malpractice litigation and medical costs in mississippi

HEALTH ECONOMICSHealth Econ. 16: 841–859 (2007)Published online 24 January 2007 in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/hec.1195

MALPRACTICE LITIGATION AND MEDICAL COSTSIN MISSISSIPPI

BRANDON ROBERTSa,* and IRVING HOCHb

aPremier Insights Inc., USAbUniversity of Texas at Dallas, USA

SUMMARY

This paper examines the impact of varying levels of malpractice litigation on area medical costs. Using a fixed-effects model and Medicare Part B as the dependent variable, the results indicate that per enrollee medicalexpenditures are positively related to the incidence of medical malpractice lawsuits. The higher cost is presumed tobe attributable to ‘defensive practices’ by area physicians based on varying degrees of perceived risk. The resultssuggest the addition to cost is substantial, possibly adding up to 25% in some jurisdictions with the impactexceeding annual dollar amounts of malpractice judgments and settlements. Copyright # 2007 John Wiley & Sons,Ltd.

Received 18 December 2005; Revised 19 August 2006; Accepted 27 October 2006

KEY WORDS: medical malpractice; litigation and medical costs

INTRODUCTION AND OVERVIEW

This paper examines the recent relationship of health care cost to litigation in the state of Mississippi.Our primary measure of health care cost is Part B Medicare expenditures per enrollee.1 Using severalalternative regression equations, the cost measure is related to a number of control variables and tolitigation measured as the number of medical malpractice lawsuits per 100 000 persons. Data coveringthe period 1998–2002 are employed, with the units of analysis the individual counties in Mississippi. Inall cases, the litigation measure was positively related to health care cost per enrollee, with therelationship always statistically significant. Drawing inferences from the statistical results necessarilyinvolved speculative assumptions, which we expand upon and discuss further, below. At one extreme,the implication was that costs imposed by litigation were close to, albeit somewhat above, direct costs,in terms of the amounts collected from successful lawsuits (presumably reflected in malpracticeinsurance).2 At the other extreme, in addition to direct costs, considerable indirect costs are generated,plausibly explained by defensive medicine practices and procedures performed in response to theperceived risks of litigation. As noted by Kessler and McClellan (1996):

Even though virtually all physicians are fully insured against the financial costs of malpractice suchas legal defense expenses, physicians may employ costly precautionary treatments in order to avoid

*Correspondence to: Premier Insights Inc., Canton, MS, USA. E-mail: [email protected] alternative measure used total expenditures per enrollee, where the total consists of Part A + Part B expenditures. Coefficientresults obtained using that alternative were similar to those of the primary measure, although explained variance was lower forthe alternative.

2We define direct costs as out-of-pocket expenses generated from levels of litigation which would include the costs of defending amalpractice suit. We assume this appears primarily in the form of malpractice insurance premiums. Indirect costs are additionalcosts primarily generated by behavior motivated to avoid litigation. Industry sources indicate that the time and expense requiredin defending a medical malpractice suit provide considerable motivation for avoidance.

Copyright # 2007 John Wiley & Sons, Ltd.

non-financial penalties such as fear of reputational harm, decreased self-esteem from adversepublicity, and the time and unpleasantness of defending a claim.

Results from national surveys of physicians suggest that defensive practices are widespread. Onesurvey, for example, found that 3 out of 4 doctors admit to practicing defensive medicine. Of thesedoctors, 68% believed that defensive medicine increased the cost of their services.3 In another survey,80% of doctors indicated they ordered more tests than they would have solely based on medical need,and 74% referred patients to specialists more often (Humphrey et al., 2002). Defensive behavior, ifindeed it exists, is likely reflected in cost. For example, in considering some effects of tort reform,Kessler and McClellan (1996) estimated defensive practice cost as comprising 5–9% of total medicalcosts. Following Kessler and McClellan, defensive medicine explicitly can be defined as the employmentof costly practices and procedures by physicians to reduce or avoid the costs (both monetary andpsychic costs) of litigation. It can be viewed as a subcategory of supply creating its own additionaldemand, often cited in the health economics literature, which in turn could be viewed as a form of theprincipal–agent problem.4

Applying our Mississippi results, we present additional evidence on these issues in the followingsections of this paper.

MISSISSIPPI AS INVESTIGATIVE LOCALE

Nearly all previous studies on medical malpractice have focused on tort reform efforts and effectsof policies ameliorating litigation pressure on the practice of medicine. To our knowledge, noother studies have attempted direct empirical measures of the effect of malpractice litigation on thecost of medical services. By limiting the analysis to a single state, Mississippi, we reduce sourcesof heterogeneity. Mississippi also implemented comprehensive tort reform subsequent to the timeperiods included in our analysis. Thus, in addition to providing a robust research design, this studyestablishes a benchmark on which to base future studies to examine ‘pre and post’ tort reformconsequences. An analysis of this sort would hold particular appeal to policy makers across a variety ofjurisdictions.

Mississippi has garnered a nationwide reputation for its litigious environment and is oftencited as an example of lawsuit abuse. Phrases and labels such as ‘the home of jackpot justice’and ‘judicial hell-hole’ have plagued the state in the last few years. One knowledgeable attorneycalled Jefferson County in Mississippi the ‘center for wealth redistribution’ and ‘ground-zero’ forlawsuits.5 A study conducted by Harris Interactive for the US Chamber of Commerce found thatMississippi’s liability system was rated by businesses across the country as the worst in the nation.6

Thus, Mississippi was a particularly inviting locale for investigating the impact of medical malpracticelitigation.

Further, previous studies generally employed multi-state analysis, with differences in state tort lawsand varying costs of malpractice insurance the source of independent variables. In contrast, state law isinvariant and malpractice insurance premiums are constant throughout the state of Mississippi (varyingonly by specialty). Hence, differences in medical cost related to litigation are likely primarily to reflectdifferences in defensive practices by physicians, based on perceived risk.

3Once burned, twice defensive. Medical Economics 79(14) (26 July 1999).4There is considerable debate on the issue of physician-induced-demand. For a discussion, see Fuchs VR. 1997. FloridianExceptionalism. Health Affairs, available online at: http://content.healthaffairs.org/webexclusives/index.dtl?year=2003. Also seePhelps CE. Health Economics, 2nd edn. Addison-Wesley Educational Publishers: New York; 246–247.

5 Jerry Mitchell, Clarion-Ledger, Jackson, Mississippi , 17 June 2001.6US Chamber of Commerce, State Liability System Ranking Study, 2003.

