ERM Determinants, Use, and Effects on the Firm
David M. Pooser
Assistant Professor of Risk Management and Insurance
School of Risk Management
Peter J. Tobin College of Business
St. John’s University
Kathleen A. McCullough
State Farm Insurance Professor in Risk Management and Insurance
Department of Risk Management & Insurance, Real Estate, and Legal Studies
College of Business
Florida State University
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David M. Pooser
850-508-7344
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ERM Determinants, Use, and Effects on the Firm
ABSTRACT
Enterprise risk management (ERM) is being implemented more frequently by
insurance firms, and regulators and ratings agencies are placing greater emphasis
on the effectiveness of firm risk management. This paper uses ERM rating data
from Standard and Poor’s Ratings Direct combined with the NAIC property and
casualty insurance annual statements to identify insurers that do and do not obtain
ERM program ratings. We examine which firm characteristics are associated
with obtaining an ERM rating, test if ERM rated firms jointly manage firm risk
using multiple risk management techniques, and test if ERM rated firms are more
resilient to shocks than non rated firms. We find that several firm characteristics
are significantly related to an ERM rating, including larger and publicly traded
firms. We find that the risk management techniques of ERM rated firms are
jointly significant in explaining firm risk, while there is no finding of joint
significance for non-ERM rated firms. This underscores the tenant that insurers
with ERM programs use a variety of techniques to jointly manage firm risk.
Finally, we find that firms with an ERM rating experience on average fewer
shocks and better performance in the variables that underlie shocks than nonERM rated firms. The results from this study are important to firm stakeholders,
regulators, and ratings agencies that seek to measure the risk management
behavior of firms with and without ERM.
SECTION I – INTRODUCTION
Risk management for corporations has been practiced since the 1960’s. It evolved from the
treatment of insurable risks to more complex financial and operational risk management methods.
Many corporations, however, viewed the risk management process as a function of second order
importance and treated individual risks in a “silo” approach – where each risk was considered to
be independent of others and was managed in a way to minimize the firm’s exposure to the risk.
A process that has started to receive industry and academic notice is enterprise risk management
(ERM). Generally speaking, ERM is the simultaneous measurement and management of all
categories of firm risk in different states of nature (e.g. a good economy or bad economy).
Because ERM is such a broad concept, clearly defining the process is difficult. The Casualty
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Actuarial Society in their “Overview of Risk Management” (2003) defines ERM as “… the
discipline by which an organization in any industry assesses, controls, exploits, finances, and
monitors risks from all sources for the purpose of increasing the organization’s short- and longterm value to its stakeholders”.1 Regardless of definition, at its most basic level, the purpose of
ERM is to create value for a firm’s shareholders.
Firms have historically performed risk management by attempting to minimize the cost of
firm risk through transfer, reduction, retention or avoidance of risk exposures. Traditional risk
management and financial risk management typically separated the management of loss
exposures using separate contracts and retentions for these risks (Dickinson, 2001). In an ERM
framework, however, firm risks are considered as a portfolio, and total firm risk is not the sum of
its individual risks (Kleffner, Lee and McGannon, 2003), due to negative or non-correlation
between risk exposures. Part of the difference between traditional risk management and ERM is
the treatment of pure, speculative, transferable and nontransferable risks simultaneously
(Dickinson, 2001).
Now, especially after the financial crises of the past decade, risk
management has shifted from what was often a minor consideration for firms, handled by the
treasurer, comptroller, or an insurance risk manager, to a very high-profile activity, overseen by
the CFO, or in some cases, a CRO (chief risk officer).
Previous research on ERM typically focused on the determinants of ERM
implementation, the effects of an ERM program on firm value, or some other link between ERM
and financial performance. This paper extends the empirical literature on ERM by testing the
characteristics of firms obtaining an ERM program rating through Standard and Poor’s Ratings
1
Other sources define or describe ERM as” “dealing with uncertainty for the organization,” (Monahan, 2008); a
method of dealing with financial, insurable, operational, and business risk that views all risks as being able to upset
firm stability, and in which risks cannot be isolated, but are affected by firm exposure to other risks (Doherty, 2000);
and a number of other definitions that involve treating all firm risk exposures simultaneously (e.g., D’Arcy, 2001;
Kleffner, Lee and McGannon, 2003; Gates, 2006).
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Direct. We also test for potential differences in the behavior of firms with and without ERM
ratings. To do this, we propose three hypotheses. First, we test whether firms with ERM ratings
are systematically different from firms without ERM ratings. Second, we test whether the ERM
rated firms differ in their use of risk management techniques from non-ERM firms. Finally, we
exam whether the presence of a rated ERM program makes the firm more insulated or resilient to
shocks to firm performance and to changes in firm performance. Together the results provide
insight into the use of rated ERM programs as well as the behavior of those firms.
This approach has several advantages. First, Hoyt and Liebenberg (2011) state that, “One
of the major challenges facing researchers is how to identify firms that engage in ERM”. Many
prior empirical analyses have employed survey data of firm ERM practices reported through
questionnaires (e.g., Kleffner, et al., 2003) or press releases and news articles (e.g., Liebenberg
and Hoyt, 2003). This paper uses Standard and Poor’s Insurer ERM Ratings Direct data to
identify insurers with ERM programs and compares ERM rated firms to those insurers that do
not obtain ERM ratings. The use of Standard and Poor’s Insurer ERM ratings means that the
tests should be less sensitive to sample bias due to missing survey responses or ambiguity in
coding news reports.2 Further, unlike some studies, the sample includes the entire population of
property and casualty firms rather than just publicly traded firms or survey respondents.3
we also employ a simultaneous equations methodology to model firm risk and risk
management technique variables.4 By modeling the system or risk management techniques and
firm risk simultaneously, we are able to test whether there is a stronger interrelation in the use of
2
It is important to note that studies that have used survey responses and news searches to analyze ERM have
provided an extremely rich and valuable framework for future tests on ERM, and that this research seeks to further
explore and clarify insurer ERM behavior and not to belittle or ignore the importance of these prior studies.
3
Several prior ERM studies rely on smaller samples due to limited survey responses or available information. For
example, Kleffner, et al. (2003), Liebenberg and Hoyt (2003), and Hoyt and Liebenberg (2011).
4
In an ERM framework, all risk management techniques are considered simultaneously, and the interrelation
between the risk management techniques is important to an insurer making its risk allocation decisions.
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risk management techniques with ERM ratings as opposed to those without. The potential
interrelation in the ERM rated firms and lack of interrelation in the non-ERM rated firms would
provide empirical evidence of differing behavior that would validate the presence of an ERM
program through the firm’s behavior.
Finally, we test whether or not firms with ERM ratings experience fewer performance
shocks than firms without ERM ratings and if these firms experience more favorable
performance in the factors underlying shocks. These tests allow me to observe whether or not
ERM is effective in insulating the firm from negative performance shocks or adverse events.
Preventing or reducing the impact from an adverse event is a valuable contribution from an ERM
program. We consider shocks to ROA, loss ratio, net income, and policyholder surplus. we also
examine the change in the loss ratio based on the presence of an ERM program rating.
Our empirical findings support the hypotheses. Related to the first hypothesis, ERM
rated firms are significantly different from non-ERM rated firms, with findings mostly consistent
with prior literature. For the second hypothesis, we find evidence that firms with ERM ratings
manage firm risk using simultaneous risk management techniques, while we do not find evidence
of this for non-ERM rated firms. For the third hypothesis, we find that ERM rated firms typically
experience fewer average performance shocks than non-ERM rated firms. we also find that
ERM rated firms experience fewer adverse changes (and experience more beneficial changes) in
the performance variables underlying shocks than non-ERM rated firms. This provides evidence
that ERM programs help to insulate these firms from adverse events.
This research has important implications for future empirical research on ERM as well as
for regulators and ratings agencies examining ERM for insurers or other financial firms. we
directly compare insurers that obtain ERM ratings and those that do not for the entire U.S.
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property and casualty insurance industry, providing insight into the firm characteristics that
differ between these groups. As requirements related to the utilization of ERM increase, this will
help regulators and firms tasked with measuring ERM behavior to understand which firms are
likely the early adopters of obtaining the ratings. The paper models the interrelation of risk
management techniques with firm risk in order to determine whether or not ERM rated firms are
more likely to jointly manage risk, which helps answer whether or not ERM does lead to
significant interrelation in a firm’s risk management techniques. This is important for evidence
that firms with ERM truly act in a manner differ from non-ERM firms as well as in a manner
consistent with the ERM framework. It also provides a potential metric with which to evaluate
the presence of a true ERM program. Finally, the finding that ERM rated firms appear to be
more insulated from various performance shocks and performance changes underscores the
potential value of an ERM program. This evidence provides motivation for the implementation
of an ERM program as well as a potential metric to analyze the effectiveness of programs.
The paper is organized as follows. Section two provides a literature review on the
development of ERM research and an overview of how ERM and insurer risk management has
been measured in prior literature. Section three develops the hypotheses and methodology for
this study and discusses the data. Section four presents the results of the empirical models.
Section five concludes.
SECTION II – LITERATURE REVIEW
Empirical ERM research has grown more complex since its inception. Early studies on ERM
performed analyses on the determinants of implementing an ERM program (Liebenberg and
Hoyt, 2003; Kleffner, Lee and McGannon, 2003). More recently, researchers have asked more
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complex questions in ERM research, including how ERM impacts the marginal cost of risk
(Eckles, Hoyt and Miller, 2010) and how ERM affects firm value (Hoyt and Liebenberg, 2011),
Below, we discuss the progression of ERM literature. we discuss methods researchers have used
to detect ERM and measure ERM activities. This literature provides a basis for performing
empirical tests on the firm characteristics of ERM rated firms, ERM effectiveness, and
behavioral differences of ERM rated insurers.
