost function to estimate scope economies. Berger et al. (2000) suggest the traditional approach may lead to misleading results because nonspecialists and specialists may use different technology. Thus, we used the preferred approach proposed in Berger et al. (2000) to estimate the scope economy for property-liability insurers. Specifically, the measurement of cost, revenue, and profit scope economies are given by the following equation: S C CS1( y1; w1) + CS 2( y2; w2) C ( 1,2) = C ( y1; y2; w) N N ( y1; y2; w) where S (1,2) C is cost scope economy; C N( ) is the estimate of translog cost function for nonspecialists. 6 Y1 (y2) is an output vector for personal line specialists (nonpersonal line specialists) 7, and w1 (w2) is an input price factor for personal line specialists (nonpersonal line specialists). If S (1,2) C > 0, the cost scope economy exists, whereas S C (1,2 ) < 0 means the cost scope diseconomy exists. The revenue scope economy is similarly defined below. S R RN ( y1; y2; w) RS1( y1; w1) RS 2 ( y2; w2) ( 1,2) = R ( y1; y2; w) N where S (1,2) R is revenue scope economy and R ( ) is the estimate of translog revenue 6 We provide the translog cost function in the later part of this section. 7 Personal lines include homeowners multiple peril, private passenger auto liability and nonpersonal lines are equal to total losses incurred minus those of personal lines. 8

11 function. If S R (1,2) >0, the revenue scope economy exists, whereas S R (1,2) < 0 means the revenue scope diseconomy exists. Finally, the profit scope economy is defined below. S Π Π N ( y1; y2; w) Π S1( y1; w1) Π S 2 ( y2; w2) ( 1,2) = Π ( y1; y2; w) N where S Π (1,2) S (1,2) Π is profit scope economy; Π ( ) is the estimate of translog profit function. If > 0, the profit scope economy exists, whereas S Π (1,2) < 0 means the profit scope diseconomy exists. Scope economy estimates for the nonspecialist firms are directly obtained from the nonspecialist insurer data. Scope economy estimates for the specialist are obtained by merging two groups of specialists into a simulated merged firm. We use the stochastic frontier approach to estimate the translog cost, revenue, and profit function. The specification of the log cost function is ln C i = ln C ( Y, w) + ( v + u ) i i i where lnc i is the logarithm of cost of insurer i; Y i is the output vector; w is input price vector; v i is random disturbance, which is assumed to be normally distributed and independent of u i ; and u i is cost inefficiency, which represents the deviation from the cost frontier and is assumed to follow half-normal distribution. The functional form is needed to estimate lnc i. We follow the model specification in Hughes (1988) and the Schmidt and Lovell (1979) to estimate lnc i. The stochastic frontier cost function is defined as follows: n ln( C i / wm ) = α 0 + βi ln( yi ) + γ j ln( w j / wm ) + ( vi + ui ) i= 1 m j= 1 Because the linear homogeneity is a necessary condition for the cost function in the input prices, one of the input prices is taken to normalize the dependent variable and other input prices. 9

12 There is no difference in which input price is used to normalize the equation (Schmidt and Lovell, 1979). The specifications of revenue and profit functions are identical to the cost function, except that the dependent variable is revenue and profit variables. Finally, we conduct regression analysis to investigate the relation between scope economies and other firm characteristics. Hypotheses related to the regression analysis are discussed below. 8 Size Cost scope economies may be negatively related to firm size. When a nonspecialist is small, sharing the fixed resources such as utilities, offices, and computers is beneficial. The benefits may be offset by the costs of control and coordination when a nonspecialist is large. There may be revenue scope economies at large scale because a large nonspecialist can benefit from branding or large spending in advertising. It is easier for producers to increase revenues by offering a complete range of products than one or two single product. Commercial Output % There may be revenue scope diseconomies when a nonspecialist issues high percentage of commercial output because policyholders are willing to pay more for tailored personal services. We also hypothesize that nonspecialists emphasizing selling commercial lines are less likely to realize profit scope economies because commercial buyers are, in general, more knowledgeable in products and can easily negotiate with several insurers, especially with the help of brokers. Cost Efficiency Managers of an insurer with high cost efficiency may be able to achieve economies of scope. In contrast, efficient managers may lack the incentive to expand the scope of operation 8 Some of the arguments used here are based on Berger (2000). However, there are significant differences between the arguments in this section and those of Berger (2000) because Berger (2000) examines both the life and the property-liability industry, whereas this article investigates commercial lines versus personal lines within the property-liability industry. 10

