Franchise Value and Insurer Performance Xuanjuan Chen, Helen Doerpinghaus, Tong Yu*

PRELIMINARY DRAFT

July 2009

____________________________ * Chen is from College of Business, Kansas State University, email: jchen@ksu.edu. Doerpinghaus is from Moore School of Business, University of South Carolina, email: doerp@moore.sc.edu. Yu is from College of Business and Administration, University of Rhode Island, email: tongyu@uri.edu. All errors are our own.

Franchise Value and Insurer Performance

Abstract

This study provides evidence that franchise value improves insurers’ operating performance.

Essentially coming from an insurer’s market power, franchise value includes the insurer’s brand

royalty, business networking, and underwriting and claim specialty. High franchise value firms

who could charge more, have lower operating costs, and have a larger client base are potentially

more profitable. On the other hand, to protect their franchise value, high franchise firms may

skip high-risk but profitable investment opportunities, leading to poorer operating performance.

Franchise value of insurance companies is notoriously difficult to measure because most of

insurance companies are private companies. Therefore we quantify franchise value using

company ratings assigned by the A. M. Best Company after controlling for various tangible firm

characteristics. We find high franchise value firms are more profitable and franchise value tends

to be persistent. Moreover, we test if franchise value effects vary across heterogeneous

environments. We find the effect of franchise value is weakened in soft markets and for insurers

that operate in more competitive sectors.

Franchise Value and Insurer Performance

1. Introduction

Products delivered by insurance companies and other financial intermediaries often are a

contract or service and its quality is largely determined by clients’ recognition. Brand loyalty and

word-of-mouth reputation are critical to a financial service firm. In the insurance industry,

franchise value, also known as intangible assets and charter value, include brand loyalty,

personnel, renewable business, and expertise in claim service and underwriting. While it is

natural to see a strong link between franchise value and insurers’ operating performance, such a

link has never been formally tested.1 The objective of this study is clear – we empirically

examine the role of franchise value in insurers’ operating performance of insurance companies

and test if the role is stable across different market conditions.

How does insurance firms’ franchise value affect their performance? A natural conjecture

is that insurers with greater franchise value are more competitive in the marketplace. Epermanis

and Harrington (2006) find insurer premiums decline in the year and the year after they

experience rating downgrades. As high franchise value firms often better ratings, their finding

may be interpreted as the evidence that franchise value favorably affects firm profitability.

Conversely, a negative relation between franchise value and firm performance is also

likely. Babbel and Merrill (2005) break down market value of insurer equity into franchise value,

market value of tangible assets, as well as other factors. They show a non-monotonic relation

between market value of insurers and firm insolvency risk. Along this line, as franchise value is

inversely related to insolvency risk, the relation between franchise value and firm performance is

uncertain.

We present a model to demonstrate the effects of franchise value on insurers’ operating

performance. Greater franchise value increases the insurers’ ability to charge a greater loading

and reduce their operating expenses, thus increasing insurer profitability. On the other hand, to

preserve franchise value, high franchise value insurers may bypass profitable but risky projects,

thus having lower profitability. The effect of franchise value on insurers’ operating performance

is determined by the relative magnitude of the two forces. The model provides two testable 1 Despite that prior studies have discussed the importance of intangible assets (or franchise value) on firm value (see Aaker 2001; Chan, Lakonishok, and Sougiannis 2001; Barth 1998; Lehmann 2004, for example), the focus of these studies is predominantly on non-financial service. As we lay out later in this study, intangible asset assets take a quite different form in financial service industries than in non-financial industries. As a result, we expect franchise value could affect firm performance differently in the financial service industry.

implications: first, franchise value typically increases insurers’ profitability; second, competition

reduces the positive effect of franchise on insurers’ performance.

We empirically examine the relationship between franchise value and insurer operating

performance. Specifically, we have the following hypotheses. First, on profitability, greater

franchise value leads to either higher unit profit margin or greater client base. Our hypothesis is

that there is a positive relation between insurers’ franchise value and profitability. Second, on

operating costs (combined ratios, loss ratios, and expense ratios), the hypothesis is that there is a

negative relation between franchise value and insurers’ operating costs. Third, franchise value

increases insurers’ profitability, resulting in greater surplus. Moreover, high franchise value

firms possess a larger client basis. Therefore, we expect franchise value positively predicts an

insurer’s tangible asset value in the subsequent year. The hypothesis is that there is a positive

relation between franchise value and insurers’ tangible asset growth rate, equity growth rate, and

premium growth rate. Finally, future franchise value – insurers with greater franchise value in

the current periods may have greater franchise values in the subsequent years. In other words,

franchise value is persistent.

The examination of the effect of franchise value is challenging as franchise value is 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. Several proxies are used 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 two 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.2

We rely on the expert-assessment on a firm financial strength -- the ratings assigned by

the Best’s rating – in assessing an insurer’s market standing. An insurer’s rating simultaneously

captures the effects of tangible and intangible assets. To be specific, two insurance companies

with the identical rating may be different in their tangible assets (e.g., different size and leverage);

their franchise value may not equate each other. We therefore construct two rating-based

2 The nature of the insurance business is substantially different from that of industrial firms and even if these accounting variables were available using them would have serious limitations.

franchise value measures that separate franchise value from tangible assets. The first is the

benchmark-adjusted rating. We break down all property-liability insurers into 125 (5 x 5 x 5)

groups based on firm total assets quintile, leverage quintile, and return on assets quintile, all in

the prior year. For each of the 125 portfolios, we calculate the equal-weighted average ratings in

each year. The difference between the rating of an individual insurer and the average rating of its

benchmark group is the benchmark-adjusted franchise value. Secondly, we follow Yu, Lin,

Oppenheimer, and Chen (2008) and use the residual term in a regression of the Best rating on

firm characteristics as a measure of the insurer’s franchise value. With the regression approach is

that we simultaneously consider multiple tangible characteristics of an insurer. A further concern

of the franchise value measures is that they could be driven by random outcomes, e.g., errors of

rating specialists. To alleviate this problem, we average the benchmark-adjusted ratings or

residual ratings in the past two years and the rating of the current year to measure an insurer’s

franchise value.

