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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 ARIA Meeting, August, 2007 2 Research Question and Research Purpose Why do we observe the long-lived coexistence of joint producers and specialist in the U.S. propertyliability insurance industry? Whether joint producers or specialist is more efficient ? 1.whether nonspecialized strategy or specialized strategy is more efficient for property-liability insurers. 2.what types of insurers are likely to realize economies of scope. 3 The Literature I Conglomeration hypothesis Teece(1980) Argued that Multiproduct enterprise is an efficient way of organizing economic activity. Panzar and Willing (1981) First to introduce the concept of economies of scope. Strategic focus hypothesis Comment and Jarrell (1995) The greater corporate focus is consistent with shareholder wealth maximization. Kanatas and Qi (2003) Integrated financial services market is less innovative than one with specialized intermediaries. Chen (JFQA,2006) focused firms exhibit significantly better post-investment operating performance than diversified firms. 4 The Literature II Insurance Related Kellner and Mathewson (1983); Meador, Ryan, and Shellhorm (1998), Cummins, Weiss, and Zi (2003), and Hirao and Inoue (2004) found results consistent with diversification strategy. Grace and Timme (1992) and Yuengert (1993) Berger et al. (2000) found mixed results. Jeng and Lai (2005) find that Keiretsu firms seem to be more cost-efficient than nonspecialized independent firms. 5 The main contribution of this study 1. This paper examines diversification/focus strategy from the perspective of a propertyliability insurer rather than two sectors of insurance industry. 2. We examines whether the technology of nonspecialists is the same as that of the specialists using the cross-frontier approach. 3. Focusing on a more recent time period enables us to determine whether the most recent wave of financial consolidation will change the focus strategy. 6 Data and Methodology Sample & Data Period: 1997-2004 Insurer’s Annual Statement from NAIC Herfindahl index of net premium is used to identify the specialists and nonspecialists. Methodology Data Envelopment Analysis (DEA) Stochastic Frontier Approach (SFA) 7 Hypotheses Development H1: The Nonspecialized Hypothesis H2: The Specialized Hypothesis H3: The technology of nonspecialists is the same as that of the specialists. H4: It is infeasible to replicate nonspecialists input-output bundle using the specialists technology. H5: There are no scope economies for either nonspecialists or specialists. 8 Inputs/Outputs using in value-added approach Cummins and Weiss (1993), Cummins, Weiss, and Zi (1999), and Cummins et al. (2004) Output Y1= Losses incurred in short-tailed personal lines Y2= Losses incurred in long-tailed personal lines Y3= Losses incurred in short-tailed commercial lines Y4= Losses incurred in long-tailed commercial lines Y5= Total Invested Assets Input X1=Labor cost X2=Business Service X3=Equity X4=Debt Input Price P1=Price of Labor P2= Price of Business Service P3= Price of Equity P4= Price of Debt 9 Table 1 Summary Statistics for non-specialists and specialists Non-specialists Mean Specialists Standard deviation Test stat Standard deviation Mean Output Y1 11,257,223 24,468,928 *** 14,374 151,373 Y2 23,298,699 50,688,162 *** 123,717 831,187 Y3 9,123,543 26,047,217 *** 4,143,038 20,745,509 Y4 46,590,136 110,748,946 *** 6,617,191 15,993,637 Y5 430,559,048 991,784,924 *** 78,075,255 193,335,845 X1 42,615 92,319 *** 5,478 12,837 X2 123,143 288,458 *** 10,451 24,917 X3 165,473,202 427,431,018 *** 27,754,645 53,933,908 X4 285,914,578 663,981,251 *** 36,906,586 77,479,769 P1 435 13 435 13 P2 281 21 281 21 P3 1.94 1.91 1.47 7.07 P4 0.038 0.0178 0.038 0.0178 Input Input prices Number of firms 133 ** 144 Note:*** Statistically significant difference at 1% level or better; ** Statistically significant difference at 5% level; * Statistically significant difference at 10% level. 