2.1 – Enterprise Risk Management Overview
The literature on ERM has developed significantly over the last 10 years.
Early research
provided very general definitions of ERM and what the process would likely mean for firms that
implemented ERM programs (e.g. Lam, 2000; D’Arcy, 2000; Harrington and Niehaus, 2002).
Soon afterwards, empirical research analyzed survey data and news reports to determine what
factors related to ERM implementation and usage (e.g. Liebenberg and Hoyt, 2003; Kleffner,
Lee and McGannon, 2003). Additionally, there have been a number of subsequent ERM studies
using news reporting data and surveys to analyze ERM, in part because of difficulties in
gathering reliable, aggregated data on ERM activities (e.g. Beasley, Clune and Hermanson, 2005;
Hoyt and Liebenberg, 2011; Altuntas, Berry-Stölzle and Hoyt, 2011).
D’Arcy (2000) provides an overview of the development of ERM and discusses the
problems arising from not considering all of a firm’s risks simultaneously. He notes that one
reason ERM use may be expanding is that advances in technology make it easier for firms to
simultaneously track different risk exposures (due to computing advances in the 1990s).
Dickinson (2001) argues that a goal of an ERM program should be decreasing inefficiencies that
increase expenses. In this way, ERM may add value to the firm.7 Additionally, Dickinson (2000)
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Harrington and Niehaus (2002) provide an analysis of an actual ERM strategy for the agricultural firm United
Grain Growers. The authors provide prima facie evidence of trends in costs between different business services.
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cites increasingly stringent government regulation and greater scrutiny on firm executives as
supporting the development of ERM.
As ERM research progressed, empirical studies emerged on the ERM topic. Two of the
earliest empirical studies on ERM are Liebenberg and Hoyt (2003) and Kleffner, Lee and
McGannon (2003). Liebenberg and Hoyt define ERM implementation as the appointment of a
chief risk officer (CRO) for firms in news outlets. They examine several factors related to ERM
implementation and find that ERM firms are typically smaller and more leveraged. Kleffner,
Lee and McGannon (2003) survey corporations on ERM usage and report the results. The
primary findings are that ERM success depends on management buy-in and that the formation of
an ERM program is limited by the difficulty of risk identification, budget constraints (especially
when risk management is not a firm priority), and uncertainty surrounding the value it adds to a
corporation. The data collection methods of these studies – using news reports and surveys –
continued into the future as well.
Gates (2006) provides a survey of reasons why corporations make ERM a priority. ERM
is generally most important to the CFO and financial auditing division of a corporation, rather
than to other managers and directors.
Firms that implement ERM often cite improved
informational efficiency, better strategic positions within their industry, and strengthened
corporate governance as major benefits. Nocco and Stultz (2006) argue that ERM is value
adding because it enables risk quantification and optimization by managers so that the firm can
choose the best operating strategy and ERM helps align risk within a firm’s culture and
This correlation among its exposures supports the concept of ERM, that risk exposures are interrelated. Through
ERM, the firm was able to identify the causes of its greatest earnings and cost volatility and more effectively
manage the risk. ERM increased the firm’s credit rating and reduced its cost of raising funds. In an ERM
framework, all risk management techniques are considered simultaneously, and the interrelation between the risk
management techniques is important to an insurer making its risk allocation decisions.
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incentivize workers to make decisions consistent with this risk culture. Mackay and Moeller
(2007) examine the value of firms that use corporate risk management. Contrary to much prior
literature, they find that corporate risk management can lead to an increase in value for a firm
when risk factors are not linearly related to revenues and costs.8
Altuntas, Berry-Stölzle and Hoyt (2010) survey German firms on their implementation of
ERM programs and provide insight into the techniques firms use in an ERM program. This
study is one of the few to provide information on several risk management techniques employed
by insurers. Ai, Brockett, Cooper and Golden (2011) theoretically examine ERM as a firm-value
enhancing operation. 9
They model how four risk types: hazard, project, financial, and
operational, affect the firm’s value. Ai, et al. (2011) notes that firm risk appetite is determined,
in part, by the firm’s current and target credit rating, because risk appetite affects the cost of
capital.
2.2 – Enterprise Risk Management and Insurer Risk Management Techniques
This paper also examines the interrelatedness of risk management techniques based on whether
or not the firm obtains an ERM rating. we seek to identify simultaneous relationships between
firm risk and risk management techniques for firms with and without an ERM rating. This test
helps provide evidence on the how ERM affects firm’s use of risk management techniques. In
order to perform an analysis on the interrelatedness of insurer risk management usage, it is
necessary to first define what methods insurers and other firms use to manage risk (especially in
an ERM program), by consulting prior literature to identify insurer risk management variables.
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This difference may be due in part to what Hoyt and Liebenberg (2011) describe as inefficiencies in the traditional
risk management strategy of treating risks as independent. Mackay and Moeller test risk management factors that
are exogenous to costs or revenues, which is part of the focus of ERM.
9
This study is one of the first theoretical examinations of ERM and one of the first to model the interrelation of risks.
9
Altuntas, Berry-Stölzle, and Hoyt (2011) conduct a survey of German insurers regarding
the firms’ decision to practice ERM and factors that led to the implementation of ERM for the
insurers. The majority of insurers in the sample of identify underwriting and investment risks as
factors that can benefit from ERM.
Additionally, 91 percent of insurers consider the
interdependencies of these risks when making decisions, up from 8 percent in 1999 (Altuntas, et
al, 2011). Ai, et al. (2011) develop a theoretical ERM framework which provides several
insights for performing an empirical study on ERM use. They identify four risk types (financial,
operational, hazard and project) which may be managed. 12 Strategic risk is related to the
competitiveness of the firm within an industry, or the firm’s competitive advantages. It is
difficult to measure how competitive a firm is within an industry because of the unobservable
factors that drive competition, including management and board decisions. However, if the
aspiration of a firm to out-perform competitors helps define strategic risk, prior literature
provides some insight on how to construct an empirical measure of strategic risk, which is
discussed below.
From the literature, we identify four enterprise risks that may have a simultaneous
influence on total firm risk and each other – operational risk, strategic risk, financial risk, and
hazard risk. Below, we discuss how each of these risks may be measured empirically.
Managing Operational Risk
Operational risk is defined for banks under Basel II as the “risk of loss resulting from inadequate
or failed internal processes, people and systems or from external events. This definition includes
legal risk, but excludes strategic and reputational risk.” (Basel Committee on Banking
Supervision, 2004). There are several definitions in insurance literature that are similar. The
12
Additionally, Gates (2006) identifies strategic risk as an exceptionally important ERM variable, which is similar
to Ai, et al. (2011)’s project risk.
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primary ‘internal processes’ of an insurance firm are risk transfer and pooling. A failure for
these processes could potentially be caused by inadequate premiums to cover losses, poor
underwriting guidelines, or some exogenous shock such as a catastrophe.
To reduce the
probability of a failure in operational risk, insurers may decide to diversify their lines of business.
Measuring Diversification: A common measure of insurer diversification is the line-of-business
Herfindahl index. Some studies have used the number of lines of insurance written, or a dummy
variable for whether or not the insurer operates in multiple lines (e.g. Liebenberg and Sommer,
2008) to measure diversification. One potential flaw with these diversification measures is that
they fail to account for the potential relatedness between lines of insurance.
The LOB
Herfindahl index, for example, measures market share per line of business but does not make any
adjustments if two lines are highly correlated in any given year. Similar criticisms can be made
for a numerical count of lines of insurance, or a simple dummy variable for diversification.
This study utilizes a measure for line of business diversification that accounts for the
correlation between income streams in lines of insurance.
This measure, the ‘modified
Hirschman-Herfindahl Index’ or modified HHI is developed in Pooser and McCullough (2012).
The modified HHI measures diversification on the same scale as the traditional HHI, but can
increase or decrease the concentration index based on the correlation between lines of insurance
written. The equation for the modified HHI is presented below. The model is similar to
Markowitz’s (1952) portfolio variance measure.
Because the measure is multiplicative,
concentration decreases as an insurer writes more business in separate lines, but if lines are
highly correlated the change in concentration may not be strictly monotonic. Unlike the measure
for portfolio variance, the modified HHI does not include a variance term. The variance in
‘returns’ on lines of insurance is influenced by the underwriting and claims standards of the
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insurer, so the interpretation of a portfolio variance equation for insurer liabilities is affected by
other factors than the concentration decision of the insurer. Therefore, the modified HHI only
communicates liability concentration.
Eq. (1)
Equation (1) shows the formula for calculating the modified HHI, where f is the firm, t is the
time period, i and j represent lines of insurance, DPW represents direct premiums written, and 
is the correlation component between two lines of insurance.
Managing Financial Risk
In addition to risk transfer and pooling, one of the primary functions of an insurance company is
intermediation. Property and liability insurers in the US collected roughly $423 billion in
premiums in 2009 and invested over one trillion dollars into capital markets (Insurance
Information Institute). Stock markets, and to a lesser extent bond markets, are often associated
with great volatility, and a significant downturn in market investments can lead to great losses
for an insurer. An insurer’s investment portfolio, including the correlation between asset returns,
affects its required risk capital. Additionally, the return on investments contributes to insurer
profitability. Some insurers may choose to decrease insurance prices (thus collecting more
premium dollars) and operate at an underwriting loss in order to access more capital for
investments (Cummins and Weiss, 1991).
