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".
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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