Tablas

1. Métodos de descomposición en
Economía utilizando STATA
Lic. David Antonio Condori Tantani
Diplomado E-learning
POSTGRADO EN INFORMATICA
LA PAZ 2016
Usuarios de STATA 2011

2. Estructura de la presentación
• Motivación y breve descripción de los principales métodos
de descomposición en economía (laboral).
• Revisión y discusión de los procedimientos existentes en
STATA.
• Algunos ejemplos a partir del análisis de los microdatos de
las Encuestas de Presupuestos Familiares 2006 y 2009.
Ficheros de datos: http://www.ine.es/prodyser/micro_epf2006.htm
Rutinas de STATA: http://www.raulramos.cat/stata2011
Métodos de descomposición en Economía utilizando STATA
Lic. David A. Condori Tantani

3. Motivación
• ¿Por qué los hombres tienen salarios superiores a los de las
mujeres?
• ¿Qué factores explican el crecimiento a lo largo del tiempo
en la desigualdad de la renta?
• ¿Por qué existen diferencias en el uso de las nuevas
tecnologías entre hombres y mujeres?
Métodos de descomposición en Economía utilizando STATA
Lic. David A. Condori Tantani

4. • Los economistas intentamos ofrecer respuestas a las
preguntas planteadas a través de la utilización de los
métodos de descomposición.
• Estos métodos se basan en las contribuciones de Oaxaca
(1973) y Blinder (1973), pero han tenido desarrollos
posteriores que han permitido ampliar el abánico de temas
susceptibles de ser analizados desde esta perspectiva.
Fortin, Lemieux, Firpo (2010), http://www.nber.org/papers/w16045
Métodos de descomposición en Economía utilizando STATA
Lic. David A. Condori Tantani

5. Oaxaca-Blinder
• Queremos explicar el salario (W) que recibe un trabajador/a
en función de su nivel de estudios (S) a través de la
estimación de un modelo de regresión (ecuación de
Mincer):
HiHiHHHi USW +⋅+= 21 ββ
( ) ( ) ( )    
tes)(coeficien
explicadano
2211
sticas)(caracterí
explicada
2
ˆˆˆˆˆ HMMiHMHHiMiHM SSSWW βββββ −⋅+−+⋅−=−
MiMiMMMi USW +⋅+= 21 ββ
Métodos de descomposición en Economía utilizando STATA
Lic. David A. Condori Tantani

6. Métodos de descomposición en Economía utilizando STATA
ANOSEST 13746 9.967772 3.736099 0 17
LWAGE 13746 6.999458 .4510732 6.214608 8.006368
Variable Obs Mean Std. Dev. Min Max
-> HOMBRE = 1
ANOSEST 9874 10.37543 4.111178 0 17
LWAGE 9874 6.752326 .4520183 6.214608 8.006368
Variable Obs Mean Std. Dev. Min Max
-> HOMBRE = 0
. bysort HOMBRE: sum LWAGE ANOSEST if LWAGE!=. & ANOSEST!=.
interaction .0014328 .0005592 2.56 0.010 .0003367 .0025288
coefficients -.2717751 .0051093 -53.19 0.000 -.2817891 -.2617611
endowments .0232102 .0029963 7.75 0.000 .0173376 .0290828
difference -.2471321 .0059579 -41.48 0.000 -.2588095 -.2354548
group_2 6.999458 .0038474 1819.25 0.000 6.991917 7.006999
group_1 6.752326 .0045491 1484.32 0.000 6.74341 6.761242
overall
LWAGE Coef. Std. Err. z P>|z| [95% Conf. Interval]
Group 2: HOMBRE = 1 N of obs 2 = 13746
Group 1: HOMBRE = 0 N of obs 1 = 9874
Model = linear
Blinder-Oaxaca decomposition Number of obs = 23620
. oaxaca LWAGE ANOSEST, by(HOMBRE) nodetail
Lic. David A. Condori Tantani

