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  • Oaxaca vs. mvdcmp commands for non-linear Blinder Oaxaca decomposition analyses with dummy variables

    Hi all,

    In the mvdcmp Stata documentation, after normalization, the contributions of each category of a categorical variable (i.e., medu) is presented separately. In this case: 1) How should the individual contributions of each of the categories (e.g., medu1, medu2, medu3) be interpreted; 2) If I wanted to know the contribution of all categories of the categorical variable (i.e., contribution of education as a whole), would it be valid to sum the contributions from each of the categories (i.e., medu1 + medu2 + medu3); and 3) If I were to do 2, how would I interpret this?

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    In the oaxaca Stata documentation, after normalization, the contributions of a categorical variable (i.e., isco) are presented as one. In this case: 4) How are the contributions of the entire categorical variable being calculated? Is this the sum of contributions from each of the levels of the categorical variable?

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    Any help would be greatly appreciated. Thank you!

  • #2
    oaxaca and mvdcmp are from SSC (FAQ Advice #12). I do not use mvdcmp, but your questions can be answered by experimentation.


    Originally posted by Tiffany Kim View Post

    In the oaxaca Stata documentation, after normalization, the contributions of a categorical variable (i.e., isco) are presented as one. In this case: 4) How are the contributions of the entire categorical variable being calculated? Is this the sum of contributions from each of the levels of the categorical variable?
    Consider this:


    Code:
    use http://fmwww.bc.edu/RePEc/bocode/h/homecomp.dta, clear
    
    oaxaca homecomp female age (educ:hsgrad somecol college) ///
                 (marstat:married prevmar) if white==1|black==1, by(black) ///
                 logit pooled
    
    
    oaxaca homecomp female age hsgrad somecol college ///
                 (marstat:married prevmar) if white==1|black==1, by(black) ///
                 logit pooled
    
    lincom _b[explained:hsgrad]+ _b[explained:somecol] + _b[explained:college]   
    Res.:

    Code:
    . oaxaca homecomp female age (educ:hsgrad somecol college) ///
    >              (marstat:married prevmar) if white==1|black==1, by(black) ///
    >              logit pooled
    
    Blinder-Oaxaca decomposition                    Number of obs     =      1,969
                                                    Model             =      logit
    Group 1: black = 0                              N of obs 1        =      1,737
    Group 2: black = 1                              N of obs 2        =        232
    
        explained: (X1 - X2) * b
      unexplained: X1 * (b1 - b) + X2 * (b - b2)
                   with b from pooled model (including group dummy)
    
    ------------------------------------------------------------------------------
                 |               Robust
        homecomp | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
    -------------+----------------------------------------------------------------
    overall      |
         group_1 |   .7322971   .0105765    69.24   0.000     .7115675    .7530267
         group_2 |   .4310345   .0324628    13.28   0.000     .3674086    .4946604
      difference |   .3012626   .0341423     8.82   0.000     .2343449    .3681802
       explained |   .0776741   .0125458     6.19   0.000     .0530848    .1022634
     unexplained |   .2235885   .0343375     6.51   0.000     .1562883    .2908887
    -------------+----------------------------------------------------------------
    explained    |
          female |  -.0026198   .0020091    -1.30   0.192    -.0065576    .0013179
             age |    .000233   .0018939     0.12   0.902    -.0034789    .0039449
            educ |   .0211536   .0066281     3.19   0.001     .0081627    .0341445
         marstat |   .0589074    .009818     6.00   0.000     .0396645    .0781502
    -------------+----------------------------------------------------------------
    unexplained  |
          female |   .0184261   .0389236     0.47   0.636    -.0578627    .0947149
             age |   .3196448   .1585697     2.02   0.044     .0088538    .6304357
            educ |  -.0390517   .0682921    -0.57   0.567    -.1729018    .0947983
         marstat |    .018174    .055918     0.33   0.745    -.0914232    .1277712
           _cons |  -.0936047   .1783961    -0.52   0.600    -.4432545    .2560452
    ------------------------------------------------------------------------------
    educ: hsgrad somecol college
    marstat: married prevmar
    
    .
    .
    . oaxaca homecomp female age hsgrad somecol college ///
    >              (marstat:married prevmar) if white==1|black==1, by(black) ///
    >              logit pooled
    
    Blinder-Oaxaca decomposition                    Number of obs     =      1,969
                                                    Model             =      logit
    Group 1: black = 0                              N of obs 1        =      1,737
    Group 2: black = 1                              N of obs 2        =        232
    
        explained: (X1 - X2) * b
      unexplained: X1 * (b1 - b) + X2 * (b - b2)
                   with b from pooled model (including group dummy)
    
    ------------------------------------------------------------------------------
                 |               Robust
        homecomp | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
    -------------+----------------------------------------------------------------
    overall      |
         group_1 |   .7322971   .0105765    69.24   0.000     .7115675    .7530267
         group_2 |   .4310345   .0324628    13.28   0.000     .3674086    .4946604
      difference |   .3012626   .0341423     8.82   0.000     .2343449    .3681802
       explained |   .0776741   .0125458     6.19   0.000     .0530848    .1022634
     unexplained |   .2235885   .0343375     6.51   0.000     .1562883    .2908887
    -------------+----------------------------------------------------------------
    explained    |
          female |  -.0026198   .0020091    -1.30   0.192    -.0065576    .0013179
             age |    .000233   .0018939     0.12   0.902    -.0034789    .0039449
          hsgrad |   .0048713   .0033115     1.47   0.141    -.0016192    .0113617
         somecol |    .000355   .0014399     0.25   0.805    -.0024671    .0031771
         college |   .0159274   .0054179     2.94   0.003     .0053086    .0265462
         marstat |   .0589074    .009818     6.00   0.000     .0396645    .0781502
    -------------+----------------------------------------------------------------
    unexplained  |
          female |   .0184261   .0389236     0.47   0.636    -.0578627    .0947149
             age |   .3196448   .1585697     2.02   0.044     .0088538    .6304357
          hsgrad |  -.0410456   .0348716    -1.18   0.239    -.1093926    .0273015
         somecol |   .0185114   .0286759     0.65   0.519    -.0376924    .0747153
         college |  -.0165176   .0180242    -0.92   0.359    -.0518445    .0188093
         marstat |    .018174    .055918     0.33   0.745    -.0914232    .1277712
           _cons |  -.0936047   .1783961    -0.52   0.600    -.4432545    .2560452
    ------------------------------------------------------------------------------
    marstat: married prevmar
    
    
    . lincom _b[explained:hsgrad]+ _b[explained:somecol] + _b[explained:college]  
    
    ( 1)  [explained]hsgrad + [explained]somecol + [explained]college = 0
    
    ------------------------------------------------------------------------------
        homecomp | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
    -------------+----------------------------------------------------------------
             (1) |   .0211536   .0066281     3.19   0.001     .0081627    .0341445
    ------------------------------------------------------------------------------

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