the determinants of state-level caps on punitive damages: theory and evidence

THE DETERMINANTS OF STATE-LEVEL CAPS ON PUNITIVE DAMAGES:THEORY AND EVIDENCE

THOMAS J. MICELI and MICHAEL P. STONE∗

Under the standard economic model of torts, punitive damages correct for imperfectdetection. Incorporating litigation costs into the model provides a justification forpunitive damage caps. At the optimum, caps balance deterrence against the costof litigation. Empirical testing of the model is performed via Cox proportional andparametric hazard analyses, using a panel dataset from 1981 to 2007. The resultsreveal a positive relationship between legal services employment (a proxy for legalcosts) and cap enactment, and a negative relationship between state gross state product(a proxy for damages) and cap enactment. Cap enactment is also influenced by politicalideology. (JEL K13, K41, L51)

I. INTRODUCTION

Despite the traditional function of the tortsystem to “make victims whole,” punitive dam-ages are monetary awards set at levels greaterthan pure compensation. These damages areimposed to punish and deter wrongdoing andare most frequently awarded in cases involv-ing business-related activity. In recent years,however, some states have placed statutoryceilings—conventionally known as damagecaps—on the magnitude of punitive damagesawards. The purpose of this article is to developa theory of these damage caps and then to testthe implication of that theory using state-leveldata.

According to the standard economic model,punitive damages are imposed as a solutionto the underdetection problem (Cooter 1982,1989). If an injurer can escape liability for atort with a positive probability, then in thosecases in which the injurer is in fact held

∗We acknowledge the helpful advice of StephenRoss, Eric Brunner, and the comments of participantsat the Department of Economics Brownbag, Univer-sity of Connecticut, November 2009. We also thank ananonymous reviewer and Wade Martin (editor) for theircomments.Miceli: Professor, Department of Economics, University of

Connecticut, Storrs, CT. Phone (860) 486-5810, Fax(860) 486-4463, E-mail [email protected].

Stone: Visiting Assistant Professor, Department of Eco-nomics, Quinnipiac University, Hamden, CT. Phone(203) 582-3941, Fax (203) 582-8664, E-mail [email protected]

liable, the injurer’s total liability (compensatoryplus punitive damages) “should equal the harmmultiplied by the reciprocal of the probabil-ity that [the injurer] will be found liable whenhe ought to be” (Polinsky and Shavell 1998,889). There are a number of reasons whyinjurers are sometimes able to escape liability.First, victims may have difficulty establish-ing the essential elements of a valid causeof action at trial. This is especially true incases where victims have difficulty proving cau-sation as a result of the background risk ofinjury. Second, litigation costs may outweighthe potential benefit of filing suit and hencedeter some victims from ever pursuing a validcause of action. Finally, injurers may sometimeshave an opportunity to conceal their identityor engage in other conscious efforts to evadeliability.

Although the foregoing considerations justifythe imposition of punitive damages, courts regu-larly neglect them, focusing instead on the men-tal state of the injurer. For example, courts tendto focus on whether a tortfeasor’s actions wereintentional or malicious (Polinsky and Shavell1998, 898–99).1 (Of course, one might argue

1. See, e.g., City of Newport v. Fact Concerts, Inc., 454U.S. 247 (1981).

ABBREVIATION

GSP: Gross State Product

1

Contemporary Economic Policy (ISSN 1465-7287)doi:10.1111/j.1465-7287.2011.00297.x© 2011 Western Economic Association International

2 CONTEMPORARY ECONOMIC POLICY

that intent could lead injurers to take consciousefforts to avoid detection.)2

The empirical literature on punitive damageshas been primarily devoted to examining thefrequency, magnitude, and determinants of puni-tive damage awards. For instance, Eisenberget al. (1997) surveyed tort cases decided in statecourts between 1991 and 1992 and found thatonly 6% of cases won by plaintiffs involvedpunitive damages. However, the study was notable to identify a predictable pattern regard-ing when they were awarded. Indeed, Polinsky(1997) argued that the results were consistentwith the possibility that punitive damages arerandomly awarded. Karpoff and Lott (1999)reached a similar conclusion based on a sam-ple of lawsuits filed against public corporationsduring 1985–1996 (most of which were prod-ucts liability). Both studies, though, did find thatthe level of punitive damages was strongly cor-related with the size of compensatory damages,as did the study by Schmit, Pritchett, and Fields(1988).3 Finally, Hersch and Viscusi (2004)found that juries were more likely than judgesto award punitive damages, and that jury awardswere generally larger. A factor complicating anystudy of punitive damages awards, however, isthat cases involving punitive damages are fre-quently reversed or reduced on appeal (Landesand Posner 1987, 302–07; Shanley 1991).

Economists clearly have an imperfect under-standing of the determinants of punitive dam-ages, but they know even less about the factorsunderlying the enactment of statutory caps onpunitive damages. This article begins to remedythis deficiency by examining caps from both atheoretical and empirical perspective. In termsof theory, we ask what economic justifications(if any) there are for limiting punitive damages.The simple “deterrence” theory described abovedoes not provide an adequate basis because a

2. There are alternative theoretical justifications forpunitive damages in the literature. Biggar (1995) maintainsthat punitive damages ensure that victims will be fullycompensated and hence will not take inefficient avoidanceactions. Sharkey (2003) argues that punitive damages benefitvictims who are not before the court. In other words, punitivedamages compensate for “societal damages.” Shavell (2004,245) claims that punitive damages can be justified whenwrongdoers obtain illicit utility from their illegal acts (i.e.,utility not recognized in the social welfare function). Finally,punitive damages have been justified to deter wealthyinjurers from wrongdoing as compensatory damages “meanless” to wealthy, as opposed to poor, injurers (Chu andHuang 2004).

3. This finding is consistent with the current constitu-tional law governing punitive damages awards.

limit on punitive damages only blunts the deter-rence function of tort law. However, when lit-igation costs are taken into account, imposinga limit potentially provides an offsetting benefitby reducing the number of costly suits filed byplaintiffs. To test the implications of our theory,we conduct an empirical analysis of the factorsthat determine a state’s decision of whether toenact a cap on punitive damages.

Previous empirical studies of damage capshave examined their impact on accident liti-gation while treating caps as exogenous. Forexample, Yoon (2001) found that a cap onnon-economic damages, including punitive dam-ages, enacted by Alabama reduced average dam-age awards relative to a control group of states.Rubin and Shepherd (2007) found that caps onnon-economic damages and an increased evi-dentiary standard for punitive damages wereboth associated with a reduction in the acci-dental death rate. Born, Viscusi, and Baker(2009) found that caps on non-economic dam-ages reduced medical malpractice losses andincreased insurer profitability (also see Vis-cusi and Born 2005). Avraham (2007) similarlyfound that caps on non-economic damages hada significant effect in reducing the number ofmalpractice claims, average awards, and totalpayments (though the last effect was weak), butcaps on punitive damages did not have a signif-icant effect. The results of these studies, how-ever, are subject to potential bias if damage capsare in fact endogenously determined. Our anal-ysis therefore will have potentially importantimplications regarding the proper specificationof empirical models of the factors that influencethe outcome of litigation.

The remainder of the article is organized asfollows. Section II develops the theoretical anal-ysis of damage caps. Section III provides a briefhistory of damage caps in the United States.Section IV describes the empirical strategy andthe data to be used. Section V then presentsthe results. Finally, Section VI offers conclud-ing comments.

II. THEORETICAL ANALYSIS

The standard economic theory of punitivedamages, based on the underdetection problem,can be formalized as follows. Let q be the prob-ability, as perceived by the injurer, that a victimwill detect and prove that the injurer is respon-sible for the victim’s damages, given by D. Asa lawsuit is the only route by which the injurer

MICELI & STONE: STATE CAPS ON PUNITIVE DAMAGES 3

faces liability, q < 1 leads to underdeterrence ifcompensation is limited to compensatory dam-ages. Suppose, however, that the court is permit-ted to add punitive damages of P in addition tocompensatory damages in those cases in whichthe injurer is found liable. (We assume through-out that liability is strict, so the injurer is heldliable whenever causation is established.) Theinjurer’s expected liability per accident is there-fore q(D + P). To achieve optimal deterrence,this term must equal actual damages per acci-dent, or q(D + P) = D, which implies that:

P = (1 − q/q)D.(1)

Thus, punitive damages should be increasingin D, decreasing in q, and 0 when q = 1 (i.e.,when detection is certain).

