Hello, I have a problem with Heckman and Oaxaca-Blinder Decomposition estimation results. The results show like this:
The estimation results with the dependent variable in the form of lwage_m1 when estimated using Heckman are different from the results in models 1 and 2 Oaxaca-Blinder Decomposition. Is there an error in my command?
Thank you in advance!
The estimation results with the dependent variable in the form of lwage_m1 when estimated using Heckman are different from the results in models 1 and 2 Oaxaca-Blinder Decomposition. Is there an error in my command?
Code:
heckman lwage_m1 fisik penglihatan pendengaran bicara f_kognitif a_limitation experience experience_sq fe
> male married yeduc agriculture mining manufactur utility construction wholesaledist transport finance_est
> ate social other_sector urban if disabilitas, select (worker=fisik penglihatan pendengaran bicara f_kogni
> tif a_limitation usia usia_sq female married child0_5 child6_11 child12_15 child16 krt yeduc urban) twost
> ep mills(imr)
note: other_sector omitted because of collinearity
note: two-step estimate of rho = -1.1400767 is being truncated to -1
Heckman selection model -- two-step estimates Number of obs = 4,191
(regression model with sample selection) Selected = 3,059
Nonselected = 1,132
Wald chi2(21) = 469.76
Prob > chi2 = 0.0000
--------------------------------------------------------------------------------
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
---------------+----------------------------------------------------------------
lwage_m1 |
fisik | -.0709475 .183754 -0.39 0.699 -.4310988 .2892038
penglihatan | .0102264 .0965127 0.11 0.916 -.1789351 .1993878
pendengaran | .0087744 .174112 0.05 0.960 -.3324789 .3500277
bicara | -.4332943 .7183682 -0.60 0.546 -1.84127 .9746816
f_kognitif | -.1327453 .2231136 -0.59 0.552 -.5700399 .3045493
a_limitation | -.1145396 .0981236 -1.17 0.243 -.3068583 .0777792
experience | .0187261 .0060162 3.11 0.002 .0069346 .0305176
experience_sq | -.0001665 .0001275 -1.31 0.191 -.0004164 .0000833
female | .0116411 .118091 0.10 0.921 -.219813 .2430951
married | .1968843 .0591124 3.33 0.001 .081026 .3127425
yeduc | .115655 .0097429 11.87 0.000 .0965594 .1347507
agriculture | .1647345 .1580484 1.04 0.297 -.1450347 .4745037
mining | .8760347 .1961874 4.47 0.000 .4915146 1.260555
manufactur | .3783642 .1483428 2.55 0.011 .0876175 .6691108
utility | .5326349 .2432612 2.19 0.029 .0558516 1.009418
construction | .2605718 .1568187 1.66 0.097 -.0467871 .5679308
wholesaledist | .1399287 .1501668 0.93 0.351 -.1543929 .4342503
transport | .337808 .1781995 1.90 0.058 -.0114565 .6870726
finance_estate | .4334911 .1607441 2.70 0.007 .1184384 .7485438
social | .1410285 .148216 0.95 0.341 -.1494696 .4315265
other_sector | 0 (omitted)
urban | -.0711167 .0854393 -0.83 0.405 -.2385747 .0963413
_cons | 13.03054 .2319722 56.17 0.000 12.57588 13.48519
---------------+----------------------------------------------------------------
worker |
fisik | -.0856003 .1802218 -0.47 0.635 -.4388285 .2676279
penglihatan | .2284432 .0797646 2.86 0.004 .0721074 .3847789
pendengaran | -.0043464 .1638559 -0.03 0.979 -.325498 .3168053
bicara | -.0857546 .6714793 -0.13 0.898 -1.40183 1.230321
f_kognitif | -.0471478 .2137479 -0.22 0.825 -.4660859 .3717903
a_limitation | -.0298112 .0959062 -0.31 0.756 -.2177838 .1581614
usia | .003353 .0135044 0.25 0.804 -.0231151 .029821
usia_sq | -.0002207 .0001723 -1.28 0.200 -.0005585 .0001171
female | -.3987747 .0530698 -7.51 0.000 -.5027896 -.2947598