10 Table 5 Cost efficiency results: 1997–2004 Year Mean 1997 1998 1999 2000 2001 2002 2003 2004 Csp(Xsp,Ysp) 0.4895 (0.2542) 0.4909 (0.1719) 0.4210 (0.1880) 0.7001 (0.1904) 0.4322 (0.2229) 0.2530 (0.2154) 0.4328 (0.2359) 0.7809 (0.1930) 0.4116 (0.1602) Cnsp(YnspXnsp) 0.6798 (0.2051) 0.7509 (0.1950) 0.7333 (0.1679) 0.7788 (0.1204) 0.7298 (0.1426) 0.5821 (0.1585) 0.4606 (0.2378) 0.7649 (0.1706) 0.6380 (0.1981) Cnsp(Xsp,Ysp) 0.9826 (1.7989) 1.0541 (2.3301) 1.1153 (2.1386) 1.0830 (1.5154) 0.9422 (1.5715) 0.8858 (1.9626) 0.9890 (2.3438) 0.9118 (0.9855) 0.8732 (0.8809) Csp(YnspXnsp) 1.4439 (1.4254) 1.3417 (0.8190) 1.3192 (0.8612) 1.1194 (0.5212) 2.8919 (2.1899) 2.5850 (2.0548) 0.5915 (0.3386) 1.0040 (0.5304) 0.6771 (0.3699) 11 Table 5 Cost efficiency results: 1997–2004 (continuous) Year Csp(Xsp,Ysp) vs. Cnsp(Xnsp,Ynsp) Csp(Xsp,Ysp) vs. Cnsp(Xsp,Ysp) Mean *** *** Cnsp(YnspXnsp) vs. Csp(YnspXnsp) *** 1997 ** *** *** 1998 *** *** *** *** *** 1999 2000 *** *** *** 2001 ** *** *** *** *** 2002 2003 2004 *** *** *** *** Note:*** Statistically significant difference at 1% level or better; ** Statistically significant difference at 5% level; * Statistically significant difference at 10% level. 12 Cummins et al., (1999) F-scores measure the dominance with respect to the frontiers. Ft (Ynp , X np ) 1 Tnp (Ynp , X np ) Tsp (Ynp , X np ) 13 Table 7 Dominance Statistics by Size Quartile Quartile 1 Mean Quartile 2 Mean Quartile 3 Mean Quartile 4 Mean Panel C: Cost Frontiers Fc(yi,xi) Nonspecialist Specialist 0.054 *** –1.184 (0.557) (2.262) 0.268 *** –0.808 (0.495) (1.132) 0.330 *** –1.342 (0.383) (3.135) 0.257 *** –1.161 (0.471) (2.237) *** *** *** *** Note:*** Statistically significant difference at 1% level or better; ** Statistically significant difference at 5% level; * Statistically significant difference at 10% level. 14 Table 8 Summary Statistics for regression Nonspecialists Variables Test stat. Specialists Mean Standard deviation Total Assets (Thousands) $736,912 $1,653,818 *** $137,014 $314,393 Commercial Insurance Output/Total Output 61.78% 21.85% *** 93.79% 23.34% Cost Efficiency 68.27% 19.92% *** 48.39% 24.86% ROE 5.34% 6.56% *** 11.04% 30.48% Capital to Asset Ratio 35.44% 13.58% *** 48.44% 25.76% WCONC 0.05% 0.21% *** 0.02% 0.09% Reinsurance Ratio 43.90% 27.55% *** 17.60% 76.70% Agent Balance (Thousands) $45,196 $100,297 *** $6,536 $32,540 Mutual Dummy 20.69% 40.53% *** 30.36% 46.01% Stock Dummy 79.31% 40.53% *** 69.64% 46.01% Vertical Integration Dummy 8.91% 28.50% *** 31.96% 46.66% N 1,010 Mean Standard deviation 1,023 Note:*** Statistically significant difference at 1% level or better; ** Statistically significant difference at 5% level; * Statistically significant difference at 10% level. 