interpreting marginal effect of logit dummy regressors at 1.

[Marzieh Goodarzi]

I have a problem interpreting the marginal effect of a dummy variable in a logit model. (I am using Stata to estimate the logit regression)

I've run a simple logit say this:
logit underline i.L i.L_underline i.L2_q i.L2_underline
where all variables are dummies. Then to compute the margins I ran this:
margins, dydx(_all) at(L2_underline=1 L_underline=1)
I wanted to know the margins where L2_underline=1 andL_underline=1.

How should I interpret the margin for L2_underline?

[Phil Bromiley]

You didn't get a quick response. You'll increase your chances of a useful answer by following the FAQ on asking questions.

It might be easier to look at predicted probabilities for the different values of the iv's. The "marginal" for a dummy is pretty much the change in probability between the dummy = 0 and dummy=1.

[Marzieh Goodarzi]

Let me ask you the above question more accurately.

I first run a probit regression. In this regression, all variables are dummy variables (binary) variables.
Then I get the margins of all variables when two explanatory variables are in the value of 1 (beside on the following code.)
These two explanatory variables are i.L2underline and i.Lunderline.
What is the interpretation of the i.L2underline coefficient?
In other words What is the interpretation of .3406954؟؟





code of stata:

. probit underline i.Lq i.Lunderline i.L2q i.L2underline [pw=Weight]

Iteration 0: log pseudolikelihood = -4148965.2
Iteration 1: log pseudolikelihood = -2930488
Iteration 2: log pseudolikelihood = -2919236.4
Iteration 3: log pseudolikelihood = -2919231
Iteration 4: log pseudolikelihood = -2919231

Probit regression Number of obs = 20,174
Wald chi2(4) = 2340.87
Prob > chi2 = 0.0000
Log pseudolikelihood = -2919231 Pseudo R2 = 0.2964

-------------------------------------------------------------------------------
| Robust
underline | Coef. Std. Err. z P>|z| [95% Conf. Interval]
--------------+----------------------------------------------------------------
1.Lq | .2236635 .0531638 4.21 0.000 .1194644 .3278627
1.Lunderline | 1.19886 .0436968 27.44 0.000 1.113215 1.284504
1.L2q | .1820238 .054248 3.36 0.001 .0756998 .2883479
1.L2underline | .890711 .0461142 19.32 0.000 .8003288 .9810932
_cons | -1.656304 .0233418 -70.96 0.000 -1.702053 -1.610555
-------------------------------------------------------------------------------

. margins, dydx(_all) at(L2underline=1 Lunderline=1)

Average marginal effects Number of obs = 20,174
Model VCE : Robust

Expression : Pr(underline), predict()
dy/dx w.r.t. : 1.Lq 1.Lunderline 1.L2q 1.L2underline
at : Lunderline = 1
L2underline = 1

-------------------------------------------------------------------------------
| Delta-method
| dy/dx Std. Err. z P>|z| [95% Conf. Interval]
--------------+----------------------------------------------------------------
1.Lq | .0753659 .0175447 4.30 0.000 .0409789 .1097529
1.Lunderline | .4457022 .0146431 30.44 0.000 .4170022 .4744022
1.L2q | .0618543 .0181925 3.40 0.001 .0261978 .0975109
1.L2underline | .3406954 .016691 20.41 0.000 .3079815 .3734092
-------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.