Property-liability Insurance Industry Research Articles

External Impacts on the Property-Liability Insurance Cycle

Traditionally, underwriting performance is considered to be a function of industry-specific institutions. Using quarterly data from 1974 through 1990, we provide evidence of a long-run link between the general economy and the underwriting performance as measured by the combined ratio. Using cointegration techniques, we estimate the long-run relationship between the general economy as measured by real gross domestic product, the short-term interest rate, and inflation. We then estimate the short-run link between the industry and the general economy using vector auto-regression technniques and find that, although the property-liability insurance industry is linked to the long-run performance of the national economy, short-run shocks in economic variables have little effect on the combined ratio.

Economic and Market Predictors of Insolvencies in the Property-Liability Insurance Industry

This article identifies a set of factors exogenous to individual property-liability insurers that are statistically related to the overall rate of insurer insolvencies. This approach represents a departure from the methodologies of prior studies which have focused primarily on firm-specific characteristics in assessing insolvency risk. The study uses quarterly data for the period 1970 through 1990. The empirical results support the hypothesis that economic and insurance market variables are important predictors of property-liability insurer failure rates. The sensitivity of the insurer insolvency rate to two of the insurance market factors is particularly striking: A 10 percent reduction in the number of property-liability insurers results in an 82 percent reduction in the insolvency rate, all else equal. A reduction of five points in the combined ratio results in an 18 percent reduction in the insolvency rate.

An Exposure-Based Analysis of Property-Liability Insurer Stock Values around Hurricane Andrew

Introduction Investors hold a set of information that generates expectations concerning risk and earnings. In an efficient market, stock prices quickly reflect all of the relevant information involving a firm. Several studies have examined the type of information investors consider relevant for pricing decisions. Dividend and earnings announcements and stock splits have been extensively covered (Charest, 1978; Aharony and Swary, 1980; Asquith and Mullins, 1983). Other studies have addressed the impact of losses on firm value. Sprecher and Pertl (1983) and Davidson, Chandy, and Cross (1987) found a negative relationship between large losses and firm value. Reilly and Drzycimski (1973) found that stock prices adjust immediately following the announcement of major world events. Shelor, Anderson, and Cross (1990) examined the effect of the October 17, 1989, California earthquake on the stock value of firms in the real estate industry. They concluded that the earthquake conveyed important new information to the market that was reflected in significant negative stock returns among real estate firms operating around San Francisco (the area sustaining the most damage from the earthquake). Real estate-related firms operating in other areas of California were generally unaffected by the earthquake. The occurrence and timing of the earthquake could not be anticipated and, thus, instantaneously introduced new and relevant information to the marketplace. The large negative returns for the sample indicated that investors viewed the earthquake as a signal of unfavorable financial conditions for the real estate industry in the San Francisco bay area. The market response to the earthquake among other California firms was consistent with that observed for an event that has no impact on market valuation. These results provide evidence that the market may discriminate among firms in relation to their geographic risk exposure. Such behavior is expected in a rational and efficient market. In a related article, Shelor, Anderson, and Cross (1992) examined the market response of property-liability insurers around the earthquake. In contrast with the real estate-related firms, the property-liability industry demonstrated a significant positive response to the earthquake, indicating that investor expectations of higher demand for insurance (positive effect) may have more than offset the potential earthquake losses (negative effect). Aiuppa, Carney, and Krueger (1993) also explored the impact of the earthquake on property-liability stock values and found a similar positive response. These results suggest that the two industries were affected by the earthquake in different ways; that is, the California earthquake stimulated industry-specific responses. This conclusion, in part, provides the motivation for this study involving the examination of the effect of Hurricane Andrew on property-liability stock values. Research Question In August 1992, Hurricane Andrew struck South Florida and Louisiana with sustained winds of 138 miles per hour. The magnitude of this storm's devastation made it the costliest natural disaster in U.S. history. The property-liability insurance industry estimates that Andrew caused more than $20 billion in property damage.(1) This dwarfs the $7 billion in claims paid after Hurricane Hugo (the previous costliest natural disaster) struck North Carolina and South Carolina in September 1989. The effect of catastrophic property damage on the value of property-liability insurers is the subject of this study; specifically, does the market demonstrate an ability to discriminate by the degree of loss exposure of property-liability insurers around significant hurricanes (such as Andrew)? Although some insurers have heavy loss exposure in certain geographic areas of the country, other firms have little or no business in the affected areas. If the market is rational and efficient, then it would be surprising to find that unexposed or lightly-exposed firms experience a similar hurricane-induced market reaction to that of heavily-exposed firms. …

Capital structure and the cost of equity capital in the property-liability insurance industry

Financial pricing models are now widely used in insurance pricing and price regulation. However, most practical applications of these models have not taken into account either the effects of firm capital structure or the differences in financial risk across lines of insurance. The objective of this paper is to provide a first step towards remedying these deficiencies by providing theoretical and empirical evidence on the effects of capital structure and line of business risk on the cost of equity capital in property-liability insurance. A theoretical model is developed that expresses the cost of capital as a function of both insurance leverage (the ratio of policy reserves to assets) and financial leverage (the ratio of financial debt to assets). The model is tested using data on traded stock property-liability insurers over the period 1980–1989. The results indicate that the cost of equity capital is sensitive to both insurance and financial leverage and that the cost of equity is higher for firms writing long-tail commercial lines such as general liability and workers' compensation. Thus, both leverage and line of business specialization should be taken into account in using the cost of equity capital in pricing property-liability insurance.

