greater effect on the lower quantiles. The first model is estimated at five different
quantiles: .90, .75, .50, .25 and .10 in order to investigate the hypothesis that hospital
competition does not affect the upper part of the distribution, but may affect the lower
part.
Table 3-6 reports the results for the respective quantile regressions. All of the
coefficients are statistically significant and, the coefficients are increasing as the quantiles
get smaller. This supports the hypothesis that competition has a greater effect for the
least complicated medical cases. For example, a one standard-deviation increase in
competition would reduce the price by approximately $225 at the median price.9
Relative to the average charge of $5950 in 1999, this implies a 3.8% change in price. If
we compare the competition effect between the 75th and 25th quantiles, we see that
competition matters much more for the lower quantile. A one standard deviation increase
in competition would reduce the price by 1.7% at the 75th percentile; a similar change
would reduce the price by 8.0% at the 25th percentile. 10 It does appear that hospital
competition matters more for the lower quantiles.
The results of the quantile regressions of the second model are reported in Table 3-
7. As with the OLS version, the results are qualitatively similar to the first model. The
coefficients on hhi are all statistically significant but not very large. The third model is
reported in Table 3-8. Again, the results are very similar to the other models. All three
9 The standard deviation of hhi is 15.6; this multiplied by the coefficient on hhi for the median regression
(14.67) implies a price change of $229.
10 The calculation for the 75th percentile is: 8.14*15.6 = $127 as compared to a price of $7200 in 1999. This
is a difference of 1.7%. A similar calculation using an average price of $3118 in 1999 yields a difference
of 8.0% for the 25th percentile.