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State guaranty fund system and price implications.
Abstract:
Since the insurance industry has developed a state guaranty fund system, the nominal value of protection has been unchanged for more than three decades. Thus, the real value of protection has declined over this time period. Relatively reduced protection from the state guaranty fund system may adversely affect the pricing behavior of property and liability (P-L) insurers. This paper examines the impact of the state guaranty fund on insurers' pricing. The analysis is made on different firm sizes, i.e., small and large P-L insurers. The results of this paper show that small insurers decrease prices due to increased risk which has been implied by the decreased value of the safety net. This paper also shows that the impact is higher during the period when the P-L industry has experienced decreasing underwriting profits. Different firm characteristics between small and large insurers are reported along with determinants of insurers' pricing.

Keywords: guaranty fund, market structure, firm size, property and liability insurance

Article Type:
Report
Subject:
Liability insurance (Prices and rates)
Author:
Choi, Byeongyong Paul
Pub Date:
05/01/2011
Publication:
Name: Journal of Academy of Business and Economics Publisher: International Academy of Business and Economics Audience: Academic Format: Magazine/Journal Subject: Business; Business, general; Economics; Government Copyright: COPYRIGHT 2011 International Academy of Business and Economics ISSN: 1542-8710
Issue:
Date: May, 2011 Source Volume: 11 Source Issue: 3
Topic:
Event Code: 740 Commodity & service prices Computer Subject: Company pricing policy
Product:
Product Code: 6360000 Financial Guaranty Insurance NAICS Code: 524126 Direct Property and Casualty Insurance Carriers SIC Code: 6351 Surety insurance
Accession Number:
272484640
Full Text:
1. INTRODUCTION

For the past decades, the insurance industry has observed various changes in economic conditions and changes in regulatory factors. The technological advancement and advances in financial engineering could change the way of insurers' doing business in various ways. Moreover, the trends of mergers and acquisitions generally affect the market structure, especially firm size and growth rate. Along with firm growth, failure of financial intermediaries may generate adverse consequences in the industry and then public concerns arise regarding this matter. In the insurance area, to protect the policyholders and other beneficiaries of policies due to the resulting insolvency of insurers, a guaranty fund system has been created and implemented for the current system since late 1960s. As a result, the presence of guaranty funds has affected the business of various sizes of insurers, especially smaller firms.

Small firms are surviving in the U.S. property and liability (P-L) insurance industry and the guaranty fund system may provide a positive impact on the persistence of small firms in the market. The guaranty fund law, which may help small firms more, protects up to $300,000 in most states and the protection cap has remained the same since 1981. However, the real value of state protection has declined over time. Then, the relatively reduced protection from the guaranty fund may adversely affect smaller firms' operation since large accounts are seeking large and stable insurers because the coverage guaranteed by the law is well below their desired protection. This research question can be tested empirically and this paper examines the impact of decreased levels of real guaranty fund protection on insurers' prices for large and small P-L insurance firms.

A company level analysis is used for the period of 1992 to 2000. Based on total assets, U.S. P-L insurers are divided into two groups, small insurers and large insurers, and then the impact of guaranty fund system on insurance prices is tested for the two groups. Also, it is tested for the two different sub-periods, 1992-1996 period and 1997-2000 period, based on the underwriting profit cycle.

This research is the first study to examine the potential impact of the guaranty fund system on firms of different sizes. Empirical results of this paper will provide useful information on the public policy issue on firms' activities of different sizes in the U.S. P-L insurance industry.

2. LITERATURE REVIEW

One of common causes of P-L insurers' failures is rapid overexpansion and diversification along with managerial inefficiencies. For an obvious reason, insurance industry concerns are about the insolvency of insurance firms. Since 1969, as a result, a guaranty fund has been developed to protect the parties in the insurance contract if an insurer goes bankrupt. By year 1981, every state has enacted its own guaranty-fund law. The coverage provided by the guaranty fund is limited and most states follow the model law suggested by the National Association of Insurance Commissioners (NAIC). On average, the guaranty-fund law covers up to $300,000 in most states and the amount of coverage hasn't been changed in over three decades.

