Name Changes and Future Growth: Evidence from the Life Insurance Industry
James M. Carson,1 Cassandra R. Cole,2 and Stephen G. Fier3
Abstract: Prior research on corporate name changes has largely focused on publicly
traded firms and generally does not identify the source of any market or performance
effect. In contrast, we consider whether name changes impact firm wealth by increas‐
ing revenue and if this revenue is driven by increased policies sold. We examine the
impact of name changes using a large sample of life insurers for which detailed
financial and operational data are available, including the number of policies in force.
The results suggest that corporate name changes have positive wealth effects for
insurers and that this effect is driven to some degree by increased policy counts for
those insurers that primarily sell to individual consumers. [Key words: name changes,
revenue and policy growth, life insurance]
1
Daniel P. Amos Distinguished Professor of Insurance, Terry College of Business, University
of Georgia, 211 Brooks Hall, Athens, GA 30602, [email protected]
Carson’s research has appeared in journals such as the Journal of Risk and Insurance, Journal of
Banking and Finance, the North American Actuarial Journal, Insurance: Mathematics and Econom‐
ics, the Journal of Insurance Issues, and Risk Management and Insurance Review. He served as
interim editor of the Journal of Insurance Regulation. He is a past president of ARIA and
WRIA and is a past editor of this Journal.
2
Robert L. Atkins Professor in Risk Management and Insurance, College of Business, Florida
State University, 821 Academic Way, Room 525, PO Box 3061110, Tallahassee, FL 32306,
[email protected], phone 850‐644‐9283, fax 850‐644‐4077
Cole’s research has appeared in journals such as the Journal of Risk and Insurance, the North
American Actuarial Journal, the Journal of Insurance Issues, and the Risk Management and Insur‐
ance Review. She is past president of SRIA and is the co‐editor of the Journal of Insurance
Regulation.
3
Liberto‐King Assistant Professor of Finance, School of Business Administration, University
of Mississippi, Holman Hall 338, PO Box 1848, University, MS 38677, [email protected],
phone 662‐915‐1353, fax 662‐915‐5821
Fier’s research has appeared in journals such as the Journal of Banking and Finance, the Journal
of Risk and Insurance, the North American Actuarial Journal, the Risk Management and Insurance
Review, and the Journal of Insurance Regulation.
1
Journal of Insurance Issues, 2016, 39 (1): 1–37.
Copyright © 2016 by the Western Risk and Insurance Association.
All rights reserved.
2
CARSON, COLE, AND FIER
INTRODUCTION
he name of a corporation can be a valuable asset to the firm, as it
conveys important information to the firm’s consumers, investors,
competitors, and other stakeholders regarding the company’s characteris‐
tics (Tadelis, 1999).4 While a firm’s name arguably represents the essence
of the firm as an entity, corporate name changes occur regularly, with one‐
third of U.S. publicly traded companies undergoing a corporate name
change since 1925 (Wu, 2010). Given the billions of dollars companies
spend to build name recognition and the costs associated with the name
change process, economic theory suggests that organizations should
engage in this strategic business decision only if the name change is
expected to generate some significant benefits for the firm.5 These benefits
could include distancing the company from bad publicity or signaling new
information to the market, such as a change in business focus. For example,
in 2009, AIG changed its name to Chartis in order to distance itself from its
acceptance of a bailout from the U.S. government (Ng, 2012).6 Additionally,
in 2001, American Heritage Life Insurance Company began to be identified
as Allstate Financial Workplace Division to reflect the fact that the company
had increased its focus on “becoming the country’s dominant provider of
insurance products at the workplace” (LifeHealthPro, 2001).7 Name changes can also occur as part of other activities, such as
mergers, or to more closely tie a company to a particular product or
affiliated company. In 2004, when Anthem Inc. and WellPoint merged, the
decision was made to use the WellPoint company name. However, with the
increase in direct marketing to consumers through the health insurance
exchanges mandated by the Affordable Care Act, the company made the
decision in 2014 to change the name from WellPoint to Anthem in an effort
to capitalize on this “better‐recognized” brand (Mathews, 2014).8 T
4
These characteristics might include the quality of the product sold by the firm, the history
of the firm, or the future plans of the firm. 5
See Koku (1997) and Cole, Fier, Carson, and Andrews (2015) for a discussion of the name
change process and the costs associated with name changes.
6
Interestingly, only three years after AIG changed its name to Chartis, it re‐marketed itself
using the AIG name. Outside of the insurance industry, Philip Morris Companies became
Altria Group Inc. While executives indicated that the name change was designed to signal to
consumers the broad nature of the products sold by the company, some felt it was simply a
move to distance the corporation from its tobacco products, which had become “synony‐
mous with cancer sticks” (Fairchild, 2013). 7
As another example, Central Reserve Life Corporation changed its name to Ceres Group
Inc. in an effort to more accurately reflect the direction of the company (Insurance Advocate,
1998). NAME CHANGES AND FUTURE GROWTH
3
Given the frequency with which name changes take place and the
importance and significant financial value that can be placed on corporate
names, a growing body of research on this subject has emerged in the
marketing and finance literature. In theory, name changes can alter con‐
sumers’ perceptions of companies and ultimately have an impact on firms’
financial performance. Pauwels‐Delassus and Descotes (2013) discuss
brand name changes and how these changes can impact the trust and
loyalty of consumers. The authors state, “(b)ecause brands … act as risk
reducers for consumers, replacing the brand might disturb the consumers’
brand trust and affect brand relationship.” In other words, while manage‐
ment should make the decision to change a name only if it maximizes value,
such a change could actually adversely affect the firm. In examining the financial impact of name changes on the firm, prior
studies have generally focused on stock price reaction to name changes,
with studies reporting mixed results. Some studies find evidence of a
positive market reaction to the announcement of a name change (e.g.,
Horsky and Swyngedouw, 1987; Bosch and Hirschey, 1989; Cooper, Dim‐
itrov, and Rau, 2001; Wu, 2010; Green and Jame, 2013).9 Alternatively, others
suggest that a name change is negatively perceived by investors (Mase,
2009) or has no impact on the value of the firm (e.g., Howe, 1982; Karpoff
and Rankine, 1994). Cooper, Gulen, and Rau (2005) examine the name changes of mutual
funds. With a sample of close to 300 funds, the authors find that those that
alter the mutual fund names to better represent a “current hot style” of
investments enjoyed an average cumulative abnormal flow, despite no
evidence of any improvement in the performance of the fund.10 Koku (1997)
uses a small sample of publicly traded firms in the services industry to
examine the impact of name changes on firms’ price‐to‐earnings ratios. The
author finds that every firm experiencing a name change also experienced
an increase in its price‐to‐earnings ratio. These results suggest that firms
that undergo name changes show improvements in financial performance. 8
At the time of the WellPoint name change, most of the company’s health insurance coverage
was sold under the Anthem name. Similarly, in 1997, First Transamerica Life Insurance
Company became Transamerica Life Insurance Company of New York. The firm made this
change so they could be more easily associated with the parent (Insurance Advocate, 1997). 9
Green and Jame (2013) differs from the other studies in that the authors consider the impact
of the change in fluency scores associated with corporate name changes. The authors find
that name changes generally increase fluency and that these increases in fluency are associ‐
ated with increases in firm value.
10
The authors identify firms with “current hot style” changes as being any change that incor‐
porated the terms “value,” “growth,” “small,” or “large” into the mutual fund name.
4
CARSON, COLE, AND FIER
Most recently, in their analysis of property‐casualty insurers, Cole,
Fier, Carson, and Andrews (2015) examine name changes within the U.S.
property‐casualty insurance market. Their focus on the property‐casualty
insurance market allows them to directly examine the effect of a name
change on changes in revenue for a large sample of both publicly traded
and non–publicly traded firms. Their results indicate that firms that
undergo a name change experience an increase in premium growth (i.e.,
revenue), and that the increase in revenue is influenced in part by the firm’s
organizational form. The results of the study suggest that the positive
market reaction to name changes that has been previously documented
may not necessarily be an irrational response, but rather it is consistent
with the exhibited increase in revenue. While Cole et al. (2015) provide
evidence of revenue growth following the occurrence of a corporate name
change, they do not provide any empirical evidence regarding the source
of the revenue growth. We extend the study of Cole et al. (2015) by re‐examining the impact
of a name change in the U.S. life insurance industry. While Cole et al. (2015)
provide evidence of increased revenues for property‐casualty insurers
following name changes, we cannot assume that such a relationship also
exists for life insurers. The life insurance industry has fundamental differ‐
ences from the property‐casualty industry, and thus it remains an empirical
question whether or not the general effect holds for life insurers.11 Addi‐
tionally, the nature of the data that are reported by life insurers to the
National Association of Insurance Commissioners (NAIC) differs from that
reported by property‐casualty insurers. Since the NAIC requires life insur‐
ers to report the number of policies in force at the end of a given year, we
are able to utilize this data to not only determine whether the occurrence
of a name change impacts revenue growth but also provide some initial
insight into the source of the revenue improvement. Our study contributes to existing literature in this area in several ways.
