U.S. Nonlife Market Exit (2007)
Kwon and Kim
A Study of Exit Forms and Insurer Characteristics:
Evidence from the U.S. Property/Liability Insurance Markets
W. Jean Kwon
St. John’s University, USA
[email protected]
Hunsoo Kim
SoonChunHyang University, Korea
[email protected]
DRAFT – DO NOT QUOTE.
American Risk and Insurance Association Conference
August 5- 8, Quebec City, Canada
This paper deals with market exit issues in the insurance industry. It examines how firm
specific factors (measured in clusters of profitability, underwriting performance, liquidity,
capital adequacy, size, business concentration and business structure) and external
factors interact with market exit choices (voluntary liquidation, involuntary liquidation,
and merger) in the regulated industry. Using the A.M. Best database of the U.S.
property-liability insurance industry for 1999-2004 and a multinomial logit regression
approach, we find that normal (non-exiting) firms and merged firms show similar
characteristics. Particularly, asset not only significantly affects the probability that a firm
continues its operation in the market. It also is a strong indicator of the firm’s
preference of voluntary liquidation or merger to involuntary liquidation, when it
considers exit from the market. Profitability and capital adequacy are also found to
affect the exit forms.
INTRODUCTION
Theory suggests that regulation is costly and prohibits the regulated market from being
contestable. A contestable market allows firms to enter it free of cost and, on exiting the market,
to liquidate their capital without any loss for an alternative use (Baumol et al., 1982). Nevertheless,
insurance markets globally have long been subject to stringent regulation. In the typical insurance
market, firms incur sunk costs of operation and the costs are often irrecoverable. 1 A mere
presence of regulation thus prohibits any market from achieving a Pareto optimal equilibrium.
Besides, an exit barrier can deter potential suppliers’ entry to the market (Ilmakunnas and Topi,
1999; and Europe Economics, 2004).
1
Contestability theory also suggests that if an exit from a market is costless, firms entering
the market would not fear of price reductions by incumbent firms.
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Kwon and Kim
Insurance regulators commonly believe that they can play a role to minimize problems of
information asymmetry (e.g., noise in valuation of insurance contracts), of potential market power
(e.g., predatory pricing by a single company or collusively by multiple companies) and of moral
hazard (e.g., deviations in consumer behavior). They also believe they can generate some social
equity effects (i.e., positive externalities) via, say, compulsory purchase of insurance by affected
citizens and mandatory participation of insurers in residual risk pools and guaranty funds in
selected lines of business. Paraphrasing it, they believe their activities protect the interests of
both the insurance company and the policyholder. Of the two types of interests, regulators tend to
emphasize that of the policyholder, thus imposing a number of regulatory measures on insurance
companies and their operations.
We can classify regulatory measures in insurance markets into four broad categories (Skipper
and Kwon, 2007). First, an applicant of insurance business is commonly subject to market entry
regulation, such as initial capital, fit-and-proper person, and license requirements. Second, rate
and product regulation (particularly in personal lines) as well as prudential (also known as
financial and solvency) regulation affect the operations of incumbent companies. On-going capital
regulation via a solvency margin or risk-based capital approach is an example of this type of
regulation. Third, market conduct regulation (including regulation of insurance intermediaries) and
corporate governance regulation, which is related in part to the regulation of accounting
transparency and investment regulation, are observed in insurance markets.
Finally, regulators (should) respond to insurers showing signs of severe financial distress or
operational difficulty. On reaching a conclusion that such a firm cannot be rehabilitated, the
regulator may locate a buyer of the firm or seek a court order to initiate the liquidation process of
the firm. The regulator’s active intervention, including overseeing the completion of the liquidation
process, is typically known as (market) exit regulation.
Kwon et al. (2005) conducted a survey of the regulatory environments that govern exit processes
of insurance companies in selected countries in Asia, Europe and North America. Their finding
confirms that insurers withdrawing from a line of insurance business or completely from the
insurance business are subject to the close control of the local regulatory authority. They also find
that exit regulation in insurance is a concern from a public policy viewpoint as well as from an
economic activity viewpoint.
Market Exit Choices
In typical markets, the exit process of a firm can be initiated by equity-holders or debt-holders.
Debt-holders may seek bankruptcy if the debtor company has defaulted on its debt. Even when
the company is not on default, equity-holders may decide to sell it via merger or acquisition or
voluntarily liquidate it.2 Of course, they more than often support business continuity and growth.
The general bankruptcy act of the country (e.g., U.S. Bankruptcy Code) prescribes the market
exit process in those markets.
A “merger” occurs when two firms dissolve their businesses and emerge into a single, newly
incorporated entity. An “acquisition" takes place when a firm retains its name, thus its business
licensure, and the target firm dissolves and becomes part of the acquiring firm.
2
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Kwon and Kim
In insurance markets, two broad types of exit guidelines are observed. In some markets
(countries), the governing law is the general bankruptcy code. In some other countries, insurance
companies are subject to the market exit procedures stipulated in insurance act and regulations
(e.g., the U.S.). In the remaining countries, insurers are subject to both the general code and the
specific act. Although the specific rules governing insurer exits differ from state to state, the exit
guidelines in the U.S. insurance acts are based mainly on the Uniform Insurers Liquidation Act of
1939 and the Insurers Rehabilitation and Liquidation Model Act of 2003 by the National
Association of Insurance Commissioners.
Insurance business is unique in that insurance companies are not much financially leveraged. For
example, the U.S. property-liability industry had 0.2 percent of their liabilities in the form of
borrowed money (A.M. Best, 2006). Conversely, their liabilities consist mainly of unearned
premiums for unexpired risks and loss (future benefit) reserves. This finding implies that the major
debt-holders (i.e., policyholders) are not likely to seek bankruptcy in the insurance market. In fact,
few countries permit such an action by policyholders.3 Instead, their agent (i.e., the insurance
regulator) is empowered to protect their monetary interests in the non-performing insurers and
may even decide the fate of those companies.
Under the normal circumstance, the exit decision of an insurance company is made by equityholders (or their management agent) or the regulator. Commonly permitted forms of exit in
insurance markets are merger/acquisition and involuntary liquidation. Indeed, regulators in all
known markets are empowered to take over the management control of insurance companies
under severe financial distress or operational difficulty. In an increasing number of countries, the
authority may even take over the control of the firm when it has failed to comply with the minimum
on-going capital guideline (i.e., minimum solvency margin or risk-based capital). 4 In selected
markets, the insurance company may initiate an exit process—what is termed as “voluntary
liquidation” in this paper.
