Congressional Budget Offi ce

Background Paper

THE CONGRESS OF THE UNITED STATES

Medical Malpractice Tort Limits and

Health Care Spending

April 2006
Pub. No. 2668
Preface

In recent years, the Congress has considered a number of legislative proposals that 

would modify the tort litigation system in the United States by imposing limits on 

medical malpractice claims, as many states have done. In this background paper, the 

Congressional Budget Office (CBO) reviews existing studies of tort limits and health 

care spending and presents an empirical analysis of a broader set of spending measures 

than has been examined previously. The analysis also performs a number of detailed 

specification tests on the results. In keeping with CBO’s mandate to provide objective, 

nonpartisan analysis, the report makes no recommendations.

Chapin White and Stuart Hagen of CBO’s Health and Human Resources Division 

prepared the report under the supervision of James Baumgardner and Bruce 

Vavrichek. Alshadye Yemane contributed to the data analysis. The report benefited 

from comments by Thomas Bradley, Thomas DeLeire, Matthew Goldberg, Robert 

Murphy, and John Sabelhaus, all of CBO. A number of outside reviewers also provided useful comments, including Michael Chernew of the University of Michigan, 

Martin Gaynor of Carnegie Mellon University, Sherry Glied of Columbia University, 

and Michelle Mello of Harvard University. (The assistance of external reviewers 

implies no responsibility for the final product, which rests solely with CBO.)

Loretta Lettner edited the report, and Christine Bogusz proofread it. Maureen 

Costantino and Christian Howlett prepared the report for publication, and Lenny 

Skutnik produced the printed copies. An electronic version of the report is available 

on CBO’s Web site, www.cbo.gov.

Donald B. Marron

Acting Director

April 2006
Note

Numbers in the tables and text of this paper may not sum to totals because of

rounding. 
v

Contents

Summary and Introduction 1

Background on Medical Malpractice and Tort Limits 1

Conflicting Estimates 2

Interpreting the Results 3

Background 4

Tort Limits Under Consideration by the Congress 4

Definitions of Defensive Medicine 5

Possible Links Between Malpractice Tort Limits and Health

Care Spending 7

The Effects of the Malpractice Environment on Health Care

Utilization and Spending: Existing Evidence 8

CBO’s Empirical Analysis 13

Methodology 13

Data Sources and Spending Measures 17

Results 20

Specification Tests 28

Graphical Comparison 28

The Addition of “Lead” Variables 30

The Addition of Controls for Medicare’s Prospective Payment

System for Hospitals 31

The Addition of State-Specific Time Trends 33

Discussion 33

Appendix A: Adjusting Health Care Spending per Capita for

Demographics 37

Appendix B: Detailed Regression Results 39

Appendix C: Regression Results, Including Leads of Tort Limits 53

Appendix D: Regression Results, Including Controls for Medicare’s 

Prospective Payment System for Hospitals 67
vi

Tables

1. Descriptive Statistics on Health Care Spending and 

Tort Limits 21

2. Summary of Regressions of Overall Health Care

Spending per Capita on Tort Limits 22

3. Summary of Regressions of Medicare Spending per

Beneficiary on Tort Limits 23

B-1. Regressions of Overall Health Care Spending per

Capita 41

B-2. Regressions of Overall Hospital Care Spending per

Capita 43

B-3. Regressions of Overall Spending on Physician and

Clinical Services per Capita 45

B-4. Regressions of Medicare Spending per Beneficiary 47

B-5. Regressions of Medicare Part A (Hospital)

Spending per Beneficiary 49

B-6. Regressions of Medicare Part B (Physician)

Spending per Beneficiary 51

C-1. Illustration of Leads of Tort Limit Variables

(Noneconomic Caps in Alaska and Alabama) 54

C-2. Regressions of Overall Health Care Spending per

Capita, Including Leads 55

C-3. Regressions of Overall Hospital Spending per Capita,

Including Leads 57

C-4. Regressions of Overall Spending per Capita for

Physician and Clinical Services, Including Leads 59

C-5. Regressions of Medicare Spending per Beneficiary, 

Including Leads 61

C-6. Regressions of Medicare Part A (Hospital) 

Spending per Beneficiary, Including Leads 63

C-7. Regressions of Medicare Part B (Physician)

Spending per Beneficiary, Including Leads 65
vii

D-1. Regressions of Medicare Spending per

Beneficiary with Prospective Payment System

Controls 69

D-2. Regressions of Medicare Part A (Hospital)

Spending per Beneficiary with Prospective Payment 

System Controls 71

Figures

1. Number of States with Tort Limits in Place, by Year,

1980 to 2003 6

2. Summary of Findings on Tort Limits 24

3. Percentage Difference in Spending for Health Care

Between States with Caps on Noneconomic

Damages and States with No Caps, 1980 to 2000 29

Box

1. The Effects of Tort Limits on Premiums for

Medical Malpractice Insurance 10

Tables (Continued)

Summary and Introduction

The tort system in the United States—which enables claimants to seek redress for 

alleged injury to their person, reputation, or property—has been the subject of 

controversy in recent years. Critics charge that the system, as it is applied to claims 

of medical malpractice, is subjective and costly and that excessive damage awards have 

increased health care spending. Many states have implemented restrictions on tort 

claims for medical malpractice, and the Congress has considered various proposals to 

establish nationwide tort limits similar to those already imposed by states.

The Congressional Budget Office (CBO) previously considered the effects of limits 

on tort claims for medical malpractice at the state level and concluded that such limits 

reduced both malpractice awards and malpractice insurance premiums. Lower malpractice premiums, in turn, would reduce providers’ costs and result in somewhat 

lower private health insurance premiums and costs for federal health programs. This 

CBO background paper is motivated by the question of whether changes in the malpractice environment could also have an effect on the utilization of health care, perhaps by altering physicians’ decisions. The analysis differs from others of its kind in 

that it looks at a broader set of spending measures than has been examined elsewhere. 

Although the analysis provides some evidence of links between tort limits and health 

care spending, the results are inconsistent and depend on the particular relationships 

and specifications tested. The mixed results also demonstrate the difficulty of disentangling any effects of tort limits from other factors that affect levels of spending for 

health care. CBO continues to monitor the work of other researchers and conduct its 

own research on the issue.

Background on Medical Malpractice and Tort Limits

Under common law, individuals may pursue civil claims against physicians or other 

health care providers for alleged “torts,” meaning breaches of duty that result in personal injury. Underpinning the system of tort law is the principle that physicians and 

other medical providers owe a duty of care to their patients, and that patients may 

attempt to recover from those providers losses that result from breaches of such duty. 

The system has twin objectives: deterring negligent behavior on the part of providers 

and compensating claimants for losses (including medical costs, lost wages, and pain 

and suffering) they incur as the result of an injury that occurs because of negligence. 1

Malpractice claims are generally pursued through the state courts, and states have 

established various rules by which those claims are adjudicated. Some recent proposals 

that have been considered by the Congress, described in more detail below, include 

setting caps on awards for noneconomic damages and punitive damages, limiting 

attorneys’ fees, restricting defendants’ liability on the basis of the degree of their 

responsibility for an injury, and allowing evidence of collateral-source benefits to be 

introduced at trial.

1. Congressional Budget Office, The Economics of U.S. Tort Liability: A Primer (October 2003).
2

Some analysts argue that new tort limits would reduce overall spending for health care 

in two ways. First, they would reduce the size of the average award paid by malpractice insurers to claimants and, thus, reduce premiums for malpractice insurance (the 

“malpractice premium” effect). A reduction in malpractice premiums would tend to 

reduce the prices that insurers and individuals pay for health care services.2 Second, 

those analysts argue that the proposed changes in tort law would reduce health care 

spending by reducing the intensity and volume of health care services provided (the 

“utilization” effect). The argument for a utilization effect is built on two premises: 

first, that fear of litigation drives medical providers to deliver additional medical services (“defensive medicine”), and second, that the proposed tort limits would reduce 

that perceived threat among physicians and thereby reduce utilization and spending.

Conflicting Estimates

Previously published estimates of the effect of proposed tort limits on federal spending vary widely. The discrepancies among those estimates are attributable primarily 

to disagreements over the existence and magnitude of possible effects of tort limits on 

utilization. In a recent cost estimate, CBO concluded that a legislative proposal containing a package of malpractice tort limits—the “Help Efficient, Accessible, LowCost, Timely Healthcare (HEALTH) Act of 2003”—would reduce federal direct 

spending on Medicare, Medicaid, and the Federal Employees Health Benefits program by about $15 billion over 10 years.3 That reduction in federal spending reflected 

the estimated reduction in malpractice premiums, which CBO concluded would 

reduce both private health insurance premiums and payment rates in the Medicare 

and Medicaid programs.4

 CBO did not, however, assign savings to effects on utilization. By contrast, the Assistant Secretary for Planning and Evaluation (ASPE) of the 

Department of Health and Human Services, for example, has estimated that a cap on 

noneconomic damages alone would reduce costs to the federal government by 

between $25 billion and $44 billion per year, an effect approximately 20 times as large 

as CBO’s estimate of the impact of the HEALTH Act of 2003 (which included a cap

2. A reduction in malpractice premiums represents a reduction in the price of one of the inputs used 

in the production of medical care. A reduction in medical malpractice premiums has a direct effect 

on Medicare’s payments for hospital and physicians’ services, because those prices are set, in part, 

on the basis of the observed level of medical malpractice premiums. Reductions in medical malpractice premiums would also reduce prices paid by insurers other than Medicare, as reductions in 

the input costs of providing medical care are passed on to insurers and patients.

3. Congressional Budget Office, Cost Estimate for H.R. 5: Help Efficient, Accessible, Low-Cost, Timely 

Healthcare (HEALTH) Act of 2003 (March 10, 2003), available at www.cbo.gov/ftpdocs/40xx/

doc4098/hr5ec.pdf. 

4. According to CBO’s estimates, the HEALTH Act of 2003 would also increase federal revenues by 

$3 billion over a period of 10 years. That increased revenue reflects the fact that the legislation 

would reduce medical malpractice premiums and, therefore, health insurance premiums. Because 

firms are competing for workers in the labor market, lower health insurance premiums would lead 

to an increase in taxable wages and salaries, which in turn would boost federal revenues.
3

on noneconomic damages as well as other tort limits).5 The vast bulk of ASPE’s estimated savings was attributable to its estimate of the effects of the proposed rule on 

utilization.

The analysis presented in this paper considers two sets of health care spending 

measures at the state level—overall health care spending per capita, and Medicare 

spending per beneficiary—over the last two and a half decades, and explores the relationship between the level of spending in each year and whether or not a given state 

had specific types of tort limits in effect in that year. The analysis focuses on the question of whether the implementation of tort limits has an impact on the level of health 

care spending; it does not examine the extent to which physicians may have adopted 

certain practice patterns in response to the threat of being sued. This analysis looks at 

a broader set of spending measures than has been examined elsewhere and performs a 

more detailed set of specification tests on the results. Those specification tests help 

assess whether the main regression results reflect the causal impact of tort limits on 

health care spending or the impact of other factors that are not included in the

analysis.

Interpreting the Results

CBO finds that the estimated effects of limits on malpractice torts vary substantially 

across different measures of health care spending and across different types of tort limits. In some cases, specific tort limits appear to be associated with reductions in health 

care spending; in other cases, there appears to be no relationship; and in still other 

cases, tort limits appear to be associated with higher spending. Analysis of overall per 

capita spending on health care at the state level reveals that eliminating joint-andseveral liability is associated with an increase in per capita spending. The estimated 

effect of implementing a package of previously proposed tort limits is near zero.

More evidence of changes in spending in response to tort limits is found when examining spending for Medicare. Imposing a cap on noneconomic damages is associated 

with reductions in Medicare spending per beneficiary, although the estimated effect of 

that tort limit falls and becomes statistically insignificant when controls are added that 

capture the effects of the implementation of Medicare’s prospective payment system 

(PPS) for hospitals. The Medicare results also show that eliminating joint-and-several 

liability is associated with statistically significant increases in spending per beneficiary.

A series of specification tests suggests that the associations between some of the tort 

limits and changes in spending may reflect other factors that are both (1) difficult to 

measure and (2) correlated with, but not caused by, the passage of malpractice tort 

limits. The mixed results presented in this analysis demonstrate the difficulty of disen5. Department of Health and Human Services, Office of the Assistant Secretary for Planning and 

Evaluation, Confronting the New Health Care Crisis: Improving Health Care Quality and Lowering 

Costs by Fixing Our Medical Liability System (July 24, 2002).
4

tangling any effects of tort limits from other factors that affect levels of health care 

spending.

Background

In surveys, many physicians report that they alter the way they practice medicine in 

response to the threat of being sued for malpractice. The measures that physicians 

take to reduce their risk of liability have been labeled “defensive medicine.” Some analysts cite defensive medicine as an important contributor to the high levels of health 

care spending in the United States. Limits on malpractice torts have been proposed to 

reduce medical providers’ exposure to the threat of being sued with the objective of 

decreasing the provision of defensive medicine. This section of CBO’s analysis 

describes the types of changes in tort laws that have been considered by the Congress, 

discusses various definitions of defensive medicine, and examines conceptually the 

ways in which tort limits might affect health care spending.

Tort Limits Under Consideration by the Congress

The Congress is considering several types of limits to the system that governs tort 

claims for malpractice:

B Cap on noneconomic damages. Imposing such a cap would limit the amount that a 

claimant could receive for noneconomic damages. Economic damages include 

medical costs and lost earnings; noneconomic damages include pain and suffering, 

mental distress, and loss of consortium. Some recent proposals would place a 

$250,000 ceiling on noneconomic damages.6

B Modification or elimination of joint-and-several liability. The principle of joint-andseveral liability allows a claimant to recover the entire amount of a damage award 

from any one of the parties found to be responsible for an injury, regardless of each 

party’s degree of responsibility for that injury. Proposals to eliminate that principle 

would specify instead that each party be responsible only for the share of damages 

equal to its degree of responsibility for the injury. Proposals to modify joint-andseveral liability might, for example, allow joint-and-several liability to be applied 

only to any defendant found to be at least 50 percent responsible for an injury.

B Cap on attorneys’ fees. Some proposals would cap the contingency fees that claimants’ attorneys can collect as a percentage of a total damage recovery. The proposed 

percentage caps could vary on the basis of the dollar amount of the total damage 

award.

6. Proposals including caps on damages often allow states that have already implemented such caps to 

retain them, regardless of whether the state caps are lower or higher than those contained in the 

federal proposal.
5

B Establishment of a collateral-source rule. Collateral-source benefits are compensation 

for an injury from sources other than a malpractice award, such as health or disability insurance. Some proposals would establish a collateral-source rule that 

allowed information on a plaintiff’s collateral-source benefits to be presented to a 

jury. Such proposals might also preclude payers of collateral benefits from receiving 

any portion of an award. Other proposals would impose a collateral-source rule 

with a mandatory offset, in which an award must be reduced by the amount of any 

collateral-source benefits received by the claimant. A third type of collateral-source 

rule—the discretionary offset—would allow the court to adjust the jury award to 

compensate for collateral-source benefits.

B Cap on punitive damages. Punitive damages are not intended to compensate the 

injured party for damages but instead to punish the defendant for egregious behavior and deter other health care providers from similar behavior. Some proposals 

would limit the situations in which plaintiffs might receive punitive damages or 

cap the amount of punitive damages that plaintiffs could receive or do both.

B Revisions to the statute of limitations. The statute of limitations specifies the period 

of time following an injury when the injured party may file a claim for damages. 

Proposals affecting that statute generally would reduce the period of time available 

to file. Two types of limits could be applied, the first based on the amount of time 

that had elapsed since the occurrence of an alleged injury, and the second based on 

the amount of time that had elapsed since the discovery of an alleged injury. One 

recent proposal would impose a filing limit of three years after the occurrence of an 

alleged injury or one year after the discovery of an alleged injury, whichever 

occurred first.

A number of states have already adopted limits similar or identical to those being 

considered in the Congress.7 States have imposed limits on torts in three waves, the 

first in the late 1980s, the second in the late 1990s, and the third in the last few years 

(see Figure 1).

Definitions of Defensive Medicine

The term “defensive medicine” has been used in different ways in the literature, 

with some researchers using the phrase more broadly and others more narrowly. The 

Office of Technology Assessment (OTA), a former Congressional research agency, 

used the term defensive medicine as an umbrella term, subdividing it into “positive” 

and “negative” defensive medicine. OTA defined positive defensive medicine as 

including “extra tests or procedures [conducted] primarily to reduce malpractice 

liability,” with negative defensive medicine including procedures or patients avoided 

by physicians out of fear of liability.8

7. Congressional Budget Office, The Effects of Tort Reform: Evidence from the States (June 2004).

8. Office of Technology Assessment, Defensive Medicine and Medical Malpractice, OTA-H-602

(July 1994).
6

Figure 1.

Number of States with Tort Limits in Place, by Year, 1980 to 

2003

Source: Congressional Budget Office.

Note: Statute of limitations is excluded from the figure because the changes that states made were 

relatively minor.

a. The principle of joint-and-several liability allows a claimant to recover the entire amount of a 

damage award from any of the parties found to be responsible for an injury, regardless of each 

party’s degree of responsibility for that injury.

b. Collateral-source benefits are compensation for an injury from sources other than a malpractice 

award, such as health or disability insurance.

More recent literature generally uses defensive medicine only to refer to those extra 

tests and procedures undertaken out of fear of liability (what OTA called positive 

defensive medicine). In previous analyses, CBO used the narrower definition, referring to defensive medicine as “services and procedures that are provided largely or 

entirely to avoid potential liability.”9 Daniel Kessler and Mark McClellan define 

defensive medicine even more narrowly, as “precautionary treatments with minimal 

expected medical benefit [provided] out of fear of legal liability.”10 Lisa Dubay and 

9. Congressional Budget Office, Cost Estimate for H.R. 5.

10. Daniel Kessler and Mark McClellan, “Do Doctors Practice Defensive Medicine?” Quarterly Journal 

of Economics, vol. 111, no. 2 (May 1996), pp. 353-390.

1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002

0

5

10

15

20

25

30

35

40

Cap on Noneconomic

Damages

Cap on Attorneys' Fees

Cap or Ban on

Punitive Damages

Modification or Elimination of Joint-and-Several Liabilitya

Collateral-Source Rule

(Jury notification)b
7

her colleagues define defensive medicine similarly to Kessler and McClellan, as “the 

extent to which fear of malpractice liability leads physicians to provide more care than 

is socially optimal.”11

Note that the definitions used by Kessler and McClellan and by Dubay and her coauthors limit the concept of defensive medicine to extra tests and procedures they 

deem socially “wasteful” (in the sense that the costs of providing those treatments 

exceed the expected clinical benefits). The current analysis does not attempt to assess 

the clinical or social value of any additional services provided or any services forgone 

because of a fear of malpractice, nor is it limited to an examination of positive defensive medicine.