First, a significant number of name changes have occurred in the life
insurance industry, thus providing a large sample with which to examine
the impact of name changes.12 Second, given that all U.S. insurers are
required to report detailed financial and organizational data to the NAIC
on an annual basis, we are able to examine the impact of insurer name
11
For example, while auto and homeowners’ insurance are generally required by state regu‐
lators and/or lenders, life insurance and annuities generally represent voluntary purchases
that often hinge on a persuasive agent to convince buyers of the need for coverage. Addi‐
tionally, the life business generally is longer‐term in nature than the property‐casualty busi‐
ness. Further, we ultimately document empirically that while the general results are
consistent for both life and property‐casualty insurers, there are non‐trivial differences
across the two groups. NAME CHANGES AND FUTURE GROWTH
5
changes for companies with varying ownership structures. Third, the use
of insurer premium data allows us to provide evidence on whether name
changes are revenue‐increasing events. Finally, the fact that life insurers
are required by the NAIC to report information that is not required of
property‐casualty insurers allows us to investigate the source of any poten‐
tial revenue growth. While prior literature has documented an association
between a name change event and revenue growth following that event, it
has yet to provide any evidence as to the cause of this increase. By focusing
on the life insurance industry, we are able to investigate whether the change
in revenue is driven by an increase in the number of policies sold, which
adds additional insight into the relation between name changes and reve‐
nue growth. As a preview to our results, we find that name changes have a positive
impact on insurers. Specifically, life insurers who change their name expe‐
rience significantly higher growth in premium income in the year follow‐
ing a name change when compared to insurers that did not change their
name. These results are consistent for all name changes as well as for
“significant” name changes in which the firm is no longer identifiable
simply by its name. These results are consistent with the findings of Cole
et al. (2015), suggesting that the positive impact of name changes is
observed within both the property‐casualty and life insurance industries.
However, unlike Cole et al. (2015), which only observes this positive effect
for stock insurers in the property‐casualty industry, we find that the
positive effect is observed for all life insurers, regardless of ownership
structure. We explain that this difference could be attributed to managerial
discretion and the different opportunities that might exist in the life insur‐
ance market as compared to the property‐casualty market. Additionally,
we find evidence that this premium growth is due, at least in part, to an
increase in policy sales. More specifically, insurers undergoing name
changes experience an increase in policies in force the year following the
name change event. However, for both premium growth and policy
growth, this result appears to be driven by insurers that sell primarily to
individual consumers (as opposed to businesses/groups). These results
build on the findings of prior research and further explain the link between
corporate name changes and revenue growth. The remainder of the paper is organized as follows. In Section 2, we
outline our hypotheses, followed by a detailed description of the data and
empirical model utilized in Section 3. In Section 4, we discuss the name
12
For the purpose of our empirical analysis, our sample consists of 223 name changes occur‐
ring between 1996 and 2012. Details on the number of name changes by year are provided in
Figure 1.
6
CARSON, COLE, AND FIER
change activity that has occurred in the U.S. life insurance industry and
then present the results of our empirical analyses in Section 5. Section 6
summarizes and concludes.
HYPOTHESES
Economic theory suggests that firms should not undertake a name
change unless firm management believes that it is a profit‐maximizing
activity. This notion has motivated several studies that have examined the
market response to corporate name changes, with many studies finding
that name changes are viewed in a positive manner by investors (e.g.,
Karpoff and Rankine, 1994; Lee, 2001; Cooper et al., 2001). Additionally,
recent research suggests that the occurrence of a name change in the prior
year is associated with an increase in revenue (premium) growth in the
following year for firms that operate in the U.S. property‐casualty insur‐
ance industry (Cole et al., 2015). Although evidence suggests that corporate
name changes are associated with an increase in revenue growth for
property‐casualty insurers, the differences between the property‐casualty
and the life insurance markets make it unclear as to whether a similar
relation would exist for life insurers. Given the findings of prior research,
our first hypothesis is stated as:
H1. The occurrence of a name change in the prior year is associated with a
positive change in revenue for life insurers.
Further, while the occurrence of a corporate name change may be
associated with an increase in revenue, the association may also be depen‐
dent on the firm’s target market. Specifically, the life insurance industry is
characterized by products that are marketed to individuals (typically
referred to as “ordinary” life products) and products that are marketed to
businesses and groups. If insurers that primarily sell to both types of
consumers experience an increase in revenue, this would suggest that any
unobserved corporate change is affecting both consumer types in a similar
manner.13 However, there may be differential effects across the two cus‐
tomer types. For example, if an association between a prior year name
change and a change in revenue was found only for those firms selling
primarily to individuals, this would indicate that either the insurer made
a change that had some impact on the price or product, or that no substan‐
tial change was made, but individuals interpreted the name change signal
13
The change would likely be related to price, quantity, coverage terms, underwriting stan‐
dards, or other associated internal changes. NAME CHANGES AND FUTURE GROWTH
7
as important.14 Given these possibilities, we test for differences across
customer types and offer our formal hypothesis in the null form as:
H2. The relation between past corporate name changes and changes in
revenue does not vary by business focus.
As well, the life insurance industry is characterized by the existence of
multiple organizational forms. The two most prevalent organizational
structures within the industry are the stock organizational form and the
mutual organizational form. As discussed below, stock insurers generally
are viewed as having a better ability to control owner‐manager conflicts
than mutual insurers, and this ability results in stock insurers having
greater managerial discretion than mutual insurers (e.g., Mayers and
Smith, 1981). Differences in risk, capital, and business focus/complexity all
have been attributed to the differences in managerial discretion that exist
across the two organizational forms. Given these differences, Cole et al.
(2015) test whether stocks and mutuals in the property‐casualty insurance
industry experience similar increases in revenue following a corporate
name change. The authors report that while stock insurers exhibit an
increase in revenue after a name change, there was no statistical relation
between name changes and revenue growth for mutual insurers. Prior
literature suggests that managerial discretion influences the operations
and decisions of management in both the property‐casualty and life mar‐
kets. However, there are still substantial differences across the two markets
to suggest that results might differ. We re‐examine the potential differences
across organizational form for the life insurance industry with Hypothesis 3:
H3. Stock insurers experience an increase in revenue following the occur‐
rence of a name change, while mutual insurers will not experience a change
in revenue following a corporate name change.
As noted previously, while much of the prior literature has focused on
documenting responses to the occurrence of a name change, little research
has examined the source of the response. By focusing on the U.S. life
insurance industry, we are able to test one possible source of this increase
in revenue—namely, increases in the number of policies in force.15 If there
is an increase in revenue following corporate name changes (as presented
by Cole et al., 2015), then one cause for this increase could be greater policy
sales. Evidence supporting an increase in policy growth around a corporate
14
Assuming corporations have greater resources (both financial and non‐financial) than
individuals and they have the ability to incur greater search costs, it is likely that corporate
entities will have the ability to make a more informed decision regarding the signal pro‐
vided by the corporate name change.
8
CARSON, COLE, AND FIER
name change would provide support for the notion that revenue growth
is attributed to policy growth and not to other potential factors such as
price increases. We formally test this relationship and present our fourth
hypothesis: H4. The occurrence of a name change in the prior year is associated with a
change in the number of policies in force. Below we provide an overview of our data and empirical methods, and we
discuss the variables used to test each of our hypotheses. DATA, EMPIRICAL METHODS, AND VARIABLES
Data
Our initial sample consists of life insurers that write business in the
U.S. and report to the NAIC for the period from 1996 through 2012. We
identify corporate name changes and other corporate events through A. M.
Best’s Corporate Changes and Retirements Database. We then match the
data obtained from this database to insurer‐specific data found in the NAIC
InfoPro Database. We then apply a number of screens to the data. First, we
remove any firms that report negative assets, premiums, surplus, policies
in force, advertising expenditures, lapses, or liabilities. Next, we remove
firms that do not report to the NAIC for at least two consecutive years.16
Finally, we remove any firms that are not of the stock or mutual organiza‐
tional forms (as in Liebenberg and Sommer, 2008).17 After applying the
screens, the final sample consists of 1,121 unique firms, with 11,396 total
firm‐year observations. Of these 1,121 unique firms, approximately 25
percent report a name change during the sample period. 15
While U.S. life insurers are required by the NAIC to report information regarding policy
issuance and policy counts, property‐casualty insurers are not required to provide this
information in their annual statements. Hence, we focus on the life insurance industry so as
to evaluate one potential cause for the previously reported increase in revenue growth,
which could not otherwise be evaluated in the property‐casualty industry. 16
We remove those firms that do not report at least two consecutive years of data to the
NAIC as two consecutive years of data are necessary for the calculation of the dependent
variables. 17
“Other” organizational forms that are not included in the study include: Blue Cross/Blue
Shield Mutuals, Blue Cross/Blue Shield Stocks, HMDIs, Limited Liability Corporations,
Non‐Profits, and Risk Retention Groups. NAME CHANGES AND FUTURE GROWTH
9
Empirical Methods
An examination of the data reveals that firms that engage in a name
change during the sample period are larger and are more often affiliated
with insurance groups in comparison to those that do not change names.
We match each firm that underwent a name change with a comparable firm
that did not change its name to help ensure that some confounding factors
not specifically controlled for in the model are not impacting the resulting
relation observed between our primary variable of interest and the depen‐
dent variable.18
In order to test our hypotheses, we estimate variations of the following
model using our matched sample:
Growth i t =  +  1 NameChange i t – 1 +  2 AdPrem i t – 1 +  3 Size i t – 1 +
 4 Liquidity  5 LOBDivers i t – 1 +  6 GEODivers i t – 1 +
 7 Reins i t – 1 +  8 Leverage i t – 1 +  9 Age i t – 1 +  10 Stock i t – 1 +
 11 Group i t – 1 +  i +  i ,t
(1)
The Growth i t variable in Equation (1) represents both premium (rev‐
enue) growth and policy growth (discussed below). The results of the
Hausman test suggest that fixed effects are preferred over random effects;
thus, we control for time‐invariant effects by including firm fixed
effects   i  in each of our models. We also control for unobservable time
effects by including yearly control variables   t . Given our use of a panel
dataset and the potential for standard errors to be biased downward, we
report standard errors that are clustered at the firm‐level (Petersen, 2009).
