The cointegrating equation is Awards (DLP) -
.859 Premiums (DPW) (.10 ) with details as shown
in the following table.
The key terms in the error-correction representation
(not shown) are .08 Awards (.054) and .4077 Premiums
(.112) (standard errors in parentheses). The larger
coefficient on the latter relative to the former
indicates that it is premiums that adjust to awards.
There are also lags of each term in the ECM. Details
are available upon request.
Premiums and Awards
Table A2 is the regression represented in Figure
Three. It shows that higher medical malpractice
awards are associated with higher medical malpractice
premiums, just as we would expect from basic economic
analysis. A one-dollar increase in awards is associated
with an increase in insurance premiums of $2.89.
In the long run we expect a one-to-one relationship
between awards and premiums, which is exactly
what we found in the cointegration analysis. The
greater than one-to-one relationship we
found here could be due to unmodeled factors or
factors specific to the 1999-2001 period. Using
data on individual-level physician malpractice
premiums and an estimated measure of individual
liability, Helland and Showalter (2006) show that
premiums increase one-to-one with awards.
A2. Premiums are higher in states
with higher awards per doctors
Premiums and Concentration Ratios
Table A3 is the regression represented in Figure
Four. It shows, contrary to the price-gouging
hypothesis, that states with higher concentration
ratios tend to have lower insurance premiums.
A ten-percentage-point increase in the concentration
ratio is associated with a decrease in insurance
premiums of $2,540, or about a 13 percent decrease
evaluated at the mean insurance premium. The negative
relationship between prices and the concentration
ratio is consistent with Demsetzs (1973
) argument that
efficient firms lower prices and increase their
market shares. Wal-Mart, for example, dominates
many markets because of its lower prices. Although
the relationship is consistent with Demsetzs
efficiency argument, our goal here is merely to
point out that this relationship is prima facie
evidence against the price-gouging hypothesis.
A3. Higher concentratIon rates lower
Multiple Regression: Premiums, Awards, Concentration
Ratios and PCFs
Table A4 demonstrates that we get similar results
to those graphed when we include awards per doctor,
the 4-firm concentration ratio, and whether a
state has a patient compensation fund (PCF) together
in a multiple regression.
Four states have mandatory PCFs: Pennsylvania,
Indiana, Kansas, and New Mexico. For a fee, these
funds provide excess professional liability insurance
to those in the medical profession. The fees were
added to the rates reported by various insurance
companies in the MLM data so that we are comparing
what physicians pay in total for $1m/$3m worth
of liability insurance in each state. The negative
coefficient on the variable indicates that total
fees are lower in states with PCFs. One explanation
fees in states with PCFs are underpriced. Pennylvania
long underpriced insurance, for example, and then
had to catch up with surcharges and, more recently,
through subsidiziation of rates by taxpayers.
In New Mexico there is no current subsidy, but
in 200 the Department of Insurance issued a report
indicating that the PCF was underfunded, thus
suggesting that taxpayers could be called upon
up some of the burden in the future.
(See Sloan et al. (2005) for more on PCFs.)
A4. Premiums are higher in states with higher awards per doctors, multiple regression
Adverse Actions Per Doctor
Table A5 presents the results mentioned in the
text, that unlike tort awards adverse actions
per doctor do not correlate with political factors
such as partisan elections or the percent below
the poverty level.
A5. Variation in adverse actions per
doctor is not associated with percent
below poverty level or partisan judicial
A6. Means and standard deviations