The Impact of No-Fault Legislation on Automobile Insurance

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The Impact of No-Fault Legislation on Automobile Insurance

Cassandra R. Cole, Kevin L. Eastman, Patrick F. Maroney, Kathleen A.

McCullough & David Macpherson

To cite this article: Cassandra R. Cole, Kevin L. Eastman, Patrick F. Maroney, Kathleen A. McCullough & David Macpherson (2012) The Impact of No-Fault Legislation on Automobile Insurance, North American Actuarial Journal, 16:3, 306-322, DOI:

10.1080/10920277.2012.10590644

To link to this article: https://doi.org/10.1080/10920277.2012.10590644

Published online: 26 Nov 2012.

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306

A UTOMOBILE I NSURANCE

Cassandra R. Cole,* Kevin L. Eastman,^† Patrick F. Maroney,^‡ Kathleen A. McCullough,^§ and David Macpherson^ن

A

BSTRACT

Since its inception, the effectiveness of no-fault legislation has been highly debated. Although some research suggests that no-fault laws are effective in reducing costs, other evidence suggests that the current no-fault systems may not meet the original objectives. This study provides a detailed assessment of the relation of no-fault laws and automobile insurance losses for the period 1994 to 2007. By examining total automobile insurance losses along with liability and personal injury protection losses, we are able to determine if and how specific provisions of the laws are related to claims costs. We find a negative relation between the presence of a no-fault law and total losses, which suggests that no-fault systems are associated with lower losses than the traditional tort system. In addition, an examination of no-fault–only states suggests that specific provisions of no-fault laws, such as thresholds and limitations on benefits, have some effect on losses.

With the sunset of Colorado’s no-fault legislation in 2003, the recent passage of Personal Injury Protection Reform in Florida, and proposed federal choice legislation, the overall impact of no- fault as well as the specific components of the laws are of heightened importance to consumers, insurers, and lawmakers.

1. I

NTRODUCTION

The issue of the effectiveness of the traditional tort system in compensating individuals involved in motor vehicle–related accidents has been debated for many years in both the academic literature and trade press, as well as by state and federal legislative bodies. In 1965 Keeton and O’Connell published a work that surmised that the existing tort system was expensive and inef- ficient and did not provide equitable claims set-

* Cassandra R. Cole is Associate Professor and Waters Fellow in the College of Business, RBA 525, Florida State University, Tallahassee, FL 32306, [email protected].

†Kevin L. Eastman is a Professor at Georgia Southern University, P.O.

Box 8151, Statesboro, GA 30460, [email protected].

‡Patrick F. Maroney is a Professor & Kathryn Magee Kip Professor in the College of Business, RBA 214, Florida State University, Tallahas- see, FL 32306, [email protected].

§Kathleen A. McCullough is an Associate Professor and State Farm Insurance Professor in the College of Business, RBB 150, Florida State University, Tallahassee, FL 32306, [email protected].

نDavid Macpherson is E. M. Stevens Professor of Economics in the Department of Economics, One Trinity Place, Trinity University, San Antonio, TX 78212-7200, [email protected].

tlements. One system intended to deal with these issues is no-fault, which created a required first- party coverage. It was expected that claims for losses such as medical expenses and lost wages would be processed quickly and efficiently within this system. In addition, by limiting individuals’

rights to sue, automobile reparations reform laws would reduce court congestion. Associated cost savings from reduced liability awards were to con- ceivably lead to reduced losses. However, one point of concern with these laws is the restrictions placed on the injured parties’ right to sue.

Although, in theory, no-fault laws should be effective in controlling claims costs and reducing insurer transactions costs, empirical evidence does not consistently support these findings. This may be partially attributable to the fact that the cost savings can vary significantly depending on the provisions of the no-fault law. In addition, even after more than 30 years, the state of no- fault legislation in the country continues to evolve. Over time, several states, including Ne- vada, Pennsylvania, Georgia, and Connecticut,

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have repealed no-fault laws.¹ In July 2003, Colo- rado’s no-fault law sunset. Recently, Florida passed significant reform aimed at reducing abuse related to personal injury project cases with respect to fraud and access to benefits. The new legislation requires insurers to give notice to insureds of suspected fraud within 30 days and provides the insurers with 60 days to investigate.

Additionally, new restrictions are placed on medical care. Specifically, the full $10,000 of Personal Injury Protection (PIP) medical benefits is available only if a physician, osteopathic physician, dentist, or a supervised physician’s assistant or advanced registered nurse practitioner deter- mines that the insured has an ‘‘emergency medical condition’’; otherwise the benefit is limited to $2,500 (Ch. 2012-197, Laws of Florida). The law also places limits on chiropractic coverage at

$2,500 with a referral from another health care provider. Finally, visits to acupuncturists and massage therapists are no longer covered, and death benefits are now $5,000 per individual in addition to medical and disability benefits (Ch.

2012-197, Laws of Florida).

The effectiveness of no-fault legislation is still the subject of much debate.² Part of the debate relates to the variation across the states in terms of the thresholds required to be pierced to sue for bodily injury as well as the types and levels of benefits provided. These differences are likely to

1Although Pennsylvania repealed its no-fault law in 1984, it reen- acted a different type of no-fault law in 1990.