13 and focus on what they do the best. Thus, efficient managers may stick to their specialized strategy. Hence, we do not have clear prediction about the sign of the coefficient. Risk There may be cost scope economies because risk-sensitive policyholders (employees) may be willing to accept lower services (wages) from nonspecialists in return for low default risk. Policyholders also may be willing to pay more from nonspecialists for similar reasons. Thus, there may be revenue scope economies. We use ROE standard deviation and capital to asset ratio as proxies for risk. Vertical Integration We define an insurer as vertically integrated if it distributes its product through exclusive agents, direct marketing, mass marketing, or a combination. An insurer is nonvertically integrated if it distributes its product through independent agent(s). We expect an insurer with a vertical integrated distribution system to be more likely to realize cost scope economies because it is able to reuse vertical integrated system to sell multiple lines of personal and business line of business. 9 Vertically integrated insurers are more likely to realize revenue scope economies because vertically integrated distributed system is more likely to develop its brand. Market Concentration and Market Share Insurers with high market concentration and market share are more likely to collude in pricing, restrain from price increases, or both (Chidambaran, Pugel, and Sauders, 1997; Nissan 2003). We use WCONC as proxy for market concentration and market share. WCONC equals weighted sum of market share per line multiplied by line-specific Herfindahl index. WCONC it = 29 l= 1 w ilt HHI lt The line-specific Herfindahl Index for each line of insurance for ith insurer in year t is calculated as follows: 9 The word reuse also is used in Berger (2000), p

14 HHI lt = n i= 1 ( ) 2 P / P ilt lt where P ilt is premium written for each line of business for ith insurer in year t. Each insurer s concentration for each line of insurance is calculated as follows: w = P / P ilt ilt it Industry concentration measurement is equal to the weighted sum of market share per line multiplied by line-specific Herfindahl index. 4. EMPIRICAL RESULTS Section 4 provides the empirical results of the data envelopment approach and the stochastic frontier approach. 4.1 Empirical Results of DEA This section reports the results of the efficiency performance by using data envelopment analysis and provides the comparisons between specialists and nonspecialists. Table 1 shows the summary statistics for the variables used in the DEA analysis. One interesting observation is that the specialists are statistically smaller than the nonspecialists in terms of all outputs and all inputs. We first test the null hypothesis that nonspecialists and specialists operate on the same frontier. If nonspecialists and specialists operate on the same frontier, then it is appropriate to use a pooled frontier to analyze differences in the two types of the insurers. Table 2 reports the results of the test of the null hypothesis that the pooled and separate frontiers are identical. Analysis of variance (ANOVA) F test, Wilcoxon Z test, Median Z test, Van Der Waerden Z test, and Savage Z test overwhelmingly reject the null hypothesis that the frontier of nonspecialists (specialists) is identical to the pooled frontier. Thus, it is not appropriate to use a pooled frontier to compare the efficiency difference between the two types of insurers. To compare the 12

