Dear Statalist,
I am developing a model to estimate the effect of managed care on the types
of services offered at drug abuse treatment facilities.
Managed care (MC) enters the model as a dummy (either a given treatment
facility has a relationship with a managed care organization or it does
not).
Each service offering (SO) also enters the model as a dummy (either the
treatment facility offers the service or it does not).
All of the data are from year 2000.
A very naive approach would use a one equation probit such as:
SO = MC + X + e
where SO = a specific service offering dummy, MC = managed care
dummy, X = rest of the regressors, e = error term.
However, there is most likely either selection or endogeneity (or both?)
between SO and MC. That is, managed care may enter a facility and, with an
eye on the bottom line, gradually alter the facility's service offerings
(this is what I'm really trying to measure). Alternatively, managed care
may seek out facilities that already have a certain suite (or portfolio) of
service offerings. Or, facilities themselves may alter their offerings in
hopes of attracting managed care patients.
In short, I don't think it's appropriate to interpret the coefficient of MC
in the above one-equation probit as the "effect of MC on the likelihood
that a particular service will be offered by a drug abuse treatment
facility" (am I wrong about this?).
What I'd like to do (I think), is to run a 2 equation model such as:
SO = MC + X + e1
MC = SO + Z + e2
where Z probably overlaps with X to a large extent.
Although -biprobit- or -sureg- will take the data and run the numbers, I am
told that both are inappropriate because they ignore the endogeneity issue.
By any chance, does anybody know whether there is user-written code that
will handle the above "simultaneous equations probit model?"
Sincerely,
Todd Olmstead
Todd Olmstead, PhD
Associate Research Scientist
School of Epidemiology and Public Health
Yale University
[email protected]
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