The sample of our study is a panel over 2000 property and liability insurance companies

over the period of 1985 to 2007. Sorting insurers into deciles based on their 3-year averaged

benchmark-adjusted ratings or residual ratings, we have the following results. First, insurers’

profitability generally increases with franchise value. When measuring franchise value with the

three-averaged benchmark-adjusted rating, for instance, we find that the averaged return on

assets for bottom insurer decile is 2.06%, while that of the top insurer decile is 3.14%. The

difference of 1.02% is significant at the 1% level (t=5.17). Second, insurers’ operating expenses

decrease with franchise value. The average loss ratio for the top decile insurers is nearly 10%

lower than the bottom decile insurers. Also, insurers in the bottom decile exceed top decile

insurers by over 4%. Third, high franchise value firms grow more rapidly than low franchise

value firms. The average asset (premium) growth rate for the top decile group is 12.69%

(14.49%) while the average growth rate for the bottom decile firms is 8.17% (9.67%). Finally,

insurers ranked in the top franchise value decile exhibit high franchise value ranks over multiple

future years. This result suggests that the top decile insurers are more likely to truely have high

franchise value rather than happen to have it by chance. The results are similar when we apply

the regression based franchise value measure. 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 stock performance.

Next we check if the result is robust after controlling for various firm characteristics. A

large set of firm characteristics are considered: lagged operating performance, Herfindahl index

for lines, level of competition of an insurer, percentage of reinsurance in an insurer’s direct

insurance premiums, percentage investments in common stocks, insurer ownership, and an

indicative variable showing an insurer is affiliated with a group. The result shows that the

coefficient on franchise value is significantly positive -- the link between operating performance

and franchise value is robust after controlling for these firm characteristics.

The most prominent stylized fact in the property casualty insurance industry is the

cyclical pattern of the business – the market is rotating with hard and soft markets where in hard

markets premiums are high and insurance supply is limited while in soft markets premiums are

low and the supply of insurance is sufficient. A further interesting question arises: how does the

franchise value effect differ across different stages of insurance cycles? To test this, we interact

franchise value with insurers’ aggregate loss ratios. The coefficient on the interactive variable is

positive: the franchise effect is weaker in soft markets when the industry aggregate loss ratio is

low. In soft markets, competition is more intensive and this limits the ability of insurers with

high franchise value to charge high premium.

Moreover, we look at the role of competition by seeing if the franchise value effects

differ across business sectors. Aligned with the result of insurance cycle effects, we find the

effect is weaker in insurance lines with more intensive competitions.

The remainder of the article is organized as follows: Section 2 presents a model on the

relation of franchise value and firm profitability. Section 3 describes data and empirical

methodology. Section 4 presents the empirical results and Section 5 concludes the paper.

2. Franchise Value and Firm Performance

2.1 The Model

Assume that losses of insurance companies follow a distribution f(l). The profit of

insurance companies is determined as the following:

ecp , (1)

where p is the premium collected by an insurer; c is the claim cost paid by an insurer; e is an

insurer’s expenses. Price of insurance is the claim cost expected by an average policyholder plus

a percentage loading λ, covering expenses and insurer profits. Assume that expenses are

proportional to the expected loss amount of an insurer:

e=β*E(l) (2)

An insurer’s assets have two components: tangible assets (t) and franchise value (f). For

simplicity, let’s assume only tangible assets are marketable.

Next, we consider alternative cases regarding the relation between in franchise value and

insurers’ profitability:

Case 1: No insolvency risk

p=(1+λ)*E(l)

c=E(l)

)()( lE (3)

Assume that λ= λ(f) and λ’(f)>0 and that β= β(f) and β’(f)<0.

Consequently, 0

f

. That is profitability is an increasing function of franchise value.

Case 2: Having insolvency risk but policyholders not aware of the risk

In this case, the claims paid depends on if insurers insolvency.

c=l if l<t (solvent)

c=t if l>t (insolvent) (4)

Then

t

tdlltfdlllfc )()(

0

t

dllftllE )()()( (5)

The second term of the expression for c is the price of a call option with an exercise price

of t.

When policyholders are not aware of the insolvency risk, insurers can charge the same

premium as in case 1. So we have

)()1( lEp (6)

Inserting (2), (5) and (6) in (1), we have

t

dllftllE ])()()()( (7)

(λ-β) increases with f. Noted earlier,

t

dllftl )()( represents the value of a call option

with an exercise price of t. Holding the total value of firms constant, the call option value

decreases in t and increases in f. Thus the effect of f on the second term for π is also positive.

Thusf

is positive in sign.

Case 3: Having insolvency risk and policyholders fully aware of the risk

When policyholders are fully aware of the insolvency risk, the price of insurance would

be:

))()()()(1(

t

dllftllEp (8)

Inserting (2), (5) and (8) in (1), we have

)(])()()([ lEdllftllEt

(9)

(λ-β) increases in f. Also,

t

dllftl )()( represents the value of a call option with an

exercise price of t. The call option value decreases in t and increases in f. The effect of f on the

second term for π is negative. The effect of f on the third term, - )(lE , is positive. Taken

together, f

is uncertain in sign.

Case 4: Having insolvency risk and policyholders partially aware of the risk

We have the following expression for the price of insurance:

))()()()(1(

t

dllftllEp (10)

An insurer’s profit equates:

t

dllftllE ])()(]1)1[()()( (11)

When η approaches to 0, we have case 2. When η approaches to 1, we have case 3.

2.2 Testable Implications

Based on the above model, the relationship between insurers’ profitability and franchise

value depends on insurers’ insolvency risk and policyholders’ perspective on this risk. When

insolvency risk is low, the relationship tends to be positive. Insurers are typically well

capitalized. As a result, the overall insolvency risk in insurance industry is low. As a result, we

propose in general, there is a positive relationship between insurers’ profitability and franchise

value:

H1(a): Insurer profitability is positively related to franchise value.