Note further that according to Equation (1),punitive damages should theoretically increasewithout limit as q becomes very small. Other-wise, deterrence is mitigated. On the basis of thisresult, Polinsky and Shavell (1998, 900) con-clude that, “in the absence of systematic bias[in jury awards of punitive damages], caps areinappropriate.”

This conclusion, however, ignores the impactof litigation costs on the effectiveness of thetort system for deterring risk.4 Generally, wewill see that adding these costs creates coun-tervailing effects on the desirability of punitivedamages. On one hand, litigation costs reinforcethe need for punitive damages because, as notedabove, they represent one way that injurers aresometimes able to avoid liability. In particular,victims whose expected recovery is less than thecost of obtaining a judgment at trial will not sue,even if detection is perfect (Hylton 1990). Onthe other hand, costly litigation reduces the ben-efits of lawsuits as a means of inducing injurersto be careful (Shavell 1982). Thus, the fact thatpunitive damages increase the number of suits,and hence litigation costs, offsets their attrac-tiveness as a deterrent.

To evaluate these effects formally, we extendthe imperfect detection model of punitive dam-ages to incorporate costly litigation and anendogenous number of suits. In particular, sup-pose that victims incur a cost cv , and injurers acost ci , when the victim files suit.5 These costs

4. See Shavell (2004, chapter 17) for a survey of theimpact of litigation costs.

5. For simplicity, we will not distinguish between trialsand settlement. On this issue, see Shavell (2004, 401–11).We also assume, as is the case in the American legal system,that both parties pay their own legal fees. On fee shiftingrules, see Shavell (2004, 428–32).

include both out-of-pocket legal fees and timecosts. The effect of the victim’s cost of litiga-tion is that, if an accident occurs and the victim’sprobability of proving causation is q, she willfile suit if and only if:

q(D + P) ≥ cv,(2)

or, if and only if:

q ≥ (cv/D + P) ≡ q̂(P ).

Now suppose that q varies across the popula-tion of potential victims according to the distri-bution function F(q), reflecting, for example,differences in the ability of victims to provecausation. In the event of an accident with arandomly chosen victim, the injurer thereforecomputes his conditional probability of facingsuit to be 1 − F(q̂(P )). As q̂(P ) is decreas-ing in P , it follows that the probability of suitis increasing in P . As a result, an increase inpunitive damages increases the expected numberof suits.

Now consider deterrence. Let r(x) be theprobability of an accident as a function ofthe injurer’s investment in care, x, wherer ′ < 0, r ′′ > 0. Thus, under strict liability andendogenous lawsuits, the injurer chooses x tominimize:

x + r(x)

⎡⎢⎣

1∫

q̂(P )

[q(D + P) + ci]f (q)dq

⎤⎥⎦ ,

(3)

where the term in square brackets is the injurer’sexpected costs (liability plus litigation costs) inthe event of an accident. (Note that the inclu-sion of litigation costs for the injurer actuallyenhances deterrence by raising the expected costof an accident, all else equal.) To conserve onnotation, call this term EC(P ). The injurer’soptimal care choice, denoted x̂(P ), thereforesolves:

1 + r ′(x)EC(P ) = 0.(4)

Performing comparative statics on Equa-tion (4) reveals that

∂x̂/∂P > 0,(5)

which follows from the fact that ∂EC(P)/∂P > 0. Thus, an increase in punitive damagesincreases the injurer’s level of care. This occursfor two reasons. First, the injurer faces greaterliability per suit, and second, the injurer expects


to face more suits. These two effects constitutethe deterrence benefits of punitive damages.

Let us now turn to the derivation of thesocially optimal level of punitive damages inthe presence of litigation costs. Social costs inthe current model are given by:

SC = x̂(P ) + r(x̂(P ))(6)

× {D + [1 − F(q̂(P ))](ci + cv)

},

where the term in braces equals damages peraccident plus expected litigation costs. Thederivative of this expression with respect toP is:

∂SC/∂P = {1 + r ′(x̂)[D + (1 − F(q̂(P )))

(7)

× (ci + cv)]}(∂x̂/∂P ) − r(x̂)f (q̂)

× (ci + cv)(∂q̂/∂P ).

The first term on the right-hand side repre-sents the deterrence effect of P , while the sec-ond term represents the litigation cost effect. Thelatter term is clearly positive (given ∂q̂/∂P <0), which reflects the increased number of law-suits as the level of punitive damages is raised.The sign of the deterrence effect, however, isambiguous. To determine its sign, substituteEquation (4) into the first term in Equation (7)and rearrange to obtain:

r ′(x̂){D + [1 − F(q̂(P ))]cv(8)

−1∫

q̂(P )

q(D + P)f (q)dq}(∂x̂/∂P ).

Now set P = 0, in which case Equation (8)reduces to:

r ′(x̂){D + [1 − F(cv/D)]cv(9)

−1∫

cv/D

qDf (q)dq}(∂x̂/∂P ).

As the expression in braces is clearly posi-tive (reflecting the fact that injurers fail to fullyinternalize the social cost of accidents), the over-all sign of this term is negative, given thatr ′ < 0. Thus, in the absence of punitive dam-ages there is underdeterrence. It follows thatthe deterrence component of social costs can bereduced by imposing punitive damages (i.e., bysetting P > 0). These gains, however, mustbe weighed against the resulting increase in

litigation costs. Assuming an interior solution,Equation (7) equals 0, or6:

− r ′(x̂){D + [1 − F(q̂(P ))]cv(10)

−1∫

q̂(P )

q(D + P)f (q)dq}(∂x̂/∂P )

= −r(x̂)f (q̂)(ci + cv)(∂q̂/∂P ).

Several implications follow from Equa-tion (10). First, as the right-hand side is positive,the left-hand side must also be positive. Thus,at the optimum there is some underdeterrence.Intuitively, when litigation is costly, punitivedamages should only be increased to the pointwhere the marginal benefit in terms of increaseddeterrence equals the marginal increase in litiga-tion costs. This conclusion mirrors a well-knownresult from the economics of law enforcement;namely, that when apprehension is costly, theexpected fine is less than the harm causedby the illegal act, which also results in someunderdeterrence (Polinsky and Shavell 2000,53–54).

Second, the zero-litigation-cost (perfect deter-rence) outcome emerges as a special case ofEquation (10). To see this, note that if ci =cv = 0, Equation (10) reduces to

−r ′(x)[D − q̄(D + P)](∂x̂/∂P ) = 0,(11)

where q̄ is the expected value of q. It followsthat

P ∗ = (1 − q̄/q̄)D,(12)

which is the analog to Equation (1).Finally, the preceding theory provides a pos-

sible economic rationale for state-level caps onpunitive damages. As argued above, a theoryof punitive damages based purely on deter-rence does not justify a limit on P ∗. How-ever, when litigation costs are incorporatedinto the theory, a cap on punitive damagesmay be justified. Specifically, an examina-tion of Equation (10) suggests that a cap willbecome more desirable as the overall cost oflawsuits, ci + cv , increases (as the marginallitigation cost term increases), and less desir-able as the damages per accident increase (asthe marginal benefit term increases).7 Empirical

6. This assumes that the second-order conditionholds.

7. Neither of these effects is a true comparative static asthe complexity of Equation (10) does not yield unambiguousresults. Predictions therefore reflect perceived dominanteffects.


testing of these predictions requires a broadunderstanding of the history behind punitivedamage caps. Accordingly, we devote the nextsection to a brief review of the history of puni-tive damage caps as a prelude to the empiricalanalysis.

