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  • oaxaca8 and rifreg

    I am using oaxaca8 package along along with the rifreg and I want to separate the unexplained part of the decomposition between the two groups. I try the split option like in simple oaxaca command but it doesn't work. I would need your help. Thanks in advance.

  • #2
    Hi Alphonse,
    Unless you are trying to do a reweighted RIF regression, I think your best option is to do as follows:
    1. Create the corresponding RIF that you want to do the analysis for, one for each group.
    2. Create a single variable using the two new variables you just created.
    3. Run a standard Oaxaca using the variable you created in 2 as your outcome/dependent variable.

    Hope this helps
    FR

    Comment


    • #3
      Hi Fernando,
      Thank you for your answer. Here is my coding:
      /*------------Step 1: RIF regression ------------------*/
      forvalues q=10(10)90{
      quietly xi: rifreg y $gender_var if manager == 0, quantile (`q') retain(rif_m`q')
      estimates store malemed`q'
      quietly xi: rifreg xi: rifreg y $gender_var if manager == 1, quantile (`q') retain(rif_f`q')
      estimates store femalemed`q'
      /*------------------Step 2: For each quantile result, use oaxaca8 to compute the decomposition -----------------*/
      oaxaca8 malemed`q' femalemed`q', weight(1) asis detail esave
      }

      My problem for this twofold decomposition is that I cannot use the split option in Step 2 to get the unexplained part for each group as we can do with oaxaca. Any help.

      Thanks
      Alphonse


      Comment


      • #4
        Here is what I have done in the past:

        Code:
        forvalues q=10(10)90{
        capture drop rif_y
        quietly rifreg y  if manager == 0, quantile (`q') retain(rif_m`q')
        quietly rifreg y if manager == 1, quantile (`q') retain(rif_f`q')
        gen double rif_y=rif_m`q'  if manager == 0
        replace rif_y=rif_f`q'  if manager == 1
        /*------------------Step 2: For each quantile result, use oaxaca8 to compute the decomposition -----------------*/
        oaxaca rif_y $gender_var, by(manager) w(1) detail
        est sto rifob_`q'
        }
        est tab rifob *
        Hope this helps

        Comment


        • #5
          Hi Fernando. Your coding solves the problem. I was thinking that it would be possible with oaxaca8 as well.

          Thanks

          Comment


          • #6
            Dear FernandoRios ,
            I am using Oaxaca-Blinder method to decompose difference in log expenditure between farm and nonfarm household. Thanks to your code above, I could run my model. But I didn't know how to interpret the result when the difference were not statistically significant at all selected quantiles. Here is the result at median, for instance. Can I conclude that there were no statistically significant differences in expenditure between two group?

            Code:
            quietly rifreg lnexpenditure if non == 1, quantile (50) retain(farm_50)
            quietly rifreg lnexpenditure if non == 0, quantile (50) retain(nonfarm_50)
            gen rif_50=farm_50  if non == 1
            (1,125 missing values generated)
            replace rif_50=nonfarm_50  if non == 0
            (1,125 real changes made)
            oaxaca rif_50 age ethnicity education householdsize proportion15 proportion65 lnfor_remittance lndomes_remittance lnincome, by(non) pool
            > ed detail
            
            Blinder-Oaxaca decomposition                    Number of obs     =      1,389
                                                              Model           =     linear
            Group 1: non = 0                                  N of obs 1      =       1125
            Group 2: non = 1                                  N of obs 2      =        264
            
            ------------------------------------------------------------------------------------
                               |               Robust
                        rif_50 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
            -------------------+----------------------------------------------------------------
            overall            |
                       group_1 |   9.764274   .0189733   514.63   0.000     9.727087    9.801461
                       group_2 |   9.776749   .0352508   277.35   0.000     9.707658    9.845839
                    difference |  -.0124751   .0400325    -0.31   0.755    -.0909374    .0659872
                     explained |   .0692789   .0311194     2.23   0.026      .008286    .1302718
                   unexplained |   -.081754   .0428163    -1.91   0.056    -.1656724    .0021643
            -------------------+----------------------------------------------------------------
            explained          |
                           age |  -.0052687    .008701    -0.61   0.545    -.0223224    .0117849
                     ethnicity |  -.0007483   .0013084    -0.57   0.567    -.0033126    .0018161
                     education |    .031386   .0093691     3.35   0.001      .013023    .0497491
                 householdsize |  -.0430033    .010735    -4.01   0.000    -.0640436    -.021963
                  proportion15 |  -.0077162   .0047679    -1.62   0.106    -.0170612    .0016287
                  proportion65 |    .000435   .0080418     0.05   0.957    -.0153266    .0161966
              lnfor_remittance |   .0000266   .0043033     0.01   0.995    -.0084078     .008461
            lndomes_remittance |  -.0022431   .0023976    -0.94   0.349    -.0069424    .0024561
                      lnincome |   .0964109   .0269999     3.57   0.000     .0434922    .1493297
            -------------------+----------------------------------------------------------------
            unexplained        |
                           age |  -.1637974   .1949282    -0.84   0.401    -.5458497     .218255
                     ethnicity |   .0137441   .0111754     1.23   0.219    -.0081592    .0356474
                     education |   .0688098   .0672642     1.02   0.306    -.0630256    .2006452
                 householdsize |   .0379496   .1020713     0.37   0.710    -.1621066    .2380057
                  proportion15 |   .0907804   .0441929     2.05   0.040     .0041639     .177397
                  proportion65 |    .057662   .0241633     2.39   0.017     .0103028    .1050212
              lnfor_remittance |   .0145628   .0081214     1.79   0.073    -.0013548    .0304804
            lndomes_remittance |    .003499   .0600728     0.06   0.954    -.1142414    .1212395
                      lnincome |   2.542524    .741905     3.43   0.001     1.088417    3.996631
                         _cons |  -2.747489   .8012333    -3.43   0.001    -4.317877     -1.1771
            ------------------------------------------------------------------------------------
            Thank you in advance for your answer.
            Nhi

            Comment


            • #7
              Hi Nhi
              Three comments
              1. I would interpret the data as evidence suggesting no difference across groups in terms of medians, but some evidence that this is because of an interaction and compensation between endowments and coefficient effects, which are different
              2. The standard errors are possibly incorrect since the recommendation is to bootstrap the whose process to obtain correct standard errors when using quantiles as outcome. Search for the latest publication by fFortin Firpo and Lemieux on using Rifregressions for wage decomposing
              3. Look in the scc archive for the program oaxaca_rif. Its a new contributed program that I prepared to elaborate the rif decomposition that can be easily used to get bootstrapped standard errors. Look into the last example in the help file
              best,
              Fernando

              Comment


              • #8
                Hi Fernando,
                Thank you for your recommendation of the latest publication by Firpo et al.(2018) and oaxaca_rif command. Literatures show that using group 0 or group 1 as the reference group dose not involve any specific estimation issues. But I've wondered which one I should choose, so I ran both of them. It seems like that there are differences between 2 models which have different reference group. The result below is too long but if you could please have a look on it. Is there any criterion showing which group should be used as the reference group. And could you give me some advices on interpreting this result, that is not statistically significant at all selected quantiles?

                results.txt

                Best regards,
                Nhi

                Comment

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