7. Métodos de descomposición en Economía utilizando STATA
interaction .0127419 .0014051 9.07 0.000 .009988 .0154958
coefficients -.2567434 .005005 -51.30 0.000 -.266553 -.2469337
endowments -.0031307 .003501 -0.89 0.371 -.0099924 .0037311
difference -.2471321 .0059583 -41.48 0.000 -.2588101 -.2354541
group_2 6.999458 .0038476 1819.17 0.000 6.991917 7.006999
group_1 6.752326 .0045494 1484.23 0.000 6.743409 6.761243
overall
LWAGE Coef. Std. Err. z P>|z| [95% Conf. Interval]
Group 2: HOMBRE = 1 N of obs 2 = 13746
Group 1: HOMBRE = 0 N of obs 1 = 9874
Model = linear
Blinder-Oaxaca decomposition Number of obs = 23620
. oaxaca LWAGE ANOSEST EXPPOT EXPPOT2, by(HOMBRE) nodetail
interaction .0014328 .0005592 2.56 0.010 .0003367 .0025288
coefficients -.2717751 .0051093 -53.19 0.000 -.2817891 -.2617611
endowments .0232102 .0029963 7.75 0.000 .0173376 .0290828
difference -.2471321 .0059579 -41.48 0.000 -.2588095 -.2354548
group_2 6.999458 .0038474 1819.25 0.000 6.991917 7.006999
group_1 6.752326 .0045491 1484.32 0.000 6.74341 6.761242
overall
LWAGE Coef. Std. Err. z P>|z| [95% Conf. Interval]
Group 2: HOMBRE = 1 N of obs 2 = 13746
Group 1: HOMBRE = 0 N of obs 1 = 9874
Model = linear
Blinder-Oaxaca decomposition Number of obs = 23620
. oaxaca LWAGE ANOSEST, by(HOMBRE) nodetail
Lic. David A. Condori Tantani

8. Métodos de descomposición en Economía utilizando STATA
interaction .0127419 .0014051 9.07 0.000 .009988 .0154958
coefficients -.2567434 .005005 -51.30 0.000 -.266553 -.2469337
endowments -.0031307 .003501 -0.89 0.371 -.0099924 .0037311
difference -.2471321 .0059583 -41.48 0.000 -.2588101 -.2354541
group_2 6.999458 .0038476 1819.17 0.000 6.991917 7.006999
group_1 6.752326 .0045494 1484.23 0.000 6.743409 6.761243
overall
LWAGE Coef. Std. Err. z P>|z| [95% Conf. Interval]
Group 2: HOMBRE = 1 N of obs 2 = 13746
Group 1: HOMBRE = 0 N of obs 1 = 9874
Model = linear
Blinder-Oaxaca decomposition Number of obs = 23620
. oaxaca LWAGE ANOSEST EXPPOT EXPPOT2, by(HOMBRE) nodetail
Raw .2471321 100%
Int -.0127419 -5.155911%
Coef .2567434 103.8891%
Char .0031307 1.266796%
Omega = 0
Int .0127419 5.155911%
Coef .2440015 98.7332%
Char -.0096113 -3.889115%
Omega = 1
Results Coef. Percentage
Number of obs (B) = 9874
Number of obs (A) = 13746
. nldecompose, by(HOMBRE) threefold: regress LWAGE ANOSEST EXPPOT EXPPOT2
Lic. David A. Condori Tantani

9. unexplained -.2567434 .005005 -51.30 0.000 -.266553 -.2469337
explained .0096113 .0034659 2.77 0.006 .0028181 .0164044
difference -.2471321 .0059583 -41.48 0.000 -.2588101 -.2354541
group_2 6.999458 .0038476 1819.17 0.000 6.991917 7.006999
group_1 6.752326 .0045494 1484.23 0.000 6.743409 6.761243
overall
LWAGE Coef. Std. Err. z P>|z| [95% Conf. Interval]
Group 2: HOMBRE = 1 N of obs 2 = 13746
Group 1: HOMBRE = 0 N of obs 1 = 9874
Model = linear
Blinder-Oaxaca decomposition Number of obs = 23620
. oaxaca LWAGE ANOSEST EXPPOT EXPPOT2, by(HOMBRE) weight(1) nodetail
.
.
.
unexplained -.2440015 .0050192 -48.61 0.000 -.253839 -.2341639
explained -.0031307 .003501 -0.89 0.371 -.0099924 .0037311
difference -.2471321 .0059583 -41.48 0.000 -.2588101 -.2354541
group_2 6.999458 .0038476 1819.17 0.000 6.991917 7.006999
group_1 6.752326 .0045494 1484.23 0.000 6.743409 6.761243
overall
LWAGE Coef. Std. Err. z P>|z| [95% Conf. Interval]
Group 2: HOMBRE = 1 N of obs 2 = 13746
Group 1: HOMBRE = 0 N of obs 1 = 9874
Model = linear
Blinder-Oaxaca decomposition Number of obs = 23620
. oaxaca LWAGE ANOSEST EXPPOT EXPPOT2, by(HOMBRE) weight(0) nodetail
Métodos de descomposición en Economía utilizando STATA
Lic. David A. Condori Tantani

