Download Franchise Value and Firm Profitability: The Case of the Property

Franchise Value and Firm Profitability:
The Case of the Property-Liability Insurance Industry
Xuanjuan Chen, Helen Doerpinghaus, Tong Yu*
September 6, 2010
____________________________
* Chen is from the College of Business, Kansas State University. Doerpinghaus is from the Darla Moore
School of Business, University of South Carolina. Yu is from the College of Business and Administration,
University of Rhode Island. Emails are [email protected]; [email protected]; [email protected]. We appreciate the
comments from Reza Mihani and participants at the American Risk and Insurance Association 2008 and
2009 meetings. Assistance from Henry Kane of A. M. Best is gratefully acknowledged. All errors are our
own.
Franchise Value and Firm Profitability:
The Case of the Property-Liability Insurance Industry
Abstract
Word-of-mouth reputation or franchise value (FV) is critical to the profitability of
a financial firm. We accommodate two channels of FV effects on the profitability of
property liability insurers. One is a reputation effect where high FV insurers charge more
to policyholders and operate with lower expenses. The other is a solvency effect where
firms with greater FV may be more prudent in risk taking, with lower insolvency risk and
greater profitability. We show that through both channels insurer profitability increases in
franchise value ceteris paribus. A more interesting prediction is that in the absence of a
major capital shock to the insurance industry the FV effect on firm profitability is greater
in more competitive soft insurance markets than in hard markets. We empirically
examine the FV effect on insurer profitability and measure FV using financial strength
ratings after controlling for tangible firm characteristics. The results are consistent with
these predictions. The study should be of interest to financial intermediaries, analysts,
investors, and regulators given recent turmoil in the financial services sector.
1
Franchise Value and Firm Profitability:
The Case of the Property-Liability Insurance Industry
“We can afford to lose money–even a lot of money. We cannot afford to lose reputation–
even a shred of reputation. … Berkshire is ranked by Fortune as the second-most
admired company in the world. It took us 43 years to get there, but we could lose it in 43
minutes.”
-- Warren Buffet, 2006
1.
Introduction
Financial firms provide contract or service agreements where contingent payoffs
occur at a future point in time. Product quality is largely derived from customer trust in
the firm, word-of-mouth reputation, and brand loyalty. Insurers are no exception.
Product quality rests not only on an insurer’s financial strength to pay claims but on
reputation-based assets known as franchise value (or intangible assets or charter value).
Insurer franchise value includes firm name recognition, brand loyalty, renewable
business, and expertise in claim service and underwriting. While franchise value is
difficult to quantify using standard accounting measures, it is critical to insurer
profitability.1
The effects of similar intangible assets on firm performance have been examined
in studies on industrial firms (see, for example, Aaker 2001; Chan, Lakonishok, and
Sougiannis 2001; Barth, Clement, Foster, and Kasznik 1998; Lehmann 2004) but have
not been modeled or empirically tested for the financial services sector, specifically the
insurance industry. This study attempts to fill this void. We develop a model on the
effects of franchise value on insurer profitability, construct an innovative measure for
intangible assets, and provide empirical evidence on the effect of franchise value on
insurer profitability.
Given the contingent nature of insurance where claims payment occurs long after
insurance is purchased, the perceived quality of an insurer, such as having a good rating,
plays a strong role in determining insurer profitability. In a simple model we consider
1
The recent experience with the American International Group (AIG) Inc after being bailed out in 2008
clearly exemplifies the franchise value effect on insurer profitability. Echoing Warren Buffet’s comments
on firm reputation, Bloomberg reports that in the third quarter of 2009 “AIG suffered an 87% quarterly
sales decline at its European life business as U.K. clients abandoned the firm due to its ‘tainted brand’”. See
http://www.bloomberg.com/apps/news?pid=email_en&sid=amabV1Im9w84 for details.
2
two channels of franchise value effects on firm profitability. In the first channel, insurers
with higher franchise value have greater name recognition and brand loyalty, enabling
them to charge a higher premium (i.e., a greater loading) and benefit from a policy
distributional cost advantage (e.g., reduced expense). While franchise value provides
insurer advantage there is also a downside to firm investment in these intangible assets:
when an insurer becomes insolvent intangible assets are not readily recovered. The
negative effect of possible insolvency however is bounded by the benefits. The difference
is referred to as the reputation effect. 2 Moreover, a second franchise value effect on
profitability comes from the reduced insolvency risk. The literature, e.g., Babbel and
Merrill (2005) and Yu et al. (2008), suggest that high franchise value firms typically are
more prudent in risk taking. The lower insolvency risk results in higher premiums (thus
greater profitability) for high franchise value firms. This is a potential additional benefit
of insurer investment in intangible assets: an improved solvency effect for the firm.
Overall we expect that insurer profitability increases with franchise value given these two
effects, reputation effects and solvency effects.
The property liability market is known to be cyclical with lower realized (than
expected) claim costs, increased insurance supply and lower prices in soft markets and
constrained supply and higher prices in hard markets (e.g., Venezian, 1985; Cummins
and Outreville, 1987; Winter, 1994). The variations in supply and shifts in prices drive
differing levels of competitions in the insurance market, which presents a natural setting
to observe a dynamic version of the franchise value effect under heterogeneous market
conditions. Specifically, in the more competitive soft markets, insurers with stronger
name recognition and underwriting and claims service expertise are able to charge a price
differential for the expense load and maintain the benefits of efficient distribution relative
to lower franchise value insurers. Thus we expect a stronger FV effect in soft markets
than in hard markets.3
2
With the reputation effect, the insurance market may not be perfectly competitive. Rather, insurers with
more franchise value have greater market power. Commonly considered in the banking literature, such
market power would be justifiable under economic models of monopolistic competition with product
differentiation (Keeley, 1990; Gan, 2004). The insurance market is known to be opaque (Ross, 1989;
Polonchek and Miller, 1999; Zhang, Cox, and Van Ness, 2009), potentially resulting in greater switching
costs and making it easier for high FV firms to retain clients.
3
An exception would be when the insurance industry is hit by a major shock leading to widespread
insolvency and clients then fly to quality. In these cases the FV effect would be stronger in hard markets
3
Testing these predictions is not an easy task since there is not a ready measure for
franchise value for insurers. Franchise value is intangible and rarely recognized in
financial statements. Lev and Zarowin (1999) and others argue that quantifying
intangibles is where the current accounting system fails most seriously in reflecting
enterprise value and performance. Other studies use various proxies to evaluate franchise
value (intangible assets): Tobin’s q (e.g., Keeley 1990; Staking and Babbel 1995; Gan
2004) or accounting entries such as research and development expenses or advertising
expenses (e.g., Chan, Lakonishok and Sougiannis 2001). For our study use of these
proxies is not appropriate for evaluating insurance franchise value since the majority of
insurance firms are privately held and firm market value and other accounting variables
are not publicly available information.4 Instead we rely on the assessment by insurance
experts of an insurer’s strength and market standing, using the A. M. Best’s Financial
Strength Rating.5 Insurer ratings have been widely used as measures of insolvency risk
and an insurer’s overall financial quality (see, for example, Best, A. M., 2008; and Adiel,
1996; Anthony and Petroni, 1997; Cummins and Danzon, 1997; Pottier, 1998; Pottier and
Sommer, 2002). 6 As an insurer’s rating would be simultaneously influenced by both
tangible and intangible values, we construct a rating-based franchise value measure that
separates franchise value from tangible assets. It is the financial strength rating of an
insurer adjusted for the average rating of firms with comparable tangible characteristics.
We measure insurer profitability using returns on assets (ROAs), return on equity
(ROEs), the insurers’ combined loss and expense ratios (abbreviated as combined ratios,
or CRs), and the economic loss ratios (the total discounted value of claims as a fraction of
when insolvency risk becomes a major consideration (as was the case in the recent financial crisis). We
nevertheless consider this type of crisis as an abnormal case.
4
The nature of the insurance business is substantially different from that of industrial firms. Even if these
accounting variables were available using them requires particular caution.
5
For robustness, we consider alternative proxies for insurer franchise value and test their effects on firm
profitability. We apply insurer regression-based residual ratings as an alternative measure, use a “young
firm” indicator variable as a proxy, and sort by insurance groups. Results are robust across measurement
conventions.
6
A Best’s Financial Rating is “an independent opinion of an insurer’s financial strength and ability to meet
its ongoing insurance policy and contract obligations” (Best A. M. (2008)). Ratings are typically considered
as a comprehensive measure of insolvency risks (e.g., Pottier and Sommer, 1999; 2002; Doherty and
Phillips, 2002). In our analysis, we use ratings as an all-inclusive measure for an insurer’s market standing.
This is reasonable given that i) high ratings are assigned to firms of greater financial strength and ii)
various attributes related to firm financial strength are included as rating determinants, including
profitability, quality of management team, firm size, and capital adequacy (e.g., Best A. M. (2008) and
Pottier and Sommer, 1999; 2002).
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premiums net of expenses, abbreviated as ELRs). To test the predictions of the model, we
use a sample of property and liability insurers over the period 1985 to 2008.
We first perform portfolio analysis to examine the average relationship between
franchise value and insurer profitability. Insurers are sorted into deciles based on their
franchise value and operating performance in the subsequent year for each decile is
estimated. As expected we see that firm profitability increases with franchise value.
These results are consistent with our expectation and the findings in Chan, Lakonishok,
and Sougiannis (2001) where industrial firms with high franchise value outperform those
with low franchise value in terms of future stock performance. Next we use regression
analysis to allow controls for a range of firm characteristics (other than franchise value)
that may affect operating performance. The set of control variables includes firm size,
ownership structure, group affiliation, product concentration (by line of coverage), use of
reinsurance, a measure for the competitiveness of the insurer, the investment percentage
in common stocks, and lagged operating performance. The result shows that the
coefficient on franchise value is positive and statistically significant, indicating that the
link between operating performance and franchise value is robust.
Next, we test the differential franchise value effects in soft and hard markets. We
measure market condition using the industry-wide average loss ratios where high industry
loss ratio indicates a hard market. Consistent with our expectations, we find that across
all profitability measures, the average spreads in firm profitability between the top and
bottom franchise value firms are positive in both hard and soft insurance markets.
Further, the average spread is greater in soft markets.
Our study is distinct from other research in that we focus on the franchise value
effect on profitability of financial service firms, where the word-of-mouth effect is
critical. The paper perhaps most closely related to our paper is Epermanis and Harrington
(2006) where they find insurer premiums decline in the year and the year after insurers
experience rating downgrades. Our paper however differs from Epermanis and
Harrington (2006) in that we specifically analyze the effect of franchise value on
profitability, rather than looking at the overall rating effects. Our work also follows
Babbel and Merrill (2005), who suggest that franchise value is inversely related to firm
insolvency risk and that increases in franchise value decrease the insurance contract put
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option value. Our analysis on the role of franchise value in different market conditions
extends the theoretical framework discussed in Babbel and Merrill (2005) and provides
empirical evidence. Other franchise value literature focuses largely on firm risk taking
(e.g., Keeley 1990; Gan 2004; Yu, Lin, Oppenheimer, and Chen 2008). Although there
are studies on the impact on profitability of franchise value, these studies are exclusively
on industrial firms.
The remainder of the article is organized as follows. Section 2 presents a model
on the effect of franchise value to firm profitability and discusses testable hypotheses.
Section 3 describes the data and empirical methodology. Section 4 presents our empirical
findings regarding hypothesized franchise value effects. Section 5 concludes the paper.
2.
Franchise Value and Firm Profitability
We first derive the relation between franchise value and cross sectional firm
profitability in a simple model. Based on the model’s predictions, we then develop testable
hypotheses to guide our subsequent empirical analysis.
2.1
The Baseline Model
We consider a simple one-period model (t=0, 1) to demonstrate the effect of
franchise value on firm profitability in the insurance industry. In the same spirit as
Keeley (1990), we model franchise value in a non-perfect competitive market setting.
The interest rate is set to be zero for simplicity.
In the model an insurer writes policies and receives premiums at t=0. The
nonstochastic claim liabilities, L, are incurred during time 0 and 1 and are fully paid at
t=1. The insurer evaluates its profitability at t=1.
At t=0, the firm’s assets (A) include both tangible assets (t) and franchise value (f). The
firm’s franchise value is F as long as the firm is solvent and drops to 0 when the firm is
insolvent.
(1)
A=τ+f
The value of tangible assets, τ, follows a geometric Brownian motion.
observe that
6
We
(2)
d