15 Table 9 Dominance Regression for nonspecialists and specialists Panel A: Cost Dominance Regression Random Effect Model Independent variables Coefficient T stat Intercept 0.4744 1.73* Q2* nonspecialist 0.4251 1.90* Q3* nonspecialist 0.5855 2.50** Q4* nonspecialist 0.6857 2.85*** Q2* specialist –0.1000 –0.69 Q3* specialist 0.0791 0.43 Q4* specialist –0.6359 –2.08** % Commercial Output –1.0134 –3.31*** % Commercial Output* nonspecialist –0.3406 –0.97 ROE Standard Deviation –0.5597 –0.68 ROE Standard Deviation* nonspecialist 0.5053 0.61 Capital to Asset Ratio 0.0525 0.12 Capital to Asset Ratio* nonspecialist –0.3549 –0.77 WCONC –10.0635 –0.33 WCONC* nonspecialist –159.8470 –1.59 Stock –0.1483 –0.78 Stock* nonspecialist 0.2493 0.98 R-square 0.0774 Hausman Test for Random Effects (P value) 19.82 (0.2284) Note:*** Statistically significant difference at 1% level or better; ** Statistically significant difference at 5% level; * Statistically significant difference at 10% level. 16 SFA variables Variables Definitions y1 losses incurred for personal line y2 losses incurred for nonpersonal lines y3 invested assets p1 price of labor=average weekly wages for insurance agent (standard industrial classification [SIC] Class 6411) by using U.S. Department of Labor data. p2 business services price index =average weekly earnings of workers in SIC 7300. Costs total expense paid of other underwriting expense Revenues totals of net premiums written + net investment income-losses incurred current year – total expenses incurred Profit revenue-cost 17 Table 10 Summary Statistics for stochastic frontier approach Nonspecialists Specialists Mean Standard deviation Test stat. Mean Standard deviation y1 34,397,479 73,221,866 *** 135,658 918,295 y2 62,233,108 144,033,832 *** 10,595,823 25,071,999 y3 428,584,725 989,856,777 *** 77,766,932 192,140,149 p1 435 13 435 13 p2 281 21 281 21 Cost ($ thousand) $47,819 $102,550 *** $5,063 $9,790 Revenue ($ thousand) $62,983 $146,969 *** $10,908 $35,355 Profit ($ thousand) $15,164 $64,700 *** $5,845 $27,689 Variables Note:*** Statistically significant difference at 1% level or better; ** Statistically significant difference at 5% level; * Statistically significant difference at 10% level. 18 Table 11 Scope Economy by Quartile Q1 Cost Scope Economy 25.35% T stat (18.85) Revenue Scope Economy –8.07% T stat (-5.17) Profit Scope Economy –39.14% T stat (–34.60) Median *** –9.93% Q3 *** (–7.39) *** 19.93% –5.63% (–4.28) *** (–19.67) *** (13.61) *** –28.30% Q4 34.24% 14.98% (12.46) *** (–48.18) *** (22.88) *** –58.07% 60.99% *** (49.29) *** 43.84% (38.7)1 Note:*** Statistically significant difference at 1% level or better; ** Statistically significant difference at 5% level; * Statistically significant difference at 10% level. *** 19 Table 12 Profit Scope Economy Regression for simulated non-specialists Independent variables Random Effect Fixed Effect Coefficient T stat Coefficient T stat Intercept –2.3521 –9.34*** –1.8529 –3.37*** Log Asset 0.1172 8.75*** 0.0923 3.09*** Commercial Output % –0.2046 –6.41*** –0.4028 –4.65*** Cost Efficiency 0.2913 6.99*** 0.3854 7.93*** ROE Standard Deviation 0.0555 1.96** 0.0482 1.57 Capital to Asset Ratio 0.1944 2.82*** 0.1469 1.43 WCONC 16.5840 1.03 30.5046 0.97 Stock –9.38E–02 –3.04*** –9.77E–02 –0.86 Vertical Integration Dummy 2.70E–03 0.05 Average value of dependent variable –12.43% –12.43% R2 9.74% 59.79% Note:*** Statistically significant difference at 1% level or better; ** Statistically significant difference at 5% level; * Statistically significant difference at 10% level. 20 Concluding remarks 1. Supports for Hypothesis1,2,4 are found. 2. The nonspecialists and specialists operate on different efficient frontiers in P/L insurance industry. 3. Nonspecialists (specialists) dominate specialists (nonspecialists) in producing nonspecialists (specialists) input–output vectors. 4. The regression results generally support the coexistence of the nonspecialized strategy and specialized strategy.

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