married | -.0488746 .0584769 -0.84 0.403 -.1634872 .065738
child0_5 | -.2976949 .1213138 -2.45 0.014 -.5354656 -.0599243
child6_11 | -.0397457 .0842714 -0.47 0.637 -.2049145 .1254232
child12_15 | .0271235 .077008 0.35 0.725 -.1238094 .1780564
child16 | .0137207 .0159459 0.86 0.390 -.0175327 .0449741
krt | .1942657 .0596117 3.26 0.001 .077429 .3111025
yeduc | -.0216708 .0052353 -4.14 0.000 -.0319318 -.0114097
urban | .3115429 .0453901 6.86 0.000 .2225801 .4005058
_cons | .9344762 .2558274 3.65 0.000 .4330637 1.435889
---------------+----------------------------------------------------------------
/mills |
lambda | -1.356478 .4597646 -2.95 0.003 -2.2576 -.4553561
---------------+----------------------------------------------------------------
rho | -1.00000
sigma | 1.3564783
--------------------------------------------------------------------------------
. heckman lwage_m1 fisik penglihatan pendengaran bicara f_kognitif a_limitation experience experience_sq fe
> male married yeduc agriculture mining manufactur utility construction wholesaledist transport finance_est
> ate social other_sector urban if !disabilitas, select (worker=fisik penglihatan pendengaran bicara f_kog
> nitif a_limitation usia usia_sq female married child0_5 child6_11 child12_15 child16 krt yeduc urban) two
> step mills(imr2)
note: a_limitation omitted because of collinearity
note: a_limitation omitted because of collinearity
note: two-step estimate of rho = -1.0661233 is being truncated to -1
Heckman selection model -- two-step estimates Number of obs = 7,154
(regression model with sample selection) Selected = 5,610
Nonselected = 1,544
Wald chi2(21) = 1148.87
Prob > chi2 = 0.0000
--------------------------------------------------------------------------------
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
---------------+----------------------------------------------------------------
lwage_m1 |
fisik | .2043803 .2349763 0.87 0.384 -.2561647 .6649254
penglihatan | .1501891 .094004 1.60 0.110 -.0340554 .3344336
pendengaran | -.1383368 .2221536 -0.62 0.533 -.5737499 .2970762
bicara | -1.730334 .7319178 -2.36 0.018 -3.164867 -.2958017
f_kognitif | -.0351052 .2977845 -0.12 0.906 -.618752 .5485417
a_limitation | 0 (omitted)
experience | .0248329 .0037229 6.67 0.000 .0175361 .0321297
experience_sq | -.0002508 .000088 -2.85 0.004 -.0004234 -.0000783
female | -.1021482 .0733697 -1.39 0.164 -.2459501 .0416537
married | .0202509 .0357313 0.57 0.571 -.0497811 .0902829
yeduc | .1079316 .0054201 19.91 0.000 .0973084 .1185547
agriculture | -.7839405 .7324081 -1.07 0.284 -2.219434 .6515531
mining | -.2878995 .7350153 -0.39 0.695 -1.728503 1.152704
manufactur | -.4480672 .7316054 -0.61 0.540 -1.881987 .985853
utility | -.359073 .7393268 -0.49 0.627 -1.808127 1.089981
construction | -.6365927 .7324041 -0.87 0.385 -2.072078 .798893
wholesaledist | -.6703694 .7316526 -0.92 0.360 -2.104382 .7636434
transport | -.5358852 .7341915 -0.73 0.465 -1.974874 .9031036
finance_estate | -.4486742 .7321423 -0.61 0.540 -1.883647 .9862982
social | -.7055753 .7313825 -0.96 0.335 -2.139059 .727908
other_sector | -.8189949 .7371812 -1.11 0.267 -2.263844 .6258537
urban | -.0281888 .0706385 -0.40 0.690 -.1666377 .11026
_cons | 13.79617 .7392255 18.66 0.000 12.34732 15.24503
---------------+----------------------------------------------------------------
worker |
fisik | -.1964087 .2876901 -0.68 0.495 -.760271 .3674536
penglihatan | .2386369 .1237973 1.93 0.054 -.0040014 .4812752
pendengaran | -.0043805 .3129323 -0.01 0.989 -.6177166 .6089556
bicara | 5.250344 . . . . .
