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Re: st: margins after xtlogit


From   "Michael N. Mitchell" <Michael.Norman.Mitchell@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: margins after xtlogit
Date   Fri, 10 Sep 2010 22:41:51 -0700

Dear Traci

I will start with a possible answer, and then work back to why this is so. After the -xtlogit- command, try this...

margins race1, predict(pu0)

This should then express the results in terms of predicted probabilities (as the -logit- model did). The reason is that the -predict- command defaults to predicting probabilities in after the -logit- command. This is described in
  -help logit postestimation- under the section on predict that will say
"pr            probability of a positive outcome; the default"

Contrast this with -help xtlogit postestimation-, in which the section about predict says that the default prediction is "xb linear prediction; the default".

I hope that helps,

Michael N. Mitchell
Data Management Using Stata      - http://www.stata.com/bookstore/dmus.html
A Visual Guide to Stata Graphics - http://www.stata.com/bookstore/vgsg.html
Stata tidbit of the week         - http://www.MichaelNormanMitchell.com



On 2010-09-10 4.59 PM, Traci Schlesinger wrote:
hi all:

i am analyzing racial disparities in pretrial diversions (a yes no,
i.e. 0/1, criminal justice outcome) using individual level data from
the SCPS, which is clustered by county--an observation for every
individual charged with a felony in sampled counties is included.  to
account for the county level sampling, i'm using xtlogit with county
level random effects.

however, i'm having difficulty interpreting the results from margins
after xtlogit.

if i estimate a model with logistic and then ask for margins on race i get:

. margins race1, post

Predictive margins                                Number of obs   =      46019
Model VCE    : OIM

Expression   : Pr(diversion), predict()

------------------------------------------------------------------------------
              |            Delta-method
              |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        race1 |
           1  |   .1025184   .0023145    44.29   0.000      .097982    .1070548
           2  |   .0848741   .0020596    41.21   0.000     .0808374    .0889109
           3  |   .0858849   .0023203    37.01   0.000     .0813372    .0904327

------------------------------------------------------------------------------

which i interpret as meaning that if everyone in my sample were white
(race1 = 1), 10% of defendants would be offered pretrial diversions.
if everyone were black (race1=2), only 8% of defendants would be
offered pretrial diversions.  (race1=3 are Latinos, with 8.5% of
people getting diversions).

however, if i estimate xtlogit --either getting my results as
coefficients or odds-ratios-- and then margins, i get the following
table.

. margins race1, post

Predictive margins                                Number of obs   =      46019
Model VCE    : OIM

Expression   : Linear prediction, predict()

------------------------------------------------------------------------------
              |            Delta-method
              |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        race1 |
           1  |  -3.580741   .2277247   -15.72   0.000    -4.027073   -3.134409
           2  |  -3.919428   .2274633   -17.23   0.000    -4.365248   -3.473608
           3  |   -3.67982   .2301685   -15.99   0.000    -4.130942   -3.228698
------------------------------------------------------------------------------

i am at a loss as to how to interpret this.  for starters, it seems
strange that all three racial groups have negative margins.  also, i'm
clearly not looking at the percent of defendants who get a pretiral
diversion any more.  i've looked through the manual, but have not been
able to figure this out.  i would appreciate any help.

cheers,
traci
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