Managerial Discretion, Regulation, and Stock Insurer Ownership Structure

Variation in operating characteristics across ownership structures in the property-liability insurance industry could be caused by regulatory and tax differences as well as controlrelated incentives. We exploit variation in ownership structure within the population of common stock insurers to help solve this identification problem. Consistent with the managerial discretion hypothesis, we find that different ownership classes operate in lines that are statistically distinguishable. Because of our focus on stock firms, this variation is not attributable to taxes and regulation alone.

Evidence on the Time Series Properties of Insurance Premiums and Causes of the Underwriting Cycle: New Support for the Capital Market Imperfection Hypothesis

Introduction Numerous studies document that underwriting profit rates and insurance prices in the property-liability insurance industry have been cyclical in the postwar period (for example, see Cummins and Outreville, 1987; Doherty and Kang, 1988; and Venezian, 1985). A hard where prices and profits are high and coverage is restricted tends to be followed by a soft where prices and profits are low and coverage is expanded. The cycle in property-liability underwriting profits has a period of about six years.(1) The of this underwriting cycle has been the subject of much debate. In a perfect market, a price cycle would occur only if insurers' expected costs--the present value of expected losses--were cyclical. Most explanations, however, attribute the cycle to imperfections on the supply side of the property-liability insurance market.(2) Cummins and Outreville (1987) present a model in which insurers set prices rationally, but in which almost all relevant information in forming expectations of future losses is contained in past loss reports. These assumptions, along with regulatory and accounting lags, induce a cycle in reported profits and prices, Venezian (1985) attributes the cycle to the extrapolative methods used in the rate setting process. Several recent articles show that capital market imperfections can generate a cycle. For example, Winter (1989) assumes that the quantity of insurance is constrained by an insurer's equity and that internal equity is a cheaper source of capital than external equity (Myers, 1984). Consequently, a reduction in capital (e.g., due to unexpected losses) causes industry supply to contract and insurance prices to increase.(3) These hypotheses have different implications for the determinants of premiums, which are tested using time series regressions similar to the causality tests proposed by Granger (1969) and Sims (1972). In a perfect market with rational expectations, insurers set premiums equal to the present value of expected future losses, where expectations are formed using all relevant information. Premiums are therefore the best predictors of future losses in the sense that premiums aggregate all information about future losses.(4) In the terminology of Sim's causality tests, premiums cause losses; that is, current premiums can be explained by future loss payments. After controlling for expected future loss payments, other information such as past losses and past surplus will not help explain premiums. In contrast, the capital market imperfection hypothesis (Winter, 1989; also see Gron, 1992, and Cummins and Danzon, 1992) implies that premiums are not the best predictors of actual losses because past surplus affects premiums. Venezian's (1985) model implies that premiums are not the best predictors of actual losses, because premiums are mechanically set based on the realizations of past losses. Accordingly, past loss experience affects premiums. Although data problems limit the strength of the conclusions, our results suggest that market imperfections play an important role in insurance pricing. Consistent with the capital market imperfection hypothesis, the evidence suggests that past values of surplus affect premiums. Weaker evidence is presented that past values of losses affect premiums, which is consistent with Venezian's hypothesis. The article proceeds as follows. In the next section, the hypotheses are described in more detail. In the third section, the methodology and data are presented along with potential econometric problems. The results are then summarized. The article concludes with a short summary and some caveats. Hypotheses Perfect Markets/Rational Expectations Hypothesis In a perfect market, the price of insurance would be determined by discounting expected cash flows by the appropriate discount rates. To be more specific, consider a competitive insurance market with no transaction costs and no information costs. …

Measuring cost efficiency in the property-liability insurance industry

Escalating prices and periodic availability problems in property-liability insurance markets are often attributed to insurance company inefficiency and lax claims settlement practices. This paper investigates these issues by estimating stochastic cost frontiers for three size-stratified samples of property-liability insurers over the period 1980–1988. A translog cost function and input share equations are estimated using maximum likelihood techniques. The results show that large insurers operate in a narrow range around an average efficiency level of about 90 percent relative to their cost frontier. Efficiency levels for medium and small insurers are about 80 and 88 percent in relation to their respective frontiers. Wider variations in efficiency are present for these two groups in comparison with large insurers. Large insurers slightly over-produce loss settlement services, while small and medium-size insurers under-produce this output. The small and intermediate size groups are characterized by economies of scale, suggesting the potential for cost reductions from consolidations in the industry.