III. A BRIEF HISTORY OF PUNITIVE DAMAGE CAPS

Despite being a permissible remedy undercommon law, punitive damages were infre-quently awarded until the early twentieth cen-tury.8 In fact, these early awards were usuallylimited to cases involving intentional torts orcriminal behavior (Ausness 1985, 7). Now, how-ever, punitive damages are awarded under avariety of different legal regimes. But, becausetheir use in products liability cases has probablyattracted the most public attention, the evolutionof punitive damages—and the resulting state-level caps—is best understood by examiningthe historical application of punitive damagesin products liability law.

The use of punitive damages in products lia-bility law arguably began in the late 1960s,9

though they were only applied sporadically dur-ing this period. The trend to award punitivedamages did not gain momentum until 1979when the Alaskan Supreme Court upheld a trialcourt’s award of punitive damages for the defec-tive design of a revolver in Sturm, Ruger &Co. v. Day.10 Two years later, the application ofpunitive damages in products liability garneredsubstantial national attention following the rul-ing in Grimshaw v. Ford Motor Co. (the FordPinto case).11

In Grimshaw, the plaintiff was awarded$2.5 million in compensatory damages and$125 million in punitive damages for injuriessustained as a result of the defective designof the Ford Pinto. Although the punitive dam-ages award was later reduced by the trialcourt judge to $3.5 million on remittitur, thecase provided the impetus for future courtsto award punitive damages. Indeed, in the

8. See Rustad and Koenig (1993, 1303), who argue thatpunitive damages gained acceptance in the early twentiethcentury as a way to protect the consumer.

9. Ausness cites Toole v. Richardson-Merrell, Inc.,60 Cal. Rptr. 398 (Cal. Ct. App. 1967) and Roginsky v.Richardson-Merrell, Inc., 378 F.2d 832 (2d Cir. 1967) tosupport this proposition.

10. 594 P.2d 38 (Alaska 1979), modified, 615 P.2d 621(Alaska 1980), cert. denied, 454 U.S. 894 (1981).

11. 174 Cal. Rptr. 348 (Cal. 1981).

4 years following Grimshaw, the number ofcases involving punitive damages “escalatedsubstantially” in areas such as motor vehiclemanufacturing, pharmaceuticals, and asbestos(Ausness 1985, 23–24).12

As courts became more willing to imposepunitive damages in the wake of Grimshaw,however, a debate arose over whether to imposestatutory caps on such awards. This debatehinged on a balancing of the effect of puni-tive damages on insurance premiums versuscorporate responsibility.13 The insurance indus-try lobbied state legislators to reform the lawof punitive damages, citing a perceived “insur-ance crisis.”14 It was feared that as jurorswere unconstrained in awarding punitive dam-ages, they would be inclined to impose size-able, yet unjustified, awards.15 This spurred“widespread alarm and cries for tort reformthroughout the United States” (O’Connell 1987,317). At the same time, however, consumeradvocacy groups lobbied in opposition to puni-tive damage caps, claiming the need topromote corporate responsibility as theirmotive.

In the years following Grimshaw, severalstates nevertheless enacted legislation aimed atreducing the incidence of punitive damages.16

Between 1985 and 1989, 12 states, beginningwith Montana, imposed some form of a dam-age cap. But, after this initial surge, a period ofrelative inactivity followed. Indeed, from 1990to 1993, only one further state, North Dakota,imposed a cap on punitive damages. Apparently,by the early 1990s, caps on punitive damageshad lost their appeal.

12. From 1975 to 1984, products liability filings surgedby 272%, while the remainder of torts increased by 17%(Galanter 1986, 21).

13. See, e.g., “Damages Cap Debated,” The WashingtonPost (February 6, 1986) (stating that Virginia’s Senate Courtof Justice Committee heard testimony regarding unjustifiablyhigh insurance premiums and that the application of apunitive damages cap would be unfair to victims of corporatewrongdoing).

14. See Greene, Goodman, and Loftus (1991) for adescription of the manners in which the insurance industryutilized various forms of media to “join the battle againstlawsuit abuse.”

15. See Viscusi (2004, 1406) (stating, “[m]uch of theconcern with respect to punitive damages stems from theimprecise guidance that juries are given in setting the awardlevels.”).

16. In addition to capping punitive damages, some statelegislatures have enacted legislation that elevates evidentiaryrequirements. Additionally, some states have passed lawsrequiring a fraction of punitive damages awards to be paidto a state fund.


In 1994, however, the notorious case ofLiebeck v. McDonald’s Restaurants17 broughtthe issue of punitive damages back to thenational spotlight. In Liebeck, the jury awardedthe plaintiff $160,000 in compensatory damagesand $2.7 million in punitive damages for injuriessustained from the defendant’s allegedly defec-tive and dangerous coffee.18 Although the judgelater reduced the punitive damages award to$480,000, the case received attention in vari-ous media outlets, including newspapers, tele-vision news coverage, magazines, talk shows,late night television, and even corporate adver-tisements (McCann, Haltom, and Bloom 2001,163–66). The ruling apparently served as a cat-alyst for further tort reform, as seven statesenacted caps on punitive damages between 1995and 1998.19

By the end of the twentieth century, the trendto cap punitive damages had again slowed; from1999 to 2007, only four additional states enactedcaps. Overall, between the Appellate Court’sruling in Grimshaw and 2007, 24 states put intoeffect some sort of cap on common law awardsof punitive damages. Figure 1 schematicallydepicts the time frame of enactment by state.

Coincidentally, in the years followingGrimshaw, a body of Constitutional law arosedictating that juries were constrained in award-ing grossly excessive punitive damages. Begin-ning with TXO Production Corp. v. AllianceResources Corp.,20 the U.S. Supreme Court heldthat grossly excessive punitive damages awards

17. Docket No. D-202 CV-93-02419 (BernalilloCounty, N.M. Dist. Ct. Aug. 18, 1994). See McCann,Haltom, and Bloom (2001) for an overview of the Liebeckcase and the verdict’s ensuing media frenzy.

18. The plaintiff in Liebeck had purchased a cup ofcoffee from a drive-through window at a McDonald’srestaurant. She then placed the cup between her legs, andwhile attempting to remove the lid, spilled the coffee onher lap, causing third degree burns to her thighs, buttocks,genitals, and groin. She sued McDonald’s, claiming that thecoffee was a defective and dangerous product as it was beingserved at an extremely hot temperature and without adequatewarning.

19. Maine capped punitive damages in wrongful deathactions during this time. See Me. Rev. Stat. 18-A, § 2-804(1997). However, as wrongful death actions are a smallsubset of all possible tort claims and the cause of actionwas not originally actionable at common law, Maine’s capis excluded from the empirical analysis.

20. 509 U.S. 443 (1993). It should be noted that the U.S.Supreme Court held that procedural due process protectionapplied to punitive damages awards 2 years earlier inPacific Mut. Life Ins. Co. v. Haslip, 499 U.S. 1 (1991).The TXO case was the U.S. Supreme Court’s first attemptto construct Constitutional limitations on the magnitude ofpunitive damages awards via substantive due process.

FIGURE 1Time Periods During Which States Imposed

Punitive Damage Caps

were governed by substantive due process. Sub-sequent rulings, such as BMW of North America,Inc. v. Gore21 and State Farm Mutual Automo-bile Insurance Co. v. Campbell,22 extended sub-stantive due process protection to require puni-tive damages awards to be commensurate withcompensatory damages. Today, while there isno precise mathematical formula for determin-ing whether a punitive damages award is grosslyexcessive, the ratio of compensatory damages topunitive damages is a factor that must be con-sidered by a jury.23 Indeed, as noted by the courtin State Farm Mutual Automobile Insurance Co.v. Campbell, “few awards exceeding a single-digit ratio between punitive and compensatorydamages, to a significant degree, will satisfy dueprocess.”24 As a result, juries must consider themagnitude of compensatory damages in award-ing punitive damages.