In order to capture the financial risks of an insurer – including the risk management
decision related to financial risks – we use the portfolio variance developed by Markowitz (1952)
as the financial risk management variable, seen below in equation (1).
Eq. (2)
12
Here,  refers to the standard deviation of a particular security (or portfolio); s refers to
the proportion of assets invested in a single asset class, and  is the correlation coefficient
between assets i and j. This measure considers the proportion of investments in each type of
financial asset, the risk of each asset, and the correlation between returns on assets – each an
element in determining risk capital for insurers. Typically, with more securities in a portfolio,
the portfolio variance increases because of the additive nature of the equation. Thus, measured
portfolio variance may increase with the size of the firm because of more available investment
dollars. However, our data consists of a fixed number of asset classes (i.e. preferred stocks,
common stocks, bonds, etc.) and therefore, the portfolio variance will be comparable between
firms regardless of size or invested assets.13 This allows for a consistent measure of asset risk.
Managing Hazard Risk
Hazard risk is defined in Ai, et al. (2011) as those risks typically managed through insurance
coverage (e.g. fire, theft). For most firms faced with a hazard risk, risk management techniques
typically include purchasing insurance or retaining the risk. The most similar function in the
insurance firm is the purchase of reinsurance. Reinsurance is purchased by insurers for a number
of reasons, including to manage earnings, to increase capacity, or to transfer risk (e.g., Mayers
and Smith, 1990; Garven and Lamm-Tennant, 2003; Cole and McCullough, 2006). Reinsurance
involves transferring the underwriting risks of an insurer to another insurer in exchange for a
premium (with loading). While reinsurance may also be viewed as a method of operational risk
management (reducing the underwriting risk), reinsurance also often provides protection against
13
Other asset management variables were considered here, and some are used as robustness tests against this
measure. Other variables considered include the portfolio variance scaled by assets, the coefficient of variation of
portfolio variance, and a concentration index of invested assets.
13
unexpected events such as catastrophes, or protection against large single losses for very large
insured clients.14
One consideration with this variable, however, is that reinsurance can be used as a
financial mechanism to smooth taxes and income between affiliated insurance firms (Powell and
Sommer, 2007). For these firms, reinsurance may be used in the smoothing of income and taxes
in addition to the transfer of risk. However, the management of risks and earnings may both be
considered in an ERM program. The reinsurance variable used in this study is the proportion of
reinsurance ceded, or:
.
Eq. (3)
Managing Strategic Risk
Measuring strategic risk management is a difficult task given available firm data. Baird and
Thomas (1985) identify over 40 variables hypothesized to affect strategic risk taking. The
strategic risk of a firm may be defined as the competitiveness of the firm within its industry, or
its competitive edge in an industry (Gates, 2006; Ai, et al., 2011).15 We create a measure for
strategic risk taking by measuring firm performance relative to an industry benchmark. Prior
literature has shown that firms alter risk taking behavior relative to their performance compared
to an industry benchmark (e.g. Gooding, Goel and Wiseman, 1996; Greve, 2008).
14
For example, a manufacturer may wish to insure its plant for $100+ million, which may exceed the highest
acceptable single loss for an insurer. The insurer may purchase layers of reinsurance on this single risk in order to
earn the business of the client.
15
The competitiveness of an insurance firm is difficult to measure, however. For example, assume two insurers sell
automobile insurance in the same market to the same consumers. The first insurer sells its product with an expected
loss ratio of 1.02, with the expectation that it will collect more gross premiums for investments (Cummins and
Weiss, 1991). The second insurer sells its product with an expected loss ratio of 0.98. It charges a higher price
because it is more financially secure and consumers are willing to pay more for safer products (Sommer, 1996).
There is strategic risk in each of these competitive strategies.
14
Because the operating performance relative to other insurers may affect the strategic risk
management decisions of an insurer, the variable used to measure strategic risk management in
this study is an aspiration level for insurer i relative to the other insurers in the sample for year t.
Several prior studies have provided insights on the measurement of aspiration. March (1988)
defines the aspiration level as a function of the previous wealth levels of the principal firm.
Miller and Chen (2004) use two measures of aspiration: own firm lagged ROA and lagged
industry median ROA. Greve (2008) uses the loss ratio of other insurers as an aspiration
measure, but controls for the size of the insurers in the sample so that similarly sized firms are
grouped. A number of other studies have defined aspiration level by using median industry
performance as a reference point (e.g., Fiegenbaum and Thomas, 1988; Fiegenbaum and Thomas,
1995; Jegers, 1991; Wiseman and Bromiley, 1991).
Strategic risk is defined in relation to the insurer’s competitive position within industry so
it is important to measure aspiration using an industry performance benchmark. However, there
is some criticism of using industry median performance levels as a reference point for aspirations,
because firms may wish to outperform the median (Gooding, Goel and Wiseman, 1996; Greve,
2008). Therefore, this study employs a 75th percentile reference point for industry ROA when
testing aspiration levels.16 The aspiration variable is constructed as shown below:
Eq. (4)
where ROA75t is the 75th percentile of risk adjusted return on assets in year t. Additionally, an
alternative measure of strategic risk management is considered – the ratio of underwriting and
claims expenses to total expenses. Insurers may be able to reduce their losses by employing
stricter underwriting and claims guidelines.
16
This will allow the insurer to charge more
Gooding, Goel and Wiseman (1996) empirically show that the aspiration reference point exceeds median returns
within an industry. In addition to using the 75th percentile of returns as the reference points, we use the 65th, 70th and
80th percentile of returns to construct a reference point, as additional robustness tests.
15
competitive prices and perhaps perform more profitably. However, allocating more resources to
underwriting and claims is costly.
2.4 – The Effect of ERM on Insulating the Firm from Adverse Changes
This study also examines the effectiveness of an ERM program by testing whether or not ERM
rated firms experience fewer performance shocks than non-ERM rated firms. To our knowledge,
no prior study has performed this type of test. However, several prior studies have examined
related issues of firm performance and ERM.
Grace, et al. (2010) use survey data from insurers that have ERM programs to perform an
efficiency analysis on firm performance. They find evidence that ERM leads lower expenses
and an increase in ROA. Eckles, Hoyt and Miller (2010) examine how ERM affects firm risk
(measured as stock return volatility) and risk adjusted returns (measured as ROA per unit of risk)
based on ERM usage. Once a program is instituted, firms realize a reduction in stock return
volatility and increased performance per unit of risk.
Pagach and Warr (2011) use the
appointment of a CRO to identify ERM programs. More volatile firms – measured as the
standard deviation of returns – are more likely to appoint a CRO.
More recently, studies have examined the effects of ERM on firm performance, such as
risk or return, and the economic question of whether or not ERM adds value to a corporation.
There is some disagreement in the literature over whether or not ERM programs improve firm
value. Hoyt and Liebenberg (2011) find that ERM is associated with significantly higher values
of Tobin’s Q, which is a measure of firm value and growth opportunities. However, Lin, Wen
and Yu (2010) find that ERM is associated with a lower Tobin’s Q. Lin, Wen and Yu (2010)
find that ERM for insurers is positively related to reinsurance purchases and derivatives use and
leads to a decrease in firm value.
16
Nearly all studies related to firm performance with an ERM program examines value or
return. Eckles, Hoyt and Miller (2010), however, provides evidence that ERM help reduce
return volatility. We expand on the prior performance literature by testing whether or not ERM
rated firms are less prone to shocks than firms without an ERM rating as well as how these firms
are affected by changes in the performance variables underlying shocks.
SECTION III – DATA AND HYPOTHESES DEVELOPMENT / METHODOLOGY
Data
As mentioned earlier, prior empirical studies on ERM most often use survey data, or press
releases and news stories that relate to a firm’s risk management in order to identify the firms
that have ERM programs. Potential drawbacks to these methods are reporting biases in surveys
and missed firms when analyzing press releases and news stories. This paper’s method for
determining ERM is more measurable and determinable. We use data from Standard and Poor’s
ERM quality ratings for insurers in the years 2009 and 2010. This data provides a quality rating
of ERM programs for insurance firms.
The quality ratings are “Excellent”, “Strong”,
“Adequate”, and “Weak”. Standard and Poor’s rates insurers’ ERM programs based on the
firm’s risk culture, risk models, management of emerging and strategic risks, and risk controls.
A summary of the number of firms rated by S&P in each category is reported in Table 1. We
combine S&P’s ERM Ratings data with data from the NAIC annual statements for propertycasualty firms from 1996 through 2010. The data is aggregated at the group level.17
17
Data is aggregated at the group level for the NAIC annual statement data, and for the S&P ERM ratings data. The
S&P ERM ratings are in almost all cases reported for the group or parent. In the cases where two individual insurers
within a group obtain separate ERM ratings, we inspect whether or not these firms have different ratings. In all
cases insurers within the same group have the same rating. Data is aggregated to the group level in order to measure
risk management activities, such as reinsurance transactions and diversification decisions, for the group, rather than
the individual insurer.
17
<Table 1 Here>
The remainder of this section develops the three hypotheses used to characterize the firms
that obtain ERM ratings, the potential the ERM helps these firms to be more insulated from loss
shock, and possible differences in the behavior of ERM rated and non-ERM rated firms.
Together the results provide insight into the use of rated ERM programs as well as the behavior
of those firms.