f_kognitif | -.2160377 .3739776 -0.58 0.563 -.9490202 .5169449
a_limitation | 0 (omitted)
usia | .0204772 .0118705 1.73 0.085 -.0027885 .043743
usia_sq | -.0004662 .0001505 -3.10 0.002 -.0007611 -.0001713
female | -.4103317 .0445936 -9.20 0.000 -.4977336 -.3229298
married | -.0041311 .0493098 -0.08 0.933 -.1007765 .0925143
child0_5 | -.0674846 .1093389 -0.62 0.537 -.2817848 .1468156
child6_11 | -.1042615 .070628 -1.48 0.140 -.2426899 .0341668
child12_15 | .0071854 .0732882 0.10 0.922 -.1364569 .1508277
child16 | -.0106009 .0139816 -0.76 0.448 -.0380043 .0168025
krt | .1492719 .0471299 3.17 0.002 .056899 .2416448
yeduc | -.0169419 .0040656 -4.17 0.000 -.0249103 -.0089736
urban | .4672862 .0366885 12.74 0.000 .3953781 .5391943
_cons | .6601781 .2101335 3.14 0.002 .248324 1.072032
---------------+----------------------------------------------------------------
/mills |
lambda | -1.024995 .3143027 -3.26 0.001 -1.641017 -.4089728
---------------+----------------------------------------------------------------
rho | -1.00000
sigma | 1.0249949
--------------------------------------------------------------------------------
. replace imr = imr2 if !disabilitas
(7,154 real changes made)
. oaxaca lwage_m1 fisik penglihatan pendengaran bicara f_kognitif a_limitation experience experience_sq fem
> ale married yeduc agriculture mining manufactur utility construction wholesaledist transport finance_esta
> te social other_sector urban imr, by(disabilitas) adjust(imr) noisily relax
Model for group 1
Source | SS df MS Number of obs = 7,154
-------------+---------------------------------- F(22, 7131) = 129.61
Model | 2030.70345 22 92.3047023 Prob > F = 0.0000
Residual | 5078.60005 7,131 .712186237 R-squared = 0.2856
-------------+---------------------------------- Adj R-squared = 0.2834
Total | 7109.3035 7,153 .993891165 Root MSE = .84391
--------------------------------------------------------------------------------
lwage_m1 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
---------------+----------------------------------------------------------------
fisik | .1102275 .1816719 0.61 0.544 -.2459033 .4663583
penglihatan | .2555275 .0745999 3.43 0.001 .1092896 .4017654
pendengaran | -.1373744 .176726 -0.78 0.437 -.4838099 .209061
bicara | -1.523368 .6026305 -2.53 0.011 -2.704703 -.3420338
f_kognitif | -.0705785 .2277831 -0.31 0.757 -.5171008 .3759439
a_limitation | 0 (omitted)
experience | .0340476 .0030281 11.24 0.000 .0281116 .0399836
experience_sq | -.0004006 .0000734 -5.46 0.000 -.0005445 -.0002567
female | -.2704931 .0588188 -4.60 0.000 -.3857954 -.1551907
married | .0456887 .0281916 1.62 0.105 -.0095753 .1009526
yeduc | .1136474 .004408 25.78 0.000 .1050064 .1222884
agriculture | -1.048878 .8451472 -1.24 0.215 -2.705618 .6078609
mining | -.4095472 .8476365 -0.48 0.629 -2.071166 1.252072
manufactur | -.580703 .8446614 -0.69 0.492 -2.23649 1.075084
utility | -.4895176 .8511105 -0.58 0.565 -2.157947 1.178911
construction | -.7839778 .8453159 -0.93 0.354 -2.441048 .8730922
wholesaledist | -.8209406 .8446964 -0.97 0.331 -2.476796 .834915
transport | -.7411244 .8466643 -0.88 0.381 -2.400838 .9185888
finance_estate | -.620179 .8450956 -0.73 0.463 -2.276817 1.036459
social | -1.038749 .8443899 -1.23 0.219 -2.694004 .6165053
other_sector | -.958218 .849035 -1.13 0.259 -2.622579 .7061424
urban | .0495319 .0570107 0.87 0.385 -.0622259 .1612897
imr | -.8434545 .2506984 -3.36 0.001 -1.334898 -.3520112
_cons | 13.62189 .8489767 16.05 0.000 11.95765 15.28614
--------------------------------------------------------------------------------
(model 1 has zero variance coefficients)
Model for group 2
Source | SS df MS Number of obs = 4,191
-------------+---------------------------------- F(22, 4168) = 78.84