Figure 1 illustrates the life cycle in the insurance market. For instance, financially and
operationally sound firms—termed as “normal firms” in this paper—usually continue operations,
merge with another or be an acquisition target. Voluntary liquidation may occur but occasionally.
Involuntary liquidation should be rare.
When an insurer experiences an extreme financial or operational difficulty, its equity-holders may
consider other options to business continuation (discussed further in the internal managerial
factor section). Ceteris paribus, they first may attempt a merger or acquisition, with which they
hope to cash out some franchise value of the firm, and later (and if permitted) initiate voluntary
liquidation, with which they forego the entire franchise value of the firm. Two conditions are
commonly attached to the “voluntary liquidation” process. First, the insurer firm must acquire prior
approval from the regulator of the exit process. Second, the firm is subject to the regulatory
oversight until the completion of the process. The regulator may attach similar conditions to
voluntary merger/acquisition.
3
Hong Kong is probably the only country where the law permits a group of 10 policyholders
or more to file a bankruptcy petition for their insurance company.
Solvency II of the European Commission also prescribes that a firm’s failure to comply with
the minimum capital threshold would give rise to tough intervention by the regulatory authority,
e.g., a freeze of assets or forced exit from the market (European Commission, 2002).
4
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Kwon and Kim
Figure 1: Life Cycle in the Insurance Market
Alternatively, the insurance regulator may intervene with the operations of non-performing
companies. It seems that regulators rarely use involuntary liquidation as their first choice of action.
Instead, they offer those companies an opportunity to return to normalcy and guide them to do so
(e.g., overseeing a corporate restructuring plan). When that attempt fails, they may place the
companies under receivership for rehabilitation. On concluding that the rehabilitation attempt has
also failed, regulators may arrange acquisition of the companies by unaffiliated insurers. In the
case that two insurers experience a similar difficulty, the regulator may propose a merger
between them as an alternative. When none of these choices work, regulators may deliver the
ultimatum—involuntary liquidation—and dissolve the companies.5 Hence, the regulator’s choices
of action in market exit regulation are: rehabilitation, merger/acquisition, and involuntary
liquidation.
The size of run off business—insurance obligations (liabilities) of insurance companies which
were liquidated or ceased operations in selected lines or territories—continues to grow. Seventyone percent of the business is related to insurers’ liabilities, and the rest to reinsurers’. The run-off
business is concentrated in the markets in Bermuda, France, Germany, Japan, the U.K. and the
U.S. (ARC, 2003). Of which, the U.S. holds the largest share with estimated liabilities of US$150200 billion (PwC, 2007).6
5
“Conservation of assets” is synonymous with liquidation in several jurisdictions.
6 A KPMG survey (2006a) reveals that the total run-off liabilities in the London market were
£38.2 billion, or around 19 percent of the U.K. nonlife insurance business. Another survey by
KPMG (2006b) for life insurance shows run-off liabilities amounting to £53 billion, or 28 percent of
liabilities of all U.K. life insurance firms.
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Kwon and Kim
In this study, we examine insurer exits based on the four forms of exits—staying firm,
merger/acquisition, voluntary liquidation and involuntary liquidation. With the furtherance of the
studies by Schary (1991) and BarNiv and Hathorn (1997), we also investigate by each stage of
choice how the permitted set of exit choices is related to the characteristics reflecting the financial
environment, internal management and the external environment. The paper is structured as
follows. Immediately after this introductory chapter, we review existing theories and literature
dealing with market exits in insurance. We then construct models to empirically examine insurer
behaviors in the U.S. property-liability market. The final chapter summarizes our findings and
policy implications.
REVIEW OF LITERATURE
Several studies examine suppliers’ entry to and entry barriers in a competitive market (e.g., Bain,
1956; Baumol et al., 1982; and Bernheim, 1984) or market exits in a competitive market (e.g.,
Resnick, 1998; and Peach, 1998). However, studies about market exits in the financial services
sector, particularly in the insurance industry, are limited in scope and deal mainly with M&A or
insolvency. For example, Altman (1968), Trieschmann and Pinches (1973), Hershbarger (1990)
and several other studies examine how to predict insurer insolvency, and Brown et al. (1999) and
Carson and Hoyt (2000) causes of insolvency. BarNiv and Hathorn (1997) examine factors
affecting bankruptcy, voluntary retirement and merger of financially distressed insurers. Similarly,
Schary (1991) explores the determinants of the form of exit in non-insurance markets. However,
no study has examined the fuller multiplicity of exit choices—merger and acquisition, voluntary
liquidation and involuntary liquidation.
Numerous factors affect the life cycle of the insurance company. We group them broadly into:
financial factors, internal management factors and external (political) factors. The financial factors
can be further classified into those related to profitability, underwriting performance, liquidity and
capital adequacy.
Financial Factors
All other things equal, the owners of the exiting firm would attempt to maximize their own wealth
before liquidation as well as the residual value of the firm at liquidation. Similarly, the non-owner
management of the firm would attempt to increase not only their economic wealth until liquidation
but also their values in the job market they wish to enter after liquidation. In other words, a firm at
a declining stage of business life cycle may form an exit strategy instead of, as Resnick (1998)
argues, making a further capital commitment to the business. Karakaya (2000) supports the
argument such that voluntary liquidation helps shareholders salvage their investment in the
liquidated firm. Peach (1998) also suggests that firm owners and their agents have every
incentive to recover their investment capital as much as legally permitted. We can observe the
strength of the wealth motive of the equity holders and management (i.e., the insurance
company) based on profitability, underwriting performance, liquidity, capital adequacy and the
capital itself.
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Profitability. Several proxy variables have been used to measure insurer profitability. For example,
the NAIC uses the ratio of 2-year investment yield average to invested assets for its IRIS analysis.
BarNiv and Hathorn (1997) use the ratio of net income to total assets (ROA) to proxy measure
the wealth motive.7 It is assumed that the higher, say, the ROA ratio, the more it is likely that the
firm continues operation or becomes an M&A target.
Underwriting Performance. Several conventional financial ratios have been used to measure the
underwriting performance of insurance companies. The loss ratio (LR), which often includes loss
adjustment expenses, represents the pure cost of insurance coverage (e.g., Angoff, 2005)
whereas the expense ratio is related to the non-claims-related activities of the insurer. The sum of
these two ratios (i.e., the combined ratio or COMB_R) thus portrays the soundness of
underwriting operations (e.g., Berger et al., 1992; Cummins and Danzon, 1997; and Hoyt and
Powel, 2005).8 Ceteris paribus, the lower the loss (or combined) ratio, the healthier the company
and the more likely it stays in business or becomes an M&A target.