 dt  dW
where  is the expected rate of return on tangible assets and  is the standard deviation of
tangible assets; W represents a Wiener process.
At t = 1, the aggregate profit made by the insurer is denoted as
(3)
  PC  E,
where P is the premium collected by an insurer; C is the claim payment made by an
insurer and E is the insurer’s expense. There is insolvency risk and insolvency is costly
due to the loss of franchise value. As the insurer holds a limited liability to policyholders
its claim costs would equal the value of tangible assets when the insurer is insolvent; the
claim costs equal the full amount of liabilities when the insurer is solvent. That is,
(4)
C= τ
if L ≥ τ (insolvent)
C=L
if L < τ
(solvent)
We have the following expression for the expected value of claim cost at t=0:
(5)
L

0
L
EXP(C )   tg ( )dt   Lg ( )d
L
 L   ( L   ) g (t )d
0
= L – PUT
(6)
L
PUT   ( L   ) g ( )d
0
Note that in Equations (5) and (6) claim liabilities, L, are fixed while the amount
of firm tangible assets, τ, is stochastic. In (6), the expected claim cost is identical to the
expected payoff of a risky bond (see Cummins, 1991). That is, the expected claim cost of
an insurer equates the payoff of a risk free bond with a face value of L minus the value of
a put option on firm tangible assets with L as the exercise price.
In our setting, insurers take a long position in the put option, entitling the firm in
the event of insolvency to receive a minimum payment of L upon liquidation at t=1. This
gives rise to a loss to policyholders, who take a short position in the put option. PUT
here is the amount that the insurer fails to deliver when it defaults. Following (5), as long
as insurers have a positive value in their claim costs (i.e., EXP(C)>0), we have L > PUT.
That is, the upper bound for the value of PUT is L.
7
With PUT clearly defined, we express premiums collected by an insurer at time 0
as below:
(7)
P  (1   )( L  PUT )
where λ (≥0) represents the percentage loading (of expected claims) that the insurer
charges policyholders. In practice, franchise value empowers insurers to charge a
relatively greater loading, thus,
(8)

0
F
Insurer expense, E, is proportional to the insurer’s expected claim costs, EXP(C):
(9)
E  e( L  PUT)
where e (≥0) is the expense ratio of the insurer. An insurer’s expenses cover the insurer’s
acquisition and underwriting expenses. Greater franchise value may help firms reduce
both costs. Thus, we expect:
(10)
e
0
F
Now, we can express the insurer’s expected profit in t=0 as below:
(11)
E ( )  P  Exp (C )  E  (  e)( L  PUT )   ( L  PUT )
where β (=λ-e) represents the net loading (i.e., the insurer’s proportional loading in
excess of its expense ratio). Equation (11) states that the insurer’s aggregate profit is
proportional to its claim liabilities in excess of the value of the firm’s insolvency put
option.
We are interested in knowing how FV affects insurers’ unit profitability. Thus, we
divide both sides of (11) by L and denote  
E ( )
and put=PUT/L (the value of the
L
insolvency put option per unit of the insurer’s liability, ranging between 0 and 1). This
gives us the following expression:
(12)
   (1  put )
Taking the derivative of π with respect to F, we have,
(13)
 
put

(1  put )  *
F 
F
F 

(1)
( 2)
8
The first term of the above expression,

(1  put ) , may be considered as the FV
F
effect on reputation (or simply the reputation effect). It is non-negative given that

 0 and 0 ≤ put ≤ 1.
F
The sign for the second term,   *
Appendix A, we show that
put
put
, depends on the sign of
. In
F
F
put
 0 under the condition that tangible assets of high-FV
F
firms exhibit first-order stochastic dominance (FSD) over tangible assets of low-FV
FSD
firms (denoted as  h   l .where h and l refer to high- and low-FV firms). Intuitively,
FSD of high-FV firms over low-FV firms suggests that high-FV firms have a greater
cumulative density in the solvency region (t>L) than do low FV firms. As a result, highFV firms are lower in their put option value. We consider this as the FV effect on
insolvency risk (or simply the solvency effect). This is consistent with empirical evidence
that high franchise value firms are more prudent in risk taking (see, e.g., Babbel and
Merrill, 2005 and Yu et al., 2008).
There is another perspective to consider. The first term in expression (13)
represents the premium component of a high FV firm that is due to its market power. The
second term represents the reduction in insurers’ risk premium to investors for high FV
firms. Since both terms are positive for high FV firms, we have the following
proposition:
Proposition 1: A sufficient condition for