Efficiency in the property-liability insurance industry

The purpose of this study is to examine the cost impact resulting from P/L insurer inefficiency. A generalized Leontief profit function which allows for allocative and scale efficiency is estimated for 100 of the largest P/L insurers over the period 1980 through 1984. Excessive costs from nonoptimal use of resources are estimated by type of inefficiency. The dollar impact of this is estimated also. Estimated inefficiency costs are 12 to 33 percent of premiums. Problems in the property-liability (P/L) insurance industry have attracted considerable attention in recent years. Price and availability crises in markets such as auto and general liability insurance have occurred, and cost pressures have continued to build as insurers compete with risk retention alternatives. Rate regulation in some states has further exacerbated these problems. Although labor saving, computer related technological changes (such as automated report writing) are available, the white collar workforce of insurers has continued to grow. Issues such as these are important not only because the P/L insurance industry is important, but because cost pressures and increasing competition exist for the growing service sector. Premium levels have captured most of the public attention. Premiums (augmented by investment income) are designed to pay covered losses as well as insurers' administrative and underwriting expenses. Losses are the largest component of premiums. In 1988, premiums written in the P/L insurance industry was $202 billion, and incurred losses equaled $160 billion (Best's Aggregates and Averages, 1989). To deal with the large loss level in general liability some states have enacted tort reform which caps the maximum amount of noneconomic loss. Interest in no-fault auto insurance with a stringent threshold has resurfaced as a potentially more efficient compensation method and strategy to lower loss costs in automobile liability insurance.

Stock Versus Mutual Ownership Structures: The Risk Implications

This article provides empirical tests of the risk differences between two types of ownership structure in the property-liability insurance industry. Empirical evidence is provided that suggests stock insurers have more risk than mutuals where the risk inherent in future cash flows is proxied by the variance of the loss ratio. Further evidence suggests that stock insurers write relatively more business than do mutuals in lines and states having higher risk. Copyright 1993 by University of Chicago Press.

Identifying Financial Distress in the Insurance Industry: A Synthesis of Methodological and Empirical Issues

12 The likelihood ratio test used in these results may be too conservative for testing hypotheses on the boundary of the parameter space. The problem may arise when comparing the EGB2 model with the probit model. 13 The NPDM slightly outperforms the MDA and most qualitative response models in the classification of insolvent firms, especially three years prior to insolvency, when the minimum number of misclassification criterion is used. References The Importance of Insolvency Prediction This study presents a methodological approach for identifying insolvent insurance companies. In this article, financial distress and insolvency are used interchangeably to describe insurers experiencing liquidation, receivership, conservatorship, restraining orders, rehabilitation, etc. Previous models for predicting financially distressed insurers are summarized and evaluated. More robust models for classifying and predicting financial distress in the insurance industry are presented, and an attempt is made to address methodological issues that previous studies have sometimes ignored. The problem of insolvency in the property-liability insurance industry merits special attention in view of the large number of failures. Since 1961, about 350 property-liability insurers have failed, more than 240 insurers have voluntarily retired, and over 500 companies have merged into other companies, resulting in more than 1,100 property-liability company retirements (compiled from A. M. Best Company, 1961-1990). Insolvency prediction models can help insurance commissioners determine whether an insurer is in danger of failing and can also help auditors decide whether an insurer is a going concern. The ability to classify and identify financial distress is important to regulators, legislators, policyholders, auditors, owners, bondholders, and even the general public. Statistical models of insolvency prediction can be constructed to help determine what accounting, financial, and other information could be employed by regulators in making decisions on the financial solidity of insurers. A number of empirical studies have compared statistical models that use insurers' financial data to predict insolvencies in the property-liability insurance industry (Trieschmann and Pinches, 1973; Pinches and Trieschmann, 1974, 1977; Harmelink, 1974; Cooley, 1975; Eck, 1982; Hershbarger and Miller, 1986; Harrington and Nelson, 1986; BarNiv and Raveh, 1986; BarNiv and Smith, 1987; Ambrose and Seward, 1988; Barniv, 1989; and McDonald, 1992). The models have impressive ability to predict insolvencies in the insurance industry. For example, Trieschmann and Pinches (1973) report that their multiple discriminant analysis (MDA) model correctly classifies 92 percent of insolvent insurers and 96 percent of solvent firms two years prior to the determination of insolvency or solvency; later studies report correct classifications ranging from 62 to 100 percent. Despite the classification success of previous studies, we should be concerned with the accuracy, reliability, and levels of significance for models and coefficients obtained by these studies. This article will review several of the important methodological issues that have been raised about models used to identify financial distress. This study's objectives are (1) to establish a general framework for multivariate prediction models that is applicable to the insurance industry; (2) to enumerate some of the methodological problems associated with insolvency prediction models for the insurance industry (most of which are relevant to binary state prediction models in general); and (3) to use the multivariate models to predict insolvencies with a high degree of accuracy and reliability by overcoming the methodological limitations encountered in previous studies. We present the current state of knowledge and illustrate the methodological considerations through the use of robust novel models and empirical applications. …

