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st: Why does the full sample change when I add variables to the 2nd stageof a Heckman model?
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From |
Gina Dokko <[email protected]> |
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To |
[email protected] |
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Subject |
st: Why does the full sample change when I add variables to the 2nd stageof a Heckman model? |
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Date |
Mon, 11 Dec 2006 16:20:23 -0500 |
I'm using a Heckman sample selection model for job performance
(supinnov). My selection model has 545 full observations, and includes
both applicants and hired workers. However, when I add certain
variables to my main 2nd stage model, the total number of observations
changes. Below, I have a baseline model, followed by a model with
variables from another source added (ctie csex cwehet density netsize).
There are randomly missing observations for the other source, so the
uncensored observations drops from 198 to 110. However, the total
number of obs. also drops by the same amount (545 to 457=88). Why does
the total number of obs. change, rather than the censored observations?
Doesn't the total number of observations reflect the sample of the
selection model? Thanks for any help you can give! -Gina
*************************
. heckman supinnov recreferral tminjob tminjobsq ed_yrs2 file_age female
propdum casdum au
> todum if adj==1, rob nolog select(occprior file_age ed_yrs2 female
recreferral urate)
Heckman selection model Number of obs
= 545
(regression model with sample selection) Censored obs
= 347
Uncensored obs
= 198
Wald chi2(9) =
10.94
Log pseudo-likelihood = -570.3193 Prob > chi2 =
0.2799
------------------------------------------------------------------------------
| Robust
| Coef. Std. Err. z P>|z| [95% Conf.
Interval]
-------------+----------------------------------------------------------------
supinnov |
recreferral | .0426596 .1285928 0.33 0.740 -.2093777
.2946968
tminjob | .0025826 .006251 0.41 0.679 -.0096691
.0148344
tminjobsq | -3.09e-06 .0000263 -0.12 0.907 -.0000547
.0000485
ed_yrs2 | .091542 .0506159 1.81 0.071 -.0076634
.1907475
file_age | -.011273 .0082991 -1.36 0.174 -.027539
.004993
female | -.2280173 .1644796 -1.39 0.166 -.5503915
.0943568
propdum | -.4738588 .2356279 -2.01 0.044 -.935681
-.0120366
casdum | -.5085815 .2335728 -2.18 0.029 -.9663757
-.0507873
autodum | -.4332596 .2199736 -1.97 0.049 -.8644
-.0021192
_cons | 4.296037 .4553609 9.43 0.000 3.403546
5.188528
-------------+----------------------------------------------------------------
select |
occprior | -.0001413 .0011995 -0.12 0.906 -.0024922
.0022097
file_age | -.0075152 .0064384 -1.17 0.243 -.0201343
.0051039
ed_yrs2 | -.1194929 .0278781 -4.29 0.000 -.174133
-.0648528
female | .4391214 .1178484 3.73 0.000 .2081427
.6701001
recreferral | .0512239 .0990166 0.52 0.605 -.142845
.2452927
urate | .5519167 .0622925 8.86 0.000 .4298257
.6740077
_cons | -2.043613 .3599999 -5.68 0.000 -2.7492
-1.338027
-------------+----------------------------------------------------------------
/athrho | -.2368843 .3414492 -0.69 0.488 -.9061124
.4323439
/lnsigma | -.143962 .0694754 -2.07 0.038 -.2801313
-.0077927
-------------+----------------------------------------------------------------
rho | -.2325507 .3229837 -.7192611
.4072783
sigma | .8659206 .0601602 .7556845
.9922376
lambda | -.2013704 .2904771 -.7706951
.3679543
------------------------------------------------------------------------------
Wald test of indep. eqns. (rho = 0): chi2(1) = 0.48 Prob > chi2 =
0.4878
------------------------------------------------------------------------------
***************************
. heckman supinnov ctie csex cwehet density netsize recreferral tminjob
tminjobsq ed_yrs2
> file_age female propdum casdum autodum if adj==1, rob nolog
select(occprior file_age ed_
> yrs2 female recreferral urate)
Heckman selection model Number of obs
= 457
(regression model with sample selection) Censored obs
= 347
Uncensored obs
= 110
Wald chi2(14) =
60.00
Log pseudo-likelihood = -352.3372 Prob > chi2 =
0.0000
------------------------------------------------------------------------------
| Robust
| Coef. Std. Err. z P>|z| [95% Conf.
Interval]
-------------+----------------------------------------------------------------
supinnov |
ctie | 1.031045 .2959833 3.48 0.000 .4509282
1.611161
csex | .2903533 .1748974 1.66 0.097 -.0524394
.6331459
cwehet | 1.556572 .4502231 3.46 0.001 .6741512
2.438993
density | .6961823 .7550956 0.92 0.357 -.7837779
2.176142
netsize | -.0698929 .0371793 -1.88 0.060 -.1427629
.0029772
recreferral | -.1106137 .1566829 -0.71 0.480 -.4177065
.1964791
tminjob | -.0181013 .0080461 -2.25 0.024 -.0338713
-.0023312
tminjobsq | .0000884 .0000307 2.88 0.004 .0000283
.0001485
ed_yrs2 | .2882946 .0565787 5.10 0.000 .1774023
.3991869
file_age | .0011888 .0116109 0.10 0.918 -.0215682
.0239459
female | -.0712856 .165677 -0.43 0.667 -.3960065
.2534354
propdum | -.6029529 .2657829 -2.27 0.023 -1.123878
-.082028
casdum | -.4712946 .2670187 -1.77 0.078 -.9946415
.0520524
autodum | -.4085019 .2482766 -1.65 0.100 -.8951152
.0781113
_cons | 4.232665 .9127007 4.64 0.000 2.443804
6.021526
-------------+----------------------------------------------------------------
select |
occprior | -.004678 .00169 -2.77 0.006 -.0079903
-.0013656
file_age | -.010334 .0071836 -1.44 0.150 -.0244136
.0037456
ed_yrs2 | -.1120664 .0327407 -3.42 0.001 -.1762371
-.0478958
female | .3216321 .136123 2.36 0.018 .0548359
.5884284
recreferral | .0707672 .1168943 0.61 0.545 -.1583414
.2998757
urate | .4512408 .0710121 6.35 0.000 .3120596
.590422
_cons | -1.782074 .3935421 -4.53 0.000 -2.553403
-1.010746
-------------+----------------------------------------------------------------
/athrho | -.8558804 .2482602 -3.45 0.001 -1.342461
-.3692994
/lnsigma | -.1011035 .1465839 -0.69 0.490 -.3884028
.1861957
-------------+----------------------------------------------------------------
rho | -.6941291 .1286447 -.8722622
-.3533788
sigma | .9038395 .1324883 .6781392
1.204658
lambda | -.6273812 .2010785 -1.021488
-.2332746
------------------------------------------------------------------------------
Wald test of indep. eqns. (rho = 0): chi2(1) = 11.89 Prob > chi2 =
0.0006
------------------------------------------------------------------------------
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