 0 is that tangible assets of high-FV firms
F
exhibit first-order stochastic dominance over tangible assets of low-FV firms.
2.2
Effect of Underwriting Cycles
The property liability insurance industry is characterized by underwriting cycles
where the supply of insurance is adequate in soft markets but the supply falls in hard
markets (Venezian, 1985; Cummins and Outreville, 1987). Soft markets are generally
viewed as a buyer’s market while hard markets are viewed as a seller’s market. The
variation in supply and shifts in prices drive changing levels of competition in the
9
insurance market -- soft markets are more competitive than hard markets. This would
result in heterogeneous FV effects on profitability across insurance underwriting cycles
and present a natural setting to further look into the franchise value effects.
Corresponding to the two components of the FV effects expressed in (13), we have the
following conditions.
First, FV potentially plays a greater role in soft markets since the reputation effect
is stronger in soft markets as insurers drop price and competition for customers increases;
any competitive advantage, including insurer reputation and name recognition, are
relatively more valuable to the insurer in a soft market relative to a hard market
environment. That is,
(14)


 h
F s F h
where the subscript s represents soft markets and h represents hard markets.
Second, the value of the put option is lower in soft markets than in hard markets
since insurers’ insolvency risk is lower in soft markets.
(15)
put s  put h
Jointly,


(1  put s ) 
(1  put h ) .
F s
F h
Third, the solvency effect may be stronger in hard markets (since high franchise
value firms typically are more prudent in risk taking, thus lowering insolvency risk and
enhancing profitability). That is, 
put
put

. Further,  s   h since more
F s
F h
intensive competition in soft markets erodes loading. Taken together,
(16)
 s *
put
put
  h *
F
F s
h
With (16) however the FV effect is stronger in hard markets. Without the
disruption of major catastrophes, insurers’ default probability is typically low and one
would expect that  s
put
put
is bounded by a low number.7 Then the FV effect
 h
F s
F h
7
Insurance companies typically are well capitalized. As reported in Cummins, Harrington, and Klein
(1995), the average number of insolvent insurers over the period 1979 to 1992 is 23, fewer than 1% of all
property casualty insurers. In addition, as reported in the Best’s Aggregates and Averages, the annual loss
10
would be stronger in soft markets. A strong franchise value effect on the option put
(
put
 0) would topple this condition however and one would expect a stronger
F s
franchise value effects in hard markets.
Proposition 2: When  s
put
put
is bounded by a relatively low number, the
 h
F h
F s
effect of franchise value on firm profitability would be stronger in soft markets, i.e.,




put
put
. Otherwise,
when  s
is unbounded.


 h
F s F h
F s F h
F h
F s
Note that the alternative case (



) is more likely to hold in the aftermath
F s F h
of a major catastrophe resulting in wide spread insolvencies and severe disruptions in
business; in that case we expect that policyholders fly to quality (as stated in footnote 3
above), thus a lower FV leads to a substantially higher put value.
2.3
Testable Hypotheses
Hypothesis 1 strictly follows from the first proposition on the relationship
between firm profitability and franchise value:
H1: Insurer profitability is positively related to franchise value.
Aligned with the first hypothesis, we have the following prediction regarding the
franchise value effects in soft and hard insurance market conditions:
H2: Insurer profitability is positively related to franchise value in both soft and hard
insurance markets.
Further, hypothesis 3 addresses the differential franchise value effects on insurer
profitability across insurance underwriting cycles. As the market is more competitive in
soft markets than in hard markets, in soft markets insurers with stronger name recognition
and underwriting and claims service expertise are able to charge a price differential for
the expense load while maintaining the benefits of efficient distribution relative to lower
franchise value insurers. As noted earlier insolvency is not widespread in the insurance
industry, following Proposition 2 we expect a greater FV effect in soft markets than in
ratios (incurred losses/earned premiums) for all property and liability insurers are not wildly fluctuating,
ranging between 70% and 100%.
11
hard markets (given that there are no major business shocks, i.e., normal business
conditions exist).
H3: In the absence of major disruption in the insurance market, the effect of franchise
value on profitability is stronger in soft markets than in hard markets.
3.
Data and Methodologies
3.1
Data
We obtain rating data from the A.M. Best Key Rating Guide Database for
property-casualty insurers (hereafter, the Best database). Financial statement data are
obtained from the National Association of Insurance Commissioners’ database for
property-casualty insurers (hereafter, the NAIC database). The firm identifier in the Best
data is the “BEST Number”, while the firm identifier in the NAIC data is the NAIC
Code. We use the link provided in the Best data to combine these two databases. Our data
spans the period of 1985 through 2008.
Following prior studies (e.g., Cummins, Dionne, Gagne, and Nouira 2009), we set
the following criteria to ensure a clean sample: (1) insurers have non-missing rating
information in the Best database, (2) if an insurer has less than $1 million total assets any
year, it is removed from our sample, (3) insurers are covered by the NAIC database with
non-negative surplus, assets, losses or expenses, (4) we remove the top and bottom 1% of
insurers based on the four performance measures, (5) insurers with non-missing
profitability measures (described in section 3.3), and (6) group level insurers are removed
from the sample. Our analysis primarily focuses on the individual firms since this
includes a much larger industry cross section.8
From 1985 to 2008, there are 74,617 firm-year observations and 5,704 unique
insurers in the NAIC data. After adhering to restrictions (2) through (6), there are 37,294
firm-year observations and 3,231 unique insurers. After matching the NAIC data with the
Best data, we obtain a sample with 23,047 firm-year observations from 2,145 unique
insurers.
8
The Best and NAIC databases both cover insurance groups and individual insurers. NAIC assign NAIC
Code no greater than 10000 for insurance groups and NAIC code exceeding 10000 for individual insurers.
We only include firms with their NAIC codes exceeding 10000.
12
3.2
Measuring Franchise Value
Measuring franchise value is challenging given that standard accounting
conventions are not adequate.9 The basis of our franchise value measure is the Best’s
rating of an insurer. According to the Best Guide, “a Best’s rating is an independent
opinion, on a comprehensive quantitative and qualitative evaluation, of a company’s
balance sheet strength, operating performance, and business profile”. In other words, the
Best’s rating of an insurer captures its financial standing in a comprehensive way. It
jointly reflects an insurer’s tangible and intangible assets (i.e., franchise value). Our
approach is to extract franchise value by removing the tangible assets component from
the Best rating.
We consider three firm characteristics for tangible assets: book value of firm
assets, leverage, and an insurer’s prior year’s operating performance. In each year, we
sequentially sort insurers into 27 (3 x 3 x 3) groups based on firm total assets, then on
leverage, and finally on return on assets, all evaluated in the prior year. For each of the 27
portfolios, we calculate the equal-weighted average rating in each year and use it as the
benchmark. Note that Best Company assigns insurers letter ratings from S (Suspension)
to A++ (superior). Consistent with prior studies (see, for example, Colquitt, Sommer, and
Godwin (1999)) we assign a numerical number to each letter rating, ranging from
RATING = 0 for the Best’s Rating of S (suspended), F (liquidation), or E (under
regulatory supervision) to RATING = 13 for the Best’s Rating of A++.10 When multiple
ratings are reported for an insurer in a year, we use the last reported rating in that year.
The difference between the rating of an individual insurer and the average rating of its
benchmark group is the benchmark-adjusted franchise value.
It is possible that an insurer may have a high raw FV measure (FVR) simply by
chance rather than because of relatively greater intangible assets. An insurer indeed with
high franchise value is expected to persistently have high franchise value over multiple
periods. We therefore compute the rolling average of FVR over three years (from year t-2
to t) to increase the likelihood that our measures capture true franchise value:
9
As noted above Lev and Zarowin (1999) and others argue that quantifying intangibles is where the current
accounting system fails most seriously in reflecting enterprise value and performance.
10
More specifically, the numerical number for each of the A.M. Best ratings is as below: 0(E, F and S),
1 (D), 2(C-), 3(C), 4(C+), 5(C++), 6(B-), 7(B), 8(B+), 9(B++), 10(A-), 11(A), 12(A+), and 13(A++).
13
(17)
3.3
FVi ,t 
1 0
FVi ,Rt