Liquidity. We find two proxies for the liquidity factor in the insurance business. Carson and Hoyt
(1995) employ the ratio of liabilities to liquid assets (LQAST), which represents the insurer’s
ability to meet claims.9 Trieschmann and Pinches (1973) use agent’s balance to surplus (AGBAL),
although this seems to reflect more of the intermediaries’ concern about the insurer’s claims
paying ability (especially when they capture a signal indicating possible insolvency of the insurer)
or merely of the insurer’s account receivable management ability rather than the pure liquidity of
the insurer. All other being equal, we expect that normal firms or potential M&A targets reveal a
higher LQAST or a lower AGBAL ratio than other firms.
Capital Adequacy. We may safely assume that (highly) adequately capitalized firms prefer staying
in business or are attractive M&A targets. Capital adequacy can be proxy measured by the NAIC
risk-based capital ratio (e.g., Cox, 2004) or a similar ratio such as the BCAR ratio by A.M. Best
Company, although both ratios reflect more about the insurer’s ability to absorb a host of risks
(shocks) than purely about its capital adequacy (Pottier and Sommer, 2002). We may also
assume that firms with a low ratio of net premiums written to policyholders’ surplus (NPWSUR) is
relatively better capitalized (Ambrose and Steward, 1988) and has a strong growth potential. The
quality of reinsurance, as measured by the surplus-aid-to-surplus ratio (SURAID), has also been
used by the NAIC as an IRIS test element to measure capital adequacy of insurance companies.
Given that such surplus aid is generally available in proportional treaty reinsurance, it is not
known a priori whether this ratio is powerful enough as a factor affecting the basket of market exit
choices.
7 One may also consider the ratio of net income to policyholders’ surplus as a proxy. For
example, Cummins and Nini (2002) and BarNiv and McDonald (1992) use the return on equity.
However, this ratio falls short of representing fully the wealth motive of a company (i.e., the firm’s
total investment performance).
8 The operating ratio can be another proxy but represents in part the investment performance
of the company.
9 A related issue in exit regulation is valuation of outstanding insurance obligations. Generally,
valuation of unearned premiums tends to be direct, but valuation of loss reserves—including
incurred-but-not-reported (IBNR) losses—can be very complicated, especially for long-tail or
volatile lines of business. In the case of a merger or acquisition involving a financially and
operationally sound firm, measurement of the franchise value of insurance business becomes
another issue.
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Kwon and Kim
Capital (Firm Size). Generally, the greater the size of capital input such as surplus or assets, the
greater the wealth motive of the equity-holders and management of the firm. Indeed, Fok et al.
(1997) find some relationship between risk and profitability of large and small insurance
companies. Assets and policyholders’ surplus are also used by Carson and Hoyt (1995) and
BarNiv and McDonald (1992).
Internal Managerial Factors
Insurers may exit markets for reasons other than financial ones.10 In fact, they may do so for a
good cause. As illustrated in Figure 1, healthy companies may voluntarily merge or be acquired
by another insurer. Through merger, they expect improvement in scale and scope economies.
Insurers may acquire other firms for similar reasons. Whether the target is financially or
operationally sound or is made available as a result of regulatory receivership should matter little
as long as the merger partner or the acquiring company believes that the target company
possesses some on or off balance-sheet value over the price they are willing to pay.
Certain internal forces (i.e., managerial decision) may pull a company out of market. A firm may
cease its operation when it no longer realizes a sustainable rate of return, which we have already
discussed in the preceding section. Other internal forces are likely ownership structure, business
concentration, and product distribution channels.
Ownership Structure. Ownership structure of the firm has been an important variable in efficiency
and performance studies since Mayers and Smith (1988), although the findings are not consistent.
Hence, it is not known whether the separation of ownership and management as in stock
companies are more likely to initiate voluntary liquidation than to wait for regulatory intervention.
The effect of the conflict between the principal and the agent can be proxy estimated using a
dummy stock-mutual classification. (Refer also to the discussion later about the relationship
between ownership structure and the political environment.)
Business Concentration (Diversification). Portfolio theory suggests that the wider the scope of
business of an insurer by line or geographically, the more diversified its insurance risk portfolios
and the less likely the firm exits the market (unless the exit is to realize a higher market value of
the firm than its intrinsic value), all others being equal. No previous studies are found to have
employed operational variables for this type of study. Nevertheless, one can attempt to proxy
measure concentration of business in the insurance market with a Herfindale index. Specifically,
the skewness of insurance risk portfolios to one or a few lines of business can be calculated
using the following equation:
2
 P remium i 
  100 ,
CONCENT   
 Total P remium 
where i stands for the line of business. A CONCENT ratio (percentage) near to zero indicates the
firm’s risk portfolios are well-diversified. A ratio near to 100 percent indicates that the company is
specialized in a very few lines of business, the extreme being the case of monocline insurers.
10
Insurance firms may change their country (state) of domicile or name, or transform a
branch into a subsidiary. Redomestication, localization and name change do not carry the
managerial intent to exit a market and are not examined in this paper.
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Kwon and Kim
Product Distribution Channels. After having observed market practices, some contend that
differences in insurer expenses can be explained by the distribution channels they use such as
direct writing and agency writing. Such a contention is probably too strong in today’s markets
where insurers increasingly prefer a mix of multiple distribution channels to a single channel.
Besides, Lee (1989) finds constant returns constant returns to scale among direct writers in the
U.S. property-liability market. Whether insurance firms would behave differently based on the
market distribution channel thus is not known a priori.
External Political Factors
The political environment is assumed to affect the speed and quality of regulatory intervention
with firms experiencing financial or operational difficulty within the jurisdiction. 11 For example, one
may examine whether insurance commissioners chosen by the public would pay more attention
to the protection of policyholders’ interest than commissioners appointed by the head of the
state.12
In the U.S. state markets where both voluntary and involuntary liquidation are permitted, it is not
known a priori which one will respond first to the sign of bankruptcy—the insurer or the regulator.
All other being equal, the management (also representing equity-holders) of a firm would have a
faster access than the regulator to signals indicating financial or operational difficulty of the firm. 13
However, whether the firm will respond fast to the signals may depend, as discussed earlier, on
the expected net worth of the firm and the strength of the wealth motive of the management.14 For
example, Lee et al. (1997) and Grace et al. (2005) find that the managers of stock insurance
companies take on additional level of risk, as compared to owners of mutual firms, when the firm
is under financial distress. Further, it can be contended that until detected by the regulator, the
management of financially troubled firms might assume greater risk until bankruptcy when the
liability of the firm is limited (e.g., stock companies) or when the guaranty fund system would
cover the losses of their policyholders and claimants (Grace et al., 2005).