An Empirical Bayes Approach to Estimating Loss Ratios

Introduction The accuracy of estimates of future loss costs plays a fundamental role in determining the underwriting profits of property-liability insurers. A standard measure of loss costs used by regulators, auditors, policyholders, and security analysts is the loss ratio. The well-known cyclical pattern in loss ratios over time (e.g., Witt, 1977, 1978) has led to a series of analyses of underwriting profit cycles. The underwriting cycle in the United States is about six years (see Cummins and Outreville, 1987; Venezian, 1985; Smith and Gahin, 1983). Explanations for the cycles fall into two categories. The first type suggests that the insurance markets are unstable such that prices fail to converge on an equilibrium due to periods of destructive competition followed by cutbacks in supply (Berger, 1988) or ratemaking with limited information (Venezian, 1985). For example, Brockett and Witt (1982) point out that an explanation for the autoregressive behavior of loss ratios is that premiums are based in part on past losses. A second type of model explains the cycle in terms of the insurance market response to external events, such as the liability insurance crisis (Harrington, 1988), interest rate changes (Doherty and Kang, 1988; Doherty and Garven, 1991), and institutional and regulatory rigidities (Cummins and Outreville, 1987). This article contributes to the analysis of underwriting profits and cycles by exploring the short-term forecasting of measures of underwriting profits. Specifically, the empirical Bayes model is proposed as a methodology for estimating loss ratios--the ratio of incurred losses to earned premiums.(1) The position taken is that of an outsider (such as a security analyst, rating agency, auditor, or insurance regulator) attempting to gauge the financial performance of an insurer. We use the loss ratio rather than the profit ratio because loss costs are the primary source of uncertainty in the determination of insurance profits. Although this analysis focuses on the loss ratio, any financial measure could be estimated using the empirical Bayes method. Hence, this analysis is intended to be an example of a technique appropriate for predicting financial measures when evaluating the financial performance of the property-liability insurer. The empirical Bayes approach is particularly appropriate for the case of loss ratios reported by the property-liability insurance industry because of data availability and comparability. The by-line loss ratio can be derived for a large number of insurers across time using the A. M. Best tapes. The empirical Bayes methodology was developed for just such a multiparameter estimation problem (Efron and Morris, 1973, 1975, 1977; Morris, 1983) and is designed for situations in which many similar parameters are to be estimated but the information on each may be weak. The procedure borrows (Tukey, 1963) from the whole set of data for estimating each parameter. Thus, empirical Bayes estimators gain their advantage over frequentist estimators by using information about all parameters in estimating each individual parameter, which is intuitively reasonable if the parameters are similar. In the case of loss ratios, although there is information across many firms, the information on each firm's by-line loss ratios may be weak. The information within the firm is considered weak when the historic trend is short or if the business is not widely underwritten such that the historic trend has high variance. An empirical Bayes model suggests that the estimate of a firm's by-line loss ratio may borrow strength from information provided by the experience reported across all firms with business in that line. The empirical Bayes model has been previously applied for actuarial purposes. For example, Morris and Van Slyke (1978) developed an empirical Bayes method for pricing insurance claims. Similarly, some applications of credibility theory have employed the empirical Bayes methodology (see, for example, Neuhaus, 1984; Norberg, 1980). …

Gaining from Loss: Property-Liability Insurer Stock Values in the Aftermath of the 1989 California Earthquake

