22. the impact on non-economical damages caps on obstretics

7/26/2019 22. the Impact on Non-economical Damages Caps on Obstretics

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The impact of non-economic damages caps on obstetrics:

Incentives versus practice style

Anca Cotet-Grecu *

Department of Economics and Legal Studies, Seton Hall University, 400 South Orange Avenue, South Orange, NJ 07079, United States

1. Introduction

There is a good deal of research on the impact of

malpractice cost and tort reform on medical practice,

including the field of obstetrics, but not all of the findings

are consistent. For instance, one strand of literature

indicates that higher malpractice premiums are associated

with an increased use of C-sections (Dubay et al., 1999;

Grant and McInnes, 2004), but some researchers find that

noneconomic damage caps, which decrease malpractice

premiums, are associated with higher rates of C-sections

(Currie and MacLeod, 2008).

This paper makes several contributions to this litera-

ture. First, I revisit two of the main findings of this

literature: that non-economic damages caps1 are associat-

ed with entry into the field of obstetricsgynecology (Klick

and Stratmann, 2007) as well as with higher rates of C-

sections (Currie and MacLeod, 2008). Using county-level

data, I find that the results hold for the 19892001 reforms,

the original period investigated by Currie and MacLeod

(2008), but not when the more recent reforms are added,

for the years spanning 19892010. Estimates lack external

validity when the researcher fails to account for all margins

of choice. I test whether general equilibrium effects

explain this observation. I find that the observed change

in C-section rates prior to 2002 is driven by areas with

significant entry and that such a change is not present in

the remaining areas where there is no/less scope for

mobility. A similar pattern exists in the case of prenatal

care initiation and of prenatal visits.

Second, I find that these changes in procedures are

associated with differences in health outcomes that follow

a similar pattern. Between 1989 and 2002, non-economic

Economics and Human Biology 17 (2015) 2941

A R T I C L E I N F O

Article history:

Received 13 May 2014

Received in revised form 11 December 2014

Accepted 11 December 2014

Available online 20 December 2014

JEL classification:

I12

I18

K13

Keywords:

Non-economic damages caps

Ob-gyn

C-sections

Infant outcomes

A B S T R A C T

This paper uses 19892010 county-level data to reexamine the effect of non-economic

damages caps on the field of obstetrics. Previous literature found that caps on damages

lead to both changes in the number of physicians and changes in treatment patterns. This

paper investigates whether the changes in procedures are attributable to changes in

incentives or to selection when new entrants could have a different practice style than

incumbents. First, I find that the relationship between non-economic damages caps and

the number of physicians and procedures identified in previous literature is not robust to

the inclusion of the newer policy changes. Second, over the period when such changes

were observed, the impact on procedures is concentrated in areas with the greatest

changes in the number of obstetricians/gynecologists per capita, suggesting that most of

the effect on procedures is driven by differences in practice style between entrants and

incumbents.

2014 Elsevier B.V. All rights reserved.

* Tel.: +1 973 761 9356.

E-mail address: [email protected] Non-economic damages compensate for past or future non-economic

losses, such as pain, suffering, emotional distress, mental anguish,

disfigurement, physical impairment, loss of consortium, loss of compan-

ionship, loss of parental guidance, loss of enjoyment of life, loss of society,

humiliation, embarrassment, inconvenience, injury to reputation, and

other such losses (Pace et al., 2004).

Contents lists available at ScienceDirect

Economics and Human Biology

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http://dx.doi.org/10.1016/j.ehb.2014.12.002

1570-677X/ 2014 Elsevier B.V. All rights reserved.
http://dx.doi.org/10.1016/j.ehb.2014.12.002http://dx.doi.org/10.1016/j.ehb.2014.12.002http://dx.doi.org/10.1016/j.ehb.2014.12.002http://dx.doi.org/10.1016/j.ehb.2014.12.002http://dx.doi.org/10.1016/j.ehb.2014.12.002http://dx.doi.org/10.1016/j.ehb.2014.12.002http://dx.doi.org/10.1016/j.ehb.2014.12.002http://dx.doi.org/10.1016/j.ehb.2014.12.002http://dx.doi.org/10.1016/j.ehb.2014.12.002http://dx.doi.org/10.1016/j.ehb.2014.12.002http://dx.doi.org/10.1016/j.ehb.2014.12.002http://dx.doi.org/10.1016/j.ehb.2014.12.002http://dx.doi.org/10.1016/j.ehb.2014.12.002mailto:[email protected]://www.sciencedirect.com/science/journal/1570677Xhttp://dx.doi.org/10.1016/j.ehb.2014.12.002http://dx.doi.org/10.1016/j.ehb.2014.12.002http://www.sciencedirect.com/science/journal/1570677Xmailto:[email protected]://dx.doi.org/10.1016/j.ehb.2014.12.002http://crossmark.crossref.org/dialog/?doi=10.1016/j.ehb.2014.12.002&domain=pdfhttp://crossmark.crossref.org/dialog/?doi=10.1016/j.ehb.2014.12.002&domain=pdf


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damages caps are associated with fewer births with low

Apgar scores and a decrease in the incidence of neonatal

injury deaths despite the higher rate of C-sections. These

findings are suggestive of an improvement in fetal health.

All these results are driven by areas with significant entry

into the field of obstetrics and are not robust to extending

the sample through 2010.