Finally, all continuous variables are winsorized at the one percent and 99
18
We use a Mahalanobis distance measure to match each name change firm (treatment firm)
with a similar firm that does not change its name (non‐treatment firm) within each sample
year using size, group affiliation, and organizational structure. In some cases, a non‐treat‐
ment firm was matched with more than one treatment firm in a given year. However, we
eliminate any duplicate matches so that each non‐treatment firm appears in the sample only
once. This is done so that fixed effects methodology can be utilized. Results are similar to
those presented here when this assumption is relaxed and non‐treatment firms are allowed
to appear in the sample multiple times in a given year if matched with more than one treat‐
ment firm. However, in these models, only year‐specific effects can be included given the
repeat of non‐treatment firms in a given year.
10
CARSON, COLE, AND FIER
percent levels and all independent variables are one‐year lags.19 Below we
discuss the variables employed in this study. Variables
Dependent Variables
We employ several dependent variables in order to evaluate: (1)
whether name changes are associated with a change in revenue, and (2)
whether the change in revenue is driven by increased policy sales.20 Below
we provide an overview of each of these dependent variables.
Revenue Growth. The relation between corporate name changes and
revenue growth is tested using a sales‐weighted industry‐adjusted growth
measure (as in Cole et al., 2015), as follows:
(1) First, we calculate revenue (premium) growth for each company, by
line of business. For the purpose of this study, we identified eleven
unique lines of business, as reported in the “Analysis of Operations by
Lines of Business” section of the NAIC annual statements.21 Following
Epermanis and Harrington (2006), growth for each line of business is
calculated as:
Revenue i ,j ,t = ln  Premiums i ,j ,t  – ln  Premiums i ,j ,t – 1 
(2)
19
To further ensure that outliers are not driving the results obtained, we also re‐estimate all
models after dropping observations with studentized residuals that are greater than 4 or
less than –4 (Choi and Weiss, 2005). In general, results are qualitatively and quantitatively
similar to those presented in this study. For instances where differences do exist, we discuss
those differences below. Results are available from the authors upon request. 20
The purpose of the study is to determine if corporate name changes are associated with
future changes in premium growth. However, as noted by an anonymous reviewer, it is pos‐
sible that a firm could experience a name change in the midst of its growth path and that a
name change may nor may not contribute to the firm’s continued growth. As such, there is
the potential for endogeneity. To address this issue, we model name change as a function of
the lag of the growth measure. If the growth measure is significant and positive, this would
provide evidence of potential endogeneity. In both the premium growth and growth in
lapse rates models, neither variable is significant. However, in the policy growth model, the
lag of the growth measure is significant and negative, suggesting that life insurers experi‐
encing policy growth in the prior year are less likely to undergo name changes than those
not experiencing policy growth. To determine if this relation impacts the results presented
in the policy growth models, we re‐estimate the models presented in Table 5, adding in a
lead name change indicator equal to one if the company experienced a name change in the
following year (see Wooldridge, 2002: 254–255 for a description of the procedure). As
expected, the lead name change variable is significant and negative. However, the results on
the lagged name change variable for those insurers focused in individual lines are consistent
with those reported in Table 5. This suggests that while insurers that are experiencing policy
growth are less likely to undergo a name change, those that do still benefit in terms of addi‐
tional growth.
NAME CHANGES AND FUTURE GROWTH
11
for insurer i, in line of business j, in year t. (2) After calculating the line-specific growth rate, we calculate the
industry growth rate for each line of business. Similar to the firmspecific growth rate, the industry growth rate is calculated for the
eleven lines of business and is given as:
Revenue j ,t = ln  Premiums j ,t  – ln  Premiums j ,t – 1  .
(3)
(3) Next, we calculate the difference between each firm-specific line-ofbusiness growth rate and the industry-specific growth rate, where:
AdjustedRevenue i ,j ,t = Premiums i ,j ,t – Premiums j ,t .
(4)
(4) Once we have calculated the adjusted growth rate, we then apply a
weight to each firm-line combination, where the weight is equal to the
proportion of firm-specific premiums that are attributed to a particular
line of business j. After the weights have been applied to each line, we
sum the weighted growth values, resulting in our final sales-weighted
industry-adjusted growth measure, given as:
11
Adjusted Growth i ,t =
  wi ,j ,t  Adjusted
Revenue i ,j ,t 
(5)
j=1
where: Premiums i ,j ,t
w i ,j ,t = ----------------------------------Premiums i ,t
(6)
and 0  w i ,j ,t  1 . Policy Growth. In addition to examining the potential effect of corporate
name changes on firm revenue growth, we also attempt to determine the
source of the revenue change by examining the change in quantity of
policies sold. First, we test the relation between the occurrence of a past
21
The eleven unique lines of business are: (1) industrial life, (2) ordinary life insurance, (3)
ordinary individual annuities, (4) ordinary supplementary contracts, (5) credit life (group
and individual), (6) group life insurance, (7) group annuities, (8) accident and health—
group, (9) accident and health—credit, (10) accident and health—other, and (11) an aggre‐
gate for all other lines of business. 12
CARSON, COLE, AND FIER
name change and the growth of end‐of‐year life policies in force. Then, we
consider the relation between a prior year name change and lapse rates. If
a name change has a positive impact on policy count as a result of increased
policy sales, we would expect to see a positive relation between a past name
change and the change in policies in force and an insignificant relation
between policy lapses and name changes.22 We obtain the number of
policies in force and the number of policies that were lapsed each year by
extracting that information from the “Exhibit of Life Insurance” that is
contained in the NAIC annual statements, and policy change is calculated
as:
Policies i ,t = ln  Policies in Force i ,t  – ln  Policies in Force i ,t = 1 
(7)
for insurer i in year t. The change in lapse activity is calculated in a similar
manner to that presented in Equation (7), but we replace policies in force
with policy lapses. Independent Variables
Name Change. The primary focus of this study is to examine the
potential effect of a corporate name change on the firm and provide a
possible explanation for the observed effect. As noted previously, prior
literature has found that the market commonly responds positively to the
announcement of a name change and that firm performance can improve
around the occurrence of a corporate name change (e.g., Horsky and
Swyngedouw, 1987; Cooper, Dimitrov, and Rau, 2001; Lee, 2001). Addition‐
ally, evidence suggests that firms in the U.S. property‐casualty insurance
industry exhibit an improvement in revenue in the year following the
occurrence of a name change (Cole et al., 2015). We account for the occur‐
rence of a prior‐year firm‐specific name change by including a binary
variable equal to one for firms that undergo corporate name changes (as
reported in Best’s Corporate Changes and Retirements Database). If a name
change has a positive impact on a life insurance firm, we anticipate a
positive relation between the name change variable and our sales growth
and policy growth measures if name changes in the life insurance industry
have a positive influence on the firm. In addition to the name change
variable, we include several other control variables, discussed below. 22
While a significant relation between name changes and lapse rates would be indicative of a
positive response to the name change, it could also suggest that the increase in policies in
force was due to the reduction in lapse activity rather than due to an increase in policy sales.
NAME CHANGES AND FUTURE GROWTH
13
Advertising Expenditures. Prior literature provides support for the
notion that advertising has the ability to communicate information to
consumers (either about the product or about the firm) and that consumers
may act on the information that is relayed through advertising by purchas‐
ing advertised products (e.g., Sheldon and Doroodian, 1989; Zheng and
Kaiser, 2008; Cole et al., 2015). However, while some studies find that
advertising can effectively impact product demand, other studies have
found that advertising may not be an effective means to influence con‐
sumer behavior (e.g., Kinnucan et al., 2001; Duffy, 2003).23 Given that
advertising has the potential to affect the consumer’s decision to purchase
a product from a firm, we control for advertising intensity by including the
ratio of advertising expenditures to total premiums.24
Firm Size. The life insurance industry is characterized by long‐term
contracts in which the insurer may not be required to make a claims
payment until decades after the inception of the policy, if ever. Because of
this, it is likely that insurance purchasers will prefer to select a firm that
has greater financial resources, as these firms tend to be viewed by con‐
sumers as less risky (e.g., Walden, 1985; Cummins and Sommer, 1996;
Sommer, 1996). We control for firm size by including the natural logarithm
of total firm assets. Liquidity. Beyond the need to pay life and annuity benefits, which
should be relatively predictable assuming a sufficiently large pool of
insureds, life insurers commonly offer products that allow insureds to take
loans on their policies and withdraw funds from their policies. These
additional options that exist within life policies, combined with the poten‐
tial for claims payments to exceed predicted amounts, require that insurers
maintain a level of liquid assets that will allow the firm to meet these
potentially unexpected financial obligations. We proxy for firm liquidity
by including the ratio of total admitted assets to total liabilities.25 A greater
value for this proxy suggests that a firm has a greater level of liquidity.
