st: Propensity Score Matching

[niken kusumawardhani <koesoemawardhanie@yahoo.com>]

Hi all,

I have a question on Propensity Score Matching. I'm trying to evaluate the
impact of migration on children's schooling. My data is cross-section and I
do not have child-level data at time before migration occured. But I have
data on household-level at time before migration occured. Therefore, I
decided to match based on household-level data, since it is measured before
participation into migration.

Since my outcome is at individual-level, there might be some individual
characteristics that affect my outcome. Estimating the impact of migration
by propensity score matching constructed based on household-level variables
won't be enough. My question is, can I estimate the impact of migration
using propensity score matching (covariates used are household-level) and
also incorporate some individual-level variables?

I'm thinking of estimating such model:

Sij = Mj + Gj + Aij + Bij + e

For Sij = schooling of child i at household j
Mj = 1 for migrant household, 0 for household without migrant
Gj = propensity score for household j (the same for all kids at one
household)
Aij = for example age of the child i
Bij = for example sex of the child i
and then since children in household are related, I'm gonna cluster the
standard errors at household level.

Is that possible to do this with Propensity Score Matching? Could someone
tell me how to do it in Stata? 

I've read a lot of references using PSM, but none of it has additional
variables to predict the ATT like in my problems.

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