Possible Links Between Malpractice Tort Limits and Health Care Spending

The links between limits on malpractice tort claims, the malpractice environment, 

and levels of health care spending are complex. The malpractice environment, from 

the point of view of medical providers, includes premiums for malpractice insurance, 

the likelihood that they will be sued for malpractice (holding constant the quality of 

medical care provided), and the financial and professional consequences of being 

sued. Providers often view the premiums they pay for malpractice insurance as a measure of their potential liability—higher premiums may increase their fear of being 

sued. The types of tort limits listed above are generally believed to help reduce malpractice premiums and make the malpractice environment less favorable to potential 

claimants and more favorable to medical providers. CBO’s analysis focuses on the 

total effect on health care spending of changes in the malpractice environment.

Proponents of limits on malpractice torts claim that the fear of being sued drives medical providers to deliver services they would otherwise not provide. Among physicians, 

the fear of being sued is prevalent and well documented.12 For physicians, the financial costs of being sued are generally limited because most are insured against malpractice claims and premiums for malpractice insurance generally are not adjusted on the 

basis of an individual physician’s claim history although being sued repeatedly may 

make malpractice coverage difficult to obtain and more expensive.13 Legal action can 

also impose other costs on physicians, however, including mental distress, lost time 

from work, and a damaged reputation. 

11. Lisa Dubay, Robert Kaestner, and Timothy Waidmann, “The Impact of Malpractice Fears on 

Cesarean Section Rates,” Journal of Health Economics, vol. 18, no. 4 (August 1999), pp. 491-522.

12. Harris Interactive, Inc., Most Doctors Report Fear of Malpractice Liability Has Harmed Their Ability 

to Provide Quality Care: Caused Them to Order Unnecessary Tests, Provide Unnecessary Treatment 

and Make Unnecessary Referrals, The Harris Poll, No. 22, May 8, 2002, available at www.

harrisinteractive.com/harris_poll/index.asp?PID=300.

13. David M. Studdert, Michelle M. Mello, and Troyen A. Brennan, “Medical Malpractice,” New 

England Journal of Medicine, vol. 350, no. 3 (January 15, 2004), pp. 283-292.
8

The types of tort limits being considered in the Congress would reduce awards to 

claimants in certain cases and could also reduce the probability that a medical provider would be sued for malpractice. The proposed limits could reduce the probability 

that claims would be filed if they affected the decisionmaking process of potential 

plaintiffs and their attorneys. Generally, plaintiffs’ attorneys receive payment in the 

form of a contingency fee, meaning they receive a percentage of any award or settlement. Plaintiffs’ attorneys, in choosing which cases to take on and which cases to pursue, assess each case to determine the likelihood of receiving an award and the probable amount of any resulting fee. If caps on awards and on attorneys’ fees reduced 

contingency payments, plaintiffs’ attorneys would be less likely to take on certain 

cases, and in the longer run, fewer attorneys might practice that branch of the law.

There are several possible paths by which medical services utilization could be affected 

by tort limits. The amount of positive defensive medicine (services provided primarily 

to avoid being sued for malpractice) might decline. Negative defensive medicine 

might be reduced as well, meaning that medical providers might be more willing to 

perform risky procedures; this could increase certain types of utilization. If medical 

providers were less likely to be sued, they might also feel less compelled to document 

and justify their clinical decisionmaking processes, which could reduce the amount of 

time required to provide a given clinical service. If they faced lower medical malpractice premiums, older physicians might choose to retire later, and younger physicians 

might be more likely to choose specialties, such as obstetrics, that today typically have 

a high risk of malpractice litigation. Those possible behavioral responses might 

increase the supply of physicians, which would tend to decrease prices paid for physicians’ services and increase the volume of such services, with ambiguous implications 

for health care utilization and spending. Medical providers might also put less effort 

into conforming to the standard of care, which could increase the rate of medical injuries resulting from negligence. An increase in the rate of such injuries could increase 

utilization and spending, if those injuries resulted in costly treatments.

In the analysis presented in this paper, health care spending is modeled as a function 

of which tort limits are in place as well as other variables commonly included in models of health care spending. Tort limits may affect the prices and quantities of health 

care services provided through several distinct channels. Some of the potential effects 

of tort limits would tend to increase utilization and spending, while other potential 

effects would tend to reduce utilization and spending. The net effect of those tort limits could be in either direction. This analysis attempts to empirically quantify that net 

effect.

The Effects of the Malpractice Environment on Health Care 

Utilization and Spending: Existing Evidence

Researchers examining the effects of the malpractice environment on health care utilization and spending have typically used one of two approaches: physician surveys and 

statistical analyses of observed patterns of health care utilization. Strong evidence sug-
9

gests that some tort limits reduce the claims paid in malpractice cases and, in the long 

run, premiums for malpractice insurance (see Box 1). Beyond the effect on malpractice premiums, the literature does not present consistent results, which is not surprising given the diversity of specific research questions and methodologies used.

When asked in surveys whether the fear of malpractice has affected their style of practice, a substantial percentage of physicians tend to agree.14 For example, a survey 

sponsored by Common Good in 2002 found that 91 percent of physician respondents reported that the fear of malpractice liability had led them to order “more tests 

than they would based only on professional judgment of what is medically needed.”15

That finding, if taken in isolation, suggests that the costs of defensive medicine could 

be substantial. 

Other findings suggest that a more nuanced interpretation of the survey results is necessary. 

B First, in surveys, a substantial percentage of physicians also report avoiding certain 

types of procedures or patients because of malpractice pressures.16 Those survey 

results suggest that malpractice pressures lead some physicians to provide additional tests and procedures (positive defensive medicine) and lead some physicians 

to avoid providing other procedures and to avoid caring for certain patients (negative defensive medicine). The net impact of such behavior on health care spending 

would depend on the magnitudes of the positive and negative defensive medicine 

effects, and also on whether the reported negative defensive medicine represented a 

shift in utilization from some physicians (those who avoid risky procedures) to 

other physicians (those who do not), or a net reduction in utilization (with some 

risky procedures not being performed at all).

B Second, physician surveys generally are characterized by low response rates.17

Response bias could undermine the validity of survey findings if physicians chose 

whether to respond on the basis of the depth of their (presumably negative) feelings about the malpractice system.

14. Office of Technology Assessment, Defensive Medicine and Medical Malpractice; and Harris Interactive, Inc., Final Report of the Fear of Litigation Study: The Impact on Medicine, Study No. 15780 

prepared for Common Good (April 11, 2002), available at http://cgood.org/assets/attachments/

57.pdf.

15. Harris Interactive, Inc., Fear of Litigation Study.

16. Office of Technology Assessment, Defensive Medicine and Medical Malpractice.

17. Ibid.
10

Box 1.

The Effects of Tort Limits on Premiums for Medical 

Malpractice Insurance

In 1993, the Office of Technology Assessment issued a report summarizing the 

first wave of studies on the experience of states that had set limits on malpractice liability in the 1970s and 1980s. The report concluded that imposing caps 

on damage awards and allowing collateral-source offsets consistently reduced 

one or more malpractice cost indicators, which include claim size, claim frequency, and premiums for malpractice insurance. Furthermore, it found 

weaker evidence that limiting the use of joint-and-several liability and reducing 

the statute of limitations for filing claims also slowed the growth of premiums 

for malpractice insurance.1

More recent research has concluded that certain types of limits can reduce 

medical malpractice premiums below what they would be in the absence of the 

limits. A 2004 study reported that a cap on noneconomic damages would 

reduce premiums per physician by about 13 percent.2 It also found that a discretionary offset for collateral-source benefits would reduce loss ratios. A 

recently released working paper found that the following tort limits were associated with substantial long-term reductions in medical malpractice insurers’ 

developed losses: caps on noneconomic damages, caps on punitive damages, 

elimination of joint-and-several liability, and changes to rules governing the 

treatment of collateral sources of damages.3 Though there is some debate over 

the strength of the link between losses incurred by medical malpractice insurers and the premiums they charge, it is reasonable to assume that reductions in 

insurers’ losses would, at least over the long term, lead to reductions in medical 

malpractice premiums.

1. Office of Technology Assessment, Impact of Legal Reforms on Medical Malpractice Costs, 

OTA-BP-H-119 (September 1993).

2. Kenneth E. Thorpe, “The Medical Malpractice ‘Crisis’: Recent Trends and the Impact of 

State Tort Reforms,” Health Affairs (Web Exclusive, January 21, 2004). 

3. Developed losses are the actual payments eventually made by malpractice insurers for 

malpractice claims. See Patricia Born, W. Kip Viscusi, and Tom Baker, The Effects of Tort 

Reform on Medical Malpractice Insurers’ Ultimate Losses, Working Paper No. 12086 

(Cambridge, Mass.: National Bureau of Economic Research), available at http://

papers.nber.org/papers/w12086.pdf.
11

B Third, the phrasing and framing of questions in the surveys can substantially affect 

the results. When physicians are asked whether malpractice concerns affect their 

practice patterns, many say yes. If the survey is structured in a more open-ended 

way, the results are substantially different. For instance, instead of asking physicians whether malpractice has affected their practice, some surveys have presented 

clinical scenarios and asked physicians which tests and procedures they would 

order and why, with malpractice concerns included among many possible rationales. In those more open-ended surveys, malpractice concerns are infrequently 

cited as the primary reason for ordering particular tests or procedures.18

Box 1.

Continued

18. Office of Technology Assessment, Defensive Medicine and Medical Malpractice.

On the basis of its own research into the effects of tort restrictions, the Congressional Budget Office (CBO) estimated that the provisions of the Help Efficient, Accessible, Low-Cost, Timely Healthcare (HEALTH) Act of 2003 (H.R. 

5) would lower premiums for medical malpractice insurance nationwide by an 

average of 25 percent to 30 percent from the levels likely to occur under current law.4 (The savings in a given state would depend in part on the restrictions already in effect there.) CBO found that the following tort limits reduced 

malpractice insurance premiums: caps on noneconomic damages, caps on 

punitive damages, and changes to rules governing the treatment of collateral 

sources of damages.

A reduction of 25 percent to 30 percent in medical malpractice premiums 

would not, by itself, have a significant impact on total health care costs, 

however. Malpractice costs amounted to an estimated $24 billion in 2002, less 

than 2 percent of overall health care spending.5 Thus, even a reduction of that 

magnitude in malpractice costs would lower health care costs by only about 

0.4 percent to 0.5 percent, and the likely effect on health insurance premiums 

would be comparably small.

4. Congressional Budget Office, Cost Estimate for H.R. 5, Help Efficient, Accessible, Low-Cost, 

Timely Healthcare Act of 2003 (March 10, 2003). 

5. According to data from the Office of the Actuary at the Centers for Medicare and Medicaid Services, health care spending in the United States totaled about $1.4 trillion in 2002 

(excluding spending on public health and capital improvements).
12

Because of the difficulties involved in interpreting survey results, most recent research 

on the potential impacts of malpractice tort limits has been based on analyses of 

observed patterns of health care utilization. Those analyses use econometric techniques to measure the association between observed levels of malpractice pressure and 

observed levels of health care utilization and spending. Analyses of this type have a 

potential advantage over surveys in that they measure actual clinical behavior rather 

than responses to hypothetical scenarios. The statistical methods and interpretations 

of those observational analyses are less straightforward than the physician surveys, 

however, because of the difficulties inherent in attempting to isolate the effects of malpractice pressure from the many other factors that affect patterns of medical practice.

Anecdotal reports have identified obstetric care, and in particular the use of cesarean 

section, as particularly vulnerable to malpractice pressures.19 Several research papers 

have measured the association between malpractice pressures and the percentage of 

deliveries by cesarean section, with conflicting results.20 In a paper on cesarean section, Dubay and her colleagues used an approach similar to the one used in this 

paper.21 They found some evidence that growth in malpractice pressures over time is 

associated with an increased probability of delivery by cesarean section. Simulating 

the impact of imposing a cap on total awards in malpractice cases on obstetrical 

spending, they concluded that, while such a limit would reduce malpractice premiums substantially, it would decrease the rate of cesarean sections by only about onehalf of 1 percentage point, with a corresponding reduction in overall obstetrical 

spending of about one-quarter of 1 percent.

Kessler and McClellan have written a series of papers examining the association 

between malpractice pressures and health care utilization and spending.22 Those studies measured health care spending among a specific population: elderly Medicare feefor-service (FFS) beneficiaries admitted to an inpatient hospital with a diagnosis of 

either ischemic heart disease (IHD) or acute myocardial infarction (AMI, or heart 

attack) between 1984 and 1994.23 The studies’ authors limited the population to 

19. Office of Technology Assessment, Defensive Medicine and Medical Malpractice.

20. A.Russell Localio and others, “Relationship Between Malpractice Claims and Cesarean Delivery,” 

Journal of the American Medical Association, vol. 269, no. 3 (January 20, 1993), pp. 366-373; and 

Laura-Mae Baldwin and others, “Defensive Medicine and Obstetrics,” Journal of the American 

Medical Association, vol. 274, no. 20 (November 22, 1995), pp. 1606-1610.

21. Dubay, Kaestner, and Waidmann, “The Impact of Malpractice Fears on Cesarean Section Rates.”

22. Daniel Kessler and Mark McClellan, “Malpractice Law and Health Care Reform: Optimal Liability 

Policy in an Era of Managed Care,” Journal of Public Economics, vol. 84, no. 2 (May 2002), 

pp. 175–197; How Liability Law Affects Medical Productivity, Working Paper No. W7533 (Cambridge, Mass.: National Bureau of Economic Research, February 2000); and “Do Doctors Practice 

Defensive Medicine?”

23. Medicare fee-for-service beneficiaries are those enrolled in the traditional Medicare program (not 

in a private managed care plan). Most Medicare beneficiaries are enrolled in the fee-for-service 

program.
13

AMI/IHD patients in order to increase the homogeneity of the patient population, 

and they chose to focus on Medicare hospital spending among fee-for-service beneficiaries primarily because of the availability of data. One of the key results from those 

papers is the finding that so-called direct tort limits—which include caps on damage 

awards, a ban on punitive damages, no mandatory prejudgment interest, and changes 

in the treatment of collateral-source benefits—have a large negative association with 

the growth in Medicare hospital spending among AMI/IHD patients. Those results 

imply that, over the long run, direct tort limits reduce Medicare hospital spending on 

AMI/IHD patients by between 4 percent and 9 percent. Those research results have 

been used as the basis for predicting large potential reductions in health care spending 

from proposed tort limits.24

Although Kessler and McClellan’s results have been widely cited, additional research is 

warranted. As previously stated, their research focused on a narrow patient population—Medicare beneficiaries with AMI/IHD—and used a narrow measure of health 

care spending—Medicare spending on short-stay hospital care.25 The approach they 

used raises questions that this analysis attempts to address. First, do the Kessler/

McClellan findings generalize to other populations and other measures of health care 

spending? Second, do the 1984-1994 results hold when longer time periods are examined? Third, are there important factors excluded from their analysis that could cause 

a spurious association between the implementation of tort limits and changes in 

Medicare hospital spending?

CBO’s Empirical Analysis

This section describes the methodology and data sources that CBO used and presents 

the empirical results on the associations between tort limits and health care spending.

Methodology

The analysis uses a panel data approach, with health care spending per capita measured for each state in each year over the span of several decades. The basic specification is:

where is a measure of the natural logarithm of per capita health care spending at 

the state-year level, is a state-fixed effect, is a year-fixed effect, is a set of 

state-year economic and demographic controls, and represents a vector of tort limits. The state-fixed effects capture state-level differences in spending levels that are 

constant over the entire period. The year-fixed effects capture national spending 

24. Department of Health and Human Services, Office of the Assistant Secretary for Planning and 

Evaluation, Confronting the New Health Care Crisis.

25. Medicare defines short-stay hospital care as care provided in a typical general hospital. Nearly all 

Medicare hospital stays take place in short-stay hospitals. Other types of hospitals include longterm care hospitals, psychiatric hospitals, and rehabilitation hospitals.

Y st αs βt X s tγ Z s tδ ε = ++ + + s t

Yst

αst βt X st

Z st
14

trends and may be thought of as factors reflecting price inflation as well as other 

sources of growth. The parameters of interest, , reflect the estimated effects of the 

presence of tort limits on health care spending. Because the dependent variables equal 

the natural logarithm of spending per capita, the coefficients of interest may be interpreted as percent effects on the level of health care spending per capita (for example, a 

coefficient of 0.010 implies an effect of 1 percent).26 The key identifying assumption, 

which is examined below, is that the imposition of tort limits is not systematically correlated with factors that are not included in the model and that have independent 

effects on health care spending.27

Health care spending per capita, by state and year, is measured among two populations: all individuals living in a given state, and all Medicare FFS beneficiaries living 

in a given state. The data set includes health care spending per capita for all services, 

as well as separate measures of hospital spending per capita and physician spending 

per capita. Thus, the data set contains six separate measures of health care spending at 

the state-year level. State-year measures of spending per capita are adjusted for demographics to the extent possible, as described in Appendix A.

A significant portion of CBO’s analysis focuses on Medicare fee-for-service beneficiaries for three reasons. First, Medicare beneficiaries account for a substantial share of 

federal spending on health care; analyses of spending among those individuals are, 

potentially, directly relevant to estimated effects of proposed tort limits on federal outlays. Second, detailed microdata on Medicare FFS spending and beneficiary demographics are available for the last three decades. Third, some of the previous literature 

on malpractice tort limits has focused on Medicare fee-for-service beneficiaries. Examining that same population in the current analysis facilitates comparisons between the 

findings presented here and those earlier findings.

Tort limits are coded in two ways. The primary set of regression specifications 

includes a separate indicator variable for each of a selected package of tort limits—

a cap on noneconomic damages, modification or elimination of joint-and-several 

liability, a cap on attorneys’ fees, allowing evidence of collateral-source benefits to be 

26. Technically, the percentage effect of an indicator variable in a semilogarithmic specification should 

be approximated as , where is the estimated coefficient for 

the indicator variable and is the estimated variance of the coefficient estimate. See P.E. 

Kennedy, “Estimation with Correctly Interpreted Dummy Variables in Semilogarithmic Equations,” American Economic Review, vol. 71, no. 4 (September 1981) p. 801. However, since all of 

the estimated coefficients on the tort limits are relatively close to zero, applying Kennedy’s correction would produce almost no difference in the reported results. For transparency and simplicity, 

CBO reports the unadjusted coefficient estimates in the results tables and interprets them as percentage effects.

27. If observable factors could be identified that were correlated with the imposition of tort limits but 

were not correlated with factors affecting health care spending that are omitted from the analysis, 

then those factors could be used in an “instrumental variables” analysis. CBO’s analysis and discussions with other researchers did not identify a suitable set of instruments.

δ

100 βhat 1 2Vhat β

hat

× exp( ) – ⁄ ( ) β

ha t

V

hat βhat ( )
15

introduced at trial, and a cap or ban on punitive damages.28 In a second specification, 

the tort limits are coded into two variables, “direct” and “indirect,” corresponding to 

the approach used in previous research by Kessler and McClellan. The direct tort limit 

variable is set equal to one if any of the following limits is in place (and zero otherwise): a cap on noneconomic damages, a cap on total damages, a ban on punitive 

damages, or a collateral-source rule (mandatory offset). The indirect tort limit variable 

is set equal to one if either of the following limits is in place (and zero otherwise): a 

cap on attorneys’ fees or modification or elimination of joint-and-several liability.