2Some states that have returned to the traditional tort system have indicated that the original goals of the no-fault system were not being achieved. Generous first-party benefits, combined with weak- ened thresholds, fraud, and abuse of the system, have contributed to no-fault laws not meeting expected goals. These issues are discussed in more detail in the trade press (see, for example, Hamblett 2003; Ruquet 2003) and academic literature (see, for example, Der- rig and Weisberg 1991; Derrig et al. 1994; Carroll and Abrahamse 2001) as well as state-commissioned studies and reports (see, for example, Fifteenth Statewide Grand Jury 2000; Select Committee on Automobile Insurance / PIP Reform 2003). Some states that chose to maintain the no-fault system have taken action to minimize these problems through increased fraud detection and prosecution, changes in thresholds, and / or potential repeal of no-fault laws. For more information on reform efforts, see Select Committee on Au- tomobile Insurance / PIP Reform (2003), Hamblett (2003), and Ru- quet (2003). Additionally, there is proposed federal legislation, known as the Auto Choice Reform Act of 2004 (S. 2931), that would encourage a choice-based system for the entire country. This is discussed in more detail in a later section of the paper.

be correlated with the variations in losses related to motor vehicle accidents across the states. The purpose of the current study is to examine these issues, focusing on the relationship between the types of automobile reparation laws and automobile insurance losses during the period from 1994 to 2007. This study is one of the few to focus specifically on no-fault states in an attempt to explain the effect of variations in no-fault provisions on average losses.³

The remainder of the paper is organized as fol- lows. The next section provides a review of prior literature relevant to this study. Then the data and methodology are discussed, and the results are presented. Finally, the implications of the results for insurers, consumers, and regulators are discussed.

2. L

ITERATURE

R

EVIEW

Numerous theoretical and empirical papers have considered the impact of no-fault legislation on factors such as premiums, claims costs, transactions costs, and frequency of accidents. Some of these focus on the expected benefits of no-fault laws. For example, Shavell (1997) argues that no- fault laws can be viewed as a type of corrective social policy that may be effective in restricting both the volume of lawsuits and the level of litigation expenditures. Others suggest that no-fault systems can reduce costs by eliminating or limiting lawsuits for general and punitive damages, although there is disagreement in the literature re- garding the extent to which these types of awards increase claims costs in the tort system (e.g., Ei- senberg et al. 1997; Eisenberg et al. 2002; Hersch and Viscusi 2004; Eaton et al. 2005).

Although some early works (e.g., O’Connell and Joost 1986) find that the average premium in no-fault states actually increased by more than that of traditional tort states, other early empirical studies find mixed evidence on the effectiveness of no-fault laws (e.g., Witt and Urrutia 1983;

3Studies in this area generally include no-fault related variables in an effort to control for differences among no-fault and tort states in the examination of variation in premiums, claims costs, transactions costs, and frequency of accidents among all states. However, few studies focus their empirical analysis on only no-fault states. Among these are Derrig et al. (1994), Devlin (2002), and Schmit and Yeh (2003).

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Rolph et al. 1985). Prior theoretical and empirical analyses of the tort system also provide mixed evidence with respect to its efficiency as compared to a no-fault system. For example, Liao and White (2002) examine the efficiency of tort and no-fault systems using a simulation of the total social costs associated with various liability rules (pure tort, mixed no-fault with low and high thresholds, pure no-fault, and choice systems).

They find that no single liability rule is always more efficient than the other systems.⁴

Cummins and Tennyson (1992) find that in the late 1980s no-fault states had lower growth rates in losses per insured car and in the number of bodily injury liability claims compared to tort states. The growth in the number of bodily injury liability claims was considerably lower in no-fault states with either verbal thresholds or significant dollar thresholds and somewhat lower in states with low-dollar thresholds, which suggests that a no-fault system with a stronger threshold can be an effective mechanism for controlling liability costs. However, other studies conclude that verbal thresholds did not result in any significant differences in claims costs when comparing no-fault and tort states (e.g., Browne and Puelz 1996).

There is also evidence that no-fault laws may reduce incentives for safe driving and increase accident rates by restricting or eliminating liability for the costs associated with those accidents (see, e.g., Landes 1982a, b; Cummins et al. 2001; Co- hen and Dehejia 2004).

Some studies consider the impact of no-fault laws on victim compensation. These studies find that combining low-dollar thresholds with generous first-party benefits typically results in higher net compensation, while combining stronger threshold requirements with less generous first- party benefits results in a reduction in compen-

4More specifically, the authors find that the pure tort rule is most efficient when the cost of care is low, whereas pure no-fault is the most efficient when the cost of care is high. Mixed no-fault and choice systems are never strongly more efficient than pure tort or pure no-fault. Mixed no-fault sometimes does better when court er- ror rates in determining negligence are high, which encourages drivers to use litigation (rather than increased care). This makes it more efficient to prevent litigation when damages are low by imposing a threshold for bringing suit. Under choice systems, drivers tend to choose the pure tort rule too often because they assume that choos- ing the pure no-fault rule will cost them their right to sue without giving them the gain from not being sued.

sation (e.g., Carroll et al. 1991; Browne and Puelz 1996; Johnson et al. 1992).⁵

Although claims costs are an important component of overall premiums, transaction costs also play a factor. Studies examining transaction costs generally find that transaction costs are reduced with the implementation of no-fault laws.

However, the results vary significantly from a low of 2% to a high of more than 20%, depending on the specific provisions of the no-fault law (e.g., Carroll et al. 1991; Carroll and Kakalik 1993;

Carroll and Abrahamse 1999).

Given that choice systems are significantly different from other no-fault settings, some authors have focused on the potential impact of choice legislation (e.g., O’Connell and Joost 1986; Pow- ers 1992; Nordman 1998; Carroll and Abrahamse 1999; Schmit and Yeh 2003). For example, Schmit and Yeh (2003) consider the effect of the implementation of a choice system in New Jersey and Pennsylvania.⁶ Their results indicate that switching from a mandatory modified no-fault system to a choice system is associated with higher attorney involvement, longer claims settlement periods, and greater variability in claims settlements. As noted by the authors, the only strong result for the change from a traditional tort system to a choice system as adopted in these states is greater variability in claims settlements.

Our study contributes to the debate on the relation of no-fault and automobile insurance losses in several key ways. First, we analyze total losses along with liability and personal injury protection losses to provide an understanding of which aspects of premiums are impacted by no-fault legislation. Second, we analyze the key components of no-fault laws to better understand how the generosity of the laws impacts the losses. Finally, this is one of the few studies that examines all states as well as no-fault states. As such, we are able to

5Other studies focus on other aspects of no-fault legislation such as the way in which thresholds are pierced and the effectiveness of thresholds. For example, Loughran (2001) finds that the percentage of injured parties that pierce thresholds has increased from nearly 17% in 1977 to 29% in 1997. A study by Maroney et al. (1991) finds that more than 57% of injured parties that pierce the threshold in Florida with a disability do so with a disability rating of 10% or less.