In addition, we have:

H1(b): Insurer operating expenses are positively related to franchise value.

H1(c): Tangible growth rate of insurance companies is positively related to franchise

value.

H1(d): Firm franchise value is persistent.

When insolvency risk is large, policyholders’ perspective is critical. We know that when

the market competition is intense, policyholders are more aware of insolvency risk, thus the sign

for f

is uncertain. When market competition is low, policyholders may be less aware of

insolvency risk, thus the sign for f

is positive. Thus we propose the second hypothesis as

below:

H2: Competition reduces the positive effect of franchise value on insurers’ profitability.

3. Data and Measures

3.1 Data

The empirical work in this paper is based on two databases and the sample spans the

period of 1985 through 2007. We obtain insurers’ ratings from the A.M. Best Key Rating Guide

Database for property-casualty insurers (hereafter, the Best database), and insurers’ demographic

and financial data from the National Association of Insurance Commissioners database for

property-casualty insurers (hereafter, the NAIC database). 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”.

As Best rating incorporates not only quantitative, but qualitative factors, it is a good proxy for

the overall financial strength of an insurer derived from both tangible assets and franchise value.

We obtain the franchise value component by removing the tangible assets component from the

Best rating. Section 3.2 provides details of the estimation of franchise value.

The Best data use best number to identify each insurer, while the NAIC data use NAIC

code. Luckily, the Best data also provides the NAIC code if an insurer is also covered by the

NAIC data. We combine the two databases using the NAIC codes in the Best data. Similar to

prior studies such as Cummins, Dionne, Gagne, and Nouira (2007), we require insurers to meet

the following criteria to be included in our sample: (1) insurers have non-missing rating in the

Best database, (2) insurers are covered by the NAIC database with non-negative surplus, assets,

losses or expenses, and (3) direct premium written is greater than zero. Note that both Best and

NAIC data provide information for insurance group (with NAIC code no greater than 10000) and

each individual insurers (with NAIC code greater than 10000). We conduct our main analyses at

individual insurer level while we also provide summary results at the group level.

Panel A of Table 1 shows the number of firm-year observations and unique insurers in

each step of sample construction. From 1985 to 2007, there are 65505 firm-year observations in

the NAIC data from 4702 unique insurers. After placing restrictions (2) and (3), there are 31841

firm-year observations and 2933 unique insurers. After matching the NAIC data with the Best

data, we obtain a sample with 22452 firm-year observations from 2192 unique insurers.

Panel B shows the number of insurers and percentage distribution of Best rating for

sample insurers in each year. Two patterns are observed. First, the majority of insurers have

ratings of A- and above, suggesting property liability insurers as a whole have high financial

strength. For example, in 2006, 79% insurers have ratings at least as high as A-. Second, there is

a decreasing trend of the number of insurers with ratings of A+ and above: in 1985 there are 44%

insurers with rating of A+ and above, yet this number drops to 20% in 2007.

3.2 Measuring Franchise value

As we show in the model part, franchise value is the present value of future cash flows

that are obtained from insurers’ intangible competitive advantage, such as high quality of service

or good reputation. The essential of the concept of franchise value is that it is additional value of

a firm beyond that is brought up from its tangible assets. Although Best ratings are not a measure

for firm valuation, it measures financial strength of insurers from a comprehensive perspective.

As a result, we can derive franchise value measures from the Best Rating by taking out the

tangible component of Best ratings.

The Best Company assigns insurers letter ratings from S (Suspension) to A++ (superior).

Consistent with prior studies like 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++.3

If multiple ratings are reported for an insurer in a year, we use the last reported rating in that

year.

We use two methods to estimate franchise value from the Best ratings. The first is what

we call a benchmark-adjusted franchise value (BFV). To obtain BFV, we first estimate the

average effects of three tangible firm characteristics on Best ratings. Specifically, in each year,

we sequentially sort insurers into 125 (5 x 5 x 5) groups based on firm total assets quintile,

leverage quintile, and return on assets quintile, all in the prior year. For each of the 125 portfolios,

we calculate the equal-weighted average ratings in each year. The difference between the rating

of an individual insurer and the average rating of its benchmark group is the benchmark-adjusted

franchise value.

Alternatively, we perform cross-sectional regression each year and use the residual term

as the proxy for franchise value, so called 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:

RATINGi,t = α0 +

10

11,,

jtijj X + i,t (12)

where 1,, tijX (j=1, 2, …, 10) are tangible characteristics potentially affecting the firm rating

measured at the end of year t-1. The residuals, εi,t, is the regression-based franchise value.

Variables used as 1,, tijX are defined in the Appendix. Relative to BFV, an obvious advantage of

RFV is that it simultaneously controls for more tangible components of the Best rating when

estimating the franchise value. A disadvantage is that it assumes a linear relation between each of

the included explanatory variables and the rating.

By construction, a high BFV/RFV indicates high intangible competitive advantage of an

insurer. Yet, it is possible that an insurer may have a high BFV/RFV just by chance instead of

high market power. As an insurer with high franchise value is more likely to have persistently

high franchise value over multiple periods, we compute the rolling average of FVs over three

years (from year t-2 to t) to increase the likelihood that our measures capture franchise value:

3 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++).

0

2,, 3

13

titi BFVBFV

and

(13)

3.3 Measuring Operating Performance and Other Firm Attributes

Two traditional operating performance 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. For both measures, there are firms with extremely large or small numbers. We remove

the top 1% and bottom 1% observations in each year for each measure to control for outliers.4

In addition to franchise value, various other firm characteristics might play a role in

determining operating performance. We construct the following control variables: the logarithm

of total assets (SIZE), the ratio of total liability to total assets (LEV), Herfindahl index by

business lines (HERFL), degree of competition faced by each insurer (COMPETE), percentage

of reinsurance business (REINS), the ratio of stock investment to total invested assets (STK),

dummy variable for group affiliation (GROUP), and type of ownership (OWNS). The appendix

provides the definition for each of the control variables.