Standard errors are estimated using the Huber-White correction, with observations 

clustered at the state level.29 Clustering at the state level allows error terms to be correlated across years within states. The measures of Medicare spending per beneficiary 

are generated using a 5 percent sample of beneficiaries, which includes between 1.5 

million and 2.2 million beneficiaries each year. 30

The main analyses (which are presented later in this paper) are weighted by population. For the analyses of overall health care spending per capita, the weight is the population in each state and year. For the analyses of Medicare spending per beneficiary, 

the weight is the number of fee-for-service Medicare beneficiaries included in the 

28. A variable for the statute of limitations was not included in this study because of insufficient variation among the states with respect to a limit of three years from the date of the injury, which has 

been included in some recent Congressional proposals. Complete elimination of joint-and-several 

liability is coded as 1.0; weakening the application of joint-and-several liability by, for example, 

limiting its application to defendants who are responsible for more than a certain share of the 

injury, is coded as 0.5, implying that modifying the law in this way is equivalent to half the effect of 

eliminating it altogether. Given that coding scheme, estimated coefficients should be interpreted as 

the effect of eliminating joint-and-several liability. An indicator variable, also referred to as a 

dummy variable, takes on values of either zero or one.

29. The analyses were performed in SAS using a routine that produces standard errors equivalent to 

those generated by the robust cluster(stateID) option in STATA. A recent paper demonstrated that 

the choice of clustering unit can have important implications for estimated standard errors and 

Type I error rates. See Marianne Bertrand, Esther Duflo, and Sendhil Mullainathan, “How Much 

Should We Trust Differences-in-Differences Estimates?” Quarterly Journal of Economics, vol. 119, 

no. 1 (February 1, 2004), pp. 249-275. In panel analyses such as this one, with serially correlated 

laws being used to predict serially correlated outcomes, clustering at the state level is appropriate; 

clustering instead at the state-year level would probably result in underestimated standard errors 

and overrejection of the null hypothesis. An alternative set of standard errors was calculated in 

STATA using bootstrapping methods, again clustering at the state level. Those bootstrapped standard errors (not shown) were quite similar to the parametric standard errors presented in the tables.

30. Despite the highly skewed nature of health care spending, that sample is large enough that standard 

errors on the estimated sample means are generally small relative to the estimated means. For each 

state-year, mean Medicare spending per beneficiary is estimated as well as the ratio of estimated 

standard error to estimated mean. Out of 1,224 state-years (51 states times 24 years), this ratio was 

below 0.1 for 1,217 state-years, below 0.05 for 1,106 state-years, and below 0.02 for 588 stateyears.
16

sample in each state and year. Appendix B presents alternative specifications, including unweighted analyses.

Health care spending per capita and Medicare spending per beneficiary are measured 

among all individuals in the relevant population, without limiting the analysis to individuals with a specific medical condition. However, spending measures are adjusted 

for demographics, and age-adjusted mortality rates are included as a control variable—those methods control to some extent for differential changes across states in 

health status. In an alternative specification, CBO included state-specific time trends, 

which provide additional controls for differential changes in health status. The results 

from that specification are reported in Appendix B.

Restricting the analysis to individuals with a specific medical condition, as other 

researchers have done, might improve the precision of the estimates but would 

adversely affect both the generalizability and the internal validity of the findings. 31

The goal of CBO’s analysis is to estimate the magnitude of the impact of proposed 

tort limits on overall health care spending. Results obtained from a sample restricted 

to a specific medical condition would have limited generalizability because they 

would not be applicable to all health care spending without making the implausible 

assumption that tort limits affected all conditions similarly. 

Restricting the analysis to patients diagnosed with a particular medical condition 

might also limit the internal validity of the analysis, because the clinical processes by 

which patients are identified as having certain medical conditions might reflect malpractice pressures. (“Internal validity” refers to the extent to which an estimated effect 

reflects the true causal impact of interest.) To use a specific example, the causal effect 

of interest might be the effect of malpractice pressures on health care spending per 

capita among individuals with heart disease. An individual who arrives at an emergency room with symptoms of heart disease may or may not be admitted to the hospital, depending to a great extent on the clinician’s judgment.32 If a physician’s decision 

to admit an individual to the hospital is affected by the fear of being sued for malpractice, then the number of individuals who have been admitted to the hospital with 

chest pain and their average underlying illness severity will be determined in part by 

malpractice pressures. Estimating the effects of malpractice pressures only among 

individuals who have been admitted to the hospital with symptoms of heart disease 

might not, therefore, measure the causal relationship of interest, that is the effect of 

malpractice pressures on per capita spending.

31. For example, see Kessler and McClellan, “Do Doctors Practice Defensive Medicine?” and Dubay, 

Kaestner, and Waidmann, “The Impact of Malpractice Fears on Cesarean Section Rates.”

32. David A. Katz and others, “Emergency Physicians’ Fear of Malpractice in Evaluating Patients with 

Possible Acute Cardiac Ischemia,” Annals of Emergency Medicine, vol. 46, no. 6 (July 2005), 

pp. 525-533; Office of Technology Assessment, Defensive Medicine and Medical Malpractice; and 

David M. Cutler, Mark B. McClellan, and Joseph P. Newhouse, “How Does Managed Care Do 

It?” Rand Journal of Economics, vol. 31, no. 3 (Autumn 2000), pp. 526–548.
17

Data Sources and Spending Measures

The data set that CBO used combines yearly state-level data on the tort rules regulating medical malpractice cases with data on overall health care spending per capita and 

Medicare spending per beneficiary, as well as demographic and macroeconomic controls. The analyses of health care spending per capita are based on the years from 1980 

to 2000, and the analyses of Medicare spending per beneficiary are based on the years 

from 1980 to 2003.

State Tort Laws. Data on state laws were gathered from several sources, with discrepancies resolved by consulting the relevant state statutes.33 In general, recent Congressional proposals were used as a guide in defining criteria for whether a state had a particular tort limit in place. For example, for changes to the collateral-source rule, states 

are coded as having imposed a limit if they allow evidence of collateral-source benefits 

to be presented to the jury in a trial. 

Overall Health Care Spending per Capita. The data source for overall health care 

spending per capita among all individuals is the state health expenditures (SHE) published by the Centers for Medicare and Medicaid Services (CMS).34 The analysis uses 

measures of spending per capita on personal health care from the SHE, as well as separate measures of spending per capita on hospital care and spending per capita on 

physician care.35 In constructing the SHE, CMS uses as its main data sources the 

Census Bureau’s Economic Census and administrative data from the Medicare and 

Medicaid programs. The Economic Census is only performed every five years; CMS 

uses other data sources to estimate trends between those censuses, including wage data 

from the Bureau of Labor Statistics and data on business receipts from the Internal 

Revenue Service.

CMS publishes two sets of spending measures: residence-based measures (in which 

spending is allocated to states on the basis of patients’ state of residence) and provider33. McCullough, Campbell & Lane, Summary of Medical Malpractice Law (last updated August 9, 

2005), available at www.mcandl.com/introduction.html; American Tort Reform Association, State 

Civil Justice Reforms (2005), available at www.atra.org/states/; National Conference of State Legislatures, Medical Malpractice Tort Reform (updated January 13, 2005), available at www.ncsl.org/

standcomm/sclaw/medmaltorttable205.htm; and National Association of Mutual Insurance Companies, Tort Reform: An Overview of State Legislative Efforts to Improve the Legal System (updated 

February 8, 2005), available at www.namic.org/reports/tortReform/overview.asp.

34. See Centers for Medicare and Medicaid Services, State Health Accounts by State of Residence, 

State of Residence Methodology, State Health Expenditure Accounts, and State Health Accounts 

(all updated March 1, 2006), available at www.cms.hhs.gov/NationalHealthExpendData/

05_NationalHealthAccountsStateHealthAccounts.asp.

35. Personal health care spending equals total spending on health care minus the following: administration of public and private insurance programs, government public health activities, research, and 

construction of facilities. Physician care includes physician and other professional services, as 

defined by the Centers for Medicare and Medicaid Services. Hospital care and physician care 

accounted for about two-thirds of all personal health care spending in 2000.
18

based measures (in which spending is allocated to states on the basis of a given provider’s location). Data for residence-based spending are available only for 1991 

through 1998. For those years, CMS’s published residence-based data are used. For all 

other years, residence-based spending is estimated by multiplying provider-based 

spending by the ratio of residence- to provider-based spending in the closest year for 

which both sets of data are available (1991 for earlier years and 1998 for later years). 

Because all of the analyses include state indicator variables, the discrepancy between 

residence- and provider-based spending only affects the results to the extent that the 

ratio of residence- to provider-based spending changed differentially across states. An 

examination of those ratios in the years for which data are available shows that most 

ratios are near 1 and that those ratios are fairly stable over time.36 Per capita measures 

of health care spending equal total spending in each state divided by that state’s population. The population for each state-year is calculated from the 2003 Area Resource 

File (ARF), which includes population counts from the decennial census and estimates for intercensal years.37

Medicare Spending per Beneficiary. The Continuous Medicare History Sample 

(CMHS) is used to calculate Medicare spending per beneficiary. That data set 

includes annual beneficiary-level demographic, spending, and utilization data for a 5 

percent national sample of beneficiaries. That sample is quite large, representing 

between 1.5 million and 2.2 million beneficiaries per year, depending on the year. 

The key dependent variables include Medicare fee-for-service outlays per fee-for-service beneficiary, as well as separate measures of per-beneficiary outlays for Part A (hospital) and Part B (physician). Medicare outlays include only the amount reimbursed 

by Medicare and do not include beneficiaries’ cost sharing or third-party payments. 

Also note that the Medicare spending data differ from the SHE data in how spending

36. In 1998, 41 states had a ratio of residence-based spending to provider-based spending of between 

0.95 and 1.05. Wyoming had the highest ratio at 1.16, and the District of Columbia had the lowest at 0.81. See Anne Martin and others, “Health Care Spending During 1991-1998: A Fifty-State 

Review,” Health Affairs, vol. 21, no. 4 (July/August 2002), pp. 112-126.

37. Department of Health and Human Services, Health Resources and Services Administration, 

Bureau of Health Professions, “User Documentation for the Area Resource File,” February 2003 

release. The state-year population for 2003 is not available in the 2003 Area Resource File; population in 2001 and 2002 is used to linearly extrapolate the population in 2003.
19

is allocated to hospital versus physician spending.38 Because utilization and spending 

data are not available for Medicare beneficiaries in private managed care plans, beneficiaries are excluded from the analysis for any year in which they were enrolled in a 

managed care plan. The measures of Medicare spending per beneficiary equal total 

Medicare outlays divided by the total person-years of enrollment (both outlays and 

person-years are limited to beneficiaries not enrolled in a managed care plan). Personyears of enrollment take into account the date of enrollment in Medicare and the date 

of death, if applicable.39

Demographic Adjustments to Health Care Spending. Measures of health care spending 

from both data sources are adjusted for state-year demographics. For the SHE data, 

the Integrated Public Use Microdata Series 5 percent census samples are used to estimate the percentage of the population in each age group for 1980, 1990, and 2000. 

State-specific linear interpolation and extrapolation are used to estimate population 

by age group in intercensal years. To adjust Medicare spending for demographics, data 

are used from the CMHS on age, sex, and race. Appendix A provides details on the 

demographic adjustments to the health care spending measures.

Demographic and Economic Controls. The regression analyses also include state-year 

measures of the following demographic and economic controls: the percentage of 

Medicare beneficiaries in managed care (from the CMHS microdata); attorneys per 

capita (compiled from various publications from the American Bar Association); gross 

state product per capita (from the Bureau of Economic Analysis); age-adjusted mortality rates (from the National Center for Health Statistics); the percentage of adults 

without a high school diploma, the percentage of people that are black, and the percentage of people residing in urban areas (all from the Area Resource File); and the 

38. State health expenditures are allocated to hospital care versus physicians’ services on the basis of the 

type of establishment providing the service (hospital versus physician’s office). Medicare fee-forservice outlays are allocated to Parts A and B on the basis of the type of service being provided. 

Medicare fee-for-service outlays for Parts A and B add up to total Medicare fee-for-service outlays, 

whereas state health expenditures for hospital care and physicians’ services do not add up to total 

state health care expenditures (minor spending categories, such as home health care and prescription drugs, make up that difference). Inpatient hospital care constitutes by far the largest component of Medicare Part A spending, but Part A also includes other services such as skilled nursing 

facility care. Likewise, physicians’ services make up the largest component of Medicare Part B 

spending, but other types of services, such as home health care, are also included in Part B. To illustrate how spending is allocated differently, skilled nursing facility care is included in Medicare Part 

A spending, whereas skilled nursing facility care is included in the hospital care component of state 

health expenditures only if the care is provided in a hospital-based facility.

39. CMS has notified CBO that an error in the programs used to construct the CMHS data set 

affected spending measures for 1998 and some later years. The error incorrectly included in spending for a given year some spending that actually occurred in other years; the result is that the 

CMHS appears to overstate spending per beneficiary in the affected years by about 15 percent or 

less. The error tends to decrease the precision of the state-year Medicare spending measures in later 

years but does not introduce a systematic bias in the results. To test the potential impact of that 

data processing error, the key regression models using Medicare spending data were rerun excluding 

1998 and later years; those results are consistent with the main conclusions of this study.
20

percentage of the workforce that is unemployed (from the Bureau of Labor Statistics).40

Table 1 presents descriptive statistics on state-level measures of tort law, health spending (in nominal dollars), and demographic and economic controls for the years 1980, 

2000, and 2003. (The year 1980 is the first year included in the analyses, 2000 is the 

latest year for which overall health care spending per capita is available, and 2003 is 

the latest year for which Medicare spending per beneficiary is available.) Of the tort 

limits listed, a substantial share of states implemented those limits during the period 

analyzed.

Results

Tables 2 and 3 summarize the results from a series of regression models, where each 

model uses either a different measure of health care spending as the dependent variable or a different set of tort limit variables. Table 2 presents analyses of state-level 

overall health care spending per capita, while Table 3 presents analyses of state-level 

Medicare spending per beneficiary. The regression results from four of the 12 models 

are also displayed visually in Figure 2. Those four models include indicator variables 

for each tort limit and use the following dependent variables: overall hospital spending per capita, overall physician care spending per capita, Medicare Part A spending 

per beneficiary, and Medicare Part B spending per beneficiary.41 A more complete set 

of results with alternative specifications is presented in Appendix B.

The results in Tables 2 and 3 and in Figure 2 present a mixed picture. Focusing first 

on tort limits with statistically significant coefficients in the models of overall health 

care spending per capita, the analyses show that eliminating the joint-and-several liability rule is associated with increases in health care spending per capita and hospital 

spending per capita.

Focusing next on tort limits that appear to have some association with spending per 

capita but do not reach statistical significance at conventional levels, there are several 

additional findings to note.42 A cap on noneconomic damages, allowing evidence of 

collateral-source benefits to be introduced at trial, a cap or ban on punitive damages, 

40. The ARF provides information on the percentage of adults without a high school diploma, the 

percentage of the population that is black, and the percentage of the population residing in urban 

areas only for the years of the decennial census (1980, 1990, and 2000). Linear interpolation and 

extrapolation are used to impute those percentages for other years.

41. Figure 2 does not show the results from the analyses of overall health care spending per capita and 

overall Medicare spending per beneficiary because those spending measures reflect, to a large 

extent, the combined effects of the other spending measures.

42. Coefficients in this category include those for which the estimated t-statistics are greater than 1 in 

absolute value but not statistically significant at the 10 percent level.
21

Table 1.

Descriptive Statistics on Health Care Spending and 

Tort Limits

Source: Congressional Budget Office.

Notes: Standard deviations appear in parentheses. Means and standard deviations of the health care 

spending measures and demographic and economic controls are weighted by state population.

N.A. = not available.

a. The principle of joint-and-several liability allows a claimant to recover the entire amount of a 

damage award from any of the parties found to be responsible for an injury, regardless of each 

party’s degree of responsibility for that injury. Modifying or eliminating joint-and-several liability 

would generally limit each party’s share of damages to his or her level of responsibility for the 

injury.

b. Collateral-source benefits are compensation for an injury from sources other than a malpractice 

award, such as health or disability insurance.

1980 2000 2003

Health Care Spending Measures (Nominal dollars)

Overall health care spending per capita 950 (140) 4,040 (500) N.A.

Overall hospital spending per capita 450 (90) 1,470 (240) N.A.

Overall physician spending per capita 220 (50) 1,170 (170) N.A.

Medicare spending per beneficiary 1,220 (210) 5,290 (720) 6,150 (730)

Medicare Part A spending per beneficiary 830 (130) 3,070 (440) 3,530 (450)

Medicare Part B spending per beneficiary 390 (90) 2,220 (330) 2,620 (340)

Number of States with Tort Limits

Cap on noneconomic damages 2 19 24

Modification or elimination of joint-and-several 

liabilitya 3 32 35

Cap on attorneys’ fees 8 15 15

Collateral-source rule (jury notification)b 38 8

Cap or ban on punitive damages 2 21 24

Statute of limitations (1-3 years from action) 50 50 50

Any “direct” tort limit 10 33 38

Any “indirect” tort limit 10 37 40

Demographic and Economic Controls

Percentage of Medicare beneficiaries in

managed care 1 (2) 19 (12) 14 (11)

Attorneys per 100,000 people 230 (61) 345 (123) 347 (135)

Gross state product per capita (Thousands of 

dollars) 12 (2) 35 (6) 38 (7)

Age-adjusted mortality rate per 100,000 people 1,040 (60) 870 (70) 840 (80)

Percentage of adults without a high school diploma 34 (6) 20 (4) 18 (4)

Percentage of people that are black 12 (8) 12 (8) 12 (8)

Percentage of people residing in urban areas 74 (13) 79 (13) 80 (13)

Percentage of the workforce that is unemployed 7 (2) 4 (1) 6 (1)
22

Table 2.

Summary of Regressions of Overall Health Care Spending per Capita on

Tort Limits

Source: Congressional Budget Office.

Notes: Estimated standard errors appear in parentheses. Standard errors are estimated using the Huber/White correction, with 

observations clustered at the state level.

Each column represents a separate regression model using state-year spending data. The dependent variables equal the 

natural logarithm of health care spending per capita. Each model includes the 50 states plus the District of Columbia 

and covers the period from 1980 through 2000. Observations are weighted by state population. The natural logarithm of 

health care spending per capita is adjusted for demographics as described in the text. All models also include state- and 

year-fixed effects and economic and demographic controls (coefficients are not shown).