15 efficiency performance between the nonspecialists and specialists, we use the cross-frontier analysis (e.g., Cummins et al., 1999). Table 3 reports the efficiency results of technical efficiency (TE). Table 3 shows that specialist TE averaged 85.76% and nonspecialist TE averaged 94.98%. The evidence suggests that specialists could have reduced their inputs by 14.24%, on average, if they had been operating with full efficiency and nonspecialists could have reduced their inputs by 5.02%. The results are not correctly interpreted as suggesting that nonspecialists are more technical efficient than specialists. The average technical efficiency score (4.14) of specialist relative to nonspecialist frontier Tnsp(Ysp,Xsp) is greater than 1. The evidence implies that it is not feasible, on average, to replicate specialist input output combination by using the nonspecialist technology. That Tnsp(Ysp,Xsp) scores are greater than 1 in all sample years strongly supports the above-mentioned conclusion. Table 3 also shows the average score (3.61) of nonspecialist relative to specialist frontier, Tsp(Ysp, Xsp), is greater than 1. In addition, Tsp(Ysp,Xsp) scores are greater than 1 in all sample years. The results suggest that it is not feasible, to replicate nonspecialist input output combination by using the specialist technology. Most of the DEA studies on financial institution evaluate the efficiency performance without specifically considering the size of the institution. For robustness, we report the efficiency scores based on the size of the insurer. The results of four different sizes are reported. Specifically, the technical efficiency scores at the first size quartile (Q1), the median (Q2), the third size quartile (Q3), and the fourth size (Q4) are reported in Table 4. We find the results of Table 4 are very similar to those of Table 3. The evidence implies that it is not feasible to replicate specialist (nonspecialist) input output combination by using the no-specialist (specialist) technology. Thus, we conclude that neither specialists nor nonspecialists dominate in terms of technical efficiency. In other words, we 13

16 reject the null hypothesis (H3) that the technology of nonspecialists is the same as that of the specialists. Tables 5 and 6 presents the cross-frontier results of cost efficiency. The results are very similar to those of Tables 3 and 4. To save space, we do not provide detailed analyses. We conclude that neither specialists nor nonspecialists dominate in terms of cost efficiency. Based on the overall evidence of technical efficiency and cost efficiency, we cannot reject the null hypothesis that it is not feasible, on average, to replicate nonspecialists (specialist) input-output combination using the specialists (non-specialist) technology. It should be noted that the above-mentioned analysis is based on the univariate analysis. We next conduct regression analyses where the dependent variable is F-scores. F-scores measure the dominance with respect to the frontiers. Based on Cummins et al. (1999), we calculate dominance statistics, F-score. Specifically, we define the F-score of technical efficiency for nonspecialists as follows: F ( Y t np, X np Tnp( Ynp, X np) ) = 1. T ( Y, X ) sp np np The F-score of technical efficiency for specialists is defined as follows: F ( Y t sp, X sp Tnp ( Ysp, X sp ) ) = 1. T ( Y, X ) sp sp sp A negative value of F t (Y i,x i ) implies that the specialist technology is dominant for producing the input output vector; a positive value of F t (Y i,x i ) implies that the nonspecialist s technology is dominant for producing the input output vector. For example, a negative value of Ft ( Ynp, X np) means the nonspecialists has higher efficiency with respect to its own frontier than it does with respect to the specialist frontier. The F-score estimation based on cost and allocative efficiency are defined similarly. Table 7 shows the results of F-scores by size quartile. F t (Y i,x i ), F a (Y i,x i ), and F c (Y i,x i ) 14

17 represent F-scores for technical, allocative, and cost efficiency, respectively. Panel A in Table 7 shows that the quartile results of F t (Ynsp, Xnsp) for nonspecialists are positive and significantly different from zero, confirming nonspecialists are superior in producing nonspecialized output vectors in terms of technical efficiency. The quartile results of F t (Ysp,Xsp) for specialists are negative and significantly different zero, confirming specialists are superior in producing specialized output vectors in terms of technical efficiency. The results of panels B and C are similar to those of panel A. The above-mentioned results are based on univariate analyses. To further analyze whether the nonspecialist dominates the specialist or vice versa, we regress F-scores on a set of independent variables representing type of insurers (nonspecialists vs. specialists), size, business mix, risk, market power, and organizational structure. We first report the evidence of the summary statistics of the independent variables for nonspecialized insurers and specialized insurers in Table 8. We summarize the most interesting results below. The evidence shows that nonspecialized insurers are much larger than specialized insurers in terms of total assets. Specialized insurers focus on more in commercial insurance than nonspecialized insurers. Specifically, the ratio of commercial output as a percentage of total output for the specialized insurers and nonspecialized insurers is 93.8 and 61.8%, respectively. We also find that the average of ROE of specialized insurers is higher than that of the nonspecialized. The regression results are shown in Table 9. Recall that a positive value of F t (Y i,x i ) implies that the nonspecialist s technology is dominant for producing the input output vector, and a negative value of F t (Y i,x i ) implies that the specialist technology is dominant for producing the input output vector. We include six size-quartile-dummy/specialization variable interaction terms in the regression models. 10 Specifically, three interaction variables (the three size quartile dummy variables interact with a dummy variable equal to 1 for nonspecialists and 0 otherwise) 10 The measure size is total output. Quartile 1 (4) includes the smallest (largest) insurers. 15

