Usuario stata

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

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.htmRutinas de STATA: http://www.raulramos.cat/stata2011


Lic. David A. Condori Tantani

http://www.ine.es/prodyser/micro_epf2006.htm

http://www.raulramos.cat/stata2011

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?



• 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



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

tes)(coeficien

explicada no2211

sticas)(caracteríexplicada

2 ˆˆˆˆˆ HMMiHMHHiMiHM SSSWW

MiMiMMMi USW 21




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 .0025288coefficients -.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 6.761242overall LWAGE Coef. Std. Err. z P>|z| [95% Conf. Interval]

Group 2: HOMBRE = 1 N of obs 2 = 13746Group 1: HOMBRE = 0 N of obs 1 = 9874 Model = linearBlinder-Oaxaca decomposition Number of obs = 23620

. oaxaca LWAGE ANOSEST, by(HOMBRE) nodetail



interaction .0127419 .0014051 9.07 0.000 .009988 .0154958coefficients -.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 6.761243overall LWAGE Coef. Std. Err. z P>|z| [95% Conf. Interval]


. oaxaca LWAGE ANOSEST EXPPOT EXPPOT2, by(HOMBRE) nodetail

interaction .0014328 .0005592 2.56 0.010 .0003367 .0025288coefficients -.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 6.761242overall LWAGE Coef. Std. Err. z P>|z| [95% Conf. Interval]


. oaxaca LWAGE ANOSEST, 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


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 6.761243overall LWAGE Coef. Std. Err. z P>|z| [95% Conf. Interval]


. 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 6.761243overall LWAGE Coef. Std. Err. z P>|z| [95% Conf. Interval]


. oaxaca LWAGE ANOSEST EXPPOT EXPPOT2, by(HOMBRE) weight(0) nodetail




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



.

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 = 28417Non-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) = 3948.04Logistic regression Number of obs = 27278

Iteration 3: log likelihood = -16931.428Iteration 2: log likelihood = -16931.588Iteration 1: log likelihood = -16972.542Iteration 0: log likelihood = -18905.449

. fairlie OCUP NATIVO SOLTERO EDAD NMIEM7, by(HOMBRE) reference(1)






_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 .1399602unexplained 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 .0287799explained 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 6.761243overall LWAGE Coef. Std. Err. z P>|z| [95% Conf. Interval]


. oaxaca LWAGE ANOSEST EXPPOT EXPPOT2, by(HOMBRE) weight(0)



.

_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 -.0493472unexplained 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 .0388058explained 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 6.761243overall LWAGE Coef. Std. Err. z P>|z| [95% Conf. Interval]


. 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 .0051529unexplained 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 .0007241explained 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 6.761243overall LWAGE Coef. Std. Err. z P>|z| [95% Conf. Interval]


. 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



_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 -.0277276unexplained 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 .017514explained 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 6.761243overall LWAGE Coef. Std. Err. z P>|z| [95% Conf. Interval]


(normalized: ESTPRIM ESTSEC ESTTER). oaxaca LWAGE ANOSEST EXPPOT EXPPOT2 normalize(ESTPRIM ESTSEC ESTTER), by(HOMBRE) weight(0)



_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 .0888984unexplained 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 .0675777explained 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 6.830707overall LWAGE Coef. Std. Err. z P>|z| [95% Conf. Interval]


> (heckman, twostep select (OCUP= NATIVO SOLTERO EDAD NMIEM7)) . oaxaca LWAGE ANOSEST EXPPOT EXPPOT2, by(HOMBRE) weight(0) model1(heckman, twostep select (OCUP= NATIVO SOLTERO EDAD NMIEM7)) model2



Std.error DO = .00072236Calculating Standard Deviation*****************************************************************percF =.99817685percM =.99636258*****************************************************************DX =-.00100188DF =.00011499DM =-.00001405D0 =.03749842D =.03659748********************************************************************** Gap in ANOSEST EXPPOT EXPPOT2 decomposition*****************************************************************. nopomatch ANOSEST EXPPOT EXPPOT2, outcome(LWAGE) by(HOMBRE) sd replace



Std.error DO = .00692002Calculating Standard Deviation*****************************************************************percF =.99817685percM =.99636258*****************************************************************DX =-.01281108DF =.0007391DM =.00024646D0 =.28253065D =.27070513********************************************************************** Gap in ANOSEST EXPPOT EXPPOT2 decomposition*****************************************************************. nopomatch ANOSEST EXPPOT EXPPOT2, outcome(WAGE) by(HOMBRE) sd replace



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 23619Decomposition 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

.2.2

5.3

.35

.4Lo

g w

age

effe

cts

0 .2 .4 .6 .8 1Quantile

Effects of coefficients (discrimination)

0.1

.2.3

.4Lo

g w

age

effe

cts

0 .2 .4 .6 .8 1Quantile

Total differential Effects of characteristicsEffects of coefficients

Decomposition of differences in distribution



QP = interaction Q x PP = price effectQ = quantity effectU = difference in residual gapE = difference in predicted gapD = 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



EC = interaction E x CC = part of D due to differences in coefficientsE = part of D due to differences in endowmentsD = 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 _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 CC = part of D due to differences in coefficientsE = part of D due to differences in endowmentsD = 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 _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


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



Otros procedimientos de interés

• decompose, gdecomp, ldecomp

• dfl (di Nardo, Fortin, Lemieux, 1996 – Van Kerm, 2003)• gfields (Fields, 2002)• shapley (Shorrocks, 1999)

• search inequalityinequal, rspread, glcurve, descogini, ineqerr,

kdensity, akdensity, changemean, … y mucho más



Usuario stata

Economy & Finance

Transcript of Usuario stata