A cursory review of two studies by theBureau of Justice Statistics shows that evenprior to the U.S. Supreme Court’s articulationof substantive due process as it applied to puni-tive damages, jury-awarded punitive damageswere proportional to compensatory damages. Asample of trial outcomes in the 75 most popu-lous counties in 1992 revealed that while 43%

21. 517 U.S. 559 (1996).22. 538 U.S. 408 (2003).23. BMW, 517 U.S. at 582.24. State Farm, 538 U.S. at 425.


of punitive damages awards exceeded compen-satory damages, only 15.4% of jury trials withvictorious plaintiffs resulted in punitive damagesexceeding a punitive-to-compensatory-damagesratio of four to one. Similarly, in 2001, 40.9%of punitive damages awards exceeded compen-satory damages, but only 15.7% of jury trialswith victorious plaintiffs exhibited a punitive-to-compensatory damages ratio greater than fourto one (Cohen 2005, 8). And a sample of trialoutcomes in urban, suburban, and rural jurisdic-tions in 2005 revealed that only 8% of casesexceeded a punitive-to-compensatory-damagesratio of ten to 1. A supermajority (76%) oftrials with punitive damages resulted in punitive-to-compensatory-damages ratios of three to oneor less (Cohen and Harbacek 2011, 5). Despitethese findings, it should be noted that duringthis same time frame, median punitive dam-ages awards increased, from $50,000 in 1992to $63,000 in 2001 to $64,000 in 2005.

IV. EMPIRICAL ANALYSIS

The empirical analysis in this article departsfrom previous studies of damage caps by treat-ing caps as endogenously determined. Thissection describes the empirical strategy and thedata used in this effort.

A. The Hazard Model

The method we use to study the enactmentof damage caps is hazard analysis, sometimesknown as survival analysis. Instead of utilizing

a binary-dependent variable to reflect whether astate has enacted a cap at a particular point intime, this approach generates properly censoredtimes-to-enactment as the dependent variable.These times-to-enactment reflect the length oftime that a state has “survived” before enactinga cap.

A hazard analysis using properly censoredtimes-to-enactment is superior to other regres-sion techniques for dealing with discrete,binary-dependent variables (e.g., probit or logitregression) for a number of reasons. First, probitand logit regressions require unreasonable distri-butional assumptions regarding the error term. Inparticular, the error term of the probit regressionis assumed to follow a normal distribution, whilethe error term of the logit regression is assumedto be an independent and identically distributedextreme value. As Figure 2 shows, however,state legislatures’ voting behavior since the early1980s has followed a crude bimodal distribu-tion. Thus, there is little reason to believe thatthe error term will be normal or extreme-valued.In addition, unlike probit and logit regressions,hazard analysis allows us to exploit the influ-ence of time on cap enactment. Finally, as not allstates have enacted caps, the times-to-enactmentare right censored, and censored data are notproperly handled by traditional probit and logitregressions. Hazard analysis is therefore supe-rior to probit and logit regression for the prob-lem at hand.

For purposes of the analysis, we assumethat once a punitive damages cap is enacted, itwill remain enacted forever. This assumption is

FIGURE 2Caps on Punitive Damages Enacted by Year, Following Avraham’s (2010) Methodology


somewhat unrealistic as Alabama25 and Ohio26

have struck down their punitive damage caps asunconstitutional. As a consequence, the modelcan be interpreted as predicting the first timethat a state will enact a cap on common lawawards of punitive damages.

The hazard model is formulated as follows.Let T be a random variable representing thetime-to-enactment of a punitive damage cap,where t is the realized value for a particularstate. The survivor function, S(t), is defined tobe the probability that a state does not enact acap until after time t .27 It is a non-increasingfunction in t and is given by

S(t) = Pr(T > t) = 1 − F(t),(13)

where F(t) is the distribution function for T .The hazard rate, h(t), is the instantaneous rate ofenactment. In other words, it is the probability ofenactment given that enactment has not alreadyoccurred. Formally,

h(t) = f (t)/S(t),(14)

where f (t) is the density function of F(t). Ifthe hazard rate is not derived from the data,it can assume different underlying distributions,including exponential, Weibull, γ, and others.28

For state j , the hazard rate is some functiong(·) such that

hj (t, x) = g(t, β0 + xjβx),(15)

where x is a vector of covariates for state j .This model can be parameterized as follows:

hj (t, x) = h0(t)exp(β0 + xjβx),(16)

where the hazard facing state j is multiplica-tively proportional to the baseline hazard, h0(t).Note that the covariates are not a function ofthe baseline hazard; this is a Cox model, con-ventionally known as the proportional hazardsmodel.

25. In Henderson v. Alabama Power Co., 627 So.2d878 (Ala. 1993), the Alabama Supreme Court struck downAlabama’s cap on punitive damages, holding that it violatedone’s right to a trial by jury as provided by the AlabamaConstitution.

26. In State ex rel. Ohio Academy of Trial Lawyers v.Sheward, 715 N.E.2d 1062 (Ohio 1999), the Ohio SupremeCourt invalidated a punitive damages cap under the one-subject rule. Specifically, the court found that a punitivedamages cap that applied to “tort and other civil actions”was overly broad as it covered unrelated areas.

27. See Klein and Moeschberger (1997) for a detailedexplanation of hazard models.

28. See Klein and Moeschberger (1997, 37) for thehazard rate, survival function, probability density function,and expected lifetime for common parametric distributions.

The Cox proportional hazards model gen-erates an empirically derived hazard function.Although the Cox model is not burdened by anydistributional assumption regarding the shape ofthe underlying hazard function, it assumes thatthe hazard ratios are not influenced by time.However, by positing a certain functional formfor the underlying hazard, we are able to exploitthe influence of time on the hazard that a stateenacts a cap. As a result, the model providesus with better estimates for our covariates. Thehazard function under this parametric model isspecified as follows:

hj (t, x(t)) = h0(t)exp(β0 + xj (t)βx),(17)

where the covariates are no longer separablefrom time. The Weibull distribution is mostcommon and yields the following hazard func-tion:

h(t) = λαtα−1,(18)

where λ is a scale parameter, α is a shape param-eter, and α, λ > 0. Note that the Weibull func-tion can only replicate monotonically increasingor decreasing hazards.

Finally, there exists the possibility of unob-served heterogeneity in the form of omittedvariable bias in the parametric hazard model.Following Nanda (2006) and De Figueiredo andVanden Bergh (2004), we incorporate hetero-geneity into the model by imposing a contin-uously distributed heterogeneity term. In thisarticle, we use a γ-shared frailty model to con-trol for unobserved heterogeneity.

B. The Data

For the different hazard analyses, we use arich panel containing data for 40 states from1981 to 2007, where the starting point is strate-gically chosen to coincide with the year inwhich the California Supreme Court renderedthe Grimshaw decision. The ruling in Grimshawis presumed to mark the beginning of the haz-ard of a cap faced by states. This presumptionis consistent with the evolution of punitive dam-ages awards in products liability law and Aus-ness’ (1985, 23) observation that despite beingawarded infrequently historically, “[t]he num-ber of punitive damages claims against prod-uct manufacturers has escalated substantially inthe four years since Grimshaw.” Additionally,the historical significance of the Liebeck casesuggests that it may have had an influence oncap enactment. To account for this possibility,


we include a dummy variable controlling forthe Liebeck ruling (denoted Liebeck_D) in theregressions below.

The panel omits data from those states thateither (1) prohibit limitations on punitive dam-ages under constitutional law, or (2) prohibitthe application of punitive damages at commonlaw. Three state constitutions prohibit limita-tions on punitive damages awards: Arizona,29

Kentucky,30 and Wyoming.31 As a cap on puni-tive damages requires a constitutional amend-ment, these states do not face the same hazardas those states without constitutional limita-tions. Moreover, long before 1981, seven statesprohibited punitive damages at common law:Connecticut, Louisiana, Massachusetts, Michi-gan, Nebraska, New Hampshire, and Washing-ton. In Connecticut, although punitive damagesare permitted to cover litigation costs, includ-ing reasonable attorney’s fees, these damagesare imposed only to “make victims whole.”32

Similarly, in Michigan, punitive damages arecompensatory in nature.33 As the recovery ofdamages greater than pure compensation is pro-hibited in Connecticut and Michigan, punitivedamages have been effectively capped at 0 inboth these states. In Louisiana, punitive dam-ages were prohibited at common law, and thiscap dates back to at least 1932.34 Similarly, inMassachusetts,35 Michigan,36 Nebraska,37 New

29. “No law shall be enacted in this State limiting theamount of damages to be recovered for causing the death orinjury of any person.” Ariz. Const. Art. 2, § 31.