18 are included in the regression models. The omitted interaction variable is the first size quartile interaction term. Similarly, we also interact the size dummies with a dummy variable equal to 1 for specialists and 0 otherwise. To examine the effect of two strategies on the relation between the firm characteristics and dominance, we add the interaction terms between strategy dummy and other firm characteristics, such as commercial output proportion, ROE standard deviation, capital to asset ratio, industry concentration, and organizational form. Panel A in Table 9 shows that the coefficients on the three size/nonspecialist variables are positive and significant in the cost dominance regression model, implying that nonspecialists have advantages in terms of cost dominance when they produce their own input output vector. The coefficients on the two of three size/specialist variables are not statistically significant, except the forth quartile interaction term. The results indicate that specialists weakly dominate for producing specialist s outputs. In the Random Effect model, the Hausman test statistics is 19.82, suggesting the Random effect model is appropriate. Panel B in Table 9 reports the results of technical dominance frontier regressions. In the random effect model, the Hausman test statistics is 45.95, suggesting we need to use the fixed effect model. The results of fixed effect model do not provide evidence that either nonspecialists dominate in terms of technical efficiency when they produce their own input output vector or that specialists dominate for producing specialist s outputs. Thus, we conclude that we cannot reject the null hypothesis that nonspecialists (specialists) dominate specialists (nonspecialists) in producing specialists (nonspecialist) input output vector. The negative coefficient on the commercial output proportion/nonspecialist term shows suggests that specialist s technology dominates the nonspecialist s technology in providing commercial line of business. The negative coefficient on the market concentration (WCONC)/nonspecialist term indicates that specialist s technology dominates the nonspecialist s technology in less competitive market. The overall results (Tables 2 9) support both the nonspecialized hypothesis and 16

19 specialized hypothesis. Thus, the specialists and nonspecialists can coexist in the U.S. property-liability insurance industry. The conclusion is robust with respect to cross-frontier and F-score analyses in terms of technical efficiency, allocative efficiency, and cost efficiency. 4.2 Empirical Results of Stochastic Frontier Approach In this section, we use the stochastic frontier approach to estimate the cost, revenue, and profit scope economies and investigate the specialized hypothesis and nonspecialized hypothesis. We adopt the variables used in the Berger et al. (2000) to estimate the cost, revenue, and profit function. The dependent variables are cost, revenue, and profits. The cost is defined as other underwriting expenses. Revenue contains the total net premium earned and net investment income less losses incurred and total loss adjustment expenses. Profits are equal to revenues minus costs. Because insurers perform risk-bearing and risk-pooling function, we adopt the value-added approach to define three outputs. Thus, the output variables include losses incurred for personal lines and losses incurred for nonpersonal lines. Moreover, because the insurers also serve as financial intermediation agent, we include invested asset as an additional output. We also include price of labor and business services price as input prices. The price of labor is from SIC 6331 (Fire, Marine, and Casualty Insurers) and the business services price index is from U.S. Department of Labor, Bureau of Labor Statistics. Table 10 presents the summary statistics for the variables used in the stochastic frontier approach. It shows that the means of the specialists are statistically smaller than those of the nonspecialists in terms of outputs, costs, revenues, and profits. More importantly, the magnitude of the difference is huge. For example, the mean of revenues for the nonspecialists is $62,983 thousand, whereas the mean for the specialized firms is $10,908 thousand. The revenue is defined as premium earned plus net investment gain minus total losses incurred minus loss adjustment expense. 17