10. Métodos de descomposición en Economía utilizando STATA
Raw .1713328 100%
Int .0129749 7.572933%
Coef .1796163 104.8347%
Char -.0212584 -12.40763%
Omega = 0
Int -.0129749 -7.572933%
Coef .1925912 112.4076%
Char -.0082834 -4.8347%
Omega = 1
Results Coef. Percentage
Number of obs (B) = 28417
Number of obs (A) = 27278
. nldecompose, by(HOMBRE) threefold: logit OCUP NATIVO SOLTERO EDAD NMIEM7
OCUP 27278 .5063788 .4999685 0 1
Variable Obs Mean Std. Dev. Min Max
-> HOMBRE = 1
OCUP 28417 .3350459 .4720148 0 1
Variable Obs Mean Std. Dev. Min Max
-> HOMBRE = 0
. bysort HOMBRE: sum OCUP
Lic. David A. Condori Tantani

11. Métodos de descomposición en Economía utilizando STATA
.
NMIEM7 .005099 .0002741 18.60 0.000 .0045618 .0056363
EDAD -.0093371 .0003832 -24.37 0.000 -.010088 -.0085861
SOLTERO .0249098 .0004716 52.82 0.000 .0239856 .0258341
NATIVO .0006068 .0000883 6.87 0.000 .0004337 .0007799
OCUP Coef. Std. Err. z P>|z| [95% Conf. Interval]
Total explained = .02125835
Difference = -.17133284
Pr(Y!=0|G=1) = .50637877
Pr(Y!=0|G=0) = .33504592
N of obs G=0 = 27278
N of obs G=0 = 28417
Non-linear decomposition by HOMBRE (G) Number of obs = 55695
.................................................. 100
.................................................. 50
1 2 3 4 5
Decomposition replications (100)
_cons 3.910227 .0934924 41.82 0.000 3.726985 4.093469
NMIEM7 -.477288 .0162986 -29.28 0.000 -.5092327 -.4453433
EDAD -.0464858 .0011812 -39.35 0.000 -.048801 -.0441706
SOLTERO -2.512533 .0469048 -53.57 0.000 -2.604464 -2.420601
NATIVO -.505698 .0577876 -8.75 0.000 -.6189595 -.3924364
OCUP Coef. Std. Err. z P>|z| [95% Conf. Interval]
Log likelihood = -16931.428 Pseudo R2 = 0.1044
Prob > chi2 = 0.0000
LR chi2(4) = 3948.04
Logistic regression Number of obs = 27278
Iteration 3: log likelihood = -16931.428
Iteration 2: log likelihood = -16931.588
Iteration 1: log likelihood = -16972.542
Iteration 0: log likelihood = -18905.449
. fairlie OCUP NATIVO SOLTERO EDAD NMIEM7, by(HOMBRE) reference(1)
Lic. David A. Condori Tantani

