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 jchen@ksu.edu; doerp@sc.edu; tongyu@uri.edu. 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.

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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.

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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

1The 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.

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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

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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. 6A 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. We

observe that

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(2) dWdtd

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) ECP ,

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

LdLgdttgCEXP )()()(

0

L

dtgLL0

)()(

= L – PUT

(6) L

dgLPUT0

)()(

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.

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With PUT clearly defined, we express premiums collected by an insurer at time 0

as below:

(7) ))(1( PUTLP

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)

0F

Insurer expense, E, is proportional to the insurer’s expected claim costs, EXP(C):

(9) )( PUTLeE

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) 0F

e

Now, we can express the insurer’s expected profit in t=0 as below:

(11) )())(()()( PUTLPUTLeECExpPE

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 L

E )( and put=PUT/L (the value of the

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)

)2()1(

*)1(F

putput

FF

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The first term of the above expression, )1( putF

, may be considered as the FV

effect on reputation (or simply the reputation effect). It is non-negative given that

0F

and 0 ≤ put ≤ 1.

The sign for the second term, F

put

* , depends on the sign of F

put

. In

Appendix A, we show that 0

F

put under the condition that tangible assets of high-FV

firms exhibit first-order stochastic dominance (FSD) over tangible assets of low-FV

firms (denoted as l

FSD

h .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, high-

FV 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 0F

is that tangible assets of high-FV firms

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

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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

h

s FF

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) hs putput

Jointly, )1()1( hh

ss

putF

putF

.

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,hs F

put

F

put

. Further, hs since more

intensive competition in soft markets erodes loading. Taken together,

(16) h

hs

s F

put

F

put

**

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 h

hs

s F

put

F

put

is bounded by a low number.7 Then the FV effect

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

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would be stronger in soft markets. A strong franchise value effect on the option put

)0(

sF

put would topple this condition however and one would expect a stronger

franchise value effects in hard markets.

Proposition 2: When h

hs

s F

put

F

put

is bounded by a relatively low number, the

effect of franchise value on firm profitability would be stronger in soft markets, i.e.,

hs FF

. Otherwise, hs FF

whenh

hs

s F

put

F

put

is unbounded.

Note that the alternative case (hs FF

) is more likely to hold in the aftermath

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%.

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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.

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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++).

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(17)

3.3 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

0

2,, 3

1

Rtiti FVFV

15

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

16

the results, where the reported portfolio performance is the time-series average of cross-

sectional 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

17

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:

1,

111,

'1,)18( titititi vXPROFIT

where tiPROFIT , is insurers’ profitability (ROA, ROE, CR, or ELR) of insurer i at the

end of year t; '1, tiX is a vector of insurer i’s franchise value and other firm characteristics

in year t-1; ki (k=1,2) is a firm-specific intercept; k

iv (k=1,2) represents the time-

specific dummy, and ),0(~ 2, vji IIDv . 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

18

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.

19

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 cross-

sectional 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).

20

(19) RATINGi,t = α0 +

10

11,,

jtijj X + i,t

where 1,, tijX (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 1,, tijX are defined in Appendix D. Relative to the benchmark-

adjusted 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

21

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.

22

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 property-

23

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.

24

Appendix A: Condition for 0

F

PUT

Suppose there are two firms (1 and 2). We assume Firm 2 has higher franchise

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,

LL

dgdg )()( 21 for all L, or

LL

dgdg0 20 1 )()( for all L. (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,

LL

dgLdgL0 20 1 )()()()( for all L. (A2)

That is,

)()( 21 PUTPUT for all L (A3)

The inequality (A3) may be rewritten as 0

F

PUT. Intuitively we see that high

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

firm 1 leads to 0

F

PUT.

25

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.

10

1,

,, )1(1 s

sst

st

tj

tjtj rER

LRELR

(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, str is the zero coupon rate for s-year

treasury constant maturity rate at the end of year t, st 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.

26

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,

17,...,2,1,)/( 2 jTPEPEHERFL itijtit (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.

17,...,2,1,)/( 2 jTPEPEHERFL jtijtjt (C2)

Second, find the weights of each of the 17 business lines for an insurer in each year.

it

ijtijt TPE

PEWGT (C3)

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:

)*/(1

17

1 ijtjtijt WGTHERFLCOMPETE (C4)

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.