B. ROBERTS AND I. HOCH842

Copyright # 2007 John Wiley & Sons, Ltd. Health Econ. 16: 841–859 (2007)

DOI: 10.1002/hec

A key aspect of the Mississippi environment is that malpractice litigation varies by jurisdiction, withsome jurisdictions considered more favorable to litigation than others. It can be assumed that theselocations are known by trial lawyers as being good venues to obtain favorable plaintiff outcomes.7

There appears to be no attempt to disguise this as a common practice, with ‘favorability’ differing at thejudicial district and county level. (Each judicial district consists of a set of counties.) Richard Scruggs, aprominent Mississippi attorney, notes that some counties and jurisdictions are known as ‘magic’ forplaintiff prospects.8 Consequently, it seems reasonable to assume that physicians are also generallyaware of such differences and are likely to react accordingly, in terms of levels of defensive practice. Thisrelationship, in turn, builds the case for our use of the county as our unit of analysis. Our key questionthen becomes: Are medical costs higher in areas with greater levels of litigation? We hypothesize that wecan answer that question by appropriate statistical analysis.

RESEARCH DESIGN AND DATA

The design of this research is a cross-sectional time series, or panel design. The econometric modeling isconducted as a pooled, fixed-effects regression. Because potential relationships are assumed to be linear,the parameter estimation is done with Ordinary Least Squares (OLS) regression. County fixed effects,rather than random effects, were used and reported here because our data included all of the counties inMississippi as opposed to a subset of counties (Gujarati, 2003; Kennedy, 1998).9

The fixed-effects framework with panel data allows for some unique controls of extraneous varianceand spuriousness (Kennedy, 1998). Previous studies have examined differences in Medicare costs acrossgeographic areas with varying degrees of success. Recent studies indicate that there was a level ofvariation not accounted for in the previous work (Fuchs et al., 2001). Medical malpractice litigation wasnot included as a potential factor in those studies.

Our research design framework addresses the possibility of such unobserved heterogeneity (Judgeet al., 1988). In a panel design, units of analysis are used as their own controls. This structure allowstesting of specific individual differences in geographic areas even if the cause cannot be identified(Sutaria and Hicks, 2003; Gujarati, 2003; Kennedy, 1998). Variation unique to specific geographies wasestimated using fixed effects for counties. In contrast to treating those effects as unidentifiable, analternative hypothesis would be to attribute them fully or in part to a particular key variable orvariables – specifically, in our case, to the level of litigation. In addition, differences in time periods werealso estimated and tested by use of year fixed effects.

The data set utilized in this analysis covers the 82 counties of Mississippi over a 5-year time period,comprising 410 total observations with 82 observations each for 1998, 1999, 2000, 2001, and 2002,respectively. The data are complete with no counties or data missing from either side of the equation.The variables employed in our analysis are listed in Table I, with corresponding operational definitionsand data sources.

The main dependent variable utilized is Part B Medicare expenditures per enrollee for the agedpopulation, obtained from the Center for Medicare and Medicaid Services (CMS) fee-for-service files.10

The Medicare cost data are tied to the county in which the enrollee resides. Our use of Medicare data

7Prior to the tort reform passed by the Mississippi Legislature in 2003, a plaintiff could file a lawsuit in the county in which theincident occurred or in the county in which the plaintiff resides.

8Scruggs cited Jefferson County in Mississippi by name and indicated that there were a ‘half dozen or so magic jurisdictionsaround the country’ that were favorable jurisdictions for plaintiffs; see ‘Tobacco Lawyers Roundtable: A Report from the FrontLines,’ DePaul Law Review, Winter 2001.

9 In practice, the random-effects model also produced significant results for litigation with an even larger coefficient, but theHausman test clearly showed fixed-effects to be the better specification.

10As noted above, the results using total Medicare expenditures as the dependent variable yielded similar results but with lessexplained variance. See Appendix D as compared to Appendix C.

MALPRACTICE LITIGATION AND MEDICAL COSTS 843


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limits the measurement of medical cost to one payment source. The aged population also was selectedbecause it includes only a specific demographic group, deemed to be the best option to limit randomnoise. The extant literature provides a considerable basis for using Medicare expenditures for the agedpopulation and examining variation in health care costs (Cutler and Sheiner, 1999; Fuchs et al., 2001;Kessler and McClellan, 1996; Skinner and Fisher, 1997).

Medicare for the 65 and over population is further divided into two parts. Part A covershospitalization, and Part B is an optional supplemental component and covers doctor visits. Nearly all(95%) of Medicare recipients who enroll in Part A voluntarily enroll in Part B.11 Part B expenditureswere chosen to help minimize uncontrolled variance and focus on the care provided by doctors. It isassumed that individual practitioners have more discretion in the care they provide than care within ahospital context. In addition, doctors operating independently may be more sensitive to the threat ofliability than an institution, thus increasing possible variance related to the dependent variable. Also,patients are less likely to travel great distances for doctor care.12 The cost data are tied to where thebeneficiary resides. Table I summarizes information on the dependent variable, whose mean value inour data was $2431.

To measure the level of malpractice litigation, the key independent variable utilized is the number ofmedical malpractice lawsuits filed in each calendar year relative to the county population. These datawere obtained from the Mississippi Supreme Court, which maintains data on filings in the statecourts. The data were obtained at the county level and represent the total number of medicalmalpractice lawsuits filed in a given year. The mean value of the litigation variable was 16.05 cases per100 000 persons.

Table I. Variables

Variable Operational Definition Data Source

Selected covariatesIncome Median Household Income 2000 US Census BureauPoverty Percent of the population that is at or below the

Poverty levelUS Census Bureau

Race Percent of the population that is black US Census BureauLn (Population) Natural log of population 2000 US Census BureauEducation Percent of 25+ with BS or higher degree US Census BureauAge % of population 65–69 US Census BureauAge % of population 70–74 US Census BureauAge % of population 75–79 US Census BureauAge % of population 80–84 US Census BureauAge % of population 85+ US Census BureauDemographics % Population growth US Census BureauHospital beds Number of hospital beds Area Resource FileNursing home beds Total nursing home beds Centers for Medicare & Medicaid

ServicesPhysicians Number of area physicians MS State Licensure Board

Main independentNumber of medical malpracticesuits per 100 000 people

Ratio of medical malpractice suits to populationannually

Mississippi Supreme Court

Primary dependentAnnual Part B Expenditures perenrollee

Annual expenditures for physician visits in wholedollars

Medicare fee-for-service files

11Center for Medicare and Medicaid Services, available online at cms.gov.12The Quality of Health Care in the United States: A Report on the Medicare Program. Dartmouth Atlas of Healthcare, 1999.