Table 2 shows the summary statistics and correlations of all the variables. We first

calculate the statistics across insurers in each year and then compute the time-series means of the

statistics. The mean benchmark-adjusted FV3 is -0.07 with minimal of -7.58 and maximum of

4.57. The mean regression-based FV3 is -0.01, with minimal of -8.09 and maximum of 3.11. The

mean return on assets is 3% per annual while the mean return on equity is 7% per annual.

Inspecting the correlations in Panel B of Table 2 reveals that the correlation between

BFV3 and RFV3 is 0.81, suggesting the two franchise value measures are highly correlated. We

also find that the correlations between BFV3 (RFV3) and ROA is 0.10 (0.07), showing a positive

relationship between franchise value and insurers’ operating performance.

4. Empirical Results

4.1 Franchise value and Operating Performance

4 Actually, we investigate the impacts of franchise value on insurers’ growth rate and operating cost as well. We report the portfolio results in Table 3. For simplicity, we focus on ROA and ROE results in Table 4 to Table 7. The regression results on growth rate and operating cost are consistent with ROA and ROE results.

0

2,, 3

13

titi RFVRFV

To start with, we form decile portfolios of insurers on lagged BFV3 and RFV3 in each

year and estimate next year operating performance on the resulting portfolios. When calculating

portfolio operating performance, we first compute the equal-weighted average performance for

each decile in each year and then compute the time-series averages of annual performance.

Table 3 reports the results. Panel A shows the results based on BFR3. That is, we sort

insurers into deciles based on 3-year averaged benchmark-adjusted franchise value. Return on

assets in general increases as BFR3 increases. ROA for bottom insurer decile is 2.06%, while

that of the top insurer decile is 3.14%. The difference of 1.02% is significant at the 1% level

(t=5.17). When we measure performance with return on equity, the results are consistent. The

difference in ROE between the top and bottom insurer deciles is 3.18% (t=5.45). Panel B sorted

insurers based on RFV3. Not surprisingly, we observe the same pattern of ROA and ROE across

franchise valued sorted portfolios. Given the high correlation between BFV3 and RFV3 and the

fact that RFV3 controls more tangible firm characteristics, we only report results based on RFV3

in later analysis.

Other than ROA and ROE, we further explore the effect of franchise value on firm

growth measures and various measures of insurers’ operating expenses. We include asset growth

(AG), equity growth (EG), and premium growth (PG) for growth rate. The middle three columns

of Panel A report the average growth measures across BFV3 sorted decile portfolios. We see that

all the three measures in general increase in franchise value deciles. The average asset growth

rate for bottom decile insurers is 8.17% while that of top decile insurers is 12.69%. The

difference of 4.51% is significant at the 1 percent level (t=3.96).

The last two columns report the averages of operating cost across franchise value decile

portfolios. We include two operating cost here: loss ratios and expense ratios. The average loss

ratio for bottom decile insurers is 79.47% while that of top decile insurers is 70.37%. The

difference of -9.10% (t=-3.97) is economically and statistically significant. The results

demonstrate that insurers with the highest franchise value have significantly lower loss ratio than

insurers with the lowest franchise value. Moreover, D10 insurers also have significantly lower

expense ratio than D1 insurers.

The portfolio analysis suggests that insurers with higher franchise value generate both

statistically and economically significant higher operating performance. As a robustness check to

this conclusion, in Figure 1 we plot the performance difference between D10 insurers and D1

insurers sorted by RFV3 for each sample year. The figure shows that the spreads are positive for

19 sample years, indicating that the outperformance of D10 firms is persistent over time.  

Moreover, there is no evident trend over time to suggest that the ability of mutual funds to profit

from the strategy has systematically changed during our sample period.

So far, we attribute superior performance of high RFV3 insurers to their high franchise

value. However, franchise value is not the only factor that affects insurers’ profitability. It is

possible that high RFV3 insurers may also simultaneously have other firm characteristics that

may at partially drive the superior performance of these insurers. We consider a rich set of

insurer characteristics that have been shown in the literature to be associated with firm

profitability, and examine insurer performance with high franchise value after controlling for

these characteristics. Specifically, we include 8 characteristics: SIZE, LEV, HERFL,

COMPETE, REINS, STK, GROUP, and OWNS.

To examine the incremental effect of franchise value on insurer operating performance,

we perform panel regressions using a fixed firm and time effect model suggested by Petersen

(2008). In this model, we assume the relationship between performance and franchise value

varies across firms and over time. More importantly, we assume that the residuals in the

regression are correlated across firms and over time. Using the estimation method suggested by

Petersen, we can obtain unbiased standard errors and thus correct significance level for

coefficients. We specify the model as below:

,ititit XPERM (14)

Where itX includes our key independent variable regression-based franchise value, lagged year

operating performance, and the eight control characteristics we discussed above. To include the

fixed firm and time effect on the coefficients, we specify ittiitX . To account for the

dependence of residuals cross firms and over time, we specify ittiit

The results are reported in Table 4. In the regression of ROA, the coefficient on RFV3 is

0.14 (t=5.45), significant at the 1% level. Similarly, in the regression of ROE, the coefficient on

RFV3 is 0.50 (t=6.26), significant at the 1% level. The evidence is supportive to the hypothesis

that franchise value enhances insurers’ operating performance.

We also find lagged ROA and ROE are highly significant in both regressions. Consistent

with prior studies, leverage is negatively related to ROA while positively related to ROE. More

concentrated insurers have higher operating performance, while competition reduces firm

performance. We also find more reinsurance coverage is associated with lower performance, and

stock insurers have significantly high performance than mutual insurers.

4.2 Persistence of Insurers’ Franchise Values

In this section, we examine whether high RFV3 insurers truly have high franchise value

or they have high RFV3 just by chance. For this purpose, we examine the persistence of RFV.