Market Reactions to Legislation and Catastrophic Loss Catastrophic losses are a major concern in the property-liability insurance industry, because earthquakes and other natural disasters create special problems for insurers. Although the probability of catastrophic loss is very small, its impact on insurance firms covering the losses is substantial. For example, insurers are expected to pay billions of dollars for damages caused by the California earthquake of October 17, 1989.(1) A number of studies have examined the impact of large losses and the impact of legislation on insurers' stock values. Sprecher and Pertl (1983) and Davidson, Chandy, and Cross (1987) find that large losses have a negative impact on stock prices. Fields and Janjigian (1989) find that the 1986 Chernobyl reactor accident had a significant negative impact on the security prices of U.S. electric utilities. Hill and Schneeweis (1983), Bowen, Castanias, and Daley (1983), and Spudeck and Moyer (1989) find that many electric utility firms experienced decreased stock prices and increased risk following the 1979 Three Mile Island incident. The impact of legislation on insurance firm value has also been documented. Fields, Ghosh, Kidwell, and Klein's (1990) examination of the impact of California Proposition 103--which imposed constraints on insurers' ability to raise rates--found that insurance firms' stock values reacted negatively several days before and after passage of the Proposition. With the exception of a study by Walker (1988), there are no analyses of the impact of catastrophic losses on insurance firm value. This question is motivated by notable natural disasters involving large property losses, such as Hurricane Hugo in September 1989 and the October 1989 California (Loma Prieta) earthquake. We chose the earthquake for study because anticipation of the hurricane may have caused an information leakage effect. The timing of the earthquake could not have been anticipated. Its occurrence sent new, relevant information to the marketplace in short order. Hypotheses Two opposing hypotheses motivate this study. First, rapid depletion of surplus accounts following a catastrophe may cause investors to discount property-liability insurer stock values, depending on the actual or implied loss exposure of individual insurers. Reinsurance, which serves to spread catastrophic losses across many insurers, might lessen the depletion of surplus accounts for some firms following the earthquake. The second hypothesis is that insurers benefit from an isolated catastrophic event because of subsequent increased consumer or institutional demand, including increases in coverage required and additional premium earnings. This should affect stock prices positively, especially if much of the property damaged in a catastrophe had not been insured, as was the case in California at the time of the Loma Prieta earthquake. A significant portion of the earthquake loss was not covered, because high earthquake insurance rates and low perceived risk discouraged many property owners from carrying coverage. Only about 30 percent of California homes and businesses had earthquake insurance to cover the losses.(2) If the second hypothesis predominates, stock price increases should be especially pronounced for companies that write large amounts of insurance in California. The expectation is that additional earnings from increased demand will more than offset the losses. Hypothesis two is at odds with the results of previous studies cited above, which indicate that losses have a negative impact on firm value. The losses reported in earlier studies may have been caused by an erosion of consumer confidence in the safety or value of a firm's product or service that lessened demand. That is, a particularly large loss (fire, airplane crash, etc.) may have had a negative effect on consumer demand for the product or service, leading to depressed stock prices. …

An Exposition of the Implications of Limited Liability and Asymmetric Taxes for Property-Liability Insurance

ABSTRACT This article elaborates upon the intuition underlying Doherty and Garven's (1986) option pricing model and extends its basic results to a further consideration of the implications of limited liability and asymmetric taxes for pricing and incentives in property-liability insurance. When compared with CAPM-based models of the insurer, a number of important insights emerge. First, the option pricing framework is shown to encompass the CAPM framework as a special case and may help to explain a number of empirical phenomena. Second, the option pricing framework is used to develop a risk hypothesis which suggests that limited liability and asymmetric taxes provide mutuals with greater disincentives for riskbearing than stock companies, even in the absence of owner/manager conflicts. Although the property-liability insurance industry has been subject to price regulation for many years, researches have only recently derived valuation formulas for property-liability insurance firms. To date, the most promising approaches apply financial theories such as the capital asset pricing model (CAPM) (see Biger and Kahane, 1978; Fairley, 1979; Hill, 1979; Hill and Modigliani, 1987; and Myers and Cohn, 1987) and the option pricing model (see Doherty and Garven, 1986; Cummins, 1988b; and Derrig, 1989). Although details vary, these models are generally organized around the principle that the rate of underwriting profit must be set so as to produce a fair, or competitive rate of return on equity.(1) In spite of their common origins, CAPM and option-based insurance pricing models produce substantially different predictions concerning pricing and incentives for property-liability insurance. It will be shown that these differences are primarily due to the manner in which the effects of insolvency and taxes are modeled. Essentially, CAPM-based models implicitly assume that shareholders have unlimited liability, whereas option-based models assume that shareholders' liability is limited. Similarly, by assuming that losses are rebated at the same rate at which gains are taxed, CAPM-based models effectively assign unlimited liability to the government, whereas option-based models limit the government's liability by assuming that gains and losses are taxed in an asymmetric fashion. This article elaborates upon the intuition underlying Doherty and Garven's (1986) option pricing model and extends its basic results to a further consideration of the implications of limited liability and asymmetric taxes for pricing and incentives in property-liability insurance. This is accomplished by comparing and contrasting option-based with CAPM-based models of the insurance firm. This analysis yields a number of important insights, such as the fact that the option pricing framework encompasses the CAPM framework as a special case. The option pricing model also has several practical advantages over the CAPM. For example, it is not plagued by the CAPM's well-known parameter estimation problems; indeed, it may help to explain the causes of these problems.(2) The option pricing model also provides an explicit linkage between fair return and the risks of insolvency and tax shield underutilization, whereas the CAPM totally ignores these effects. The option pricing model also calls attention to some important incentive effects concerning risk-bearing that are not captured by the CAPM. Under the CAPM, asset and liability is not particularly important so long as these claims are priced to yield appropriate risk-adjusted rates of return. However, under the option pricing model, the extent to which firms will seek to increase or avoid through their investment and underwriting policy choices depends upon the likelihood of being taxed or becoming insolvent. Consequently, the application of the option pricing framework makes it possible to develop a risk hypothesis which predicts that mutual insurers will seek less exposure to than stock companies. …