27

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:

RATINGi,t = α0 +

10

11,,

jtijj X + i,t (D1)

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:

(D2)

0

2,, 3

1

CStiti FVFV

28

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31

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

Franchise Value and Operating Performance Measures

FV 1034 0.03 0.25 1.46 -7.48 4.00

ROA (%) 1034 2.81 2.93 3.99 -17.55 17.96

ROE (%) 1034 6.92 7.76 12.08 -83.21 45.72

CR (%) 1034 106.65 104.36 25.06 40.14 486.41

ELR (%) 887 95.00 93.50 23.61 7.60 251.48

Insurer’ Characteristics

SIZE 1034 600.58 80.39 2773.13 1.86 59,456.71

LEV 1034 0.60 0.64 0.17 0.02 0.91

HERFL 1031 0.44 0.36 0.27 0.11 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

32

Panel B: Correlations among Variables

Panel C: Insurer Characteristics across Franchise Value Ranks

ROA ROE CR ELR SIZE LEV HERFL HERFS COMPETE REINS GROUP STOCK

FV 0.09 0.11 -0.08 -0.07 0.09 -0.02 -0.18 -0.11 -0.02 0.19 0.22 0.05

ROA 0.89 -0.55 -0.67 0.00 -0.23 0.10 0.00 0.01 -0.07 -0.01 0.07

ROE -0.48 -0.60 0.02 0.00 0.04 -0.03 -0.01 -0.06 0.03 0.08

CR 0.92 0.01 0.02 -0.06 -0.02 0.00 0.11 0.05 0.00

ELR 0.01 0.09 -0.15 0.01 -0.01 0.12 0.04 -0.05

SIZE 0.08 -0.09 -0.18 -0.06 -0.07 0.12 0.00

LEV -0.23 -0.15 -0.07 0.02 0.15 0.11

HERFL 0.20 0.27 -0.23 -0.32 -0.06

HERFS -0.09 -0.21 -0.30 -0.22

COMPETE 0.04 -0.10 0.02

REINS 0.28 0.19

GROUP 0.34

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)

33

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 ROA ROE CR ELR D1 2.03 4.74 111.55 99.59 2 2.25 5.57 107.34 95.53 3 2.79 6.79 107.25 94.57 4 2.76 6.66 106.73 95.43 5 3.00 6.80 106.97 93.16 6 2.91 6.41 105.64 95.28 7 2.91 6.90 105.76 94.62 8 2.89 7.19 105.22 94.04 9 2.82 7.24 106.29 94.43

D10 3.04 7.95 105.05 93.60 D10-D1 1.01*** 3.22*** -6.50*** -5.99*** (t-stat) (5.77) (7.33) (-4.49) (-5.25)

34

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 time-series dependence in the residual term. Note: *** p<0.01, ** p<0.05, * p<0.10.

ROA ROE CR ELR

INTERCEPT 1.04** -1.95** 42.68*** 47.91*** (2.40) (-2.27) (4.18) (18.28) FV 0.18*** 0.55*** -0.92*** -0.99*** (7.24) (7.90) (-5.30) (-5.88) Lag ROA 0.45*** (23.00) Lag ROE 0.40*** (15.20) Lag CR 0.66*** (6.48) Lag ELR 0.54*** (10.49) SIZE 0.10** 0.37*** -0.34* -0.31** (2.22) (2.80) (-1.74) (-2.05) LEV -0.40 5.44*** -6.48* -3.27 (-1.37) (5.05) (-1.80) (-1.47) HERFL 0.96*** 2.91*** -4.53*** -8.12*** (5.40) (5.35) (-3.11) (-7.94) HERFS 0.06 0.23 -0.67 0.48 (0.66) (0.95) (-0.89) (1.20) COMPETE -0.00* -0.01* 0.02** 0.02** (-1.74) (-1.86) (2.03) (2.47) REINS -0.88*** -2.22*** 2.37 4.77*** (-5.63) (-4.35) (1.47) (5.10) GROUP -0.13* -0.36* 0.94 0.64 (-1.82) (-1.77) (1.19) (1.35) STOCK 0.53*** 1.42*** -0.87 -2.00*** (3.50) (3.46) (-1.31) (-2.72) Firm Dummies Yes Yes Yes Yes Year Dummies Yes Yes Yes Yes N 18,740 18,740 18,740 15,544 Adj. R2 0.223 0.191 0.280 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 Newey-West (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 1.04*** (5.70)

0.55** (2.09)

Hard 1.62 1.60 2.15 2.01 2.24 2.20 2.27 2.35 1.98 2.11 0.49*** (2.79)

Panel B: ROE

Soft 5.74 6.84 8.10 8.26 8.13 7.72 8.46 8.57 8.92 9.87 4.13*** (7.20)

1.59* (1.77)

Hard 3.40 3.87 5.04 4.53 5.02 4.66 4.81 5.36 5.00 5.94 2.54*** (3.48)

Panel C: CR

Soft 110.05 103.89 104.06 102.09 100.51 101.67 101.51 100.67 101.71 101.48 -8.57*** (-3.81)

-4.83* (-1.92)

Hard 115.88 111.95 111.50 112.92 110.91 110.92 111.41 111.28 112.40 112.15 -3.74*** (-3.28)

Panel D: ELR

Soft 99.28 93.40 91.16 92.03 89.78 92.10 91.12 90.36 90.37 89.86 -9.42*** (-7.61)

-7.25*** (-4.74)

Hard 99.92 97.90 98.36 99.21 96.92 98.82 98.50 98.13 98.94 97.75 -2.17** (-2.41)

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.