There were no prior studies to examine the relationship between the guaranty fund system and insurance prices. Many studies, however, focused on the impact of guaranty fund on the firm risk-taking behavior (e.g., Cummins, 1988, Lee, Mayers and Smith, 1997, and Downs and Sommer, 1999). Cummins (1988) argued that the guaranty fund system should be changed to risk-based assessment to reduce high-risk strategies which were caused by the current flat premium rate assessment in which the level of firm's risk was not linked to the assessment. Lee, Mayer and Smith (1997) asserted the risk-subsidy hypothesis and provided evidence from stock companies. Under the risk-subsidy hypothesis, the enactment of state guaranty fund law has increased the risk of insurers' asset portfolios. Downs and Sommer (1999) examined the risk-subsidy hypothesis for the stock companies with the perspective of inside ownership structure. They also support the risk-subsidy hypothesis and find a positive relationship between the inside ownership and risk-taking behavior.

Unlike other prior research, this paper directly addresses the impact of the state protection system on P-L insurers' operation. Since the nominal value of state protection was unchanged at $300,000, the real value of guaranty funds has declined over time. By year 2002, the real value has dropped more than 50 percent of the original value started in 1981. The limited and declined real value of guaranty fund protection impacts on the large accounts that need more than the statutory coverage. So, their tendency is to seek large and well-established insurers who may have a conventional conception of 'Too Big To Fail.' Consumers also tend to find financially sound firms because of limited protection (Klein and Barth, 1995). Furthermore, there exists a prevailing perception that small insurers are riskier than larger insurers. Hence, the decreased level of guaranty fund protection might adversely affect small firms. That is, there might be a tendency to decrease prices to mitigate the increased risk due to decreased protection from the safety net (see Epermanis and Harrington, 2006). The impact might be greater for smaller firms since their business is more tied to guaranty fund protection.

In summary, we can state the following testable hypothesis for P-L insurers:

Hypothesis 1. Since insurers have to mitigate the risks, the decreased level of guaranty fund protection negatively impacts on insurers' prices. The price reduction is relatively higher for small insurers compared to large insurers.

The P-L insurance industry generally experiences an underwriting cycle measured by the industry's combined ratio (see Figure 1). A value of 100 indicates a break-even point and high value of the combined ratio indicates losses from underwriting performance. More competition and price reductions are characterized during the period when the underwriting cycle is downward, i.e., 1992-1996 period. On the other hand, less competition and rising prices are commonly found during the period when the underwriting cycle is upward trend, i.e., 1997-2000 period. Browne and Hoyt (1995) and Kleffner and Lee (2009) show a high correlation between the underwriting cycle and insurers' insolvency. Thus, we expect that the impact of the guaranty fund system on prices is different for the different underwriting cycles in the following way:

Hypothesis 2. The impact of guaranty fund protection is greater during the period when the industry experiences poor underwriting performance compared to the period when the industry experiences improved underwriting profits.

3. DATA AND MODEL SPECIFICATION

3.1 Data

Various firm-specific variables are obtained from the Annual Statement from NAIC Database. Gross Domestic Product (GDP) is available from the U.S. Department of Commerce. To get the real value of a guaranty fund, the cap amount has been deflated using the personal consumption expenditures implicit price deflator obtained from Bureau of Economic Analysis, U.S. Department of Commerce. Data for the insurer agency system is obtained from Best's Key Rating Guide (A.M. Best Co.). The sample period is 1992-2000.

