For my Dissertation I am calculating a multilevel regression with data from a cross-national survey. Apart from the national differences I have got a special focus on differences between postcommunist and capitalist societies. For the latter I engage a matching-procedure additionally.
However, I am not sure if it is statistically feasible to include country-level variables in my propensity score calculation. My outcome-variable is life satisfaction.
These are my results with the country-level variables:
Variable Sample Treated Controls || Difference S.E. T-stat
stflife Unmatched 6.52477649 7.30727985 || -.782503361 .010444271 -74.92
ATT 6.5630657 6.1991459 || .363919805 .047303311 7.69
Note: S.E. does not take into account that the propensity score is estimated.
psmatch2: psmatch2: Common
Treatment support
assignment Off suppo || On suppor Total
Untreated 0 || 154,234 154,234
Treated 2,333 || 52,921 55,254
Total 2,333 || 207,155 209,488
And here without country-level variables:
Variable Sample Treated Controls || Difference S.E. T-stat
stflife Unmatched 6.42518045 7.31894856 || -.893768108 .009584001 -93.26
ATT 6.42515767 6.51730024 || -.092142568 .015272386 -6.03
Note: S.E. does not take into account that the propensity score is estimated.
psmatch2: psmatch2: Common
Treatment support
assignment Off suppo || On suppor Total
Untreated 0 || 171,802 171,802
Treated 1 || 69,132 69,133
Total 1 || 240,934 240,935
The code I used for these outputs:
- Code: Alles auswählen
psmatch2 postcom fem agea agesquared income attend edu_local subhealth domicil unemployed SocialTrust actandinst regulatory supranational gdppc socspend domicil_C Gini, out(stflife) common
est store pps1
psmatch2 postcom fem agea agesquared income attend edu_local subhealth domicil unemployed SocialTrust actandinst regulatory supranational, out(stflife) common
est store pps2
Nevertheless, I guess both results are quite strong.
So eventuallly my question is if it is feasible and worth including country-level variables?? And why ?
Fritz