International P/L Insurance Output, Input, and Productivity Comparisons

This research provides (bilateral) divisia and multilateral divisia indexes of output, input, and productivity for the property-liability (P-L) insurance industry for the following countries: United States, West Germany, Switzerland, France, and Japan. The time period studied is 1975 to 1987. The results indicate that considerable diversity exists among different countries, with Japan showing the weakest productivity growth. The United States and West Germany are associated overall with high productivity.

McCarran-Ferguson Act reform: More competition or more regulation?

The antitrust exemptions provided by the McCarran-Ferguson Act are often identified as the cause of a variety of problems that have plagued the property-liability insurance industry in the last decade. In particular, proponents of repeal of the Act suggest that it has facilitated anticompetitive behavior by insurers, which in turn contributed to the liability and auto insurance crises of the 1980s. We examine industry structure, behavior, and performance and assess possible market imperfections that may justify price regulation and special antitrust treatment. We find that the major barrier to effective competition is state rate regulation rather than anticompetitive behavior. We examine evidence on the causes of the liability and auto insurance crises and conclude that they are readily explained by changes in market conditions and regulatory constraints rather than anticompetitive behavior. While there is no need for the broad antitrust exemptions contained in the Act, there is a danger that repeal will lead to more inefficient price regulation unless reform of the Act includes restrictions on state rate regulation. We propose reform legislation that both narrows the industry's antitrust exmption and promotes competition.

Efficiency in the Property-Liability Insurance Industry

Efficiency in the Property-Liability Insurance Industry

Risk/Return Relationships for Life-Health, Property-Liability, and Diversified Insurers

Risk/Return Relationships for Life-Health, Property-Liability, and Diversified Insurers Abstract This study focuses on a risk-return comparisson for life-health, property-liability, and diversified insurers. Included is an analysis of risk-return relationships using both mean-variance and CAPM approaches. Both accounting and market data are used to measure profitability. The results suggest that for the time period of the study, 1973 through 1987, investment in individual life-health and property-liability insurers was better than investment in diversified insurers. Evidence also indicates that accounting measures of profitability may be poor proxies for market performance. This study undertakes to compare the mean returns for life-health, property-liability, and diversified insurers. Mean returns are compared both with and without adjustment for risk. Several measures of risk are used and both book and market returns are considered. The goal of the study is to assess whether identifiable risk-return advantages existed for investors in each of the three insurer segments during the period 1973 through 1987. A number of studies have been published on the topic of insurance company profitability. The Arthur D. Little (1967) report concluded that insurers were not earning excessive profits, especially on a risk-adjusted basis. Forbes (1971) and Trieschmann (1971) came to a similar conclusion. Fairley (1979) used the capital asset pricing model (CAPM) to determine the appropriate rate for automobile insurance. His findings suggested premium rates lower than those desired by insurers. He argued that only systematic risk, as measured in the CAPM, should be considered. However, Harrington (1983), in a study of life-health insurance stocks from 1961 through 1976, found evidence of a significant relationship between mean returns and both systematic and unsystematic risk. Cummins and Harrington (1988) analyzed the risk-return relationship of property-liability insurance stocks during the period 1970 through 1983. Their results were inconsistent with CAPM for the time period 1970 through 1980, but were consistent with it for the period 1980 through 1983. Cox and Griepentrog (1988) provided empirical results indicating that both systematic and unsystematic risk are important. Pritchett and Wilder (1986), in an extensive study of life-health insurers from 1950 through 1980, used mean-variance and CAPM, as well as accounting rate of return data. They concluded that life-health insurers had higher returns and greater volatility from 1950 through 1963 than during the period from 1963 through 1980. The Insurance Services Office (ISO) (1987) conducted a profitability study on the property-liability industry for the period 1970 through 1986. The study focused primarily on book rate of return and total risk. The ISO study concluded that for the time period studied, the property-liability insurance industry earned below average returns and had greater variability of returns than most of the other 89 industries examined. Research Issues This study, while considering risk-return issues similar to those investigated by previous studies, differs in several ways. First, the study focuses on a comparison of risk and return for life-health, property-liability, and diversified insurers. A key question is asked: Do investors gain by purchasing the stock of a diversified insurer versus separately buying the stock of a life-health insurer and of a property-liability insurer? Portfolio theory suggests that there would be no return advantage on a risk-adjusted basis. However, if efficiencies result from the combination of life-health and property-liability operations, then a risk-return advantage might be observed. None of the previous risk-return studies focused simultaneously on the three segments of insurers. …

