Impact of State Laws Limiting Malpractice Awards on Geographic Distribution

Results 

There are 196 observations for each variable in our analyses of State data (observations for 49 States at four points in time), and Table 2 presents a list of the variables and their respective means. The average number of active, non-Federal physicians practicing per 100,000 residents in each State was 208, and the average percent of the population in each State over the age of 65 is 13 percent. The average unemployment rate is 5.53 percent, and the average number of beds per 1,000 residents is 4.03. 

Observations from each of the four time periods in our analyses (1985, 1990, 1995, and 2000) from each of the 49 States in our sample were combined to estimate the impact of State laws that limit payments in malpractice cases on physician availability. Table 3 presents the estimates of the coefficients of each variable derived using ordinary least squares estimation techniques. The coefficients of the independent variables in the equation were estimated using 196 observations, and the independent variables explain 52 percent of the variation between the square of the difference between the estimated and actual value of the dependent variable.

All variables entered the equation with the expected signs, and all but one were statistically significant at a 95-percent confidence level. The coefficient for States with a cap on damage awards in malpractice cases is about 24 (Table 3). This implies that States with a cap average 24 more physicians per 100,000 residents than States without such a cap. Thus, States with caps have about 12 percent more physicians per capita than States without a cap (12% = 24/208).

The coefficient for the variable measuring the proportion of the population 65 years of age or older in Table 3 indicates that States with a greater proportion of elderly citizens have more physicians. For each percentage-point increase in the age variable, the number of physicians per 100,000 residents increased by about 5. Thus, we would expect Florida, which averaged 18.5 percent of its population 65 years of age or above, to have about 42 more physicians per 100,000 residents than Georgia, which averaged 10.2 percent of its population age 65 or older over the four time periods.

Table 3 also shows that a 1-percentage-point increase in the unemployment rate was associated with a decrease in over 6 physicians per 100,000 residents, and that a 1-percentage-point increase in the proportion of a State's domestic product attributable to farm activities was associated with a decrease of about 5 physicians per 100,000 residents. Income was positively related to physician availability as hypothesized, and an increase of $1,000 per year in income was related to an increase of slightly more than 1 physician per 100,000 residents.

Population density as measured by the number of residents in thousands per square mile was also positively related to physician supply as anticipated, and an increase of 1,000 residents per square mile in a State was associated with an increase of about 17 physicians per 100,000 residents. 

Table 4 presents estimates of coefficients after including dummy variables for three of the four time periods (1990 is the reference time period). This model also was estimated using the ordinary least squares regression technique, and the coefficients for each of the three nonreference time periods were statistically significant. Nevertheless, the size and sign of the coefficient for the variable for States with a law capping damage awards were still positive, statistically significant, and of similar magnitude as that in the model with time variables.

Indeed, the magnitude of the coefficient for the damage caps variable was robust across a diversity of models. In each of four equations that was estimated using data from a single time period (results not reported here), the coefficient for the damage cap variable was positive and was only slightly less than the coefficient in the combined runs. Furthermore, the coefficients were statistically significant in three of the four equations.

We also estimated our model setting the independent variable for caps equal to 1 only for States listed in a 2003 report by the U.S. Department of Health and Human Services with a cap on noneconomic damage awards of less than $350,000 (California, Hawaii, Indiana, Michigan, Montana, New Mexico, North Dakota, South Dakota, Utah, and Wisconsin)41 and zero otherwise. We then estimated our model where the dummy variable was equal to 1 for the other nine States with a cap on malpractice damage awards above $350,000 (Colorado, Idaho, Kansas, Louisiana, Maryland, Massachusetts, New Mexico, Virginia, West Virginia) and zero otherwise. We found the coefficient for the cap variable in each of these models to be positive, but it was statistically significant only in the model where the dummy variable was equal to 1 for States with a cap on noneconomic damages of less than $350,000.

Variables with coefficients that are not statistically significant are considered to have effects that are not distinguishable from a zero-effect. Thus, a State that passes legislation capping payments for noneconomic damages in malpractice cases at relatively high levels might not realize an increase in the number of physicians practicing in the State.

Ohio, Oregon, and Texas had provisions that set limits on noneconomic damages in malpractice cases that were struck down by their State Supreme Court, and these limits were in effect for more than 4 years.a  We estimated our State data model setting our cap variable equal to 1 during the time periods the State law capping noneconomic payments in malpractice cases was in effect for Ohio, Oregon, and Texas in addition to setting it equal to 1 for our original 19 States. The coefficient for the cap variable remained positive, significant, and of similar magnitude. 

While the State data provided a picture of liability caps over the years 1985, 1990, 1995, and 2000, we next used county data to provide a finer, more detailed analysis of the final 5-year period: 1996-2000. Table 5 presents the means of each of the variables used in our analyses based on 14,640 observations from county data over the 5 years from 1996 through 2000.

The average number of physicians per 100,000 population was 117 over this time period. This figure is significantly lower than the 208 physicians per 100,000 that we found in our analyses of State data from 1985, 1990, 1995, and 2000. The reason for this is that most counties are rural with a low number of doctors; and since each county has equal weight in the county analysis, the average number of doctors per 100,000 population across all counties (117) is lower than the average number of doctors across all States (208), which is skewed upward by the highly populated metropolitan areas of the State. 