Model | 1452.99884 22 66.045402 Prob > F = 0.0000
Residual | 3491.78834 4,168 .837761118 R-squared = 0.2938
-------------+---------------------------------- Adj R-squared = 0.2901
Total | 4944.78719 4,190 1.18014014 Root MSE = .91529
--------------------------------------------------------------------------------
lwage_m1 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
---------------+----------------------------------------------------------------
fisik | -.2038522 .1176891 -1.73 0.083 -.4345855 .0268811
penglihatan | .0717836 .0613202 1.17 0.242 -.0484368 .192004
pendengaran | -.1338948 .1096671 -1.22 0.222 -.3489008 .0811112
bicara | -.701344 .4615609 -1.52 0.129 -1.60625 .2035616
f_kognitif | -.0308184 .1416699 -0.22 0.828 -.3085671 .2469302
a_limitation | -.1576363 .0626078 -2.52 0.012 -.2803809 -.0348917
experience | .0350608 .0039297 8.92 0.000 .0273565 .0427651
experience_sq | -.0004197 .0000851 -4.93 0.000 -.0005865 -.0002528
female | -.147916 .0753432 -1.96 0.050 -.2956288 -.0002032
married | .1290023 .0377216 3.42 0.001 .0550478 .2029567
yeduc | .1247427 .0062497 19.96 0.000 .1124899 .1369954
agriculture | -.5851329 .1768263 -3.31 0.001 -.9318068 -.2384591
mining | .2080187 .2022441 1.03 0.304 -.1884875 .6045249
manufactur | -.1475967 .173529 -0.85 0.395 -.4878061 .1926128
utility | 0 (omitted)
construction | -.2522498 .1780028 -1.42 0.157 -.6012302 .0967305
wholesaledist | -.4041729 .1744301 -2.32 0.021 -.7461489 -.0621968
transport | -.1742709 .1898261 -0.92 0.359 -.5464314 .1978896
finance_estate | -.0919337 .1802492 -0.51 0.610 -.4453183 .2614508
social | -.5813041 .1725649 -3.37 0.001 -.9196233 -.2429849
other_sector | -.6158203 .2006119 -3.07 0.002 -1.009127 -.222514
urban | -.0596762 .0544685 -1.10 0.273 -.1664634 .047111
imr | -1.366203 .2915065 -4.69 0.000 -1.937712 -.7946952
_cons | 13.34408 .2046559 65.20 0.000 12.94285 13.74532
--------------------------------------------------------------------------------
(model 2 has zero variance coefficients)
Blinder-Oaxaca decomposition Number of obs = 11,345
Model = linear
Group 1: disabilitas = 0 N of obs 1 = 7154
Group 2: disabilitas = 1 N of obs 2 = 4191
--------------------------------------------------------------------------------
lwage_m1 | Coef. Std. Err. z P>|z| [95% Conf. Interval]
---------------+----------------------------------------------------------------
overall |
group_1 | 14.06757 .0117998 1192.19 0.000 14.04444 14.0907
group_2 | 13.87324 .0168119 825.21 0.000 13.84029 13.9062
difference | .1943272 .0205395 9.46 0.000 .1540704 .2345839
---------------+----------------------------------------------------------------
adjusted |
group_1 | 14.38083 .0938206 153.28 0.000 14.19694 14.56471
group_2 | 14.4923 .1331057 108.88 0.000 14.23142 14.75318
difference | -.1114756 .1628479 -0.68 0.494 -.4306516 .2077004
endowments | .2203864 .0626612 3.52 0.000 .0975726 .3432002
coefficients | -.1730132 .166392 -1.04 0.298 -.4991355 .1531092
interaction | -.1588488 .0632823 -2.51 0.012 -.2828798 -.0348179
---------------+----------------------------------------------------------------
endowments |
fisik | .0024861 .0014923 1.67 0.096 -.0004388 .005411
penglihatan | -.0111082 .0094992 -1.17 0.242 -.0297264 .0075099
pendengaran | .0019656 .0016356 1.20 0.229 -.0012401 .0051714
bicara | .0004733 .0004777 0.99 0.322 -.0004629 .0014096
f_kognitif | .0002559 .0011774 0.22 0.828 -.0020518 .0025635
a_limitation | .1387921 .0551292 2.52 0.012 .0307409 .2468433
experience | .0155076 .0088034 1.76 0.078 -.0017468 .032762
experience_sq | .0062314 .0048671 1.28 0.200 -.0033079 .0157707
female | .0224148 .0115026 1.95 0.051 -.00013 .0449595
married | .0051894 .0018803 2.76 0.006 .0015041 .0088747