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In addition to the model discussed here, several alternative models were explored, including the use oflawsuits per 100 area physicians as the litigation variable and the use of judicial districts for fixed-effects.The results paralleled those presented here but with less explained variance.

Beside the main independent variable, other factors are known to cause variation in medicalcosts and specifically, in Medicare expenditures across geographic areas. Previous studies, as well asMedicare expenditures reports, provide substantial help in identifying these factors (Fuchs et al.,2001; Cutler and Sheiner, 1999). Among these factors are age of the population, income,educational levels, cost of living, and proximity to medical care; all have been identified as likelyinfluences on the dependent variable. Consequently, we include versions of most of those controlvariables in our model. Annual per capita Medicare expenditures are defined as a function of14 key variables, including age, income, education, poverty, population size, and area availabilityof health services.13 Because the data are observed across time periods, trend is controlled byyear dummies. Medicare expenditures are higher for persons in nursing homes; therefore, the numberof area nursing home beds is controlled in the model. Finally, because the demographic dataare 2000 census figures and are static, demographic changes are accounted for by includingpopulation growth in the model, measured as annual percent change in population from 1990 to2000.14

Because this analysis is done within one state and with only one payment source, employing fixedpayment rules, regional cost-of-living differences are not a factor. Local cost-of-living differences (at thejurisdiction or county level) are likely to be accounted for by some of the control variables (income andlog of population) or later, by county dummies (Hoch, 1976a, b, 1987, 1999).

As noted above, the panel data set covers the years 1998–2002. A simple pooled regression exposesthe results to possible temporal and spatial heterogeneity. The base model is elaborated in this analysisby incorporating area and time fixed-effects.

Mississippi has 22 separate judicial districts that cover its 82 counties, including a set of countieswithin each district. The legal system in terms of civil litigation is administered at the county and districtlevel; corresponding area fixed-effects are introduced at the county level.

MODEL SPECIFICATION

The basic regression equation for the model is given as

Yit ¼ aþ Tt þ Ai þ BXit þ CZit þ uit

where Yit is the average annual Medicare Part B expenditures per enrollee, Tt the time fixed-effectsdummy, Ai the area fixed-effects dummy, B the medical malpractice litigation coefficient, Xit the medicalmalpractice litigation variable, C the selected covariate coefficients, Zit the vector of selected covariatesand uit the uncorrelated disturbance term.

The formal statement of the hypothesis to be tested is as follows:H1. The per capita cost for Part B Medicare expenditures for the aged population is positively related

to the number of lawsuits per capita.The model was estimated initially without the litigation variable and without time and area fixed-

effects. The model was then elaborated, first by including malpractice litigation and then the fixed-effectsparameters. The base model was estimated with only the independent control variables to assess howwell these variables predicted Medicare Part B expenditures. The litigation variable was then added and

13Log of population was used as a measure of population size as it is believed to be a better form in regional studies of this type(see Hoch, 1976a, b, 1987, 1999).

14Population growth as a control variable was chosen as opposed to extrapolating figures for different years to avoid introducingautocorrelation into the model.



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an F Test conducted to determine if the models with and without litigation were significantly different.Finally, area and time fixed-effects were added to the model to control for possible time period andindividual county effects.

OLS diagnostics: Once the initial model was estimated with OLS, the results were analyzed to identifypossible violations of OLS assumptions. Multicollinearity was determined not to be an issue in themodel. The mean Variance Inflating Factor (VIF) for all of the independent variables was 6.31, andonly one (age 75–79) had a VIF>10, with VIF=12.08.15 It was expected that age categories werecorrelated with each other but these variables seemed too important to eliminate. In practicedependency among the independent variables was not deemed troublesome enough to warrant anychanges to the model specification.

Correlation of the error terms was not identified as a problem in the model. Plots of the residualsagainst time revealed no correlation and the estimated Durbin–Watson statistic was 2.036. Plots of theresiduals against the malpractice litigation variable indicated non-constant error variance suggesting aheteroskedastic error term.16 To remedy this variance, Weighted Least Squares (WLS) were utilized forthe parameter estimates, with the main independent variable (lawsuits per 100 000 population) used asthe weight (Gujarati, 2003; Judge et al., 1988).

PARAMETER RESULTS

As shown by the first column of results in Table II, the WLS estimates for the base model withonly the control variables was significant, with a50:01 and an R-squared of 0.7614. All of thevariables had the expected parameter signs. The model indicates that per-enrollee expenditureswere significantly higher in 2000, 2001, and 2002 relative to 1998, which was used as the base year.A one-point increase in the percent of population at or below the poverty level adds $48.93 toper-enrollee expenditures, and the addition of one nursing home bed adds $1.03 to per-enrolleeexpenditures. Both variables were significant at the one percent level. A one-percent increase inpopulation size is associated with a $149.66 decrease in medical expenditures.17 Population size wassignificant at a50:01:

All of the age variables were significant, with increases in the percent of the population in the65–69 and the 80–84 categories reflecting higher per-enrollee costs and the 70 – 74, 75 – 79, and85 and over categories indicating lesser per-enrollee costs. Population growth was also positive andsignificant, with a one-percentage-point increase in annual population growth adding $164.60 toannual cost per enrollee. Income was negative but not significant, as was percent of the population25 and over with a BS degree or greater. The number of hospital beds, the number of areaphysicians, and the percent of the population that is black were not significant. The results areconsistent with past studies as well as with records maintained by the Centers for Medicare andMedicaid Services.18

Addition of litigation variable: The second column of results in Table II shows the effect ofadding the litigation variable to the model. That variable was measured as the ratio of medicalmalpractice lawsuits per 100 000 persons; it was independently significant at a50:01 with t ¼ 7:80: Itscoefficient implies that an additional lawsuit per 100 000 persons adds $2.49 to annual per-enrolleeexpenditures.

15VIF ¼ 1=1� ðrx2x3 Þ2 where rx2x3 is the correlation coefficient of x2 and x3.

16The plot of residuals is available on request from the senior author, email [email protected] the positive relationship between poverty and medical expenditures, the negative effect for population size is likely due tomany of the poor counties in Mississippi being small in population size and larger counties having more sophisticatedfacilities.