6New Jersey switched from a mandatory modified no-fault system to a choice system, while Pennsylvania switched from a traditional tort system to a choice no-fault system.

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Table 1 Summary Statistics

Variable

Tort States

Mean Std. Dev. Min Max

No-Fault States

Mean Std. Dev. Min Max

Dependent variables

Total losses 20.1449 1.0085 17.9084 22.8300 20.7292 1.2184 18.0426 22.6900

Liability losses 20.1149 1.0065 17.8252 22.8292 20.3694 1.1720 17.7961 22.3887

PIP losses 14.8827 2.1973 9.5625 20.1605 19.3076 1.4552 16.4775 21.7768

No-fault variables

Add-on measure 0.2679 0.4432 0.0000 1.0000

Optional add-on measure 0.1875 0.3907 0.0000 1.0000

Mandatory add-on measure 0.0804 0.2721 0.0000 1.0000

Choice measure 0.2394 0.4278 0.0000 1.0000

Verbal threshold measure 0.3989 0.4910 0.0000 1.0000

Mixed threshold measure 0.5213 0.5009 0.0000 1.0000

Internal time limit measure 0.4947 0.5013 0.0000 1.0000

Limit on medical benefits measure 0.8298 0.1016 0.6000 1.0000

Wage replacement measure 0.3138 0.4653 0.0000 1.0000

Hard to pierce 0.0745 0.2632 0.0000 1.0000

Other independent variables

Lawyers per capita 2.8210 0.7789 1.6415 5.4897 0.5745 0.4957 0.0000 1.0000

Rate law 0.5714 0.4953 0.0000 1.0000 0.1584 0.0771 0.0463 0.3484

Repair costs 7.6658 0.1363 7.2867 7.9873 10.6490 0.1879 10.1546 11.1130

Median income 10.5775 0.2000 10.0607 11.1284 26.6527 4.7757 16.8000 38.7000

Percent of population with bachelor’s degree 24.3131 6.6934 11.4000 90.6000 0.3721 0.2306 0.0339 0.8711

Traffic density proxy 0.2855 0.1739 0.0296 0.8491 0.1351 0.0439 0.0642 0.2360

Motor vehicle–related deaths 0.1804 0.0796 0.0156 1.3815 0.1250 0.0309 0.0540 0.2120

Percent uninsured persons 0.1465 0.0401 0.0640 0.2580 3.0125 1.2130 0.6608 5.5667

Weather index 3.1210 1.2679 0.4475 6.0558 0.0427 0.0788 0.0000 0.3588

Percent residual market 0.0124 0.0496 0.0000 0.5436 20.9774 3.8093 11.5707 28.8733

Volume of beer consumed 23.4110 3.5326 13.9955 35.2655 11.4399 0.8330 9.6661 13.5544

Note:Total losses⫽natural logarithm of the sum of total liability losses in the state; Liability losses⫽natural logarithm of the total liability losses in the state; PIP losses⫽natural logarithm of the total personal injury protection losses in the state; Add-on measure⫽dummy variable equal to 1 if add-on state, 0 otherwise; Optional add-on measure⫽dummy variable equal to 1 if optional add-on state, 0 otherwise; Mandatory add-on measure⫽dummy variable equal to 1 if mandatory add-on state, 0 otherwise; Choice measure⫽dummy variable equal to 1 if choice state, 0 otherwise; Verbal threshold measure ⫽dummy variable equal to 1 of the state has a pure verbal threshold, 0 otherwise; Mixed threshold measure⫽dummy variable equal to 1 if the state has a mixed threshold measure, 0 otherwise; Internal time limit measure ⫽ dummy variable equal to 1 if the state has internal time limits on wage loss benefits and / or replacement services benefits, 0 otherwise; Limit on medical benefits measure⫽dummy variable equal to 1 if a separate limit is applicable to medical expense benefits, 0 otherwise; Wage replacement measure⫽percentage of wages replaced; Hard to pierce⫽dummy variable equal to 1 if mixed threshold state if the dollar threshold is equal to or greater than $5,000 and individuals cannot collect noneconomic damages for fractures, or if verbal threshold state and individuals cannot collect for noneconomic damages for fractures, 0 otherwise; Lawyers per capita⫽lawyers to population; Rate law indicator⫽dummy variable equal to 1 if a rate-regulated state, 0 otherwise; Repair costs⫽natural logarithm of gross collision repair costs;

Median income⫽natural logarithm of median household income; Percentage of population with bachelor’s degree⫽percent of population with bachelor’s degree or higher; Traffic density proxy⫽annual average daily traffic in urban areas to total annual average daily traffic; Motor vehicle–related deaths⫽number of motor vehicle fatalities to population; Percent uninsured persons⫽percentage of population in the state without health insurance; Weather index⫽precipitation index; Percent residual market⫽residual market direct premiums written to total direct premiums written in the state; Volume of beer consumed⫽volume of beer consumed per capita.

provide evidence as to the impact of specific provisions of no-fault laws on losses in these states.

3. D

ATA

, M

ETHODOLOGY

,

AND

V

^ARIABLE

D

^ESCRIPTION

3.1 Data

Based on a sample period from 1994 through 2007, this study examines the relation between no-fault legislation and automobile insurance losses. As shown in the Appendix, data for the study were obtained from a variety of sources. The

American Insurance Association’s publication Summary of Automobile Insurance Laws is the primary source of data on the type of no-fault law present in each state as well as the specific provisions of the law. State loss data were obtained from the National Association of Insurance Commissioners Property-Casualty Database. The losses for each state include both losses and loss adjustment expenses for personal lines automobile insurance coverage. The legal and demographic control variables are obtained from a variety of other sources, including the United

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States Census Bureau, the Federal Highway Ad- ministration, the Property Casualty Insurers As- sociation of America, the American Bar Asso- ciation, the Insurance Research Council, the Property Casualty Insurers Association of Amer- ica, the National Oceanic and Atmospheric Ad- ministration, the AIPSO, and the National Insti- tute on Alcohol Abuse and Alcoholism. Summary statistics for the variables included in the models are presented in Table 1.