Below is a brief overview of the hypotheses development and proposed
methodology.
3.1 – Firm Factors Related to an ERM Program
Prior studies such as Hoyt and Liebenberg (2011) and McShane, Nair, and Rustambekov (2011)
have analyzed the motivations for adopting an ERM program, as well as the firm characteristics
most associated with ERM programs.18 Drawing from these studies, we develop an initial list of
firm operational, organizational, and financial traits likely associated with an ERM rating from
S&P. Both Hoyt and Liebenberg (2011) and McShane, Nair and Rustambekov (2011), include
variables for insurer size and leverage. These studies predict a positive relationship between size
and an ERM program and an ambiguous relationship between leverage and ERM.
Hoyt and Liebenberg (2011) and McShane, et al. (2011) also include variables for firm
complexity measure in their ERM analyses. Their complexity measures are proxies for the
firm’s operational diversification. 19 The authors differ in their predictions of the impact of
complexity on ERM. However, Hoyt and Liebenberg (2011) find that ERM firms are less
18
Hoyt and Liebenberg (2011) collect data on insurers with ERM programs based on results from searches of news
sources and press releases. Their non-ERM observations are firms that will adopt ERM in future years. This study
observes firms that obtain ERM ratings and firms that do not obtain ERM ratings for the full sample of U.S.
property and casualty insurers. McShane, et al. (2011) also use the S&P ERM ratings data, but use only this sample
of firms.
19
Hoyt and Liebenberg (2011) consider international diversification, and McShane, et al. (2011) use the four-digit
SIC codes in which the insurer operates to measure complexity.
18
frequently internationally diversified. Our measures for firm complexity are the modified HHI,
the firm’s geographic concentration, and the insurer’s exposure to industry concentration.20
We test for differences in the capital-asset ratio, which is a measure of firm liquidity.
This variable is similar to the free capital variable in Hoyt and Liebenberg (2011) as well as
McShane, et al. (2011). Although these studies do not predict a certain relationship between this
variable and ERM, less risky firms may require less free capital due to a reduced probability of
distress. Thus a negative relationship is expected between the capital-asset ratio and ERM.
Additionally, Hoyt and Liebenberg (2011) include a variable for the volatility of firm
returns, a variable for change in firm value, and a reinsurance variable. Firms with ERM
programs are expected to have lower volatility of earnings. We measure this volatility using the
standard deviation of ROA, which is often used in the insurance literature to measure firm risk or
earnings volatility (e.g., Liebenberg and Sommer, 2008). Hoyt and Liebenberg (2011) predict
that the change in firm value will be negatively related to ERM adoption because firms with
declines in earnings will be likely to implement ERM in order to signal an improvement to
shareholders.
We include a variable for the percentage change in policyholder surplus to
measure the change in value.21 The authors predict that reinsurance usage could be positive or
negative, because the insurer may utilize other risk management techniques to reduce
dependency on reinsurance, or it may rely more heavily on reinsurance. we include a variable
for the proportion of reinsurance ceded to measure this effect.22
20
These variables are used in our examination of insurer risk management techniques and are discussed in greater
detail below.
21
Policyholder surplus (PHS) is similar to a publicly traded firm’s equity. It is the insurer’s assets minus its
liabilities. In prior studies, PHS has proxied for firm equity (e.g., Liebenberg & Sommer, 2008).
22
Additionally, this variable is used in the methodology testing hypothesis three as one of the risk management
techniques.
19
We include a series of risk management variables to test whether insurers that are
designated as ERM firms differ in their use of risk management techniques.
These risk
management techniques are proposed in section 2.2 and examined in greater detail in hypothesis
two. They also are included in hypothesis one in order to observe differences in their usage
between firms with and without ERM ratings. These variables are the portfolio variance of
invested assets, the modified HHI developed in Pooser and McCullough (2012), the proportion
of reinsurance ceded, and two variables that help determine the influence of firm strategic risk –
an aspiration variable that measures the difference between firm performance and an industry
benchmark as well as the proportion of expenses spent on claims and underwriting.
We expect that ERM firms will have a lower portfolio variance in order to reduce the
probability of an adverse market event having a negative impact on its assets. We also test for a
difference in the firm’s asset concentration.23 There is no a priori prediction for this variable.
The modified HHI variable measures the concentration of a firm’s lines of business accounting
for the potential relatedness of these lines.24
ERM can benefit both diversified and concentrated
firms. Thus, we do not expect a clear relationship between the modified HHI and ERM use.25
The strategic risk of a firm may be defined as the competitiveness of the firm within its industry,
or its competitive edge in an industry (Gates, 2006; Ai, et al., 2011). The strategic risk variables
are the aspiration level – the difference in firm performance from an industry benchmark – and
the proportion of expenses in claims and underwriting. ERM firms should more effectively
23
This variable is used as an instrument to the asset portfolio variance variable.
The modified HHI is a measure of the firm’s liability management and operational risk management.
25
Geographic concentration and the firm’s exposure to industry concentration are used as instruments to the
modified HHI in the multivariate model for hypothesis three. we predict that ERM firms will be more
geographically diversified, but as discussed previously there will be no significant difference for the modified HHI.
The firm’s exposure to industry concentration may be higher for ERM firms, since ERM may give these firms an
operational advantage in their lines of business.
24
20
manage strategic risk (Gates, 2006); therefore, we predict a negative relationship between ERM
use and the strategic risk variables.26
Since our measure of ERM is based on an insurer obtaining an outside rating from S&P,
we also consider an agency theory perspective where the insurers obtain the ERM designation as
a method of bonding that helps align shareholder and manager values.27 Hoyt and Liebenberg
(2011) include an institutional ownership variable in order to test for potential agency and
monitoring motives. They predict that firms with stronger shareholder groups will be more
likely to demand ERM implementation. We include variables for stock or mutually owned
insurers, and for publicly traded insurers. Consistent with prior predictions, we expect that stock
firms are more likely to implement ERM programs in order to satisfy shareholders, and that this
effect will be even greater for publicly traded insurers.
A listing of proposed variables and expected signs is contained in Table 2. Combined,
these factors lead to the first testable hypothesis.
Hypothesis 1 – ERM and Non-ERM firms have different firm characteristics including financial
variables, risk variables, risk management techniques, and agency variables.
<Table 2 Here>
We perform two tests of this hypothesis. First, we perform basic t-tests to characterize
the differences between ERM and non-ERM firms. Then, following the examples of Liebenberg
and Hoyt (2003), and Hoyt and Liebenberg (2011), we perform logistic regression models to test
26
The magnitude of the aspiration variable decreases as firm ROA performance approaches the industry benchmark..
Gates (2006) suggests that ERM will help reduce governance risk by defining the amount of risk the firm can take,
and clearly communicating this throughout the firm. ERM should create a risk reference point for managers that
helps determine their risk-taking behavior.
27
21
the determinants of obtaining an ERM rating.28 These models’ specifications are based on the
variables that are contained in Hoyt and Liebenberg (2011). We then expand their model to
include measures of risk management that are used later to characterize potential differences in
behavior between ERM and non-ERM firms.
3.2 – Differences in Risk Management Techniques
In order to examine how ERM affects insurers in a more dynamic framework, we examine the
risk management techniques of insurers based on obtaining an ERM rating and potential agency
motives based on firm ownership structure. One of the hallmarks of ERM is the fact that all of
the risks and management of those sources of uncertainty are managed jointly. If ERM rated
firms truly do this, the risk management patterns of ERM and non-ERM rated firms will differ.
we perform simultaneous equations regression analysis for samples of ERM rated and non-rated
insurers and analyze whether or not the interrelation between these risk management techniques
differs significantly between the samples. We also perform this analysis for insurers organized
as stock firms because a stronger ERM rating presence is found among stock insurers.
Hypothesis 2 – The risk management techniques of insurers will differ based on their ERM
behavior, ownership structure, and experience with performance shocks.
We test Hypothesis two using a three stage least squares (3SLS) regression model, whose
structure is seen in equations (5) through (9) below. The system measures overall firm risk as a
function of several risk management techniques. This methodology allows me to account for the
potential that the risk management techniques are jointly determined and endogeneity between
28
Liebenberg and Hoyt (2003)’s logistic regression results show that firms more likely to appoint a Chief Risk
Officer are smaller and more highly leveraged. However, in Hoyt and Liebenberg (2011) the results indicate that
firms with a Chief Risk Officer are larger with lower levels of leverage. The 2011 study uses a larger dataset and
benefits from industry and academic development of ERM, so we focus on the comparison with this study.
22
the firm risk variable and risk management techniques.29 Equation (5) models firm risk as a
function of the risk management techniques discussed in section 2.3. This methodology controls
for the interrelation between these potentially simultaneously determined risk management
variables. Therefore, we can test the joint significance of the risk management variables in order
to determine whether or not the insurer’s overall risk is jointly determined by these variables,
which is one of the definitions of ERM behavior. We expect this joint relationship to be
significant for the ERM rated firms, but not significant for the non-ERM rated firms.
Eq. (5)
Eq. (6)
Eq. (7)
Eq. (8)
Eq. (9)
This methodology was developed by Zellner and Theil (1962).
It is a form of a
simultaneous equations model that allows for the joint estimation of a system of equations with
29
Born, et al. (2009) employs a similar simultaneous equations methodology in assessing several dimensions of firm
risk. The authors examine risk management, capital management, and financial management, as methods for
measuring liability and asset management. This study uses several manifest variables to proxy for the latent risk
management variables in the structural equation model. we include variables for reinsurance use, portfolio variance,
asset concentration, premium growth, and the capital-asset ratio, which are found in, or relate closely to, manifest
risk, asset, and capital management variables used in Born, et al. (2009).