30. “The General Assembly shall have no power to limitthe amount to be recovered for injuries resulting in death,or for injuries to person or property.” Ky Const. § 54.

31. “No law shall be enacted limiting the amount ofdamages to be recovered for causing the injury or death ofany person.” Wyo. Const. Art. 10 § 4(a).

32. As stated by the court in Doroszka v. Lavine, “thepurpose [of punitive damages] is not to punish the defendantfor his offense but to compensate the plaintiff for hisinjuries.” Doroszka v. Lavine, 150 A. 692-93 (Conn., 1930).As a result, punitive damages are limited to the expenses oflitigation, including reasonable attorney’s fees, less taxablecosts. Berry v. Loiseau, 614 A.2d 414 (Conn., 1992).

33. Peisner v. Detroit Free Press, 304 N.W.2d 814(Mich. App., 1981). According to the Michigan SupremeCourt, “An award of exemplary damages is consideredproper if it compensates a plaintiff for the ‘humiliation,sense of outrage, and indignity’ resulting from injuries ‘mali-ciously, willfully and wantonly’ inflicted by the defendant.”Kewin v. Massachusetts Mutual Life Ins. Co., 295 N.W.2d50 (Mich., 1980).

34. See Killebrew v. Abbot Laboratories, 359 So.2d1275 (La., 1932).

35. See Burt v. Advertiser Newspaper Co., 28 N.E. 1(Mass., 1891).

36. See Ford v. Cheever, 63 N.W. 975 (Mich., 1895).37. See Boyer v. Barr, 8 Neb. 68 (1878).

Hampshire,38 and Washington,39 punitive dam-ages have been prohibited at common law, dat-ing back to the 1800s. As these ten states areanomalies, we omit them from the empiricalanalysis.

The dummy variable PD_Cap takes the value“1” if state j enacted a broad-based cap on puni-tive damages before July 1 of time t . Avraham(2010) provides these data.40 As noted above,once a cap is enacted, it is presumed that it willremain active forever, and therefore, situationsin which a cap is struck down as unconstitu-tional are ignored. As a result, the dependentvariable reflecting the time-to-enactment is gen-erated as the difference between 1981 and thefirst time in which the dummy variable PD_Captakes the value “1,” regardless of whether thecap was later modified by the state legislatureor the courts.

The model in Section II predicted that a capis more desirable as litigation costs increase,and less desirable as accident losses increase.We include several explanatory variables to testthese predictions. The variable Jud_Leg_Expcaptures inflation-adjusted per capita state andlocal government expenditures for judicial andlegal services (hereinafter “judicial and legalexpenditures”). A state’s judicial and legalexpenditures include spending associated witha state’s civil and criminal courts, such as “lawlibraries, grand juries, petit juries, medical andsocial service activities (except public defenseand probation, where separately identifiable),court reporters, judicial councils, bailiffs, and‘register of wills,’ and similar probate func-tions.” These data do not include expendituresfrom the private sector and are available fromthe U.S. Department of Justice, Bureau of Jus-tice Statistics.41 As judicial and legal expendi-tures are a proxy for litigation costs, we expectthe coefficient on Jud_Leg_Exp to be positive;that is, a higher value should increase the like-lihood of a cap.

LS_Emp reflects the per capita (in ten thou-sands) quantity of private sector legal servicesjobs (including both full- and part-time employ-ment) in the legal services industry, as defined

38. See Fay v. Parker, 53 N.H. 342 (1872).39. See Spokane Truck & Dray Co. v. Hoefer, 25 P. 1072

(Wash., 1891).40. The substance of state caps varies in ways that

would be difficult to quantify. Thus, we simply treat it as aqualitative variable.

41. These data include expenditures for both civil andcriminal courts. Unfortunately, data on civil courts alone arenot available.


and reported by the Bureau of Economic Anal-ysis. Danzon (1986) finds a positive correlationbetween the number of lawyers and the fre-quency of legal filings. However, she did notfind any evidence demonstrating that lawyershave “any systematic impact on the frequencyof claims filed” (Danzon 1986, 70). Along thesame lines, as Lee, Browne, and Schmit (1994,302–3) maintain, the relationship between thenumber of attorneys and the number of civilfilings is ambiguous.42 As the number of legalservice jobs increases, the number of lawsuitsshould increase,43 but the cost of legal servicesshould also decrease. In addition, an increase inlegal service employment will likely increase thepolitical influence of lawyers, which would workagainst enactment of a cap. On the basis of theseoffsetting factors, we cannot make any a prioripredictions regarding the sign of the coefficienton the variable LS_Emp.

The variable CM_GSP captures real percapita construction and manufacturing grossstate product (GSP) for each state.44 These datawere obtained from the Bureau of EconomicAnalysis. Viscusi (1991, 169) finds a correlationbetween construction and manufacturing activi-ties and products liability insurance premiums.Particularly, he determines that manufacturingactivities are responsible for approximately halfof all paid products liability insurance premi-ums, while construction activities account forroughly one-quarter of all premiums. Giventhese findings, there is arguably a correlationbetween construction and manufacturing activ-ities and the number of accidents. Therefore,we expect the coefficient on CM_GSP to benegative as increases in construction and manu-facturing activities ought to increase the overallcosts of accidents, thus dissuading legislatorsfrom capping punitive damages.

The variable Ins_GSP measures real percapita insurance GSP. Grace and Leverty (2008)utilize these data in their investigation of thelikelihood that various tort reform measuresare held unconstitutional.45 These data are also

42. While legal service employment is not identical tothe number of attorneys, the a priori expectations are thesame.

43. This may occur as the result of lawyers acceptingmarginal cases in the short run, or moving to high-accidentjurisdictions in the long run.

44. Its value is therefore the sum of construction GSPand manufacturing GSP divided by the population of statej for time t .

45. It should be noted that Grace and Leverty (2008)utilized hazard analysis in their study.

available from the Bureau of Economic Anal-ysis. This variable is included to control forthe political influence of the insurance lobby.46

Given the anecdotal evidence supporting theproposition that the insurance lobby was influ-ential in the enactment of various tort reformmeasures, we expect the coefficient on Ins_Empto be positive. In addition, we recognize thatstates differ with respect to whether directlyassessed punitive damages are insurable. Insur-ers have different incentives to lobby for caps onpunitive damages when insurance policies arepermitted by law to cover liability for punitivedamages. Accordingly, we include the dummyvariable Insurable_D to control for whether astate permits the insurability of directly assessedpunitive damages.

Following Grace and Leverty (2008), weinclude two variables to capture the political ide-ology of each state. The variable Per_Dem_Legcaptures the percentage of democrat legislatorsin state j for time t . Its value is the sum ofdemocrat legislators divided by the total num-ber of legislators in each state. These data areobtained from the Partisan Divisions of Ameri-can State Governments dataset, which is avail-able from the Interuniversity Consortium forPolitical and Social Research. The coefficienton Per_Dem_Leg is expected to be negative,reflecting the view that democrats generallyoppose tort reform while republicans usuallysupport it. In addition, the variable Poli_Indexcaptures the political ideology of residents instate j for time t . These data are made availableby Berry et al. (2010). This [0, 1] index approxi-mates the unweighted average political ideologyof residents in a state by using interest groupideology ratings on candidates in addition toactual election results. Low values of Poli_Indexreflect a conservative state, while high valuesof Poli_Index reflect a liberal citizenry. Thus,we expect a negative relationship between thisvariable and the hazard of cap enactment.