Diversification. Firms have the ability to diversify both geographically
and across lines of business. Both forms of diversification should have the
effect of reducing firm risk if the diversification allows the firm to: (1)
23
Kinnucan et al. (2001) find that advertising has no effect on the demand for non‐alcoholic
beverages. However, they do find that advertising has an effect on the distribution of
demand among the non‐alcoholic beverage space. 24
A similar measure of advertising intensity has been used in prior research (e.g., Gardner
and Grace, 1993; Kim, Mayers, and Smith, 1996; Choi and Weiss, 2005; and Choi and
Elyasiani, 2011). 25
A similar measure of liquidity is used by Cummins, Tennyson, and Weiss (1999), who use
the ratio of cash and invested assets to liabilities as their proxy of liquidity in the life insur‐
ance industry. 14
CARSON, COLE, AND FIER
increase the size of the risk pool, and (2) further diversify the firm’s overall
book of business. The increased risk pool should allow the firm to more
accurately predict future losses if the pool consists of relatively homoge‐
neous individuals, while writing across additional lines and states should
allow the firm to take advantage of coinsurance effects.26 If diversification
(either product or geographic) is associated with a reduction in risk, then
one may anticipate that consumers will prefer policies from more diversi‐
fied firms.27 We account for product diversification by including the com‐
plement of the line‐of‐business Herfindahl‐Hirshman Index (HHI), calcu‐
lated as the sum of squared product shares, based on direct premiums
written across eleven lines of business. The line of business diversification
variable is given as:
11
LOB Div i ,t = 1 –

j=1
 Premiums i ,j ,t
 -----------------------------------
 Premiums i ,t 
2
(8)
for insurer i, in line of business j, in a given year t. Similarly, we calculate
our geographic diversification measure as the complement of the geo‐
graphic HHI, which is calculated using direct premiums written across the
50 U.S. states and the District of Columbia. The geographic diversification
variable is calculated as:
51
GEO Div i ,t = 1 –

s=1
 Premiums i ,s ,t
 -----------------------------------
 Premiums i ,t 
2
(9)
for insurer i, in state s, in a given year t. Reinsurance Utilization. As firms write greater levels of insurance cov‐
erage, their ability to write future business declines due to surplus drain
(Babbel and Merrill, 2005).28 Given this relation, we also control for the
effect that reinsurance might have on revenue and policy growth. We proxy
26
Coinsurance effects refer to instances where a firm operates in multiple businesses (or
lines of business) where the cash flows are not perfectly correlated (Tong, 2012). 27
Cummins and Nini (2002) also note that consumers may prefer insurers that are diversi‐
fied across lines of business if they prefer “one‐stop shopping.” 28
Surplus drain refers to a situation where expenses must be recognized immediately while
premiums must be recognized only when earned. This effectively causes a situation where
writing greater amounts of business reduces the level of surplus, as expenses are greater
than the recognized (earned) premiums. NAME CHANGES AND FUTURE GROWTH
15
for reinsurance utilization by including the ratio of reinsurance assumed
minus reinsurance ceded, scaled by total assets. Leverage. Prior literature argues that while leverage can benefit the
firm, increasing levels of insurance leverage can have the effect of increas‐
ing the likelihood of insurer insolvency (e.g., Carson and Hoyt, 1995;
Staking and Babbel, 1995). If increasing levels of leverage are associated
with increased risk, one may anticipate an inverse relation between lever‐
age and premium/policy growth. However, given the construction of our
leverage variable, it is also possible for a positive relation to exist. Specifi‐
cally, we account for insurance leverage using the ratio of premiums to
policyholders’ surplus. Thus, it is possible that a positive relation exists
between our leverage measure and growth, as an increase in premiums
could result in an increase in insurer leverage (Cole et al., 2015). Firm Age. It is generally argued that there is an inverse relation between
the growth opportunities available to a firm and the age of a firm, where
younger firms have the ability to grow faster than older firms (e.g., Evans,
1987; Variyam and Kraybill, 1992; Ranger‐Moore et al., 1995; Choi, 2010).
We control for firm age by including the age of the firm, based on when
the firm was first established. Organizational Form. The life insurance industry is largely populated
by firms that are organized either as stock insurers or as mutual insurers.
Prior literature contends that an important difference between these two
organizational forms is the ability to control owner‐manager conflicts.
Specifically, it is argued that the stock organizational form is better
equipped to control potential owner‐manager conflicts than the mutual
form (Mayers and Smith, 1981). The outcome of this difference is that stock
insurers are afforded greater levels of managerial discretion than are
managers of mutual insurers. Previous studies of the managerial discretion
hypothesis find organizational form is associated with the type and com‐
plexity of business lines written (Mayers and Smith, 1988; Pottier and
Sommer, 1997), the riskiness of the firm (Lamm‐Tennant and Starks, 1993),
executive compensation (Mayers and Smith, 1992), and the level of capital‐
ization (Harrington and Niehaus, 2002).29 We control for organizational
form by including a binary variable equal to one for stock insurers. Group Membership. Life insurers can either be organized as single
unaffiliated firms or as members of a group. Prior literature notes several
important differences between the two types of firms, including risk
29
Also consistent with the managerial discretion hypothesis, Berry‐Stölzle, Liebenberg,
Ruhland, and Sommer (2012) find that mutual insurers in the property‐casualty insurance
industry are more likely to select related rather than unrelated lines of business when diver‐
sifying. 16
CARSON, COLE, AND FIER
(Cummins and Sommer, 1996; Sommer, 1996) and access to internal capital
markets (e.g., Powell and Sommer, 2007; Powell, Sommer, and Eckles, 2008;
Fier, McCullough, and Carson, 2013). Given the differences that exist
between affiliated and unaffiliated firms, we control for group membership
by including a binary variable equal to one for firms that are members of
a group. Summary statistics are provided in Table 1. In examining these statis‐
tics, we find that mean value of the NameChange variable is approximately
4.6 percent. This is higher than the value of 2.1 percent that was reported
by Cole et al. (2015) for the property‐casualty industry.30 However, it should
be noted that the authors used the entire available sample in their analysis
while we utilize a matched sample in our analysis. Therefore, it is not
surprising that this variable is higher in our matched sample. Second, on
average, firms experienced positive growth for both premiums written and
policies in force during the full sample period. We also find that the average
firm has approximately $6.36 billion in total assets, is relatively liquid, is
diversified geographically but concentrated across product lines, and
tends to have a leverage ratio near two. Finally, the overwhelming majority
of the life insurers in the sample are of the stock organizational form while
over 80 percent of the firms are members of a group. In the next section,
we discuss trends in name changes and the prevalence of name changes
during the sample period.
Name Change Activity in the U.S. Life Insurance Industry
Prior to estimating our empirical models, we first present a brief
overview of name change activity in the life insurance market for the period
from 1996 through 2012. Figure 1 presents the number of name changes
that took place during the sample period. The figure suggests that, on
average, approximately 42 name changes took place on an annual basis,
with the most name changes (60 name changes) occurring in 1996 and the
fewest (16 name changes) in 2008.31 This evidence suggests that while the
majority of firms are not undertaking name changes, the occurrence of a
name change is not a unique event in the life insurance industry. While Figure 1 suggests that name changes occur with some frequency
within the life insurance industry, the potential exists for the events to occur
in conjunction with other events, such as mergers, changes in ownership,
30
As indicated earlier, approximately 25 percent of the unique firms examined experienced a
name change during the sample period. However, the NameChange variable is equal to one
only in the year in which the name change occurred. 31
For an overview of the name change process in the insurance industry, see Cole et al.,
(2015). NAME CHANGES AND FUTURE GROWTH
17
Table 1. Summary Statistics and Variable Definitions
Variable
Definition
Mean Std. dev.
Premium‐
Growth
Sales‐weighted industry‐adjusted premium growth
0.0585
0.5542
PolicyGrowth
Natural logarithm of year‐end policies in force in year t minus the natural logarithm of year‐end policies in force in year t–1
0.0208
0.3543
LapseGrowth
Natural logarithm of policy lapses in year t minus 0.0150
the natural logarithm of policy lapses in year t – 1. 0.7139
NameChange
Binary variable equal to 1 for firms that experienced 0.0459
a name change in the prior year
0.2092
AdPrem
Ratio of advertising expenditures to total premi‐
ums
0.0090
0.0304
Size
Natural logarithm of total assets
19.5186
2.8663
Liquidity
Ratio of total admitted assets to total liabilities
3.2718
14.4605
LOB Divers
One minus the line‐of‐business Herfindahl‐
Hirschman Index, calculated using direct premi‐
ums written in eleven different lines of business
0.3111
0.2328
GEO Divers
One minus the geographic Herfindahl‐Hirschman Index, calculated using premiums written in 50 U.S. states and the District of Columbia
Reins
Reinsurance assumed minus reinsurance ceded, scaled by total assets
Leverage
Ratio of premiums to policyholders’ surplus
2.1028
2.4611
Age
Age of the firm, based on when the firm was first established
48.0209
34.4163
Stock
Binary variable equal to 1 for firms that are of the stock organizational form, 0 otherwise
0.9093
0.2872
Group
Binary variable equal to 1 for firms that are mem‐
bers of an insurance group, 0 otherwise
0.8238
0.3810
0.5930
–0.0558
0.3911
0.2847
Note: All continuous variables are winsorized at the 1st and 99th percentiles to reduce potential bias induced by extreme outliers.
and changes in domicile. In order to further examine name change activity
in the U.S. life insurance market, we also present the number of name
changes that occurred at the same time as some other corporate events.32
On average, approximately 20 percent of name changes occur in a year
when another major corporate event takes place. This indicates that while
other corporate events can be associated with the occurrence of a name
change, name changes frequently occur in the absence of any other
18
CARSON, COLE, AND FIER
70
60
50
40
30
20
10
0
1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
No Other Corporate Change
Other Corporate Change
Fig. 1. Life insurer name changes from 1996 through 2012.