3  2
Profitability and Firm Characteristics Measures
Four traditional profitability measures are used in our analysis. The first is return
on assets (ROA), calculated as net income scaled by beginning period total assets. The
second is return on equity (ROE), calculated as net income scaled by beginning period
total equity. The third is the combined ratio (CR), calculated as the ratio of losses (claims
paid) and expenses to the premium earned. The last profitability measure is the economic
loss ratio (ELR). The calculation of ROA, ROE, and CR is straightforward based on
balance sheet and income statement information of insurers covered by the NAIC
database. The estimation of ELR follows the spirit of Winter (1994) with estimation
details provided in Appendix B. For all profitability measures, we remove the top 1% and
bottom 1% observations in each year to control for the impact of outliers.
It should be noted that we look at how insurer profitability responds to franchise
value in the prior year. Also note that insurer franchise value is the three-year rolling
average of an insurance financial strength rating adjusted for the average rating of firms
with comparable tangible characteristics in the preceding year. As a result, our data (with
insurer characteristics information starting from 1985) facilitates an analysis of insurer
profitability starting from 1989.
Various firm characteristics, other than franchise value, potentially play a role in
determining operating performance. We construct the following control variables: total
assets (SIZE), the ratio of total liabilities to total assets (LEV), the Herfindahl index by
business line (HERFL), the Herfindahl index across different states (HERFS), the degree
of competition faced by each insurer (COMPETE), the percentage of reinsurance in direct
insurance business (REINS), an indicator for group affiliation (GROUP), an indicator for
stock ownership (STOCK). Appendix C provides definitions for these variables.
Panel A of Table 1 reports summary statistics for insurer franchise value,
operating performance measures and various firm characteristics used in the analysis.
For each variable, we include the time-series averaged number of observations, mean and
median, standard deviation, minimum and maximum. The average number of firms is
14
1034. The mean of franchise value is 0.03 with minimal of -7.48 and maximum of 4.00.
The mean return on assets is 2.81% per annual while the mean return on equity is 6.92%
per annual. While the average combined ratio is 106.65%, the average economic loss
ratio is 95%, suggesting that with consideration of the time value of money, on average
insurers make a profit from their underwriting business. Most insurers are affiliated with
groups (70%) and have stock ownership (70%).
In Panel B, we show the correlations among variables. Franchise value is
positively correlated with ROA (0.09) and ROE (0.11) but negatively correlated with
combined ratios (-0.08) and economic loss ratio (-0.07), which is consistent with positive
expectations about FV on profitability. The four measures of insurers’ profitability are
highly correlated with each other. The correlation between ROA and combined ratio is 0.55 and the correlation between ROE and ROA is 0.89. GROUP is highly correlated
with REINS (0.28) indicating that firms affiliated with a parent tend to purchase more
reinsurance. In addition, GROUP is positively correlated with STOCK (0.34) (i.e.,
affiliated firms are more likely to be stock companies).
In Panel C, we take a snapshot of insurer characteristics across franchise value
deciles. In each year, insurers are classified into deciles based on their franchise value:
D1 insurers have the lowest franchise value, while D10 insurers have the highest. Firm
characteristics in the same year are evaluated and presented. Relative to D1 firms, firms
in the D10 group are larger in firm size and have a longer history of operation. Also, D10
firms are relatively more diversified, buy more reinsurance, and engage in more
diversified business lines. Lastly, high franchise value firms are more likely to be
affiliated with a group and they are more likely to have stock ownership.
4.
Main Empirical Results
4.1
Franchise Value and Firm Profitability
Our first hypothesis posits a positive relationship between franchise value and
insurer profitability. We explore the relationship using portfolio analysis. We sort
insurers into deciles based on franchise value in each year and estimate insurer combined
ratio, return on assets, and return on equity in the subsequent year. D1 insurers have the
lowest franchise value, while D10 insurers have the highest. Panel A of Table 2 reports
15
the results, where the reported portfolio performance is the time-series average of crosssectional means of the profitability measures. The cross-sectional means are calculated
using equal-weights.
The first column reports insurer return on assets on FV-sorted deciles. Return on
assets increases when franchise value increases. Return on assets is 2.03% for D1
insurers and it is 3.04% for D10 insurers. Their difference of 1.01% is significant at the
1% level (t=5.77). The second column reports return on equity on FV-based deciles.
There is still a pattern of increasing return on equity when franchise value increases. The
third column shows the combined ratio across franchise value deciles. For property and
liability insurers, the combined ratio is a typical measure of operating performance. The
evidence suggests that the combined ratio decreases as franchise value increases. The
combined ratio for D1 insurers is 111.55%, while that of the D10 insurers is 105.05%.
The difference of 6.50% is significant at the 1% level (t=-4.49). Another interesting
pattern we can observe from column one is that for all insurer deciles, the combined
ratios are greater than 1. That is, during our sample period from 1985 to 2008, on
average, premium earned is smaller than associated claims without considering the time
value of money. When looking at the economic loss ratio, we find ELR for D1 insurers is
99.59% and it is 93.60% for D10 insurers. Their difference of 5.99% (t=-5.25) is
significant, both statistically and economically.
In Figures 1 and 2, we look at the difference in the alternative profitability
measures between D10 and D1 portfolios sorted by insurer franchise values over time. In
Figure 1, we show the return spread in each of our sample years. The plots show that D10
portfolios stably beat D1 portfolios in almost every year over our sample period. In
Figure 2, we look at the D10-D1 spread in each of the five years subsequent to the
franchise value measurement year. We find that that D10 firms are remarkably more
profitable in every year of the subsequent five years since the franchise value evaluation
year.
Next, we use regression analysis to examine whether high franchise value firms
tend to have better operating performance. The advantage of regression relative to
portfolio analysis is that it allows us to control for other factors that may also affect
insurer profitability. Franchise value is not the only factor that affects insurer
16
profitability. According to prior studies, we consider eight insurer characteristics that
have been shown to be associated with firm profitability: SIZE, LEV, HERFL, HERFS,
COMPETE, REINS, GROUP, and STOCK. The definitions of these variables are
provided in Appendix C.
We include fixed firm and time effects in the regression by including both firm
and year indicator variables in the regressions. Moreover, as our sample firms come from
the same industry for multiple years, the residuals may be correlated across firms and/or
across time. We follow Petersen (2009) to allow for correlation of residuals using the
two-way clustering method. The model is specified as follows:
(18)
PROFITi,t     1 X ' i,t 1  i1  vt1   i1,t
where PROFIT i ,t is insurers’ profitability (ROA, ROE, CR, or ELR) of insurer i at the
end of year t; X i',t 1 is a vector of insurer i’s franchise value and other firm characteristics
in year t-1;  ik (k=1,2) is a firm-specific intercept; v ik (k=1,2) represents the timespecific dummy, and vi , j ~ IID (0,  v2 ) . ε is a random error term assumed to be
heteroskedastic and correlated within firms and years.
We present the results in Panel B of Table 2. The franchise value coefficient in
the ROA regression is 0.18 (t=7.24), and it is 0.55 (t=7.90) in the ROE regression. In the
CR regression, the FV coefficient is -0.92 (t=-5.30), and it is -0.99 (t=-5.88) in the ELR
regression. The impact of franchise value seems to be substantial. For instance, when
franchise value increases by 1 unit, the average decrease in ELR is nearly 1%. The
evidence suggests that, after controlling for the impacts of other insurer characteristics,
franchise value still has significant impact on insurer underwriting profitability and
comprehensive profitability.
We also find that lagged-year firm profit, firm size, leverage, Herfindahl index by
insurance line, competition, reinsurance, and ownership structure, all have impact on at
least one of the performance measures. The coefficient of SIZE in the ROA regression is
0.10 (t=2.22), suggesting that larger insurers are more profitable. Moreover, the use of
reinsurance is negatively related to future performance. This result is consistent with the
finding in Cole and McCullough (2006) that firm reinsurance use is negatively associated
with firm profitability. It is either due to the fact that reinsurance is a costly risk transfer
17
mechanism (thus lowering insurers’ profitability) or less financially strong insurers buy
relatively more reinsurance.
4.2
Do Underwriting Cycles Affect Franchise Value Effects?
In this section, we examine the differential franchise value effects in soft and hard
markets. We classify our sample into soft and hard market years based on the industry
average loss ratio. Soft market years refer to those years when the industry average loss
ratios are no greater than the median during our sample period; hard market years refer to
periods when the industry average loss ratios are greater than the median during our
sample period.11
We calculate the time-series averages of cross-sectional means of insurer
performance across franchise value deciles in soft and hard markets and report the results
in the Panel A of Table 3. In both markets, we find D10 insurers significantly outperform
D1 insurers in both soft and hard markets. This finding supports the second hypotheses,
indicating that the positive franchise value effect on firm profitability is neutral of market
conditions.
More interestingly, the performance differential in soft markets is significantly
greater than that in hard markets. For instance, D10 insurers outperform D1 insurers by
1.04% per year in soft markets, while they outperform D1 insurers by 0.49% in hard
markets. Their difference of 0.55% is statistically significantly different from zero
(t=2.09). We reach the same conclusion when measuring firm profitability using ROE,
CR and ELR. The pattern of lower profit spreads in hard markets can be visualized in
Figure 1 – where the D10-D1 spreads are low in the years conventionally considered to
be hard markets, e.g., 1992, 1993, 2000, and 2001.
Next, we perform regression analysis to examine the importance of market
conditions on the franchise value effect on firm profitability. We use the product term,
FV*SOFT, to capture the impact of underwriting cycle on the FV effect. SOFT is defined
as (1 – industry average loss ratio for property liability insurers). As reported in Panel B
of Table 3, we see that franchise value is positively related to future performance
11
We perform the analyses here and subsequently using combined ratios to quantify different stages during
insurance underwriting cycles. The results are similar to what we report here.
18
regardless of the choice of insurer profitability measures. The coefficients on FV*SOFT
have the opposite sign to the coefficients on FV. For instance, in the regression of ROA,
the coefficient on FV is 0.59 (t=1.83), while the coefficient on FV*SOFT is 0.64 (t=1.98). That is, the franchise value effect will be stronger when the industry loss ratio is
lower. We interpret this as evidence confirming the third hypothesis that the role of
franchise value on performance is weaker in hard markets.
A caveat on our empirical finding is that though we find stronger FV effects in
soft markets, this is attributable to the relatively smooth condition in the insurance
market. Recall our proposition 2 does not provide a sure prediction regarding the impact
on insurance underwriting cycle on FV effects. We might expect a stronger FV effect in
hard markets where policyholders may escape from firms with lower franchise value. In
Cummins, Doherty, and Lo (2002), they conduct a simulation analysis which shows the
U.S. property and liability insurance industry can fund a USD100 billion loss event (with
a total equity capital of USD350 billions).12 In case this limit is reached, we may observe
the FV effect is otherwise stronger in hard markets. This prediction is echoed by the
casual observation that some banking giants, like Goldman Sachs and Bank of America,
took large market shares and turned highly profitable shortly after the 2007-2008
financial crises while most financial institutions are still battling with survival and lost
clients.
4.3
Robustness Checks
4.3.1 Regression-Based Franchise Value Measures
In the above analysis, we use benchmark-adjusted insurer ratings as our franchise
value proxy. As a robustness check, we construct regression-based insurer ratings as the
franchise value. Specifically, we perform cross-sectional regression each year and use the
residual term as the proxy for franchise value, the regression-based franchise value
(RFV). We follow Pottier and Sommer (1999) to include a set of firm characteristics that
may affect the Best’s rating. RFV is the residual term in the following annual crosssectional regressions:
12
The largest catastrophe in history is Hurrican Katrina, leading to an estimated insured losses of roughly
USD45 billion (Kunreuther and Michel-Kerjan, 2007).
19
10
(19)
RATINGi,t = α0 +

