where outcome is an indicator of either a healthy birth or a neonatal death. For the
models that estimate the probability of a healthy birth, the dependent variable, is a
dummy variable that takes the value of one if a baby has a 5-minute Apgar score greater
than or equal to 8 depending on the model. Because the 5-minute Apgar score is a
measure of health at the 5-minute mark, it is an imperfect measure of hospital quality at
best. It is likely that many quality related problems would occur after the 5-minute mark
and would not be picked up in this data. Nonetheless, it is reasonable to believe that some
quality related problems would occur before the Apgar score. Because the vast majority
of problems will not be captured in the Apgar score, the estimates can be considered a
lower bound. The fact that we find any results with the healthy dependent variable,
suggests that these laws have an effect.
Because the Apgar score may not pick up all health problems, the same model is
estimated with the dependent variable an indicator of neonatal death. That is, the variable
is equal to one if the baby died in its first year of life and is zero otherwise. If CON laws
have an effect, we would expect the results to have opposite signs because of the nature
of the indicators. For example, if CON laws are beneficial then the estimated coefficients
on the noCON indicators will be negative if the dependent variable is healthy (indicating
that dropping a state's CON program reduces the probability of a healthy baby).
Similarly, the estimated coefficients will be positive for the noCON indicators if the
dependent variable is death (indicating that dropping a state's CON program increases the
probability of a baby dying in its first year).
The variables of interest are the noCON variables; noCON89 is a dummy variable
that takes the value of one if the state has dropped its Certificate of Need program by