12. difference -.2471321 .0059583 -41.48 0.000 -.2588101 -.2354541
group_2 6.999458 .0038476 1819.17 0.000 6.991917 7.006999
group_1 6.752326 .0045494 1484.23 0.000 6.743409 6.761243
overall
LWAGE Coef. Std. Err. z P>|z| [95% Conf. Interval]
Group 2: HOMBRE = 1 N of obs 2 = 13746
Group 1: HOMBRE = 0 N of obs 1 = 9874
Model = linear
Blinder-Oaxaca decomposition Number of obs = 23620
. oaxaca LWAGE ANOSEST EXPPOT EXPPOT2, by(HOMBRE) nodetail
Métodos de descomposición en Economía utilizando STATA
_cons -.253217 .0256104 -9.89 0.000 -.3034125 -.2030215
EXPPOT2 .2243474 .0191586 11.71 0.000 .1867972 .2618976
EXPPOT -.3237409 .0324256 -9.98 0.000 -.3872939 -.2601879
ANOSEST .1086091 .0159958 6.79 0.000 .0772579 .1399602
unexplained
EXPPOT2 .0193913 .0067165 2.89 0.004 .0062272 .0325554
EXPPOT -.0454813 .0068799 -6.61 0.000 -.0589655 -.031997
ANOSEST .0229593 .0029697 7.73 0.000 .0171387 .0287799
explained
unexplained -.2440015 .0050192 -48.61 0.000 -.253839 -.2341639
explained -.0031307 .003501 -0.89 0.371 -.0099924 .0037311
difference -.2471321 .0059583 -41.48 0.000 -.2588101 -.2354541
group_2 6.999458 .0038476 1819.17 0.000 6.991917 7.006999
group_1 6.752326 .0045494 1484.23 0.000 6.743409 6.761243
overall
LWAGE Coef. Std. Err. z P>|z| [95% Conf. Interval]
Group 2: HOMBRE = 1 N of obs 2 = 13746
Group 1: HOMBRE = 0 N of obs 1 = 9874
Model = linear
Blinder-Oaxaca decomposition Number of obs = 23620
. oaxaca LWAGE ANOSEST EXPPOT EXPPOT2, by(HOMBRE) weight(0)
Lic. David A. Condori Tantani

13. Métodos de descomposición en Economía utilizando STATA
.
_cons -.0863557 .0178037 -4.85 0.000 -.1212504 -.051461
EXPPOT2 .1663749 .0187925 8.85 0.000 .1295423 .2032075
EXPPOT -.250333 .0324129 -7.72 0.000 -.313861 -.1868049
ESTSEC -.0194887 .0028643 -6.80 0.000 -.0251025 -.0138748
ESTPRIM -.0650163 .0079946 -8.13 0.000 -.0806854 -.0493472
unexplained
EXPPOT2 .0230256 .0079679 2.89 0.004 .0074087 .0386425
EXPPOT -.0495804 .007483 -6.63 0.000 -.0642468 -.0349139
ESTSEC .0018083 .001329 1.36 0.174 -.0007966 .0044131
ESTPRIM .032433 .0032515 9.97 0.000 .0260602 .0388058
explained
unexplained -.2548186 .0049956 -51.01 0.000 -.2646099 -.2450274
explained .0076865 .003499 2.20 0.028 .0008286 .0145445
difference -.2471321 .0059584 -41.48 0.000 -.2588105 -.2354538
group_2 6.999458 .0038477 1819.12 0.000 6.991917 7.006999
group_1 6.752326 .0045495 1484.18 0.000 6.743409 6.761243
overall
LWAGE Coef. Std. Err. z P>|z| [95% Conf. Interval]
Group 2: HOMBRE = 1 N of obs 2 = 13746
Group 1: HOMBRE = 0 N of obs 1 = 9874
Model = linear
Blinder-Oaxaca decomposition Number of obs = 23620
. oaxaca LWAGE ESTPRIM ESTSEC EXPPOT EXPPOT2, by(HOMBRE) weight(0)
_cons -.1971357 .0181032 -10.89 0.000 -.2326174 -.1616541
EXPPOT2 .1663749 .0187925 8.85 0.000 .1295423 .2032075
EXPPOT -.250333 .0324129 -7.72 0.000 -.313861 -.1868049
ESTTER .0259728 .003221 8.06 0.000 .0196598 .0322858
ESTSEC .0003023 .0024749 0.12 0.903 -.0045483 .0051529
unexplained
EXPPOT2 .0230256 .0079679 2.89 0.004 .0074087 .0386425
EXPPOT -.0495804 .007483 -6.63 0.000 -.0642468 -.0349139
ESTTER .0358869 .0027258 13.17 0.000 .0305444 .0412294
ESTSEC -.0016456 .0012091 -1.36 0.173 -.0040154 .0007241
explained
unexplained -.2548186 .0049956 -51.01 0.000 -.2646099 -.2450274
explained .0076865 .003499 2.20 0.028 .0008286 .0145445
difference -.2471321 .0059584 -41.48 0.000 -.2588105 -.2354538
group_2 6.999458 .0038477 1819.12 0.000 6.991917 7.006999
group_1 6.752326 .0045495 1484.18 0.000 6.743409 6.761243
overall
LWAGE Coef. Std. Err. z P>|z| [95% Conf. Interval]
Group 2: HOMBRE = 1 N of obs 2 = 13746
Group 1: HOMBRE = 0 N of obs 1 = 9874
Model = linear
Blinder-Oaxaca decomposition Number of obs = 23620
. oaxaca LWAGE ESTSEC ESTTER EXPPOT EXPPOT2, by(HOMBRE) weight(0)
_cons -.08635567 -.19713574
ESTTER .02597285
EXPPOT2 .16637491 .16637491
EXPPOT -.25033295 -.25033295
ESTSEC -.01948867 .0003023
ESTPRIM -.06501626
unexplained
ESTTER .03588689
EXPPOT2 .02302563 .02302563
EXPPOT -.04958036 -.04958036
ESTSEC .00180828 -.00164564
ESTPRIM .03243298
explained
unexplained -.25481864 -.25481864
explained .00768652 .00768652
difference -.24713211 -.24713211
group_2 6.9994581 6.9994581
group_1 6.752326 6.752326
overall
Variable TER PRIM
Lic. David A. Condori Tantani