20 Berger et al. (2000) suggest scope economies may differ at different scale. We thus evaluate scope economies at different valuation points based on the size of total assets. The samples are divided into the Q1, the median, the Q3, and Q4, that is, 25th, 50th, 75th, and 100th percentiles of the data. Table 11 shows the estimates of cost, revenue, and profit scope economies for property-liability insurers by size quartile. The estimate of the cost scope economy at Q1 is 25.35% and statistically significant, indicating that it is more costly for two specialists to produce two outputs of two types of products separately than a nonspecialized firm producing both outputs at Q1. The evidence of large cost scope economies at Q1 suggests production complementarities for different lines of business. Thus, small specialists are not able to fully use resources such as computer systems, investment department, and managerial expertise. Another possible reason is that small specialists incur more costs than nonspecialists to provide higher quality services. If this is the case, we should observe diseconomies of revenue scope for nonspecialists. The estimated cost scope diseconomies is 9.93% at the median, 28.30% at Q3, and 58.07% at Q4, respectively. The cost scope diseconomies suggest larger nonspecialists incur additional expenses and costs. For example, the additional costs include coordination between different lines of business, training costs for actuaries, claim adjusters, and underwriters for different line of business. The estimate of revenue scope economy is 8.07% at Q1, suggesting that small specialists benefit from revenue scope diseconomies. One possible reason is small specialists may be more able to provide high-quality tailored products and service; therefore, specialists could charge more price from the customers. This possible explanation is consistent with the above results that smaller specialists incur more costs to provide high-quality products than nonspecialists. The estimated revenue scope economy is 19.93, 34.24, and 60.99% at the median, 18

21 Q3, and Q4, respectively. The estimated revenue scope economy for nonspecialists is very large for the three largest quartiles, implying policyholders are willing to pay higher price, purchase more insurance from nonspecialists than specialists, or both. The possible reason is that larger nonspecialists benefit through providing a broader range of products. The profit scope diseconomy is and 5.63% at Q1 and Q2, respectively. In contrast, we find profit scope economies (14.98 and 43.84%) at Q3 and Q4. Apparently, the revenue scope economies result in profit scope economies for the large nonspecialists. The above-mentioned results are based on the univariate analysis. We next provide the results of regressions that control the characteristics of different insurers. Table 12 shows the regression results for cost, revenue, and profit scope economies for the simulated merged insurers. The simulated merged insurers are formed by combining the personal line specialists and commercial line specialists. In Panels A, B, and C of Table 12, we report both the random effects and fixed effects results. The coefficient of log asset is statistically positive in the revenue and profit scope economy models, suggesting that larger insurers benefit from scope economies. The results of log asset in revenue model and profit model are consistent with the results in Table 11. Next, we examine the relationship between scope and product mix. The coefficients of percentage of commercial short-tail line output is negative and statistically significant at the 1% level in the cost scope economy model, suggesting that specialists benefit from cost efficiency when selling more commercial short-tail line of insurance. The cost efficiency variable in the revenue scope economy model and the profit scope economy model is statistically positive, implying nonspecialists that are more cost efficient are more likely to benefit from revenue and profit scope economies. Standard deviation of return of equity is a measurement of risk taking behavior. In the profit scope economy model, the coefficient of standard deviation of return of equity is positive and 19