3.2 Methodology

In our assessment of the potential relation between no-fault legislation and premiums, we di- vide our empirical analysis into two sections. In the first section, we model the relation between the general types of no-fault laws and average state total losses and liability losses while controlling for legal and demographic variations among states. We construct two model variations.

In the first variation, we examine a more general classification of laws that categorizes the regulatory regimens as no-fault states, choice states, add-on states, or tort states. In the second, we consider a more detailed specification that in- cludes the type of add-on law and type of threshold for the no-fault states.

In the second part of the analysis, we conduct a more in-depth examination of the relation between the various no-fault provisions and average total, liability, and PIP losses on the sample of modified no-fault states.⁷ We include variables representing whether or not the threshold is hard to pierce and a proxy for limitations on wage replacement. We also estimate a version that in- cludes the type of threshold and the presence of restrictions on time limits and medical benefits.

As in the prior section, we include a series of legal and demographic control variables.

Based on the fact that there are likely factors related to the political and regulatory environment that may impact the states’ choices of no- fault laws that also may impact average losses in the state, we use a two-stage least-squares ap- proach. The Breusch-Pagan test indicates the presence of heteroskedasticity and the Wool- dridge test indicates the presence of autocorre-

7States with add-on plans are not included in this analysis.

lation. As such, we use a procedure that calcu- lates Newey-West standard errors as well as allows the specification of the autocorrelation (first- order autocorrelation). The empirical framework can be described in the following manner:

LAWît ⫽ ␣ ⫹冘␤Yît⫹ εît, (1) Lossesît⫽ ␣ ⫹冘 ␤LAWît ⫹冘 ␦Xît⫹ εît (2) In the equations, Losses represents total losses and loss adjustment expenses in statei in yeart.

LAW represents a vector of variables representing the automobile reparations laws in stateiin year t, and X represents a vector of legal and demographic factor controls for state i in year t. The LAW variables are instrumented with a series of variables (noted asYin eq. [1]) designed to capture the political and economic characteristics of the state that are likely to have impacted the passage of legislation. To help find suitable instruments, many of the variables are based on the historical characteristics of the political environment rather than the current environment, which would likely be more closely related to the loss levels. The instruments included in the models vary but include historical income, population density, regulation, spending, percent Demo- cratic governor from 1969 to 1978, average Americans for Democratic Action score for members of Congress from 1969 to 1978, and an index of labor regulations in 1919. It should be noted that the amount of money spent by the automobile industry on lobbying would likely be a good instrument for the no-fault laws. However, this information is not available historically, or consistently for all the years of the sample. We do construct a trended version of this variable and include it as an instrument for robustness pur- poses. The results for the no-fault variables are generally consistent with what is reported here with the exception of the optional add-on provi- sion being significant and negative. Tests for valid instruments pass at conventional levels. Excep- tions to this are noted in the results. Observa- tions with incomplete data were dropped. Also, as in other prior studies, the District of Columbia is not included in this analysis (see, for example, Harrington 1994; Cummins et al. 2001; Devlin 2002).

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Table 2

No-Fault States and Add-On States

Panel A: No-Fault States

State Threshold Type

Colorado Verbal and dollar threshold amount⫽$2,500 Florida Pure verbal

Hawaii Verbal and dollar threshold amount⫽$5,000 Kansas Verbal and dollar threshold amount⫽$2,000 Kentucky^a Verbal and dollar threshold amount⫽$1,000 Massachusetts Verbal and dollar threshold amount⫽$2,000 Michigan Pure verbal

Minnesota Verbal and dollar threshold amount⫽$4,000 New Jersey^a Pure verbal

New York Pure verbal

North Dakota Verbal and dollar threshold amount⫽$2,500 Pennsylvania^a Pure verbal

Utah Verbal and dollar threshold amount⫽$3,000 Panel B: Add-On States

State First-Party Benefits Arkansas Optional

Delaware Mandatory

Maryland Mandatory

New Hampshire Optional

Oregon Mandatory

South Dakota Optional

Texas Optional

Virginia Optional Washington Optional Wisconsin Optional

Source:American Insurance Association.

Note: Although there has been some variation in state laws and thresholds over the years, the data presented here are as of 2003 and are relatively consistent over the sample period.

aChoice states.

3.3 Variable Description: All States Models

In this section we first outline the hypotheses and variables used to model the relation of losses with automobile reparations laws in all states. We also discuss the legal and demographic controls. This allows us to create an initial picture of whether the presence of no-fault legislation is related to lower premiums through contrasting states with and without no-fault laws.

3.3.1 Classification of No-Fault Laws

As shown in Panel A of Table 2, current no-fault laws can be categorized as either modified plans or choice plans.⁸ Under modified no-fault plans, a threshold must be pierced before an individual can sue the negligent party for noneconomic

8For detailed descriptions of these types of plans, see Maroney (1984), Powers (1992), Nordman (1998), and Maroney et al. (1999).

damages. The thresholds can be based on verbal or monetary descriptions. In some modified no- fault states with verbal thresholds, the injured party must incur a ‘‘serious injury’’ as specifically defined in the law. Other states rely on a combi- nation of verbal and monetary thresholds under which the injured party can pierce the threshold by either sustaining a serious injury or having medical expenses that exceed a certain dollar amount. It should be noted that although an individual must pierce the threshold to sue for noneconomic damages such as pain and suffering, individuals can always sue for noncompensated economic damages even if the threshold is not pierced.

Traditionally, it is argued that no-fault laws are designed to provide accident victims with a source of recovery for economic damages that is quick and efficient. In addition, since no-fault laws restrict an individual’s right to sue for noneconomic damages to cases of serious injury, one expects that the presence of no-fault laws should reduce claims payments and associated liability defense expenses and therefore liability insurance premiums. Based on this argument, we hypothesize that the presence of no-fault laws is negatively related to both liability and total losses.