One

explanation

relies

on

the

observation

that

liability-sensitive physicians choose to locate where malpractice

liability is low. When non-economic damages caps reduce

liability, the resulting increase in competition in these

areas could improve access to care, leading to better fetal

health. At the same time, entrants could have a different

practice style than the average provider in a particular

area. For instance, if liability-sensitive physicians have

poorer diagnostic skills and use surgical intervention more

often (Currie and MacLeod, 2013), states with higher

proportions of entrants would experience larger increases

in C-section rates. This mechanism can explain why the

impact of non-economic damages caps does not work in

the

opposite

direction

of

the

effect

of

an

increase

inmalpractice liability. This paper thus contributes to the

literature by reconciling apparently conflicting findings in

previous research. It is plausible that most of the change in

treatment patterns due to non-economic damages caps is

driven by selection and not by the impact of changes in

incentives.

This mechanism can also explain the lack of robustness

of the estimated effect of non-economic damages caps over

time. More recent reforms might not be associated with

significant entry, possibly because the scope of mobility

decreases as more states pass such legislation or because

successful past repeals reduce expected gains from

mobility.

2. The economics of tort reform

Where medical liability insurance is an important

component of the operating cost of a medical practice,

legislation that reduces malpractice insurance premiums

should increase the profitability of the medical profession

and induce entry into the medical field. Non-economic

damages caps reduce insurer payouts (Sloan et al., 1989;

Avraham, 2007; Hyman et al., 2009) and the probability of

lawsuits (Kessler and McClellan, 1997; Browne and Puelz,

1999) and, thus, they are expected to increase the supply of

medical

care.Previous literature has found evidence of an increase in

the number of doctors (Encinosa and Hellinger, 2005;

Kessler et al., 2005; Matsa 2007) and more so in high-risk

specialties (Klick and Stratmann, 2007) such as obstetrics

gynecology. This evidence is not always consistent, howev-

er, and Yang et al. (2008) find no evidence that tort reform

increased the number of obstetriciangynecologists.

Nevertheless, even when the number of physicians does

not change, a decrease in cost could still shift the supply of

medical care and decrease prices. As predicted, the changes

in the cost structure of the industry because of non-

economic damages caps (and other tort reforms) have been

found

to

be

associated

with

higher

prevalence

of

healthinsurance coverage for the most price-sensitive groups

(Avraham and Schanzenbach, 2010). This in itself would

lead us to believe that tort reforms lead to better health.

Previous literature, however, has found only very limited

evidence of health improvements. In particular, Klick and

Stratmann (2007) found that non-economic damages caps

are associated with a reduction in black infant mortality

rates. One proposed explanation is that changes in

physicians

incentives

lead

to

changes

in

treatmentpatterns (Currie and MacLeod, 2008) that are not

necessarily associated with better health (Kessler and

McClellan, 1996) and may in fact lead to more medical

mistakes (Cotet, 2012).

The case of obstetrics is especially confusing because of

apparently conflicting results. There is some evidence that

C-section rates are responsive to financial incentives

(Gruber et al., 1999), but other evidence indicates that

C-sections are not sensitive to medical malpractice risk

(Kim, 2007). Previous literature found that higher mal-

practice premiums are associated with an increased use of

C-sections (Dubay et al., 1999; Grant and McInnes, 2004).

At

the

same

time,

there

is

evidence

that

noneconomicdamages caps, which decrease malpractice premiums

(Thorpe, 2004; Danzon et al., 2004), are associated with

higher rates of C-sections (Currie and MacLeod, 2008).

There are arguments to be made for both sets of results.

C-sections could be used to protect against malpractice

claims that argue that negative infant outcomes arose from

waiting too long to perform surgery (see DAttilo versus

Viscarello 2011, which resulted in a $58 million award; or

Fielding and Martinez versusJohn Hopkins Hospital 2012,

which resulted in a $55 million award). Therefore, C-

sections could be one more way physicians practice

defensive medicine. At the same time, C-sections entail

risks.

They

could

lead

to

injury

or

infections

and

couldaffect future fertility, all of which could lead to malpractice

lawsuits. Thus, physicians may be reluctant to perform C-

sections in an effort to avoid litigation.

Ultimately, whether C-sections rates are too high boils

down to determining whether at the margin additional

procedures are associated with a net benefit. Previous

literature found no evidence that higher rates of C-sections

are associated with any changes in infant health (Baicker

et al., 2006), findings that do not appear to suggest that

physicians avoid C-sections because of potential liability.

In addition, there may be general equilibrium effects. To

the extent that the litigiousness of a place affects

physicians

scope

of

practice

and

location

choices,

changesin working environment driven by the decline in the

likelihood of being sued due to non-economic damages

caps could lead to geographical mobility (Matsa, 2007).

Such mobility could differentially alter treatment choices

and outcomes in the affected areas. Because the scope of

mobility decreases as more states pass such legislation or

fall under a federal tort reform, predictions based on early

reforms may under/overestimate the impact of tort reform.

Interestingly if the estimated effect of caps is driven by

such mechanisms, all theories regarding the use of C-

sections predict the same outcome. If a high rate of C-

sections performed indicates a risk-taking physician, lower

liability

could

attract

the

most

risk-taking

physicians,leading to an increase in C-sections in states with caps. In

A. Cotet-Grecu / Economics and Human Biology 17 (2015) 294130


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fact the same result would occur if C-sections were a form

of defensive medicine. A risk-adverse physician that

performs many C-sections might find a state with caps

particularly attractive. C-sections might also increase

where competition improves pregnancy health and more

difficult pregnancies result in live births if these additional

births are more likely to be associated with surgical

deliveries.

