nsw_pscore.do

* Reload experimental group data
use https://github.com/scunning1975/mixtape/raw/master/nsw_mixtape.dta, clear
drop if treat==0

* Now merge in the CPS controls from footnote 2 of Table 2 (Dehejia and Wahba 2002)
append using https://github.com/scunning1975/mixtape/raw/master/cps_mixtape.dta
gen agesq=age*age
gen agecube=age*age*age
gen edusq=educ*edu
gen u74 = 0 if re74!=.
replace u74 = 1 if re74==0
gen u75 = 0 if re75!=.
replace u75 = 1 if re75==0
gen interaction1 = educ*re74
gen re74sq=re74^2
gen re75sq=re75^2
gen interaction2 = u74*hisp

* Now estimate the propensity score
logit treat age agesq agecube educ edusq marr nodegree black hisp re74 re75 u74 u75 interaction1 
predict pscore

* Checking mean propensity scores for treatment and control groups
su pscore if treat==1, detail
su pscore if treat==0, detail

* Now look at the propensity score distribution for treatment and control groups
histogram pscore, by(treat) binrescale