23
endogenous variables, and is equivalent to estimating a two stage least squares and seemingly
unrelated regression model together. The methodology allows for the estimation of a system of
equations with potential contemporaneous correlation between the different elements of the
system. The 3SLS methodology controls for the correlation between the error terms of each
equation in the system. A weighting matrix derived from the residuals of a 2SLS regression is
applied to the estimators of the 3SLS system, which are composed of instruments that are
assumed to be exogenous to all of the models in the system.
Under 3SLS, each equation in the system contains exogenous independent variables.
Some exogenous variables may overlap between the different equations, but the methodology
requires that each equation – and more specifically, each endogenous variable – in the system
contain its own instruments for estimation (Wooldridge, 2002).
For these equations, the
endogenous risk management techniques are the Portfolio Variance, Modified HHI, Reinsurance
Use, and Aspiration variables discussed in section 2.2 and hypothesis one. The firm risk variable
is the five year prior standard deviation of the insurer’s return on assets. Each of these variables
serves as a dependent variable in one equation and independent variables in each other equation
in the simultaneous equation model.
The instruments for each endogenous variable are selected so that they have explanatory
power for an endogenous variable, but do not bias the error term for each equation. For the
measure of firm risk – SDROA – firm ROA is used as an instrument. For line of business
concentration, geographic and industry concentration indices are selected as instruments. The
concentration of firms within a certain geographic regions and industry concentration within a
line of insurance will likely affect the diversification decisions of an insurer. A concentration
index of assets lagged one period is used as an instrument for portfolio variance as described
24
below in equation (5). The previous year’s asset concentration is likely an important predictor in
current year asset allocation. The proportion of premiums ceded to reinsurance is estimated with
the growth of direct premiums and net income as instruments. Finally, the Kenney ratio is used
as an instrument for aspiration level.
The control variables in each equation are firm size, a dummy variable for publicly traded
insurers, and the capital-asset ratio. We also include year dummy variables. Since we are testing
these models based on presence of an ERM rating, the years in the sample are 2009-2010.
According to results from the three stage least squares model, all instruments are valid for the
equations, and each equation has significant explanatory power within the system. There is no
evidence of multicollinearity in any equation.
We test equation (5) by measuring the joint significance of the risk management variables
in predicting the firm risk variable for different samples of insurers. First, we differentiate the
sample by comparing firms with an ERM rating and firms without an ERM rating. This will
help provide evidence on whether or not ERM leads to greater simultaneous management of firm
risk. We predict that the joint significance of the risk management techniques will be stronger
for firms with an ERM rating because this indicates the simultaneous management of firm risks
while accounting for the interrelation between risk management techniques. This test provides
evidence the risk management patterns differ based on whether or not the insurer obtains an
ERM rating. Additionally, we perform this same test for a sample of only stock insurers. The
tests for hypothesis one showed that stock insurers are more likely to have a rated ERM program.
If agency motives affect the implementation of ERM, there may be measurable differences in
risk management techniques for stock insurers.
3.3 – Testing The Effectiveness of an ERM Program
25
The univariate and multivariate tests comparing firm characteristics for firms with and without
ERM ratings provide insight into what types of firms are more likely to practice ERM. Once we
have characterized the types of firms most likely to have ERM ratings, the next step is to
determine whether this creates a significant difference for the firm through either the potential
impact of risk and/or behavior related to risk management. Here, we observe if an ERM
program is associated with greater resilience to performance shocks and to the changes in firm
factors underlying shocks. If an ERM program is effective, the firm likely should be more
resilient and/or insulated from risk in the form of shocks and changes in performance. We aim to
test this prediction using several empirical tests. First, we measure whether firms with ERM
ratings have fewer shocks than firms without ERM ratings in a univariate setting. We expand
this measure to include a multivariate model using logistic regression analysis. We then test if
ERM rated firms experience more favorable changes in the performance variables underlying
shocks than non-rated firms. Thus, our third hypothesis is as follows.
Hypothesis 3 –Firms with an ERM program are more insulated to adverse events than firms
without ERM programs. ERM firms will have more favorable performance changes than nonERM firms and will experience fewer adverse shocks.
The first challenge is in defining what constitutes a shock. While prior literature provides
little guidance in developing a clear definition of what constitutes a shock, inferences may be
made by analyzing some research on the materiality of adverse events in accounting, and risky
debt and underwriting cycles in insurance. For example Lai, Witt, Fung, MacMinn, and Brockett
(2000) note that a loss shock for an insurer is an event that reduces surplus and might reduce the
insurer’s credibility in paying its current and future claims. In the accounting literature, a
material loss event is a misstatement or restatement of some firm characteristic that damages
26
firm credibility in the eyes of creditors and shareholders. Chewning and Higgs (2002) note that
SFAS defines a loss event as material if it exceeds 5-10 percent of income or assets.30 Although
some inference must be made between the streams of literature, it is argued that a sharp
reduction in a dimension of insurer performance constitutes a shock event.
We first test whether firms with ERM ratings have fewer shocks to their surplus or to
their net income. Using values from the accounting literature, we define two levels of shocks to
the surplus and net income variables – a 5 percent (or greater) reduction and a 10 percent (or
greater) reduction. As an alternative, we also consider shocks to the insurer’s loss ratio and
return on assets. We consider two levels of shocks for these variables as well. We define shock
events as a one or two standard deviation reduction in the mean value of the change in ROA and
a one or two standard deviation increase to mean value of the change in the overall loss ratio.
We use the change in value for these variables, rather than the value, because this change better
represents a shock to performance. An insurer that consistently operates with a loss ratio above
industry mean or median is not experiencing a shock. The mean value and standard deviation of
the change in ROA and the loss ratio are calculated for each firm each year. We measure the
mean and standard deviation using the five prior-year values for each variable. This way, a
shock to the firm’s performance is relative to prior changes in ROA or the loss ratio for the firm,
rather than for the entire industry.
In order to test hypothesis three, we first examine the existence of shocks firms with
ERM ratings and without ERM ratings in univariate and multivariate frameworks. This portion
of the analysis is focused on the existence of shocks, with shocks defined through dummy
variables described above. We then turn to an analysis of the underlying measure used to define
30
SFAS is the Statement of Financial Accounting Standards, issued by the Financial Accounting Standards Board.
27
the shock in a continuous framework.31 Specifically, we look at the change in loss ratio rather
than dummy variable for shocks based on this variable. 32 We perform OLS regression and
quantile regression analyses on the change in loss ratio and ROA to test the relation of this
variable to the existence of an ERM rating. The OLS regression allows me to determine the
overall relation between an ERM rating and the change in the loss ratio. The quantile regression
allows me to determine if this relation changes at different points of the distribution for the
change in loss ratio.33 Combined, the analysis allows a test of both whether there is a difference
in the existence of loss shocks for ERM and non-ERM rated firms as well as insight into the
differences in changes in the loss ratio, controlling for the magnitude of the change. Since the
shocks are more localized in the high quantile for the change in loss ratio this provides more
information on the underlying factors related to the shock.
SECTION IV – RESULTS
Summary statistics for the variables of the entire sample of firms in the years 2009 and 2010 are
shown in Table 3. This time period corresponds with the data period for ERM ratings. There are
a total of 850 firm year observations in 2009 and 809 observations in 2010. There are 84 firms
with ERM ratings in 2009 and 80 firms with ERM ratings in 2010. Below, we present and
discuss results corresponding with our three hypotheses.
<Table 3 Here>
31
Shocks to the loss ratio or ROA are defined as a one or two standard deviation increase in the change in loss ratio
or ROA.
32
The loss ratio is a measure of insurer operating profitability. The loss ratio is the proportion of losses to premiums,
so a value of 100 (percentage points) indicates that the insurer collects premium revenue equal to its losses. A
higher value indicates greater losses relative to premiums collected, or less operating profitability, so firms with an
increasing loss ratio experience decreasing operational profitability.
33
Prior studies, such as Viscusi and Born (2005) and Born, Viscusi, and Baker (2009) have also used quantile
regression based on the distribution of insurer losses. The use of quantile regression with this type of data allows
one “to evaluate the potential differential influence on loss levels” of the independent variables.
28
Section 4.1 – Examining Differences in Firm Characteristics Related to an ERM Rating
Hypothesis one predicts that a number of firm factors are related to obtaining an ERM rating.
These firm factors and their expected relationship with the presence ERM ratings are
summarized in Table 1. We conduct univariate t-tests of the mean values of each variable based
on ERM rating, and a multivariate analysis of the propensity to obtain an ERM rating. The
findings for the univariate tests are presented in Table 4.
<Tables 4 Here>
Consistent with prior literature, we find that firms with an ERM rating tend to be larger,
less leveraged, have greater operational diversification, and have lower levels of liquidity or free
capital. Also in line with previous works, we find that firms with an ERM rating have a lower
proportion of reinsurance usage. We observe no significant difference in our sample for firm
ROA and standard deviation of ROA, premium growth, income growth, surplus growth, or
industry concentration.
Although we did not have ex-ante predictions for the difference in line of business
concentration or reinsurance use, we find that ERM rated firms have significantly lower values
for each of these variables. This indicates that firms with an ERM rating tend cede fewer
premiums dollars to reinsurance and have higher levels of lines of business diversification. In
line with prior literature, we predicted a negative relation between the claims and underwriting
expense ratio and ERM rated firms because prior literature on cites increased efficiency as a
benefit to an ERM program. Consistent with this prediction, we find a significantly lower value
for this expense ratio for ERM rated firms.