The Effect of Proposition 103 on Insurers: Evidence from the Capital Market

Effect of Proposition 103 on Insurers: Evidence from the Capital Market Introduction On November 8, 1988, California voters passed an insurance-reform ballot initiative known as Proposition 103. In essence, the initiative was a populist response to rising automobile insurance premiums in a state where property-liability insurance rates were already among the highest in the nation. Proposition 103's most controversial provision mandates a minimum 20 percent reduction in property-liability insurance premiums from their November 1987 levels. An exemption from the rate cut and any future rate increases were to be permitted only if an insurer was substantially threatened by insolvency. On May 4, 1989, the California Supreme Court upheld the constitutionality of the rate rollback but overturned the insolvency provision and ruled that insurers were entitled to an adequate return. Comments in the financial press indicate that Proposition 103 has evoked a considerable debate about its effect on the property-liability insurance industry. Opponents of Proposition 103 have deemed it to be one of the most expensive political defeats any American industry has faced. This view is based on several features of Proposition 103 (see Gross, 1989; and Schmitt and Steptoe, 1988). First, the 20 percent rollback in premiums is estimated to produce an annual loss of $6 billion for the insurance industry. Second, revenues earned by the property-liability industry are further reduced because Proposition 103 introduces more competition into the insurance market by allowing Califoria banks to act as insurance brokers or agents. Third, because of the proposition, the insurance industry is no longer exempt from the state's antitrust laws. This would effectively curtail the widespread practice of exchanging and pooling actuarial and other data. Insurers contend that exchanging information is useful in setting accurate and fair rates. Moreover, they argue that removal of the exemption threatens the soundness of medium-size companies which depend on pooled actuarial data to make sound estimates of risk. An alternative view expressed by some groups is that the regulatory changes instituted by Proposition 103 may ultimately benefit the insurance industry. For example, some industry experts argue that by injecting the adequate return standard into Proposition 103, the decision rendered by the state high court is favorable to insurers in California. As most California auto insurers claim to be earning little or no return in the state (see Hill and Celis, 1989), the adequate return standard provides a loophole that may largely negate the 20 percent rate reduction provision. Moreover, some observers believe that under Proposition 103 insurance rates will increase rather than fall if insurers can demonstrate that their current rate structure deprives them of an adequate return. (1) According to Standard and Poor's, the adequate return clause was a positive development for insurance firms operating in California. Indeed, as a result of the court decision, the rating agency removed two large California insurers, Safeco and Allstate, from its credit-watch list (see Schmitt and Wells, 1989). While the debate on the effects of Proposition 103 on the insurance industry continues, no empirical evidence has been presented on how the California initiative affected the shareholders of property-liability insurers. This study examines share price reactions of property-liability insurers around key dates surrounding Proposition 103. findings indicate that Proposition 103 is viewed by the capital market as an unfavorable development for insurers. Data and Research Methods A preliminary sample of firms most likely to be affected by the consequences of Proposition 103 is identified from the list of The Two Hundred Largest American Property-Casualty Companies published in Best's Key Rating Guide (1988) (2) and from articles discussing the California initiative in the Wall Street Journal (WSJ). …

Insurance Firm Market Response to California Proposition 103 and the Effects of Firm Size