A number of variables are included to cap-ture the economic climate of each state. Thevariable Unemp captures each state’s unemploy-ment rate for time t . This variable is included tocontrol for the income effect of tort filings. AsDanzon (1986) and Lee, Browne, and Schmit(1994) maintain, the opportunity cost to lawyers

46. The authors considered using profitability measuresinstead of a size measure to capture the political influenceof the insurance lobby in each state. Unfortunately, insurerprofitability measures, such as state-level loss ratios, areproprietary and thus were not available for this study.


TABLE 1Summary Statistics 40 State Characteristics (1981–2007)

States without a Capa States with a Capb All Data

Variable N Mean (SD) N Mean (SD) N Mean (SD)

Judicial and legal expenditures, percapita (Jud_Leg_Exp)

681 0.0819 (0.0519) 319 0.0947 (0.0360) 1,000 0.0860 (0.0478)

Legal services employment, per capita(10,000s) (LS_Emp)

595 39.6769 (11.4412) 205 43.2530 (8.2483) 800 40.5933 (10.8225)

Real construction and manufacturingGSP, per capita (CM_GSP )

719 0.0071 (0.0022) 361 0.0073 (0.0023) 1,080 0.0072 (0.0023)

Real insurance GSP, per capita(Ins_GSP )

719 0.0008 (0.0008) 361 0.0008 (0.0003) 1,080 0.0008 (0.0006)

Political index (Poli_Index ) 719 50.8883 (15.6986) 361 46.3015 (10.7948) 1,080 49.3551 (14.4068)Percent democrat legislature

(Per_Dem_Leg)719 0.5862 (0.1733) 361 0.4956 (0.1168) 1,080 0.5559 (0.1624)

Unemployment rate (Unemp) 719 6.0868 (2.1614) 361 5.0512 (1.2171) 1,080 5.7406 (1.96)Income, per capita ($1,000s) (PCI ) 719 19.9086 (7.9295) 361 26.9495 (7.088) 1,080 22.2621 (8.3453)Real GSP, per capita (GSP ) 719 0.0351 (0.0102) 361 0.0382 (0.0066) 1,080 0.0361 (0.0093)Non-farm employment, per capita

(NF_Emp)719 0.5506 (0.058) 361 0.5856 (0.0444) 1,080 0.5623 (0.0563)

Population growth rate (Pop_GR) 719 0.0094 (0.0099) 361 0.0129 (0.0116) 1,080 0.0105 (0.0106)Punitive damages insurable dummy

(Insurable_D)719 0.5716 (0.4952) 361 0.5069 (0.5006) 1,080 0.5500 (0.4977)

Liebeck dummy (Liebeck_D) 719 0.3908 (0.4883) 361 0.7729 (0.4196) 1,080 0.5185 (0.4999)West dummy (West_D) 719 0.2406 (0.4278) 361 0.2687 (0.4439) 1,080 0.2500 (0.4332)Midwest dummy (Midwest_D) 719 0.2281 (0.4199) 361 0.2936 (0.4561) 1,080 0.2500 (0.4332)South dummy (South_D) 719 0.3380 (0.4733) 361 0.3740 (0.4845) 1,080 0.3500 (0.4772)

aIncludes data for all years in which the variable PD_Cap = 0.bIncludes data for all years in which the variable PD_Cap = 1.

and plaintiffs is lower during periods of highunemployment, and as a result, increases in theunemployment rate ought to be associated withlower litigation costs. In contrast, the expectedcost of accidents decreases as unemploymentincreases as a customary element of the dam-age measure for a cause of action in tort is lostwages. Given these offsetting effects, the pre-dicted sign of Unemp is ambiguous.

The variable PCI measures the per capitaincome for state j. We expect it to be neg-atively correlated with the hazard that a stateenacts a punitive damages cap as increases inper capita income increase the cost of accidents.Similarly, the real per capita gross state product,GSP, proxies for the cost of accidents and there-fore should have a negative sign. The variableNF_Emp is included to control for per capitanon-farm employment, as these changes are notproperly reflected in the unemployment rate.Like Unemp, the expected sign of this variableis ambiguous as it likely increases both litiga-tion and accident costs. Finally, as most of theexplanatory variables are measured in per capita

terms, the variable Pop_GR is included to cap-ture the percent changes in a state’s population.Ceteris paribus, increases in population increaseactivity levels, which ought to increase the num-ber of accidents and hence the litigation rate. Atthe same time, however, increases in the popu-lation growth rate will likely increase the cost oflitigation as a result of greater demand for legalservices. Thus, the predicted sign is ambiguous.

We also include dummy variables based oncensus region. As the panel data may sufferfrom heteroskedasticity or autocorrelation, thestandard errors are clustered at the state level.

Table 1 provides summary statistics for thedata. The first column (“States without a Cap”)includes the sample average and standard devi-ation for the explanatory variables for all yearsbefore which a cap was enacted (if at all) forevery state, while the second column (“Stateswith a Cap”) includes the sample average andstandard deviation for only those years after(and including) the first year in which a stateenacted a cap. The third column provides sum-mary statistics for the entire dataset.


V. RESULTS

Figures 3 and 4 provide the Kaplan-Meiersurvival estimate and the smoothed hazard esti-mate, respectively, for cap enactment. As onecan infer from Figure 2, these estimates demon-strate that the instantaneous probability of capenactment has fluctuated considerably since1981. Moreover, these figures also demonstratethat cap enactment is unlikely for the first fewyears in which we have available data. Thesefigures support our decision to base the range ofour dataset on our understanding of the historyof punitive damage caps.

FIGURE 3Instantaneous Probability of Cap Enactment

for All Analysis Times, Given That EnactmentHas Not Already Occurred

0.00

0.25

0.50

0.75

1.00

Pro

babi

lity

of S

urvi

val

0 10 20 30Analysis Time

Kaplan-Meier Survival Estimate

FIGURE 4The Hazard of Cap Enactment for All

Analysis Times, Given That Enactment HasNot Already Occurred

.025

.03

.035

.04

.045

Haz

ard

0 5 10 15 20 25Analysis Time

Smoothed Hazard Estimate

The hazard is run using five different specifi-cations, the last of which is presumed to be themost reliable. The results from the Cox propor-tional hazards model are presented in column 1of Table 2. Note that only the coefficients on theeconomic variables exhibit some statistical sig-nificance. In particular, the coefficients on theunemployment rate and per capita income arepositive and significant at the 1% level, whilethe coefficient on the population growth rate ispositive and significant at the 5% level. Thecoefficients on non-farm employment and GSPare significant at the 10% level. The negativesign on GSP is in line with the predictions ofthe model as it serves as a proxy for the costof accidents. The estimate for insurance GSP isnegative and not statistically significant, whilethe coefficients on judicial and legal expendi-tures, construction and manufacturing GSP, andlegal services employment are all positive andnot statistically significant. The adjusted R2 isonly 0.14, suggesting that this unrestrictive spec-ification does not explain much variation in thedata. Moreover, these results may be unreli-able because, as noted, the Cox model doesnot permit the hazard of cap enactment to beinfluenced by time. As a result, positing a func-tional form for the underlying hazard may benecessary.

Accordingly, columns 2 and 3 of Table 2show the estimates when we assume thatthe underlying hazard function conforms to aWeibull distribution, first with current (column2) and then with lagged (column 3) covari-ates. Judicial and legal expenditures are posi-tive in both models and significant at the 10%level in the model using lagged covariates.Legal services employment is positive, statis-tically significant at the 10% level under themodel with current covariates, and significantat the 5% level under the specification withlagged covariates. This provides some evidencethat the effect of increased court caseloads dom-inates the reduction in the cost of legal services,though as noted, it could also reflect the politi-cal influence of the lawyer’s lobby. Constructionand manufacturing GSP is positive under bothspecifications, but neither coefficient is statis-tically significant. Insurance GSP is negativeunder both specifications, but neither estimateis statistically significant.