* = p<0.10; ** = p<0.05; *** = p<0.01; n.a. = not applicable.

a. The principle of joint-and-several liability allows a claimant to recover the entire amount of a damage award from any of the

parties found to be responsible for an injury, regardless of each party’s degree of responsibility for that injury. Modifying or 

eliminating joint-and-several liability would generally limit each party’s share of damages to his or her level of responsibility 

for the injury.

b. Collateral-source benefits are compensation for an injury from sources other than a malpractice award, such as health or 

disability insurance.

Overall Health Care 

Spending

per Capita

Overall Hospital Care 

Spending

per Capita

Overall Spending on 

Physician and

Clinical Services 

per Capita

Tort Limit Model 1 Model 2 Model 1 Model 2 Model 1 Model 2

Cap on Noneconomic Damages -0.014

(0.010) n.a.

-0.016

(0.012) n.a.

-0.018

(0.017) n.a.

Elimination of Joint-and-Several 

Liabilitya

 0.040

(0.011)

***

n.a.

 0.047

(0.015)

*** 

 n.a.

 0.022

(0.021) n.a.

Cap on Attorneys’ Fees 0.007

(0.021) n.a.

-0.018

(0.023) n.a.

 0.001

(0.035) n.a.

Collateral-Source Rule (Jury 

notification)b

-0.032

(0.024) n.a.

-0.037

(0.030) n.a.

-0.007

(0.020) n.a.

Cap or Ban on Punitive Damages -0.007

(0.011) n.a.

-0.019

(0.014) n.a.

 0.002

(0.014) n.a.

Sum of Tort Limit Coefficients -0.005

(0.034) n.a.

-0.044

(0.043) n.a.

-0.001

(0.031) n.a.

Any “Direct” Tort Limit n.a. -0.008

(0.011)

n.a. -0.024

(0.014) n.a.

-0.003

(0.014)

Any “Indirect” Tort Limit n.a. 0.020

(0.015)

n.a. 0.023

(0.019) n.a.

 0.000

(0.019)
23

Table 3.

Summary of Regressions of Medicare Spending per Beneficiary on

Tort Limits

Source: Congressional Budget Office.

Notes: Estimated standard errors appear in parentheses. Standard errors are estimated using the Huber/White correction, with 

observations clustered at the state level.

Each column represents a separate regression model using state-year spending data. The dependent variables equal 

the natural logarithm of spending per beneficiary. Each model includes the 50 states plus the District of Columbia and 

covers the period from 1980 through 2003. Observations are weighted by the number of Medicare fee-for-service beneficiaries in the sample. The natural logarithm of spending per beneficiary is adjusted for demographics as described in 

the text. All models also include state- and year-fixed effects and economic and demographic controls (coefficients are 

not shown).

* = p<0.10; ** = p<0.05; *** = p<0.01; n.a. = not applicable.

a. The principle of joint-and-several liability allows a claimant to recover the entire amount of a damage award from any of the

parties found to be responsible for an injury, regardless of each party’s degree of responsibility for that injury. Modifying or 

eliminating joint-and-several liability would generally limit each party’s share of damages to his or her level of responsibility 

for the injury.

b. Collateral-source benefits are compensation for an injury from sources other than a malpractice award, such as health or 

disability insurance.

Medicare

Spending per

Beneficiary

Part A (Hospital)

Spending per

Beneficiary

Part B (Physician) 

Spending per 

Beneficiary

Tort Limit Model 1 Model 2 Model 1 Model 2 Model 1 Model 2

Cap on Noneconomic 

Damages

-0.040

(0.014)

*** n.a. -0.052

(0.017)

*** n.a. -0.016

(0.015)

n.a.

Elimination of Joint-andSeveral Liabilitya

 0.053

(0.018)

*** n.a. 0.056

(0.024)

** n.a. 0.040

(0.019)

** n.a.

Cap on Attorneys’ Fees -0.005

(0.024)

 n.a. -0.011

(0.029)

n.a. 0.002

(0.015)

n.a.

Collateral-Source Rule 

(Jury notification)b

-0.043

(0.036)

 n.a. -0.061

(0.042)

n.a. 0.015

(0.026)

n.a.

Cap or Ban on Punitive 

Damages

 0.017

(0.015)

 n.a. 0.021

(0.018)

n.a. 0.001

(0.015)

n.a.

Sum of Tort Limit 

Coefficients

-0.018

(0.049)

n.a. -0.047

(0.055)

n.a. 0.043

(0.036)

n.a.

Any “Direct” Tort Limit n.a. -0.035

(0.019)

* n.a. -0.046

(0.023)

* n.a. -0.014

(0.017)

Any “Indirect” Tort Limit n.a. 0.028

(0.027)

 n.a. 0.023

(0.032)

n.a. 0.037

(0.018)

**
24

Figure 2.

Summary of Findings on Tort Limits

(Percentage impact of tort limit on per capita spending)a

Source: Congressional Budget Office.

Note: The vertical lines indicate a 90 percent confidence interval. A coefficient estimate is statistically significant at the 10 percent level if the 90 percent confidence interval line does not 

cross the zero line.

a. The percentage impacts are the coefficients from regressions of the natural log of spending per 

capita.

b. The principle of joint-and-several liability allows a claimant to recover the entire amount of a 

damage award from any of the parties found to be responsible for an injury, regardless of each 

party's degree of responsibility for that injury. Modifying or eliminating joint-and-several liability 

would generally limit each party’s share of damages to his or her level of responsibility for the 

injury

c. Collateral-source benefits are compensation for an injury from sources other than a malpractice 

award, such as health or disability insurance.

-20

-15

-10

-5

0

5

10

A

B B A

P

C

C

D

D

D

E

E E

Cap on

Noneconomic

Damages

Modification or

Elimination of

Joint-and-Several

Liability

Collateral-Source

Rules (Jury

notification)

Cap or Ban

on Punitive

Damages b c

Hospital Spending

per Capita

H

Cap on

Attorneys'

Fees

H

H

H

H

P

P P

P

A

A

A

A

B

B

B

B

Physician Spending

per Capita

Medicare Part A

Spending per

Beneficiary

Medicare Part B

Spending per

Beneficiary

H P A B
25

and any direct tort limit are each associated with reductions in health care spending 

for at least one of the three categories of spending per capita. Eliminating joint-andseveral liability is associated with an increase in spending per capita on physician care, 

and any indirect tort limit is associated with an increase in hospital spending per capita and overall spending per capita.

The results in Table 2 can be used to estimate the impact on spending of changing 

from a legal regime in which no tort limits are in place to one in which all five of the 

selected tort limits are in place. That estimated impact can be calculated by summing 

the coefficients for the individual tort limits. For health care spending per capita and 

physician spending per capita, the sum of the coefficients for the individual tort limits 

is near zero. The sum of the coefficients in the model of hospital spending per capita 

is -0.044, which has a t-statistic greater than 1 in absolute value but is not statistically 

significant at conventional levels.

The results in Table 3 using Medicare spending data provide more support for the 

proposition that tort limits affect health care spending. The cap on noneconomic 

damages and the direct tort limit variable (which includes a cap on noneconomic 

damages) both produce statistically significant negative coefficients for the measures 

of Medicare spending per beneficiary and Medicare Part A (hospital) spending per 

beneficiary.43 The coefficients on eliminating joint-and-several liability are statistically significant and positive for all three Medicare spending categories, and in the 

model of Medicare Part B spending per beneficiary the indirect limit variable (which 

includes the variable for eliminating joint-and-several liability) has a statistically significant positive coefficient.

Focusing next on coefficients in the Medicare spending models that appear to have 

some association with health care spending but do not reach statistical significance at 

conventional levels, noneconomic caps are associated with a reduction in Medicare 

Part B spending, and allowing evidence of collateral-source benefits to be introduced 

at trial is associated with reductions in spending in the models of total Medicare 

43. Short-stay hospital spending makes up the bulk of Medicare Part A spending. A separate set of 

analyses was run in which Medicare short-stay hospital spending per beneficiary was regressed on 

tort limits and the other controls included in the main analysis. In the analyses of Medicare shortstay hospital spending per beneficiary, capping noneconomic damages was associated with a statistically significant 4.3 percent reduction in short-stay hospital spending, while eliminating jointand-several liability was associated with a statistically significant 5.4 percent increase in short-stay 

hospital spending. CBO performed further analyses to identify whether the associations between 

those tort limits and Medicare short-stay hospital spending per beneficiary were driven by changes 

in hospital stays per beneficiary or changes in payments per hospital stay. For both tort limits (capping noneconomic damages and eliminating joint-and-several liability), the associations with 

changes in Medicare short-stay hospital spending per beneficiary are attributable primarily to 

changes in payments per stay, rather than changes in stays per beneficiary, suggesting that most of 

the observed effect of tort limits on Part A spending is not due to changes in utilization.
26

spending and Medicare Part A spending. A cap or ban on punitive damages and any 

indirect tort limit were associated with increases in spending in one or more of the 

models. Considering the total effect of all of the tort limits together, the sums of the 

coefficients of the individual limits are not statistically significant for any of the three 

measures of Medicare spending.44

In several of those analyses, two results recur: capping noneconomic damages is associated with reductions in health care spending, and eliminating joint-and-several liability is associated with increases in spending. A simple correlation analysis (weighted 

by state population) shows that those two tort limits are only weakly positively correlated (they have a correlation coefficient of 0.24).45 Two additional regressions were 

run to test for the possibility that the correlation might be producing misleading 

results in the main analysis, which includes both variables. Each alternative regression 

included only a single tort limit variable, either a cap on noneconomic damages or the 

elimination of joint-and-several liability, along with the controls included in the main 

models. Both of those alternative regressions yielded essentially the same estimates of 

the tort limit coefficients and with the same level of statistical significance as when the 

two variables were included together along with the other three tort limit variables.

That eliminating joint-and-several liability would be associated with increases in 

health care spending seems counterintuitive, although it is consistent with Kessler and 

McClellan’s findings.46 Eliminating joint-and-several liability would limit each defendant’s financial liability to his or her share of responsibility for an alleged injury, which 

would reduce the awards actually paid in cases where some of the defendants did not 

have adequate resources to pay their full share of the award. By reducing the expected 

award payment, eliminating joint-and-several liability would, thus, reduce the attractiveness of filing a malpractice claim. That outcome would be expected to reduce the 

overall cost to providers of malpractice claims and, therefore, to lessen the pressure on 

them to practice defensive medicine. W. Kip Viscusi and others (1993) and Patricia 

Born and others (2006) provide support for that hypothesis: they found that eliminat44. In the analysis of Medicare Part B spending per beneficiary, the sum of the coefficients of the individual tort limits equals 0.043; that estimate is not statistically significant at conventional levels but 

has a t-statistic greater than 1.

45. Technically, the correlation of interest is not the simple correlation between the two tort limits but 

rather the partial correlation between the two tort limits after adjusting for all the other covariates 

included in the analysis. To measure that partial correlation, the two tort limits were regressed separately on the control variables included in the main analysis and the residuals were calculated. The 

correlation between the residuals from those regressions equals only 0.04. That very small correlation is consistent with the subsequent finding that the respective coefficients on a cap on noneconomic damages and elimination of joint-and-several liability are unchanged if either of those two 

tort limits is dropped from the regression.

46. See Kessler and McClellan, “Do Doctors Practice Defensive Medicine?” The authors find that any 

“indirect reform,” which includes eliminating joint-and-several liability, is associated with an 

increase in Medicare short-stay hospital spending on heart attack patients of 1.8 percent (AMI 

patients) to 3.4 percent (IHD patients).
27

ing joint-and-several liability appeared to reduce medical malpractice insurers’ losses 

and medical malpractice insurance premiums.47 Other research regarding the effect of 

eliminating joint-and-several liability on medical malpractice premiums has been 

inconclusive.48

There is an alternative hypothesis that is consistent with a positive relationship 

between eliminating joint-and-several liability and increases in health care spending. 

Although eliminating joint-and-several liability might reduce overall malpractice premiums as described above, it could increase malpractice premiums for at least some 

physicians. That could occur if the elimination of joint-and-several liability made 

physicians responsible for paying a larger share of damage awards (and made hospitals 

responsible for paying a smaller share of damage awards), and if from the physician’s 

point of view, paying a larger share of damage awards more than offset any reduction 

in the number of claims filed. Given that physicians could see their malpractice premiums increase as a result of the elimination of joint-and-several liability, it is conceivable that eliminating joint-and-several liability might increase both the practice of 

defensive medicine and health care costs in general. CBO has not found any direct 

empirical evidence to support that hypothesis, however.

The fact that results found using the Medicare data differ from those found using the 

broader measures of state health care spending could call into question the quality of 

the broader state data. Indeed, as noted earlier, those data are based in part on the 

Economic Census, which is conducted only once every five years. Substantial interpolation is required for between-census years. To test the influence of the betweencensus-year observations on the results of the analyses of overall health care spending, 

a separate analysis was conducted that included only the years in which the Economic 

Census was performed. The results of that analysis, although it included less than 

one-fifth of the data available, are very similar to the main results presented in 

Table 2. The only systematic difference is that the standard errors are slightly larger 

in the models that include only the census years. Moreover, when the models of 

Medicare spending are reestimated using only data for every fifth year, the results are 

very similar to those originally displayed in Table 3 for Medicare. Thus, that further 

analysis indicates that lower-quality data do not appear to explain the weaker results 

found using the SHE data versus the Medicare data.

47. See W. Kip Viscusi and others, “The Effect of 1980s Tort Reform Legislation on General Liability 

and Medical Malpractice Insurance, Journal of Risk and Uncertainty, vol. 6, no. 2 (April 1993), 

pp.165-186; and Patricia Born, W. Kip Viscusi, and Tom Baker, The Effects of Tort Reform on Medical Malpractice Insurers' Ultimate Losses, Working Paper No. 12086 (Cambridge, Mass.: National 

Bureau of Economic Research, March 2006), available at http://papers.nber.org/papers/

w12086.pdf.

48. CBO was unable to identify a statistically significant relationship between the joint-and-several 

liability rule and medical malpractice premiums in previous analyses of legislation imposing 

malpractice tort limits.
28

Specification Tests

The key identification assumption in the analyses presented in Tables 2 and 3 is that 

the imposition of state-level tort limits is not correlated with factors that are not 

included in the model and that are driving differential spending trends across states. 

CBO undertook three tests of the validity of that assumption. The first two tests 

involved comparing spending trends in states that implemented tort limits with states 

that did not. If, in the period prior to implementation of tort limits, the trends in 

states that later implemented tort limits differed substantially from trends in states 

that did not, then that would suggest that there may be important differences between 

those two groups of states. For example, implementation of tort limits could be part 

of a broader set of initiatives designed to limit growth in health care spending.

Graphical Comparison

The first specification test is a simple graphical analysis of spending trends in states 

that passed tort limits versus states that did not. The test consists of analyzing the percentage difference in spending per capita between states that imposed a cap on noneconomic damages at some point versus states that never imposed a noneconomic 

cap.49 That analysis focuses on caps on noneconomic damages for two reasons: first, 

based on the Medicare spending results described earlier, that type of limit appears to 

have a relatively strong association with reductions in health care spending; and, second, noneconomic caps have been found in earlier CBO research to be strongly associated with reductions in malpractice claims and malpractice insurance premiums. 

Both overall health care spending per capita and Medicare spending per beneficiary 

are examined. Each state is assigned to one of three groups: states that had a noneconomic cap in place in 1980 (two states), states that imposed a noneconomic cap at 

some point between 1981 and 2003 (27 states), and states that had never imposed a 

noneconomic cap during the period (22 states).

The results, shown in Figure 3, indicate that in 1980 states that later imposed noneconomic caps had higher spending than states that did not impose caps (4.9 percent 

higher health care spending per capita and 5.9 percent higher Medicare spending per 

beneficiary). For overall health care spending per capita, that spending gap declined to 

0.1 percent by 1986. For Medicare spending per beneficiary, the spending gap widened from 1980 to 1982, then shrank somewhat, and then began to fall dramatically 

beginning in 1986. It should be noted that the wave of noneconomic caps, and tort 

limits in general, did not begin until 1986. (The number of states imposing noneconomic caps in each year is indicated in Figure 3 by the vertical bars.)

49. The percentage difference in spending equals the difference in per capita spending (states that 

imposed caps versus states that did not) divided by spending per capita in states that did not 

impose caps. All spending measures are adjusted for demographics. Mean spending per capita in 

each group of states (those that imposed caps and those that did not) equals the populationweighted mean of the state-level measures of spending per capita.
29

Figure 3.

Percentage Difference in Spending for Health Care Between 

States with Caps on Noneconomic Damages and States with 

No Caps, 1980 to 2000

(Percent) (Number of states)

Source: Congressional Budget Office.

1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000

-8

-4

0

4

8

12

16

-8

-4

0

4

8

12

16

Percentage Difference

in State Health Care

 Expenditures per Capita

(Left scale)

Number of States

Imposing Noneconomic Caps

(Right scale)

1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000

-8

-4

0

4

8

12

16

-8

-4

0

4

8

12

16

Personal Health Care Spending per Capita

Medicare Spending per Beneficiary

Percentage Difference

in Medicare Spending

per Beneficiary

(Left scale)

Number of States

Imposing Noneconomic Caps

(Right scale)
30

The key findings from Figure 3 are that states that later imposed noneconomic caps 

started the period with higher overall spending per capita, and that between 1982 and 

1986 the gap in spending shrank. After the wave of noneconomic caps began in 1986, 

that differential trend continued, with overall spending per capita in states that 

imposed noneconomic caps eventually dropping well below spending in states that 

did not impose caps. For Medicare spending per beneficiary, states that imposed noneconomic caps began the period with higher spending, but the gap did not begin to 

shrink until 1986. The key questions, which cannot be resolved with the available 

data, are: 1) whether the observed differential trends in overall health care spending 

per capita after 1986 in those states that capped noneconomic damages were merely a 

continuation of a spending reduction process or trend that had already begun in those 

states or were caused by passage of tort limits; and 2) whether the differential trend in 

Medicare spending per beneficiary beginning in 1986 in those states that capped noneconomic damages was caused by the implementation of that tort limit or by other 

factors.

The Addition of “Lead” Variables

To test more specifically for differences in spending trends between states that later 

imposed caps and states that did not, a second specification test was performed in 

which a set of “lead” variables was added to the models in Tables 2 and 3. For each 

tort limit, a variable was created that was set to 1 in a given year if the state imposed 

that tort limit one to three years in the future and set to zero otherwise. If the coefficients on those lead variables were negative and statistically significant, for example, 

that would indicate that spending had begun to decline in states that later implemented tort limits prior to implementation of the tort limits themselves. That finding 

would suggest that passage of tort limits occurred after relative spending reductions 

had begun, calling into question whether tort limits caused the observed relative 

spending declines.50 Appendix C illustrates the construction of the lead variables for 

two selected states—Alaska and Alabama—and provides detailed regression results 

from models that include the lead variables.51

The discussion of the results using the lead variables will focus on those tort limits 

that had statistically significant coefficients in the main analyses in Tables 2 and 3. In 

the original models of overall health care spending per capita, the only tort limit with 

a statistically significant coefficient was the elimination of joint-and-several liability in 

50. Relative spending means spending relative to the national trend in health care spending. The tort 

limit variables are defined on the basis of the date of implementation, which may differ from the 

date on which the tort limit is passed by the state legislature. Conceivably, physicians might adjust 

their practice patterns in response to a tort limit prior to the tort limit’s date of implementation if 

physicians either mistakenly assumed that the tort limit would go into effect immediately upon 

passage, or if they simply adjusted in anticipation of changing conditions.

