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Note: This FAQ is for Stata 10 and older versions of Stata. In Stata 11, the margins command replaced mfx.

Can I use mfx on survey data with unweighted means?

Title   Marginal effects after estimations with weights
Author May Boggess, StataCorp
Date April 2004; minor revisions Sept 2005

Yes, using the nowght option. Let’s first make sure we understand how mfx handles weights for survey data, and then we'll see how to ignore the weights when we need to.

 . clear

 . set mem 2M
 (output omitted)

 . webuse nhanes2d

 . svy: logit highbp height black, offset(hlthstat)
 (running logit on estimation sample)

 Survey: Logistic regression

 Number of strata   =        31                  Number of obs      =     10349
 Number of PSUs     =        62                  Population size    = 1.171e+08
                                                 Design df          =        31
                                                 F(   2,     30)    =     10.30
                                                 Prob > F           =    0.0004
 
 ------------------------------------------------------------------------------
              |             Linearized
       highbp |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
 -------------+----------------------------------------------------------------
       height |   .0216824   .0049413     4.39   0.000     .0116046    .0317603
        black |  -.0805318   .1565209    -0.51   0.611    -.3997582    .2386946
        _cons |  -8.669328   .8423297   -10.29   0.000    -10.38727   -6.951385
     hlthstat |   (offset)
 ------------------------------------------------------------------------------

 . mfx, predict(p)

 Marginal effects after svy:logit
       y  = Pr(highbp) (predict, p)
          =  .06750846
 ------------------------------------------------------------------------------
 variable |      dy/dx    Std. Err.     z    P>|z|  [    95% C.I.   ]      X
 ---------+--------------------------------------------------------------------
   height |   .0013649      .00033    4.19   0.000   .000727  .002003   168.462
    black*|  -.0049292      .00943   -0.52   0.601  -.023409   .01355   .095527
 hlthstat |  (offset)                                                   2.39875
 ------------------------------------------------------------------------------
 (*) dy/dx is for discrete change of dummy variable from 0 to 1
 
 . summarize height [aweight=finalwgt] if e(sample)

     Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
 -------------+-----------------------------------------------------------------
       height |   10349   117131111     168.462   9.698747      135.5        200

 . replace height = r(mean)
 (10351 real changes made)

 . summarize black [aweight=finalwgt] if e(sample)

     Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
 -------------+-----------------------------------------------------------------
        black |   10349   117131111    .0955274   .2939563          0          1

 . replace black = r(mean)
 black was byte now float
 (10351 real changes made)

 . summarize hlthstat [aweight=finalwgt] if e(sample)

     Variable |     Obs      Weight        Mean   Std. Dev.       Min        Max
 -------------+-----------------------------------------------------------------
     hlthstat |   10349   117131111    2.398746   1.163176          1          8

 . replace hlthstat = r(mean)
 hlthstat was byte now float
 (10351 real changes made)

 . predict y, p

 . summarize y

     Variable |       Obs        Mean    Std. Dev.       Min        Max
 -------------+--------------------------------------------------------
            y |     10351    .0675085           0   .0675085   .0675085

 . replace black=0
 (10351 real changes made)

 . predict p0, p

 . replace black=1
 (10351 real changes made)

 . predict p1, p

 . generate marg=p1-p0

 . summarize marg

     Variable |       Obs        Mean    Std. Dev.       Min        Max
 -------------+--------------------------------------------------------
         marg |     10351   -.0049292           0  -.0049292  -.0049292

In the previous example, we correctly calculated the predicted value for y, and we even calculated the marginal effect for black and found that checked out OK, too. We also saw that all the means and the offset have taken the weight into account, as well.

mfx obtains the pweight variable from svyset and uses it as an aweight variable with summarize. This gives the same mean as svy: mean but not the same standard deviation. However, because we don’t need the standard deviation, we can get away with this here.

This trick is actually making things a bit easier because using svy: mean would change our estimation results, and we wouldn't be able to use mfx after svy: mean.

Now let’s redo the above example without weights by specifying the nowght option of mfx:

 . clear

 . set mem 2M
 (output omitted)

 . webuse nhanes2d

 . svy: logit highbp height weight black, offset(hlthstat)
 (running logit on estimation sample)

 Survey: Logistic regression

 Number of strata   =        31                  Number of obs      =     10349
 Number of PSUs     =        62                  Population size    = 1.171e+08
                                                 Design df          =        31
                                                 F(   3,     29)    =     55.69
                                                 Prob > F           =    0.0000
 
 ------------------------------------------------------------------------------
              |             Linearized
       highbp |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
 -------------+----------------------------------------------------------------
       height |  -.0142804   .0053408    -2.67   0.012    -.0251729   -.0033879
       weight |   .0490184   .0037007    13.25   0.000     .0414709     .056566
        black |  -.2857725   .1423184    -2.01   0.053    -.5760329    .0044878
        _cons |  -6.318211   .8832654    -7.15   0.000    -8.119643    -4.51678
     hlthstat |   (offset)
 ------------------------------------------------------------------------------

 . mfx, predict(p) nowght

 Marginal effects after svy:logit
       y  = Pr(highbp) (predict, p)
          =  .06758168
 ------------------------------------------------------------------------------
 variable |      dy/dx    Std. Err.     z    P>|z|  [    95% C.I.   ]      X
 ---------+--------------------------------------------------------------------
   height |  -.0008999      .00036   -2.49   0.013  -.001609 -.000191   167.653
   weight |   .0030889      .00035    8.76   0.000   .002398   .00378   71.8998
    black*|  -.0163564      .00788   -2.08   0.038  -.031792 -.000921   .104938
 hlthstat |  (offset)                                                   2.59349
 ------------------------------------------------------------------------------
 (*) dy/dx is for discrete change of dummy variable from 0 to 1

 . summarize height if e(sample)

     Variable |       Obs        Mean    Std. Dev.       Min        Max
 -------------+--------------------------------------------------------
       height |     10349    167.6526    9.655687      135.5        200
 
 . replace height = r(mean)
 (10351 real changes made)

 . summarize weight if e(sample)

     Variable |       Obs        Mean    Std. Dev.       Min        Max
 -------------+--------------------------------------------------------
       weight |     10349    71.89977    15.35705      30.84     175.88

 . replace weight = r(mean)
 (10351 real changes made)

 . summarize black if e(sample)

     Variable |       Obs        Mean    Std. Dev.       Min        Max
 -------------+--------------------------------------------------------
        black |     10349    .1049377    .3064879          0          1

 . replace black = r(mean)
 black was byte now float
 (10351 real changes made)

 . summarize hlthstat if e(sample)

     Variable |       Obs        Mean    Std. Dev.       Min        Max
 -------------+--------------------------------------------------------
     hlthstat |     10349    2.593487    1.221695          1          8

 . replace hlthstat = r(mean)
 hlthstat was byte now float
 (10351 real changes made)

 . predict y, p

 . summarize y

     Variable |       Obs        Mean    Std. Dev.       Min        Max
 -------------+--------------------------------------------------------
            y |     10351    .0675817           0   .0675817   .0675817

 . replace black=0
 (10351 real changes made)

 . predict p0, p

 . replace black=1
 (10351 real changes made)

 . predict p1, p

 . generate marg=p1-p0

 . summarize marg

     Variable |       Obs        Mean    Std. Dev.       Min        Max
 -------------+--------------------------------------------------------
         marg |     10351   -.0163564           0  -.0163564  -.0163564

nowght does exactly what we want. All the means have been calculated without weights. Even the offset is calculated without weights. The predicted y value and the marginal effect for black checks out OK.

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