Note: Data obtained from Best’s Corporate Changes and Retirements Database. Values for
“No Other Corporate Change” represent firms that experienced name changes in a given
year and did not experience another corporate event (as tracked by Best’s Corporate Changes
and Retirements Database). Values for “Other Corporate Change” represent firms that
experienced name changes in a given year and experienced another corporate event (as
tracked by Best’s Corporate Changes and Retirements Database). contemporaneous events. Taken together, the evidence presented in Figure
1 suggests that name changes occur on a relatively frequent basis in the life
insurance industry and they generally occur outside of other major corpo‐
rate events.
Given that a large proportion of the sample is composed of firms that
are members of groups, we take a closer look at whether name changes
occur simultaneously among group members.33 First, we calculate a mea‐
sure that captures the percentage of members of the group that change
names during the sample period by summarizing the number of firms that
underwent a name change across group codes and dividing by the number
of firms in the group. The sample average is five percent. We also examine
32
“Other” corporate events are those that are tracked by Best’s Corporate Changes and
Retirements database. “Other events” include: ceased operations, domiciliary change, disso‐
lution, liquidation, runoff, mergers, ownership changes, surrender/suspension of license,
and other events. We recognize the potential for these other events to impact the results of
our empirical analyses and re‐estimate each of our models to account for the potential effect
that contemporaneous events could have. 33
The number of group members within a group range from one to 13 in our matched sam‐
ple, and we identify group membership based on NAIC group code. For those firms identi‐
fied as belonging to a group but in which only one life insurer appears in the sample, the
other group members are property/casualty and/or health insurers. NAME CHANGES AND FUTURE GROWTH
19
the subset of firms in which all firms in the group changed names in the
same year. This produced a sample of eight groups and all groups had only
two or three life insurers in the group. Collectively, this suggests that for
firms that are members of a group, name changes tend to occur indepen‐
dently of one another and that it is common for individual group members
to experience a name change without other members undergoing a similar
change. In addition to examining the independence of name changes within
groups, we also consider the impact of name changes on the premium
growth of other members that do not undergo a name change. For those
insurers in which at least one group member changes its name (other than
that firm), the average premium growth for the other members of the group
is 6.6 percent. When we examine separately firms in which only one
member of the group changes its name, the premium growth rate is
approximately 5.5 percent, while when multiple group members engage
in name changes, the premium growth rate is more than 12 percent. This
suggests that for firms that do not engage in name changes, there is some
spill‐over effect or benefit if they are affiliated with a group in which one
of the member firms changes its name and that the spill‐over effect is
greater when multiple other firms engage in name changes. The fact that
a spill‐over effect might take place among group members is further
support for our inclusion of a group control variable. Given the potential costs of a name change (both monetarily and with
regard to potential loss of brand recognition), it would be anticipated that
firms make the decision to change a name as part of a profit‐maximizing
strategy, suggesting that the decision to undergo a name change should
either enhance revenue or reduce costs. Cole et al. (2015) provide evidence
that name changes in the property‐casualty insurance industry are associ‐
ated with increased revenue growth; however, they do not provide further
evidence on the source of that revenue growth. In the next section, we first
examine whether the occurrence of a name change in the life insurance
industry is associated with revenue growth, and then test for a potential
cause of the growth. RESULTS
Name Changes and Revenue Growth
The first objective of this study is to examine whether prior year
corporate name changes influence the premium (revenue) growth of life
insurers, similar to what prior literature has found for property‐casualty
insurers. We estimate four different specifications of Equation (1) using the
20
CARSON, COLE, AND FIER
sales‐weighted industry‐adjusted premium growth measure in Equation
(5).34 These results are presented in Table 2. As shown in Model 1, the
coefficient on the lagged NameChange variable is significant and positive,
suggesting that a prior‐year corporate name change is associated with an
increase in life insurer premium growth. This result is consistent with
Hypothesis 1 and suggests that either: (1) a corporate name change pro‐
vides a positive signal to insurance purchasers regarding the quality of the
firm or its products, or (2) the name change is an observable signal of
(possibly unobservable) internal corporate changes that impact the opera‐
tions of the firm.35
In addition to finding that prior corporate name changes are associated
with improved revenue growth for life insurers, we also find that there are
differences across firms based on the focus of the firm. Specifically, in
Columns 2 and 3 of Table 2, we re‐estimate Equation (1) for subsets of firms
that primarily focus on either individual lines of business or business/
group lines of business. We follow Cole et al. (2015) and classify individual
(business/group) focused firms as those that write over 75 percent of
premiums in individual (business/group) lines of business.36 While we
presented Hypothesis 2 in the null form, we do report a difference between
firms with an individual focus and firms with a business/group focus. The
results suggest that while name changes are associated with an increase in
premium growth for those firms that sell primarily to individuals, there is
no statistically significant relation between prior‐year corporate name
changes and revenue growth for firms selling primarily to businesses and
groups.
The difference in results between the two consumer types indicates
that either: (1) (on average) name changes are associated with other internal
changes that affect the individual market, but not the business/group
market; or (2) the name changes are (on average) unrelated to actual
changes in either market, but individual consumers are still responding to
the change. If a name change does represent a non‐event but consumers
still respond to the event, such a finding would be consistent with some of
34
We examine the variance inflation factors (VIFs) for each of the models estimated and find
no VIF in excess of two. Since Kennedy (1998) states that a VIF that is greater than 10 is
cause for concern, we conclude that multicollinearity is not an issue for the models esti‐
mated in this study. 35
For instance, a firm may make changes to its underwriting standards, its policy language,
its prices, or some other aspect of its business. To some extent, the inclusion of firm fixed
effects should control for some of these unobservable factors. 36
For the purpose of this study, individual‐focused lines include the following: (1) ordinary
life insurance, (2) ordinary annuities, and (3) ordinary supplementary contracts. All other
lines of business are classified as business/group lines of business. NAME CHANGES AND FUTURE GROWTH
21
Table 2. Life Insurer Name Changes and Premium Growth
Variable
NameChange
Full sample
0.1233***
[0.047]
AdPrem
Size
Liquidity
2.9576***
0.1112*
[0.063]
3.5248***
Business focus
0.1050
[0.084]
3.4063**
No other
events
0.1271***
[0.047]
2.7333***
[0.841]
[1.025]
[1.413]
[0.853]
–0.1439***
–0.1853***
–0.0693*
–0.1360***
[0.028]
[0.049]
[0.037]
[0.027]
0.0032
0.0071
[0.002]
[0.009]
0.0037**
[0.002]
LOB Divers
Individual focus
0.1904*
0.5199***
0.0780
0.0037**
[0.001]
0.1962**
[0.101]
[0.182]
[0.163]
[0.100]
–0.3715***
–0.4694***
–0.1223
–0.3887***
[0.111]
[0.163]
[0.219]
[0.111]
Reins
–0.0793
0.0501
–0.0582
–0.0928
[0.056]
[0.166]
[0.057]
[0.057]
Leverage
–0.0456***
–0.0765***
–0.0337***
–0.0472***
[0.006]
[0.011]
[0.010]
[0.006]
–0.0010
–0.0036
–0.0006
–0.0004
[0.005]
[0.005]
[0.005]
[0.005]
0.1178
0.1430
0.0265
0.1176
[0.091]
[0.167]
[0.068]
[0.092]
–0.0581
–0.1447
[0.063]
[0.126]
GEO Divers
Age
Stock
Group
Constant
2.9635***
[0.562]
Observations
R‐squared
4.0345***
[0.971]
0.1418*
[0.072]
1.2210*
[0.686]
–0.0849
[0.062]
2.8210***
[0.556]
4,805
2,259
1,750
4,786
0.0963
0.1505
0.0890
0.0949
*, **, and *** denote statistical significance at the 10 percent, 5 percent, and 1 percent
levels, respectively. The dependent variable is sales‐weighted industry‐adjusted pre‐
mium growth. All models are estimated with firm fixed effects and year control vari‐
ables. Standard errors are presented below coefficients in brackets and are clustered at
the firm level (Petersen, 2009). The column titled “Full sample” presents results for the
full sample of firms. The column titled “Individual focus” presents the results for firms
with a focus on individual lines of life insurance business, where individual‐focused
firms are those firms that write more than 75 percent of total premiums in individual
lines of Table continues
22
CARSON, COLE, AND FIER
business (ordinary life, ordinary annuity, and ordinary supplementary). The column titled
“Business focus” presents the results for firms with a focus on business/group lines of life
insurance business. The column titled “No other events” presents the results for firms that
did not experience other corporate events (as reported by AM Best) during the year of, the
year prior to, or the year following the name change. All independent variables are lagged
one year. NameChange = binary variable equal to 1 for firms that experienced a name change;
AdPrem = ratio of advertising expenditures to total premiums; Size = natural logarithm of total
assets; Liquidity = ratio of total admitted assets to total liabilities; LOB Divers = one minus the
line‐of‐business HHI, calculated using direct premiums written; GEO Divers = one minus the
geographic HHI, calculated using premiums written in 50 U.S. states and the District of
Columbia; Reins = reinsurance assumed minus reinsurance ceded, scaled by total assets;
Leverage = ratio of premiums to policyholders’ surplus; Age = age of the firm, based on when
the firm was first established; Stock = binary variable equal to 1 for firms that are of the stock
organizational form; Group = binary variable equal to 1 for firms that are members of an
insurance group.