51. In general, tort limits only affect claims involving injuries that occur after the law goes into effect. 

Thus, it is unlikely that providers would have any incentive to alter their practice patterns prior to 

the effective date of the limit.
31

the models of health care spending per capita and hospital spending per capita. When 

the lead variables are included in those models, the elimination of joint-and-several 

liability one to three years in the future is associated with statistically significant 

increases in current-year spending, which suggests that relative spending in states that 

eliminated joint-and-several liability began to increase before that tort limit was 

implemented (see Table C-2).

In the models of total Medicare spending per beneficiary and Medicare Part A spending per beneficiary, the lead variables for caps on noneconomic damages have negative 

coefficients, though the coefficient on the lead variable in the model of Medicare 

spending per beneficiary is not statistically significant. The lead variables for any 

direct reform are near zero (see Tables C-5 and C-6). In the three models of Medicare 

spending per beneficiary, none of the lead variables on eliminating joint-and-several 

liability is statistically significant, nor is the lead variable on any indirect reform in the 

model of Medicare Part B spending per beneficiary (see Table C-7).

The results on the lead variables provide evidence that, in some cases, the relative 

spending changes associated with certain tort limits began prior to implementation of 

those limits. That suggests that other factors might have been driving differential 

trends in states that implemented tort limits. One such factor may have been general 

concern at the state level with high health care spending, which could have resulted in 

state or private-sector actions to rein in rising costs, in addition to the imposition of 

tort limits.

The Addition of Controls for Medicare’s Prospective Payment

System for Hospitals

The fact that tort limits have stronger associations with changes in Medicare spending 

than with changes in spending among each state’s entire population seems at odds 

with other evidence that elderly people are less likely to file malpractice claims and are 

paid smaller amounts in malpractice cases in which they prevail. That raises the question of whether certain policy changes specific to Medicare might have coincided with 

the implementation of tort limits and been driving Medicare spending trends, confounding the estimates of the effects of tort limits. The most obvious candidate for 

such a policy change is the implementation, beginning in 1983, of Medicare’s new 

prospective payment system for hospitals. That new payment system, which was 

phased in over several years, dramatically affected the payments that hospitals received 

from Medicare. The new PPS compressed payment rates differentially across states, 

meaning that states with hospitals that previously received high average payments per 

discharge from Medicare tended to have their average payment rates reduced, and 

states with hospitals that previously received low average payments per discharge 

tended to have their average payment rates increased. The fact that states that later 

implemented noneconomic caps began the 1980s with higher Medicare Part A spending per beneficiary suggests that those states were more likely than states that did not 

pass tort limits to have their Medicare hospital payment rates negatively impacted by 

the new PPS.
32

To test more formally whether the states most heavily impacted by PPS were driving 

the Medicare Part A spending results, CBO performed a separate set of analyses of 

Medicare Part A spending in which states were excluded if they were among either the 

top or bottom 10 in terms of their 1983 average Medicare hospital payment per discharge. States with an unusually high or low hospital payment per discharge were 

likely to be more strongly impacted by the new Medicare PPS than other states.52 The 

results of those analyses were quite similar to the main Medicare Part A results in 

Table 3, however, suggesting that the estimates reported in Table 3 are probably not 

driven by a small number of states that were heavily impacted by the new Medicare 

PPS.

To further test the possibility that the Medicare Part A results in Table 3 were driven 

by changes in the hospital payment system, two additional variables were added to the 

Medicare Part A model, representing the degree of cost-reducing pressure imposed on 

each state’s hospitals as a result of the imposition of Medicare’s hospital PPS.53 States 

were likely to have experienced stronger pressure to reduce hospital spending under 

the hospital PPS if, prior to the implementation of the PPS, they received higher average payments per discharge from Medicare, or if they had an unusually high number 

of short-stay hospital beds per capita.54 When PPS “pressure” variables are included in 

the Medicare Part A model, the coefficient on the noneconomic cap is less than half as 

large (-0.023, compared with -0.052 in the Part A model) and is no longer statistically 

significant at conventional levels, and the coefficient on any direct tort limit is also 

less than half as large (-0.018, compared with -0.046 in the Part A model) and is no 

longer statistically significant (see Tables 3 and D-2). The coefficient on eliminating 

joint-and-several liability remains positive and statistically significant when the PPS 

controls are included. Those findings suggest that the main results on noneconomic 

caps in the Medicare models may have been driven in part by the effects of the implementation of Medicare’s hospital PPS.

52. The hospital price for each state was set equal to Medicare payments per discharge in 1983, 

adjusted for beneficiary demographics and for a state-level aggregation of Medicare’s hospitalspecific local wage indexes for 1983.

53. The results of these tests are available in Appendix D.

54. Two PPS pressure variables were constructed: the natural logarithm of Medicare short-stay hospital 

payments per short-stay hospitalization in 1982 (adjusted for demographics and a state-level aggregate of Medicare’s hospital wage index for 1983) and the natural logarithm of short-term general 

hospital beds per capita in 1983 (adjusted for state population density). The year 1982 was used for 

the payments per stay, rather than 1983, because 1982 was the last year of full cost reimbursement 

(The Tax Equity and Fiscal Responsibility Act of 1982 (TEFRA), which began the changes to the 

system of full cost reimbursement, was first implemented in fiscal year 1983, and the prospective 

payment system was first implemented in fiscal year 1984). Each of those PPS pressure variables 

was interacted with each of the year indicator variables, and those interactions were included in the 

alternative specifications. This allows for a flexible time trend that varies with each PPS pressure 

variable.
33

The Addition of State-Specific Time Trends

The final specification test of the models presented in Tables 2 and 3 includes statespecific time trends in the regression specifications. Adding state-specific time trends 

allows for the possibility that states that adopted tort limits had different underlying 

time trends from states that did not adopt limits. (The detailed results from the 

regressions that include the time trends are presented in the far right column of the 

tables in Appendix B.) For all six spending measures, the analyses that include statespecific time trends produce coefficients on eliminating joint-and-several liability that 

are closer to zero than the coefficients in the main analysis (this comparison is 

between the coefficients presented in Tables 2 and 3 and the coefficients in the outer 

right column of Appendix Tables B-1 through B-6). Also, for five of the six spending 

measures, the coefficients on eliminating joint-and-several liability are less statistically 

significant (or not statistically significant at all) in the analyses that include statespecific time trends compared with the main analyses. The coefficient estimates on a 

cap on noneconomic damages were generally similar between the main analyses and 

the analyses that included state-specific trends. For two spending measures (overall 

health care spending per capita and hospital spending per capita) the coefficient on a 

cap on noneconomic damages is not statistically significant in the main analyses but is 

statistically significant in the analyses that include state-specific time trends. The fact 

that the coefficient estimates for eliminating joint-and-several liability move toward 

zero when state-specific time trends are added suggests that states that implemented 

that tort limit may have had different underlying spending trends than other states, 

and that those underlying trends may be confounding the results in the main analyses.

Discussion

In previous work, CBO analyzed the effect of limits on tort claims imposed by states 

and found that those limits reduced premiums for medical malpractice insurance. 

Lower malpractice premiums would, in turn, reduce providers’ costs and therefore 

result in some lowering of private health insurance premiums and the cost of federal 

health programs. A remaining question has been whether there is an additional effect 

of tort limits, a so-called utilization effect—by which changes in the malpractice environment might affect utilization by affecting physicians’ behavior. 

This empirical analysis examined the question of whether the imposition of state-level 

tort limits affects health care spending at the state level. The statistical approach is 

multiple regression with state- and year-level fixed effects. Two sets of tort limit variables were analyzed: the first set included a separate indicator variable for each of five 

different tort limits—a cap on noneconomic damages, eliminating joint-and-several 

liability, a cap on attorneys’ fees, allowing evidence of collateral-source benefits to be 

introduced at trial, and a cap or ban on punitive damages; the second included direct 

and indirect limit variables, similar to the variables used by Kessler and McClellan.55

55. See, for example, Kessler and McClellan, “Do Doctors Practice Defensive Medicine?”
34

The analysis used two sets of data on health care spending for each state in each year 

since 1980: overall health care spending per capita calculated from CMS’s state health 

expenditures, and Medicare spending per beneficiary calculated from Medicare’s 

administrative files. Each of the two data sets has advantages and disadvantages. The 

Medicare data, because they are based on administrative claims, are arguably a more 

accurate measure of spending than the measures of state-level health spending per 

capita. Medicare spending, however, only accounts for about one-fifth of total health 

care spending and is not necessarily representative of overall spending. Overall health 

care spending per capita includes spending for each state’s entire population, regardless of age or insurance status. Although data on state health expenditures are a better 

measure of spending for each state’s population as a whole, they are based on a mix of 

data sources, including primarily the Economic Census, which is conducted only 

once every five years.

For two of the three measures of Medicare spending per beneficiary considered here 

(Medicare spending per beneficiary and Medicare Part A spending per beneficiary), a 

cap on noneconomic damages is associated with a statistically significant reduction in 

spending. The coefficients on direct tort limits are also negative and statistically significant for those two spending measures, probably reflecting the fact that noneconomic caps are included in the definition of that variable. In contrast, eliminating 

joint-and-several liability is associated with statistically significant increases in all three 

measures of Medicare spending, with coefficient estimates that are larger in absolute 

value than the negative coefficients on the cap on noneconomic damages. Finally, the 

sum of the coefficients of the individual tort reforms is not statistically different from 

zero for any of the three measures of Medicare spending that CBO considered.

In the analyses of overall health care spending per capita, eliminating joint-and-several 

liability is associated with statistically significant increases in spending in both the 

model of health care spending per capita and the model of hospital spending per capita. Caps on noneconomic damages are associated with reductions in spending per 

capita for all three spending measures, though those coefficients are not statistically 

significant at conventional levels. As in the analysis of Medicare spending, the sums of 

the coefficients are not statistically significant at conventional levels for any of the 

spending measures.

Specification tests, however, raise some questions as to whether those associations 

between certain tort limits and spending reflect causal impacts. In several of the cases 

where a tort limit was found to have a statistically significant association with changes 

in spending, either positive or negative, there was evidence that relative spending was 

already moving in the direction of the estimated effect prior to the implementation of 

those tort limits. This evidence of a “lead” or anticipatory effect is consistent with the 

possibility of there being an outside factor that may have caused changes in relative 

spending in states that passed tort limits, and that was correlated with the passage of 

tort limits. One possible such factor is the general concern among the states about 

high medical costs, and efforts, in addition to placing limits on torts for medical mal-
35

practice, to control those costs. Another possible factor is changes in Medicare’s payment policies for hospitals that took place during the period of the analysis. Variables 

were added to the main models to control for the differential impact across states of 

the introduction of Medicare’s PPS for hospitals. The addition of those PPS control 

variables produced an estimated impact of noneconomic caps on Medicare Part A 

spending that was substantially closer to zero than in the results in Table 3, and not 

statistically significant.

The potentially lower quality of the data on state health expenditures could be a reason that less evidence of a relationship between tort limits and spending is found 

using those data than using the Medicare data. However, further analysis indicates 

that the quality of those data does not appear to have had an important impact on the 

results presented here.

Rules governing medical malpractice claims are one of a host of factors potentially 

affecting the delivery and cost of health care services in the United States. Although 

this analysis provides some evidence of links between tort limits and health care 

spending, the results are inconsistent and depend on the particular relationships and 

specifications tested. The mixed results also demonstrate the difficulty of disentangling any effects of tort limits from other factors that affect levels of spending for 

health care. CBO continues to monitor the work of other researchers and conduct its 

own research on the issue.

37

Appendix A:

Adjusting Health Care Spending per 

Capita for Demographics Appendix A: Adjusting Health Care Spending per Capita for Demographics

State-year measures of health care spending per capita are adjusted to incorporate demographics to the largest extent possible given the available data. A crude method is 

used for adjusting measures of overall health care spending per capita, and a separate 

method is used for adjusting measures of Medicare spending per beneficiary. A more 

refined method can be used for adjusting Medicare spending measures because the 

Medicare spending data are available at the person-year level.

To adjust health care spending per capita, a predicted spending level for each state in 

each year is generated on the basis of the age composition of that state’s population:

where is the measure of health care spending per capita in age group for service 

, is the population in age group in state in year , and is the total population in state in year . A national predicted spending level is also generated for 

each year:

where is the U.S. population in age group in year , and is the U.S. population in year .

Adjusted health care spending per capita for service in state in year equals:

where is unadjusted health care spending per capita for service . To estimate agegroup-specific health care spending per capita, , the Medical Expenditure Panel 

Surveys (MEPS) for 1996 through 2000 are pooled, and separate measures are calculated for spending on all health care, hospital care, and physician and clinical services.1

 To make spending from different years of the MEPS comparable, spending is 

inflated using the gross domestic product (GDP) deflator to a common year. Spending per capita is then estimated separately for 10-year age groups (less than 6, 6 to 14, 

1. Joel Cohen and others. “The Medical Expenditure Panel Survey: A National Health Information 

Resource,” Inquiry, vol. 33, no. 4 (Winter 1996), pp. 373-389.

H

ˆ

stj Σa

z aj

n ast

Nst

= ---------

z aj a

j n ast a s tNst

s t

H

ˆ

tj Σa

z aj

n at

Nt

= ------

n at a tNt

t

j st

H

 ̃

stj H stj

Hˆ

tj

H

ˆ

stj

= --------

H stj j

z aj
38

15 to 24, and so on, and concluding with ages 85 and up). Note that only the relative 

level of spending across age groups is relevant for the demographic adjustment; the 

level does not matter, which makes it possible to use the same measures of age-groupspecific health care spending per capita for adjusting spending in all years. It is possible to calculate separate age-group-specific health care spending for each year, but relative spending across age groups has not changed so dramatically that such a measure 

is necessary.2

For adjusting Medicare spending, the Continuous Medicare History Sample includes 

detailed data on demographics and spending. That allows adjustment of Medicare 

spending per beneficiary using a more sophisticated approach. Predicted Medicare 

spending per beneficiary in state s in year t equals:

where is national Medicare spending per beneficiary in demographic group in 

year , is the number of Medicare beneficiaries in demographic group in state 

 in year , and is total Medicare beneficiaries in state in year . Year-specific 

Medicare spending in each demographic group equals:

where indexes beneficiaries, equals spending by beneficiary in demographic 

group in year , and equals the number of beneficiaries in demographic group 

 in year . National Medicare spending per beneficiary is also calculated:

where indexes beneficiaries, equals spending by beneficiary in year , and 

equals the number of beneficiaries in year . Adjusted Medicare spending per beneficiary for state in year equals:

where is unadjusted Medicare spending per beneficiary in state and year .

2. Ellen Meara, Chapin White, and David M. Cutler, “Trends in Medical Spending by Age, 1963-

2000,” Health Affairs, vol. 23, no. 4 (July/August 2004), 176-183.

M

ˆ

st Σ

d

mdt

b dst

Bst

= ---------

mdt d

t b dst d

s tBst s t

mdt

Σ

i

midt

B dt

= -----------------

i midt i

d tBdt

d t

Mt

Σ

i

mit

B t

= --------------

i mit i tBt

t

s t

M ̃

st Mst

Mt

M

ˆ st

= -------

Mst s t
39

Appendix B:

Detailed Regression Results Appendix B: Detailed Regression Results

The regression results that appear in this appendix include the findings presented in 

the main section of the analysis (see Tables 2 and 3 on pages 22 and 23 and Figure 2 

on page 24). The appendix also features other regression output and the results from 

additional specifications.

41

Table B-1.

Regressions of Overall Health Care Spending per Capita

Continued

Main Results 

(Weighted by 

Population)

Unweighted 

Results

Indirect/Direct 

Tort Limit

Variables

State-Specific

Time Trends

Tort Limit Variables

Cap on noneconomic damages -0.014

(0.010)

-0.011

(0.007)

n.a. -0.011

(0.006)

*

Elimination of joint-and-several liabilitya 0.040

(0.011)

*** 0.029

(0.008)

*** n.a. 0.014

(0.005)

**

Cap on attorneys’ fees 0.007

(0.021)

 0.015

(0.016)

n.a. 0.008

(0.010)

Collateral-source rule (jury notification)b -0.032

(0.024)

-0.017

(0.017)

 n.a. -0.015

(0.012)

Cap or ban on punitive damages -0.007

(0.011)

-0.011

(0.010)

n.a. -0.001

(0.007)

Sum of tort limit coefficients -0.005

(0.034)

 0.005

(0.028)

n.a. -0.004

(0.018)

Any “direct” tort limit n.a. n.a. -0.008

(0.011)

n.a.

Any “indirect” tort limit n.a. n.a. 0.020

(0.015)

n.a.

Demographic and Economic Controls

Percentage of Medicare beneficiaries in 

managed care

-0.203

(0.085)

** -0.233

(0.052)

*** -0.223

(0.084)

** -0.234

(0.057)

***

Attorneys per capitac 0.160

(0.056)

*** 0.175

(0.043)

*** 0.185

(0.063)

*** 0.004

(0.080)

Gross state product per capitac 0.184

(0.046)

*** 0.181

(0.031)

*** 0.207

(0.052)

*** 0.183

(0.035)

***

Age-adjusted mortality ratec 0.089

(0.192)

 0.179

(0.113)

 0.126

(0.212)

 0.188

(0.069)

***

Percentage of adults without a high school diploma -1.947

(0.291)

*** -1.614

(0.213)

*** -1.603

(0.229)

*** -0.110

(0.577)

Percentage of people that are black -0.609

(0.644)

-0.674

(0.369)

* -0.324

(0.982)

-3.020

(1.051)

***

Percentage of people residing in urban areas -0.207

(0.285)

-0.058

(0.202)

-0.215

(0.296)

-0.617

(0.351)

*

Percentage of the workforce that is unemployed 0.794

(0.414)

* 0.580

(0.270)

** 0.952

(0.463)

** 0.096

(0.146)
42

Table B-1.

Continued

Source: Congressional Budget Office.

Notes: Estimated standard errors appear in parentheses. Standard errors are estimated using the Huber/White correction, with 

observations clustered at the state level. 

Each column represents a separate regression model. The dependent variable equals the natural logarithm of spending 

per capita. The natural logarithm of spending per capita is adjusted for demographics as described in Appendix A. All 

models include state- and year-fixed effects (coefficients are not shown). “Any ‘direct’ tort limit” equals one if any of 

the following limits are in place (and zero otherwise): a cap on noneconomic damages, a cap on total damages, a ban 

on punitive damages, or a collateral-source rule (mandatory offset). “Any ‘indirect’ tort limit” equals one if either of the 

following limits is in place (and zero otherwise): a cap on attorneys’ fees or modification or elimination of joint-andseveral liability.