14. Métodos de descomposición en Economía utilizando STATA
_cons -.056726 .037474 -1.51 0.130 -.1301737 .0167216
ESTTER .025924 .0032397 8.00 0.000 .0195744 .0322736
ESTSEC -.0067038 .0015754 -4.26 0.000 -.0097915 -.0036161
ESTPRIM -.042871 .0072424 -5.92 0.000 -.0570659 -.0286761
EXPPOT2 .1628606 .0193317 8.42 0.000 .1249712 .20075
EXPPOT -.2490752 .0323043 -7.71 0.000 -.3123905 -.1857598
ANOSEST -.0840635 .0287433 -2.92 0.003 -.1403994 -.0277276
unexplained
ESTTER .0086331 .0009322 9.26 0.000 .0068061 .0104601
ESTSEC -7.80e-06 .000041 -0.19 0.849 -.0000882 .0000726
ESTPRIM .0078755 .0009461 8.32 0.000 .0060212 .0097298
EXPPOT2 .0207843 .0071965 2.89 0.004 .0066795 .0348892
EXPPOT -.0475207 .0071789 -6.62 0.000 -.0615912 -.0334502
ANOSEST .0137584 .0019162 7.18 0.000 .0100027 .017514
explained
unexplained -.2506549 .0049645 -50.49 0.000 -.2603851 -.2409247
explained .0035228 .0035922 0.98 0.327 -.0035179 .0105634
difference -.2471321 .0059586 -41.47 0.000 -.2588108 -.2354535
group_2 6.999458 .0038478 1819.08 0.000 6.991917 7.007
group_1 6.752326 .0045497 1484.14 0.000 6.743409 6.761243
overall
LWAGE Coef. Std. Err. z P>|z| [95% Conf. Interval]
Group 2: HOMBRE = 1 N of obs 2 = 13746
Group 1: HOMBRE = 0 N of obs 1 = 9874
Model = linear
Blinder-Oaxaca decomposition Number of obs = 23620
(normalized: ESTPRIM ESTSEC ESTTER)
. oaxaca LWAGE ANOSEST EXPPOT EXPPOT2 normalize(ESTPRIM ESTSEC ESTTER), by(HOMBRE) weight(0)
Lic. David A. Condori Tantani