Alternatively,

if

the

most

liability

sensitivephysicians that drive the increase in supply tend to have

poorer diagnostic skills they may perform differentially

more C-sections (Currie and MacLeod, 2013). In all these

situations, there would be an amplification effect: the

coefficients obtained would overestimate the incentive

effect.

3. Econometric strategy

The following equation describes the empirical model2:

Yct uCAPs;t1 bXc s t ac gtr vst ect (1)

where s stands to state, c for county, r for region, t for yearand ectis the stochastic error term.

Yctcould be one of the following outcomes variables in

county c year t: obstetricians per 10,000 women; prenatal

visits per live births; C-section rate among live births;

births per 100 women of fertile age (1544 years old);

percent mothers who initiated prenatal care during the

first trimester, second trimester, third trimester, or did not

receive prenatal care; percent premature births (


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overall level of health of the population in a county do not

confound the effect of non-economic damages caps. The

equation also includes year fixed effects, gtr, meant tocapture time-varying differences in the dependent variable

common to all counties, such as changes in federal-level

healthcare policies. Because there are regional differences

in physicians practice style that may change over time the

year

effects

are

allowed

to

vary

by

region.State-specific trends in medical-care utilization rates

could impact a states likelihood to adopt a non-economic

damages cap. To control for such a possibility, the model

includes state-specific time trends, vst. Controlling forthese trends reduces the burden of the assumption of

reforms exogeneity. Conditional on county, region by year

fixed effects, and state-specific trends, the us are identifiedfrom year-by-year changes in legislation, after controlling

for shocks common to all counties, differential shocks to

different regions, and state-specific trends.

One more issue about the estimation strategy should be

mentioned. The independent variable of interest varies

only

at

the

state

level.

Moreover,

it

could

be

that

the

errorterms are correlated within states over time, although the

fact that laws are also repealed during the period

investigated is useful for identification because it helps

ensure that general trends in outcomes are not confounded

for the effect of caps. Nevertheless, misspecification of the

autocorrelation process can lead to downward bias in the

standard-error estimates (Bertrand et al., 2004). Conse-

quently, robust standard errors clustered at the state level

that allow for heteroskedasticity and autocorrelation of

unspecified form are calculated and reported throughout

the paper6.

The key identifying restriction in this paper is that the

adoption

of

non-economic

damages

caps

is

exogenous.

Theliterature indicates that the timing of the adoption is mostly

the resultof vagaries of the political process (Rubin,2005).

A significant portion of the reforms is not specific to

medical malpractice, but is instead broad tort reform,

applying to accidents and product liability as well

(Avraham and Schazenbach, 2010). Thus, it is unlikely

that tort reforms were passed in response to changes in

the obstetrics practice (Currie and MacLeod, 2008).

Numerous papers, among which Rubin and Shepherd

(2007), Avraham and Schazenbach (2010), and Cotet

(2012) are just a few examples, investigated this point

using a variety of balancing tests and graphical evidence

and

found no

evidence

of

endogeneity

so

I

will

not

repeatthe exercise here. However, because this paper investi-

gates the robustness of the results over time in Table 1, I

present summary statistics indicating whether the

distribution of observable covariates is similar in all

period investigated (Heckman and Hotz, 1989).

4. Data

The physician data comes from the Area Resource File

(ARF), which contains county level information about the

number of obstetricgynecologists starting in 1995. This

data are also available separately by age.

For the investigation of obstetric procedures and birth

outcomes I use the 19892010 National Center for Health

Statistics (NCHS) Natality Files, which provide a census of all

births. This dataset contains information on the newborns

health, the demographic characteristics of the parents, the

pregnancy

history

of

the

mother,

and the

prenatal carehistory of the mother. In addition, these data provide

information about childbirth procedures. Over the period

of thisstudy, therewere significant changes in thecontent

of the birth certificate. As a result, some of the data from

the revised (2003) andunrevised (1989) birth certificates

may not be comparable. I concentrate on C-sections

because these data are consistent over the entire period

investigated, while the prevalence of stimulation of labor

may notbe fullyconsistent7. The dataon health outcomes

is fully consistent over time. Although some states revised

theprenatalcare initiationquestions on birth certificates I

retain the data on prenatal care initiation, an outcome

variable.

If the measurement error

in

this dependentvariable is not correlated with the independent variables,

it does not bias the estimates. All regressions that use

Natality data control for an indicator variable for the

states that revised birth certificatedafter the revision. All

conclusions drawn based on results obtained using data

from thetwo certificates combined are reinforced byother

empirical tests usingonly data prior toany revision in any

states, i.e. before 2003.

Because in some regressions the period investigated is

quite long (22 years) and there are changes over time in the

prevalence of multiple births, I retain only the sample of

singleton births. This ensures that the observed changes in

the

prevalence

of

C-sections

and

poorer

birth

outcomes

arenot driven by trends in multiple births known to be more

likely to be associated with surgical delivery and higher

incidence of prematurity and low birth weight. The

retained data are then collapsed at county level to allow

the investigation of the differential effect of tort reform

function of local characteristics. These data are then

merged with the state legislative data.

State by state legislative data are taken from Avrahams

(2011) Database of State Tort Law Reforms (DSTLR

4 dataset). The impact of the law is measured by

introducing a dummy variable indicating whether the

state has a cap on non-economicdamages in a given year,

regardlessof

the

value

of

thecap.

If

theeffective

date

of

thereform was on or after July 1st, it was coded as belonging

to the following year.