yeduc | .0318478 .0106265 3.00 0.003 .0110202 .0526754
agriculture | -.0056176 .0039448 -1.42 0.154 -.0133492 .0021141
mining | .0004014 .0006605 0.61 0.543 -.0008932 .0016959
manufactur | .0013188 .0019237 0.69 0.493 -.0024515 .0050892
utility | 0 (omitted)
construction | -.0013789 .0016507 -0.84 0.404 -.0046142 .0018564
wholesaledist | .002052 .0029862 0.69 0.492 -.0038008 .0079048
transport | .0003199 .0006566 0.49 0.626 -.0009671 .0016068
finance_estate | -.0016074 .0031822 -0.51 0.613 -.0078444 .0046296
social | .0087568 .0058975 1.48 0.138 -.0028021 .0203158
other_sector | .0034453 .0018694 1.84 0.065 -.0002186 .0071092
urban | -.0013599 .0013529 -1.01 0.315 -.0040116 .0012918
---------------+----------------------------------------------------------------
coefficients |
fisik | .0047963 .0033587 1.43 0.153 -.0017866 .0113791
penglihatan | .0323119 .0170161 1.90 0.058 -.001039 .0656628
pendengaran | -.0000623 .0037221 -0.02 0.987 -.0073574 .0072328
bicara | -.0007846 .0008238 -0.95 0.341 -.0023992 .0008301
f_kognitif | -.0004079 .0027529 -0.15 0.882 -.0058036 .0049877
a_limitation | .1387921 .0551292 2.52 0.012 .0307409 .2468433
experience | -.0173227 .0848198 -0.20 0.838 -.1835666 .1489211
experience_sq | .0088413 .052101 0.17 0.865 -.0932749 .1109575
female | -.0568868 .0443695 -1.28 0.200 -.1438493 .0300758
married | -.0597568 .033782 -1.77 0.077 -.1259683 .0064547
yeduc | -.1136132 .0783157 -1.45 0.147 -.2671091 .0398826
agriculture | -.0493511 .0919133 -0.54 0.591 -.2294979 .1307957
mining | -.0104622 .0148142 -0.71 0.480 -.0394975 .0185731
manufactur | -.0860839 .1714114 -0.50 0.616 -.422044 .2498762
utility | -.0033873 .0059226 -0.57 0.567 -.0149954 .0082208
construction | -.0416146 .0676438 -0.62 0.538 -.1741939 .0909647
wholesaledist | -.0657322 .1360557 -0.48 0.629 -.3323964 .2009321
transport | -.0160953 .0246801 -0.65 0.514 -.0644674 .0322767
finance_estate | -.0310065 .0507568 -0.61 0.541 -.130488 .0684749
social | -.1509537 .2844213 -0.53 0.596 -.7084093 .4065019
other_sector | -.0061274 .015628 -0.39 0.695 -.0367577 .024503
urban | .0740822 .0534931 1.38 0.166 -.0307624 .1789267
_cons | .2778114 .8732958 0.32 0.750 -1.433817 1.98944
---------------+----------------------------------------------------------------
interaction |
fisik | -.0038304 .0027139 -1.41 0.158 -.0091495 .0014888
penglihatan | -.0284336 .0149857 -1.90 0.058 -.0578051 .0009379
pendengaran | .0000511 .0030534 0.02 0.987 -.0059334 .0060356
bicara | .0005548 .0006653 0.83 0.404 -.0007492 .0018587
f_kognitif | .0003301 .0022282 0.15 0.882 -.0040371 .0046974
a_limitation | -.1387921 .0551292 -2.52 0.012 -.2468433 -.0307409
experience | -.0004481 .0022084 -0.20 0.839 -.0047766 .0038803
experience_sq | -.0002832 .0016825 -0.17 0.866 -.0035809 .0030145
female | .018575 .0145308 1.28 0.201 -.0099048 .0470548
married | -.0033515 .0020256 -1.65 0.098 -.0073215 .0006186
yeduc | -.0028327 .0021646 -1.31 0.191 -.0070753 .0014099
agriculture | -.0044522 .0087567 -0.51 0.611 -.0216151 .0127107
mining | -.0011916 .0023087 -0.52 0.606 -.0057166 .0033334
manufactur | .00387 .0083982 0.46 0.645 -.0125902 .0203301
utility | -.0009236 .0018066 -0.51 0.609 -.0044645 .0026174
construction | -.0029067 .0054954 -0.53 0.597 -.0136774 .0078641
wholesaledist | .0021159 .0052748 0.40 0.688 -.0082225 .0124544
transport | .0010405 .0024112 0.43 0.666 -.0036853 .0057664
finance_estate | -.0092358 .0153191 -0.60 0.547 -.0392607 .0207891
social | .006891 .0136349 0.51 0.613 -.0198328 .0336149
other_sector | .0019156 .0049511 0.39 0.699 -.0077885 .0116197
urban | .0024886 .0020491 1.21 0.225 -.0015276 .0065048
--------------------------------------------------------------------------------
(adjusted by imr)
