# Re: st: Computing interaction effects after svy:heckman

 From "Stas Kolenikov" To statalist@hsphsun2.harvard.edu Subject Re: st: Computing interaction effects after svy:heckman Date Tue, 12 Aug 2008 10:44:19 -0500

```-margeff- and -inteff- are user written additions, probably with a
very specific objective in mind. The reason for interactions in
regression models is to modify the main effects, so that effects of
years of study depend on the place where you live. Probably you would
want to report the results separately for rural and urban locations.

Note that your model is only identified by the variables -race- and
-region-. I presume those are categorical, and should enter through
the set of dummies, e.g. as -xi : ... i.race i.region)-. Even with
that, I would not say that's a terribly strong form of identification,
especially if those are not terribly significant in the selection
equation.

On Tue, Aug 12, 2008 at 10:22 AM, Joao Ricardo F. Lima
<jricardofl@gmail.com> wrote:
> Dear Statalisters,
>
> I'm using the heckman procedure with survey data. I have an
> interaction term (yearsstudy_placelive) that is a product of a
> continuous variable and a dummy variable. My model is
>
> *****************
> svy linearized: heckman lnincome age age2 years_study place_live
> yearsstudy_placelive, select (ocup = age age age2 years_study
> place_live yearsstudy_placelive race region) log
> ****************
>
> where place_live is a dummy (0=rural; 1=urban)
>
> Iīm using margeff, because itīs easy to calculate the total effect of
> age. However, I actually donīt known how to calculate the effect of
> the interaction term in the regression model. I only can use inteff
> after probit or logit.
>
> Then, I really appreciate an advice of how I can calculate this effect.
>
> Thanks a lot,
>
> Joao Lima
> --

--
Stas Kolenikov, also found at http://stas.kolenikov.name
Small print: I use this email account for mailing lists only.

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