Although, as Avraham (2011) indicates, many states

experimented with non-economic damages caps before

1989, there was significant legislative activity both in the

period of reference, 19892001, and in recent period,

20022010. Specifically, given that, as will be shown

below, this analysis suggests there is a one-year lag in the

effect of caps, the investigation of the 19892001 birth

cohorts covers fifteen reforms: six instances of caps

6

The

standard

errors

are

essentially

the

same

when

clustering

by

stateand year; results are available on request.

7

Some

results

regarding

the

prevalence

of

induction

are

discussed

inthe paper. The data on induction is consistent over the entire period.



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adoptions (IL 1995, WI1995, MT 1996, ND 1996, SD, 1996,

OH 1997) and nine instances of caps repeals (WA 1989, MN

1990, NH 1991, WI 1991, OH 1992, AL 1993, IL 1998, OH

1998, OR 2000) as shown in Fig. 1. The analysis of 1989

2010 birth cohorts covers an additional ten reforms: nine

instances of adoptions (FL 2003, MS 2003, NV 2003, OH

2003, OK 2004, TX 2004, GA 2005, IL 2006, SC 2006) and

one repeal (IL 2008) as shown in Fig. 2.

The source of fetal death data is the NCHS Fetal Deaths

Files. At the time of this research Fetal Death data are

available only until 2006. Because different states have

different gestation thresholds for reporting fetal death, I

retain only the records indicating a gestation that would

ensure data reliability and comparability across states.

Table 1

Summary statistics.

19892001 19892010

Mean Standard error Mean Standard error

Ob-gyn 2.420 (1.473) 2.440 (1.454)

C-sections 21.057 (4.369) 24.375 (6.223)

Prenatal visits 11.298 (1.139) 11.277 (1.181)

Prenatal care initiation

1st Trim 80.083 (8.583) 79.417 (9.538)

2nd Trim 15.283 (6.014) 15.794 (6.711)

3rd Trim 3.230 (2.141) 3.394 (2.346)

None 1.404 (1.608) 1.396 (2.087)

Births 6.380 (2.635) 6.438 (2.719)

Gestation


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Specifically, I use fetal deaths per 1000,000 women aged

18448 occurring after 28 weeks of gestation9.

The source of neonatal mortality data is the Com-

pressed Mortality Files compiled by NCHS. They contain

information

from

all

death

certificates

filed

in

the

50

statesand the District of Columbia. The per-capita personal

income data come from the Bureau of Economic Analysis.

The county level population data by gender, race and age

come from National Center for Health Statistics (NCHS)

Bridged Race Population Estimates. County level hospital

data as well as data regarding urban-rural states come

from the Area Resource File. The state-level education and

marital status data for women come from the Current

Population Survey, March Supplement.

5. Results

5.1.

Main

results

5.1.1. Effect on supply

First I show results obtained for the period running up

to and including 2001, and subsequently I extend the

period to cover all reforms passed until 2010. The

2001 cut-off was chosen to match the period used by

previous literature as closely as possible. Both Klick and

Stratmann (2007), who find effects on physicians in high-

risk specialties, such as obstetrics and gynecology, and

Currie and MacLeod (2008), who find effects on C-sections,

use data up to and including 2001.

In Table 2, I present results suggesting that during the

19952001

period,

non-economic

damages

caps

wereassociated with an increase in the number of ob-gyns per

10,000 women. The adoption of non-economic damages

caps is associated with 0.103 more ob-gyn per 10,000

women, the equivalent of a 4.26% increase in the supply of

ob-gyn. This estimate is very close to the Klick and

Stratmanns (2007) findings of a 4.1% increase in supply of

physicians in high-risk specialties. However, the results do

not hold when the period is extended to 2010. The

estimated effect during the period 20022010 is signifi-

cantly different from the estimated effect during the pre-

2002 period (p-value 0.049). It is possible that scope of

mobility decreases as more states pass such legislation. An

alternative

explanation

is

that,

over

time

the

high

rate

ofsuccessful repeals decreased the trust that a policy will

Table 2

Non-economic damages caps and the supply of medical care: the case of obstetrics.

Dep. Var. Ob/Gyn [1] Prenatal visits [2] Cesarean [3]

Panel A: 19892001

NE Cap 0.103** (0.049) 0.102*** (0.037) 0.412** (0.200)

Obs. 17,696 32,458 32,582

Panel B: 19892010

NE

Cap

0.033

(0.030)

-0.174

(0.138)

0.453

(0.275)Obs. 40,048 54,496 54,728

Panel C: 20022010

NE Cap 0.005 (0.016) -0.147 (0.147) 0.254 (0.182)

Obs. 22,352 22,038 22,146

The dependent variables are: obstetriciansgynecologists per 10,000 women in column 1, average number of prenatal visit per live birth in column 2, and C-

sections per 100 live births in column 3. Due to data availability column 1 only uses the data after 1995. NE Cap stands for non-economic damages caps

reforms. The regressions reported in column 1 control for percent white (omitted), percent black, and percent other race; the age structure: percent under

15 years old (omitted), 1544, 4564, 65 and older; log of county income per capita; and for state level variables such as womens education: percent with

less than a high-school degree (omitted), percent with a high-school degree, percent with some college, and percent with a college degree or more

education; and marital status. The regressions reported in columns 2 and 3 control for birth parity: first child, second child, third child, fourth child, fifth or

subsequent child (omitted); gender; mothers age: under 25 (omitted), 2534; over 35 years old; mothers education: less than high-school education

(omitted), high-school, some college, college or more; mothers race: white (omitted), black, other race; Hispanic; marital status; and log of county income

per capita. All regressions control for joint and several liability reforms, punitive damages reforms, collateral source reforms, total damages reforms. All

regressions include county fixed effects, year fixed effects that are allowed to vary by region and state specific time trends. The regressions reported in

column 1 are weighted by the number of women in a county-year. The regressions reported in columns 23 are weighted by the number of live births in acounty-year. Robust standard errors clustered at state level are reported in parentheses.