18Medicare Expenditures Reports and Surveys, available online at hhs.gov/cms.



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An F test comparing the restricted and unrestricted models indicates that the model containing thelitigation variable differs significantly from the restricted model. Adding this variable increased theR-squared from 0.7614 to 0.7936. The critical value of F(18, 390), a50:01 is 1.88. The F test calculation is

Table II. Weighted least squares with and without litigation Y=Part B per-EnrolleeAnnual Expenditures

Only control variables Adding lawsuits per 100k

TimeYr1999 �1.117 �6.006

(�55.645) (�51.826)Yr2000 134.782 125.325

(54.357)* (50.638)*Yr2001 279.089 265.242

(55.251)** (51.487)**Yr2002 473.595 325.166

(45.796)** (46.706)**

DemographicsLn (Population) �149.657 �124.694

(49.007)** (45.753)**Annual % Pop growth 164.6 156.555

(27.327)** (25.471)**Ln (Median HH Income) �59.006 �224.284

(�213.632) (�202.248)% at or 5 poverty 48.928 57.465

(7.576)** (7.140)**% 25+ with BS or > �6.349 �7.823

(�3.894) (�3.632)*% Black 2.814 0.811

(�1.715) (�1.618)

Age% 65–69 501.222 397.406

(76.564)** (72.537)**% 70–74 �284.605 �363.712

(�89.955)** (�84.388)**% 75–79 �556.13 �130.877

(�127.653)** (�130.797)% 80–84 731.577 543.788

(129.478)** (122.965)**% 85+ �338.446 �350.433

(�100.704)** (�93.799)**

Medical servicesPhysicians �0.046 �0.176

(�0.185) (�0.173)Nursing Home Beds 1.026 0.949

(0.292)** (0.272)**Hospital Beds �0.155 �0.125

(�0.107) (�0.1)

Litigation 2.485Lawsuits per 100k Population (0.319)**Constant 2896.63 �435.003

(�2408.04) (�2282.98)Observations 410 410R-squared 0.7614 0.7936

Standard errors in parentheses.

*Significant at 5%; ** significant at 1%.



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as follows:

F ¼ ½ð0:7936� 0:7614Þ=18�=½ð1� 0:7936Þ=ð410� 20Þ� ¼ 3:38; > Fc ¼ 1:88

County fixed-effects: Executing the model first with the control variables and all 81 county dummiesrevealed a problem of exact collinearity among the regressors (concentrated in the county dummyvariables). With elimination of exact collinearity, a considerable collinearity problem remained.The level of collinearity was reduced to reasonable levels by excluding counties that were within plusor minus 5% of the mean value for the dependent variable.19 The mean value for Part B expenditureswas $2431, with a standard deviation of $386 over the 5-year period. Counties that had a meanexpenditure between $2309 and $2553 were excluded from the model, resulting in the exclusion of 25counties, with 57 retained. Results for the independent variables, exclusive of dummy variables, appearas Table III.

The coefficient for the lawsuits variable dropped from $2.49 to $1.40 with the countydummies included, but remained significant (a50:01) with t ¼ 6:64: The intercept shifting valuesfrom the fixed-effects model are listed for significant counties as Appendix A (positive shifts)and Appendix B (negative shifts). Some specific cases are worthy of comment (possibleslope differences were tested for by interaction terms for the dummies but none were found to besignificant). Thus, the 9th and the 22nd Districts contain some of the counties perceived as‘plaintiff friendly,’ with results for those counties shown as Table IV. The 22nd districtconsists of Copiah, Claiborne, and Jefferson counties. Copiah County, the largest of the three,is the only county not characterized as plaintiff friendly; it had a negative and significantcoefficient. Claiborne County’s coefficient was positive and significant with t ¼ 7:45; as was JeffersonCounty’s with t ¼ 7:32: These results meant that enrollee expenditures were $893 higher in ClaiborneCounty and $1227 higher in Jefferson County relative to the average value of 2431, equating to 36.7 and50.5%, above the mean respectively.

In the 9th district, Issaquena and Sharkey counties were very small in population size, with fewphysicians and few malpractice lawsuits over the sum of the periods of our study. However, both have areputation for being target venues for lawsuits directed at other economic activities, presumably helpingto explain their high positive and significant intercept shifters of $2311 and $518, respectively. It can besurmised that awareness of the litigious climate made enrollees from those counties more subject todefensive medical practice. Warren County, with an average number of malpractice suits, had a positiveand significant intercept shifter of $310.

ESTIMATING THE IMPACT OF LITIGATION

We turn now to considering the impact of litigation in terms of direct versus indirect costs imposed onhealthcare expenditures. As noted in the introduction, inferences drawn from the statistical resultsnecessarily involve some speculative assumptions, primarily reflecting interpretations of the results for

19We could not include all of the counties (save one) in our equation because of exact collinearity. In our initial regression, 12independent variables were found to have an exact linear relationship with one another; of these, 9 were counties and 3 wereother explanatories, meaning we would need to eliminate 13 variables. It seemed best to retain all of the control variables sincetheir impact on costs were well established in the literature; hence, we decided to eliminate collinear counties. First, weeliminated 13 counties and obtained an R square result of 0.9617. However, although no longer faced with exact collinearity, thecalculated Variance Inflating Factor (VIF) was extremely high, indicating a serious collinearity problem remained. This helpedestablish the case for dropping counties whose costs per enrollee were within 5% of the mean of the dependent variable,presumably indicating little impact on the relationship. In effect, this treated those counties as comprising a single district withzero impact on the regression equation. Our R square in this case was 0.9572 and our test results for collinearity indicated a verysubstantial drop in the problem. To address an anonymous reviewer’s concerns we provide the results of 3 alternativeapproaches in Appendix C; none changed the significance of litigation or the magnitude of the effect. Appendix D showscorresponding comparisons using total Medicare costs.



