10
mhbounds
|
d |
Coef. |
Std. Err. |
z |
P>|z| |
[95% Conf. |
Interval] |
|
age |
.3316904 |
.1203295 |
2.76 |
0.006 |
.0958489 |
.5675318 |
|
age2 |
-.0063668 |
.0018554 |
-3.43 |
0.001 |
-.0100033 |
-.0027303 |
|
education |
.8492683 |
.3477041 |
2.44 |
0.015 |
.1677807 |
1.530756 |
|
educ2 |
-.0506202 |
.0172492 |
-2.93 |
0.003 |
-.084428 |
-.0168124 |
|
married |
-1.885542 |
.2993282 |
-6.30 |
0.000 |
-2.472214 |
-1.298869 |
|
black |
1.135973 |
.3517793 |
3.23 |
0.001 |
.446498 |
1.825447 |
|
hispanic |
1.96902 |
.5668567 |
3.47 |
0.001 |
.8580017 |
3.080039 |
|
re74 |
-.0001059 |
.0000353 |
-3.00 |
0.003 |
-.000175 |
-.0000368 |
|
re75 |
-.0002169 |
.0000414 |
-5.24 |
0.000 |
-.000298 |
-.0001357 |
|
re742 |
2.39e-09 |
6.43e-10 |
3.72 |
0.000 |
1.13e-09 |
3.65e-09 |
|
re752 |
1.36e-10 |
6.55e-10 |
0.21 |
0.836 |
-1.15e-09 |
1.42e-09 |
|
blacku74 |
2.144129 |
.4268089 |
5.02 |
0.000 |
1.307599 |
2.980659 |
|
_cons |
-7.474742 |
2.443502 |
-3.06 |
0.002 |
-12.26392 |
-2.685566 |
Note: 22 failures and 0 successes completely determined.
There are observations with identical propensity score values.
The sort order of the data could affect your results.
Make sure that the sort order is random before calling psmatch2.
|
Γ Sample ∣ |
Treated |
Controls |
Difference |
S.E. | ||
|
Variable | ||||||
|
> |
T-stat |
__________________L | ||||
|
employment |
Γ Unmatched ∣ |
.756756757 |
.885140562 |
-.128383805 |
.024978843 | |
|
> |
-5.14 | |||||
|
ATT I |
.756756757 |
.664864865 |
.091891892 |
.047025406 | ||
|
> |
1.95 | |||||
|
I | ||||||
Note: S.E. for ATT does not take into account that the propensity score is esti
> mated.
|
psmatch2: Treatment |
psmatch2: | |
|
support |
Total | |
|
Untreated |
2,490 |
2,490 |
|
Treated |
185 |
185 |
|
Total |
2,675 |
2,675 |
What can be seen from the output is that we get a significant positive treatment
effect on the treated of 0.0919. That is the employment rate of participants is 9.2%-
points higher when compared to matched control group members. Since psmatch2
automatically produces the variables .treated, .weight, and .support we do not have
to specify those when using mhbounds.
. mhbounds employment, gamma(1 (0.05) 1.5)
Mantel-Haenszel (1959) bounds for variable employment
Gamma Q_mh+ Q_mh- p_mh+ p_mh-
-------------------------------------------------
1 1.83216 1.83216 .033464 .033464
1.05 1.62209 2.04761 .052392 .020299
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