* Significant at 10% significance level.

** Significant at 5% significance level.

*** Significant at 1% significance level.

Fig. 2. Non-economic damages caps changes legislative changes 2002

2010 (light gray for adoptions, dark gray for repeals, black for states that

both adopted and repealed a cap).

8 This specification is preferable to using fetal deaths/conceptions

because of the measurement error in the variable conceptions (i.e. live

births + fetal deaths). Such measure of conceptions would exclude all fetal

deaths before 28 weeks of gestation, which could be affected more by the

impact of non-economic damages cap leading to attenuation in the

estimated effect of the reforms.9

The

results

are

robust

to

using

all

fetal

deaths

occurring

after24 weeks of gestation. Results not reported but available on request.



7/13

stay in effect and, thus, the incentives to choose a state

with caps over one without.

The estimates reported in column 3 of Table 2 are also

consistent with previous literature (Currie and MacLeod,

2008) and indicate that during the 19892001 period there

was a positive, significant correlation between non-

economic damages caps and the prevalence of C-sections

among

live

births.

The

estimated

effect

is

small,

a0.412 increase in C-sections per 100 live births, the

equivalent of a 2% increase calculated at the mean10. In

addition, during that period non-economic damages caps

were associated with 0.102 more prenatal visits for the

average women, the equivalent of a 0.9% increase.

There is a lag between the year of adoption and any

significant change in medical practice, providing reassur-

ance that the causality runs from the legislation to the

likelihood of surgical intervention during birth and not the

other way around11. When investigating the effect of this

reform on procedures over the entire 19892010 period,

however, neither the estimated effect on prenatal visits nor

that

on

C-sections

is

significant.

Two

standard

deviationsaround the estimated effect on prenatal visits before

2002 excludes the estimated effect over the 20022010

period, and in fact the estimated effects are of different

signs. The difference between estimates appears to be

much smaller in the case of C-sections, although the

0.254 estimated coefficient is the equivalent of 0.9%

increase in C-sections, half the effect over the pre-2002

period, and statistically insignificant. The lack of statistical

significance is not driven by larger standard errors but by

the smaller coefficient12. The smaller, insignificant effect of

newer reforms leads to smaller and insignificant estimates

on the pooled sample as well.

5.1.2. Buyers response

The second question is whether such supply changes

elicited any demand response that could have led to

changes in procedures. Do women respond to changes in

medical practice? A noisy way to address this question is to

look at the number of births. Women residing close to a

state border could respond by choosing a physician in the

border state. This measure is noisy because it is unlikely

pregnant women are very mobile and because our data

retain only live births, which contain a supply component:

the quality of care. When non-economic damages caps

affect the quality of care and thus are correlated with the

likelihood of survival in utero, the information contained in

the number of live births captures both a demand response

and a supply response. The results reported in Table 3

indicate noeffecton live births per 100 women of fertile age.

A better way to investigate this hypothesis is to look at

the initiation of an episode of care. While suppliers,

physicians, may decide the course of treatment, they can

only

do

so

if

women

initiate

an

episode

of

care.

Thus,

thetiming of prenatal-care initiation represents a cleaner

source of information about how women respond to

changes in medical practice. I find that non-economic

damages caps reforms passed during the 19892001

period were associated with earlier prenatal-care initia-

tion. This is consistent with previous literature indicating

that some of the savings experienced by physicians in

states with caps are passed to consumers: damage caps

increase health insurance coverage (Avraham and Schan-

zenbach, 2010).

These results are also not robust on the more

comprehensive sample and in fact change signs. Although

the

hypothesis

that

these

results

are

driven

by

measure-ment error in the dependent variables due to birth

certificates revision cannot be completely rejected, it does

not seem to pose a significant problem. The exclusion of

the control variable for revised data alters neither the sign

nor significance of the coefficients. In addition, the results

are consistent with those obtained on the sample of states

that did not implement revised birth certificates.

5.2. Falsification tests

5.2.1. Effect on outcomes

One way to investigate the plausibility of the results is

to

check

whether

other

variables

related

to

the

supply

ofmedical care changed in the same manner.

Table 4 reports the effect of non-economic damages

caps on a variety of birth outcomes. Interestingly, these

changes are associated with better health outcomes.

Between 1989 and 2001, non-economic damage caps are

associated with fewer births with low Apgar scores and

fewer instances of neonatal mortality due to injury.

Although there is a positive correlation between non-

economic damages caps and neonatal mortality due to

adverse effects of medical care, this association is not

significant at conventional significance levels13.

These effects, however, are not robust to using the more

comprehensive

sample,

which

is

fully

consistent

withprevious results indicating that the newer reform have less

bite.10 Currie and MacLeod (2008) found a 5% increase in the probability of C-

section, but they retain multiple births, while this paper does not, for

reasons explained in the data section. The C-section rate is higher among

multiple births.11 The estimated contemporaneous effect on ob-gyn is 0.031 with

standard error 0.022 and not statistically significant. The contemporane-

ous effect on C-sections is 0.267 with a standard error of 0.138 and, thus,

smaller and significant only at 10%. The lead policy variable is not

statistically significant.12 It is also not true that the lack of significance is driven by a longer lag

between the adoption and the response, such as may be expected if

people wait to see whether the reform will stick or be reversed. An event

analysis performed on the 20022010 data indicates statistically

insignificant

effects

at

all

lags;

results

reported

in

Appendix

Table

A2,available on request.