j 1
j
X j ,i ,t 1 + i,t
where X j ,i ,t 1 (j=1, 2, …, 10) are tangible characteristics potentially affecting the firm
rating measured at the end of year t-1. The residual, εi,t, is the regression-based franchise
value. Variables used as X j ,i ,t 1 are defined in Appendix D. Relative to the benchmarkadjusted franchise value, an advantage of RFV is that it simultaneously controls for more
factors that may affect the Best ratings. However, we are very conservative in estimating
franchise value as some factors may have both tangible and intangible components.
RFV results are reported in the first two columns of Table 4. For brevity, we only
report the results in the regressions using ROA. The results are consistent with what we
reported in Tables 2 and 3. The first column shows the effect of franchise value on
performance without any interaction term. Franchise value is positive and statistically
significant; for example, the coefficient on FV is 0.12 (3.35). The next column examines
the impact of the insurance market cycle on the role of franchise value. Here again the
evidence suggests that the effect of franchise value is weaker in hard markets.
4.3.2 Profitability of Young Firms
It requires time for a firm to develop its franchise value. Firms with relatively
short history (young firms) would have little franchise value. If franchise value plays a
role in determining insurers’ profitability, we expect young firms to be less profitable
than experienced firms.
To test this conjecture, we alternatively measure franchise value using an
indicator variable, YOUNG, equal to one if the age of an insurer is less than or equal to 10
years, and zero otherwise. With this definition, about one-fourth of our sample firms in
each year are young firms and the rest are not. We perform regression analysis to see if
YOUNG affects future firm profitability.
As reported in the third and fourth columns of Table 4, young firms tend to have
worse performance, setting everything else equal. In column four the coefficient on
YOUNG is -2.53 (-1.93), which is significant at 10% level. The result shows that on
average ROA of young firms is 2.53 percent lower than older firms. It indicates that firms
20
with little or no franchise value are not as profitable as firms with greater franchise value
(experienced firms).
4.3.3 Franchise Value Effect at the Group Level
Our analysis up to this point focuses on individual firm level analysis, including
firms affiliated with each other and stand-alone insurers. One reason for this is that firms
in the same insurance group may exhibit different characteristics. For instance, Zanjani
(2009) finds that affiliates that are less integrated into the parent organization in terms of
risk sharing agreements and ownership relations tend to have lower insolvency
probabilities, while flagship companies---large companies at the center of ownership and
reinsurance relationships within the group---tend to be at higher risk. Another reason is
that information on insurer groups may be incomplete and less accurate. Some parent or
holding companies of insurance firms are not insurance companies and thus they do not
report their financial statements to NAIC. Moreover, A.M. Best and NAIC may use
different criteria for group insurers. Due to these considerations, evaluating franchise
value at the individual firm level analyses is more appropriate. For the sake of
completeness we provide group level analysis since insurers in the same insurance group
may share in franchise value from their parent company and/or investment in franchise
value may be decided at the group level.
The A.M. Best data only provide the NAIC numbers for individual insurers
covered in its database so we must aggregate data in order to do the analysis. We
manually match insurance groups in the NAIC database with those in the A.M. Best
database. First, we identify all insurance groups in the NAIC database (As mentioned
before, insurance groups are assigned an NAIC number of no greater than 10000).
Second, we identify all insurance groups in the A.M. Best data using two criteria: there is
no NAIC code for an insurer and the aggregate codes indicate that observations are at the
group or sub-group level. Finally, we compare company and group names in the two data
bases and add the A.M. Best number to the NAIC insurer group if they exactly match.
The average number of property and liability insurance groups is 111 and the average
group size is $3673 million, much larger than at the individual firm level.
21
Regression results for analysis at the group level are reported in Table 4. The
results here are consistent with our prior results: franchise value is positively associated
with future firm profitability and the effect of franchise value is stronger in soft market.
5.
Conclusions
This study examines the franchise value (or intangible asset) effects on firm
profitability in the property liability insurance sector. High franchise value (FV) insurers
benefit from name recognition and brand loyalty and could charge more to policyholders
and operate with lower expenses, thus increasing profitability (a “reputation effect”).
Prior research indicates that high franchise value firms typically are more prudent in risk
taking, thus lowering insolvency risk and enhancing profitability (a “solvency” effect).
FV effects on firm profitability may vary also with market conditions, specifically hard
and soft markets which characterize the property-liability insurance industry. Given
these varying FV effects the purpose of our paper is to look at the overall role of FV for
property liability insurers.
We develop a model which predicts that insurer profitability increases in
franchise value all else being equal. Both reputation effects and solvency effects are
accommodated as are market conditions. We expect that FV effects on profitability are
generally stronger in soft insurance markets than in hard markets in the absence of major
catastrophes. We construct a new empirical measure of FV and use a large data set from
1985-2008 to test the model. We find evidence consistent with our predictions: firm
performance is positively related to franchise value and FV effects differ according to
market conditions, with greater franchise value effects in soft markets.
Franchise value is an important component contributing to a firm’s sustainable
growth and long-term profitability. An interesting aspect of our model is that it shows
that, regardless of market conditions (i.e., soft or hard markets), whether normal business
conditions exist or it is a crisis period following an exogenous industry shock, franchise
value persistently plays a positive role in improving firm profitability. The channel or
reason (i.e., the reputation or insolvency effect) by which franchise value positively
affects insurer profitability varies, but the positive relationship holds. Given the
robustness of the results we expect that our findings are not limited to the property22
liability insurance industry but well may hold for other financial service firms. The study
should be of general interest to financial intermediaries, analysts, investors, and
regulators given recent turmoil in the financial services sector.
23
PUT
0
F
Suppose there are two firms (1 and 2). We assume Firm 2 has higher franchise
Appendix A: Condition for
value than firm 1 (i.e., F2>F1). We also assume that when firms have higher franchise
value, they are more likely to maintain solvency given the same amount to debt. That is,