However, others argue that no-fault laws may not be effective in controlling liability insurance costs. Some critics of no-fault laws suggest that the design of the laws makes it possible for individuals to overutilize the generous first-party benefits or exaggerate or falsify injuries to gain access to the generous first-party benefits. This is often referred to as build-up (e.g., Derrig and Weisberg 1991; Carroll and Abrahamse 2001). If an individual is able to build up enough in first- party benefits to pierce the threshold and regain the right to sue the negligent party, this can result in higher first-party claims costs and possibly third-party liability claims costs, because first- party benefits are often used to establish noneconomic damages for third-party claims. If this is the case, we hypothesize that the presence of a no-fault law will have no effect or may actually increase claims costs. The no-fault variable used to test this hypothesis is an indicator variable equal to one for no-fault states and zero otherwise.

Kentucky, New Jersey, and Pennsylvania have choice no-fault plans in which each driver can se-

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lect coverage under either the traditional tort system or the no-fault system. If drivers elect coverage under the traditional tort system, they maintain their right to sue for both noneconomic and economic damages. If drivers elect coverage under the no-fault system, in exchange for allowing the right to sue to be restricted to only cases in which a serious injury has occurred, the driver is given first-party benefits for medical expenses, lost wages, and replacement services. In addition, the driver is given some protection against being sued by others because of his or her negligence.⁹ Theoretically individuals who elect the no-fault coverage will receive benefits equal to those provided by mandatory no-fault systems including faster processing of first-party claims and lower premiums. By restricting an individual’s right to sue, choice no-fault laws are expected to reduce attorney involvement, the number of motor vehicle lawsuits filed, and ultimately liability insurance premiums.¹⁰Based on these arguments, we hypothesize that choice would be negatively related to total and liability losses.

Complications may arise with the choice system. The first can occur when a driver who has elected to remain under the traditional tort system is injured by an individual who has elected coverage under the no-fault system. Because this coverage is subject to policy limits, the possibility

9It should be noted that proposed federal no-fault legislation would require all states to implement a choice system, meaning that all drivers would be given the option of being covered under the traditional tort system or a modified no-fault system. The default for drivers who did not choose would be coverage under the tort system. Additionally, a state could be exempt from the law if (1) it passed a statute indicating it was exempt or (2) it could prove that coverage under the no-fault system would not result in premium savings of at least 30%. The proposed bill was referred to the Com- mittee on Commerce, Science, and Transportation in October 2004.

There has been no action on the bill since that time. The merits of the federal plan have been assessed in a number of studies (see, for example, Detlefsen 1998; Carroll and Abrahamse 1999; Kabler 1999;

Maroney et al. 1999), but the possibility of a federal choice system increases the importance of understanding the impact of no-fault on costs and premiums.

10Existing choice no-fault laws require a premium reduction for those who choose the no-fault option (KRS 304.39-330; Pa.B. Doc.

No. 03-1869). In some cases, the lower price for drivers electing no- fault coverage versus tort coverage comes from the fact that tort drivers are required to purchase higher limits of liability insurance and ‘‘connector’’ coverage that covers them in the event they are in an accident with a no-fault driver.

of uncompensated damages exists. As choice no- fault was originally conceived, the solution was to provide ‘‘tort maintenance’’ or ‘‘connector’’ coverage to those selecting the tort system, which would allow tort drivers to collect both economic and noneconomic damages from their own insurers when injured by no-fault drivers. Note that if the choice system had a pure no-fault component, the no-fault driver would be completely exempt from lawsuits. However, the three choice states have some form of modified no-fault in which no-fault drivers can be sued for either (1) uncompensated economic damages only or (2) both uncompensated economic damages and noneconomic damages. Specifically, the existing choice no-fault systems in Pennsylvania and New Jersey require all drivers, including those who opt for the tort system, to purchase a type of PIP coverage that effectively serves as tort maintenance coverage for those who select the tort system.

Since the PIP coverage does not provide coverage for noneconomic damages, tort selectors may sue at-fault drivers who choose the no-fault option both for any uncompensated economic damages and for noneconomic damages. In New Jersey a state fund (the New Jersey Automobile Insurance Risk Exchange) has been established ‘‘for the purpose of compensating members of the exchange for claims paid for non-economic loss and claims adjustment expenses which would not have been incurred had the tort limitation option . . . been elected by the insured party filing a claim for noneconomic losses.’’

Another potential problem with the choice system is that since individuals have to rely on first- party benefits for compensation of medical expenses, lost wages, and other economic losses, a large amount of personal injury protection coverage is needed to provide adequate protection.

In this case even if liability costs are controlled, the first-party coverage might be too expensive for lower income individuals (O’Connell and Joost 1986). Finally, some authors argue that there is a potential problem of adverse selection associated with choice systems as ‘‘bad’’ drivers would be more apt to select no-fault coverage to obtain lower premiums and tort exemptions (see, for example, Carr 1989; Kabler 1999). Given the potential costs and benefits of a choice system, the net effect of a choice system on total and liability losses is uncertain. To test these compet-

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ing hypotheses, an indicator variable equal to one if the state is a choice state and zero otherwise is included.

3.3.2 Classification of Add-On Laws

In addition to modified and choice plans, add-on plans are often referred to as no-fault plans. Add- on plans are distinctly different in that, although first-party benefits are required or mandated to be offered, no restrictions are in place on an individual’s right to sue. Thus, it is hypothesized that add-on laws will increase average liability losses and total losses in comparison to no-fault states. Because these laws alter the way in which individuals are compensated in comparison to the traditional tort system, these laws are distin- guished from both no-fault systems and traditional tort systems. The 10 states with mandatory or optional add-on plans are listed in panel B of Table 2. In the empirical analysis, these states are identified using an indicator variable equal to one if the state has an add-on plan and zero otherwise.