13 I find no evidence of a statistically significant change in the

prevalence of low birth weight, high birth weight, or in the average

birth weight. There is some evidence that during the 19892001 period

caps were associated with an increase in births at 39 weeks of gestation

and a decrease in those at more than 41 weeks of gestation. These results

are more suggestive of scheduled C-sections than of decisions taken at the

time of delivery and thus do not provide evidence that changes in health

drive the change in procedures. In addition, I find no evidence of a

statistically significant effect on induction over the entire sample or

within groups defined by gestation, which would suggest that a vaginal

delivery

was

attempted.

These

results

are

not

reported

but

are

availableon request.

A. Cotet-Grecu /Economics and Human Biology 17 (2015) 2941 35


8/13

There are two possible explanations for these results.

One is a demand-based explanation. For instance, women

that care more about their health are more likely to be

aware of their health risks and, thus, start prenatal care

earlier and have better outcomes than women diagnosed

later during pregnancy. If these women with high-risk

pregnancies that warrant surgical intervention are more

mobile in their search for the right physician, non-

economic damages caps could be associated with more

C-sections and better outcomes. This explanation has

significant caveats. These women may be reluctant to

travel too far. Expecting to need more care, they could also

be

more

likely

to

avoid

a

state

with

a

decreased

cost

ofmalpractice even when the price of care is lower.

Alternatively, women that care more about their health

and have healthier births but also request surgical

intervention may be more mobile. In both situations, the

births in states with caps could be significantly different

from those occurring in the non-cap states. I follow Currie

and MacLeod (2008) to define high-risk women as those

suffering from following conditions: anemia, cardiac, or

lung conditions, diabetes, herpes, eclampsia, or incompe-

tent cervix, previous large or preterm deliveries, renal

failure, rh problems, uterine bleeding, or other risk factors.

Between 1989 and 2001 I find no evidence of a significant

change in the proportion of births to high-risk women

(coefficient

2.984

with

standard

error

of

1.865).

It

is

moredifficult to test the hypothesis that women that care more

Table 4

The impact of non-economic damages caps on birth outcomes.

Dep. Var. Premature births [1] 5 min Apgar


9/13

about their health are more mobile. The increase in the

proportion of women that delay prenatal care reported in

Table 3 could be consistent with this hypothesis. However,

the

lack

of

a

significant

change

in

the

number

of

births

instates with caps, also reported in Table 3, seems more

suggestive of a response to lower prices, than of a selection

story.

A more fruitful area of investigation is a supply-based

explanation. The gains from easier access to specialized

medicalcare, ob-gyn,more than offsetany cost associated

with the risk of injury during surgical intervention, thus

leading to better overall outcomes. This explanation is

plausible. Evenwhen newentrants have lower diagnostic

skills and, thus, are both more prone to intervene

surgically (Currie and MacLeod, 2013) and more prone

to errors, the increase in competition may ensure that

only the most error-free physicians can maintain asuccessful practice. Thus, the risk of iatrogenic injury

might be small. The results regardingthe effect of caps on

mortality frommedical care adverse effects provide some

support for this proposed explanation.

Thehypothesis that the results are driven by selection

could also explain why none of the results obtained from

the 19892001 data can be replicated with the extended

19892010 sample14. In the following, I concentrate on

testing the supply-based explanation using the sample of

reforms passed before 2002, when the reforms are known

to be binding and produce an effect in the total

population. For instance, thepositivecorrelation between

caps and C-sections could be explained by increased risk

taking

by ob-gyns or

by a

change in

the

characteristics ofob-gyns due to selection. To separate one explanation

from the other I present results from a series of

falsification tests that show that most of the effect on

procedures is concentrated in areas with higher entry,

with little to no effect in areaswhere the structure of ob-

gyn population remained substantially the same, sugges-

tive of selection effects.

5.2.2. Effect by age of physicians

The hypothesis that the effect is driven by selection

relies on the assumption that the ob-gyn entrants are

different from the incumbents. Due to data availability, I

cannot

test

directly

whether

their

practice

style

isdifferent. However, because practice style is built through

education and experience, significant changes in the

demographics of ob-gyns provide indirect evidence of

differences in practice style. I find evidence of a statistically

significant increase in the number of ob-gyns under

35 years old in states that adopted caps before 2002

(see Table 5). The adoption of caps is associated with a

0.011 increase in ob-gyn per 10,000 women, the equivalent

of a 10% increase calculated at the mean of the data. There

is also some weak evidence, significant at the 10%

significance level, of an increase in the number of

physicians 6574 years old. Although quite noisy the

estimate

is

large.

It

represents

an

almost

10%

increase,

asignificant change if we are to compare it with the

estimated effect on physicians age 35 to 44, the equivalent

of a 2% increase.

These findings are consistent with previous literature

(Baicker and Chandra, 2005) and plausible, because young

ob-gyns at the beginning of their career are more mobile.

Once physicians have established their practices, which

often rely on referrals, changing their locationwould bevery

costly. At the same time, the prospect of lower practice costs

may induce some ob-gyns to defer retirement.

5.2.3. Effect by rurality

According

to

the

2003

Socioeconomic

Survey

ofAmerican College of Obstetricians and Gynecologists

Table 5

the impact of non-economic damages cap on the supply of Ob-Gyn by age of the physician.