L
L
0

g1 ( )d   g 2 ( )d for all L, or
L
L
g1 ( )d   g 2 ( )d for all L.
0
(A1)
The inequality (A1) is equivalent to the condition that, given the differential
distribution of tangible assets, firm 2 has first-order stochastic dominance over firm 1.
Consequently,

L
0
L
( L   ) g1 ( )d   ( L   ) g 2 ( )d for all L.
0
(A2)
That is,
PUT1 ( )  PUT2 ( ) for all L
The inequality (A3) may be rewritten as
(A3)
PUT
 0 . Intuitively we see that high
F
franchise value firms have more density in the solvency region and thus have lower value
in their default options.
Taken together, the first-order stochastic dominance of tangible assets of firm 2 to
PUT
firm 1 leads to
 0.
F
24
Appendix B: Calculation of Economic Loss Ratios
Economic loss ratio (ELR) is an estimate of the aggregate discounted value of
claims as a fraction of premiums net of expenses.
ELR j ,t 
LR j ,t
1  ER j ,t
10
 ts
 (1  r
s 1
t
s
)s
(B1)
where LRj,t represents the loss ratio of insurer j at the end of year t, ERj,t represents the
expense ratio of insurer j at the end of year t, rt s is the zero coupon rate for s-year
treasury constant maturity rate at the end of year t,  ts is the proportion of claims paid in
each year since an insurance policy is written in year t, and s is the number of years for
the claim payment year since policy year t. It ranges from 1 to 10.
ELR is first used in Winter (1994) for industry-wide discounted loss ratios.
Different from the Winter (1994) procedure, we estimate ELR for each firm in each year
using the Chain Ladder approach (Taylor, 2000). The idea of the Chain Ladder method is
to estimate the proportions of claim development pattern using all of the historical claim
payment information contained in Schedule P, Part 3 of the National Association of
Insurance Commissioners (NAIC) database. The details on the Chain Ladder method are
provided in Taylor (2000).
We use zero coupon rates corresponding to the gap between the claim payment
year and the year when the insurance policy is written. To get zero coupon rates for
bonds maturing between years 1 through 10, we collect the yields to maturity of 3-month
and 6-month Treasury bills from the St. Louis Federal Reserve Bank's FRED database
and the yields to maturity for treasury bonds with maturity closest to 1, 2, 5, 7, and 10
year from the CRSP monthly databases. We apply the extended Neslson-Siegel model
(Bliss, 1977) to estimate the term structure of risk-free zero-coupon interest rates.
25
Appendix C: Firm Operating Performance and Franchise Value: Control Variables
In the panel regression of operating performance on franchise value, we include control
variables in the regression that may affect performance.
 Lag CR: the combined ratio in the lagged year for each insurer.
 Lag ROA: the return on assets in the lagged year for each insurer.
 Lag ROE: the return on equity in the lagged year for each insurer.
 SIZE: the logarithm of total assets (measured in US$ billions)
 LEV: the ratio of total liability to total assets
 HERFL: the Herfindahl index that measures the concentration degree of an insurer. It
is the sum of squared ratio of premium earned in a business line of an insurer to the
total premium earned by the insurer. That is,
HERFL


it

 ( PE
ijt
/ TPE it ) 2 ,
j  1, 2 ,..., 17
(C1)
where PE is the premium earned of insurer I in line j and year t, and TPE is the total
premium earned by insurer i in year t. the business line classification j is based on the
Best Average & Aggregates.
HERFS: ∑(PWi,s /TPWi)2 where PWi,s is premiums written in state s (s=1,2,…,51) of
insurer i, and TPWi is total premium written for all sates. HERFS indices evaluate
geographical diversifications. The higher the HERFS index measures, the lower
diversification is.
COMPETE: the degree of competition faced by an insurer. The procedure to estimate
this involves three steps: first, compute the Herfindahl index for each of the 17
insurance lines in each year using the top 10 insurers’ premium earned.
(C2)
HERFL jt   ( PE ijt / TPE jt ) 2 ,
j  1, 2 ,..., 17
Second, find the weights of each of the 17 business lines for an insurer in each year.
WGT
ijt

PE ijt
TPE
(C3)
it
Third, calculate the sum of the product of the Herfindahl index for each line in each
year and the weights of each line for each insurer:
17
(C4)
COMPETE ijt  1 /(  1 HERFL jt * WGT ijt )



REINS: the ratio of reinsurance ceded divided by the sum of direct premiums written
and reinsurance assumes for each insurer in each year.
GROUP: the dummy variable that equals one if an insurer belongs to an insurance
group and equals zero otherwise
STOCK: the dummy variable that equals one for stock insurers and zero otherwise.
26
Appendix D: Constructing Regression-based Franchise Value
Following Pottier and Sommer (1999), we perform annual cross-sectional regressions of
insurer Best’s ratings on tangible firm characteristics. The residuals are the FVCS (i.e.,
cross sectional regression based FV measure). The regression is specified below:
10
RATINGi,t = α0 +   j X j ,i ,t 1 + i,t
(D1)
j 1
where X includes SIZE, LEV, HERFL, REINS, ROA, CHGNPW, LONGTAIL, JUNK,
CASH, and STK. The definitions of SIZE, LEV, HERFL, REINS, and ROA are provided in
Appendix C. The definitions of other variables are provided below:






CHGNPW: the difference between the net written premiums in this period and in the
prior period, scaled by lagged net written premium
LONGTAIL: net premiums written (NPW) in long-tail lines of insurance divided by
total NPW. We include auto liability, other liability, farm owners/homeowners
/commercial multiple peril, medical malpractice, workers compensation, aircraft, and
boiler and machinery as long-tail lines.
ROA: the ratio of net income to end-of-year total asset
JUNK: risky bond investment divided by invested assets
CASH: cash divided by invested assets
STK: the ratio of equity investment divided by total invested assets
We compute the rolling average of FVCS over three years (from year t-2 to t) to increase
the likelihood that our measures capture true franchise value:
FVi ,t 
1 0
 FVi,CSt
3  2
(D2)
27
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29
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30
Table 1: Summary Statistics
Panel A presents the time-series averages of cross sectional statistics, including the number of observations,
mean, median, standard deviations, minimum, and maximum, of franchise value (FV), insurers’ operating
performance (ROA, ROE, CR, and ELR), and various firm characteristics, including firm size (SIZE),
leverage (LEV), Herfindahl index across business lines (HERFL), Herfindahl index across states (HERFS),
the level of competition (COMPETE), reinsurance (REINS), a dummy for group affiliated firms (GROUP),
and a dummy for stock ownership firms (STOCK). Definitions are provided in Appendix C. Panel B
reports the correlations between all the variables. Panel C reports time series averages of firm
characteristics across insurer deciles sorted by insurer franchise value in the same period. D10 insurers
have the highest franchise values while D1 insurers have the lowest franchise value. We also report the
differences among insurer characteristics between the D10 and D1 deciles. Inside the parentheses are the
Newey-West (1987) adjusted t-statistics with a 2-year lag. Note: *** p<0.01, ** p<0.05, * p<0.10.
Panel A: Summary Statistics
N
Mean
Median
Std
Min
Max
1.46
3.99
12.08
25.06
23.61
-7.48
-17.55
-83.21
40.14
7.60
4.00
17.96
45.72
486.41
251.48
Franchise Value and Operating Performance Measures
FV
ROA (%)
ROE (%)
CR (%)
ELR (%)
1034
1034
1034
1034
887
0.03
2.81
6.92
106.65
95.00
0.25
2.93
7.76
104.36
93.50
Insurer’ Characteristics
SIZE
LEV
HERFL
1034
1034
1031
600.58
0.60
0.44
80.39
0.64
0.36
2773.13
0.17
0.27
1.86
0.02
0.11
59,456.71
0.91
1.00
HERFS
987
0.53
0.46
0.38
0.03
1.00
COMPETE
1031
59.16
49.92
31.37
18.06
206.51
REINS
1025
0.37
0.33
0.27
0.00
0.99
GROUP
1034
0.70
1.00
0.46
0.00
1.00
STOCK
1034
0.70
1.00
0.46
0.00
1.00
31
Panel B: Correlations among Variables
FV
ROA
ROE
CR
ELR
SIZE
LEV
HERFL
HERFS
COMPETE
REINS
GROUP
STOCK
0.09
0.11
-0.08
-0.07
0.09
-0.02
-0.18
-0.11
-0.02
0.19
0.22
0.05
0.89
-0.55
-0.67
0.00
-0.23
0.10
0.00
0.01
-0.07
-0.01
0.07
-0.48
-0.60
0.02
0.00
0.04
-0.03
-0.01
-0.06
0.03
0.08
0.92
0.01
0.02
-0.06
-0.02
0.00
0.11
0.05
0.00
0.01
0.09
-0.15
0.01
-0.01
0.12
0.04
-0.05
ROA
ROE
CR
ELR
SIZE
0.08
LEV
-0.09
-0.18
-0.06
-0.07
0.12
0.00
-0.23
-0.15
-0.07
0.02
0.15
0.11
0.20
0.27
-0.23
-0.32
-0.06
-0.09
-0.21
-0.30
-0.22
0.04
-0.10
0.02
0.28
0.19
HERFL
HERFS
COMPETE
REINS
GROUP
0.34
Panel C: Insurer Characteristics across Franchise Value Ranks
RANK
SIZE
LEV
HERFL
HERFS
COMPETE
REINS
GROUP
STOCK
D1
131.74
0.61
0.53
0.67
61.50
0.31
0.54
0.72
2
213.54
0.62
0.50
0.58
60.67
0.31
0.57
0.66
3
321.82
0.59
0.49
0.55
60.53
0.32
0.61
0.66
4
491.55
0.60
0.44
0.49
61.06
0.34
0.67
0.69
5
507.28
0.58
0.44
0.50
60.37
0.34
0.69
0.70
6
703.68
0.57
0.43
0.49
59.44
0.36
0.72
0.68
7
924.11
0.57
0.39
0.47
60.05
0.39
0.76
0.68
8
749.97
0.58
0.41
0.47
58.97
0.41
0.74
0.70
9
1414.22
0.59
0.39
0.47
56.00
0.44
0.83
0.75
D10
732.72
0.59
0.37
0.55
55.09
0.51
0.89
0.83
D10-D1
600.98***
-0.02***
-0.15***
-0.12***
-6.41***
0.20***
0.35***
0.11***
(t-stat)
(4.23)
(-2.61)
(-11.24)
(-6.45)
(-4.02)
(14.72)
(10.94)
(6.67)
32
Table 2: Insurer Profitability Sorted by Franchise Value
This table reports the average profit of property liability insurance companies across decile groups sorted
by franchise value in the prior year. Franchise value is measured as the average of the benchmark-adjusted
Best rating in the prior three years. D10 insurers have the highest franchise values. Four profitability
measures are used: return on assets (ROA), return on equity (ROE), combined ratio (CR), and economic
loss ratio (ELR). All the numbers reported are in percent. The differences of insurer characteristics between
the D10 and D1 are also reported. Inside the parentheses are the Newey-West (1987) adjusted t-statistics
with a 2-year lag. Note: *** p<0.01, ** p<0.05, * p<0.10.
FV Rank
D1
2
3
4
5
6
7
8
9
D10
D10-D1
(t-stat)
ROA
2.03
2.25
2.79
2.76
3.00
2.91
2.91
2.89
2.82
3.04
1.01***
(5.77)
ROE
4.74
5.57
6.79
6.66
6.80
6.41
6.90
7.19
7.24
7.95
3.22***
(7.33)
33
CR
111.55
107.34
107.25
106.73
106.97
105.64
105.76
105.22
106.29
105.05
-6.50***
(-4.49)
ELR
99.59
95.53
94.57
95.43
93.16
95.28
94.62
94.04
94.43
93.60
-5.99***
(-5.25)
Table 3: Panel Regressions of the Franchise Value Effect on Firm Profitability
This table reports the coefficients from panel regressions of insurer performance (in percentage) on
franchise value and other firm characteristics. Besides franchise value, dependent variables include lagged
profitability, firm size (SIZE), leverage (LEV), Herfindahl index of premiums earned across business lines
(HERFL), Herfindahl index of premiums earned across states (HERFS), the level of competitions
(COMPETE), reinsurance ratio (REINS), group dummy (GROUP), and stock ownership dummy
(STOCK). All the independent variables are measured at the end of the prior year. Fixed firm and fixed
year effects are included. Reported in the parentheses, t-statistics are adjusted for cross-sectional and timeseries dependence in the residual term. Note: *** p<0.01, ** p<0.05, * p<0.10.
INTERCEPT
FV
Lag ROA
ROA
1.04**
(2.40)
0.18***
(7.24)
0.45***
(23.00)
ROE
-1.95**
(-2.27)
0.55***
(7.90)
CR
42.68***
(4.18)
-0.92***
(-5.30)
0.40***
(15.20)
Lag ROE
0.66***
(6.48)
Lag CR
Lag ELR
SIZE
LEV
HERFL
HERFS
COMPETE
REINS
GROUP
STOCK
Firm Dummies
Year Dummies
N
Adj. R2
ELR
47.91***
(18.28)
-0.99***
(-5.88)
0.10**
(2.22)
-0.40
(-1.37)
0.96***
(5.40)
0.06
(0.66)
-0.00*
(-1.74)
-0.88***
(-5.63)
-0.13*
(-1.82)
0.53***
(3.50)
Yes
Yes
18,740
0.223
0.37***
(2.80)
5.44***
(5.05)
2.91***
(5.35)
0.23
(0.95)
-0.01*
(-1.86)
-2.22***
(-4.35)
-0.36*
(-1.77)
1.42***
(3.46)
Yes
Yes
18,740
0.191
34
-0.34*
(-1.74)
-6.48*
(-1.80)
-4.53***
(-3.11)
-0.67
(-0.89)
0.02**
(2.03)
2.37
(1.47)
0.94
(1.19)
-0.87
(-1.31)
Yes
Yes
18,740
0.280
0.54***
(10.49)
-0.31**
(-2.05)
-3.27
(-1.47)
-8.12***
(-7.94)
0.48
(1.20)
0.02**
(2.47)
4.77***
(5.10)
0.64
(1.35)
-2.00***
(-2.72)
Yes
Yes
15,544
0.309