For each year Y0, we sort insurers into deciles based on their RFV3. D10 decile has insurers

with the highest RFV3 and D1 decile has insurers with the lowest RFV3. We trace the average

RFV of each insurer decile for the subsequent five years, Y1 to Y5.5 The results are in Table 5.

The average RFV of D10 insurers for Y1 to Y5 are 1.89 percent, 1.79 percent, 1.66 percent, 1.53

percent, and 1.44 percent, respectively, all significantly positive. D10 insurers’ RFV in Y1 to Y5

are also significantly higher than those of D1 insurers. These results suggest that D10 insurers

are more likely to have high RFV3 due to their high franchise value rather than by chance.

Figure 1 provides transformation matrix of D10 and D1 firms in the subsequent five

years. It shows what fraction of the D10/D1 insurers remains in the lowest FV3 decile in the

subsequent years. After one year, 53% of D10 firms remain in the same decile and after five

years 33% of D10 firms stay in D10, showing a high percentage for D10 insurers tend to remain

in the highest decile. Panel B reports the transition matrix for D1 insurers. We observe a similar

pattern.

4.3 Market Conditions and the Franchise Value Effects

The property casualty insurance industry is characterized by underwriting cycles. During

soft market conditions premium rates are stable or falling and insurance is readily available, and

during hard markets rates rise, coverage becomes more difficult to find, and insurer profits

increase (Venezian, 1985; Cummins and Outreville, 1987; Harrington and Yu, 2003; and Leng

2006). Naturally, the role of franchise value on performance may differ in different market

conditions.

5 Some funds in our sample report portfolio holdings semiannually. The results for Q1 and Q3 are based only on funds reporting portfolio holdings quarterly, whereas the results for Q2 and Q4 are based on both funds reporting quarterly and funds reporting semiannually. For robustness, we repeat our analysis after excluding semiannually-reporting funds, and obtain similar results.

We use the industry average loss ratio to measure market conditions. Specifically, we use

two variables to capture the effect of insurance cycles, SOFT and LR, where LR is the industry

average loss ratio and SOFT is a dummy variable that equals 1 if the industry average loss ratio

is less than the median of industry average loss ratio in all sample years. It equals to 0 if

otherwise. We construct interactive variables of franchise value and market condition:

BFV3*SOFT and BFV3*LR.

The first two columns in Table 6 report results using SOFT dummy and its interaction

with franchise value. The coefficient on FV3 is 0.16 (t=4.54), indicating that in hard market, high

franchise value is associated with high future operating performance. More importantly, the

coefficient on FV3*SOFT is -0.05 (t=-1.99), suggesting significant weaker effect of franchise

value on profitability in soft market. This result is consistent with the expectation.

The other variable to proxy market conditions is the industry average loss ratio (LR). We

obtain the industry average loss ratio in each sample year from the Best Average & Aggregates

Book. As in soft market insurers have lower loss ratio due to less claim payments, we expect the

sign of the coefficient on FV3* LR is positive. As reported in the last two columns in Table 6,

the coefficient on FV3* LR in the regression of ROA is 0.08(t=1.86) and the coefficient on

FV3*LR in the regression of ROE is 0.14 (t=-1.73). The results are consistent with our

prediction. The coefficient on the interactive variable is positive: the franchise effect is weaker

in soft markets when the industry aggregate loss ratio is low. In soft markets, competition is

more intensive and franchise value becomes lower on average. Putting together, the effect of

franchise value diminishes in its magnitude, consistent with our model.

4.4 Competitions and the Franchise Value Effects

Gan (2004) find that in the banking industry, competition reduces franchise value and

reduced franchise value induces risk taking. However, there has been little research on the role of

franchise value on operating performance of firms with different competition levels. Some

insurance business lines, like auto insurance, have quite homogeneous products and thus high

competition among insurers. Some other insurance lines, like surety insurance, have customized

products thus low competition among insurers. Based on the model, we propose that in more

competitive lines, the role of franchise value is weaker.

We construct a variable, COMPETE, to measure the degree of competition faced by each

insurer in each year given their underwriting business. We first define 17 insurance business

lines using Best Average &Aggregate classification. We calculate the annual Herfindahl index

for each of the 17 lines using premium earned of top 10 insurers in that line. COMPETE is the

premium-weighted average of the 17 Herfindahl indexes. As we are interested in the role of

franchise value on performance under different competition level, we use an interaction term,

RFV3*COMPETE, to examine the relation. As shown in Table 7, the coefficient on

FV3*COMPETE is -0.06 (t=-1.75) in the ROA regression and it is -0.24 (t=-2.18) in the ROE

regression. The evidence supports that franchise value has less effect on performance in more

competitive lines.

5. Conclusions

In this paper, we explore the impact of franchise value on insurers’ operating

performance. Franchise value comes from insurers’ market power. Insurers develop their

franchise value through improving brand royalty, business networking, and improving their

underwriting and claim specialty. These efforts increase insurer ability to charge higher

premiums, to reduce operating costs, and to expand their client bases, potentially increasing

insurer profitability. However, there has been little exploration on this relationship in academic

research because of the difficulty to measure franchise value.

We quantify franchise value using company ratings assigned by the A. M. Best Company

after controlling for various tangible firm characteristics. We provide empirical evidence that

firm performance is positively correlated with franchise value. Moreover, we test if franchise

value effects differ in heterogeneous market conditions (soft versus hard markets) and across

business sectors (competitive versus monopolistic insurance sectors). The overall evidence

suggests that the effect of franchise value is weaker in soft markets and for insurers engaging less

in competitive business lines.

Appendix A: 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 RFVE measure. The regression is specified as below:

RATINGi,t = α0 +

10

11,,

jtijj X + i,t (A1)

X includes, and ROA as defined in Appendix A. It also involves the following new variables: Size: the logarithm of total assets (measured in US$ billions)

LEV: the ratio of total liability to total assets

HERFL: this is 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 (A2)

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.

