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# Re: st: Stata implementation of difference-in-differences with binary outcomes

 From Steve Samuels To statalist@hsphsun2.harvard.edu Subject Re: st: Stata implementation of difference-in-differences with binary outcomes Date Fri, 16 Apr 2010 11:14:12 -0400

```I agree with Maarten.

"(possibly with the -vce(robust)- "
I'd say "necessarily, " or just use the "robust" option, in order to
assure correct standard errors and tests.

In my experience, a the estimated DiDs and their CIs do not have
boundary problems: the possible range is -2 to +2, with the average
usually close to the middle.

Steve

On Fri, Apr 16, 2010 at 10:57 AM, Maarten buis <maartenbuis@yahoo.co.uk> wrote:
> --- On Fri, 16/4/10, C Engelbrecht wrote:
>> But what if the outcome variable is binary? How should I
>> model the difference of two latent variables, as is the
>> case in Probit / Logit? The usual DID is based on
>> differencing Y across these groups, but what should we
>> do now that we only have a latent Y*?
>
> Difference in difference is all about getting at a causal
> effect, which is usually difined as a difference in
> averages. This also exists and is meaningful when the
> dependent variable is binary, that is the risk difference.
> You can calculate it using a linear probability model,
> which is just a fancy name of using -regress- on a binary
> variable (possibly with the -vce(robust)- option.
>
> There is often some uneasyness in specifying "the effect"
> as linear in the probability metric, as that can
> eventually lead to predictions outside the range [0, 1].
> However, if you define the effect interms of odds ratios
> or probit coefficients, you won't get the causal effects
> either, see for example: Mood 2010, Allison 1999, or
> Neuhaus and Jewell 1993.
>
> So my guess would be that the linear probability model
> is in this case the lesser of two evils.
>
> Hope this helps,
> Maarten
>
> Allison, Paul D. 1999. "Comparing Logit and Probit
> Coefficients Across Groups." Sociological Methods &
> Research 28:186–208.
>
> Mood, Carina. 2010. "Logistic regression: Why we cannot
> do what we think we can do, and what we can do about
> it." European Sociological Review 26:67–82.
>
> Neuhaus, John M. and Nicholas P. Jewell. 1993. "A
> Geometric Approach to Assess Bias Due to Omited
> Covariates in Generalized Linear Models." Biometrika
> 80:807–815.
>
> --------------------------
> Maarten L. Buis
> Institut fuer Soziologie
> Universitaet Tuebingen
> Wilhelmstrasse 36
> 72074 Tuebingen
> Germany
>
> http://www.maartenbuis.nl
> --------------------------
>
>
>
>
>
>
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--
Steven Samuels
sjsamuels@gmail.com
18 Cantine's Island
Saugerties NY 12477
USA
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```