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the county dummy variables. Initially, if we apply the rule that the regression relationship holds at themeans of the dependent and independent variables, we obtain a preliminary impact estimate using theinitial case (of Table IV). Here, the mean of litigation per 100 000 persons was 16.05. Multiplying that

Table III. Weighted least squares with litigation & county/time fixed-effects Y=Part B per-enrollee annual expenditures

(1)Base=25 Counties Where

Mean of Y� 5% of Overall Mean

TimeYr1999 43.102

(25.976)+Yr2000 156.191

(25.602)**Yr2001 304.823

(26.414)**Yr2002 412.127

(25.318)**DemographicsLn(Population) �60.165

(97.282)Annual % Pop Growth �16.981

(37.004)Ln(Median HH Income) 707.137

(503.132)% at or 5 poverty 11.58

(9.485)% 25+ with BS or > �0.758

(4.562)% Black 1.458

(2.319)Age% 65–69 �86.781

(133.713)% 70–74 169.687

(108.824)% 75–79 204.401

(139.541)% 80–84 �337.392

(170.255)*% 85+ 107.003

(129.157)

Medical servicesPhysicians 0.462

(0.383)Nursing home beds 0.09

(0.495)Hospital beds �0.207

(0.138)

LitigationLawsuits per 100k Population 1.4

(0.211)**Constant �5021.282

(4961.245)Observations 410R-squared 0.96


+Significant at 10%; * significant at 5%; ** significant at 1%.



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figure by its corresponding coefficient of 2.485 yields $39.88, which is 1.6% of the mean Part Bexpenditures of $2431. In contrast, between 1993 and 2003, the annual dollar amount of settlementsaveraged 0.30% of annual medical expenditures.20 Hence, treating the 0.3% as equivalent to direct costsyields an initial estimate of indirect costs as 1.3% of total costs (1.6–0.3=1.3). After introducing thecounty dummies, the litigation coefficient fell to 1.4. Multiplying this value by the mean of 16.05 yields$22.47, which accounts for approximately one percent of average expenditures (0.93% to be precise),not varying much from the original estimate, and again above the direct cost estimate. Further, if weagain calculate values at the means, now assuming that all the coefficients of the county dummies reflectthe effects of litigation (and treating each dummy’s average value as 1

82) we obtain an additional cost

increment of $51.29, added to the $22.47 from the litigation variable, per se, which yields a total of$73.36, corresponding to 3% of the average cost of $2431. Finally, we can push this hypothesis to anextreme formulation, based on an ‘optimistic’ interpretation of negative county dummy coefficients,which occurred in a number of cases (see the appendix tables). If the negative cases were to be viewed asreductions in defensive medicine costs, the most negative dummy value of roughly �$600 (for CalhounCounty, which also had zero lawsuits) corresponds to the maximum absence of defensive medicine, sothat the mean of $2431 includes $600 of defensive medicine. Under this interpretation, then, defensivemedicine amounts to roughly 25% of total costs.

Another extreme interpretation occurs if we limit our conclusions only to the counties in Districts 9and 22 which are regarded as ‘plaintiff friendly’, corresponding to the assumption that only theirdummy variables reflect the litigation climate; we can then infer that much defensive medicine occurs inthose counties. If for each such county, we multiply the number of lawsuits times 1.4 and add the countydummy coefficient to that result, and then multiply that total by the number of county enrollees, we will

Table IV. County and time fixed-effects 9th and 22ndjudicial district counties

Dummy Coefficients

22nd DistrictClaiborne 892.867

(119.768)**Copiah �252.868

�(65.379)**Jefferson 1227.70

(167.674)**

9th DistrictIssaquena 2311.04

(312.690)**Sharkey 518.264

(107.202)**Warren 310.62

(127.505)*

LitigationLawsuits per 100k 1.4

(0.211)**Constant �5021.28

(�4961.245)Observations 410R-squared 0.96


*Significant at 5%; **Significant at 1%.

20Based on data from the National Practitioner Databank and total medical expenditures from CMS. The same figure heldnationally and for the entire state of Mississippi.



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obtain a hypothesized litigation related cost; we then can derive an estimate of the percent of totalexpenditures attributable to defensive medicine by dividing through by total county costs. For example,44% of Jefferson County’s total cost is accounted for, 28% of Claiborne’s, 20% of Sharkey’s and 15%of Warren’s, indicating the likelihood of a great deal of defensive medicine occurring in the plaintiff-friendly counties.

In contrast to these estimates, at the other extreme, we can take the much more cautious approach ofassuming that the county fixed effects only reflect unexplained latent differences among counties. This isessentially the position of Gujarati, Kennedy, and Sutaria and Hicks, as cited earlier. That position wasanticipated by Maddala (1971) who argued, ‘rarely is it possible to give a meaningful interpretation tothe dummy variables.’ Here, although the litigation impact is restricted only to the effect of thecoefficient of 1.40, sans the dummy variable effects, there is nevertheless some evidence suggesting thatthere are considerable indirect costs of litigation. The maximum county value for the lawsuits variablewas 277 lawsuits per 100 000 people (for Jefferson County in 2002). Applying the 1.4 coefficient to the277 implies a potentially added amount of $387.80 in expenditures per enrollee or 15.9% of the mean of$2431.

Finally, a parallel approach we draw on is a comparison of the model’s estimated impact on theactual dollar amounts of malpractice settlements in a major area of the state. The National PractitionerDatabank of the federal government’s Health and Human Services is a database which maintainsmedical malpractice judgments and settlements nationwide. Physicians are required to reportmalpractice settlement payments to the databank, though the identities of the individual physiciansare not disclosed. The data are provided publicly at the state level, but an extract was obtained by thisstudy for the larger Metropolitan Statistical Areas (MSAs) in the country.21 This data allows acomparison of the actual dollar amounts paid out in settlements to the impact of litigation estimated bythe model for the Jackson, Mississippi MSA.22

The Jackson MSA consists of 5 counties: Copiah, Hinds, Madison, Rankin, and Simpson counties.The MSA spans four separate judicial districts. Hinds County contains the state capital and also theUniversity of Mississippi Medical Center, along with several other medical facilities. It is also the mostpopulous in the state and is a separate judicial district (District 7). Madison and Rankin countiescomprise District 20, and are some of the more affluent counties in the state. Copiah County is part ofthe 22nd District, and Simpson County is part of District 13. The MSA represents some diversity in itscomposition and may represent an appropriate application of the model parameters.

Table V compares the dollars paid out in malpractice settlements for 1998–2002 to the estimatedimpact of malpractice litigation generated by the model. Applying the number of lawsuits per 100000persons and the coefficient generated by the county fixed-effects model, we calculate an aggregateimpact based on the number of enrollees.23 Medicare Part B expenditures comprise about 8% of totalmedical expenditures in the state, so the assumption is made that litigation would affect all medical cost,not just Medicare Part B. Hence, the total Medicare effect is divided by 0.08 to estimate the total stateeffect.24 The data of Table V suggest that the annual amount paid to settle medical malpractice lawsuitsare just part of the true effect of litigation.