REINS: it is the ratio of reinsurance ceded divided by the sum of direct premiums written and reinsurance assumes for each insurer in each year.

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: it is 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: it is the ratio of equity investment divided by total invested assets

Appendix B: Measures of Insurers’ Growth Rate and Operating Cost Asset growth: this is the percentage increase of total assets over the last year

)3(1,

1, ATA

TATAAG

ti

tiitit

where TA is the total assets of an insurer

Equity growth: it is the percentage increase of equity over the last year

)4(1,

1, ATE

TETEEG

ti

tiitit

where TE is the total equity of an insurer

Premium growth: it is the percentage increase of premium over the last year

)5(1,

1, APE

PEPEPG

ti

tiitit

where PE is the premium earned of an insurer

Expense ratio: it is the sum of losses incurred and loss expenses incurred divided by premium earned in each year

Loss ratio: it is the ratio of other underwriting expenses incurred to premium earned in each year

Appendix C: Control Variables in the Regression of Performance on Franchise Value

In the panel regression of operating performance on franchise value, we include nine control

variables in the regression that may affect performance. SIZE, LEV, HERFL, REINS, and STK

are defined in Appendix A. The rest are defined as below:

LAGDEP: in the regression of ROA, it is the lagged year ROA, and in the regression of

ROE, it is the lagged year ROE.

COMPETE: it measures the overall competition degree faced by an insurer. The procedures

to estimate it involves three steps: first, compute the Herfindahl index for each of the 17

insurance lines in each year using top 10 insurers’ premium earned.

17,...,2,1,)/( 2 jTPEPEHERFL jtijtjt (A6)

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

it

ijtijt TPE

PEWGT (A7)

Third, calculate the sum of the product of herfindahl index for each line in each year and the

weights of each line for each insurer:

17

1* ijtjtijt WGTHERFLCOMPETE (A8)

GROUP: it is the dummy variable that equals to one if an insurer belongs to an insurance group and equals zero otherwise

OWNS: it is the dummy variable that equals to one for stock insurers and zero otherwise.

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Demsetz, Rebecca, Marc Saidenberg, and Philip Strahan, 1996, Banks with Something to Lose: the Disciplinary Role of Franchise Value, FRBNY Economic Policy Review, October 1996.

Epermanis, Karen and Scott E. Harrington, 2006, Market Discipline in Property/Casualty Insurance: Evidence from Premium Growth Surrounding Policyholder Rating Changes. Journal of Money, Credit, and Banking, 38: 1515-1544.

Fama, E.F. and K.R. French, 1993, Common risk factors in the returns on stocks and bonds, Journal of Financial Economics 33, 3–56.

Gan, Jie, 2004, Banking market structure and financial stability: Evidence from the Texas real estate crisis in the 1980s, Journal of Financial Economics 73, 567-601.

Harrington, Scott E., 1992, Rate Suppression, Journal of Risk and Insurance 59: 185-202.

Harrington, Scott E. and Tong Yu, 2003, Do Property and Liability Insurance Underwriting Margins Have Unit Roots? Journal of Risk and Insurance, 70: 735-753.

Hirschey, M., and JJ. Weygandt, 1985, Amortization Policy for Advertising and Research and Development Expenditures. Journal of Accounting Research 23: 326–335. Keeley, M., 1990, Deposit Insurance, Risk, and Market Power in Banking, The American Economic Review 80, 1183-1200.

Kohlbeck, Mark J., and Terry Warfield, 2002, The Role of Unrecorded Intangible Assets in Residual Income Valuation: The Case of Banks, Working paper Lamm-Tennant, Joan and Laura T. Starks, 1993, Stock Versus Mutual Ownership Structures: the Risk Implications, Journal of Business, 66: 29-46. Lehmann, D. R. 2004. Linking marketing to financial performance and firm value. Journal of Marketing 68:73-75.

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Table 1 Insurer Sample Selection and Rating Distributions

This table shows the number of sample firms following each sample selection procedure and Best rating distribution during 1986 through 2007. Panel A: Insurer Sample Construction Procedure

# of Firm-year # of Insurers NAIC p&l Insurers 65505 4702 NAIC p&l Insurers with positive assets, equity, and premium

31841 2933

Insurers with rating (merge with A.M.Best) 22452 2192

Panel B: Percentage Distribution of Best Ratings over Time

Year # of

Insurers RT >= A+ A+ > RT >= A- A-> RT >= B- C++ >= RT >= C- RT < C-

1986 793 0.44 0.32 0.22 0.03 0.00 1987 811 0.42 0.36 0.20 0.02 0.00 1988 850 0.41 0.38 0.19 0.01 0.00 1989 774 0.42 0.41 0.16 0.01 0.00 1990 762 0.40 0.46 0.14 0.01 0.00 1991 833 0.38 0.45 0.15 0.01 0.00 1992 887 0.33 0.51 0.15 0.01 0.00 1993 1124 0.32 0.50 0.16 0.01 0.01 1994 1218 0.29 0.51 0.18 0.01 0.00 1995 1245 0.25 0.54 0.20 0.01 0.00 1996 1177 0.22 0.56 0.21 0.01 0.00 1997 1135 0.22 0.55 0.21 0.01 0.00 1998 1094 0.23 0.53 0.23 0.01 0.00 1999 1082 0.24 0.52 0.22 0.01 0.00 2000 1151 0.27 0.52 0.21 0.01 0.00 2001 1152 0.25 0.55 0.20 0.01 0.00 2002 1172 0.20 0.57 0.21 0.01 0.00 2003 1251 0.20 0.55 0.23 0.02 0.00 2004 1237 0.20 0.56 0.22 0.02 0.00 2005 1111 0.22 0.55 0.22 0.02 0.00 2006 1132 0.21 0.58 0.20 0.01 0.00 2007 870 0.20 0.60 0.19 0.01 0.00