Conversely, the weaker the wealth motive of the management or the smaller the net worth of the
firm they can recover from taking on an additional level of risk, the less likely that the
management takes a prompt action of liquidation. A slow response by the management to the
signals may lead to the regulator’s taking over the control of the firm.
11
Of course, other external forces, such as changes in government policy, certain
developments in the legal environment and catastrophic insured losses, can affect the national or
regional market. In this paper, we only examine state-specific external factors.
12
In Florida, the insurance commissioner is appointed by the head of the financial services
regulation who is appointed by the governor.
13
This is an extension of the agency theory by Jensen and Meckling (1976): the manager has
superior information to the shareholder or the policyholder about the firm.
14 For foreign direct investment, Nees (1981) and Boddewyn (1983) argue that the fact that a
firm is exiting an overseas market would often signal a failure where the investors play a waiting
game. They may decide to withdraw from the market only after having tried to revive the firm,
thus the return of their investment, and only with a support of the top management and others.
See also Matthyssens and Pauwels (2000).
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Table 1: Market Exit Factors in Insurance
Financial
Category
Profitability
Underwriting
Performance
Liquidity
Capital
Adequacy
Capital
Managerial
Political
a
b
Variable
ROA
INVYD
LR
CR
LQAST
AGBAL
NPWSUR
BCARb
Ln(Asset)
SURPLUS
CONCENTb
DISTRIb
OWNER
COMM
Description
Net income ÷ Assets
Two-year investment yield average based on cash + invested assets
(IRIS6)a
Loss ratio (Losses & LAE incurred ÷ Premiums earned)
Combined ratio
Liabilities ÷ Liquid assets (IRIS8)
Agents’ balance to surplus (IRIS9)
Net premiums written ÷ Surplus (IRIS2)
Best’s Capital Adequacy ratio
Log of total assets
Policyholders’ surplus
Line concentration (a Herfindale index)
Agency vs. direct vs. mixed marketing channels
Dummy (stock vs. mutual ownership)
Dummy (appointed vs. elected commissioner)
IRIS refers to the Insurance Regulatory Information System of the NAIC.
Due to data instability, these factors are not examined in this paper.
The conflict resulting from this type of information asymmetry is less likely to arise when the law
bars voluntary liquidation. However, such a measure does not guarantee that the regulator will
effectively assist insurance companies under distress. Besides, whether or not voluntary
liquidation is permitted may matter little in markets where the regulator’s decision reflects more of
the interest of the industry than of the consumer, i.e., when evidence of capture theory of
regulation is found. Similarly, political traits and the philosophy of the regulatory authority, let
alone the operating efficiency of the authority, can make a difference in the speed and the cost of
the exit from the market.
We list these factors in Table 1. The table also offers specifics of the candidate variables for the
empirical investigation of this study.
Ranking Market Exit Choices
By default, every company wishes to continue operations. If, however, it decides to exit the
market—for a good cause or not—the company needs to rank the choices given to them. If it is
financially and operationally sound, the only reasonable choice under the usual circumstance
would be merger/acquisition at the right price.
In the case of a company experiencing a severe financial or operational distress and wishing to
exit the market, there are several choices but the company may not be able to fully control its
rank of preference. If the signs signaling the distress are not captured by the regulator or if the
regulator decides to let the company find a reasonable solution, it may prefer merger/acquisition
followed by voluntary liquidation. With the regulatory intervention, the company is likely subject to
the following sequence of actions—rehabilitation, merger/acquisition and involuntary liquidation.
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Merger/acquisition remains as a choice for all types of insurance companies. To date, however,
no data of the U.S. insurance markets are known to have separated all mergers and acquisitions
by the relevant status of the firm (i.e., normal firm, voluntary liquidation or involuntary liquidation).
Nonetheless, data compiled by SNL (1999-2007) show that the average purchase price to book
value of all mergers and acquisitions in the U.S. property-liability insurance market was almost
2.0 for the 1997-2006 period.15 The median price to earnings ratio also ranged between 10 and
20 in most quarter-year periods except for 2006.16 These findings imply that generally mergers
and acquisitions in the market occur when the other party in the contract captures some on- or
off-balance sheet value from the merger/acquisition contract.
EMPIRICAL INVESTIGATION
The model comprises one dependent variable with four codes (see below) and multiple variables
representing financial, managerial and political factors affecting exit decisions. For the empirical
investigation, we employ a multinomial logistic (MNL) regression model expressed as:17
pj 
e
 'j X
je
 'j X
for j = 1,…, k+1.
where X is a vector of independent variables and  is a vector of parameters.
By setting k+1 to 0 (a zero vector) for normalization, we obtain:
pk 1 
1
je
'j X
.
This results in the j logit having the following form:
log
pj
 'j X for j = 1,…, k+1.
p k 1
In this logit form, the right-hand-side of the equation is the natural log of odd-ratio; that is, the risk
ratio of two probabilities of, say, voluntary vs. involuntary liquidation. Using maximum likelihood
estimation (MLE), we estimate variable parameters in a linear form.
The dependent variables are divided into four mutually exclusive business status depicted in
Figure 1. They are coded as:18
15
The average is based on quarterly-year observation of the ratio and the data have some
missing observations due to data unavailability or non-existence of transactions during the
quarter-year period.
16
Again, there are several missing observations.
17 The assumptions of normality and homoscedasticity would be violated with OLS regression
model. Refer to Borooah (2001) for further discussion of MNL. See also BarNiv and McDonald
(1992) for various types of methodologies that others have used to measure/predict insurer
insolvency.
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Kwon and Kim
= 0 for normal insurer (non-exiting firm);
= 1 for insurer exiting through voluntary liquidation;
= 2 for insurer exiting through merger or acquisition; and
= 3 for insurer forced to exit through involuntary liquidation.
The data used for our study include normal firms as well as those exited the insurance market
during 1999~2004. There are two main data sources. One is A.M. Best database for propertycasualty firms. The other is the market exit information published annually in Best’s Reports
(property-casualty edition). We keep only stock and mutual companies and only pure exit cases;
that is, firms under rehabilitation (receivership) are excluded from the study. Neither do we
consider insurers that changed names or redomesticated to a new jurisdiction. In this paper, we
use observations one-year prior to the declaration of exit. Non-insurance specific data are
collected from U.S. state governments and other reliable sources. The final data set has 2,200
observations.
Due to the instability of the initial estimation for CONCENT, BCAR and for DISTRI (due to use of
multiple marketing channels by a number of insurers), we decide to exclude these two variables
from further estimation.19
Preliminary Findings
Findings from the Pearson correlation matrix show that the profitability variables (INVYD and
ROA) are highly correlated. Similar high correlations are found for the variables representing
underwriting performance (LR and CR) and capital (ASSET and SURPLUS). We find no strong
correlations for variables representing liquidity (LQAST and AGBAL).