Table 4: Effect of Franchise Value in Different Market Cycles
This table reports the spreads of insurer profitability across franchise-value sorted decile portfolios respectively in soft and hard markets. Four profitability
measures are used: return on assets (ROA), return on equity (ROE), combined ratio (CR), and economic loss ratio (ELR). Franchise value is measured as the
average of the benchmark-adjusted Best rating in the prior three years. D10 insurers have the highest franchise values. D10-D1 is the difference in performance
between D10 and D1 insurers. Soft markets are periods when the industry average loss ratios are below the median loss ratio over the sample period. Hard
markets are periods when the industry average loss ratios exceed the median industry loss ratio over the sample period. Inside the parentheses are the NeweyWest (1987) adjusted t-statistics with a 2-year lag. Note: *** p<0.01, ** p<0.05, * p<0.10.
D1
2
3
4
5
6
7
8
9
D10
D10-D1
Soft-Hard
(t-stat)
Panel A: ROA
Soft
2.35
2.74
3.27
3.33
3.56
3.44
3.39
3.47
3.45
3.39
Hard
1.62
1.60
2.15
2.01
2.24
2.20
2.27
2.35
1.98
2.11
1.04***
(5.70)
0.49***
(2.79)
0.55**
(2.09)
4.13***
(7.20)
2.54***
(3.48)
1.59*
(1.77)
-8.57***
(-3.81)
-3.74***
(-3.28)
-4.83*
(-1.92)
-9.42***
(-7.61)
-2.17**
(-2.41)
-7.25***
(-4.74)
Panel B: ROE
Soft
5.74
6.84
8.10
8.26
8.13
7.72
8.46
8.57
8.92
9.87
Hard
3.40
3.87
5.04
4.53
5.02
4.66
4.81
5.36
5.00
5.94
Panel C: CR
Soft
110.05
103.89
104.06
102.09
100.51
101.67
101.51
100.67
101.71
101.48
Hard
115.88
111.95
111.50
112.92
110.91
110.92
111.41
111.28
112.40
112.15
Panel D: ELR
Soft
99.28
93.40
91.16
92.03
89.78
92.10
91.12
90.36
90.37
89.86
Hard
99.92
97.90
98.36
99.21
96.92
98.82
98.50
98.13
98.94
97.75
Table 5: Effect of Franchise Value in Different Market Cycles
This table reports the coefficients from panel regressions of insurer performance on franchise value in
different market conditions. Franchise value is measured as the average of the benchmark-adjusted Best
ratings in the prior three years. All other independent variables are measured at the end of the prior year.
SOFT is measured as (1 - industry average loss ratio). dependent variables include lagged profitability, firm
size (SIZE), leverage (LEV), Herfindahl index of premiums earned across business lines (HERFL),
Herfindahl index of premiums earned across states (HERFS), the level of competitions (COMPETE),
reinsurance ratio (REINS), group dummy (GROUP), and stock ownership dummy (STOCK). All the
independent variables are measured at the end of the prior year. Fixed firm and fixed year effects are
included. Reported in the parentheses, t-statistics are adjusted for cross-sectional and time-series dependence
in the residual term. Note: *** p<0.01, ** p<0.05, * p<0.10.
INTERCEPT
FV
FV*SOFT
SOFT
ROAt-1
ROA
(1)
16.66***
(8.24)
0.59*
(1.83)
0.64**
(1.98)
10.92***
(3.71)
0.24***
(9.18)
ROE
(2)
18.99***
(3.08)
0.20
(0.14)
0.43**
(2.14)
32.03***
(4.63)
CR
(3)
32.31**
(2.24)
-5.81**
(-2.40)
5.38*
(1.87)
-99.95***
(-4.69)
1.19***
(21.33)
ROEt-1
0.44***
(12.10)
CRt-1
ELRt-1
SIZE
LEV
HERFL
HERFS
COMPETE
REINS
GROUP
STOCK
N
Adj. R2
ELR
(4)
0.57
(0.05)
-7.97***
(-6.13)
9.01***
(5.44)
-62.59***
(-5.27)
0.17***
(3.37)
-2.64***
(-7.35)
0.99***
(3.98)
0.03
(0.28)
-0.00
(-0.66)
-0.94***
(-4.46)
-0.13
(-1.36)
0.84***
(5.04)
18,590
0.143
0.25**
(2.06)
12.34***
(7.85)
2.45***
(4.15)
-0.01
(-0.05)
-0.00
(-1.01)
-2.39***
(-4.46)
-0.16
(-0.74)
1.24***
(3.01)
18,590
0.207
-0.23*
(-1.69)
-6.51
(-1.47)
-3.48**
(-2.08)
-0.27
(-0.30)
0.01
(0.70)
8.66***
(4.33)
1.37
(1.12)
-1.02
(-0.95)
18,590
0.097
0.52***
(9.12)
-0.14*
(-1.91)
-3.87*
(-1.81)
-7.23***
(-7.31)
0.95**
(2.29)
0.01
(1.58)
5.58***
(6.50)
0.44
(0.99)
-1.97***
(-2.73)
15,544
0.331
Table 6: Robustness of the Effects of Franchise Value
This table reports three different robustness checks. The dependent variable is ROA. First, we measure
franchise value using the residual terms in the regression of Best Ratings on insurer characteristics
(regression-based FV). Next, we look at whether firms without franchise value perform less well. We use a
dummy variable, YOUNG, to identify such firms. Third, we perform the analysis for insurance groups. The
specific model used for the panel data includes fixed firm and fixed time effects and assumes cross-sectional
and time-series dependence in the residual term (Petersen, 2009). Note: *** p<0.01, ** p<0.05, * p<0.10.
Regression-based FV
FV
Young Firm for Low FV
(2)
0.12***
0.16**
0.13**
0.15
(3.35)
(2.16)
(2.04)
(2.15)
FV*SOFT
(3)
(4)
Group Level Analysis
(1)
(5)
0.87*
(6)
1.10***
(1.90)
(3.21)
YOUNG
0.00
-2.53*
(0.04)
(-1.93)
0.03
YOUNG*MARKET
(1.63)
MARKET
-0.11***
-0.12***
(-3.71)
ROAt-1
SIZE
LEV
HERFL
HERFS
COMPETE
REINS
0.49***
(-3.64)
(7.33)
-0.06***
-0.06***
-0.06***
-0.06***
0.49***
-0.72
(-10.35)
(-9.06)
(-10.47)
(-9.23)
(7.52)
(-0.71)
0.21***
0.16***
0.24***
0.19***
0.24***
1.68***
(3.92)
(3.02)
(4.49)
(3.62)
(3.05)
(4.35)
-2.97***
-2.70***
-3.07***
-2.81***
-0.66
0.32
(-7.75)
(-6.29)
(-8.15)
(-6.62)
(-0.69)
(1.10)
1.06***
0.81***
1.08***
0.82***
1.65***
-0.01
(4.36)
(3.01)
(4.42)
(2.97)
(4.06)
(-0.86)
0.11
0.02
0.10
0.01
0.29
-0.25
(0.83)
(0.15)
(0.76)
(0.06)
(1.06)
(-0.52)
-0.00
-0.00
-0.00
-0.00
-0.01
0.49***
(-1.32)
(-0.72)
(-1.27)
(-0.70)
(-1.00)
(7.33)
-0.84***
-0.93***
-0.79***
-0.89***
-0.10
0.25***
(-0.20)
(3.21)
(-4.24)
(-4.43)
(-3.98)
(-4.21)
GROUP
-0.10
-0.10
-0.11
-0.11
(-1.03)
(-0.97)
(-1.09)
(-1.00)
STOCK
0.81***
0.79***
0.81***
0.79***
(5.58)
(5.47)
(5.54)
(5.58)
INTERCEPT
9.43***
18.09***
9.42***
18.52***
-0.17
-0.19
(12.68)
(9.18)
(12.61)
(8.75)
(-0.18)
(-0.21)
N
16,261
15,655
16,265
15,659
1,328
1,321
Adj. R2
0.139
0.164
0.137
0.162
0.244
0.246
37
Figure 1: Difference in Profitability across D10 and D1 Portfolios over Time
The figure shows return spreads between D10 and D1 portfolios sorted by insurer intangible assets.
We use four operating performance measure (1) ROA; (2) ROE; (3) CR; and (4) ELR. Profitability
is measured in percentages.
(i) ROA
ROA Spreads
2
1
0
-1
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
2000
2002
2004
2006
2008
2000
2002
2004
2006
2008
2000
2002
2004
2006
2008
(ii) ROE
8
ROE Spreads
6
4
2
0
-2
-4
1990
1992
1994
1996
1998
(iii) CR
CR Spreads
0
-5
-10
-15
1990
1992
1994
1996
1998
(iv) ELR
ELR Spreads
0
-5
-10
-15
1990
1992
1994
1996
1998
38
Figure 2: Difference in Profitability 5 Years Subsequent to FV Measurement Year
The figure shows the spreads in insurer profitability between D10 and D1 portfolios sorted by
insurers’ intangible assets in five years subsequent to the franchise value measurement year. Panel
(i) is for the ROA; Panel (ii) is for ROE; Panel (iii) is for CR; and Panel (iv) is for ELR.
Profitability is measured in percentage.
Diff in ROA
3
2.5
2
1.5
Diff in ELR
Diff in CR
Diff in ROE
(i) ROA
0.8
0.6
0.4
1
2
3
(ii) ROE
4
5
1
2
3
(iii) CR
4
5
1
2
3
(iii) ELR
4
5
1
2
3
4
5
-2
-4
-6
-2
-4
-6
39