The estimates in columns 2 and 3, however,are susceptible to measurement error. In partic-ular, the legislative process is sometimes slowand methodical, and as a result, it may take


TA

BL

E2

Reg

ress

ion

Res

ults

Var

iabl

e(1

)(2

)(3

)(4

)(5

)

Jud_

Leg

_Exp

8.89

17(9

.542

9)4.

6753

(8.1

05)

9.70

08∗

(5.8

438)

14.0

974∗

(8.5

621)

14.0

974

(10.

637)

LS_

Em

p0.

0186

(0.0

39)

0.08

38∗

(0.0

431)

0.09

57∗∗

(0.0

418)

0.09

85∗∗

(0.0

49)

0.09

85∗∗

(0.0

499)

CM

_GSP

24.6

364

(145

.316

4)66

.375

5(1

11.0

237)

90.1

639

(126

.521

8)73

.341

4(1

69.8

832)

73.3

414

(159

.615

3)In

s_G

SP−1

98.2

42(7

73.5

334)

−896

.654

2(6

22.7

262)

−941

.916

7(7

14.9

006)

−519

.188

4(8

05.4

771)

−519

.187

9(1

167.

082)

Pol

i_In

dex

−0.0

464

(0.0

337)

−0.0

297

(0.0

246)

−0.0

433

(0.0

287)

−0.0

809∗

(0.0

443)

−0.0

809∗

∗(0

.035

2)P

er_D

em_L

eg−2

.814

9(2

.362

5)−2

.589

9(2

.051

6)−3

.706

(2.3

345)

−3.5

556

(2.6

132)

−3.5

556

(2.6

241)

Une

mp

0.66

21∗∗

∗(0

.218

2)0.

1289

(0.1

704)

0.36

44∗

(0.1

868)

0.68

64∗∗

(0.2

982)

0.68

64∗∗

(0.2

963)

PC

I0.

4865

∗∗∗

(0.1

739)

−0.1

799

(0.1

656)

−0.2

564

(0.1

786)

−0.2

672

(0.2

421)

−0.2

672

(0.2

415)

GSP

−250

.828

5∗(1

34.6

883)

−15.

3121

(63.

7666

)−4

9.79

67(4

4.69

51)

−97.

9661

∗(5

4.72

95)

−97.

9661

(66.

8413

)N

F_E

mp

17.8

842∗

(9.5

954)

8.03

97(8

.998

4)15

.459

2∗(9

.257

1)25

.823

1∗∗

(13.

1861

)25

.823

1∗∗

(12.

7875

)P

op_G

R44

.224

9∗∗

(17.

5943

)16

.506

7(3

0.53

51)

22.5

24(2

5.84

11)

37.1

861

(30.

157)

37.1

862

(33.

6938

)In

sura

ble_

D−0

.417

3(0

.695

1)−0

.354

3(0

.640

2)−0

.419

3(0

.639

5)−0

.623

1(0

.662

)−0

.623

1(0

.652

9)L

iebe

ck_D

0.40

24(·)

−0.3

868

(0.5

794)

−0.4

678

(0.5

138)

−1.0

142

(0.7

511)

−1.0

142

(0.9

391)

Wes

t_D

−0.9

84(1

.215

)−0

.053

1(1

.306

1)−0

.940

3(1

.467

)−2

.248

9(1

.955

3)−2

.248

9(1

.543

8)M

idw

est_

D0.

708

(0.9

425)

1.36

97(1

.103

3)1.

1062

(1.0

933)

0.45

61(1

.053

4)0.

4561

(1.1

975)

Sout

h_D

0.78

8(0

.988

)1.

3513

(1.1

185)

1.13

14(1

.222

6)0.

2148

(1.3

248)

0.21

48(1

.401

2)C

onst

ant

−11.

1217

∗(6

.388

8)−1

4.66

44∗∗

(6.3

471)

−19.

9186

∗∗(8

.708

7)−1

9.91

86∗∗

(8.1

238)

Cov

aria

tes

Cur

rent

Cur

rent

Lag

ged

Lag

-Avg

.L

ag-A

vg.

Haz

ard

Cox

Wei

bull

Wei

bull

Wei

bull

Wei

bull

Wei

bull

shap

e(α

)2.

7680

3.14

793.

6081

3.60

81H

eter

ogen

eity

term

γ

R2

.137

5L

ogli

keli

hood

−59.

0578

−33.

5369

−31.

7889

−28.

5924

−28.

5924

∗ Sig

nific

ant

at<

.10;

∗∗si

gnifi

cant

at<

.05;

∗∗∗ s

igni

fican

tat

<.0

1.


many years for a state legislature to approve andimplement a proposed cap on punitive damages.Therefore, to control for measurement error, weuse 3-year lagged averages as explanatory vari-ables (columns 4 and 5).47 Under the first of thespecifications utilizing 3-year lagged averages(column 4), we do not control for unobservedheterogeneity in the form of omitted variablebias. Under this specification, the coefficientsfor judicial and legal expenditures and legal ser-vices employment continue to be positive. Theestimate for judicial and legal expenditures isstatistically significant at the 10% level whilethe estimate for legal services employment issignificant at the 5% level. The coefficient onthe political index is negative and significant atthe 10% level, supporting the conjecture thata democrat-dominated electorate opposes tortreform. Similarly, the coefficient on the percent-age of democrats in the legislature is negativethough not statistically significant. The coeffi-cient on GSP is negative, as expected, and it issignificant at the 10% level. Finally, the coeffi-cients on the unemployment rate and non-farmemployment are both positive and significant atthe 5% level.

Unlike the specifications using current andlagged values, these estimates are less likelyto suffer from measurement error. At the sametime, however, they are susceptible to bias fromunobserved heterogeneity. To control for thispossibility, the final column in Table 2 pro-vides estimates using 3-year lagged averagesunder a shared γ-frailty model. These resultsare the most precise of any of the specifica-tions as they are least likely to suffer fromomitted variable bias. After controlling for unob-served heterogeneity, the sign on judicial andlegal expenditures is positive and stable. Theestimate for legal services employment is posi-tive, stable, and statistically significant at the 5%level. Insurance GSP and construction and man-ufacturing GSP are not statistically significant,and neither is of the expected sign. The politicalindex is negative, stable, and significant at the5% level. The percent of democrat legislatorsis also negative, as expected, but it is not sta-tistically significant. Of the economic controls,only the estimates for unemployment and non-farm employment are statistically significant;both are positive and significant at the 5% level.

47. Note that the percentage of democrat legislators isnot 3-year lagged averaged. Rather, only one-period laggedvalues are used for this political control.

The former most likely reflects the reductionin the opportunity cost of litigation that resultsfrom increases in unemployment, while the lat-ter likely reflects the increases in court-ordereddamages that result from lost wages or lost earn-ing capacity in periods of high job growth.

Under all specifications, the Liebeck dummyis not statistically significant, nor is the dummyfor whether a state permits insurance coveragefor directly assessed punitive damages. Finally,under all specifications, punitive damage capsare least likely to be enacted in the western partof the country, though the census region dum-mies do not exhibit statistical significance underany of the models.48

Taken as a whole, these results provide somesupport for the economic model. The estimatefor judicial and legal expenditures is positiveacross all specifications and statistically signif-icant at the 10% level under two of the mod-els. This is consistent with the prediction thatincreases in the cost of litigation encourage leg-islators to enact caps on punitive damages. Thepositive, significant (at the 5% or 10% levelunder the parametric specifications), and stableestimate on legal services employment suggestthat increases in the size of the legal servicessector are associated with increases in litiga-tion costs, and hence with an increase in thelikelihood of a cap. Although construction andmanufacturing GSP did not influence cap enact-ment under any of our specifications, anotherproxy for damages, state GSP, was negativeunder all of the models, and it was significantat the 10% level in two of the specifications.Finally, as expected, states with a high percent-age of democrat legislators and those with alarge proportion of democratically aligned resi-dents were less likely to enact caps on punitivedamages.