We conclude that litigation has a statistically significant relation to Medicare expenditures, a resultthat holds under a variety of model specifications. One contrary specification is worth consideration inpassing: to wit, reverse causation: that litigation is caused by increased illness. The argument would runthis way: sicker patients require more intervention (more tests and procedures); with greater

21The authors are particularly grateful to Dr Robert Oshel for his expertise and assistance in preparing these data.22MSA definitions are based on revised MSA boundaries released in June 2003 by the OMB.23The MSA had a population of 497 197 in 2000. The total number of lawsuits per 100000 is given as (497 197/100 000)/totallawsuits. Since the MSA includes 5 counties, the sum of lawsuits per 100,000 produced a larger number, so this approach is moreconservative than an aggregate number. The cost figure then is lawsuits per (100 000*$1.40)(enrollees) for Part B.

24Calculated fom data from CMS, available online: http://www.cms.hhs.gov/NationalHealthExpendData/



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intervention, there is greater probability of some dire occurrence or misadventure; hence, a greaterlikelihood of litigation. But note that our control variables explained a great deal of variance, suggestingan ‘equalization’ of sickness levels across counties, with the litigation variable highly significant despitethe strong contributions of the control variables. (Put more explicitly: if reverse causation held, highcorrelation with the control variables would cause litigation to account for roughly 0.30% of total cost-equaling direct cost-if it attained statistical significance.)

We also found that plaintiff-friendly counties tend to have much higher levels of expenditure than theaverage level. The statewide effect of litigation on expenditures as a result of defensive practices, at aminimum, is 3 times its direct cost (0.93% vs 0.30%). There is evidence as well (in part involvingspeculative hypotheses) that the induced indirect costs of litigation, in terms of defensive medicine, canbe considerable. The speculative hypotheses turn on interpretations of dummy variable results; at oneextreme, the reduction in coefficient value for litigation which occurs when county dummies areintroduced, occurs because some counties foster litigation by being ‘plaintiff friendly’; at the otherextreme, some counties have underlying latent ‘unobservable for now’ characteristics (explainable by alarge variety of possible variables) which happen to be well correlated with litigation levels.25

SUGGESTIONS FOR FUTURE RESEARCH

Future research might well profit by more conclusive insight into those alternative extremeexplanations, finding where in-between the truth lies.

A more general perspective on future research possibilities also involves some focus on the countydummy variables. To start with, our coverage of Medicare costs in Mississippi has some appealingcharacteristics. Medicare enrollees constitute a fairly homogeneous group, likely eliminating manydifferences attributable to gender (costs of pregnancy and premature births, for example). As notedabove, the Mississippi coverage yields constant tort law and insurance coverage. Further, medicalpractice is likely more homogeneous than would occur over a set of diverse states.

A starting point for future research is afforded by our results that the litigation coefficient droppedfrom $2.49 to $1.40 upon the introduction of the county dummies. We can, in effect, view the $1.40result as the ‘base’ defensive medicine effect that holds for all physicians in the state, while the roughlyone dollar increment ($2.49–1.40) may well reflect the impact of ‘litigation friendly’ counties. Futureresearch might further explore county characteristics to establish how other county variables enter.Medicare costs involve the setting of prices for specific procedures by the Medicare administration.County differences in cost per enrollee will then reflect differences in number of procedures or mix ofprocedures, price having been set administratively. Those differences, in turn, can be attributed topatient or physician characteristics. Our control variables, as the name implies, controlled for patientcharacteristics at the county level, in line with a considerable literature (as noted above). But additionalinformation on patient characteristics at the enrollee level, including gender, could be of help. For

Table V. Model litigation impact estimates and National Practitioner Databank payments Jackson,Mississippi MSA

Year Enrollees Lawsuits filed Per 100k Total settlements Estimated impact from model Settlements as % of impact

1998 47 496 88 17.70 $6 225 500 $ 14 711 239 42.31999 47 652 112 22.53 $6 327 278 $ 18 784 892 33.72000 48 082 114 22.93 $6 542 488 $ 19 292 874 33.92001 48 321 144 28.96 $14 430 594 $ 24 491 081 58.92002 48 871 289 58.13 $8 955 926 $ 49 711 700 18.0

25This discussion is extended in Appendix F.



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example, some patients just turning 65 might have had pre-existing conditions they put off treating untilthey were eligible for Medicare coverage. At the physician level, increases in number of procedures ormore involved procedures are both consistent with defensive medicine. However, detailed informationon those measures could be worth exploring in future research. We remain convinced that some of thecounty effects reflect a response to litigation. Refinements in measuring incidence and types of litigationcould also pay off in future research endeavors.

Finally, as noted previously, this study was conducted prior to significant legislative changes in tortlaw specifically affecting medical malpractice litigation. Our research furnishes a foundation on which tobuild future models that could study the impact of tort reform on health care costs. Studies whichmeasure the effects of legislative changes, as well as time-lags and jurisdictional variations associatedwith those changes would likely have broad policy significance.

APPENDIX A: COUNTIES WITH SIGNIFICANTLY HIGHER PER-ENROLLEEEXPENDITURES RESULTS FROM COUNTY FIXED-EFFECTS REGRESSION MODEL

Additional cost per enrolleea

Claiborne 892.867(7.45)**

Coahoma 226.427(3.97)**

Grenada 494.341(6.95)**

Holmes 570.635(4.19)**

Issaquena 2311.040(7.39)**

Jefferson 1227.703(7.32)**

Leake 399.641(3.79)**

Marion 358.814(2.66)**

Pearl River 238.560(2.20)*

Perry 507.446(3.44)**

Quitman 372.076(4.11)**

Scott 227.326(1.73)+

Sharkey 518.264(4.83)**

Stone 415.960(3.99)**

Warren 310.620(2.44)*

Wilkinson 758.583(6.72)**


(6.64)**Constant �5021.282


Absolute value of t statistics in parentheses.+Significant at 10%; * significant at 5%; ** significant at 1%.aRelative to mean value of 2431.