22 significant at the 5% level. The results are consistent with our expectation that ROE standard deviation is positively related to profit scope economies. Capital to asset ratios is statistically positive in the cost and profit scope economy models, respectively, but statistically negative in the revenue scope economy model. Capital to asset ratios in the revenue scope economy model is consistent with out expectation that risk sensitive policyholders are more willing to pay more to nonspecialists to reduce risk. The coefficient of WCONC is statistically negative in the cost scope economy model but statistically positive in the revenue scope economy model. The results indicate that specialists who are exposed to less competitive market are more likely to realize the cost scope economy. In contrast, nonspecialists in a less competitive market are more like to benefit from the revenue scope economy. Thus, policyholders are more likely to purchase their policies from nonspecialists when the insurance market is more competitive. We find vertical integration is positively and significantly related to cost scope economies, suggesting that nonspecialists using vertically integrated distribution system are more likely to realize scope economies. Finally, we follow the literature (e.g., Berger et al., 2000) and use stock variable to control organizational structure. The coefficients of stock are negative and statistically significant in the cost scope economy and profit economy models, respectively. Table 13 presents the regression results for the cost, revenue, and profit scope economy models based on nonspecialized firms with panel data from 1997 to The main results in Table 13 provide additional evidence for the coexistence of specialists and nonspecialists. Specifically, we find many highly significant coefficients in the profit scope economy equations, suggesting that nonspecialization production is more efficient for some types of insurers and specialization production is more efficient for other types of insurers. The overall evidence indicates that insurers with the following characteristics are more likely to realize profit scope economies, i.e., insurers that (1) are large, (2) specialize in personal 20

23 lines, (3) are less cost efficient, (4) have lower risk, (5) have high leverage, (6) in a less competitive market environment, or (7) are mutuals more likely to realize profit scope economies. 11 The above-mentioned results suggest that nonspecialists are more efficient for some types of insurers, whereas specialists are more efficient for other types of insurers. Therefore, the evidence supports that the nonspecialized hypothesis and the specialized hypothesis can coexist. Based on the overall evidence of scope economies, we reject the null hypothesis (H5) that there are no scope economies for the specialists and/or non-specialists. 5. CONCLUSION Whether the nonspecialized strategy or the specialized strategy is more efficient in the insurance industry has been an interesting question. This research question is important for managers, investors, and other stakeholders. This paper tests both the nonspecialized hypothesis and the specialized hypothesis in the property-liability insurance industry for sample period from 1997 to We use the data envelopment analysis (nonparametric approach) and the stochastic frontier approach (parametric approach) to examine the efficiency performance and scope economies for nonspecialists and specialists. The results of the DEA approach show that the nonspecialists and specialists operate on different efficient frontiers. The evidence of cross-frontier analysis suggests that nonspecialists (specialists) dominate specialists (nonspecialists) in producing nonspecialists (specialists) input output vectors. These results are robust with respect to different size quartiles. The regression results generally support the coexistence of the nonspecialized strategy and specialized strategy. The above-mentioned results are robust with respect to technical efficiency scores and cost efficiency scores. We summarize the evidence of the stochastic analysis next. The results of the scope 11 It should be noted some of the above-mentioned results based on the nonspecialist data are different from those based on the simulated nonspecialist data. Two possible reasons are first, the data are different and second, the sample sizes are different. 21

24 economy analyses suggest that small nonspecialists are more likely to realize cost scope economies, whereas large nonspecialists are more likely to suffer scope diseconomies. We also find small nonspecialists suffer revenue and profit scope diseconomies, whereas large nonspecialists enjoy revenue and profit scope economies. The lack of scope economies at all size is consistent with two possible explanations. First, nonspecialists and specialists are approximately efficient. Thus, neither the nonspecialized hypothesis nor the specialized hypothesis is valid. Second, both hypotheses are valid. Nonspecialists may be more efficient for some types of insurers and specialists may be more efficient for other types of insurers. The following regression results confirm the latter statement. Specifically, the regression evidence indicates that insurers with the following characteristics are more likely to realize profit scope economies, i.e., insurers that (1) are large, (2) specialize in personal lines, (3) are less cost efficient, (4) have lower risk, (5) have high leverage, (6) in a less competitive market environment, or (7) are mutuals are more likely to realized profit scope economies. The above-mentioned results suggest that nonspecialists are more efficient for some types of insurers, whereas specialists are more efficient for other types of insurers. The overall empirical evidence supports that both the nonspecialist hypothesis and the specialist hypothesis hold for different type of property-liability insurers. 22

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