the market‐based name change research that finds that positive market
responses to name changes are potentially irrational and that consumers
will respond to cosmetic changes to the firm (Cooper et al., 2005). The final column in Table 2 presents the results after removing any
firms that experienced other corporate events (as identified through Best’s
Corporate Changes and Retirements Database) at the time of the name
change. Specifically, we re‐estimate Equation (1) excluding firms that expe‐
rienced other corporate events either in the year before, the year of, or the
year following the name change. If the occurrence of other contemporane‐
ous corporate events is the cause of the increased revenue growth that has
been observed in the previous models, then the coefficient on the
NameChange variable should be statistically insignificant (or the size of the
coefficient should be greatly reduced). The results presented in the final
column in Table 2 provide further evidence that prior‐year name changes
are associated with increases in revenue growth, even after accounting for
other corporate events. Additionally, the coefficient on the NameChange
variable is nearly identical to the coefficient on the NameChange variable in
the full‐sample model. Overall, these results indicate that, similar to the
property‐casualty insurance industry, life insurers experience an increase
in revenue growth in the year following a name change.37 37
While not reported, we also re‐estimated the models across business focus (i.e., individual
and business/group) after removing firms that experienced other corporate events around
the time of a name change. The results from these models were similar to those reported in
Table 2, with two exceptions. For those focused in individual lines, the coefficient on the
Liquidity variable is significant and positive and the coefficient on the Group variable is sig‐
nificant and negative. NAME CHANGES AND FUTURE GROWTH
23
In addition to the significant relation between prior‐year name
changes and premium growth, we also report significant relations between
premium growth and some of the other control variables included in our
models. First, we find that AdPrem is significant and positive in all four
models, indicating that advertising does appear to have some influence on
consumer behavior in the life insurance market. Among other possible
reasons, the finding of a positive relation between advertising expenditures
and life insurer revenue growth may be the result of a change in the
perception of the firm by consumers or simply reflect an increase in
awareness of the existence of the firm and the products it offers as a result
of advertising.38 We also find that larger firms have lower levels of premium
growth than smaller firms. If larger firms have greater market share, these
firms may have fewer growth opportunities than smaller firms. Liquidity
is significant and positive in Model 1 and Model 4, providing limited
evidence that liquidity has a positive impact on premium growth.39 LOB
Divers is positive and significant in all but one of the models while GEO
Divers is negative and significant, providing evidence that both line‐of‐
business diversification and geographic diversification influence premium
growth. Additionally, there is evidence that firms with greater levels of
leverage experience higher levels of premium growth than do other firms.
As premiums increase, firms experience surplus drain, which has the effect
of reducing surplus while increasing premiums, both of which would
result in an increased leverage ratio. Finally, the group variable is signifi‐
cant and positive, but only for insurers focused on commercial business. The results presented in Table 2 suggest that corporate name changes
are associated with an increase in premium growth in the life insurance
industry. However, it must be recognized that name changes can vary
based on the degree to which the new name differs from the original name.
For example, a company can make a small adjustment to its name, like
38
Since the ratio of advertising expenditures to premiums may mask the degree to which
advertising expenditures change from year to year, we also re‐estimate the models replacing
AdPrem with a variable capturing the change in advertising expenses from the prior year to
the current year. In this model specification, the NameChange variable is significant in Model
1 and Model 4, but not in Model 2 (for insurers focused on individual lines) and the adver‐
tising variable is significant and positive in all models but Model 3. We do not present mod‐
els using this variable in the main results because there is significant variation in this
variable, even when winsorized at the one and 99 percent level. Specifically, the variable
ranges from –1 to nearly 38 during the sample period, suggesting significant outliers exist. 39
When re‐estimating the models after removing outliers with studentized residuals greater
than |4|, the coefficient on the Liquidity variable is positive and significant at the 10 percent
level of significance for the subsample that contains only those firms with a business focus.
All other results are qualitatively and quantitatively similar to those presented in Table 2. 24
CARSON, COLE, AND FIER
changing “Life” to “Life and Annuity” to better identify a refocus or
expansion of its business. Such a change does not significantly alter the
identity of the firm and may even go unnoticed to the average consumer.
In these cases, the firm would still be “recognizable” to consumers as the
original entity, but would still be classified as a firm undergoing a name
change by A. M. Best and for the purpose of this study. In other instances,
a name change may be more significant and reach a point where the
average consumer is unlikely to recognize the firm following the change.
For example, in 2003 CGU Life Insurance Company of America changed
its name to Aviva Life Insurance Company.40 These more significant name
changes may have a different impact than the less significant or minor
name changes. Given potential differences that may exist based on the degree to
which the firm’s name has changed, we re‐estimate the models presented
in Table 2 using a variable equal to one only for firms identified as having
undergone a significant name change and zero for all other firms, effec‐
tively considering firms with insignificant name changes as not having
experienced a name change. We classify firms as having undergone a
significant name change if the firm’s new name is unrecognizable relative
to the original name. These results are presented in Table 3 and are
consistent with those discussed above for all variables included in the
model. Focusing on the variable of interest, similar to the results reported in
Table 2, we find that prior‐year significant name changes (SigNameChange)
are positively and significantly associated with an increase in premium
growth, but that the results are largely driven by firms with an individual
focus. The results are again consistent with Hypothesis 1 and suggest that
firms with a focus on individual lines that undergo significant name
changes in the prior year experience an increase in premium growth.41
Results on the other controls variables in the models are qualitatively and
quantitatively similar to those presented in Table 2. In the next section, we
40
The firm changed its name to Aviva because “the name reflects the company’s focus on
helping people meet their financial goals, is memorable, and travels well across languages
and cultures” (Aviva, 2003). 41
We re‐estimated the models presented in Table 3 after omitting observations with studen‐
tized residuals that exceed |4|, and the results are largely qualitatively and quantitatively
similar to those presented in Table 3. Differences include a positive and significant coeffi‐
cient on the Liquidity variable for the subsample of firms with a business focus, and a nega‐
tive and significant coefficient on the Age variable for the subsample of firms with an
individual focus. Both variables are significant at the 10 percent level, and all other results
are similar to those presented in Table 3. NAME CHANGES AND FUTURE GROWTH
25
Table 3. Significant Name Changes and Premium Growth
Variable
SigNameChange
Full
sample
0.1130**
[0.054]
AdPrem
Size
Liquidity
2.9686***
0.1353*
[0.075]
3.5332***
Business
focus
0.0617
[0.087]
3.4212**
No other
events
0.1191**
[0.054]
2.7438***
[0.840]
[1.016]
[1.426]
[0.852]
–0.1436***
–0.1856***
–0.0686*
–0.1357***
[0.028]
[0.049]
[0.037]
[0.027]
0.0032
0.0075
[0.002]
[0.009]
0.0037**
[0.002]
LOB Divers
Individual
focus
0.1891*
0.5166***
0.0724
0.0037**
[0.002]
0.1949*
[0.101]
[0.182]
[0.163]
[0.100]
–0.3717***
–0.4643***
–0.1289
–0.3884***
[0.111]
[0.163]
[0.220]
[0.111]
Reins
–0.0793
0.0504
–0.0574
–0.0928
[0.056]
[0.166]
[0.057]
[0.057]
Leverage
–0.0457***
–0.0766***
–0.0342***
–0.0473***
[0.006]
[0.011]
[0.010]
[0.006]
–0.0010
–0.0035
–0.0003
–0.0004
[0.005]
[0.005]
[0.004]
[0.005]
0.1196
0.1520
0.0318
0.1193
[0.093]
[0.169]
[0.066]
[0.093]
–0.0579
–0.1438
[0.063]
[0.126]
GEO Divers
Age
Stock
Group
Constant
2.9581***
[0.562]
Observations
R-squared
4.0287***
[0.973]
0.1432**
[0.072]
1.2011*
[0.687]
–0.0848
[0.062]
2.8153***
[0.556]
4,805
2,259
1,750
4,786
0.0956
0.1510
0.0874
0.0942
*, **, and *** denote statistical significance at the 10 percent, 5 percent, and 1 percent
levels, respectively. The dependent variable is sales‐weighted industry‐adjusted pre‐
mium growth. All models are estimated with firm fixed effects and year control vari‐
ables. Standard errors are presented below coefficients in brackets and are clustered at
the firm level (Petersen, 2009). The column titled “Full sample” presents results for the
full sample of firms. The column titled “Individual focus” presents the results for firms
with a focus on individual lines of life insurance business, where individual‐focused
firms are those firms that write more than 75 percent of total premiums in individual
lines of business (ordinary life, ordinary annuity, and ordinary supplementary). The
column
Table continues
26
CARSON, COLE, AND FIER
titled “Business focus” presents the results for firms with a focus on business/group lines
of life insurance business. The column titled “No other events” presents the results for
firms that did not experience other corporate events (as reported by AM Best) during the
year of, the year prior to, or the year following the name change. All independent variables
are lagged one year. SigNameChange = binary variable equal to 1 for firms that experienced
a significant name change (one in which the firm is no longer recognizable by name);
AdPrem = ratio of advertising expenditures to total premiums; Size = natural logarithm of
total assets; Liquidity = ratio of total admitted assets to total liabilities; LOB Divers = one
minus the line‐of‐business HHI, calculated using direct premiums written; GEO Divers =
one minus the geographic HHI, calculated using premiums written in 50 U.S. states and
the District of Columbia; Reins = reinsurance assumed minus reinsurance ceded, scaled
by total assets; Leverage = ratio of premiums to policyholders’ surplus; Age = age of the
firm, based on when the firm was first established; Stock = binary variable equal to 1 for
firms that are of the stock organizational form; Group = binary variable equal to 1 for firms
that are members of an insurance group.
examine the relation between premium growth and corporate name
changes and how the relation differs across organizational structure.42 Name Changes and Organizational Form
The previous section provides evidence that prior‐year corporate
name changes in the life insurance industry are associated with increases
in premium growth. However, the question remains as to whether a
differential effect exists between stock and mutual insurers. As noted
previously, prior literature suggests there are significant differences
between stock and mutual insurers, with most of the focus on differences
in the level of managerial discretion afforded firm management. While
Cole et al. (2015) provide evidence that there are differences between the
organizational forms as they relate to the effect of name changes on revenue
growth, we contend that the insurance market (property‐casualty versus
life) could also influence this relation across the two organizational forms.