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APPENDIX B: COUNTIES WITH SIGNIFICANTLY LOWER PER ENROLLEEEXPENDITURES RESULTS FROM COUNTY FIXED-EFFECTS REGRESSION MODEL

Reduced cost per enrolleea

Adams �268.128(3.08)**

Calhoun �598.140(4.33)**

Choctaw �409.714(2.50)*

Clay �475.995(5.93)**

Copiah �252.868(3.87)**

DeSoto �303.560(1.66)+

Hinds �560.011(1.74)+

Kemper �261.082(1.76)+

Lafayette �298.318(2.40)*

Lee �272.353(3.09)**

Leflore �167.265(2.23)*

Lowndes �276.063(3.01)**

Monroe �280.073(2.97)**

Noxubee �475.403(6.34)**

Oktibbehaq �336.135(2.19)*

Panola �219.928(2.04)*

Simpson �278.399(3.11)**

Tishomingo �212.641(2.42)*

Tunica �364.001(2.56)*

Union �216.130(2.45)*

Webster �343.367(1.96)+


(6.64)**Constant �5021.282


Absolute value of t statistics in parentheses.+ Significant at 10%; * significant at 5%; ** significant at 1%.aRelative to mean value of 2431.



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APPENDIX C: WEIGHTED LEAST SQUARES FIXED-EFFECTS MODELS Y=PART B PERENROLLEE EXPENDITURES

(1) (2) (3) (4)Dropped Dropped Dropped Dropped

25 Counties 13 Counties 13 Counties 1 countyNearest Mean Randomly Nearest Mean Randomlya

Timeyr1999 43.102 25.746 25.746 25.746

(25.976)+ (25.904) (25.904) (25.904)yr2000 156.191 138.368 138.368 138.368

(25.602)** (25.637)** (25.637)** (25.637)**yr2001 304.823 281.725 281.725 281.725

(26.414)** (27.170)** (27.170)** (27.170)**yr2002 412.127 384.49 384.49 384.49

(25.318)** (26.277)** (26.277)** (26.277)**

DemographicsLn(population) �60.165 �1652.774 2954.47 �25.476

(97.282) (611.852)** (1160.940)* (72.211)Annual % Pop Growth �16.981 �60.968 39.664 452.753

(37.004) (121.328) (1807.416) (48.819)**Ln(Median HH Income) 707.137 5231.289 �6324.737 0

(503.132) (4273.716) (12 263.168) 0% at or 5 poverty 11.58 134.822 �207.949 122.094

(9.485) (149.724) (132.24) (13.376)**% 25+ with BS or > �0.758 82.809 17.898 16.097

(4.562) (77.887) (239.783) (5.497)**% Black 1.458 5.383 �16.981 �1.486

(2.319) (20.459) (44.003) (3.034)

Age% 65–69 �86.781 �361.566 �2749.369 538.937

(133.713) (477.802) (1056.943)** (130.095)**% 70–74 169.687 2751.536 �134.175 �102.034

(108.824) (1338.840)* (508.022) (145.002)% 75–79 204.401 �1318.699 2046.88 0

(139.541) (930.263) (5376.126) 0% 80–84 �337.392 �833.019 1907.749 0

(170.255)* (1274.923) (4209.581) 0% 85+ 107.003 �284.89 429.3 520.798

(129.157) (714.711) (2093.099) (155.698)**

Medical servicesPhysicians 0.462 3.693 3.693 3.693

(0.383) (0.932)** (0.932)** (0.932)**Nursing home beds 0.09 �0.211 �12.836 �2.823

(0.495) (0.532) (4.075)** (1.066)**Hospital beds �0.207 �0.443 �0.443 �0.443

(0.138) (0.148)** (0.148)** (0.148)**

LitigationLawsuits per 100k population 1.4 1.424 1.424 1.424

(0.211)** (0.208)** (0.208)** (0.208)**Constant �5021.282 �40283.252 44199.358 �2871.338

(4961.245) (42951.08) (126410.465) (856.550)**Observations 410 410 410 410R-squared 0.96 0.96 0.96 0.96

Standard errors in parentheses.+ Significant at 10%; * significant at 5%. In addition to the 6 covariates listed as eliminated, 6 additional counties were alsoeliminated; ** significant at 1%.a0 indicates variable omitted because of exact collinearity.



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APPENDIX D: WEIGHTED LEAST SQUARES FIXED-EFFECTS MODELS Y=TOTAL PERENROLLEE EXPENDITURES (PART A þ PART B)

(1) (2) (3) (4)Dropped Dropped Dropped Dropped

25 Counties 13 Counties 13 Counties 1 countyNearest Mean Randomly Nearest Mean Randomlya

Timeyr1999 �241.007 �256.676 �256.676 �256.676

(57.868)** (54.364)** (54.364)** (54.364)**yr2000 �2.587 �29.212 �29.212 �29.212

(57.033) (53.804) (53.804) (53.804)yr2001 435.880 394.364 394.364 394.364

(58.842)** (57.022)** (57.022)** (57.022)**yr2002 906.616 876.690 876.690 876.690

(56.401)** (55.147)** (55.147)** (55.147)**

DemographicsLn(Population) �131.322 948.193 4703.923 �238.088

(216.716) (1284.097) (2436.471)+ (151.550)Annual % Pop Growth 35.825 1032.963 1274.650 799.339

(82.434) (254.631)** (3793.234) (102.456)**Ln(Median HH Income) 3263.297 �14 413.071 �8693.336 0.000

(1120.830)** (8969.271) (25 736.781) (0.000)% at or 5 poverty 53.673 �590.574 �81.597 188.748

(21.131)* (314.227)+ (277.532) (28.072)**% 25+ with BS or > �47.991 �428.678 �113.029 �0.635

(10.163)** (163.462)** (503.234) (11.536)% Black 1.105 110.760 �38.089 �10.296

(5.166) (42.937)* (92.349) (6.367)

Age% 65–69 �783.350 161.356 �3982.661 450.185

(297.874)** (1002.765) (2218.213)+ (273.032)% 70–74 141.829 �2518.139 �866.827 432.808

(242.428) (2809.832) (1066.188) (304.316)% 75–79 2090.818 2793.194 2722.775 0.000

(310.857)** (1952.347) (11 282.906) (0.000)% 80–84 �1362.988 �11 442.536 3462.043 0.000

(379.278)** (2675.689)** (8834.672) (0.000)% 85+ 130.222 5154.882 921.869 537.280

(287.723) (1499.969)** (4392.799) (326.763)

Medical ServicesPhysicians �1.573 1.380 1.380 1.380

(0.854)+ (1.956) (1.956) (1.956)Nursing home beds 2.255 �0.499 �14.487 0.272

(1.103)* (1.116) (8.551)+ (2.238)Hospital beds �0.237 �0.054 �0.054 �0.054

(0.307) (0.311) (0.311) (0.311)

LitigationLawsuits per 100k 1.914 1.887 1.887 1.887

(0.470)** (0.436)** (0.436)** (0.436)**Constant �28 465.992 168 225.913 54 472.065 �666.085

(11 052.195)* (90 141.674)+ (2 652 98.358) (1797.645)Observations 410 410 410 410R-squared 0.94 0.95 0.95 0.95

Standard errors in parentheses.+ Significant at 10%; * significant at 5%; ** significant at 1%.a0 indicates variable omitted because of exact collinearity.