[FIGURE 1 OMITTED]

3.2 Model Specification

General demand and supply equation deliver the equilibrium point of insurance price and then a reduced form is obtained to analyze the impact of the guaranty fund on firm prices. Insurers' prices are set based on the level of market interest rates, capital adequacy ratio, product specialization, level of reinsurance utilization, and general economic conditions. These can be captured in a model of demand for and supply of prices of each insurer. The regression model is represented by the following:

[Price.sub.i,t] = [[beta].sub.0] + [[beta].sub.1]Guaranty [Fund.sub.t] + [[beta].sub.2]Real [GDP.sub.t] + [[beta].sub.3][Interest.sub.t] + [[beta].sub.4]Market [Share.sub.i,t] + [[beta].sub.5] [Advertisement.sub.i,t] + [[beta].sub.6][Leverage.sub.i,t] + [[beta].sub.7][Diversification.sub.i,t] + [[beta].sub.8][Agent.sub.i,t] + [[beta].sub.9][Ownership.sub.i,t] + [[beta].sub.10][Group.sub.i,t] + [[epsilon].sub.i,t] (1)

where i indicates the insurer and t refers to the year. Price is estimated as premiums earned divided by losses and loss expenses incurred (i.e., the inverse of loss ratio and seeWinter, 1994, Cummins and Danzon, 1997, and Choi and Weiss, 2005). In this paper, premiums, losses and loss adjustment expenses related to workers' compensation were deleted to calculate price, advertisement, and leverage variables, since the workers' compensation line is not affected by the limit of guaranty fund law.

There is a problem of using 'real value of guaranty fund' since the value has been continuously decreased over time; even though the magnitude of decreased level has not been the same (depends on the price index to get the real value). Thus, the inclusion of real value of guaranty fund would be spurious in the regression model (Greene, 1993). Since the real value of the fixed guaranty fund has declined over the period, a decreased level of protection (high risk to the insurer) is implicitly offered. Generally reinsurance is utilized as a mechanism which enables insurers to achieve more efficient risk sharing, thus supplementing underwriting capacity (Garven and Grace, 200i). That is, insurers tend to use more reinsurance when they face more risks. Thus, reinsurance utilization is, instead, used for 'Guaranty Fund' variable as a proxy. Bassett and Brady (2002) also used a proxy variable, studying the impact of the decline in the real value of deposit insurance on the bank performance.

This variable is constructed as follow:

reinsurance ceded/direct premiums written + reinsurance assumed

Many prior studies used this definition of reinsurance utilization (e.g., Regan and Tzeng, 1999, Garven and Lamm-Tennant, 2003, Viswanathan and Cummins, 2003, and Cole and McCullough, 2006). Insurers may try to mitigate increased risk through decreasing prices, if statutory protection is important for the pricing. That is, because of risk-sensitive demand, increased risk lowers prices that risk-sensitive customers are willing to pay (Epermanis and Harrington, 2006). Insurers' risk taking behaviors from the perspective of 'incentives for safety' have been studied in the insurance literature, e.g., Lee, Mayers, and Smith, 199i, Downs and Sommer, 1999, Bohn and Hall, 1999, and Epermanis and Harrington, 2006. The expected sign of this variable is negative and the magnitude of this impact is more significant for small insurers and much less (or nil) for large insurers as stated in Hypothesis 1.

To reflect the business cyclical economic fluctuation, two cyclical variables are included in the price equation. Firstly, real GDP growth is measured by dividing the current year's real GDP by the previous year's real GDP. It is expected to be positive if the GDP growth boosts demand for insurance. It is also expected that this variable captures the riskiness of insurers at different points in the business cycle (see Bassett and Brady, 2002). Secondly, the market interest rate measured by a one-year T-bill rate is included (in Interest variable). Two major insurers' earnings are generally from underwriting business and investment activities. And, market interest rate is sensitive to the insurance business through investment activities

Leverage is used to identify the capital adequacy of an insurer. It is defined as the ratio of premiums written to surplus and is the most widely used leverage ratio in insurance (Cummins and Weiss, 1992). As the leverage ratio increases, the insurer's ability to cover unexpected losses is reduced. So, an increase in leverage is associated with higher risk. High risk may be compensated by a decreased price. Therefore, the expected sign on this variable is negative.