3.3.3 Legal and Demographic Controls In addition to the variables of interest within each model, we incorporate variables traditionally found to impact automobile insurance costs and the regulatory environment. These include factors such as lawyers per capita, a rate regulation variable, average gross collision repair costs, median household income, an educational attainment variable, a traffic density proxy, motor vehicle–related deaths per capita, percent of population without health insurance, a weather index, a measure of the size of the residual market, and a measure of alcohol consumption.

Prior studies find that when attorneys are involved in motor vehicle–related claims, the claims settlements are higher (e.g., Rolph et al.

1985). The higher settlements may actually result from the involvement of the attorneys (for example, by allowing plaintiffs to obtain more equitable settlements or by advising plaintiffs on how to build up damages) or merely represent the increased likelihood of attorney involvement in cases for which potential damages are more significant. In either case, we expect that attorney involvement will be associated with higher losses.

We also control for variation in costs, consumer buying decisions, claims patterns, and physical

environment. We hypothesize that individuals with higher levels of education and higher levels of income are more likely to purchase levels of coverage above state mandated minimum levels.

Given that higher limits will likely be related to higher losses, we hypothesize that the education and income variables will be positively related to losses. The percent of the population without health insurance also is included in the model to control for individuals’ incentives to file claims.

As noted by Cummins and Tennyson (1996),

‘‘drivers with legitimate claims may be less likely to file a liability suit if their medical costs are covered by health insurance.’’ As such, we would expect this variable to be negatively related to losses.

The size of the residual market also is included in the models as it may reflect the riskiness of the state given that those participating in residual markets are likely those with prior accidents (e.g., Grabowski et al. 1989).¹¹This could lead to greater future loss costs. The quality of the insureds of the residual market may also be the result of the level of regulatory stringency in the state. For example, Bouzouita and Bajtelsmit (1997) suggest that in states with less stringent rate regulation, insurers may change underwrit- ing standards such that drivers are unable to obtain coverage in the traditional market and are forced into the involuntary market. The size of the residual market could also serve as a measure of regulatory stringency (see, for example, Gra- bowski et al. 1989; Klein et al. 2002). In each case, like Weiss et al. (2010), we would expect to find a positive relation between residual markets and loss costs. Additionally, we include a proxy for alcohol consumption, measured as the gallons of beer consumed per capita. Given that alcohol can impair the ability of drivers to operate their vehicles safely, greater alcohol consumption in a state is expected to be positively associated with the number of car accidents in a given state, and therefore total losses. This argument relies on the assumption that those persons consuming alco-

11It should be noted that the size of the residual market also is impacted by the minimum required limits of the state. Given that the required limits are low in some states, this could result in lower total premium volume within the state; therefore premium volume may not necessarily reflect the number of drivers participating in the residual market.

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hol are driving under the influence. To the extent that this is not the case, we would expect to find no relation between alcohol consumption and automobile insurance losses.¹² A gross collision repair costs variable is included as a proxy for the cost of automobile goods and services. We also include an indicator variable to control for state rate regulation given that rate regulation could indirectly impact loss costs by influencing the average price of coverage and therefore the volume of coverage purchased. In addition, a motor vehicle–related deaths per capita variable is included to control for the potential severity of automobile claims. Specifically, a fatality is likely to result in a wrongful death lawsuit regardless of whether it occurs in a tort state or a no-fault state (see, for example, Cummins and Tennyson 1996).

As such, we hypothesize that motor vehicle–

related deaths per capita will be positively related to losses in all models.

For environmental controls, we include the percentage of average daily traffic on urban roads as a proxy for traffic density given that greater levels of urbanization and/or population density are generally associated with higher claims levels.

Cummins et al. (2001) use a similar measure in their work to test the theory that greater rural interstate miles driven are associated with higher fatality rates. We also include a weather index that captures precipitation rates. Given that adverse weather conditions can increase both the frequency and severity of accidents, we expect this variable to be positively associated with losses. However, Cummins et al. (2001) suggest that adverse weather conditions decrease travel and speed of travel. To the extent that this is the case, it is possible that the weather index would be negatively related to total losses.

Finally, a fraud proxy similar to that used by Cummins and Tennyson (1996) is included in an alternate version of the model. This variable is not included in the primary analysis because the data are available only to 2004 and therefore

12It should be noted that, as discussed in Cummins et al. (2001), there is some prior evidence suggesting that driving under the influence is a factor in fatal car accidents. If this is the case, there would be strong correlation between our alcohol consumption variable and the number of motor vehicle–related deaths variable. However, we do not find this to be the case as the correlation level is only around 20% and is actually negative.

would reduce the sample period by more than 25%. The variable is defined as the frequency of bodily injury liability claims relative to the frequency of property damage liability claims. There is a greater potential for fraud with policies cov- ering bodily injury losses because certain types of injuries (e.g., soft tissue injuries) and/or certain elements of these losses (e.g., general damages for pain and suffering) are more difficult to verify objectively than property damage losses.¹³ Thus, we hypothesize that fraud is associated with increased claims costs

3.4 Variable Description:

No-Fault–Only Models

To better understand the differences among the no-fault states, we create a second set of models in which we examine only no-fault states. In addition to most of the legal and demographic environment variables outlined above, we include additional characteristics related to the provisions of no-fault laws described in this section.

Due to the presence of multicollinearity in the reduced sample, the educational attainment measure and the traffic density proxy are not included in these models. Also, we estimate these models for total losses, liability losses, and personal injury protection losses. By considering the impact of the factors on the cost of all three coverages, we create a more complete picture of the relation between the various facets of no-fault legislation and claims costs.

3.4.1 General No-Fault Variables

Several no-fault related variables are included in the analysis. The first is a single measure indicating whether or not a state’s threshold is hard to pierce. We consider states with a mixed threshold hard to pierce if the dollar threshold amount is equal to or greater than $5,000 and individuals cannot collect noneconomic damages in the case of fractures, while we consider a verbal threshold hard to pierce only if individuals cannot collect

13It should be noted that cases in which soft tissue injuries occur, and possibly claims for general damages, also can be cases that are more likely to be litigated. This can occur as a result of the greater likelihood of disagreement between the injured party and the insurer due to asymmetric information. For a more detailed discussion of this argument, see Cooter and Rubinfeld (1989) and Shavell (2003).