* = p<0.10; ** = p<0.05; *** = p<0.01; n.a. = not applicable.

a. The principle of joint-and-several liability allows a claimant to recover the entire amount of a damage award from any of the

parties found to be responsible for an injury, regardless of each party’s degree of responsibility for that injury. Modifying or 

eliminating joint-and-several liability would generally limit each party’s share of damages to his or her level of responsibility 

for the injury.

b. Collateral-source benefits are compensation for an injury from sources other than a malpractice award, such as health or 

disability insurance.

c. The natural logarithm of these variables is used in the model.

d. “Partial R-squared” statistics reflect the explanatory power of the tort limit and control variables only and do not reflect the 

explanatory power of the state- and year-fixed effects. (The R-squared statistics calculated for models that include stateand year-fixed effects are quite high—above 0.97—in all models.)

Main Results 

(Weighted by 

Population)

Unweighted 

Results

Indirect/Direct 

Tort Limit

Variables

State-Specific

Time Trends

Model Characteristics

Number of observations 1,071 1,071 1,071 1,071

Degrees of freedom 987 987 990 936

Years included 1980-2000 1980-2000 1980-2000 1980-2000

Partial R-squaredd 0.2186 0.1394 0.2050 0.5591

p-value on F test of joint significance of tort limits 0.0002 *** 0.0066 *** 0.3944 0.0089 ***

Weight Population Unweighted Population Population
43

Table B-2.

Regressions of Overall Hospital Care Spending per Capita

Continued

Main Results 

(Weighted by

Population)

Unweighted

Results

Indirect/Direct 

Tort Limit

Variables

State-Specific

Time Trends

Tort Limit Variables

Cap on noneconomic damages -0.016

(0.012)

 0.012

(0.016)

n.a. -0.018

(0.010)

*

Elimination of joint-and-several liabilitya 0.047

(0.015)

*** 0.035

(0.014)

** n.a. 0.011

(0.010)

Cap on attorneys’ fees -0.018

(0.023)

-0.019

(0.019)

n.a. -0.002

(0.014)

Collateral-source rule (jury notification)b -0.037

(0.030)

-0.030

(0.025)

n.a. -0.005

(0.017)

Cap or ban on punitive damages -0.019

(0.014)

-0.042

(0.016)

** n.a. -0.007

(0.011)

Sum of tort limit coefficients -0.044

(0.043)

-0.044

(0.038)

n.a. -0.022

(0.024)

Any “direct” tort limit n.a. n.a. -0.024

(0.014)

n.a.

Any “indirect” tort limit n.a. n.a. 0.023

(0.019)

n.a.

Demographic and Economic Controls

Percentage of Medicare beneficiaries in 

managed care

-0.477

(0.106)

*** -0.452

(0.092)

*** -0.506

(0.105)

*** -0.336

(0.082)

***

Attorneys per capitac 0.128

(0.071)

* 0.169

(0.079)

** 0.154

(0.085)

* 0.075

(0.133)

Gross state product per capitac 0.038

(0.067)

 0.039

(0.043)

 0.070

(0.072)

 0.170

(0.064)

**

Age-adjusted mortality ratec 0.216

(0.254)

 0.310

(0.152)

** 0.222

(0.267)

 0.195

(0.138)

Percentage of adults without a high school 

diploma

-1.821

(0.465)

*** -1.465

(0.426)

*** -1.387

(0.377)

*** -0.991

(0.952)

Percentage of people that are black -1.850

(0.910)

** -1.997

(0.688)

*** -1.323

(1.264)

-2.942

(1.467)

*

Percentage of people residing in urban areas 0.014

(0.420)

 0.189

(0.306)

-0.047

(0.428)

-0.567

(0.423)

Percentage of the workforce that is unemployed 0.578

(0.416)

 0.420

(0.316)

 0.657

(0.485)

 0.134

(0.257)
44

Table B-2.

Continued

Source: Congressional Budget Office.

Notes: Estimated standard errors appear in parentheses. Standard errors are estimated using the Huber/White correction, with 

observations clustered at the state level. 

Each column represents a separate regression model. The dependent variable equals the natural logarithm of spending 

per capita. The natural logarithm of spending per capita is adjusted for demographics as described in Appendix A. All 

models include state- and year-fixed effects (coefficients are not shown). “Any ‘direct’ tort limit” equals one if any of 

the following limits are in place (and zero otherwise): a cap on noneconomic damages, a cap on total damages, a ban 

on punitive damages, or a collateral-source rule (mandatory offset). “Any ‘indirect’ tort limit” equals one if either of the 

following limits is in place (and zero otherwise): a cap on attorneys’ fees or modification or elimination of joint-andseveral liability.

* = p<0.10; ** = p<0.05; *** = p<0.01; n.a. = not applicable.

a. The principle of joint-and-several liability allows a claimant to recover the entire amount of a damage award from any of the

parties found to be responsible for an injury, regardless of each party’s degree of responsibility for that injury. Modifying or 

eliminating joint-and-several liability would generally limit each party’s share of damages to his or her level of responsibility 

for the injury.

b. Collateral-source benefits are compensation for an injury from sources other than a malpractice award, such as health or 

disability insurance.

c. The natural logarithm of these variables is used in the model.

d. “Partial R-squared” statistics reflect the explanatory power of the tort limit and control variables only and do not reflect the 

explanatory power of the state- and year-fixed effects. (The R-squared statistics calculated for models that include stateand year-fixed effects are quite high—above 0.97—in all models.)

Main Results 

(Weighted by

Population)

Unweighted

Results

Indirect/Direct 

Tort Limit

Variables

State-Specific

Time Trends

Model Characteristics

Number of observations 1,071 1,071 1,071 1,071

Degrees of freedom 987 987 990 936

Years included 1980-2000 1980-2000 1980-2000 1980-2000

Partial R-squaredd 0.2236 0.1523 0.2140 0.5099

p-value on F test of joint significance of tort limits 0.0212 ** 0.0370 ** 0.2178 0.2423

Weight Population Unweighted Population Population
45

Table B-3.

Regressions of Overall Spending on Physician and Clinical

Services per Capita

Continued

Main Results 

(Weighted by

Population)

Unweighted 

Results

Indirect/Direct 

Tort Limit

Variables

State-Specific

Time Trends

Tort Limit Variables

Cap on noneconomic damages -0.018

(0.017)

-0.024

(0.015)

n.a. -0.002

(0.010)

Elimination of joint-and-several liabilitya 0.022

(0.021)

 0.011

(0.017)

 n.a. 0.019

(0.013)

Cap on attorneys’ fees 0.001

(0.035)

 0.027

(0.033)

n.a. 0.012

(0.019)

Collateral-source rule (jury notification)b -0.007

(0.020)

 0.005

(0.019)

 n.a. -0.017

(0.011)

Cap or ban on punitive damages 0.002

(0.014)

 0.012

(0.012)

n.a. 0.000

(0.008)

Sum of tort limit coefficients -0.001

(0.031)

 0.033

(0.040)

n.a. 0.013

(0.021)

Any “direct” tort limit n.a. n.a. -0.003

(0.014)

n.a.

Any “indirect” tort limit n.a. n.a. 0.000

(0.019)

n.a.

Demographic and Economic Controls

Percentage of Medicare beneficiaries in 

managed care

-0.154

(0.103)

-0.209

(0.088)

** -0.150

(0.111)

-0.425

(0.076)

*** 

Attorneys per capitac 0.364

(0.109)

*** 0.324

(0.081)

*** 0.382

(0.100)

*** -0.045

(0.086)

Gross state product per capitac 0.458

(0.076)

*** 0.376

(0.052)

*** 0.465

(0.083)

*** 0.243

(0.069)

*** 

Age-adjusted mortality ratec -0.141

(0.228)

 0.062

(0.207)

-0.090

(0.232)

 0.303

(0.111)

*** 

Percentage of adults without a high school 

diploma

-1.487

(0.405)

*** -1.446

(0.333)

*** -1.350

(0.322)

*** 0.273

(1.053)

Percentage of people that are black 0.938

(0.703)

 0.836

(0.372)

** 0.917

(0.924)

-6.202

(2.319

** 

Percentage of people residing in urban areas -0.818

(0.326)

** -0.699

(0.325)

** -0.843

(0.340)

** -0.703

(0.506)

Percentage of the workforce that is unemployed 1.987

(0.610)

*** 1.513

(0.405)

*** 2.094

(0.664)

*** 0.529

(0.174)

*** 
46

Table B-3.

Continued

Source: Congressional Budget Office.

Notes: Estimated standard errors appear in parentheses. Standard errors are estimated using the Huber/White correction, with 

observations clustered at the state level. 

Each column represents a separate regression model. The dependent variable equals the natural logarithm of spending 

per capita. The natural logarithm of spending per capita is adjusted for demographics as described in Appendix A. All 

models include state- and year-fixed effects (coefficients are not shown). “Any ‘direct’ tort limit” equals one if any of 

the following limits are in place (and zero otherwise): a cap on noneconomic damages, a cap on total damages, a ban 

on punitive damages, or a collateral-source rule (mandatory offset). “Any ‘indirect’ tort limit” equals one if either of the 

following limits is in place (and zero otherwise): a cap on attorneys’ fees or modification or elimination of joint-andseveral liability.

* = p<0.10; ** = p<0.05; *** = p<0.01; n.a. = not applicable.

a. The principle of joint-and-several liability allows a claimant to recover the entire amount of a damage award from any of the

parties found to be responsible for an injury, regardless of each party’s degree of responsibility for that injury. Modifying or 

eliminating joint-and-several liability would generally limit each party’s share of damages to his or her level of responsibility 

for the injury.

b. Collateral-source benefits are compensation for an injury from sources other than a malpractice award, such as health or 

disability insurance.

c. The natural logarithm of these variables is used in the model.

d. “Partial R-squared” statistics reflect the explanatory power of the tort limit and control variables only and do not reflect the 

explanatory power of the state- and year-fixed effects. (The R-squared statistics calculated for models that include stateand year-fixed effects are quite high—above 0.97—in all models.)

Main Results 

(Weighted by

Population)

Unweighted 

Results

Indirect/Direct 

Tort Limit

Variables

State-Specific

Time Trends

Model Characteristics

Number of observations 1,071 1,071 1,071 1,071

Degrees of freedom 987 987 990 936

Years included 1980-2000 1980-2000 1980-2000 1980-2000

Partial R-squaredd 0.1160 0.0952 0.1122 0.3957

p-value on F test of joint significance of tort limits 0.2863 0.2727 0.9825 0.2839

Weight Population Unweighted Population Population
47

Table B-4.

Regressions of Medicare Spending per Beneficiary

Continued

Main Results 

(Weighted by 

Number of

Beneficiaries)

Unweighted 

Results

Indirect/Direct 

Tort Limit

Variables

State-Specific

Time Trends

Tort Limit Variables

Cap on noneconomic damages -0.040

(0.014)

*** -0.044

(0.013)

*** n.a. -0.038

(0.010)

***

Elimination of joint-and-several liabilitya 0.053

(0.018)

*** 0.032

(0.016)

* n.a.

.

 0.027

(0.015)

*

Cap on attorneys’ fees -0.005

(0.024)

 0.019

(0.026)

n.a. -0.023

(0.014)

Collateral-source rule (jury notification)b -0.043

(0.036)

-0.008

(0.031)

n.a. 0.004

(0.019)

Cap or ban on punitive damages 0.017

(0.015)

 0.002

(0.015)

n.a. 0.009

(0.011)

Sum of tort limit coefficients -0.018

(0.049)

 0.001

(0.050)

n.a. -0.020

(0.024)

Any “direct” tort limit n.a. n.a. -0.035

(0.019)

* n.a

Any “indirect” tort limit n.a. n.a. 0.028

(0.027)

n.a

Demographic and Economic Controls

Percentage of Medicare beneficiaries in managed 

care

-0.176

(0.143)

-0.145

(0.095)

-0.149

(0.126)

-0.158

(0.082)

*

Attorneys per capitac 0.105

(0.061)

* 0.090

(0.060)

 0.137

(0.088)

 0.007

(0.085)

Gross state product per capitac -0.202

(0.082)

** -0.057

(0.062)

-0.179

(0.086)

** -0.110

(0.078)

Age-adjusted mortality ratec 0.157

(0.223)

 0.314

(0.172)

* 0.156

(0.222)

 0.111

(0.138)

Percentage of adults without a high school 

diploma

-1.591

(0.406)

*** -1.547

(0.416)

*** -1.431

(0.314)

*** 0.466

(0.988)

Percentage of people that are black -0.240

(0.926)

 2.074

(0.670)

*** -0.017

(1.104)

-5.566

(2.614)

**

Percentage of people residing in urban areas -0.090

(0.307)

 0.152

(0.283)

 0.031

(0.292)

-0.663

(0.589)

Percentage of the workforce that is unemployed 0.759

(0.401)

* 0.323

(0.322)

 0.941

(0.432)

** 0.106

(0.277)
48

Table B-4.

Continued

Source: Congressional Budget Office.

Notes: Estimated standard errors appear in parentheses. Standard errors are estimated using the Huber/White correction, with 

observations clustered at the state level. 

Each column represents a separate regression model. The dependent variable equals the natural logarithm of spending 

per capita. The natural logarithm of spending per capita is adjusted for demographics as described in Appendix A. All 

models include state- and year-fixed effects (coefficients are not shown). “Any ‘direct’ tort limit” equals one if any of 

the following limits are in place (and zero otherwise): a cap on noneconomic damages, a cap on total damages, a ban 

on punitive damages, or a collateral-source rule (mandatory offset). “Any ‘indirect’ tort limit” equals one if either of the 

following limits is in place (and zero otherwise): a cap on attorneys’ fees or modification or elimination of joint-andseveral liability.

* = p<0.10; ** = p<0.05; *** = p<0.01; n.a. = not applicable.

a. The principle of joint-and-several liability allows a claimant to recover the entire amount of a damage award from any of the

parties found to be responsible for an injury, regardless of each party’s degree of responsibility for that injury. Modifying or 

eliminating joint-and-several liability would generally limit each party’s share of damages to his or her level of responsibility 

for the injury

b. Collateral-source benefits are compensation for an injury from sources other than a malpractice award, such as health or 

disability insurance.

c. The natural logarithm of these variables is used in the model.

d. “Partial R-squared” statistics reflect the explanatory power of the tort limit and control variables only and do not reflect the 

explanatory power of the state- and year-fixed effects. (The R-squared statistics calculated for models that include stateand year-fixed effects are quite high—above 0.97—in all models.)

Main Results 

(Weighted by 

Number of

Beneficiaries)

Unweighted 

Results

Indirect/Direct 

Tort Limit

Variables

State-Specific

Time Trends

Model Characteristics

Number of observations 1,224 1,224 1,224 1,224

Degrees of freedom 1,137 1,137 1,140 1,086

Years included 1980-2003 1980-2003 1980-2003 1980-2003

Partial R-squaredd 0.1261 0.0767 0.1030 0.3177

p-value on F test of joint significance of tort limits 0.0005 *** 0.0062 *** 0.2114 0.0044 ***

Weight Number of

beneficiaries

Unweighted Number of

beneficiaries

Number of

beneficiaries
49

Table B-5.

Regressions of Medicare Part A (Hospital) Spending per Beneficiary

Continued

Main Results 

(Weighted by

Number of

Beneficiaries)

Unweighted 

Results

Indirect/Direct 

Tort Limit

Variables

State-Specific

Time Trends

Tort Limit Variables

Cap on noneconomic damages -0.052

(0.017)

*** -0.051

(0.015)

*** n.a. -0.051

(0.015)

***

Elimination of joint-and-several liabilitya 0.056

(0.024)

** 0.031

(0.019)

 n.a. 0.035

(0.023)

Cap on attorneys’ fees -0.011

(0.029)

 0.021

(0.035)

n.a. -0.032

(0.022)

Collateral-source rule (jury notification)b -0.061

(0.042)

-0.014

(0.036)

 n.a. -0.001

(0.023)

Cap or ban on punitive damages 0.021

(0.018)

 0.005

(0.017)

n.a. 0.008

(0.016)

Sum of tort limit coefficients -0.047

(0.055)

-0.008

(0.060)

n.a. -0.042

(0.032)

Any “direct” tort limit n.a. n.a. -0.046

(0.023)

* n.a.

Any “indirect” tort limit n.a. n.a. 0.023

(0.032)

n.a.

Demographic and Economic Controls

Percentage of Medicare beneficiaries in 

managed care

-0.030

(0.149)

-0.030

(0.098)

-0.006

(0.130)

-0.028

(0.116)

Attorneys per capitac 0.190

(0.064)

*** 0.181

(0.062)

*** 0.225

(0.096)

** 0.036

(0.096)

Gross state product per capitac -0.151

(0.076)

* -0.046

(0.059)

-0.130

(0.082)

-0.064

(0.101)

Age-adjusted mortality ratec -0.066

(0.248)

 0.135

(0.205)

-0.061

(0.249)

-0.046

(0.188)

Percentage of adults without a high school 

diploma

-1.350

(0.427)

*** -1.305

(0.426)

*** -1.162

(0.334)

*** -0.498

(1.340)

Percentage of people that are black -0.449

(0.981)

 1.572

(0.596)

** -0.162

(1.311)

-5.032

(3.376)

Percentage of people residing in urban areas -0.128

(0.362)

 0.217

(0.329)

 0.061

(0.355)

-0.510

(0.762)

Percentage of the workforce that is unemployed 1.056

(0.469)

** 0.481

(0.379)

 1.267

(0.473)

** 0.215

(0.450)
50

Table B-5.

Continued

Source: Congressional Budget Office.

Notes: Estimated standard errors appear in parentheses. Standard errors are estimated using the Huber/White correction, with 

observations clustered at the state level.

Each column represents a separate regression model. The dependent variable equals the natural logarithm of spending 

per capita. The natural logarithm of spending per capita is adjusted for demographics as described in Appendix A. All 

models include state- and year-fixed effects (coefficients are not shown). “Any ‘direct’ tort limit” equals one if any of 

the following limits are in place (and zero otherwise): a cap on noneconomic damages, a cap on total damages, a ban 

on punitive damages, or a collateral-source rule (mandatory offset). “Any ‘indirect’ tort limit” equals one if either of the 

following limits is in place (and zero otherwise): a cap on attorneys’ fees or modification or elimination of joint-andseveral liability.

* = p<0.10; ** = p<0.05; *** = p<0.01; n.a. = not applicable.

a. The principle of joint-and-several liability allows a claimant to recover the entire amount of a damage award from any of the

parties found to be responsible for an injury, regardless of each party’s degree of responsibility for that injury. Modifying or 

eliminating joint-and-several liability would generally limit each party’s share of damages to his or her level of responsibility 

for the injury.

b. Collateral-source benefits are compensation for an injury from sources other than a malpractice award, such as health or 

disability insurance.

c. The natural logarithm of these variables is used in the model.

d. “Partial R-squared” statistics reflect the explanatory power of the tort limit and control variables only and do not reflect the 

explanatory power of the state- and year-fixed effects. (The R-squared statistics calculated for models that include stateand year-fixed effects are quite high—above 0.97—in all models.)