Table 1A lists the number of physicians per 100,000 county residents State by State for States that had caps in the year 2000. In contrast, Table 1B lists the number of physicians per 100,000 county residents State by State for States that either did not have caps or had their caps overturned in court. Table 5 shows that about 10 percent of all counties had a hospital that operated a residency training program, and the average unemployment rate was 5.3 percent. About 22 percent of counties had a high HMO enrollment rate (i.e., an HMO penetration rate greater than 30 percent). 

Table 6 presents results using county data for the years 1996 through 2000.The coefficient for the variable of interest is 13.65. That is, counties in States without caps have 111.83 doctors per 100,000 population, while counties in States with caps have 13.65 more doctors per 100,000 population (i.e., 125.48 doctors). The mean number of doctors—111.83 in noncapped States and 125.48 in capped States—is simulated from a linear prediction of the regression results in Table 6. Thus, States with caps have 12.2 percent more doctors per county than States without caps (i.e., 12.2%=13.65/111.83). This county coefficient is about half the absolute size of the coefficient found using State data because the number of doctors per 100,000 residents is lower at the county level than at the aggregate State level. However, the percentage impact is about the same (12 percent). The coefficient of each of the other variables in the equation was of the expected sign, and all coefficients were statistically significant at a 99 percent level of confidence.


 a Alabama, Florida, Idaho, Illinois, and Washington also had statutes overturned but they were in effect less than 4 years. Idaho overturned a statute that capped noneconomic damages that applied only to medical liability cases, but another statute that capped noneconomic damages in all liability cases was passed and is still in effect.


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Discussion

Between 1970 and 2000, the supply of physicians per capita increased at a faster rate in those States that passed tort reform laws that capped damage payments in malpractice cases (see Table 1A and Table 1B). In 1970, before any States had enacted caps, the average number of physicians per 100,000 population per county was 69 in States that eventually enacted caps between 1970 and 2000, compared with 67 in States that never enacted caps. This difference (69 vs. 67) is statistically insignificant (P=0.22). However, by the year 2000, the States that had enacted caps had a significantly higher number of doctors per 100,000 population per county (135) compared with States that did not enact caps (120) (P=0.006).

This trend indicates that caps may have possibly increased the availability of physicians. To examine whether this was indeed the case, we controlled for other State and county characteristics that may have also impacted physician availability (such as medical residency programs, HMO penetration, etc.). In particular, this study utilizes information about such numerous State characteristics in the years 1985, 1990, 1995, and 2000, as well as information about numerous county characteristics in 1996, 1997, 1998, 1999, and 2000 to ascertain the relationship between State tort reform laws that cap damage payments in malpractice cases and the supply of physicians. This study finds evidence supporting the claim that States with caps on noneconomic damages awards or caps on total damage awards benefit from about 12 percent more physicians per capita than States without such laws.

This evidence was derived first in analyses where the State was the unit of observation and then in analyses where the county was the unit of observation. We found that the magnitude of the impact of laws limiting damage payments using State data and county data was similar. Furthermore, we found that the magnitude of the coefficient of the variable representing the existence of a State law limiting damage payments was similar across various specifications of each type of model. The robustness of this finding supports the argument that State laws limiting noneconomic damages in medical malpractice cases increase the number of physicians who practice in the State.

Nevertheless, this study has limitations. First, there are factors other than those included in our model that affect the supply of physicians. For example, the proportion of the population without health insurance is likely to be related to physician supply through its influence on the demand for physician services. Nonetheless, the proportion of people without health insurance is likely related to the unemployment level in a State as well as to the proportion of its production attributable to farm activities. Thus, there are variables in our analysis that are likely to account for at least some of the influence of these omitted variables. In any event, the variables in our model explain more than half of the variation around the mean in our State analyses, and this is quite large for a model that is estimated with predominantly cross-sectional data.

Second, there are other State laws that may affect physician location decisions. For example, some States have passed laws that permit awards in malpractice cases to be made over a period of time (i.e., they permit periodic payments) and laws that eliminate or weaken the "joint and several liability" principle (the common rule of joint and several liability calls for losing defendants to pay all the damage in spite of their level of fault). Although such laws may be related to the decision of a physician on whether or not to practice in a given geographic area, these types of laws are not nearly as conspicuous as laws that cap payments. Previous research has shown that laws that indirectly affect the level of malpractice damage awards (e.g. laws permitting periodic payments) have less impact on malpractice premiums than laws that directly limit malpractice damage awards.42

Finally, this study employs State and county data. Consequently, there may be problems with aggregation bias (i.e., the relationships that exist at the individual level may be obscured when observations are viewed as a group).43,44 There is, however, justification for estimating an equation using State and county data because the independent variable of interest in this study is whether or not a State has a law that limits damage awards in malpractice cases, and we are interested in the impact of this type of State law on the supply of physicians.

Although it is not possible to conduct a randomized trial to confirm the findings of this study, future studies should include more variables and utilize data from more time periods. Future studies also should focus on important questions such as: how the level at which noneconomic damages is capped is related to the supply of physicians; whether or not physician supply is related to the length of time since the law has been in effect; and whether or not other types of state tort reform laws such as those that eliminate or weaken the principle of joint and several liability are related to physician supply.

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References 

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Current as of July 2003
Internet Citation: Impact of State Laws Limiting Malpractice Awards on Geographic Distribution. July 2003. Agency for Healthcare Research and Quality, Rockville, MD. http://www.ahrq.gov/research/findings/final-reports/tortcaps/tortcap2.html