Advertisement represents advertising intensity which is measured as the ratio of advertising expenses to net premiums written. It is used to control the fact that advertising may promote more sales and affect prices. This variable is also included in the model to control for the possibility that the two groups (small vs. large insurers) vary as to the advertising ratio used in a P-L market. The effect from this variable is uncertain. Increased costs due to high advertisement expenses may lead to high prices. On the other hand, if advertisements generate more sales then insurers may reduce prices.

Market Share measures the changes in market share on the individual firm and it is calculated based on the premiums written each year. Rapid market growth is considered one of the causes of market failure and affects prices. Specifically, A.M. Best (2001) reports that a rapid growth is one of the primary causes of P-L insurers' failure since it represents 15.1% of total P-L insurers impairment studied for the period of 1969 to 2006. Also, studies provide evidence of high premium growth prior to the insolvencies (Grace, Harrington, and Klein, 1995, and Bohn and Hall, 1999). Growing firms are more likely to charge higher prices if they try to increase market power. A positive relationship is expected if firms are able to increase prices as their market share increases. It is due to the fact that those firms can charge higher prices with market power. We may have more relative market power if consumers spend significant search costs to find products and if consumers have a loyalty to market leaders due to high market share, i.e., a large and well-established company must be good (Choi and Weiss, 2005).

The lines of business an insurer writes can affect the overall risk and performance of the firm. Diversification is measured using the Herfindahl index which is the sum of square of each of four major areas; short-tail personal lines, long-tail personal lines, short-tail commercial lines, and long-tail commercial lines. A higher value of the Herfindahl index indicates a more specialized (less diversified) company. Insurers that specialize in a few lines may gain greater expertise in administering these lines, leading to a positive relationship between diversification and price. On the other hand, it may be more difficult to achieve economies of scope or cross-sell business so that price might be reduced for such an insurer.

Three dummy variables are included to control the firm characteristics of P-L insurance companies; Agent dummy, Stock dummy, and Group dummy. Insurance is distributed to customers in a number of different ways, and a different distribution system is variously related to costs and prices. Thus, the distribution system may play a significant role in determining prices. For example, direct writers rely relatively more on factors such as advertising and computer automation in distributing insurance. However, an independent agency system depends more on the capacity and expertise of agents. So, their commission rates are higher than other distribution systems. Insurers using independent agents are assigned one and zero otherwise.

Organizational forms for insurance companies include mutuals and stock organizations, each of which has its own objective function and is effective in solving specific incentive conflicts among the contractual parties such as owners, managers, and policyholders (Mayers and Smith, 1994 and He and Sommer, 2010). For instance, in mutual organizations the conflict between policyholders and owners are eliminated because the policyholder-owner functions are merged. Controlling for organizational form allows for the possibility of differing levels of risk behavior among stock and mutual firms (Lamm-Tennant and Starks, 1993, Doherty and Dionne, 1993, Smith and Stutzer, 1995, and Weiss and Choi, 2008). The binary variable takes the value of one for stock companies and zero otherwise.

Controlling for group membership allows for the different behavior between group members and non- group members in insurance prices. Large insurers are more likely to be affiliated with a group than small insurers. Insurers affiliated with a group are assigned one and zero otherwise.

4. EMPIRICAL RESULTS

The potential sample of insurers consists of all P-L insurers that write P-L insurance and that report data to the NAIC for the period 1992 to 2000. From this potential sample, insurers with negative surplus, assets or premiums were deleted. Insurers are allowed to enter and exit the U.S. market over the sample period, in order to avoid problems associated with survivor bias for the equation (1). Based on total assets, large insurers are defined as the top 100 insurers and a rank below 1,000 are grouped as small insurers. No evidence of multicollinearity among variables is found.