ROA ROE CR ELR (1) (2) (3) (4) INTERCEPT 16.66*** 18.99*** 32.31** 0.57 (8.24) (3.08) (2.24) (0.05) FV 0.59* 0.20 -5.81** -7.97*** (1.83) (0.14) (-2.40) (-6.13) FV*SOFT 0.64** 0.43** 5.38* 9.01*** (1.98) (2.14) (1.87) (5.44) SOFT 10.92*** 32.03*** -99.95*** -62.59*** (3.71) (4.63) (-4.69) (-5.27) ROAt-1 0.24*** (9.18) ROEt-1 1.19*** (21.33) CRt-1 0.44***

(12.10) ELRt-1 0.52*** (9.12) SIZE 0.17*** 0.25** -0.23* -0.14* (3.37) (2.06) (-1.69) (-1.91) LEV -2.64*** 12.34*** -6.51 -3.87* (-7.35) (7.85) (-1.47) (-1.81) HERFL 0.99*** 2.45*** -3.48** -7.23*** (3.98) (4.15) (-2.08) (-7.31) HERFS 0.03 -0.01 -0.27 0.95** (0.28) (-0.05) (-0.30) (2.29) COMPETE -0.00 -0.00 0.01 0.01 (-0.66) (-1.01) (0.70) (1.58) REINS -0.94*** -2.39*** 8.66*** 5.58*** (-4.46) (-4.46) (4.33) (6.50) GROUP -0.13 -0.16 1.37 0.44 (-1.36) (-0.74) (1.12) (0.99) STOCK 0.84*** 1.24*** -1.02 -1.97*** (5.04) (3.01) (-0.95) (-2.73) N 18,590 18,590 18,590 15,544 Adj. R2 0.143 0.207 0.097 0.331

37

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 Young Firm for Low FV Group Level Analysis

(1) (2) (3) (4) (5) (6)

FV 0.12*** 0.16** 0.13** 0.15

(3.35) (2.16) (2.04) (2.15)

FV*SOFT 0.87* 1.10***

(1.90) (3.21)

YOUNG 0.00 -2.53*

(0.04) (-1.93)

YOUNG*MARKET 0.03

(1.63)

MARKET -0.11*** -0.12*** 0.49***

(-3.71) (-3.64) (7.33)

ROAt-1 -0.06*** -0.06*** -0.06*** -0.06*** 0.49*** -0.72

(-10.35) (-9.06) (-10.47) (-9.23) (7.52) (-0.71)

SIZE 0.21*** 0.16*** 0.24*** 0.19*** 0.24*** 1.68***

(3.92) (3.02) (4.49) (3.62) (3.05) (4.35)

LEV -2.97*** -2.70*** -3.07*** -2.81*** -0.66 0.32

(-7.75) (-6.29) (-8.15) (-6.62) (-0.69) (1.10)

HERFL 1.06*** 0.81*** 1.08*** 0.82*** 1.65*** -0.01

(4.36) (3.01) (4.42) (2.97) (4.06) (-0.86)

HERFS 0.11 0.02 0.10 0.01 0.29 -0.25

(0.83) (0.15) (0.76) (0.06) (1.06) (-0.52)

COMPETE -0.00 -0.00 -0.00 -0.00 -0.01 0.49***

(-1.32) (-0.72) (-1.27) (-0.70) (-1.00) (7.33)

REINS -0.84*** -0.93*** -0.79*** -0.89*** -0.10 0.25***

(-4.24) (-4.43) (-3.98) (-4.21) (-0.20) (3.21)

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

38

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.

1990 1992 1994 1996 1998 2000 2002 2004 2006 2008-1

0

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1990 1992 1994 1996 1998 2000 2002 2004 2006 2008-4

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1990 1992 1994 1996 1998 2000 2002 2004 2006 2008-15

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eads

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1990 1992 1994 1996 1998 2000 2002 2004 2006 2008-15

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39

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.

1 2 3 4 5

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