Dear Austin,
Thank you for getting back. I should have provided a little bit of
background. Data is from random cross-sectional survey of insured and
uninsured households in Vietnam. Households were sampled randomly, though
insured were over-sampled to have a decent number of them.
My main outcome variable of interest is medical expenditure at health
centres or hospitals. Therefore those who didn't seek medical advice have
zero actual expenditure, though latent expenditure may be different. I wish
to determine the impact of insurance on medical expenditure. Insurance
membership is voluntary and my assumption is that the sick are more likely
to buy insurance. Therefore I would like to adjust for this bias
(endogeneity). Also because not all sick seek medical advice and therefore
have medical expenditure, I would like to adjust for this sample selection
as well which is attributed to the decision to seek care given illness. The
independent variables used in med expenditure equation are age, sex, rural,
province, income, job dummies, years of schooling, self-assessed health
status and severity of illness. Instrumental variables for insurance
equation are 'level of worry about future health' and 'dummy variable for
membership in mass organisation/s'. I don't have an instrumental variable
for 'decision to seek med advice' variable.
Hope you can help more after this description.
Many thanks,
Shehzad
-----Original Message-----
From: [email protected]
[mailto:[email protected]] On Behalf Of Austin Nichols
Sent: 12 November 2007 03:08
To: [email protected]
Subject: Re: st: using treatment and selection models for my data
Shehzad Ali <[email protected]>:
Whether those models are appropriate depends on the causal model you
propose that connects these constructs (you do not even mention what
the x variables are), and how you think the data are generated. What
is the source of bias in regressing medical expenditures on insurance?
Are the errors normally distributed? Etc. etc.
Are we to understand that medical expenditure is zero when someone
does not seek medical advice? In that case, modeling expected med exp
as exp(Xb) may make sense, using e.g. ivpois, in which case you might
predict med exp of 0.03 for those folks who have zero expenditure. If
the desired med exp is low, then it is no surprise that these folks
don't seek medical advice.
I think you probably have a complicated story in mind, where people
have unobserved heterogeneity in preferences (discount rates, risk
prefs, price elasticity etc.) and are induced (possibly in unforeseen
ways) by past choices to optimize at corner solutions, but it is
impossible to comment on your use of the empirical models without
seeing a cogent theory to motivate them.
On Nov 11, 2007 2:14 PM, Shehzad Ali <[email protected]> wrote:
> Hello listers,
>
> I have a query about using biprobit, treatreg and heckman steps in stata
and
> generating IMRs. I am using a three-part model for medical expenditure.
> Here is a summary:
>
> I have 1,500 in the sample who felt sick, of which 1,000 sought medical
> advice and hence had medical expenditure. Here the first selection bias is
> with
> regards to seeking treatment when ill. Then in the sample, 700 individuals
> are insured and 1,300 are not. This is the second selection bias which is
> related to insurance purchasing decision. So I need to take into account
two
> selection equations for my medical expenditure (outcome) equation, i.e.
> sought medical advice when ill, and bought insurance. I have to bear in
mind
> that insurance decision also affects decision to seeking care when ill and
> medical expenditure when treatment is sought.
>
> Here is what I am thinking of doing:
>
> First part of the model:
>
> biprobit (eq1: visit_when_ill = insurance x1 x2 x3) (eq 2: insurance=x1 x2
> x3 x4)
>
> Here first eq is for decision to seek care when ill and second decision is
> to buy insurance.
>
> Predict imr1, xb
>
> Second part:
>
> heckman med_expenditure x1 x2 x3 imr1 insurance, treat(insurance= x1 x2 x3
> x4)
>
> predict imr2,xb
>
> Third part:
>
> treatreg med_expenditure x1 x2 x3 insurance imr2, treat(insurance=x1 x2 x3
> x4)
>
> Is this the right approach to take? Any help would be greatly appreciated.
>
> With sincere thanks,
>
> Shehzad
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