We look at the differences in performance and market share changes between the two groups. First, the rate of return on total assets (ROA) is calculated each year for the small firms and large firms. Figure 2 highlights the different movements of ROA for two groups. It shows that large insurers generate higher ROA than small firms every year except in 1992. Changes in market share measured by premiums written for the two groups are also pictured in Figure 2 for the sample period. On average, large firms account for 53.9 percent of total property-liability premiums written in the U.S., while small firms represent 3.3 percent of the total market.

Not surprisingly, significantly different firm characteristics are reported in Table 1 between small insurers and large insurers. Small insurers tend to spend more on advertisements compared to their total premiums' income and show significantly higher levels of leverage. On the other hand, large insurers are more diversified in their business structure, use more reinsurance transactions, and are less likely to use an independent agency system. About 74.7 percent of large insurers are stock companies and 95.6 percent of them are members of insurance groups, whereas, for small firms, 61.3 percent are stock ownership companies and only 43.4 percent are members of insurance groups.

[FIGURE 2 OMITTED]

Table 2 shows that the reinsurance utilization variable is negative for small insurers and significantly related to insurance price at the 1 percent level. However, this variable is not significantly related to price for large insurers. Thus, there is strong evidence that small insurers are more likely to decrease price due to increased risk in order to attract customers in the competitive market. Otherwise, consumers do not purchase the product provided by relatively high-risk insurers (smaller companies), implied by reduced level of real guaranty fund protection, if prices are same. These results are consistent with the risk-sensitive demand and franchise value hypothesis by Epermanis and Harrington (2006) and provide support for Hypothesis 1. This may result in small insurers having a relatively less competitive position in the market and may impact adversely on future firm growth.

Table 3 shows that there exist some differences between the two periods. The coefficient on the reinsurance utilization variable for small insurers is -0.3639 for the first period, while it is -0.4012 for the second period. Both are significant and negative like that of Table 2. However, the impact of statutory protection for small insurers is greater during the period when the industry has experienced decreasing underwriting profits. This result provides support for the second hypothesis.

The results in Table 2 indicate that two economic cyclical variables are all insignificant in the price equation. Thus, real GDP changes and market interest rates do not affect insurance price. With respect to the leverage variable, the results for both large and small insurers are negative and significant for the 1992-1996 period only. Maybe, publicly available information (e.g., NAIC's Insurance Regulatory Information System) may prevent those higher risky firms from charging high prices. However, interestingly, it is positive and significant for small insurers during the 1997-2002 period.

Advertisements are strongly related to low prices for small insurers as reported in Tables 2 and 3. This suggests that small insurers do not charge higher prices even though they have increased costs from advertisement spending. This variable is not significantly related to large insurers' pricing. The results for market share change provide that these coefficients are negative and significant for the 1992-2000 period and the 1997-2000 period for large insurers only. Thus, these findings suggest that large firms sacrifice their prices to gain more market share during this time period and that no market power theory is implied in this market. Tables 2 and 3 show no significance for this variable for small firms. Diversification is positively related to both small and large insurers' price, except for large insurers for the 1992-1996 period. Thus, non-diversified and specialized insurers are more likely to charge higher prices and no economies of scope are presented in the P-L insurance industry during the sample period. The coefficients on agent variables are positive and significant for the small insurers for the three different periods, but they are negative and significant at the 5 percent level for the large insurers for the 1992-1996 period. This suggests that large insurers can utilize agents' expertise more efficiently than small insurers and that this efficient utilization may lead to lower prices. The results in Table 3 indicate that large stock companies are more likely to deliver their products at higher prices than non-stock companies. Small insurers are more likely to charge lower prices if they are affiliated with a group. But, for the large insurers, the coefficients on group dummy variables are not significant.

5. CONCLUSIONS

This paper analyzes the effects of guaranty fund protection on insurance prices. The analysis is made on the different firm sizes; small insurers and large insurers. The empirical analysis also uses two data sets based on the underwriting performance cyclical movement.