Given the differences between stock and mutual insurers, we re‐estimate
Equation (1) separately for stock and mutual insurers. The results for our
test of Hypothesis 3 are presented in Table 4. Across both the stock and mutual models, we again find that the
coefficient on the NameChange variable is positive and significant, indicat‐
ing that name changes are positively associated with increases in revenue
42
We re‐estimate Tables 3 through 6 after replacing the NameChange variable with the
SigNameChange variable, and the results are qualitatively and quantitatively similar to those
reported in this paper. Results obtained from these specifications are available from the
authors upon request. NAME CHANGES AND FUTURE GROWTH
27
for both organizational forms. Additionally, it is interesting to note that the
coefficient on the NameChange variable is very similar for both the stock
and mutual subsamples (0.1248 v. 0.1240), suggesting that name changes
have a similar effect on both groups of insurers. The finding of a positive
relation between premium growth and the mutual organizational form in
the life insurance industry differs from what Cole et al. (2015) find for the
property‐casualty market; in their study, the authors find that while stock
insurers experience an increase in revenue following a past name change,
mutual insurer premium growth was unaffected by past corporate name
changes. One possible explanation for this difference is grounded in the
managerial discretion hypothesis. Cole et al. (2015) argue that the increase
in premium could be due to the ability of stock insurer management to
better utilize managerial discretion in increasing premium while mutuals
are more constrained. While prior literature does document evidence in
favor of managerial discretion in the life insurance market, mutual life
insurers likely have fewer constraints than do their property‐casualty
counterparts as they relate to business lines, since the contracts are often
long term and there are fewer lines. Specifically, there are only 11 lines of
business to write on the life side relative to the over 30 lines that are
available to property‐casualty insurers (based on the 2012 NAIC annual
statements). If the name change signals that the insurer may enter new lines
or emphasize certain lines more than they had previously, it is likely that
mutuals will be less constrained in taking advantage of these opportunities
than would mutuals writing in the more diverse property‐casualty market.
This potential explanation is consistent with prior literature that finds that
line‐of‐business specialization is unrelated to firm identification as either
a stock or a mutual (Pottier and Sommer, 1997).43 Name Changes and Policy Growth
To this point, our study has confirmed that those firms operating in
the U.S. life insurance industry experience an increase in revenue growth
following a corporate name change, which is consistent with the findings
of prior literature that focuses on the property‐casualty market (Cole et al.,
2015). We have also shown that this relationship holds for both stock and
mutual insurers, and that this result differs from the findings in past
research that have focused exclusively on the property‐casualty insurance
market. However, the question remains as to what causes this improve‐
43
When omitting observations with studentized residuals that are greater than 4 or less than
–4, the coefficient on the LOB Divers variable is insignificant for the subsample of stock
insurers. 28
CARSON, COLE, AND FIER
Table 4. Name Changes, Organizational Form, and Premium Growth
Stock Organizational Form
Variable
Mutual Organizational Form
Coefficient
Standard Error
NameChange
0.1248**
0.051
0.1240*
0.065
AdPrem
2.9417***
0.850
7.0360
5.111
–0.1392***
0.028
–0.2450**
0.097
0.0038**
0.002
–0.0543
0.075
LOB Divers
0.1939*
0.105
0.3427
0.386
GEO Divers
–0.3869***
0.109
0.0107
0.865
Reins
–0.0864
0.059
0.0455
0.256
Leverage
–0.0448***
0.006
–0.0814**
0.034
Age
–0.0002
0.006
0.0123
0.121
Group
–0.0567
0.073
–0.0444
0.120
0.573
3.8447
10.674
Size
Liquidity
Constant
Observations
R‐squared
2.9459***
Coefficient
4,378
427
0.0958
0.1483
Standard Error
*, **, and *** denote statistical significance at the 10 percent, 5 percent, and 1 percent
levels, respectively. The dependent variable is sales‐weighted industry‐adjusted pre‐
mium growth. All models are estimated with firm fixed effects and year control variables.
Standard errors clustered at the firm level are presented in the columns titled “Standard
error” (Petersen, 2009). The columns titled “Stock organizational form” contain results
only for those firms that are of the stock organizational form. The columns titled “Mutual
organizational form” contain the results only for those firms that are of the mutual
organizational form. All independent variables are lagged one year. NameChange = binary
variable equal to 1 for firms that experienced a name change; AdPrem = ratio of advertising
expenditures to total premiums; Size = natural logarithm of total assets; Liquidity = ratio
of total admitted assets to total liabilities; LOB Divers = one minus the line‐of‐business
HHI, calculated using direct premiums written; GEO Divers = one minus the geographic
HHI, calculated using premiums written in 50 U.S. states and the District of Columbia;
Reins = reinsurance assumed minus reinsurance ceded, scaled by total assets; Leverage =
ratio of premiums to policyholders’ surplus; Age = age of the firm, based on when the
firm was first established; Stock = binary variable equal to 1 for firms that are of the stock
organizational form; Group = binary variable equal to 1 for firms that are members of an
insurance group.
ment in revenue for insurance companies. One of the primary benefits of
examining this issue within the life insurance industry is that while prop‐
erty‐casualty insurers do not report information on the number of policies
in force in a given year, life insurers are required to report this information
to the NAIC on an annual basis. This unique feature of the U.S. life
insurance industry allows us to directly examine one potential cause for
NAME CHANGES AND FUTURE GROWTH
29
the revenue growth—namely, increases in the quantity of coverage in force
in a given year. We re‐estimate Equation (1) and replace the sales‐weighted
industry‐adjusted sales measure with our measure of policy growth.44 The
results from this set of estimated models are presented in Table 5.
The results in Table 5 provide the first evidence of a source of the
revenue increases that are exhibited in this study and in prior literature
(i.e., Cole et al., 2015). We find that the number of policies in force increases
following a past name change, but only for insurers focused on individual
lines. Specifically, the results in Table 5 indicate that, consistent with the
results in Table 2, life insurers that sell primarily to individuals experience
an increase in policy growth following a name change, but life insurers that
sell primarily to businesses/groups do not experience increased policy
growth. This finding provides a greater understanding of why both this
study and the work of Cole et al. (2015) report evidence of an increase in
revenue for insurers focused on individual lines as opposed to those that
focus on businesses and groups. As discussed previously, if businesses
represent sophisticated insurance purchasers (or at least exhibit a greater
level of sophistication than individual consumers), these findings may
again suggest an irrational response to a cosmetic change in name. Alter‐
natively, the findings may indicate that the changes that take place largely
impact individual consumers. In addition to our primary findings, we also report a relation between
some of our control variables and policy growth. Specifically, we find that
advertising expenditures are positively associated with policy growth,
implying that firms that exhibit a greater level of advertising intensity
experience a greater degree of policy growth. We also find some evidence
that size is negatively related to growth in insurance policies, while liquid‐
ity is positively related to growth in policies.45 Finally, there is some
evidence of a negative impact of diversification on policy growth and a
positive impact of reinsurance on policy growth, but only for those insurers
focused on individual lines.
The previously reported results suggest that a name change can have
the effect of increasing premium growth and that the growth might be
44
We calculate policy growth as the difference between the natural logarithm of the total
number of policies in force in year t and the natural logarithm of the total number of policies
in force in year t–1. 45
For models that exclude observations with studentized residuals that exceed |4|, the coef‐
ficient on the Size variable is also negative and significant at the 5 percent level of signifi‐
cance for the model that does not include observations that experienced other corporate
events. All other results are qualitatively and quantitatively similar to those presented in
Table 5. 30
CARSON, COLE, AND FIER
Table 5. Life Insurer Name Changes and Policy Growth
Variable
NameChange
Full
sample
0.0309
[0.033]
AdPrem
Size
Liquidity
1.3215***
Individual
focus
0.0977**
[0.042]
1.7147***
[0.482]
[0.644]
–0.0397**
–0.0746***
[0.018]
[0.028]
0.0025*
0.0033**
Business
focus
No other
events
–0.0460
0.0353
[0.061]
[0.033]
0.9744**
[0.492]
0.0130
[0.035]
0.0010
1.3634***
[0.486]
–0.0359**
[0.018]
0.0025*
[0.001]
[0.002]
[0.003]
[0.001]
LOB Divers
–0.0283
–0.0551
–0.1231
–0.0200
[0.057]
[0.072]
[0.118]
[0.057]
GEO Divers
–0.0953
–0.3048***
–0.0600
–0.1000
[0.093]
[0.106]
[0.169]
[0.092]
Reins
Leverage
Age
Stock
0.0235
0.0297
[0.054]
0.0421
[0.093]
[0.055]
[0.052]
–0.0068
–0.0073
–0.0008
–0.0072
[0.005]
[0.006]
[0.007]
[0.005]
–0.0016
–0.0041
–0.0027
–0.0015
[0.006]
[0.003]
[0.008]
[0.006]
0.1480***
[0.040]
Group
0.0837**
[0.034]
Constant
0.7815*
[0.416]
Observations
R-squared
0.2534***
0.0703
[0.052]
0.0844
[0.066]
1.7599***
[0.562]
0.3403**
[0.142]
0.1105**
[0.050]
–0.3765
[0.661]
0.1470***
[0.040]
0.0790**
[0.034]
0.7085*
[0.419]
4,805
2,259
1,750
4,786
0.0441
0.1085
0.0372
0.0438
*, **, and *** denote statistical significance at the 10 percent, 5 percent, and 1 percent
levels, respectively. The dependent variable is policy growth, calculated as the natural
logarithm of policies in force in year t minus the natural logarithm of policies in force in
year t–1. All models are estimated with firm fixed effects and year control variables.
