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st: Oaxaca decomposition for beginners


From   A.K.Abdul-Karim@warwick.ac.uk
To   statalist@hsphsun2.harvard.edu
Subject   st: Oaxaca decomposition for beginners
Date   Fri, 18 Apr 2008 13:23:22 +0100 (BST)

Hello statalist members,

I'm new to statistics and Stata. I just follow the example in the help
file but unfortunately I'm not able to interpret the output. I wish to
test for wage discrimination amongst European and non-European footballers
using the Oaxaca method. Here is what I did:

. reg logwages caps apps asiprob golprob ycprob rcprob if eu==0

. est sto noneu

. reg logwages caps apps asiprob golprob ycprob rcprob if eu==1

. est sto yeseu
. oaxaca yeseu noneu
(high estimates: noneu; low estimates: yeseu)

                                                 Mean prediction 1 = 
10.29027
                                                 Mean prediction 2 = 
10.08027
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf.
Interval]
-------------+----------------------------------------------------------------
  difference |   .2100054   .0809862     2.59   0.010     .0512754   
.3687354
------------------------------------------------------------------------------

Linear decomposition
------------------------------------------------------------------------------
       Total |      Coef.   Std. Err.      z    P>|z|     [95% Conf.
Interval]
-------------+----------------------------------------------------------------
three-fold   |
  endowments |   .0831522   .0588726     1.41   0.158     -.032236   
.1985405
coefficients |   .1430246   .0879633     1.63   0.104    -.0293803   
.3154294
 interaction |  -.0161714   .0717465    -0.23   0.822     -.156792   
.1244493
------------------------------------------------------------------------------

. oaxaca noneu yeseu, weight(1 0.5 0 omega)
(high estimates: noneu; low estimates: yeseu)

                                                 Mean prediction 1 = 
10.29027
                                                 Mean prediction 2 = 
10.08027
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf.
Interval]
-------------+----------------------------------------------------------------
  difference |   .2100054   .0809862     2.59   0.010     .0512754   
.3687354
------------------------------------------------------------------------------

Linear decompositions
------------------------------------------------------------------------------
       Total |      Coef.   Std. Err.      z    P>|z|     [95% Conf.
Interval]
-------------+----------------------------------------------------------------
W=1          |
   explained |   .0669809   .0689001     0.97   0.331    -.0680609   
.2020226
 unexplained |   .1430246   .0879633     1.63   0.104    -.0293803   
.3154294
-------------+----------------------------------------------------------------
W=,5         |
   explained |   .0750665    .053101     1.41   0.157    -.0290095   
.1791426
 unexplained |   .1349389   .0758584     1.78   0.075    -.0137409   
.2836186
-------------+----------------------------------------------------------------
W=0          |
   explained |   .0831522   .0588726     1.41   0.158     -.032236   
.1985405
 unexplained |   .1268532   .0796571     1.59   0.111    -.0292718   
.2829781
-------------+----------------------------------------------------------------
W=omega      |
   explained |   .0824304   .0512448     1.61   0.108    -.0180075   
.1828683
 unexplained |    .127575    .067142     1.90   0.057     -.004021    
.259171
------------------------------------------------------------------------------


My questions are:
1) Am I doing the right thing? (i.e. decomposition)
2) How do I use the result to make an interpretation? How much of the
difference is explained vs. unexplained?
3) What is the command weight(1 0.5 0 omega) for?

I'm really new to all this and hope you guys don't feel annoyed if my
questions are too basic.

Thanks.
Khalis.


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