Under 35 3544 4554 5564 6574 over 75

Panel A: 19952001

NE Cap 0.011** (0.005) 0.003 (0.004) 0.004 (0.005) 0.002 (0.003) 0.004* (0.002) 0.001 (0.002)

Obs. 17,695 17,695 17,695 17,695 17,695 17,695

Panel B: 19952010

NE

Cap

0.004*

(0.002)

0.003

(0.004)

0.004

(0.003)

0.000

(0.003)

0.002

(0.001)

0.000

(0.001)Obs. 40,047 40,047 40,047 40,047 40,047 40,047

Panel C: 20022010

NE Cap 0.002 (0.002) 0.003 (0.002) 0.001 (0.002) 0.000 (0.003) 0.001 (0.002) 0.001 (0.001)

Obs. 22,352 22,352 22,352 22,352 22,352 22,352

The dependent variables are the number of ob-gyn per 10,000 women by age of physician as noted in the heading of each column. The model specification

used for these regressions is identical to the one used in regressions reported in Table 2 column 1.

* Significant at 10% significance level.

** Significant at 5% significance level.*** Significant at 1% significance level.

14

Another

explanation

is

that

the

most

recent

reforms

are

not

binding.However, there is no evidence in support of that theory as the magnitude

of the cap is similar over time. Yet another explanation is repeals have

adoptions do not have symmetric effects. A large share of the variance in

the pre-2001 period is repeals whereas only 1 of the changes in the

2002 to 2010 period is a repeal. However, previous literature found there

is no difference between the effect of adoptions and repeals in the case of

medical procedures (Cotet, 2012). There is one paper that reports a

difference. Yoon (2001) found that the amount that plaintiffs recovered

fell by $23,000 in the period following the enactment of damage caps in

Alabama relative to the control states; while following the repeal of the

damage cap, the amount that plaintiffs recovered increased by $48,000

relative to the levels in the control states, However, two unusually high

payouts in the post-repeal period accounted for that asymmetry: after

omitting those cases, the decrease after repeal was found to be about

$20,000, an amount in line with the increase after enactment (Elliott,

2004).

There

is,

thus,

no

reason

to

believe

that

adoptions

and

repeals

haveasymmetric effects.



10/13

(ACOG) Fellows, the vast majority of ob-gyns, 88%, practice

in metropolitan areas. This number did not change

between 1991 and 2003. This is quite plausible, given

the nature of the demand for their services and the

infrastructure used in the production of these services.

Natality data indicate that 98% of births took place in a

hospital. At the same time, ARF data indicate that

approximately 10% of counties had no hospital and another

40% had only one hospital. In fact, over the period

investigated,

80%

of

counties

had

a

maximum

of

twohospitals. Because deliveries take place in hospitals, ob-

gyns have to locate close to hospitals, which, as the data

indicate, tend to be located in metropolitan areas and other

urban areas. New entrants would also look for a location

reasonably close to a hospital. It is likely that there is more

entry into this field in metropolitan/urban areas. If our

results are driven by changes in the characteristics of the

body of ob-gyns practicing in an area, as opposed to a

change in the incentives of existing ob-gyns, we should

also observe larger changes in procedures and outcomes in

metropolitan/urban areas.

To test this hypothesis, I use the ARF 1995 classification

of

counties

rurality

and

define

urban

as

including

allcounties in metropolitan areas regardless of population

and counties with an urban population of 20,000 or more

adjacent to a metropolitan area. For the sake of brevity, I

report only the regressions for those outcome variables

previously found to be significantly affected by non-

economic damages caps. For all other variables, the effect is

not significant on either the urban or the rural sub-

sample15. The results reported in Table 6 are consistent

with the theory delineated above. Two standard deviations

around the effect on prenatal care initiation, or Apgar

scores estimated for urban counties exclude the effect in

rural areas. One standard deviation around the effect on

ob-gyns, C-sections or neonatal injury mortality in urban

areas excludes the effect estimated for rural areas.

5.2.4. Effect by number of hospitals

Although, ob-gyns prefer metropolitan locations, the

choice of location also depends on the demand for their

services. Matsa (2007) finds that the supply of physicians

increases more in relatively underserved areas16. There are

thus

two

margins

of

choice:

infrastructure

availability,

i.e.hospitals, and the demand for ob-gyn services. The

availability of hospitals captures the existence of infra-

structure that could be used for obstetric services. It

represents a measure of desirability of an area from a

professional point of view. It also captures the availabil-

ity of location options for an ob-gyn, thus being a

measure of desirability from a personal point of view. If a

county only has only one hospital then location options

are very limited because the ob-gyn likely cannot locate

too far from the hospital. This implies that ob-gyns may

locate in areas with at least one hospital but that are

relatively underprovided. To test this hypothesis, in

Table 7, I

split

the sample

by the

number of

hospitals

per10,000 people. Most of the effect is concentrated in areas

with a low number of hospitals per capita, with some

effect on prenatal initiation but no effect on outcomes in

areas with no hospitals or with many hospitals. This is

credible because even women in areas without a

significant influx of ob-gyns might benefit from lower

prices and, thus, they may be more likely to initiate care

Table 6

The impact of non-economic damages capseffect in rural versus urban areas.

Panel A. Supply

Ob/Gyn Prenatal visits Cesarean

All


11/13

Table 8

The impact of non-economic damages caps by Lag ObGyn density.

Panel A. Supply

Ob/Gyn Prenatal visits Cesarean

All


12/13

earlier, perhaps with a primary physician or general

practitioner. These results provide further support for

the hypothesis that the estimated effect of non-

economic damages caps is driven mostly by entry rather

than by changes in the practice style of existing

physicians.

5.2.5.