Table 2 shows descriptive statistics of independent variables by exit type. As expected,
investment yields (INVYD) and returns on assets (ROA) show similar patterns. However, their
means and medians are lowest for merged firms and highest for involuntary liquidated firms,
which appear to be counter-intuitive; that is, financially troubled firms tend to maintain their
profitability during the one year period prior to exit,
Underwriting performance measured by the loss ratio shows voluntarily exiting firms have the
highest ratio on average as well as in terms of median. In contrast, involuntarily exited firms had
the lowest ratio, indicating that they are on run-off and have probably met most of the insurance
obligations. The combined ratio is also lowest for involuntarily exiting firms. This contrasts to the
relatively high combined ratios for firms exiting voluntarily or through merger/acquisition. The high
loss ratio for voluntary liquidation is probably indicative to the failure in underwriting, claims
management, excess marketing, or some combination of these effects.
18
Since there is no particular order among the four alternatives, we may apply an unordered
multiple choice regression model, which is equivalent to a multinomial logit regression model.
19
A preliminary examination shows normal firms with the lowest business concentration ratio
(CONCENT), implying that they tend to maintain more diversified risk portfolios, in terms of
premium volume by line, than any other firms.
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Kwon and Kim
Liquidity measured by the liquidity ratio (LQSAT) and agent’s balance (AGBAL) is again lowest for
involuntary liquidation. Merger/acquisition follows it with the second lowest ratios for both
variables. Voluntarily liquidated firms have the highest ratio of liability to liquid asset (LQAST) and
the highest amount of agents’ balances expressed as a percentage of policyholders’ surplus
(AGBAL). This implies a possibility that insurance intermediaries may hold premium forwarding to
insurers whose financial soundness they doubt; that is, those intermediaries protect policyholders
and claimants from those unhealthy firms.
Regulators often use the ratio of net premiums to surplus (NPWSUR) as a proxy measure of the
underwriting leverage of the insurer. They supplement this with the surplus ratio (SURAID) to
measure the primary insurer’s dependency on (proportional treaty) reinsurance for capital surplus.
The summary statistics show that voluntarily liquidated firms tend to behave differently from other
types of insurers. Although not proven in this paper, we suspect that the management of a typical
to-be-voluntarily liquidated firm might attempt to maximize the residual value of the firm by
engaging in, say, cash flow underwriting, or using reinsurance for the sake of generating ceding
commissions. This contrasts to the case of merger/acquisition which shows the lowest NPWSUR
ratio. This may imply that the merger partner or acquiring firm probably captures some growth
potential (a type of franchise value) of the target firm. The average for normal firms is in line with
the industry average.
The cases of normal operation and merger/acquisition show large asset sizes, in terms of mean
and median. In fact, asset sizes of merged firms appear not to be quite different from those of
normal insurers, an observation consistent with most other variables in the table. In contrast,
firms exiting through involuntary liquidation tend to have the smallest asset size among all types
of firms. The political environment factor measured by COMM (1 for elected commissioner)
shows the highest average for merger/acquisition and the lowest average for involuntary
liquidation.
12
U.S. Nonlife Market Exit (2007)
Kwon and Kim
Table 2: Descriptive Statistics of Independent Variables
Comparison of means and medians of normal, voluntarily liquidated, merged/acquired, and involuntarily liquidated property-liability insurers.
Analysis based on the data 1 year prior to actual exits.
Type
All types
Involuntary
liquidation
Merger/
acquisition
Voluntary
Liquidation
Normal Firm
Mean
N
SD
Minimum
Max
INVYD
ROA
LR
COMB_R
LQAST
AGBAL
NPWSUR
ASSET
SURPLUS
OWNER
5.1188
5.0077
72.5996
107.0286
63.6374
14.4680
91.9451
10.9228
23432.16
.18
COMM
.20
2096
2096
2096
2096
2096
2096
2096
2096
2096
2096
2096
1.73459
1.76316
122.41554
128.59833
71.57419
51.74131
104.96792
1.85240
21351.961
.388
.401
.00
.00
-199.80
-99.90
-99.00
.00
.00
6.55
-1869
0
0
26.00
26.00
1999.80
999.90
999.00
999.00
999.00
18.00
99400
1
1
Median
5.0000
5.2000
70.8000
102.7000
63.0000
5.0000
71.0000
10.6750
16084.00
.00
.00
Mean
5.4182
5.3655
159.3673
174.1836
152.7636
109.0182
366.0364
10.4369
13854.00
.09
.25
55
55
55
55
55
55
55
55
55
55
55
2.60833
2.54582
328.80953
191.84462
191.02171
264.25377
387.13779
1.47357
21214.043
.290
.440
0
N
SD
Minimum
.00
.00
-1.50
.00
.00
.00
.00
6.70
-9644
0
15.00
14.70
1999.80
999.90
999.00
999.00
999.00
13.86
76819
1
1
Median
5.0000
5.4000
95.7000
129.2000
107.0000
13.0000
196.0000
10.1200
6144.00
.00
.00
Mean
4.4118
4.2882
56.1059
161.2941
43.8824
5.2353
40.5294
11.0071
26433.82
.29
.35
17
17
17
17
17
17
17
17
17
17
17
1.80481
1.70803
46.85062
251.40335
33.91328
6.31990
38.83317
2.28989
25378.174
.470
.493
Minimum
1.00
1.00
-3.40
-99.90
.00
.00
.00
7.22
739
0
0
Max
8.00
7.60
147.60
999.90
103.00
17.00
103.00
15.33
81684
1
1
Median
4.0000
4.4000
71.7000
111.5000
48.0000
1.0000
48.0000
10.4300
13568.00
.00
.00
Mean
5.6563
5.4094
29.5500
51.2719
40.2813
13.8125
99.5938
8.9553
7408.78
.13
.09
32
32
32
32
32
32
32
32
32
32
32
3.11717
3.23054
50.13764
69.68934
45.87798
39.10238
195.98483
1.72564
12063.540
.336
.296
0
Max
N
SD
N
SD
Minimum
.00
.00
-96.20
-52.50
.00
.00
.00
6.50
270
0
20.00
19.50
127.10
193.80
162.00
186.00
999.00
14.26
67892
1
1
Median
5.5000
5.3500
.0000
.0000
16.0000
.0000
.0000
8.7000
5242.00
.00
.00
Mean
5.1286
5.0170
74.0151
108.3158
65.3732
16.7509
98.5114
10.8826
22982.84
.18
.20
Max
N
SD
Minimum
Max
Median
2200
2200
2200
2200
2200
2200
2200
2200
2200
2200
2200
1.79010
1.81623
131.14331
131.78267
77.59701
67.12846
128.79135
1.86098
21401.683
.386
.402
.00
.00
-199.80
-99.90
-99.00
.00
.00
6.50
-9644
0
0
26.00
26.00
1999.80
999.90
999.00
999.00
999.00
18.00
99400
1
1
5.0000
5.2000
70.9500
103.1000
64.0000
4.0000
71.0000
10.6250
15456.00
.00
.00
13
U.S. Nonlife Market Exit (2007)
Kwon and Kim
Evaluation of Exit Preference – Multivariate Model
The key objective of this study is to identify the factors correlating exit choices (affecting exit
decisions) and their impact on each stage of the life cycle of insurance companies. In this paper,
we attempt to find a multivariate model explaining the dependent variable—exit choices—using a
multi-stage process. For the first multivariate model, we choose one variable for each of the
seven factor categories. Like BarNiv and Harthorn (1997) we select ROA for profitability instead
of INVYD, because the latter explains only investment performance rather than overall profitability.