Table 2

Insurers’ Summary Statistics Panel A of this table shows the summary statistics of all insurers covered in the NAIC property-liability database and our sample insurers. It presents the time-series averages of cross sectional statistics, including number of observations, mean, median, standard deviations, minimum, and maximum. The definitions of BFV3, RFV3, ROA, and ROE are provided in section 3 and those of other insurer characteristics are provided in Appendix A. Panel B reports the correlations between all the variables. The sample period is from 1985 to 2007. Panel A: Summary statistics of variables

N Mean Median Std Min Max

Franchise Value Measure

BFV3 1021 -0.07 0.06 1.46 -7.58 4.57

RFV3 1020 -0.01 0.26 1.49 -8.09 3.11

Operating Performance Measures

ROA 1021 0.03 0.03 0.04 -0.17 0.19

ROE 1021 0.07 0.08 0.12 -0.81 0.48

Insurers’ Characteristics

SIZE 1021 0.37 0.09 0.84 0.01 8.09

LEV 1021 0.60 0.64 0.17 0.03 0.90

PERFL 1021 0.44 0.37 0.26 0.13 1.00

COMPETE 996 0.59 0.50 0.30 0.18 2.01

REINS 1021 0.36 0.31 0.26 0.00 0.97

STK 1021 0.14 0.09 0.15 0.00 0.75

GROUP 1020 0.74 1.00 0.44 0.00 1.00

OWNS 1020 0.71 1.00 0.45 0.00 1.00

Panel B: Correlations among Variables

RFV3 ROA ROE SIZE LEV HERFL COMPETE REINS STK GROUP OWNS

BFV3 0.81 0.10 0.10 0.03 -0.07 -0.11 -0.01 0.16 -0.06 0.11 0.02

RFV3 0.07 0.10 0.02 0.01 -0.03 0.02 0.04 0.00 0.04 0.00

ROA 0.88 -0.01 -0.29 0.13 0.01 -0.07 0.01 -0.02 0.05

ROE 0.05 -0.03 0.07 0.00 -0.05 -0.04 0.01 0.06

SIZE 0.19 -0.18 -0.06 -0.04 0.20 0.19 0.02

LEV -0.22 -0.04 0.05 -0.25 0.12 0.10

HERFL 0.24 -0.21 -0.05 -0.29 -0.05

COMPETE 0.05 -0.03 -0.11 0.02

REINS -0.10 0.23 0.18

STK 0.02 -0.18

GROUP 0.34

Table 3 Insurers’ Profitability, Growth, and Operating Cost Sorted by Lagged Franchise Value This table reports average profitability, growth rate, and operating cost of insurer deciles sorted on franchise value in the lagged year. D10 insurers have highest franchise values. Insurer profitability measures include return on assets and return on equity. Growth measures include total asset growth, equity growth, and premium growth. Operating cost measures include loss ratio and expense ratio. Panel A reports results sorted by three-year average benchmark-adjusted franchise value. Panel B reports results sorted by three-year average regression-based franchise value. The t-statistics are in parentheses, and are computed using the Newey-West procedure with a 3-year lag. The sample period is from 1985 to 2007. Panel A: Sorted by the three-year Average Benchmark-adjusted Franchise Value

Rank ROA ROE AG EG PG LR ER 1 2.06 4.99 8.17 10.95 9.67 79.47 34.77 2 2.53 6.49 9.63 11.27 10.60 75.01 32.08 3 2.56 6.17 9.07 10.30 8.19 74.22 33.03 4 2.70 6.28 8.59 10.86 8.44 73.55 33.25 5 2.94 6.82 9.55 11.12 11.21 72.77 32.63 6 3.15 7.26 9.06 10.13 10.81 71.79 32.39 7 3.14 7.10 10.35 11.50 10.71 71.57 32.47 8 3.21 7.74 10.30 12.01 11.94 70.70 32.99 9 3.01 7.66 11.07 12.44 11.61 70.22 32.04 10 3.14 8.17 12.69 14.07 14.49 70.37 30.48

10-1 1.02 3.18 4.51 3.12 4.81 -9.10 -4.29 t 5.17 5.45 3.96 2.71 3.16 -3.97 -3.57

Panel B: Sorted by the three-year Average Regression-based Franchise Value

Rank ROA ROE AG EG PG LR ER 1 2.20 5.16 9.15 11.52 9.39 77.34 35.36 2 2.60 6.22 9.94 11.50 9.51 72.31 33.96 3 2.85 6.33 9.35 10.10 10.86 73.37 33.83 4 2.71 6.02 9.54 10.88 10.59 73.29 33.82 5 2.98 6.60 10.23 11.09 11.02 71.84 32.70 6 2.69 6.48 10.33 10.96 10.36 73.39 32.36 7 2.92 6.98 10.89 10.97 10.80 73.64 31.90 8 3.02 7.39 10.78 11.20 10.35 73.50 31.77 9 3.06 8.28 11.14 11.68 11.85 72.21 31.36 10 3.21 9.16 12.54 13.73 12.83 72.75 29.60

10-1 1.02 4.00 3.39 2.21 3.44 -4.59 -5.76 t 4.93 5.78 2.28 1.99 2.35 -2.50 -4.48

Table 4 Regression of Operating Performance on Franchise value This table reports the coefficients from panel regressions of insurer performance on franchise value, lagged performance, and tangible firm characteristics. The dependent variables include return on assets (ROA) and return on equity (ROE) The key independent variable is the three-year average regression-based franchise value (RFV3). The specific model used for the panel data is a fixed firm and fixed time effect model assuming cross-sectional and time-series dependence in the residual term. The sample period is from 1985 to 2007.