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APPENDIX E: WEIGHTED LEAST SQUARES FIXED-EFFECTS MODELS Y=PART B PERENROLLEE EXPENDITURES

(2) (3)(1) Dropped 9 Dropped

Dropped 9 Collinear Counties 25 CountiesCollinear & 3 Collinear Nearest MeanCounties Explanatories*** (Original Model)

Timeyr1999 25.746 43.102

(0.99) (1.66)+yr2000 138.368 156.191

(5.40)** (6.10)**yr2001 281.725 } 304.823

(10.37)** (11.54)**yr2002 384.490 412.127

(14.63)** (16.28)**

DemographicsLn(Population) �49.731 �60.165

(0.62) (0.62)Annual % Pop Growth 348.149 �16.981

(2.67)** (0.46)Ln(Median HH Income) 0.000 707.137

(.) } (1.41)% at or 5 poverty 49.816 11.580

(4.21)** (1.22)% 25+ with BS or > 1.763 �0.758

(0.02) (0.17)% Black 1.455 1.458

(0.03) (0.63)

Age% 65–69 903.594 �86.781

(1.13) (0.65)% 70–74 �625.756 169.687

(0.69) (1.56)% 75–79 0.000 204.401

(.) } (1.46)% 80–84 0.000 �337.392

(.) (1.98)*% 85+ 240.676 107.003

(0.51) (0.83)

Medical servicesPhysicians 3.693 0.462

(3.96)** (1.20)Nursing home beds �2.839 } 0.090

(1.68)+ (0.18)Hospital beds �0.443 �0.207

(2.98)** (1.50)

LitigationLawsuits per 100k Population 1.424 1.400

(6.85)** (6.64)**Constant �231.034 } �5021.282

(0.03) (1.01)Observations 410 410R-squared 0.96 0.96Variance Inflating Factor 845.63 31.03

Absolute value of t statistics in parentheses.+ Significant at 10%; * significant at 5%; ** significant at 1%; ***the ex post results obtained by the computer eliminating the 3covariates of column 1 were exactly the same as the ex ante results obtained by deleting those 3 variables in an initial specification.(�) and non-entry: variable automatically eliminated because of exact collinearity.



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APPENDIX F: NOTES ON DUMMY INTERPRETATIONS

The county-level dummies as a group were highly significant in our models as were some of theindividual counties. If we regard the model without fixed-effects as the restricted model and the modelwith the dummies as the unrestricted, an F Test indicated that the two models were significantlydifferent. Further, the addition of the county dummies reduced the litigation coefficient from 2.49 to1.40, a reduction of 43%. Tests of possible slope differences between counties via interaction terms(litigation*county) were insignificant suggesting the differences in the counties were reflected only invarying intercepts.

Interpretation of dummy effects are often assumed to be unknown, attributed to some latentdifferences unique to the specific unit of analysis, observation, or group. Hoch (1976a, b) however,found that the potential cause of these differences is sometimes identifiable. In a study of Californiadairy farm production functions using approximately 10 000 observations on 1000 individual dairyfarms, original estimates of returns to scale obtained by summing Cobb–Douglas production elasticitieswere very close to one (indicating constant returns to scale). With introduction of firm effects (firmdummy variables), the elasticity sum typically fell to 0.7–0.8, indicating decreasing returns to scale.A plausible explanation was that the original case (without firm effects) measured returns to scale withall factors variable, including managerial ability; with the firm dummies now treating managerial abilityas fixed to the firm, the drop in coefficient sum could be explained. This explanation received someconfirmation by development of outside information. The field workers who had developed the datahad a long-standing advisory relation with the individual producers; they were asked to rate the qualityof management of each producer using a simple scale with 1 the poorest and 4 the best of managerialability. Those ratings were correlated with the estimated firm effects, and highly significant positivecorrelations were obtained. Hence, outside information was used to lend some support to theexplanation of results (Hoch, 1976b).

In the present study, we utilized some of the information generated from the model to consider thelitigation climate as a partial cause of the county effects. Some of the counties that have a reputation forbeing plaintiff-friendly are small with very few practicing physicians and thus do not have a largenumber of medical malpractice lawsuits. Some of these counties, however, have a reputation forbeing targets of other types of lawsuits, some involving fraudulent claims. For example, in 2005a number of individuals in Jefferson County (mentioned specifically by Richard Scruggs as aplaintiff-friendly jurisdiction) were convicted in federal court for filing false claims. [See US Departmentof Justice press release available online:http://www.usdoj.gov/criminal/press room/press releases/2005 4162 FalseClamSubmission080205.pdf. Also see Clarion Ledger, Jackson, MS, 5 August 2005:Jimmie E. Gates]. In our analysis Jefferson County generated one of the largest coefficients ($1227)which was 50% greater than the mean per enrollee expenditure. In addition, five of the six othercounties in the 9th and 22nd Districts were positive and significant (Table IV). Given our findings, theprevious work cited, and the anecdotal evidence, it seemed reasonable to consider this interpretation ofthe dummies as a possible partial explanation.

ACKNOWLEDGEMENTS

Brandon Roberts is President of Premier Insights, Inc. a research consulting firm he founded in 1995.This paper is based on part of the senior author’s PhD dissertation; he received his doctorate in theSchool of Social Sciences at the University of Texas at Dallas (UTD). Irving Hoch is Professor ofEconomics Emeritus at UTD, and served as co-chair of the dissertation committee. The dissertation andpaper benefited from the insights and suggestions of Euel Elliott (co-chair), Anthony Champagne,Donald Hicks, Paul Jargowsky, Richard Scotch and Barry Seldon, all UTD faculty members.Comments by two anonymous reviewers were stimulating and helpful and are gratefully acknowledged.



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malpractice litigation and medical costs in mississippi

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