Effect

by

lag

ob-gyn

per

10,000

womenAnother measure of an areas suitability for new

entrants is the number of ob-gyn in previous period. A

low number of ob-gyn per women signals under-provision,

but the survival of any ob-gyn practices signals the

existence of basic infrastructure. The results reported in

Table 8 support the theory that before 2002 entry took

place mostly in areas relatively underserved, albeit not in

areas with most problems. The effect on procedures,

prenatal visits and C-sections, is also larger in the same

areas.

Of more interest perhaps is the effect of caps on health

outcomes, Apgar scores and neonatal mortality, which

seems

to

be

concentrated

in

areas

with

significantconcentrations of ob-gyn. These results match the results

regarding prenatal care initiation. In areas with high initial

concentrations of ob-gyn caps are associated with fewer

women delaying prenatal care initiation until the third

trimester. Thus, it could be that the effect on outcomes is

mostly due to the demand response to caps.

All this evidence is consistent with relatively small

changes in the practice style of incumbent ob-gyns and

larger changes in the overall practice style of ob-gyns in

states with caps due to entry. Thus, failure to account for

this source of variation would bias the estimate of ob-gyns

response to financial incentives. The overall conclusion is

that

the

lack

of

robustness

of

the

effect

of

non-economicdamages caps over time is likely driven by weaker supply

responses, either because of the signal contained in the

numerous repeals of caps or due to the reduced scope of

mobility.

6. Discussion and conclusions

Empirical results suggest that during the 19892001

period, non-economic damages caps reforms were associ-

ated with an increase in the number of ob-gyns, increases

in the number of prenatal visits and the rate of C-sections,

and better birth outcomes. The results are not robust to

the

addition

of

more

recent

reforms

taking

place

during

the20022010 period, however. This paper investigates the

role of selection among new entrants in explaining these

inconsistencies. It also provides some guidance toward

understanding the inconsistencies found in the literature

between the estimated effect of malpractice premiums on

procedures and that of non-economic damages caps that

reduce malpractice premiums on procedures.

First, the results reported in this paper suggest that the

earlier laws implementing caps on non-economic

damages produced changes in procedures and outcomes

in areas with more entry. In these areas, the demographic

structure of ob-gyns changed toward more young

physicians,

suggestive

of

changes

in

the

average

practicestyle through selection. Thus, non-economic damages

caps likely affected the market for obstetric services

through changes in access, changes in the incentives of

ob-gyns, and changes in the characteristics of service

provided by the average ob-gyn due to selection among

ob-gyns.

Failure to account for all these margins of choice

threatens the external validity of the estimated effect of

this

policy

on

physicians

behavior,

and,

thus,

extrapola-tion from state- to federal-level policies may not be

appropriate. When a sizeable part of the effect of the policy

is driven by young physicians location choices, the

estimated coefficients overestimate the impact of changes

in the incentives faced by the average ob-gyn.

Second, the evidence presented in this paper suggests

that although earlier non-economic damages caps reforms

led to changes in medical practice that mirror the effect of

higher malpractice premiums (i.e., they are positively

associated with higher C-sections rates and more care),

the results need not be inconsistent with each other. Rather,

the correlation between malpractice premiums and proce-

dures

reflects

incentive effects,

while

the

associationbetween non-economic damages caps and procedures

reflects selection effects that could obscure the effect of

changes in incentives. Because malpractice premiums were

found to lead to more C-sections (Dubay et al., 1999; Grant

and McInnes, 2004) while non-economic damages caps

were found to act mostly though selection, it is more

plausible that in general, C-sections are not a sign of risk-

taking among ob-gyns.

In addition, the results are consistent with the idea that

non-economic damages caps lead to changes in outcomes

through increased pressure of competition. Both the lower

cost of malpractice and the shift in supply toward

physicians

with

less

experience

would

suggest

greaterpotential for medical errors. The results do not completely

reject the hypothesis that non-economic damages caps

increase medical errors, because although the estimated

effect on infant mortality from medical care adverse effects

is not statistically significant, it is positive and economi-

cally significant. The implied effect, albeit very imprecisely

estimated, is more than 30% increase in infant mortality

due to adverse effects from medical care. Increased

competition, however, can offset some of this increase in

medical errors by easing access and putting pressure on

physicians to perform, thus, leading to a net positive effect

on outcomes, as identified in this paper.

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Avraham, R., Schanzenbach, M.M., 2010. The impact of tort reform onprivate health insurance coverage. Am. Law Econ. Rev. 12 (2), 263264.

Baicker, K., Chandra, A., 2005. The Effect of Malpractice Liability on theDelivery of Health Care, 8. Forum for Health Economics & Policy:Frontiers in Health Policy Research (Article 4).

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Further

reading

National Center for Health Statistics, 1999. Bridged-race IntercensalEstimates of the July 1, 1990July 1, 1999, United States ResidentPopulation by County, Single-Year of Age, Sex, Race, and HispanicOrigin, Prepared by the U.S. Census Bureau with Support from theNational Cancer Institute. National Center for Health Statistics Avail-able on the internet athttp://www.cdc.gov/nchs/nvss/bridged_race.htm.

National Center for Health Statistics, 2010. Postcensal estimates of theresident population of the United States for July 1, 2000-July 1, 2010,by year, county, single-year of age (0, 1, 2,. . ., 85 years and over),bridged race, Hispanic origin, and sex (Vintage 2010). Prepared Undera Collaborative Arrangement with the U.S. Census Bureau. NationalCenter for Health Statistics Available fromhttp://www.cdc.gov/nchs/nvss/bridged_race.htm.

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