15. Métodos de descomposición en Economía utilizando STATA
_cons -.2057391 .0356934 -5.76 0.000 -.2756969 -.1357813
EXPPOT2 -.0736814 .0267852 -2.75 0.006 -.1261793 -.0211834
EXPPOT -.203953 .0403399 -5.06 0.000 -.2830176 -.1248883
ANOSEST .0335772 .0282256 1.19 0.234 -.0217441 .0888984
unexplained
EXPPOT2 .0212043 .0035783 5.93 0.000 .0141911 .0282176
EXPPOT -.0694218 .0064712 -10.73 0.000 -.0821051 -.0567385
ANOSEST .0597605 .0039885 14.98 0.000 .0519432 .0675777
explained
unexplained -.4497963 .0221315 -20.32 0.000 -.4931733 -.4064193
explained .011543 .0045549 2.53 0.011 .0026156 .0204704
difference -.4382532 .0226215 -19.37 0.000 -.4825906 -.3939159
group_2 7.229495 .0103095 701.25 0.000 7.209289 7.249701
group_1 6.791242 .0201357 337.27 0.000 6.751776 6.830707
overall
LWAGE Coef. Std. Err. z P>|z| [95% Conf. Interval]
Group 2: HOMBRE = 1 N of obs 2 = 9283
Group 1: HOMBRE = 0 N of obs 1 = 6580
Model = linear
Blinder-Oaxaca decomposition Number of obs = 15863
> (heckman, twostep select (OCUP= NATIVO SOLTERO EDAD NMIEM7))
. oaxaca LWAGE ANOSEST EXPPOT EXPPOT2, by(HOMBRE) weight(0) model1(heckman, twostep select (OCUP= NATIVO SOLTERO ED
Lic. David A. Condori Tantani

16. Métodos de descomposición en Economía utilizando STATA
Std.error DO = .00072236
Calculating Standard Deviation
*****************************************************************
percF =.99817685
percM =.99636258
*****************************************************************
DX =-.00100188
DF =.00011499
DM =-.00001405
D0 =.03749842
D =.03659748
*****************************************************************
***** Gap in ANOSEST EXPPOT EXPPOT2 decomposition
*****************************************************************
. nopomatch ANOSEST EXPPOT EXPPOT2, outcome(LWAGE) by(HOMBRE) sd replace
Lic. David A. Condori Tantani

17. Métodos de descomposición en Economía utilizando STATA
Std.error DO = .00692002
Calculating Standard Deviation
*****************************************************************
percF =.99817685
percM =.99636258
*****************************************************************
DX =-.01281108
DF =.0007391
DM =.00024646
D0 =.28253065
D =.27070513
*****************************************************************
***** Gap in ANOSEST EXPPOT EXPPOT2 decomposition
*****************************************************************
. nopomatch ANOSEST EXPPOT EXPPOT2, outcome(WAGE) by(HOMBRE) sd replace
Lic. David A. Condori Tantani

18. Métodos de descomposición en Economía utilizando STATA
The variance has been estimated by bootstraping the results 2 times
Number of quantile regressions estimated 100
Number of observations in group 1 13746
Number of observations in group 0 9873
Total number of observations 23619
Decomposition of differences in distribution using quantile regression
(bootstrapping ..)
Fitting base model
. rqdeco LWAGE ANOSEST EXPPOT EXPPOT2, by(HOMBRE) qlow(0.1) qhigh(0.9) qstep(0.1) vce(boot) reps(2) noprint
.2.25.3.35.4
Logwageeffects
0 .2 .4 .6 .8 1
Quantile
Effects of coefficients (discrimination)
0.1.2.3.4
Logwageeffects
0 .2 .4 .6 .8 1
Quantile
Total differential Effects of characteristics
Effects of coefficients
Decomposition of differences in distribution
Lic. David A. Condori Tantani

19. Métodos de descomposición en Economía utilizando STATA
QP = interaction Q x P
P = price effect
Q = quantity effect
U = difference in residual gap
E = difference in predicted gap
D = difference in differential
Total -.0073548 .0050216 -.0104891 -.0018873
U Q P QP
Decomposition of diffence in residual gap:
EXPPOT2 -.0025347 .0035399 -.004537 -.0015375
EXPPOT .0078312 4.64e-06 .0078278 -1.29e-06
ANOSEST -.0039431 -.0071249 .0043113 -.0011295
Total .0013535 -.0035803 .0076021 -.0026683
E Q P QP
Decomposition of difference in predicted gap:
Total -.0060013 .0013535 -.0073548
D E U
Difference in (components of) differentials:
Sample 2 -.2531201 .0109148 -.2640349
Sample 1 -.2471187 .0095614 -.2566801
ferential effect gap
raw dif- quantity residual
Decomposition of individual differentials:
. jmpierce2 est2006M est2006H est2009M est2009H, detail
Lic. David A. Condori Tantani