Main Results 

(Weighted by

Number of

Beneficiaries)

Unweighted 

Results

Indirect/Direct 

Tort Limit

Variables

State-Specific

Time Trends

Model Characteristics

Number of observations 1,224 1,224 1,224 1,224

Degrees of freedom 1,137 1,137 1,140 1,086

Years included 1980-2003 1980-2003 1980-2003 1980-2003

Partial R-squaredd 0.0787 0.0469 0.0508 0.2161

p-value on F test of joint significance of tort limits 0.0018 *** 0.0154 ** 0.1388 0.0106 **

Weight Number of

beneficiaries

Unweighted Number of

beneficiaries

Number of

beneficiaries
51

Table B-6.

Regressions of Medicare Part B (Physician) Spending per Beneficiary

Continued

Main Results 

(Weighted by 

Number of

Beneficiaries)

Unweighted 

Results

Indirect/Direct 

Tort Limit

Variables

State-Specific 

Time Trends

Tort Limit Variables

Cap on noneconomic damages -0.016

(0.015)

-0.030

(0.014)

** n.a. -0.008

(0.009)

Elimination of joint-and-several liabilitya 0.040

(0.019)

** 0.030

(0.017)

* n.a. 0.003

(0.013)

Cap on attorneys’ fees 0.002

(0.015)

 0.011

(0.014)

n.a. -0.011

(0.017)

Collateral-source rule (jury notification)b 0.015

(0.026)

 0.017

(0.029)

n.a. 0.039

(0.022)

*

Cap or ban on punitive damages 0.001

(0.015)

-0.009

(0.015)

n.a. 0.008

(0.009)

Sum of tort limit coefficients 0.043

(0.036)

 0.019

(0.041)

n.a. 0.031

(0.024)

Any “direct” tort limit n.a. n.a. -0.014

(0.017)

n.a.

Any “indirect” tort limit n.a. n.a. 0.037

(0.018)

** n.a.

Demographic and Economic Controls

Percentage of Medicare beneficiaries in 

managed care

-0.462

(0.142)

*** -0.386

(0.122)

*** -0.426

(0.133)

*** -0.339

(0.075)

***

Attorneys per capitac -0.008

(0.071)

-0.049

(0.075)

 0.017

(0.076)

-0.025

(0.071)

Gross state product per capitac -0.348

(0.123)

*** -0.115

(0.084)

-0.316

(0.121)

** -0.249

(0.090)

***

Age-adjusted mortality ratec 0.522

(0.207)

** 0.581

(0.212)

*** 0.492

(0.202)

** 0.272

(0.187)

Percentage of adults without a high school 

diploma

-2.157

(0.442)

*** -2.157

(0.495)

*** -2.096

(0.375)

*** 2.120

(1.072)

*

Percentage of people that are black -0.026

(1.181)

 2.849

(0.914)

*** 0.014

(1.129)

-6.159

(2.258)

***

Percentage of people residing in urban areas 0.040

(0.320)

 0.066

(0.314)

-0.063

(0.278)

-0.895

(0.562)

Percentage of the workforce that is unemployed 0.060

(0.506)

-0.014

(0.419)

 0.113

(0.547)

-0.242

(0.292)
52

Table B-6.

Continued

Source: Congressional Budget Office. 

Notes: Estimated standard errors appear in parentheses. Standard errors are estimated using the Huber/White correction, 

with observations clustered at the state level. 

Each column represents a separate regression model. The dependent variable equals the natural logarithm of spending 

per capita. The natural logarithm of spending per capita is adjusted for demographics as described in Appendix A. All 

models include state- and year-fixed effects (coefficients are not shown). “Any ‘direct’ tort limit” equals one if any of 

the following limits are in place (and zero otherwise): a cap on noneconomic damages, a cap on total damages, a ban 

on punitive damages, or a collateral-source rule (mandatory offset). “Any ‘indirect’ tort limit” equals one if either of the 

following limits is in place (and zero otherwise): a cap on attorneys’ fees or modification or elimination of joint-andseveral liability.

* = p<0.10; ** = p<0.05; *** = p<0.01; n.a. = not applicable.

a. The principle of joint-and-several liability allows a claimant to recover the entire amount of a damage award from any of the

parties found to be responsible for an injury, regardless of each party’s degree of responsibility for that injury. Modifying or 

eliminating joint-and-several liability would generally limit each party’s share of damages to his or her level of responsibility 

for the injury.

b. Collateral-source benefits are compensation for an injury from sources other than a malpractice award, such as health or 

disability insurance.

c. The natural logarithm of these variables is used in the model.

d. “Partial R-squared” statistics reflect the explanatory power of the tort limit and control variables only and do not reflect the 

explanatory power of the state- and year-fixed effects. (The R-squared statistics calculated for models that include stateand year-fixed effects are quite high—above 0.97—in all models.) 

Main Results 

(Weighted by 

Number of

Beneficiaries)

Unweighted 

Results

Indirect/Direct 

Tort Limit

Variables

State-Specific 

Time Trends

Model Characteristics

Number of observations 1,224 1,224 1,224 1,224

Degrees of freedom 1,137 1,137 1,140 1,086

Years included 1980-2003 1980-2003 1980-2003 1980-2003

Partial R-squaredd 0.2110 0.1191 0.2074 0.4493

p-value on F test of joint significance of tort limits 0.3815 0.1068 0.1307 0.3478

Weight Number of

beneficiaries

Unweighted Number of

beneficiaries

Number of

beneficiaries
53

Appendix C:

Regression Results, Including

Leads of Tort Limits Appendix C: Regression Results, Including Leads of Tort Limits

For each tort limit examined in this analysis of leads of tort limits, a “change lead 1-to3” variable is created that equals one if the tort limit is put in place one to three years 

in the future and otherwise equals zero. The construction of the change lead 1-to-3

variables is illustrated for noneconomic caps in Table C-1.

Tables C-2 through C-7 present the regression results including the lead variables. In 

the models of health care spending per capita, only two of the coefficients on the 

“limit in place” variables are statistically significant: eliminating joint-and-several liability in the models of health care spending per capita and hospital spending per capita. In both of those cases, the “change lead 1-to-3” variable has the same sign as the 

limit in place variable and is statistically significant.

In the models of Medicare spending per beneficiary, eight of the coefficients on the 

limit in place variables are statistically significant: noneconomic caps in the models of 

Medicare spending per beneficiary and Medicare Part A spending per beneficiary; 

eliminating joint-and-several liability in all three models of Medicare spending per 

beneficiary; direct tort limits in the models of Medicare spending per beneficiary and 

Medicare Part A spending per beneficiary; and indirect tort limits in Medicare Part B 

spending per beneficiary. In four of those eight cases, the change lead 1-to-3 variable 

has the same sign as the limit in place variable, but only one is statistically significant: 

the change lead 1-to-3 variable for a cap on noneconomic damages in the model of 

Medicare Part A spending per beneficiary.
54

Table C-1.

Illustration of Leads of Tort Limit Variables (Noneconomic Caps in

Alaska and Alabama) 

Source: Congressional Budget Office. 

Note: n.a. = not applicable.

State

Noneconomic 

Cap in Place

Change Lead 

1-to-3 State

Noneconomic 

Cap in Place

Change Lead 

1-to-3

1977 AK 0 0 AL 0 0

1978 AK 0 0 AL 0 0

1979 AK 0 0 AL 0 0

1980 AK 0 0 AL 0 0

1981 AK 0 0 AL 0 0

1982 AK 0 0 AL 0 0

1983 AK 0 1 AL 0 0

1984 AK 0 1 AL 0 1

1985 AK 0 1 AL 0 1

1986 AK 1 0 AL 0 1

1987 AK 1 0 AL 1 0

1988 AK 1 0 AL 1 0

1989 AK 1 0 AL 1 0

1990 AK 1 0 AL 1 0

1991 AK 1 0 AL 1 0

1992 AK 1 0 AL 0 0

1993 AK 1 0 AL 0 0

1994 AK 1 0 AL 0 0

1995 AK 1 0 AL 0 0

1996 AK 1 0 AL 0 0

1997 AK 1 0 AL 0 0

1998 AK 1 0 AL 0 0

1999 AK 1 0 AL 0 0

2000 AK 1 0 AL 0 0

2001 AK 1 0 AL 0 0

2002 AK 1 n.a. AL 0 n.a.

2003 AK 1 n.a. AL 0 n.a.

2004 AK 1 n.a. AL 0 n.a.
55

Table C-2.

Regressions of Overall Health Care Spending per Capita, Including Leads

Continued

Individual Tort Limit

Variables

Indirect/Direct Tort

Limit Variables

Tort Limit Variables

Cap on noneconomic damages

Change lead 1-to-3 -0.009 (0.009) n.a.

Limit in place -0.017 (0.012) n.a.

Elimination of joint-and-several liabilitya

Change lead 1-to-3 0.018 * (0.010) n.a.

Limit in place 0.050 *** (0.015) n.a.

Cap on attorney fees 

Change lead 1-to-3 0.002 (0.011) n.a.

Limit in place 0.008 (0.025) n.a.

Collateral-source rule (jury notification)b

Change lead 1-to-3 0.002 (0.011) n.a.

Limit in place -0.032 (0.027) n.a.

Cap or ban on punitive damages 

Change lead 1-to-3 0.000 (0.008) n.a.

Limit in place -0.007 (0.013) n.a.

Any “direct” tort limit 

Change lead 1-to-3 n.a. 0.008 (0.006)

Limit in place n.a. -0.003 (0.013)

Any “indirect” tort limit

Change lead 1-to-3 n.a. 0.007 (0.011)

Limit in place n.a. 0.022 (0.017)

Demographic and Economic Controls 

Percentage of Medicare beneficiaries in managed care -0.205 ** (0.082) -0.232 *** (0.081)

Attorneys per capitac 0.157 *** (0.057) 0.188 *** (0.062)

Gross state product per capitac 0.189 *** (0.045) 0.210 *** (0.051)

Age-adjusted mortality ratec 0.079 (0.194) 0.124 (0.210)

Percentage of adults without a high school diploma -1.949 *** (0.293) -1.582 *** (0.226)

Percentage of people that are black -0.635 (0.630) -0.346 (0.989)

Percentage of people residing in urban areas -0.212 (0.288) -0.220 (0.299)

Percentage of the workforce that is unemployed 0.810 ** (0.395) 0.974 ** (0.461)
56

Table C-2.

Continued

Source: Congressional Budget Office. 

Notes: Estimated standard errors appear in parentheses. Standard errors are estimated using the Huber/White correction, with 

observations clustered at the state level.

* = p<0.10; ** = p<0.05; *** = p<0.01; n.a. = not applicable.

a. The principle of joint-and-several liability allows a claimant to recover the entire amount of a damage award from any of the

parties found to be responsible for an injury, regardless of each party’s degree of responsibility for that injury. Modifying or 

eliminating joint-and-several liability would generally limit each party’s share of damages to his or her level of responsibility 

for the injury.

b. Collateral-source benefits are compensation for an injury from sources other than a malpractice award, such as health or 

disability insurance.

c. The natural logarithm of these variables is used in the model.

d. “Partial R-squared” statistics reflect the explanatory power of the tort limit and control variables only and do not reflect the 

explanatory power of the state- and year-fixed effects. (The R-squared statistics calculated for models that include stateand year-fixed effects are quite high—above 0.97—in all models.)

Individual Tort Limit

Variables

Indirect/Direct Tort

Limit Variables

Model Characteristics

Number of observations 1,071 1,071

Years included 1980–2000 1980–2000

Partial R-squaredd 0.2266 0.2053

p-value on F test of joint significance of lead variables 0.0482 ** 0.2653

Weight Population Population
57

Table C-3.

Regressions of Overall Hospital Spending per Capita, Including Leads

Continued

Individual Tort Limit

Variables

Indirect/Direct Tort

Limit Variables

Tort Limit Variables

Cap on noneconomic damages

Change lead 1-to-3 -0.011 (0.012) n.a.

Limit in place -0.019 (0.014) n.a.

Elimination of joint-and-several liabilitya

Change lead 1-to-3 0.028 ** (0.013) n.a.

Limit in place 0.061 *** (0.018) n.a.

Cap on attorneys’ fees 

Change lead 1-to-3 0.016 (0.015) n.a.

Limit in place -0.011 (0.028) n.a.

Collateral-source rule (jury notification)b

Change lead 1-to-3 -0.001 (0.018) n.a.

 Limit in place -0.038 (0.035) n.a.

Cap or ban on punitive damages 

Change lead 1-to-3 0.004 (0.011) n.a.

Limit in place -0.019 (0.018) n.a.

Any “direct” tort limit 

Change lead 1-to-3 n.a. -0.002 (0.010)

Limit in place n.a. -0.022 (0.017)

Any “indirect” tort limit

Change lead 1-to-3 n.a. 0.031 ** (0.015)

Limit in place n.a. 0.035 (0.021)

Demographic and Economic Controls 

Percentage of Medicare beneficiaries in managed care -0.479 *** (0.107) -0.511 *** (0.105)

Attorneys per capitac 0.124 *

 (0.070) 0.160 * (0.082)

Gross state product per capitac 0.048 (0.060) 0.086 (0.068)

Age-adjusted mortality ratec 0.200 (0.256) 0.209 (0.264)

Percentage of adults without a high school diploma -1.815 *** (0.474) -1.360 *** (0.376)

Percentage of people that are black -1.878 ** (0.874) -1.394 (1.256)

Percentage of people residing in urban areas 0.007 (0.421) -0.051 (0.431)

Percentage of the workforce that is unemployed 0.612 (0.386) 0.668 (0.479)
58

Table C-3.

Continued

Source: Congressional Budget Office. 

Notes: Estimated standard errors appear in parentheses. Standard errors are estimated using the Huber/White correction, with 

observations clustered at the state level.

* = p<0.10; ** = p<0.05; *** = p<0.01; n.a. = not applicable.

a. The principle of joint-and-several liability allows a claimant to recover the entire amount of a damage award from any of the

parties found to be responsible for an injury, regardless of each party’s degree of responsibility for that injury. Modifying or 

eliminating joint-and-several liability would generally limit each party’s share of damages to his or her level of responsibility 

for the injury.

b. Collateral-source benefits are compensation for an injury from sources other than a malpractice award, such as health or 

disability insurance.

c. The natural logarithm of these variables is used in the model.

d. “Partial R-squared” statistics reflect the explanatory power of the tort limit and control variables only and do not reflect the 

explanatory power of the state- and year-fixed effects. (The R-squared statistics calculated for models that include stateand year-fixed effects are quite high—above 0.97—in all models.)

Individual Tort Limit

Variables

Indirect/Direct Tort

Limit Variables

Model Characteristics

Number of observations 1,071 1,071

Years included 1980–2000 1980–2000

Partial R-squaredd 0.2402 0.2214

p-value on F test of joint significance of lead variables 0.0745 * 0.1369

Weight Population Population
59

Table C-4.

Regressions of Overall Spending per Capita for Physician and Clinical

Services, Including Leads

Continued

Individual Tort Limit

Variables

Indirect/Direct Tort

Limit Variables

Tort Limit Variables

Cap on noneconomic damages

 Change lead 1-to-3 -0.005 (0.015) n.a.

 Limit in place -0.020 (0.021) n.a.

Elimination of joint-and-several liabilitya

 Change lead 1-to-3 0.006 (0.013) n.a.

 Limit in place 0.025 (0.025) n.a.

Cap on attorneys’ fees 

 Change lead 1-to-3 -0.024 (0.032) n.a.

 Limit in place -0.009 (0.045) n.a.

Collateral-source rule (jury notification)b

 Change lead 1-to-3 -0.010 (0.018) n.a.

 Limit in place -0.012 (0.022) n.a.

Cap or ban on punitive damages 

 Change lead 1-to-3 -0.002 (0.013) n.a.

 Limit in place 0.000 (0.019) n.a.

Any “direct” tort limit 

 Change lead 1-to-3 n.a. 0.021 ** (0.010)

 Limit in place n.a. 0.005 (0.016)

Any “indirect” tort limit

 Change lead 1-to-3 n.a. -0.025 ** (0.011)

 Limit in place n.a. -0.011 (0.021)

Demographic and Economic Controls 

Percentage of Medicare beneficiaries in managed care -0.154 (0.098) -0.164 (0.111)

Attorneys per capitac 0.360 *** (0.109) 0.381 *** (0.100)

Gross state product per capitac 0.459 *** (0.078) 0.450 *** (0.081)

Age-adjusted mortality ratec -0.138 (0.225) -0.076 (0.229)

Percentage of adults without a high school diploma -1.486 *** (0.398) -1.340 *** (0.321)

Percentage of people that are black 0.921 (0.712) 0.964 (0.944)

Percentage of people residing in urban areas -0.828 ** (0.324) -0.851 ** (0.337)

Percentage of the workforce that is unemployed 1.998 *** (0.606) 2.127 *** (0.668)
60

Table C-4.

Continued

Source: Congressional Budget Office. 

Notes: Estimated standard errors are in parentheses. Standard errors are estimated using the Huber/White correction, with 

observations clustered at the state level.

* = p<0.10; ** = p<0.05; *** = p<0.01; n.a. = not applicable.

a. The principle of joint-and-several liability allows a claimant to recover the entire amount of a damage award from any of the

parties found to be responsible for an injury, regardless of each party’s degree of responsibility for that injury. Modifying or 

eliminating joint-and-several liability would generally limit each party’s share of damages to his or her level of responsibility 

for the injury.

b. Collateral-source benefits are compensation for an injury from sources other than a malpractice award, such as health or 

disability insurance.

c. The natural logarithm of these variables is used in the model.

d. “Partial R-squared” statistics reflect the explanatory power of the tort limit and control variables only and do not reflect the 

explanatory power of the state- and year-fixed effects. (The R-squared statistics calculated for models that include stateand year-fixed effects are quite high—above 0.97—in all models.)

Individual Tort Limit

Variables

Indirect/Direct Tort

Limit Variables

Model Characteristics

Number of observations 1,071 1,071

Years included 1980–2000 1980–2000

Partial R-squaredd 0.1219 0.1211

p-value on F test of joint significance of lead variables 0.9165 0.0282**

Weight Population Population
61

Table C-5.

Regressions of Medicare Spending per Beneficiary, Including Leads

Continued

Individual Tort Limit

Variables

Indirect/Direct Tort

Limit Variables

Tort Limit Variables

Cap on noneconomic damages

Change lead 1-to-3 -0.018 (0.013) n.a.

Limit in place -0.058 *** (0.021) n.a.

Elimination of joint-and-several liabilitya

Change lead 1-to-3 -0.001 (0.016) n.a.

Limit in place 0.060 ** (0.024) n.a.

Cap on attorneys’ fees 

Change lead 1-to-3 0.003 (0.017) n.a.