The mean values for the aspiration and portfolio
variance variables are not significantly different based on ERM rating.
29
Related to agency theory motives for the use of ERM, Hoyt and Liebenberg (2011)
predict that firms with more institutional ownership will be more likely to have ERM because of
shareholder pressure on managers. We test for this motive by including a dummy variable for
whether or not the firm is publicly traded. Publicly traded insurers are stock organized and have
a much more widely held owner base than privately owned insurers. We also include a variable
for organizational form in order to determine if stock or mutual insurers are more associated with
obtaining an ERM rating. Consistent with the prior finding, we see that ERM rated firms are
significantly more likely to be organized as stock insurers and more often publicly traded. The
insurers in our sample are far more likely to be organized as stock firms than mutual (almost 80%
stock firms in the ERM sample, versus 60% in the non-ERM sample). Additionally, 57% of the
ERM firms are publicly traded, while this only accounts for 7% of the non-ERM firms. The
propensity for ERM rated firms to be stock organized and publicly traded may indicate a
motivation to bond manager and shareholder incentives. An ERM program should set clear risk
goals for the organization and incentivize managers to meet these goals, so an ERM rating may
be most valuable to the shareholders of stock organized and publicly traded insurers.
In the second tests of hypotheses one, we report initial results for the determinants of
obtaining an ERM rating in Table 5. This regression model is a reexamination of Hoyt and
Liebenberg (2011)’s analysis of ERM implementation using the entire sample of insurers in the
NAIC property and casualty database, rather than publicly traded insurers. In addition, we add to
this model by including the risk management technique variables in order to measure their
impact on obtaining an ERM rating.34 Since the ERM variable is a dummy (1/0) variable, we
estimate the results of the multivariate regression using a logistic model. We use robust standard
34
The reinsurance and line of business concentration variables appear in the original iteration of the model, so the
only variables added to the second iteration are portfolio variance and the aspiration variable.
30
errors to control for potential heteroskedasticity. We find no evidence of multicollinearity in the
regression equations. The results of this multivariate model are presented in table 5.
<Table 5 Here>
Consistent with the findings of Hoyt and Liebenberg (2011), we find that larger firms are
more likely to be rated for ERM and that reinsurance usage is lower among firms with an ERM
rating. We also find that publicly traded firms are more likely to be ERM rated, perhaps because
the shareholders of these firms seek a method of bonding manger incentives. While we predicted
a negative relation between an ERM rating and the capital-asset ratio, we find a positive
coefficient for this variable. Hoyt and Liebenberg predict a negative relation for this variable
because ERM firms may require less free capital in order to operate safely. Differences between
our findings for model one and the findings of Hoyt and Liebenberg (2011) may be caused by
different samples.35
In the second model we include variables for the firm’s aspiration level and portfolio
variance. We do not find significant results for either variable and the prior significant results
remain unchanged. The risk management variables, including portfolio variance, the modified
HHI, reinsurance use, and the aspiration level, are analyzed in greater detail in hypothesis two.
Section 4.2 –Testing for Simultaneity in Risk Management Techniques
Hypotheses one examines the impact of an ERM rating on dimensions of firm characteristics by
looking for differences in firm characteristics based on the presence of an ERM rating.
Hypothesis two examines ERM in a more dynamic framework.
35
We examine the joint
My measure of ERM activity is whether or not an insurer obtains an ERM program rating through Standard and
Poor’s Ratings Direct, while the Hoyt and Liebenberg measure of ERM activity is based on survey data from news
outlets and press releases. Additionally, their sample consists of only stock insurers that are publicly traded. My
sample includes all of the firms in the NAIC property-casualty database with available financial and operational data.
31
management of firm risk controlling for the interrelation of the risk management techniques and
other firm characteristics.
Since ERM’s hallmark is managing risk jointly, we expect to find joint significance of
the risk management variables for the ERM rated sample of firms. If other firms are truly not
practicing ERM we expect their behavior do be different. While they likely use the same
techniques they may not use them jointly as in an ERM program.
In order to find support for hypothesis two, we should observe joint significance in the
risk management variables that explain firm risk for insurers with an ERM rating. An ERM
program treats all firm risks simultaneously, and finding joint significance in the risk
management techniques indicates a firm decision to manage overall risk using different
techniques jointly. We do not expect to find joint significance in the risk management variables
in the sample of insurers without an ERM rating as traditional ERM programs operate in silos in
which the risk management techniques are often thought of independently. We first test the
simultaneous equations model based on the full sample of firms, differentiating by firms with
and without an ERM rating. We also test this model using only stock insurers, with and without
an ERM rating. We observe a greater tendency for stock insurers to adopt an ERM rating in the
tests for the first hypothesis.
Testing for differences in the joint significance of the risk
management variables for the stock subsample provides a robustness test for the propensity of
ERM rated firms to simultaneously manage firm risk.
In Table 6 we report whether the
simultaneous equations model is significant for each model specification, and if the risk
management technique variables are jointly significant in each model. The full results from the
simultaneous equations models, and a discussion of the findings, are presented in the appendix.
<Table 6 here>
32
For the simultaneous equations models, we report the model significance for each
equation and the joint significance of the risk management techniques on firm risk. If the model
is overall significant then there is predictive power in its independent variables. Further, joint
significance of the risk management techniques shows that these risk management variables are
significant determinants of firm risk, controlling for their impact on each other (the interrelation
of these techniques). Joint significance provides evidence of simultaneous management for firm
risk, one of the most important aspects of ERM.
For the full sample of firms, we separate the system of equations by firms that obtain an
ERM rating and those that do not. We find that the firm risk equation is significant for both
ERM rated and non-ERM rated insurers. However, we do not find joint significance in the risk
management techniques of firms without an ERM rating in explaining firm risk. We do find
joint significance in the risk management techniques of firms with an ERM rating. In order to
determine whether or not organizational structure impacts these findings, we also perform this
analysis for only stock insurers. Again, the results show that the firm risk equation is significant
for both ERM rated and non-ERM rated firms. However, we do not find joint significance in the
risk management techniques for stock insurers without an ERM rating in explaining firm risk.
We do find joint significance for stock insurers with an ERM rating. These results indicate a
tendency for firms with ERM ratings to simultaneously manage firm risk, controlling for the
interrelation between these risk management techniques and their impact on each other, using the
hypothesized risk management techniques.36 This provides strong evidence for the presence of
36
For robustness, we also consider levels of the aspiration variable at the 50 th, 65th, and 85th percentile of industry
ROA. At the 65th and 85th percentile levels the results are the same. At the 50 th percentile level the joint effect of
risk management techniques on firm risk is marginally significant for the non-ERM rated sample. However, this
makes an assumption that the aspiration to perform is only set at an industry median, which may not be valid for
most firms in the industry. we also substitute the traditional HHI measure for the modified HHI. The statistical
significance of the equations and joint significance of the risk management techniques does not change.
33
what can be called ERM behavior – the simultaneous management of firm risk using multiple
methods of risk management. This ERM behavior is present in firms with an ERM rating, but is
not found for firms that do not obtain an ERM rating.
Section 4.3 – Examining the Effectiveness of ERM in Preventing Performance Shocks
Hypothesis three predicts that firms with an ERM rating will be more effective in preventing
adverse events, or that firms with an ERM rating are more insulated from performance shocks.
<Table 7 Here>
In the univariate tests presented in Table 7, we consider eight categories of performance
shocks. We include two severity levels for shocks to net income, policyholder surplus, ROA,
and the loss ratio. The results indicate that, on average, ERM rated firms have fewer shocks than
non-ERM rated firms. We find significantly fewer shocks for ERM rated firms for five of the
eight shock categories. Additionally, we do not find significantly more shocks for ERM rated
firms under any shock category. For shocks to the loss ratio or ROA, all differences between
ERM rated and non-rated firms are significant. The average number of shocks per firm is about
six percent lower for ERM rated insurers than non-rated insurers. The difference in shocks is
also significantly lower for ERM rated insurers using a five percent drop in net income as the
shock definition. .
In addition to the univariate tests we also examine shocks to the change in the loss ratio
in a multivariate framework. This is done in a logistic model focused on shock variables related
to change in loss ratio. In Table 8, we present results from a logistic regression using the loss
ratio shock indicator as the dependent variable. This regression is performed for the ERM
sample years, 2009-2010, and utilizes robust standard errors. The primary result from this
regression analysis is that the ERM indicator variable is negatively and significantly related to
34
the dependent variable. This provides further evidence that firms which experience a shock to
the loss ratio are significantly less likely to have obtained an ERM rating. In addition to this
result, firms that experience a loss ratio shock tend to have lower values of leverage (measured
using the Kenney ratio) and have negative growth in policyholder surplus. The decrease in
policyholder surplus may be caused by the increase in the loss ratio from greater losses for the
insurer.
<Table 8 Here>
To better understand the relation of ERM ratings and loss shocks, we now test for the
effect of an ERM program rating on the change in the loss ratio.
Tables 9 presents the
distribution of the change in loss ratio as it relates to the loss ratio shock dummy variable. This
table also shows the number of firms experiencing a loss ratio shock based on the respective
change as well as the number of firms with ERM program ratings across the distribution of the
change.
