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Re: st: Regression Discontinuity Design

From   Austin Nichols <>
Subject   Re: st: Regression Discontinuity Design
Date   Fri, 7 Oct 2011 07:55:09 -0400

Nyasha Tirivayi <>
You do not have a good RD design, partly because you do not appear to
be confident of the existence of a discontinuity in treatment, but
mainly because you do not have adequate sample size.  6 communities
are hypothesized to lie on either side of the cutoff; if assumptions
are correct, communities close to the cutoff can be treated as being
randomly assigned treatment.  People in those communities can also be
treated as being randomly assigned treatment under the stronger
assumption that community is fixed and people do not change community.
 But you do not have 400 observations on the assignment variable with
which to construct a local linear regression of the effect of the
assignment variable on treatment; you have 6. The problem here is that
you will really want to cluster on community, but you cannot cluster
when you have 6 clusters (and when you cluster in the first stage, you
really only have 6 obs, not 400). Even 400 obs probably would not be
enough to identify any reasonably small effect using an RD method,
which needs a very large sample size to work well.  The first thing to
do in such cases, if you are not sure how much power you might have,
is to run a quick simulation. There are IV methods one might use,
perhaps based on distance to clinic, but you are not really explicit
about what your estimand is.  What are you trying to estimate?  What
is the outcome variable?

On Thu, Oct 6, 2011 at 6:39 PM, Nyasha Tirivayi <> wrote:
> Hello
> I have questions about implementing a regression discontinuity
> approach. I have cross sectional data from 200 households on a social
> program and 200 control households. The program was targeted at two
> levels- geographically and at household level.
> The geographic placement of the social program in communities appears
> to have been done based on HIV prevalence rates of more than 20.5% for
> 3 "treated" communities and less than 20.5% for 3 "control
> communities". Two clinics do not follow this cutoff making it a fuzzy
> discontinuity design at community level. After geographic placement,
> households were then selected based on a means tested score. However
> we do not have access to this data. We have data from 200 randomly
> sampled households who are actually in the social program and residing
> in the treated communities and from 200 control households with
> similar household characteristics to the treated households but
> residing in the control communities.
> My questions are as follows:
> 1. Would it be valid to use the community level discontinuity for
> impact evaluation? What software can I use in Stata?
> 2. If so would an RD approach based on 8 communities be valid? Is the
> sample of communities too small?
> 3. If RD is no appropriate what other methods besides propensity score
> matching can I use, that can also take care of unobservables even with
> cross sectional data?
> Kindly advise
> Regards
> N.Tirivayi
> Maastricht University
> Netherlands
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