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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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