<Table 9 Here>
We first perform an OLS regression using the change in loss ratio from the prior year as
the dependent variable. 37 We perform this test for the ERM data period (2009-2010) using
robust standard errors. Additionally, we perform a quantile regression analysis with the same
construction as the OLS regression. The quantile regression examines the determinants of the
dependent variable at different points of its distribution – here the 25th, 50th, and 75th percentiles.
The quantile regression is also performed for the ERM data period and utilizes bootstrapped
standard errors.
37
A negative value for this variable indicates that the firm’s loss ratio decreased, leading the firm to be more
operationally profitable in the current year, and vice versa.
35
The results of the change in loss ratio OLS regression model in Table 10 shows that the
ERM rating indicator variable is negative and significantly related to the change in the loss ratio.
This means that firms with a lower, or decreasing, loss ratio have a greater propensity to have an
ERM program rating. Consistent with the finding that ERM rated firms are less likely to
experience a loss ratio shock, this provides support for hypothesis three. Firms with ERM tend
to be more insulated from shocks to performance or adverse events – here defined as increases to
the loss ratio. However, does this finding hold when different levels of the change in loss ratio
are examined? That is, does an ERM rating relate to the change in the loss ratio at different
distributional points for the change in loss ratio? The results of the quantile regression help
provide an answer for this question.
<Table 10 Here>
Table 10 also presents the results of the quantile regression for the change in loss ratio
variable. We use three quantile points – the 25th, 50th, and 75th percentile of the change in loss
ratio.. 39 In the quantile regression, the coefficient for the ERM rating indicator variable is
negative and significant at each quantile of the change in loss ratio. The magnitude of the
coefficient changes slightly in each quantile, but is mostly stable. This finding further supports
the value of ERM. Firms with an ERM rating tend to have lower or negative changes in the loss
ratio, even controlling for potential differences in the change in loss ratio distribution. In this
case, we see both fewer shocks for ERM rated firms in our first tests as well as lower changes in
loss ratios which provides evidence of the way in which these firms are insulated from the
shocks.
39
Some prior studies, such as Viscusi and Born (2005) and Born, Viscusi, and Baker (2009),
have used the 10th, 25th, 50th, 75th, and 90th percentiles in the quantile regression. However,
because the ERM indicator variable has so few observations in the 10th and 90th percentile of the
change in loss ratio we use fewer quantile points. For example, only 6 firms in the upper 10th percentile of
the change in loss ratio have an ERM program rating.
36
The detail provided in this section of the paper is important for insurer stakeholders
examining an ERM decision. Regulators and ratings agencies will likely view these results
favorably, since ERM rated firms tend to have better operating performance with respect to
losses. Insurers considering ERM may be more likely to implement in order to reap the benefits
of better risk identification and organizational communication which lead to better performance.
Consumers may view an insurer with ERM as a better or safer choice when making purchasing
decisions.
Taken with the results from hypotheses one and two, the evidence indicates that ERM
rated firms tend to participate in what is one of the hallmarks of ERM – jointly managing firm
risks. In addition, this form joint of risk management benefits the firm since ERM rated firms
also experience fewer shocks and better performance related to the loss ratio. Where prior
empirical research on ERM has focused on whether or not ERM adds value to the firm, these
results show other benefits in enacting ERM – including enhanced firm stability.
SECTION V - CONCLUSION
This paper examines insurer ERM behavior by testing three hypotheses related to insurer ERM.
These hypotheses examine whether firm characteristics that differ due to ERM, differences in the
use of risk management techniques based on firm risk based on the presence of a rated ERM
program, and the potential for ERM to help firms be insulated from and/or reduce shock as well
as increase firm performance. In testing these hypotheses we find several firm factors that are
significantly related to obtaining an ERM program rating. This evidence is important for future
research utilizing Standard and Poor’s ERM Rating data, as well as to research examining the
firm’s decision to implement of ERM.
Understanding the types of firms utilizing ERM
37
programs is important to better understanding the impact of the programs. Additionally, we
provide an examination of insurer risk management techniques, and relate these techniques to
ERM by observing differences in risk management usage between firms with ERM ratings, and
those without. If a firm is engaged in an ERM program, there should be a statistical interrelation
between the risk management techniques. We find that firms with an ERM rating jointly manage
firm risk using several risk management techniques. We do not see evidence that firms without
ERM ratings use risk management techniques jointly. This result is consistent for the full sample
of insurers and for a sample of only stock insurers.
Finally, we provide evidence of the
effectiveness of ERM by showing that firms with an ERM rating experience fewer adverse
events and performance downturns than other firms in the industry. ERM rated firms experience
fewer negative performance shocks and have more favorable performance in the variables
underlying shocks.
This study contributes to the ERM literature by providing an additional dimension of
empirical ERM research. Prior research on ERM typically focuses on the ability of ERM to add
firm value. We examine firm characteristics related to ERM implementation, the effectiveness
of an ERM program in reducing or preventing shocks to firm performance, and differences in the
risk management techniques of ERM and non-ERM insurers. To our knowledge, no prior ERM
studies have examined the effects of ERM on firm behavior in this manner.
We use the Standard and Poor’s ERM Ratings dataset as an outside, verified measure of
ERM implementation combined with the NAIC’s U.S. property and casualty insurance data. This
is in contrast to prior studies that have gathered data using surveys (e.g., Kleffner, et al., 2003,
Beasley, et al., 2005, Altuntas, et al., 2010) or news reports and press releases for publicly traded
insurers (e.g., Liebenberg and Hoyt, 2003, Hoyt and Liebenberg, 2011). The construction of the
38
dataset and tests also allows me to use the entire sample of property and casualty insurers rather
than only ERM insurers or publicly traded insurers, which creates a broader study than some of
the prior empirical work.
The empirical results in this study provide academic researchers, ratings agencies, and
regulators with insight into which types of insurers are adopters of ERM.
The tests for
differences in firm behavior related to the management of risk across ERM and non-ERM firms
not only provides evidence that ERM rated firms jointly use risk management techniques as the
ERM framework suggests when compared to non-ERM firms, but it also provides a potential
means to test for the presence of an ERM program within insurers. This will be useful for those
tasked with validating the presence of ERM such as regulators, rating agencies, or other
interested stakeholders. Finally, the results that firms with rated ERM programs experience
fewer shocks and adverse events helps to bolster the motivation for firms to implement an active
enterprise risk management program. This should also provide motivation to other stakeholders,
such as regulators and rating agencies, to support the implementation of ERM.
The importance of these findings will grow as ERM implementation is expected to
continue with ratings agencies and regulators increasing their requirements for risk quantification
and risk management. Additionally, greater scrutiny on managers and directors in the post SOX
and financial crisis environment is leading many firms to pursue an ERM strategy. As data
becomes available in the future for further tests on ERM, the hypotheses developed in this paper
provide a basis for further empirical tests of the effectiveness of ERM and the change in behavior
of ERM firms, which may be applied across industries.
39
Table 1 – ERM Sample Statistics
Year
Weak
2009
9
2010
7
Insurers by ERM Rating
Adequate Strong
Excellent Total
57
14
4
84
56
13
4
80
40
Table 2 –Descriptions of Firm Variables with Expected Relation to the Presence of an ERM
Program
Variable
Description
ERM Related Variables From Prior Literature
Size
Natural Log of Net Admitted Assets
Kenney
Net Premiums Written Scaled by Policyholder
Surplus
CapAsset
Policyholder Surplus Scaled by Net Admitted
Assets
GrowthNI
Change in Net Income from Prior Year
GrowthPrem
Change in Direct Premiums Written from Prior
Year
GrowthPHS
Change in Policyholder Surplus from Prior Year
DPW
Direct Premiums Written (in $1,000s)
Insurer Risk Management Techniques and Risk Variables
SDROA
Standard Deviation of ROA – Firm Risk
ROA
Return on Assets
PortVar
Portfolio Variance
AssetHHI
Concentration Index of Invested Assets
MODHHI
Modified Line of Business HHI
GeoHHI
Firm Concentration by State
WCONC
Index Measuring the Firm’s Exposure to Industry
Concentration
Reinsurance
Proportion of Premiums Ceded to Reinsurance
Aspir
Performance Distance from Industry Aspiration
XPRatio
Ratio of Claims and Underwriting Expenses to
Total Expenses
Potential Agency Related Variables
Mutual
Dummy Variable for Mutual Insurer
Public
Dummy Variable for Publicly Traded Insurer
Expected Relation
to ERM
+
+
+/+
+
+/+
+/+/+
+/-
+
41
Table 3 – Summary Statistics for All Firm Variables
Variable
Mean
Size
Kenney
CapAsset
GrowthNI
GrowthPrem
GrowthPHS
DPW
SDROA
ROA
PortVar
AssetHHI
MODHHI
GeoHHI
WCONC
Reinsurance
Aspir
XPRatio
Mutual
Public
10.573
1.682
0.483
-0.003
0.148
0.100
195,779
0.032
0.019
5.691
0.627
0.731
0.711
0.109
0.224
0.050
0.412
0.401
0.133
25th
Percentile
9.070
0.632
0.328
-0.021
-0.031
-0.010
4,294
0.014
0.000
1.867
0.495
0.506
0.371
0.051
0.050
0.016
0.255
0
0
Median
10.446
1.232
0.438
0.002
0.044
0.067
19,695
0.024
0.025
2.460
0.615
0.754
0.992
0.099
0.156
0.032
0.338
0
0
75th
Percentile
11.978
2.096
0.606
0.023
0.158
0.160
85,635
0.040
0.050
3.440
0.766
1.000
1.000
0.163
0.335
0.057
0.444
1
1
n. obs.