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noneconomic damages in the case of fractures.

The hard-to-pierce variable is designed to determine if the overall stringency of the collective no- fault measures is related to losses. In many ways this variable is a more comprehensive measure than including the threshold variables because it captures the level of stringency of both the verbal component and the monetary component of the threshold. Therefore, if states have collective no- fault measures that are by design difficult to pierce, we hypothesize that this variable will be negatively related to liability and possibly total losses. However, it may not have any impact on personal injury protection losses as it does not change the benefits provided by this coverage.

For robustness, in a second version of the model, we used the presence of a verbal threshold as a more traditional proxy for how difficult the threshold is to pierce. We also expect a negative and significant relation.

In addition to the threshold variable described above, we use the percentage of lost wages replaced. For states that replace the full amount of lost wages, this variable is equal to 100%. We expect the percentage of wages replaced is expected to be positive in the personal injury protection loss model as the larger the percentage of wages replaced, the larger the potential total first-party claims. Also, since more coverage is provided for this loss by the first-party coverage, then the amount submitted as an economic loss to the liability coverage of the negligent party is reduced.

Therefore, we expect the variable to be negative in the liability loss model. Given that higher percentages of wages replaced can lead to greater build-up of first-party costs, we expect a positive relation with liability losses. Because of the potential opposing effects, we cannot predict the net effect on total losses.

In a second version of the models, we include additional measures such as internal time limitations and limitations on medical benefits to further capture the variation in generosity across states. The internal time limitation variable is equal to one if there are internal time limits on wage loss benefits and/or replacement services benefits (specifically, if there are restrictions on the number of weeks or years that these benefits will be covered) and zero otherwise. The limit on medical benefits is equal to one if a separate limit

is applicable to medical expense benefits and zero otherwise. We expect that no-fault laws with more generous first-party benefits, or those that do not impose a time limit on wages and/or replacement services benefits, will be associated with higher claims costs.

3.4.2 Legal and Demographic Control Variables

We also include variables relating to the legal and demographic environments described earlier in the no-fault–only models. We expect the hypotheses discussed above to hold for the variables in this section with respect to their relation to total premiums and liability premiums. We also expect the income variable to be positively related to personal injury protection costs because of the higher level of wage replacement and other benefits likely to be paid out under this coverage for individuals with higher levels of income. We include motor-vehicle deaths per capita, the presence of rate regulation, a repair cost measure, the percent of uninsured persons, the volume of beer consumed per capita, the weather index, and the size of the residual market to control for the potential variations that exist across states. If the lawyers per capita variable is positively related to personal injury protection losses, this may serve as evidence of the involvement of lawyers in the build-up of first-party claims or the likelihood of lawyers to be involved in cases in which the damages or injuries are more significant. Finally, states with higher levels of fraud are expected to have higher personal injury protection claims in the same way liability and overall claims costs are affected.

4. R

ESULTS

We summarize the results of the study in this section. First, we present the results of the empirical models including all states, followed by the results for the no-fault–only states. In each case, we correct the models for the presence of heteroskedasticity and autocorrelation along with the potential endogeneity of the LAW variables.

Although there are some significant pairwise correlations among the variables of interest, the variance inflation factors do not detect multicollinearity except where noted earlier.

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Table 3 All States Models

Total Losses Liability Losses

No-fault measure ⫺1.5444*** ⫺2.1911*** ⫺1.6374*** ⫺2.2447***

[0.476] [0.704] [0.436] [0.636]

Add-on measure ⫺0.7153* ⫺0.8956** ⫺0.7550** ⫺0.9243**

[0.380] [0.455] [0.354] [0.422]

Choice measure 1.2722** 1.1949**

[0.585] [0.531]

Optional add-on measure ⫺0.4880 0.8987

[0.503] [1.010]

Mandatory add-on measure ⫺2.6048 ⫺4.7882**

[1.682] [2.147]

Verbal threshold measure ⫺0.7315 ⫺0.0074

[0.742] [0.946]

Mixed threshold measure ⫺2.4278** ⫺2.1472**

[0.967] [0.959]

Lawyers per capita 0.1700*** 0.2138*** 0.0012 0.1504*** 0.1915*** 0.0026

[0.061] [0.078] [0.076] [0.056] [0.072] [0.096]

Rate law 0.0539 0.0323 ⫺0.0507 0.0634 0.0430 ⫺0.2301

[0.101] [0.114] [0.159] [0.091] [0.103] [0.223]

Repair costs ⫺0.4809 ⫺0.8913* ⫺1.5464* ⫺0.4675 ⫺0.8529* ⫺2.6063***

[0.361] [0.488] [0.922] [0.332] [0.450] [0.989]

Median household income 0.3259 0.2706 1.2650* 0.2720 0.2202 2.1459**

[0.315] [0.362] [0.755] [0.293] [0.336] [0.901]

Percentage of population with 0.0086 0.0133 0.0102 0.0099 0.0142* 0.0019

bachelor’s degree [0.007] [0.008] [0.008] [0.006] [0.008] [0.011]

Traffic density proxy 1.0105*** 0.7431** 0.5547 1.0006*** 0.7494** 0.8478**

[0.270] [0.319] [0.353] [0.245] [0.291] [0.415]

Motor vehicle–related deaths ⫺1.5593 ⫺1.8902 ⫺1.9327 ⫺1.5046 ⫺1.8154 ⫺0.7890

[1.083] [1.245] [1.219] [1.035] [1.183] [1.098]

Percent uninsured persons 1.0172 0.5286 3.5333 1.6748 1.2159 8.2293**

[1.688] [1.946] [2.538] [1.560] [1.783] [3.248]

Weather index ⫺0.0200 ⫺0.0692 ⫺0.0204 0.0025 ⫺0.0438 0.0644

[0.044] [0.055] [0.065] [0.040] [0.049] [0.080]

Percent residual market 1.9919** 2.5883** 0.5758 1.9157*** 2.4759** 0.4830

[0.812] [1.076] [0.789] [0.741] [0.980] [0.762]

Volume of beer consumed per capita ⫺0.0168 ⫺0.0179 ⫺0.0596* ⫺0.0133 ⫺0.0143 ⫺0.0839**

[0.012] [0.015] [0.035] [0.012] [0.014] [0.036]

Young male drivers 0.9203*** 0.9476*** 0.7371*** 0.8822*** 0.9078*** 0.4313**

[0.093] [0.100] [0.169] [0.086] [0.091] [0.214]

Constant 10.2584*** 13.9353*** 11.9825*** 10.8483*** 14.3019*** 13.5555**

[2.965] [3.994] [4.276] [2.706] [3.640] [5.397]

Observations 711 711 711 711 711 711

Note:Standard errors in brackets.