The regression results show the existence of guaranty fund impact on insurance prices. However, these findings are not applied to large firms. For small firms only, the results from the guaranty fund related model suggest some evidence that the guaranty fund effect exists in the U.S. P-L insurance market. The decreased level of guaranty fund protection impacts negatively on prices for small insurers. In addition, we find some evidence that small insurers tend to decrease prices more during the period when the industry's underwriting profits are decreasing. Those findings together suggest that small insurers mighthave disadvantages in pricing in the P-L insurance market to compete with firms of other sizes or biggerfirms. This position may adversely impact corporate growth for small insurers. The implications of this research suggest that regulators should consider changing the level of current guaranty fund protection.

However, economic cyclical variables, i.e., real GDP growth and market interest rates, do not provide a significant impact on insurance prices. Mixed results are found in firm-specific variables for small and large firms. Moreover, along with those findings, different firm characteristics are observed between small and large insurers.

The result findings from this paper provide new information on the impact of the guaranty fund system in the U.S. P-L insurance industry. Since no prior research examines these causal relations in this industry, the empirical findings from this study could provide valuable input for the public and regulators. The results of this paper provide and meet demanding information market needs.

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Byeongyong Paul Choi, Howard University, Washington, DC, USA

Dr. B. Paul Choi earned his Ph.D. at the Temple University in 2002. He is an assistant professor of Finance, International Business and Insurance at Howard University and leads Howard's insurance and actuarial science program. Dr. Choi holds a Chartered Property Casualty Underwriter (CPCU) designation.
TABLE 1: DESCRIPTIVE STATISTICS: SMALL VS. LARGE FIRMS

                           Small Firm

Variables                  Mean           Std. Dev.

Price                           1.7164       1.1490
Asset Size                 $12.78        $8.93
Asset Growth                    1.1102       0.3158
Reinsurance Utilization         0.1085       0.2527
Leverage                        1.6314      35.1142
Advertisement                   0.0164       0.3240
Market Share Change             5.1278      89.0438
Diversification                 0.6774       0.2537
Agent Dummy                     0.7326       0.4426
Stock Ownership Dummy           0.6130       0.4871
Group Dummy                     0.4336       0.4956
Observations                     6,268

                           Large Firm

Variables                  Mean           Std. Dev.    t-test

Price                           1.3757        0.8684   ***
Asset Size                 $5,181.29     $7,848.90     ***
Asset Growth                    1.1072        1.1587
Reinsurance Utilization         0.2308        0.2510   ***
Leverage                        0.9904        0.6068
Advertisement                   0.0052        0.0242   ***
Market Share Change             2.7214       36.7285
Diversification                 0.5064        0.2041   ***
Agent Dummy                     0.6000        0.4902   ***
Stock Ownership Dummy           0.7467        0.4352   ***
Group Dummy                     0.9556        0.2062   ***
Observations                       900

Note: Units of observation are insurance companies.
Dollars are in millions.

Small firms are companies below 1,000 rank and Large firms
are top 100 companies. *** significant at 1% level;
** significant at 5% level; * significant at 10% level.

TABLE 2. PRICE REGRESSIONS: 1992 TO 2000

                            Large Firm

Independent Variable        Coeff.      Std. Err.

Intercept                   -3.3231     4.9252
Reinsurance Utilization     0.0179      0.0909
Real GDP Growth             3.7900      4.6479
Market Interest (T-Bill)    8.0488      3.5966       **
Leverage                    -0.0030     0.1019
Advertisement               -1.0049     0.6603
Market Share Change         -0.0003     0.0001       ***
Diversification             1.0775      0.3209       ***
Agent Dummy                 -0.1326     0.0912
Stock Ownership Dummy       0.1250      0.0946
Group Dummy                 -0.0692     0.3249

Observations                         900

Adjusted [R.sup.2]          0.0707

                            Small Firm

Independent Variable        Coeff.      Std. Err.