Limit in place 0.002 (0.030) n.a.

Collateral-source rule (jury notification)b

Change lead 1-to-3 0.030 *

 (0.015) n.a.

Limit in place -0.035 (0.035) n.a.

Cap or ban on punitive damages 

Change lead 1-to-3 -0.001 (0.011) n.a.

Limit in place 0.019 (0.018) n.a.

Any “direct” tort limit 

Change lead 1-to-3 n.a. 0.000 (0.015)

Limit in place n.a. -0.045 * (0.026)

Any “indirect” tort limit

Change lead 1-to-3 n.a. -0.008 (0.018)

Limit in place n.a. 0.031 (0.031)

Demographic and Economic Controls 

Percentage of Medicare beneficiaries in managed care -0.075 (0.142) -0.082 (0.123)

Attorneys per capitac 0.145 *

 (0.078) 0.206 * (0.113)

Gross state product per capitac -0.229 *** (0.085) -0.185 ** (0.089)

Age-adjusted mortality ratec 0.177 (0.273) 0.190 (0.280)

Percentage of adults without a high school diploma -1.892 *** (0.410) -1.530 *** (0.326)

Percentage of people that are black -0.116 (0.915) 0.234 (1.182)

Percentage of people residing in urban areas -0.132 (0.361) -0.042 (0.354)

Percentage of the workforce that is unemployed 0.519 (0.413) 0.807 * (0.474)
62

Table C-5.

Continued

Source: Congressional Budget Office. 

Notes: Estimated standard errors are in parentheses. Standard errors are estimated using the Huber/White correction, with 

observations clustered at the state level.

* = p<0.10; ** = p<0.05; *** = p<0.01; n.a. = not applicable.

a. The principle of joint-and-several liability allows a claimant to recover the entire amount of a damage award from any of the

parties found to be responsible for an injury, regardless of each party’s degree of responsibility for that injury. Modifying or 

eliminating joint-and-several liability would generally limit each party’s share of damages to his or her level of responsibility 

for the injury.

b. Collateral-source benefits are compensation for an injury from sources other than a malpractice award, such as health or 

disability insurance.

c. The natural logarithm of these variables is used in the model.

d. “Partial R-squared” statistics reflect the explanatory power of the tort limit and control variables only and do not reflect the 

explanatory power of the state- and year-fixed effects. (The R-squared statistics calculated for models that include stateand year-fixed effects are quite high—above 0.97—in all models.)

Individual Tort Limit

Variables

Indirect/Direct Tort

Limit Variables

Model Characteristics

Number of observations 1,122 1,122

Years included 1980–2001 1980–2001

Partial R-squaredd 0.1256 0.0945

p-value on F test of joint significance of lead variables 0.4614 0.9101

Weight Number of

beneficiaries

Number of

beneficiaries
63

Table C-6.

Regressions of Medicare Part A (Hospital) Spending per Beneficiary, 

Including Leads

Continued

Individual Tort Limit

Variables

Indirect/Direct Tort

Limit Variables

Tort Limit Variables

Cap on noneconomic damages

Change lead 1-to-3 -0.034 *

 (0.017) n.a.

Limit in place -0.079 *** (0.026) n.a.

Elimination of joint-and-several liabilitya

Change lead 1-to-3 -0.008 (0.021) n.a.

Limit in place 0.061 *

 (0.031) n.a.

Cap on attorneys’ fees 

Change lead 1-to-3 0.013 (0.022) n.a.

Limit in place 0.002 (0.034) n.a.

Collateral-source rule (jury notification)b

Change lead 1-to-3 0.035 (0.022) n.a.

Limit in place -0.054 (0.041) n.a.

Cap or ban on punitive damages 

Change lead 1-to-3 0.011 (0.015) n.a.

Limit in place 0.028 (0.023) n.a.

Any “direct” tort limit 

Change lead 1-to-3 n.a. -0.007 (0.019)

Limit in place n.a. -0.062 * (0.032)

Any “indirect” tort limit

Change lead 1-to-3 n.a. -0.001 (0.026)

Limit in place n.a. 0.029 (0.040)

Demographic and Economic Controls 

Percentage of Medicare beneficiaries in managed care 0.088 (0.157) 0.065 (0.137)

Attorneys per capitac 0.236 *** (0.081) 0.311 ** (0.124)

Gross state product per capitac -0.189 ** (0.081) -0.136 (0.086)

Age-adjusted mortality ratec 0.019 (0.331) 0.025 (0.335)

Percentage of adults without a high school diploma -1.626 *** (0.469) -1.190 *** (0.383)

Percentage of people that are black -0.285 (0.997) 0.108 (1.491)

Percentage of people residing in urban areas -0.172 (0.453) 0.014 (0.451)

Percentage of the workforce that is unemployed 0.808 (0.495) 1.134 ** (0.527)
64

Table C-6.

Continued

Source: Congressional Budget Office. 

Note: Estimated standard errors appear in parentheses. Standard errors are estimated using the Huber/White correction, with 

observations clustered at the state level.

* = p<0.10; ** = p<0.05; *** = p<0.01; n.a. = not applicable.

a. The principle of joint-and-several liability allows a claimant to recover the entire amount of a damage award from any of the

parties found to be responsible for an injury, regardless of each party’s degree of responsibility for that injury. Modifying or 

eliminating joint-and-several liability would generally limit each party’s share of damages to his or her level of responsibility 

for the injury.

b. Collateral-source benefits are compensation for an injury from sources other than a malpractice award, such as health or 

disability insurance.

c. The natural logarithm of these variables is used in the model.

d. “Partial R-squared” statistics reflect the explanatory power of the tort limit and control variables only and do not reflect the 

explanatory power of the state- and year-fixed effects. (The R-squared statistics calculated for models that include stateand year-fixed effects are quite high—above 0.97—in all models.)

Individual Tort Limit

Variables

Indirect/Direct Tort

Limit Variables

Model Characteristics

Number of observations 1,122 1,122

Years included 1980–2001 1980–2001

Partial R-squaredd 0.0927 0.0538

p-value on F test of joint significance of lead variables 0.3589 0.9332

Weight Number of

beneficiaries

Number of

beneficiaries
65

Table C-7.

Regressions of Medicare Part B (Physician) Spending per Beneficiary, 

Including Leads

Continued

Individual Tort Limit

Variables

Indirect/Direct Tort 

Limit Variables

Tort Limit Variables

Cap on noneconomic damages

Change lead 1-to-3 0.018 (0.013) n.a.

Limit in place -0.014 (0.018) n.a.

Elimination of joint-and-several liabilitya

Change lead 1-to-3 0.015 (0.016) n.a.

Limit in place 0.049** (0.021) n.a.

Cap on attorneys’ fees

Change lead 1-to-3 -0.004 (0.019) n.a.

Limit in place 0.005 (0.022) n.a.

Collateral-source rule (jury notification)b

Change lead 1-to-3 0.021* (0.013) n.a.

Limit in place 0.026 (0.024) n.a.

Cap or ban on punitive damages

Change lead 1-to-3 -0.029** (0.013) n.a.

Limit in place -0.011 (0.018) n.a.

Any “direct” tort limit

Change lead 1-to-3 n.a. 0.018 (0.015)

Limit in place n.a. -0.009 (0.020)

Any “indirect” tort limit

Change lead 1-to-3 n.a. -0.016 (0.014)

Limit in place n.a. 0.034* (0.018)

Demographic and Economic Controls

Percentage of Medicare beneficiaries in managed care -0.401*** (0.136) -0.376*** (0.125)

Attorneys per capitac 0.027 (0.085) 0.054 (0.091)

Gross state product per capitac -0.358*** (0.124) -0.329*** (0.120)

Age-adjusted mortality ratec 0.423* (0.212) 0.429* (0.220)

Percentage of adults without a high school diploma -2.466*** (0.425) -2.295*** (0.356)

Percentage of people that are black 0.031 (1.288) 0.235 (1.153)

Percentage of people residing in urban areas 0.058 (0.349) -0.165 (0.292)

Percentage of the workforce that is unemployed -0.188 (0.501) -0.035 (0.581)
66

Table C-7.

Continued

Source: Congressional Budget Office. 

Notes: Estimated standard errors appear in parentheses. Standard errors are estimated using the Huber/White correction, with 

observations clustered at the state level.

* = p<0.10; ** = p<0.05; *** = p<0.01; n.a. = not applicable.

a. The principle of joint-and-several liability allows a claimant to recover the entire amount of a damage award from any of the

parties found to be responsible for an injury, regardless of each party’s degree of responsibility for that injury. Modifying or 

eliminating joint-and-several liability would generally limit each party’s share of damages to his or her level of responsibility 

for the injury.

b. Collateral-source benefits are compensation for an injury from sources other than a malpractice award, such as health or 

disability insurance.

c. The natural logarithm of these variables is used in the model.

d. “Partial R-squared” statistics reflect the explanatory power of the tort limit and control variables only and do not reflect the 

explanatory power of the state- and year-fixed effects. (The R-squared statistics calculated for models that include stateand year-fixed effects are quite high—above 0.97—in all models.)

Individual Tort Limit

Variables

Indirect/Direct Tort 

Limit Variables

Model Characteristics

Number of observations 1,122 1,122

Years included 1980–2001 1980–2001

Partial R-squaredd 0.2058 0.1833

p-value on F test of joint significance of lead variables 0.1327 0.2343

Weight Number of

beneficiaries

Number of

beneficiaries
67

Appendix D:

Regression Results, Including Controls 

for Medicare’s Prospective Payment 

System for Hospitals Appendix D: Regression Results, Including Controls for Medicare’s Prospective Payment System for Hospitals

The regression results in this appendix place the main findings on Medicare spending 

side-by-side with results from an alternative specification that includes controls for 

Medicare’s prospective payment system for hospitals.

69

Table D-1.

Regressions of Medicare Spending per Beneficiary with 

Prospective Payment System Controls

Continued

Main Results Indirect/Direct Tort Limit Variables

 Without

PPS Controls

With 

PPS Controls

Without

PPS Controls

With

PPS Controls

Tort Limit Variables

Cap on noneconomic damages -0.040

(0.014)

*** -0.016

(0.014)

n.a. n.a.

Elimination of joint-and-several liabilitya 0.053

(0.018)

*** 0.041

(0.019)

** n.a. n.a.

Cap on attorneys’ fees -0.005

(0.024)

 0.002

(0.023)

n.a. n.a.

Collateral-source rule (jury notification)b -0.043

(0.036)

-0.028

(0.027)

n.a. n.a.

Cap or ban on punitive damages 0.017

(0.015)

 0.017

(0.015)

n.a. n.a.

Sum of tort limit coefficients -0.018

(0.049)

 0.015

(0.041)

-0.007

(0.022)

 0.008

(0.020)

Any “direct” tort limit n.a. n.a. -0.035

(0.019)

* -0.012

(0.018)

Any “indirect” tort limit n.a. n.a. 0.028

(0.027)

 0.020

(0.023)

Demographic and Economic Controls

Percentage of Medicare beneficiaries in 

managed care

-0.176

(0.143)

-0.163

(0.123)

-0.149

(0.126)

-0.131

(0.107)

Attorneys per capitac 0.105

(0.061)

* 0.187

(0.055)

*** 0.137

(0.088)

 0.216

(0.071)

*** 

Gross state product per capitac -0.202

(0.082)

** -0.062

(0.078)

-0.179

(0.086)

** -0.035

(0.080)

Age-adjusted mortality ratec 0.157

(0.223)

-0.029

(0.162)

 0.156

(0.222)

-0.073

(0.146)

Percentage of adults without a high school 

diploma

-1.591

(0.406)

*** -1.156

(0.405)

*** -1.431

(0.314)

*** -0.941

(0.341)

*** 

Percentage of people that are black -0.240

(0.926)

 0.680

(0.614)

-0.017

(1.104)

 0.835

(0.768)

Percentage of people residing in urban areas -0.090

(0.307)

-0.299

(0.284)

 0.031

(0.292)

-0.204

(0.264)

Percentage of the workforce that is unemployed 0.759

(0.401)

* 1.031

(0.361)

*** 0.941

(0.432)

** 1.181

(0.352)

*** 
70

Table D-1.

Continued

Source: Congressional Budget Office.

Notes: Estimated standard errors appear in parentheses. Standard errors are estimated using the Huber/White correction, with 

observations clustered at the state level. 

Each column represents a separate regression model. The dependent variable equals the natural logarithm of spending 

per capita. The natural logarithm of spending per capita is adjusted for demographics as described in Appendix A. All 

models include state- and year-fixed effects (coefficients are not shown). “Any ‘direct’ tort limit” equals one if any of 

the following limits are in place (and zero otherwise): a cap on noneconomic damages, a cap on total damages, a ban 

on punitive damages, or a collateral-source rule (mandatory offset). “Any ‘indirect’ tort limit” equals one if either of the 

following limits is in place (and zero otherwise): a cap on attorneys’ fees or modification or elimination of joint-andseveral liability.

* = p<0.10; ** = p<0.05; *** = p<0.01; PPS = prospective payment system; n.a. = not applicable.

a. The principle of joint-and-several liability allows a claimant to recover the entire amount of a damage award from any of the

parties found to be responsible for an injury, regardless of each party’s degree of responsibility for that injury. Modifying or 

eliminating joint-and-several liability would generally limit each party’s share of damages to his or her level of responsibility 

for the injury.

b. Collateral-source benefits are compensation for an injury from sources other than a malpractice award, such as health or 

disability insurance.

c. The natural logarithm of these variables is used in the model.

d. “Partial R-squared” statistics reflect the explanatory power of the tort limit and control variables only and do not reflect the 

explanatory power of the state- and year-fixed effects. (The R-squared statistics calculated for models that include stateand year-fixed effects are quite high—above 0.97—in all models.) 

Main Results Indirect/Direct Tort Limit Variables

 Without

PPS Controls

With 

PPS Controls

Without

PPS Controls

With

PPS Controls

Model Characteristics

Number of observations 1,224 1,224 1,224 1,224

Degrees of freedom 1,091 1,091 1,094 1,094

Years included 1980-2003 1980-2003 1980-2003 1980-2003

Partial R-squaredd 0.1261 0.1269 0.1030 0.1131

p-value on F test of joint significance of tort limits 0.0005 *** 0.0134 ** 0.2114 0.6573

Weight Number of

beneficiaries

Number of

beneficiaries

Number of

beneficiaries

Number of

beneficiaries
71

Table D-2.

Regressions of Medicare Part A (Hospital) Spending per Beneficiary with 

Prospective Payment System Controls

Continued

Main Results Indirect/Direct Tort Limit Variables

 Without

PPS Controls

 With

PPS Controls

Without

PPS Controls

With 

PPS Controls

Tort Limit Variables

Cap on noneconomic damages -0.052

(0.017)

*** -0.023

(0.016)

n.a. n.a.

Elimination of joint-and-several liabilitya 0.056

(0.024)

** 0.045

(0.025)

* n.a. n.a.

Cap on attorneys’ fees -0.011

(0.029)

-0.005

(0.027)

n.a. n.a.

Collateral-source rule (jury notification)b -0.061

(0.042)

-0.045

(0.032)

n.a. n.a.

Cap or ban on punitive damages 0.021

(0.018)

 0.022

(0.019)

n.a. n.a.

Sum of tort limit coefficients -0.047

(0.055)

-0.006

(0.043)

n.a. n.a.

Any “direct” tort limit n.a. n.a. -0.046

(0.023)

* -0.018

(0.023)

Any “indirect” tort limit n.a. n.a. 0.023

(0.032)

 0.014

(0.027)

Demographic and Economic Controls

Percentage of Medicare beneficiaries in managed 

care

-0.030

(0.149)

-0.042

(0.125)

-0.006

(0.130)

-0.009

(0.110)

Attorneys per capitac 0.190

(0.064)

*** 0.274

(0.054)

*** 0.225

(0.096)

** 0.307

(0.074)

***

Gross state product per capitac -0.151

(0.076)

* 0.010

(0.071)

-0.130

(0.082)

 0.036

(0.077)

Age-adjusted mortality ratec -0.066

(0.248)

-0.251

(0.174)

-0.061

(0.249)

-0.300

(0.163)

*

Percentage of adults without a high school 

diploma

-1.350

(0.427)

*** -0.865

(0.429)

** -1.162

(0.334)

*** -0.595

(0.370)

Percentage of people that are black -0.449

(0.981)

 0.671

(0.677)

-0.162

(1.311)

 0.894

(1.014)

Percentage of people residing in urban areas -0.128

(0.362)

-0.373

(0.340)

 0.061

(0.355)

-0.214

(0.314)

Percentage of the workforce that is unemployed 1.056

(0.469)

** 1.370

(0.408)

*** 1.267

(0.473)

** 1.546

(0.393)

***
72

Table D-2.

Continued

Source: Congressional Budget Office.

Notes: Estimated standard errors appear in parentheses. Standard errors are estimated using the Huber/White correction, with 

observations clustered at the state level. 

Each column represents a separate regression model. The dependent variable equals the natural logarithm of spending 

per capita. The natural logarithm of spending per capita is adjusted for demographics as described in Appendix A. All 

models include state- and year-fixed effects (coefficients are not shown). “Any ‘direct’ tort limit” equals one if any of 

the following limits are in place (and zero otherwise): a cap on noneconomic damages, a cap on total damages, a ban on 

punitive damages, or a collateral-source rule (mandatory offset). “Any ‘indirect’ tort limit” equals one if either of the 

following limits is in place (and zero otherwise): a cap on attorneys’ fees or modification or elimination of joint-and-several liability.

* = p<0.10; ** = p<0.05; *** = p<0.01; PPS = prospective payment system; n.a. = not applicable.

a. The principle of joint-and-several liability allows a claimant to recover the entire amount of a damage award from any of the

parties found to be responsible for an injury, regardless of each party’s degree of responsibility for that injury. Modifying or 

eliminating joint-and-several liability would generally limit each party’s share of damages to his or her level of responsibility 

for the injury.

b. Collateral-source benefits are compensation for an injury from sources other than a malpractice award, such as health or 

disability insurance.

c. The natural logarithm of these variables is used in the model.

d. “Partial R-squared” statistics reflect the explanatory power of the tort limit and control variables only and do not reflect the 

explanatory power of the state- and year-fixed effects. (The R-squared statistics calculated for models that include stateand year-fixed effects are quite high—above 0.97—in all models.)

Main Results Indirect/Direct Tort Limit Variables

 Without

PPS Controls

 With

PPS Controls

Without

PPS Controls

With 

PPS Controls

Model Characteristics

Number of observations 1,224 1,224 1,224 1,224

Degrees of freedom 1,091 1,091 1,094 1,094

Years included 1980-2003 1980-2003 1980-2003 1980-2003

Partial R-squaredd 0.0787 0.0794 0.0508 0.0538

p-value on F test of joint significance of tort limits 0.0018 *** 0.0637 * 0.1388 0.7284

Weight Number of

beneficiaries

Number of

beneficiaries

Number of

beneficiaries

Number of

beneficiaries