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
More intriguing information
1. Foreign Direct Investment and the Single Market2. The name is absent
3. On s-additive robust representation of convex risk measures for unbounded financial positions in the presence of uncertainty about the market model
4. The name is absent
5. DISCUSSION: ASSESSING STRUCTURAL CHANGE IN THE DEMAND FOR FOOD COMMODITIES
6. ADJUSTMENT TO GLOBALISATION: A STUDY OF THE FOOTWEAR INDUSTRY IN EUROPE
7. The Demand for Specialty-Crop Insurance: Adverse Selection and Moral Hazard
8. An Efficient Secure Multimodal Biometric Fusion Using Palmprint and Face Image
9. The Prohibition of the Proposed Springer-ProSiebenSat.1-Merger: How much Economics in German Merger Control?
10. Pricing American-style Derivatives under the Heston Model Dynamics: A Fast Fourier Transformation in the Geske–Johnson Scheme