We select LR for underwriting performance because it better represents pure underwriting
performance than COM_R. For liquidity, LQSAT is preferred to AGBAL because AGBAL does not
capture the entirety of the insurer’s liquidity. For size, we choose Ln(Asset) because several
insurers have negative SURPLUS.
For each of the other categories, we have only one variable for each factor—NPWSUR for capital
adequacy, OWNER for organizational structure (stock vs. mutual) and COMM political
environment (appointed vs. elected). Later we also run the alternative model using the other
variables not selected for the first model.
Table 3 shows the parameter estimates of explanatory variables and goodness-of-fit of the model.
Our multi-logit model is good for empirical testing at 1 percent level of significance, with the
explanatory power of 20.2 percent. We find no sign of multicolliniarity among the final
independent variables.
Table 3: Log-likelihood Test Result
X Parameter
Intercept
ROA
LR
LQAST
NPWSUR
Ln(Asset)
COMM
OWNER
Model fit:
-2log likelihood
854.042
862.160
863.757
859.381
925.503
900.664
858.951
859.625
2
8.118
9.715
5.39
71.461
46.621
4.908
5.582
Probability a
0.044
0.021
0.149
0.000
0.000
0.179
0.134
log-likelihood = 854.042 (significant at 1% level)
Pseudo R2 a = 0.220
a Nagelkerke
The discussion of findings in this section begins with the case of normal insurance companies. As
elaborated earlier in the paper, a normal firm may stay normal, be a merger/acquisition target or
become financially/operationally unstable. When it in fact experiences such instability, it may still
consider merger/acquisition or attempt voluntary liquidation. If the regulator captures that
instability, the company may be subject to involuntary liquidation as well. We discuss each case
below.
Base Group—Normal Firm. Using the group of normal firms (coded 0 in the dependent variable)
as the reference, we can estimate the impact of each of the variables in the final model on the
firm’s choice of exit from the market, or:
14
U.S. Nonlife Market Exit (2007)
log
Kwon and Kim
p1
= – 2.658 + 0.118ROA* + 0.001LR* + 0.002LQAST* + 0.005 NPWSUR*
p0
– 0.304Ln(Asset)* – 0.158COMM + 0.751OWNER
log
p2
= – 4.053 – 0.208ROA – 0.001LR – 0.006LQAST – 0.010 NPWSUR*
p0
+ 0.220Ln(Asset) – 0.851COMM* – 0.753OWNER
log
p3
= + 2.354 + 0.141ROA* – 0.010LR* – 0.002LQAST – 0.004 NPWSUR*
p0
– 0.849Ln(Asset)* + 0.832COMM + 0.653OWNER
where the asterisk (*) denotes statistical significant at the p = 0.1 level or lower.
See also Table 4-A for a summary of the same results. The findings show that whether a firm is
likely to continue its operations or to be subject to voluntary liquidation (denoted as log p0 / p1 ) is
affected by all five financial factors but not by managerial or political factor.
As shown in the last column of Table 4-A, the eβ—a measure of factor influence for a unit change
in the denominator—for capital adequacy indicates that one unit “increase” in NPWSUR
increases the risk ratio (p1/p0) by 1.005 times. That is, a rise in NPWSUR (net premium written to
surplus) is more related to voluntary liquidation rather than staying in business. We find a similar
relationship for profitability, underwriting performance and liquidity, but the actual impact is
negligent as the ratios are almost one. If, however, the firm becomes large in size in terms of
In(Asset), the more it is likely to consider to stay in the market rather than considering voluntary
liquidation.
In the case between normal firms and merger/acquired firms, the result shows that NPWSUR is
the only variable significant at 0.1 level and improvements in capital adequacy may affect the
company more in favor of staying in a market. However, the actual impact (0.999) may be
negligent. Given also that other factors are found statistically insignificant, we conclude that there
is little difference between normal firms and merged/acquired firms. This is consistent with our
discussion in the paper as well as in the preliminary statistics.
In the case between normal firms and involuntarily liquidated insurers, we find that a rise in
profitability or capital adequacy is likely to cause insurers to consider involuntary liquidation rather
than staying in the market. It is more likely so when the decision is based on the overall
profitability (INVYD) than capital adequacy (NPWSUR). Although the actual impact of the
premium-to-surplus ratio is negligent, the result for ROA is not in line with our assumption—an
issue warranting further investigation.
15
U.S. Nonlife Market Exit (2007)
Kwon and Kim
Table 4-A: Parameter Estimates
(Base =0, NORMAL firms)
3. Involuntary
liquidation
2. Merger/
acquisition
1. Voluntary
liquidation
X Parameter
Std. Error
Wald
 Estimate
Intercept
- 2.658
1.150
5.344
ROA
0.118
0.067
3.099
LR
0.001
0.001
3.705
LQAST
0.002
0.001
4.134
NPWSUR
0.005
0.001
65.511
Ln(Asset)
- 0.304
0.099
9.326
COMM
- 0.158
0.362
0.191
OWNER
0.751
0.512
2.152
Intercept
- 4.053
1.544
6.888
ROA
- 0.208
0.138
2.248
LR
- 0.001
0.003
0.178
LQAST
- 0.006
0.008
0.533
NPWSUR
0.010
0.006
3.182
Ln(Asset)
0.220
0.138
2.550
COMM
- 0.851
0.519
2.686
OWNER
- 0.753
0.563
1.784
Intercept
2.354
1.539
2.341
ROA
0.141
0.069
4.209
LR
- 0.10
0.004
5.878
LQAST
- 0.002
0.004
0.318
NPWSUR
0.004
0.001
8.445
Ln(Asset)
- 0.849
0.160
28.120
COMM
0.832
0.621
1.797
OWNER
0.653
0.572
1.301
a p-value for Z test
b Factor change in odds for unit increase in variable X
j
* indicates .05 level significance, and ** .01 level significance.