Insurance Firm Market Response to California Proposition 103 and the Effects of Firm Size Introduction California voters, dissatisfied with high insurance rates, approved Proposition 103 on November 8, 1988. The key provision was an immediate 20 percent reduction of automobile, homeowner, commercial, and municipal liability insurance rates. Rates also were to be frozen for one year, unless the insurance commissioner of all rate increases, repeal of the insurance industry's state antitrust exemption, allowing banks to sell insurance, and limiting the determinants of automobile liability rates to the insured's accident record. A major result of Proposition 103 was the movement of the insurance regulatory system in California from one of open competition to prior approval of rates. Brostoff (1988) and Aschkenasy (1989), among others, recognize that consumer dissatisfaction with the insurance industry is spreading, and legislation similar to Proposition 103 is being considred in at least 14 other states. Some consumers believe insurance rates, especailly those for auto insurance, are unfairly high. The public interest theory of regulation (Needham, (1983)) assumes that regulation is a government response to public demands for the rectificatin of inequitable practices (primarly in price setting) by business organizations. Regulation in the insurance industry is an example of public interest regulation because its purpose is to enforce the requirement that rates remain adequate, reasonable, and fair. Various researchers have examined the methods used for property-liability insurance rate regulation. Biger and Kahane (1978), Fairley (1979), and Hill (1979) proposed CAPM type models to determine fair rates of return for the property-liability insurance industry. Joskow (1973), Samprone (1979), Harrington (1984, 1987), and Grabowski, Viscusi and Evans (1989) analyze accounting data to determine the impact of open competition versus regulated ratemaking on insurer profitability. The findings of these studies are inconsistent with regard to whether open competition or regulated ratemaking serve to maximize insurer profitability. Peltzman (1976) developed a general model dealing with the influence of regulation based on the premise that regulation creates a wealth transfer. Industry and special interest groups compete for the benefits of regulatory change. Under Peltzman's model, regulatory change often transfers wealth to insurers since their financial interests are greater than that of individual consumers. Consumers' financial interests or costs are directly related to their involvement in the regulatory policy change. Higher levels of consumer involvement are generally associated with fewer industry benefits from the regulatory change. Proposition 103 provided short-term benefits for consumers. The most immediate attraction was the 20 percent reductionand freezing of automobile and other insurance rates for one year. Since consumers had such a large financial advantage from the enactment of Proposition 103, it is likely that the insurance industry suffered. The purpose of this research is to examine the impact of the passage of California Proposition 103 on insurance stock values. It is hypothesized that the passage had a significant negative impact on property-liability insurers, especially those doing large volumes of business in California. Fields, Ghosh, Kidwell, and Klein (1990) have examined California's Proposition 103. They found that for several days surrounding the election, insurance firms' stock values reacted negatively. The decrease in value is directly related to the proportion of the firm's revenues that are affected by the referendum. Profitable companies were found to have less negative effect. Szewczyk and Varma (1990) also analyze Proposition 103. Their study examines the effect of Proposition 103 on the common stock values of property-liability insurers around both the passage of the measure and the court decision upholding the rate rollback and overturning the insolvency provision. …

The Relationship between Voluntary and Involuntary Market Rates and Rate Regulation in Automobile Insurance

The Relationship Between Voluntary and Involuntary Market Rates and Rate Regulation in Automobile Insurance Introduction Cross-subsidization from low-cost to high-cost consumers often has occurred in industries subject to price regulation when production costs differ across consumers.(1) The potential for increased political support from engaging in cross-subsidization may help to explain the existence of price regulation in competitively structured industries, especially when nonprice competition is likely to impede the use of price regulation to achieve profits for producers. A possible example is the competitively structured market for private passenger auto insurance, which has long been subject to a two-tiered system of rate regulation. Joskow (1973) suggested a producer protection motive for auto insurance rate regulation (also see Ippolito, 1979). While this motive probably has some historical validity, empirical evidence suggests that rate regulation on average has decreased the ratio of premiums to losses in private passenger auto insurance during the 1970s and early 1980s (Grabowski, Viscusi and Evans, 1989, Harrington, 1984a and 1987, and Pauly, Kleindorfer, and Kunreuther, 1986; also see U.S. General Accounting Office, 1986).(2) The overall market for auto insurance has two components: the voluntary market, in which firms willingly contract with consumers, and the involuntary (residual) market, in which insurers are forced to issue contracts to certain persons. Voluntary market rates are regulated in about half of the states; involuntary market rates are regulated in all states.(3) In principle, involuntary markets may be needed as a result of adverse selection, which conceivably could prevent a viable voluntary market for certain consumers. Whether unregulated markets could fail in some instances due to a lemons phenomenon that could not be overcome by price and quantity competition (e.g., Rothschild and Stiglitz, 1976) is uncertain. However, it is unlikely that adverse selection can explain the substantial size of the involuntary market in a number of states. A more likely explanation is that involuntary market rate regulation produces rate levels that essentially crowd out the voluntary market for some consumers (Mintel, 1983). Involuntary market rate regulation enables regulators to reduce rates for certain consumers. Mandatory pooling ensures that any adverse financial results are spread broadly among companies. The relationship between voluntary and involuntary market rates and rate regulation in auto insurance and, more generally, the property-liability insurance industry has received little attention in the literature. The fact that auto insurance involuntary markets in some states consistently produce substantial accounting losses is well known (see Lee, 1977, Mintel, 1983, and Grabowski, Viscusi, and Evans, 1989). Little is known about the extent to which voluntary market rates are affected by involuntary market results, or whether voluntary market rate regulation affects the relationship between involuntary market results and voluntary market premiums. Previous studies of the impact of voluntary market rate regulation have focused exclusively on the relationship between aggregate premiums and losses for the total market (i.e., for the voluntary and involuntary market combined).(4) This study analyzes the relationship between voluntary and involuntary market rates and rate regulation. A multiple regression model is estimated with cross-state data to provide evidence of the impact of voluntary market rate regulation and a measure of involuntary market deficits (defined below) on the overall ratio of premiums to losses in the private passenger auto insurance market from 1979 through 1981. As in previous work, the results suggest that voluntary market rate regulation reduced the ratio of premiums to losses for the overall market. They also suggest that voluntary market rate regulation reduced voluntary market premiums in states with small involuntary market deficits. …