1,562
1,562
1,562
1,413
1,424
1,413
1,573
1,131
1,562
1,573
1,573
1,571
1,536
1,571
1,505
1,562
1,562
1,526
1,573
42
Table 4 – Mean Value and T-Tests of Firm Variables by ERM-rating Type
2009-2010
ERM
Non-ERM
Rated Firms
Rated Firms
Variable
Mean
Mean
ERM Related Variables From Prior Literature
Size
13.7348
10.6521
Kenney
1.1922
1.5250
CapAsset
0.4294
0.4990
GrowthNI
0.0074
-0.0015
GrowthPrem
0.1154
0.0967
GrowthPHS
0.0943
0.0697
DPW
2,120,643
101,682
T-Test of
the Difference
Significance
***
*
***
***
Insurer Risk Management Techniques and Risk Variables
SDROA
0.0317
0.0323
ROA
0.0056
0.0071
PortVar
3.0836
4.3613
AssetHHI
0.6973
0.6375 ***
MODHHI
0.6346
0.7767 ***
GeoHHI
0.3393
0.7069 ***
WCONC
0.1325
0.1279
Reinsurance
0.1586
0.2221 **
Aspir
0.0454
0.0495
XPRatio
32.2044
44.1187 ***
Potential Agency Related Variables
Mutual
0.2152
0.4029 ***
Public
0.5696
0.0723 ***
1
*,**,*** indicate significance at the 0.1, 0.05, and 0.01 levels, respectively
43
Table 5 – Logistic Regression Results: Determinants of ERM
Model 1
Model 2
VARIABLES
ERM Related Variables from Prior Literature
Size
Kenney
CapAsset
GrowthPHS
0.7980***
(0.1254)
0.1610
(0.2030)
2.8837*
(1.5095)
0.4384
(0.9291)
0.8030***
(0.1264)
0.1502
(0.2110)
2.8707*
(1.5431)
0.5049
(0.9739)
Potential Agency Related Variables
Public
Mutual
2.1132***
(0.3915)
-0.6253
(0.4717)
2.0900***
(0.3982)
-0.6361
(0.4685)
Insurer Risk Management Techniques and
Risk Variables
SDROA
ModHHI
GeoHHI
WCONC
Reinsurance
-0.5122
(3.7144)
-0.8704
(0.7828)
0.5726
(0.5845)
-0.0503
(2.7606)
-2.6434*
(1.4839)
-1.2945
(6.2043)
-0.8822
(0.7836)
0.5802
(0.5919)
-0.0664
(2.8123)
-2.6149*
(1.4616)
0.5905
(2.9977)
-0.0126
(0.0154)
-13.9574***
(2.3389)
-13.9484***
(2.3465)
Aspir
Portvar
Constant
Observations
1,056
1,056
Pseudo R2
0.3966
0.3975
1
Dependent Variable = 1 for Firms with ERM Rating
2
Robust Standard Errors reported below coefficient estimate
3
*,**,*** corresponds with significance at the 0.1, 0.05, and 0.01 levels, respectively
44
Table 6 – Simultaneous Equations Models for Firm Risk and Risk Management
Techniques
Dependent Variable
Model Significance
Joint Significance
Dependent Variable
Model Significance
Joint Significance
Dependent Variable
Model Significance
Joint Significance
Firm
Risk
<0.0001
0.0003
Firms with ERM Rating
Portfolio Modified Reinsurance
Variance
HHI
Use
<0.0001
0.0006
<0.0001
0.0010
0.0058
0.0249
Aspiration
<0.0001
<0.0001
Firm
Risk
0.0018
0.1567
Firms without ERM Rating
Portfolio Modified Reinsurance
Variance
HHI
Use
<0.0001
0.0003
<0.0001
<0.0001
0.0014
0.0034
Aspiration
<0.0001
<0.0001
Firm
Risk
<0.0001
<0.0001
Stock Firms with ERM Rating
Portfolio Modified Reinsurance
Variance
HHI
Use
0.0445
0.0120
<0.0001
0.6835
0.8948
0.0879
Aspiration
<0.0001
<0.0001
Stock Firms without ERM Rating
Firm
Portfolio Modified Reinsurance
Risk
Variance
HHI
Use
Aspiration
Model Significance
<0.0001
0.0285
0.2475
0.0004
<0.0001
Joint Significance
0.4184
0.0226
0.1592
0.8988
0.1903
The reported statistics in each cell are the p-values for overall model significance, and the joint
significance for the risk management techniques in predicting the dependent variable. The highlighted
cells are the p-values for the model significance and joint significance for the firm risk variable.
Dependent Variable
45
Table 7 – Description of Shock Events and Average Number of Shocks for Firms With
ERM Ratings and Firms Without ERM Ratings
Variable
Variable Description
2 SD ROA
1 SD ROA
2 SD LR
1 SD LR
5 Pct NI
10 Pct NI
5 Pct PHS
10 Pct PHS
Two Standard Deviation decrease in the change in ROA
One Standard Deviation decrease in the change in ROA
Two Standard Deviation increase in the change in loss ratio
One Standard Deviation increase in the change in loss ratio
Five Percent decrease in Net Income
Ten Percent decrease in Net Income
Five Percent decrease in Policyholder Surplus
Ten Percent decrease in Policyholder Surplus
Total Sample
Average Shocks Per Firm
ERM Rated
Non-ERM
Firms
Rated Firms
2 SD ROA
0.0711
0.0182
0.0742
1 SD ROA
0.1952
0.1429
0.1983
2 SD LR
0.0960
0.0536
0.0985
1 SD LR
0.2080
0.1071
0.2140
5 Pct NI
0.1154
0.0694
0.1178
10 Pct NI
0.0453
0.0278
0.0462
5 Pct PHS
0.1812
0.2083
0.1797
10 Pct PHS 0.1224
0.1111
0.1230
1
*,**,*** indicate significance at the 0.1, 0.05, and 0.01 levels, respectively
T-Test of
the Difference
**
*
*
***
*
46
Table 8 – Logistic Regression Results for Loss Ratio Shock
VARIABLES
ERM Indicator
Size
ROA
CapAsset
Kenney
GrowthPHS
PortVar
MODHHI
Reinsurance
Aspir
Mutual
Public
Constant
Loss Ratio
Shock
-1.030**
(0.521)
0.047
(0.056)
-3.628
(2.415)
-0.952
(0.661)
-0.252***
(0.096)
-1.302**
(0.585)
-0.0003
-0.006
0.229
(0.380)
0.650
(0.438)
-1.855
(2.646)
-0.129
(0.172)
-0.130
(0.353)
-1.062
(1.007)
Observations
949
Pseudo R-squared
0.0271
Dependent Variable = Shock to Loss Ratio Indicator
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.10
47
Table 9 – Distribution of the Change in Loss Ratio
Difference in Loss Ratio
All Firms
ERM Rated
Non-ERM Rated
10th
-21.606
-31.589
-21.209
25th
-8.592
-14.595
-8.326
50th
0.000
-1.277
0.000
75th
7.436
6.316
7.687
90th
19.044
18.983
19.044
Number of Firms
with Loss Ratio
Shock by Quantile
n
1,424
72
1,352
0-25
25-50
50-75
75-100
0
0
40
167
1
81.1% of loss shocks are at or above the 75th percentile of the difference in loss ratio.
Number of Firms
with ERM Rating
by Quantile
0-25
21
25-50
17
50-75
19
75-100
15
48
Table 10 – OLS and Quantile Regression Results for the Difference in Loss Ratio
VARIABLES
Difference in
Loss Ratio Value
ERM Indicator
Size
ROA
CapAsset
Kenney
GrowthPHS
PortVar
MODHHI
Reinsurance
Aspir
Mutual
Public
Constant
OLS
Regression
-6.148*
(3.308)
0.202
(0.427)
-34.615***
(11.413)
-7.734
(6.941)
-0.754
(0.692)
-9.540***
(2.970)
-0.037
(0.038)
0.425
(3.098)
-0.602
(6.061)
19.819
(13.836)
-2.572
(1.626)
0.188
(2.286)
3.322
(7.285)
25th
Percentile
50th
Percentile
75th
Percentile
-8.592
0.000
7.436
-6.574**
(3.566)
1.238***
(0.404)
-19.897
(15.049)
3.961
(4.724)
1.558***
(0.440)
-10.041***
(3.133)
-0.109*
(0.058)
2.845
(2.933)
-10.601**
(4.242)
-15.220
(16.213)
-5.188***
(1.185)
1.614
(2.341)
-22.205***
(7.459)
-4.135**
(2.319)
0.405*
(0.219)
-21.117***
(7.382)
-1.483
(1.948)
0.267
(0.275)
-7.669***
(1.790)
-0.011
(0.072)
2.248
(1.629)
-3.246*
(1.847)
7.924
(10.031)
-1.165
(0.853)
0.642
(1.354)
-3.764
(3.749)
-5.308**
(2.686)
-0.003
(0.332)
-56.275***
(9.132)
-5.921
(3.713)
-0.926***
(0.317)
-4.412*
(2.356)
0.009
(0.054)
2.030
(1.903)
4.130
(2.932)
50.736***
(11.056)
0.495
(0.996)
4.253*
(2.225)
8.776
(5.621)
Observations
1,336
1,336
1,336
Dependent Variable = Change in Loss Ratio from prior year
Bootstrapped standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.10
1,336
49
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