ROA ROE INTERCEPT 1.53*** -0.32 (4.00) (-0.45) RFV3 0.14*** 0.50*** (5.45) (6.26) LAGDEP 0.46*** 0.42*** (22.80) (15.48) SIZE 0.09 0.34 (1.44) (1.64) LEV -0.41+ 5.57*** (-1.71) (4.93) HERFL 0.85*** 2.50*** (4.63) (4.64) COMPETE -0.42** -1.27*** (-2.40) (-2.78) REINS -0.82*** -2.18*** (-5.71) (-5.10) STK 0.00 -0.25 (0.00) (-0.25) GROUP -0.02 0.14 (-0.22) (0.62) OWNS 0.46*** 1.18*** (4.08) (3.66) Adj. R2 0.225 0.179

Table 5 Persistence of Franchise Value This table reports averaged regression-based franchise value (in percentage points) from year one (Y1) to year t (Y5) insurer deciles sorted on the three-year regression based franchise value (RFV3) in year zero. D10 insurers have highest RFV3 values. D1 insurers are the 10% of insurers with lowest RFV3. The sample period is from 1985 to 2007.

Rank of RFV3 RFV(Y1) RFV(Y2) RFV(Y3) RFV(Y4) RFV(Y5) 1 -2.92 -2.64 -2.24 -1.96 -1.70 2 -1.37 -1.23 -1.03 -0.86 -0.73 3 -0.67 -0.60 -0.48 -0.40 -0.30 4 -0.22 -0.18 -0.17 -0.14 -0.13 5 0.12 0.12 0.15 0.14 0.18 6 0.41 0.40 0.38 0.36 0.33 7 0.66 0.62 0.55 0.52 0.50 8 0.98 0.94 0.86 0.80 0.78 9 1.30 1.22 1.14 1.08 0.99 10 1.89 1.79 1.66 1.53 1.44

10-1 4.81 4.43 3.90 3.49 3.14 t 80.79 79.59 53.76 40.99 37.92

Table 6 Effect of Franchise value on Performance in Soft and Hard Markets

This table reports the coefficients from panel regressions of insurer performance on franchise value in different market conditions. The specific model used for the panel data is a fixed firm and fixed time effect model assuming cross-sectional and time-series dependence in the residual term. The dependent variables include return on assets (ROA) and return on equity (ROE). The key independent variables are the three-year average regression-based franchise value (RFV3) and the interaction of RFV3 and market conditions. In the first two columns, we measure market conditions with a annual dummy variable, SOFT. SOFT equals to 1 if the industry average loss ratio in a year is less than the median of the industry average loss ratio in all sample years. It equals to 0 if otherwise. In the last two columns, we measure market conditions using the industry average loss ratio itself in each year. The sample period is from 1985 to 2007.

ROA ROE ROA ROE INTERCEPT 0.94*** -2.05*** 10.85*** 26.35*** (2.77) (-2.68) (5.63) (4.10) RFV3 0.16*** 0.64*** 0.08 0.31 (4.54) (5.13) (2.12) (2.39) SOFT 1.08*** 3.09***

(3.81) (3.45)

RFV3*SOFT -0.05 -0.22

(-1.99) (-2.31)

LR -0.12*** -0.34*** (-4.95) (-4.43) RFV3*LR 0.08 0.14 (1.86) (1.73) LAGDEP 0.45*** 0.41*** 0.45*** 0.41*** (23.62) (16.31) (26.55) (17.90) SIZE 0.05 0.23 0.02 0.14 (0.79) (1.05) (0.33) (0.71) LEV -0.41 5.62*** -0.36 5.75*** (-1.61) (4.39) (-1.57) (4.11) HERFL 0.67*** 1.99*** 0.62*** 1.83*** (3.54) (3.46) (3.17) (3.18) COMPETE -0.32** -1.02** -0.21 -0.72+ (-2.10) (-2.42) (-1.38) (-1.67) REINS -0.91*** -2.44*** -0.94*** -2.52*** (-6.27) (-5.58) (-6.31) (-5.67) STK 0.12 0.02 0.24 0.39 (0.34) (0.02) (0.72) (0.37) GROUP -0.00 0.17 0.01 0.20 (-0.05) (0.67) (0.09) (0.79) OWNS 0.46*** 1.17*** 0.45*** 1.14*** (3.98) (3.52) (3.85) (3.43) Adj. R2 0.238 0.189 0.244 0.194

Table 7 Competition and the Effect of Franchise value This table reports the coefficients from panel regressions of insurer performance on franchise value in different competition levels. The specific model used for the panel data is a fixed firm and fixed time effect model assuming cross-sectional and time-series dependence in the residual term. The dependent variables include return on assets (ROA) and return on equity (ROE). The key independent variables are the three-year average regression-based franchise value (RFV3) and the interaction of RFV3 and COMPETE. The variable COMPETE measures the degree of competition of all the business lines an insurer are engaged. The sample period is from 1985 to 2007.

ROA ROE INTERCEPT 1.53*** -0.32 (4.00) (-0.44) FV3 0.17*** 0.62*** (4.64) (5.60) FV3*COMPETE -0.06 -0.24 (-1.75) (-2.18) LAGDEP 0.46*** 0.42*** (22.82) (15.50) SIZE 0.09 0.34 (1.43) (1.63) LEV -0.41+ 5.58*** (-1.71) (4.94) HERFL 0.86*** 2.51*** (4.65) (4.67) COMPETE -0.42** -1.28*** (-2.40) (-2.79) REINS -0.82*** -2.18*** (-5.71) (-5.10) STK 0.00 -0.23 (0.01) (-0.23) GROUP -0.02 0.14 (-0.23) (0.59) OWNS 0.46*** 1.18*** (4.07) (3.63) Adj. R2 0.225 0.180

Figure 1: Persistence of Franchise value for Top and Bottom Decile Insurers

1 2 3 4 5 6 7 8 9 1012

34 5

0

0.5

IA Rankt+1

D10 Firms

Year

Pro

b

1 2 3 4 5 6 7 8 9 1012

34 5

0

0.5

IA Rankt+1

D1 Firms

Year

Pro

b

We look at the persistence of franchise value for D10 and D1 insurers shorted by RFV3. In each year t, we sort insurers into deciles based on their RFV3. We plot the percentage distribution of these firms across RFV3 deciles in the following five years.