20. Métodos de descomposición en Economía utilizando STATA
dC D E C EC
Total .0024382 .0005665 .0042345 -.0023628
_cons 0 0 0 0
EXPPOT2 .0016642 .0065639 -.0036595 -.0012401
EXPPOT .0036177 7.52e-06 .0036108 -5.97e-07
ANOSEST -.0028438 -.0060049 .0042832 -.0011221
dE D E C EC
Decomposition of difference in differentials:
Total -.0060013 .0024382 -.0073548 -.0010847
_cons .0503392 0 .0503392 0
EXPPOT2 .020389 .0016642 .0229236 -.0041989
EXPPOT -.0785843 .0036177 -.0864155 .0042135
ANOSEST .0018548 -.0028438 .0057979 -.0010993
dD dE dC dEC
Difference in (components of) differentials:
Total -.2531201 -.0007417 -.2640349 .0116566
_cons -.2034218 0 -.2034218 0
EXPPOT2 .2639641 .0210333 .256053 -.0131222
EXPPOT -.4474175 -.0418527 -.4271677 .0216029
ANOSEST .1337551 .0200777 .1105015 .0031758
Sample 2 D E C EC
Total -.2471187 -.0031799 -.2566801 .0127413
_cons -.2537609 0 -.2537609 0
EXPPOT2 .2435752 .019369 .2331294 -.0089233
EXPPOT -.3688332 -.0454705 -.3407522 .0173894
ANOSEST .1319002 .0229215 .1047036 .0042751
Sample 1 D E C EC
Decompositions of individual differentials:
. smithwelch est2006M est2006H est2009M est2009H, detail
EC = interaction E x C
C = part of D due to differences in coefficients
E = part of D due to differences in endowments
D = differential / difference in component of differential
Total -.0010847 -.0041468 .0033676 -.0003054
_cons 0 0 0 0
EXPPOT2 -.0041989 -.003024 -.0008775 -.0002974
EXPPOT .0042135 -2.88e-06 .004217 -6.97e-07
ANOSEST -.0010993 -.00112 .0000281 -7.35e-06
dEC D E C EC
Total -.0073548 .0020308 -.0086806 -.000705
_cons .0503392 0 .0503392 0
EXPPOT2 .0229236 -2.87e-06 .0229268 -2.82e-07
EXPPOT -.0864155 -.0030433 -.0826341 -.000738
ANOSEST .0057979 .005077 .0006876 .0000333
dC D E C EC
Total .0024382 .0005665 .0042345 -.0023628
_cons 0 0 0 0
EXPPOT2 .0016642 .0065639 -.0036595 -.0012401
EXPPOT .0036177 7.52e-06 .0036108 -5.97e-07
ANOSEST -.0028438 -.0060049 .0042832 -.0011221
dE D E C EC
Decomposition of difference in differentials:
Total -.0060013 .0024382 -.0073548 -.0010847
_cons .0503392 0 .0503392 0
EXPPOT2 .020389 .0016642 .0229236 -.0041989
EXPPOT -.0785843 .0036177 -.0864155 .0042135
ANOSEST .0018548 -.0028438 .0057979 -.0010993
dD dE dC dEC
Difference in (components of) differentials:
Total -.2531201 -.0007417 -.2640349 .0116566
Lic. David A. Condori Tantani

21. Síntesis
• Naturaleza de la variable: continua vs discreta
• Descomposición agregada vs detallada (identificación)
• Problemas de selección (Heckman vs Matching)
• Descomposición en la media o a lo largo de la distribución
(solo para variables continuas …)
• Comparación de las diferencias entre grupos a lo largo del
tiempo
oaxaca nldecompose fairlie nopomatch rqdeco
jmpierce2 smithwelch
Métodos de descomposición en Economía utilizando STATA
Lic. David A. Condori Tantani

22. Otros procedimientos de interés
• decompose, gdecomp, ldecomp
• dfl (di Nardo, Fortin, Lemieux, 1996 – Van Kerm, 2003)
• gfields (Fields, 2002)
• shapley (Shorrocks, 1999)
• search inequality
inequal, rspread, glcurve, descogini, ineqerr, kdensity,
akdensity, changemean, … y mucho más
Métodos de descomposición en Economía utilizando STATA
Lic. David A. Condori Tantani