Proba
0,021
0.078
0.054
0.042
0.000
0.002
0.662
0.142
0.009
0.134
0.673
0.466
0.074
0.110
0.101
0.182
0.126
0.040
0.015
0.573
0.004
0.000
0.180
0.254
eβ b
*
*
*
*
**
*
1.125
1.001
1.002
1.005
0.738
0.854
2.120
*
*
*
*
*
*
**
0.813
0.999
0.994
0.990
1.246
0.427
0.471
1.151
0.990
0.998
1.004
0.428
2.298
1.921
Table 4-B: Parameter Estimates
(Base =2, merger/acquisition)
3. Involuntary
liquidation
1. Voluntary
liquidation
X Parameter
Std. Error
Wald
 Estimate
Intercept
1.396
1.919
0.529
ROA
0.325
0.153
4.494
LR
0.003
0.003
0.633
LQAST
0.008
0.008
1.039
NPWSUR
0.015
0.006
6.699
Ln(Asset)
-0.524
0.169
9.543
COMM
0.693
0.630
1.208
OWNER
1.504
0.759
3.926
Intercept
6.408
2.172
8.707
ROA
0.348
0.154
5.110
LR
-0.008
0.005
2.688
LQAST
0.003
0.009
0.142
NPWSUR
0.014
0.006
5.706
Ln(Asset)
-1.069
0.211
25.721
COMM
1.682
0.806
4.360
OWNER
1.406
0.800
3.087
a p-value for Z test
b Factor change in odds for unit increase in variable X
j
* indicates .05 level significance, and ** .01 level significance.
16
Proba
0.467
0.034
0.426
0.308
0.010
0.002
0.272
0.048
0.003
0.024
0.101
0.706
0.017
0.000
0.037
0.079
eβ b
*
*
**
*
*
*
*
**
*
*
1.384
1.003
1.008
1.015
0.592
1.999
4.499
1.417
0.992
1.003
1.014
0.343
5.379
4.078
U.S. Nonlife Market Exit (2007)
Kwon and Kim
Table 4-C: Parameter Estimates
(Base =1, voluntary liquidation)
3. Involuntary
liquidation
X Parameter
Std. Error
 Estimate
Intercept
- 1.396
1.919
ROA
- 0.325
0.153
LR
- 0.003
0.003
LQAST
- 0.008
0.008
NPWSUR
- 0.015
0.006
Ln(Asset)
0.524
0.169
COMM
- 0.693
0.630
OWNER
- 1.504
0.759
a p-value for Z test
b Factor change in odds for unit increase in variable X
j
* indicates .05 level significance, and ** .01 level significance.
Wald
0.529
4.494
0.633
1.039
6.699
9.543
1.208
3.926
Proba
0.467
0.034
0.426
0.308
0.010
0.002
0.272
0.048
eβ b
*
*
**
*
0.722
0.997
0.992
0.985
1.688
0.500
0.222
Base Group—Merger/Acquisition. We discussed that a failure in merger/acquisition attempt by
firms experiencing financial or operational difficulty may be followed by liquidation. The liquidation
is likely voluntary if the firm is ahead of making the decision; otherwise, we expect involuntary
liquidation initiated by the regulator. Table 4-B summarizes our findings.
This table shows Ln(Asset) and NPWSUR are significant at 0.01 level, and ROA and OWNER at
0.05 level. In the case of voluntary liquidated firms and merger/acquired firms, an increase in the
firm size, Ln(asset), is very likely to lead a merger/acquisition deal. But a rise in NPWPLUS is
likely to lead the firm to voluntary liquidation instead of merger/acquisition. For OWNER, a unit
increase, from zero to one, is likely to lead to a voluntary liquidation rather than an M&A exit.
In the case of involuntary liquidation and merger/acquisition, our findings suggest that all factors
except the liquidity factor (LQSAT) significantly affect the exit decision. Specifically, a rise in
profitability, capital adequacy, the fact that the commissioner is elected, and stock ownership of
the insurer are likely to lead the regulator to explore a merger/acquisition opportunity before
deciding to liquidate the affected insurer. Of the factors, the most powerful is COMM—a finding
supporting our argument that in the states where the insurance commissioner is elected, the
regulatory authority is likely to take care of policyholders’ interests in the non-performing insurers
by taking over insurance companies. Among the financial factors, ROA is found the most powerful.
Base Group—Voluntary Liquidation. Finally, we examine the relationship between voluntary and
involuntary liquidations. We this examination, we test whether the wealth motive of equity-holders
or their agent-management plays a role in market exit decisions.
Our findings suggest that larger insurers—thus probably with a stronger wealth motive by equityholders and their agents—are more likely to respond to signals indicating financial or operational
difficulty before the signals are captured by the regulator; that is, they are likely to initiate
voluntary liquidation. However, the finding that stock companies are much less likely to initiate
voluntary liquidation is not in line with our assumption.