***p⬍0.01, **p⬍0.05, *p⬍0.1.

Note:Total losses⫽natural logarithm of the sum of total liability losses in the state; Liability losses⫽natural logarithm of the total liability losses in the state; No-fault measure⫽dummy variable equal to 1 if no-fault state, 0 otherwise; Add-on measure⫽dummy variable equal to 1 if add-on state, 0 otherwise; Choice measure⫽dummy variable equal to 1 if choice state, 0 otherwise; Optional add-on measure⫽dummy variable equal to 1 if optional add-on state, 0 otherwise; Mandatory add-on measure⫽dummy variable equal to 1 if mandatory add-on state, 0 otherwise; Verbal threshold measure⫽dummy variable equal to 1 of the state has a pure verbal threshold, 0 otherwise; Mixed threshold measure⫽dummy variable equal to 1 if the state has a mixed threshold measure, 0 otherwise; Lawyers per capita⫽lawyers to population;

Rate law indicator⫽dummy variable equal to 1 if a rate-regulated state, 0 otherwise; Repair costs⫽natural logarithm of gross collision repair costs; Median income ⫽natural logarithm of median household income; Percentage of population with bachelor’s degree ⫽percent of population with bachelor’s degree or higher; Traffic density proxy⫽annual average daily traffic in urban areas to total annual average daily traffic; Motor vehicle–related deaths⫽number of motor vehicle fatalities to population; Percent uninsured persons⫽percentage of population in the state without health insurance; Weather index⫽precipitation index; Percent residual market⫽residual market direct premiums written to total direct premiums written in the state; Volume of beer consumed⫽volume of beer consumed per capita; Young male drivers⫽natural logarithm of young male drivers.

4.1 All States Models

The models in this section test the relation of the presence of no-fault, choice, and add-on legislation to the cost of automobile insurance. We present results of the models in Table 3. The presence of no-fault laws is associated with 78% lower

liability losses and 81% lower total losses.¹⁴ This provides initial evidence that no-fault laws may be effective in containing automobile loss costs

14The coefficients were converted into percentages with the following formula: [exp(␤)⫺1]⫻100.

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Table 4

Summary of No-Fault State Laws

FL HI KS KY MA MI MN NJ NY ND PA UT

Hard to pierce 1 1 0 0 0 1 0 0 0 0 1 0

Verbal threshold 1 0 0 0 0 1 0 1 1 0 1 0

Mixed threshold 0 1 1 1 0 0 1 0 0 1 0 1

Internal time limit 0 0 1 0 1 1 0 0 1 0 0 1

Limit on medical benefits 0 1 1 0 0 0 1 1 0 0 1 1

Wage replacement 60% 100% 85% 85% 75% 85% 85% 100% 80% 85% 80% 85%

when compared to tort states. Further, we find that the presence of add-on measures also relates to lower loss levels.

When replacing the no-fault indicator variable with variables denoting the type of threshold, we see that only mixed threshold no-fault states are associated with lower losses. The effect is large because mixed threshold no-fault states have 91%

lower total losses and 88% lower liability losses.

In addition, the result for the add-on measure ap- pears to be driven by states with mandatory add- on provisions because the optional add-on provi- sion is not significant. These differences further underscore the importance of a more detailed study of the provisions of no-fault laws. Finally, choice states have a positive and significant relation to both total and liability losses in all models.

The results related to the demographic and legal controls are generally as expected. The lawyers per capita measure is generally positive and significant, indicating higher levels of losses with increased attorney involvement, which is consistent with prior literature. Further, the traffic density proxy is significant and positive in all models, indicating that greater levels of urbanization are associated with higher losses. As hypothesized, both increases in the percentage of residual markets and the percentage of young male drivers are positively associated with higher loss levels. This is likely the result of a larger higher risk portion of the population. Contrary to expectations, repair costs are negatively associated with losses, but generally only marginally so. This may be due in part to the fact that the main drivers of automobile losses, especially liability losses, are associated with bodily injury rather than physical damage losses. Finally, additional variables are

significant in some of the models and generally as expected.¹⁵

4.2 No-Fault–Only States

To better understand the relationship of the various provisions of no-fault laws and premiums, we take a closer look at only no-fault states. We summarize the provisions of the no-fault law for each state as of the final sample year in Table 4. As shown in the table, four states have thresholds that are hard to pierce. Three of these states have verbal thresholds as well as internal time limits or replacement services benefits. Also, half of the no-faults states have separate limits on medical benefits including two of the states with thresholds that are hard to pierce. Finally, wage replacement benefits range from 60% to 100% with Ha- waii and New Jersey having the most generous benefits.

We present the results of these models in Table 5. The hard to pierce threshold measure is significant and negative in the personal injury protection models and is associated with 62% to 63%

lower losses. This result is consistent with expectations as it indicates that states with overall provisions that are designed to be more stringent are effective in reducing losses overall or for liability.

However, this result may serve as additional evidence that not just the presence of no-fault laws but the specific provisions selected by states result in varying impacts on losses. For this reason, we look more closely at some of the key provisions of the laws. As expected, we find that

15Household income is significant and positive in two of the models;

the percentage of uninsured persons is significant and positive in one of the models; and volume of beer consumed is significant and negative in one of the models (contrary to expectations).