VI. CONCLUSION

Under the traditional economic model ofpunitive damages, which views them as a

48. It should be noted that despite the apparent bimodalnature of the estimated hazard function (see Figure 4), thehazard analyses utilized in this paper properly relied uponthe Weibull hazard function. All estimates obtained utilizingthe Weibull hazard were able to reject the null hypothesis(at the 1% level) of the Wald test. Interestingly, the Weibullshape parameter ranged between 2.8 and 3.6 in the fourparametric specifications. This result suggests that the longera state “survives” before enacting a cap, the more likely itis that it will enact a cap on punitive damages. We shouldexpect to see more caps on punitive damages.


solution to the problem of underdetection, thereis no theoretical justification for imposing a capon their amount. However, by incorporating lit-igation costs into the model, we were able toprovide a theoretical justification for a statu-tory cap on damages as a way of balancing thedeterrence benefits against the cost of additionalsuits. We tested the predictions of the theo-retical model by running Cox and parametrichazard analyses on a rich panel dataset cov-ering 27 years. After controlling for measure-ment error and unobserved heterogeneity, wefound some evidence that increases in legal ser-vices employment (a proxy for litigation costs)increased the likelihood of cap enactment, andthat higher state GSP (a proxy for damages)reduced the likelihood of a cap. We also foundevidence that increases in the quantity of legalservices increased the hazard of cap enactment,and that democratic-leaning states were lesslikely to enact caps.

REFERENCES

Ausness, R. C. “Retribution and Deterrence: The Role ofPunitive Damages in Products Liability Litigation.”Kentucky Law Journal, 74, 1985, 1–125.

Avraham, R. “An Empirical Study of the Impact of TortReforms on Medical Malpractice Settlement Pay-ments.” Journal of Legal Studies, 34, 2007, S183–229.

. “Database of State Tort Law Reforms (DSTLR3rd).” Northwestern Law and Economics ResearchPaper No. 06-08; University of Texas Law, Law andEconomics Research Paper No. 184, 2010. AccessedMay 1, 2011. http://ssrn.com/abstract=902711.

Berry, D., E. Ringquist, R. Fording, and R. Hanson. “Mea-suring Citizen and Government Ideology in the Ameri-can States, 1960–2008.” American Journal of PoliticalScience, 42, 2010, 327–48.

Biggar, D. “A Model of Punitive Damages in Tort.” Inter-national Review of Law and Economics, 15, 1995,1–24.

Born, P., W. K. Viscusi, and T. Baker. “The Effects of TortReform on Medical Malpractice Insurers’ UltimateLosses.” Journal of Risk and Insurance, 76, 2009,197–219.

Chu, C., and C. Huang. “On the Definition and Efficiency ofPunitive Damages.” International Review of Law andEconomics, 24, 2004, 241–54.

Cohen, T. “Punitive Damage Awards in Large Counties,2001.” Bulletin. NCJ 208445, Washington, DC: U.S.Department of Justice, National Bureau of JusticeStatistics, 2005.

Cohen, T., and K. Harbacek. “Punitive Damage Awardsin State Courts, 2005.” Special Report. Bulletin.NCJ 233094. Washington, DC: U.S. Departmentof Justice, National Bureau of Justice Statistics,2011.

Cooter, R. “Economic Analysis of Punitive Damages.”Southern California Law Review, 56, 1982, 79–101.

. “Punitive Damages for Deterrence: When and HowMuch.” Alabama Law Review, 40, 1989, 1143–96.

Danzon, P. “The Frequency and Severity of Medical Mal-practice Claims: New Evidence.” Law and Contempo-rary Problems, 49, 1986, 57–84.

De Figueiredo, R., and R. Vanden Bergh. “The PoliticalEconomy of State-Level Administrative ProcedureActs.” Journal of Law and Economics, 47, 2004,569–88.

Eisenberg, T., J. Goerdt, B. Ostrom, D. Rottman, andM. Wells. “The Predictability of Punitive Damages.”Journal of Legal Studies, 26, 1997, 623–61.

Galanter, M. “The Day After the Litigation Explosion.”Maryland Law Review, 46, 1986, 3–39.

Grace, M., and J. Leverty. “How Tort Reform Affects Insur-ance Markets.” Northwestern Law Research Sympo-sium on Insurance Markets and Regulation, 2008.

Greene, E., J. Goodman, and E. Loftus. “Jurors’ Attitudesabout Civil Litigation and the Size of DamagesAwards.” American University Law Review, 40, 1991,805–20.

Hersch, J., and W. K. Viscusi. “Punitive Damages: HowJudges and Juries Perform.” Journal of Legal Studies,33, 2004, 1–36.

Hylton, K. “The Influence of Litigation Costs on Deter-rence under Strict Liability and under Negligence.”International Review of Law and Economics, 10, 1990,161–71.

Karpoff, J., and J. Lott. “On the Determinants and Impor-tance of Punitive Damage Awards.” Journal of Lawand Economics, 42, 1999, 527–73.

Klein, J., and M. Moeschberger. Survival Analysis: Tech-niques for Censored and Truncated Data. New York:Springer, 1997.

Landes, W., and R. Posner. The Economic Structure ofTort Law. Cambridge, MA: Harvard University Press,1987.

Lee, H., M. Browne, and J. Schmit. “How Does Joint andSeveral Tort Reform Affect the Rate of Tort Filings?Evidence from the State Courts.” Journal of Risk andInsurance, 61, 1994, 295–316.

McCann, M., W. Haltom, and A. Bloom. “Law and Soci-ety Symposium: Java Jive: Genealogy of a JuridicalIcon.” University of Miami Law Review, 56, 2001,113–78.

Nanda, A. “Property Condition Disclosure Law: Why DidStates Mandate ‘Seller Tell All’?” Department ofEconomics Working Paper, University of Connecti-cut, 2006. http://www.econ.uconn.edu/working/2006-16.pdf.

O’Connell, J. “Symposium Issues in Tort Reform: BalancedProposals for Product Liability Reform.” Ohio StateLaw Journal, 48, 1987, 317–28.

Polinsky, A. M. “Are Punitive Damages Really Insignif-icant, Predictable, and Rational? A Comment onEisenberg et al.” Journal of Legal Studies, 26, 1997,663–77.

Polinsky, A. M., and S. Shavell. “Punitive Damages: AnEconomic Analysis.” Harvard Law Review, 111, 1998,869–962.

. “The Economic Theory of Public Enforcementof Law.” Journal of Economic Literature, 38, 2000,45–76.

Rubin, P., and J. Shepherd. “Tort Reform and AccidentalDeaths.” Journal of Law and Economics, 50, 2007,221–38.

Rustad, M., and T. Koenig. “The Historical Continuityof Punitive Damages Awards: Reforming the TortReformers.” American University Law Review, 42,1993, 1269–333.

Schmit, J., S. Pritchett, and P. Fields. “Punitive Damages:Punishment or Further Compensation?” Journal of Riskand Insurance, 55, 1988, 453–66.


Shanley, M. “The Distribution of Post-Trial Awards.” Jour-nal of Legal Studies, 20, 1991, 463–81.

Sharkey, C. “Punitive Damages as Societal Damages.” YaleLaw Journal, 113, 2003, 347–453.

Shavell, S. “The Social versus Private Incentive to BringSuit in a Costly Legal System.” Journal of LegalStudies, 11, 1982, 333–9.

. Foundations of Economic Analysis of Law. Cam-bridge, MA: Belknap Press, 2004.

Viscusi, W. K. “The Dimensions of the Product LiabilityCrisis.” Journal of Legal Studies, 20, 1991, 147–77.

.“The Blockbuster Punitive Damages Awards.”Emory Law Journal, 53, 2004, 1405–55.

Viscusi, W. K., and P. Born. “Damages Caps, Insurability,and the Performance of Medical Malpractice Insur-ance.” Journal of Risk and Insurance, 72, 2005, 23–43.

Yoon, A. “Damage Caps and Civil Litigation: An Empir-ical Study of Medical Malpractice Litigation in theSouth.” American Law and Economics Review, 3,2001, 199–227.

the determinants of state-level caps on punitive damages: theory and evidence

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