Standard errors are presented below coefficients in brackets and are clustered at the firm
level (Petersen, 2009). The column titled “Individual focus” presents the results for firms
with a focus on individual lines of life insurance business, where individual‐focused
firms are those firms that write more than 75 percent of total premiums in individual
lines of business (ordinary life, ordinary annuity, and ordinary supplementary). The
Table continues
NAME CHANGES AND FUTURE GROWTH
31
column titled “Business focus” presents the results for firms with a focus on business/
group lines of life insurance business. The column titled “No other events” presents the
results for firms that did not experience other corporate events (as reported by AM Best)
during the year of, the year prior to, or the year following the name change. All
independent variables are lagged one year. NameChange = binary variable equal to 1 for
firms that experienced a name change; AdPrem = ratio of advertising expenditures to total
premiums; Size = natural logarithm of total assets; Liquidity = ratio of total admitted assets
to total liabilities; LOB Divers = one minus the line-of-business HHI, calculated using
direct premiums written; GEO Divers = one minus the geographic HHI, calculated using
premiums written in 50 U.S. states and the District of Columbia; Reins = reinsurance
assumed minus reinsurance ceded, scaled by total assets; Leverage = ratio of premiums to
policyholders’ surplus; Age = age of the firm, based on when the firm was first established;
Stock = binary variable equal to 1 for firms that are of the stock organizational form; Group
= binary variable equal to 1 for firms that are members of an insurance group.
attributable to the growth in the number of policies that are in force.
However, it is possible that policy growth will appear to be increasing
when in actuality, lapses are declining, which creates the appearance that
the number of policies sold has increased.46 Given this potential, we also
examine the impact of a prior year name change on policy lapse rates in an
attempt to disentangle these effects47. We re‐estimate the Equation (1) using
a measure of policy lapse growth as the dependent variable. The measure
is calculated as the natural logarithm of policy lapses in year t minus the
natural logarithm of policy lapses in year t–1. These results are presented
in Table 6. We find that the coefficient on the NameChange variable is
insignificant across each of the four models, suggesting that firms with a
prior‐year name change do not exhibit a statistically significant change in
their lapse rates. However, when re‐estimating the models after removing
observations with studentized residuals that are greater than 4 or less than
–4 (Choi and Weiss, 2005), we find a positive and significant coefficient on
the NameChange variable for the sample of firms with an individual focus,
which suggests that corporate name changes result in a decrease in lapse
activity. Given these mixed results, we are unable to reject Hypothesis 4.
However, the results do indicate that name changes are associated with
policy growth (as presented in Table 5) and are related to either: (a) no
change in lapse rates, or (b) a decline in lapse rates. As noted earlier and
discussed in Cole et al. (2015), the increase in premium growth observed
46
We thank an anonymous reviewer for providing this insight. Similar to the policy growth data, information regarding policy lapses is only available
through the NAIC for life insurers and is not available for property‐casualty insurance
firms. 47
32
CARSON, COLE, AND FIER
Table 6. Life Insurer Name Changes and Policy Lapse
Variable
NameChange
AdPrem
Size
Liquidity
Full
sample
Individual
focus
Business focus
No other
events
0.0833
0.2003
–0.2646
0.0813
[0.100]
[0.122]
[0.221]
[0.099]
2.6424**
3.1574**
1.4580
2.7359**
[1.172]
[1.577]
[1.902]
[1.235]
–0.1280**
–0.1897***
–0.0084
–0.1204**
[0.050]
[0.060]
[0.104]
[0.050]
0.0043*
0.0051**
–0.0021
0.0041*
[0.002]
[0.002]
[0.012]
[0.002]
LOB Divers
–0.1753
–0.0475
–0.4373
–0.1897
[0.156]
[0.215]
[0.495]
[0.160]
GEO Divers
–0.6222**
–0.6859
–0.3382
–0.6322**
[0.267]
[0.423]
[0.554]
[0.276]
Reins
Leverage
Age
0.4721***
Group
Constant
R-squared
0.4753***
[0.250]
[0.243]
[0.168]
–0.0188*
–0.0067
–0.0311
–0.0215*
[0.010]
[0.015]
[0.022]
[0.011]
0.0021
0.0091
[0.007]
[0.007]
0.0091*
0.2200***
0.2082
0.3323**
0.2248***
[0.170]
0.0089
–0.0679
0.0284
0.0141
[0.096]
[0.134]
[0.137]
[0.097]
2.5298**
4.1703***
[1.348]
[0.167]
0.0089*
[0.005]
[0.077]
[1.070]
Observations
0.4029*
[0.167]
[0.005]
Stock
0.5441**
0.0745
[2.114]
[0.081]
2.3963**
[1.088]
2,609
1,317
829
2,601
0.0374
0.0747
0.0351
0.0372
*, **, and *** denote statistical significance at the 10 percent, 5 percent, and 1 percent
levels, respectively. The dependent variable is the change in policy surrenders, calculated
as the natural logarithm of policies lapsed in year t minus the natural logarithm of
policies lapsed in year t–1. All models are estimated with firm fixed effects and year
control variables. Standard errors are presented below coefficients in brackets and are
clustered at the firm level (Petersen, 2009). The column titled “Individual focus” presents
the results for firms with a focus on individual lines of life insurance business, where
individual‐focused firms are those firms that write more than 75 percent of total
premiums in individual lines of business (ordinary life, ordinary annuity, and ordinary
Table continues
NAME CHANGES AND FUTURE GROWTH
33
supplementary). The column titled “Business focus” presents the results for firms with a
focus on business/group lines of life insurance business. The column titled “No other
Events” presents the results for firms that did not experience other corporate events (as
reported by AM Best) during the year of, the year prior to, or the year following the name
change. All independent variables are lagged one year. NameChange = binary variable
equal to 1 for firms that experienced a name change; AdPrem = ratio of advertising
expenditures to total premiums; Size = natural logarithm of total assets; Liquidity = ratio of
total admitted assets to total liabilities; LOB Divers = one minus the line‐of‐business HHI,
calculated using direct premiums written; GEO Divers = one minus the geographic HHI,
calculated using premiums written in 50 U.S. states and the District of Columbia; Reins =
reinsurance assumed minus reinsurance ceded, scaled by total assets; Leverage = ratio of
premiums to policyholders’ surplus; Age = age of the firm, based on when the firm was
first established; Stock = binary variable equal to 1 for firms that are of the stock organiza‐
tional form; Group = binary variable equal to 1 for firms that are members of an insurance
group. could be attributed to increased pricing or an increase in policy sales. The
results presented in Tables 5 and 6 suggest that the increase in premium
growth for the life insurance industry is largely driven by policy growth,
possibly combined with a decline in lapse activity, and not by increased
pricing. It is also possible that the findings of a positive relation between a
name change and premium growth documented in the property‐casualty
industry by Cole et al. (2015) may be the result of policy growth. CONCLUSION
A growing body of literature has investigated corporate name changes
and their potential impact on firms. Many of these studies have tested for
a market response to corporate name changes, but little empirical work has
focused on the relation between name changes and their impact on future
revenue. While Cole et al. (2015) provide evidence for property‐casualty
insurers that prior‐year name changes are related to increases in premium
growth, they do not provide evidence as to the source of the revenue
increase. Our study contributes to existing literature by examining the
effect of name changes with a specific emphasis on the life insurance
industry and by considering a potential source of the revenue increase
following a name change. We find, consistent with Cole et al. (2015), that a prior‐year name
change is associated with an increase in revenue for life insurers. These
results are consistent for the full sample of firms and are robust to the
removal of firms that experienced other contemporaneous corporate
events. We also find that while name changes are related to an increase in
revenue growth, this is the case only for insurers that primarily sell to
34
CARSON, COLE, AND FIER
individuals. In contrast to Cole et al. (2015), our findings also suggest that
both stock and mutual insurers exhibit an increase in revenue growth, with
a larger increase for stock insurers. Our major contribution is that we are able to test for the source of the
increase in revenue following corporate name changes. Since life insurers
are required to report the number of policies in force on an annual basis,
we are able to provide evidence as to whether the positive consumer
response is driven by an increase in the number of policies sold. We provide
evidence that the occurrence of a name change is related to some combi‐
nation of increased policy sales and decreased lapse activity, suggesting
that the premium growth experienced by life insurers that undergo a name
change is due, at least in part, to selling more policies and likely not due to
an increase in pricing. However, this finding holds only for the sample of
firms that sell primarily to individuals. To our knowledge, this is the first study that examines the effect of
name changes on revenue growth in conjunction with attempting to iden‐
tify one of the potential sources of the revenue growth. While the results
indicate that increase sales activity (as proxied by growth in the number of
policies in force) is one possible explanation for the increase in revenue,
additional research should be conducted that more explicitly examines the
potential effects that changes in price or underwriting policy have on this
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