17
U.S. Nonlife Market Exit (2007)
Kwon and Kim
Table 5-A: Comparison between Two Competing Models
(Base =0, NORMAL firms)
3. Involuntary
liquidation
2. Merger/
acquisition
1. Voluntary
liquidation
First Model
Alternative Model
Parameter

Proba
Intercept
ROA
LR
LQAST
NPWSUR
Ln(Asset)
COMM
OWNER
Intercept
ROA
LR
LQAST
NPWSUR
Ln(Asset)
COMM
OWNER
Intercept
ROA
LR
LQAST
NPWSUR
Ln(Asset)
COMM
OWNER
- 2.658
0.118
0.001
0.002
0.005
- 0.304
- 0.158
0.751
- 4.053
- 0.208
- 0.001
- 0.006
0.010
0.220
- 0.851
- 0.753
2.354
0.141
- 0.10
- 0.002
0.004
- 0.849
0.832
0.653
0,021
0.078
0.054
0.042
0.000
0.002
0.662
0.142
0.009
0.134
0.673
0.466
0.074
0.110
0.101
0.182
0.126
0.040
0.015
0.573
0.004
0.000
0.180
0.254
eβ b
1.125
1.001
1.002
1.005
0.738
0.854
2.120
0.813
0.999
0.994
0.990
1.246
0.427
0.471
1.151
0.990
0.998
1.004
0.428
2.298
1.921
18
Parameter

Proba
Intercept
INVYD
COMB_R
AGBAL
NPWSUR
SUR_100
COMM
OWNER
Intercept
INVYD
COMB_R
AGBAL
NPWSUR
SUR_100
COMM
OWNER
Intercept
INVYD
COMB_R
AGBAL
NPWSUR
SUR_100
COMM
OWNER
-5.406
.102
.001
.002
.004
-.002
-.072
.722
-2.315
-.229
.001
-.016
-.009
.001
-.884
-.659
-3.989
.139
-.012
-.001
.002
-.011
.867
.394
.000
.162
.099
.211
.000
.071
.846
.157
.010
.126
.195
.554
.076
.313
.090
.252
.000
.045
.001
.778
.084
.000
.161
.488
eβ b
1.107
1.001
1.002
1.004
.998
.930
2.058
.795
1.001
.985
.991
1.001
.413
.517
1.149
.988
.999
1.002
.989
2.379
1.483
U.S. Nonlife Market Exit (2007)
Kwon and Kim
Table 5-B: Comparison between Two Competing Models
(Base =2, merger/acquisition)
3. Involuntary
liquidation
1. Voluntary
liquidation
First Model
Alternative Model
Parameter

Proba
Intercept
ROA
LR
LQAST
NPWSUR
Ln(Asset)
COMM
OWNER
Intercept
ROA
LR
LQAST
NPWSUR
Ln(Asset)
COMM
OWNER
1.396
0.325
0.003
0.008
0.015
-0.524
0.693
1.504
6.408
0.348
-0.008
0.003
0.014
-1.069
1.682
1.406
0.467
0.034
0.426
0.308
0.010
0.002
0.272
0.048
0.003
0.024
0.101
0.706
0.017
0.000
0.037
0.079
eβ b
1.384
1.003
1.008
1.015
0.592
1.999
4.499
1.417
0.992
1.003
1.014
0.343
5.379
4.078

Proba
-3.090
.331
.000
.017
.014
-.003
.811
1.381
-1.674
.368
-.013
.015
.011
-.012
1.750
1.053
.007
.046
.954
.514
.009
.050
.203
.071
.188
.025
.000
.579
.035
.000
.030
.191
Parameter
Intercept
INVYD
COMB_R
AGBAL
NPWSUR
SUR_100
COMM
OWNER
Intercept
INVYD
COMB_R
AGBAL
NPWSUR
SUR_100
COMM
OWNER
eβ b
1.392
1.000
1.017
1.014
.997
2.251
3.979
1.445
.987
1.015
1.011
.988
5.757
2.866
Table 5-C: Comparison between Two Competing Models
(Base =2, voluntary liquidation)
3. Involuntary
liquidation
First Model
Alternative Model
Parameter

Proba
eβ b
Parameter

Proba
eβ b
Intercept
ROA
LR
LQAST
NPWSUR
Ln(Asset)
COMM
OWNER
- 1.396
- 0.325
- 0.003
- 0.008
- 0.015
0.524
- 0.693
- 1.504
0.467
0.034
0.426
0.308
0.010
0.002
0.272
0.048
0.722
0.997
0.992
0.985
1.688
0.500
0.222
Intercept
INVYD
COMB_R
AGBAL
NPWSUR
SUR_100
COMM
OWNER
1.416
.037
-.013
-.003
-.002
-.009
.939
-.328
.217
.702
.000
.445
.049
.002
.189
.664
1.038
.987
.997
.998
.991
2.557
.720
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Kwon and Kim
Alternative Models
We also run an alternative model using alternative variables—INVYD instead of ROA, COMB_R
for LR, AGBAL for LQAST and SUR_100 for Ln(Asset)—while keeping the original variable—
NPWSUR, CONMM and OWNER—when no alternatives are available for the category. We
compare the empirical result of the alternative model with that of the first model with respect to
parameter estimates and p-values. For example, Table 5-A shows compassion result with
base=0. Results of two models appear very similar except liquidity variables of p1/p0.
Table 5-B shows comparison results of two models with the base of merger/acquisition. From the
comparison, wee find little difference between voluntary liquidated firms and merger/acquired
firms. For involuntary liquidated firms and merger/acquired firms, two differences are noted. As
the proxy of underwriting performance, LR of the first model is not statistically significant but
COM_R of the alternative model is highly significant. Nevertheless, the odd ratios of these
variables (0.992 and 0.987) are not much different. The other difference comes from OWNER,
which is insignificant in the alternative model but significant in the first model.
Table 5-C shows comparison results of two models with the base of voluntary liquidation. There
are several difference for p3/p1. For the alternative models, COM_R and SUR_100 (are
statistically significant but ROA, Ln(Asset) and OWNER are significant.
CONCLUSIONS
This paper discusses insurer preferences in market exit decision theoretically. We have also
examined their exit preferences based on the status of insurers and on the financial, managerial
and political factors.
The descriptive analysis indicates similar characteristics between normal and merged/acquired
firms; our MNL regression results confirm that similarity. (The logit regression shows a significant
overall fit.) This finding supports BarNiv and Hathorn (1997)’s argument that 54 to 80 percent of
merged insurers are financially sound. However, U.S. property-liability insurance companies show
differences when it comes to a choice of action—one between merger/acquisition and liquidation
and the other between voluntary and involuntary liquidations. In general, profitability, capital
adequacy and the capital itself (firm size) frequently affect the decision-making process. In
contrast, liquidity and underwriting performance are found to be insignificant or cause little impact
on the process in all cases.
We find that ownership structure influences when insurers considers voluntary vs. involuntary
liquidation. However, the finding does not support our contention. We also find that elected
insurance commissioners may prefer merger/acquisition to involuntary liquidation, as the former
is likely to fully protect the interests of policyholders of the non-performing insurers. Finally, the
findings for the influence of capital do not always support our contentions. This remains an area
calling for further investigation.
20
U.S. Nonlife Market Exit (2007)
Kwon and Kim
In today’s insurance markets, a well-structured exit guideline is indicative of a transparent
regulatory environment and helps the market attract more firms and improve its financial and
operational stability. In fact, one of the IAIS principles (ICP 16) deals specifically with insurer’s
winding-up and exit from the insurance market. The principle states that regulatory authorities
need to clearly define insolvency, to establish the criteria and procedure for dealing with
insolvency, and to give priority to the protection of policyholders (IAIS, 2003). Merger with a
healthier insurance company or a complete closure of the business are two of the means the IAIS
suggests that troubled firms may use. Nevertheless, the best choice would be helping nonperforming insurers to return their operations to normalcy while the regulator continues to oversee
the rehabilitation program.
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