Intercept                   -0.9745     2.6855
Reinsurance Utilization     -0.3783     0.0653       ***
Real GDP Growth             2.0153      2.6114
Market Interest (T-Bill)    0.8692      1.9248
Leverage                    0.0000      0.0003
Advertisement               -0.1061     0.0343       ***
Market Share Change         -0.0001     0.0001
Diversification             1.1612      0.0846       ***
Agent Dummy                 0.1153      0.0444       ***
Stock Ownership Dummy       0.0018      0.0349
Group Dummy                 -0.0937     0.0405       **

Observations                       6,268

Adjusted [R.sup.2]          0.0612

*** significant at 1% level, ** significant at 5% level,
and * significant at 10% level.

Note: Standard errors are heteroscedastic-consistent
estimators following the method of White (1980).

TABLE 3
PRICE REGRESSIONS: BY UNDERWRITING PROFIT CYCLE

                            1992 to 1996

                            Large Firm

Independent Variable        Coeff.      Std. Err.

Intercept                   -0.0852     6.5358
Reinsurance Utilization     0.0052      0.1200
Real GDP Growth             1.9800      6.5099
Market Interest (T-Bill)    4.5554      3.5169
Leverage                    -0.2594     0 0872       ***
Advertisement               -0.6904     0.6137
Market Share Change         -0.0422     0.0395
Diversification             0.4316      0.2919
Agent Dummy                 -0.3245     0.1518       **
Stock Ownership Dummy       0.2979      0.1261       **
Group Dummy                 -0.6679     0.4669

Observations                        500
Adjusted [R.sup.2]          0.1068

                            1992 to 1996

                            Small Firm

Independent Variable        Coeff.      Std. Err.

Intercept                   1.0563      3.5070
Reinsurance Utilization     -0.3639     0.0758       ***
Real GDP Growth             0.0071      3.4149
Market Interest (T-Bill)    2.5238      2.1053
Leverage                    -0.0002     0.0000       ***
Advertisement               -0.0878     0.0280       ***
Market Share Change         -0.0001     0.0001
Diversification             1.1261      0.0950       ***
Agent Dummy                 0.0965      0.0536       *
Stock Ownership Dummy       0.0291      0.0451
Group Dummy                 -0.1416     0.0451       ***

Observations                       3,645
Adjusted [R.sup.2]          0.0741

                            1997 to 2000

                            Larqe Firm

Independent Variable        Coeff.      Std. Err.

Intercept                   18.5577     23.2571
Reinsurance Utilization     0.1417      0.1434
Real GDP Growth             17.1597     21.5185
Market Interest (T-Bill)    13.5064     15.1322
Leverage                    0.2761      0.1802
Advertisement               -2.0319     1.3203
Market Share Change         0.0002      0.0001       *
Diversification             1.6635      0.5182       ***
Agent Dummy                 0.0454      0.1129
Stock Ownership Dummy       -0.0030     0.1294
Group Dummy                 0.4075      0.4510

Observations                400
Adjusted [R.sup.2]          0.1055

                            1997 to 2000

                            Small Firm

Independent Variable        Coeff.      Std. Err.

Intercept                   4.6856      10.0911
Reinsurance Utilization     -0.4012     0.1164       ***
Real GDP Growth             -2.8774     9.4422
Market Interest (T-Bill)    -10.9264    8.1682
Leverage                    0.0023      0.0005       ***
Advertisement               -0.1727     0.0462       ***
Market Share Change         0.0000      0 0001
Diversification             1.2139      0.1553       ***
Agent Dummy                 0.1364      0.0738       *
Stock Ownership Dummy       -0.0317     0.0552
Group Dummy                 -0.0287     0.0757

Observations                2,623
Adjusted [R.sup.2]          0.0490

*** significant at 1% level, ** significant at 5% level,
and * significant at 10% level.

Note: Standard errors are heteroscedastic-consistent estimators
following the method of White (1980).
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Copyright 2011 Gale, Cengage Learning. All rights reserved.