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re: st: Regression Discontinuity Design


From   "Ariel Linden, DrPH" <ariel.linden@gmail.com>
To   <statalist@hsphsun2.harvard.edu>
Subject   re: st: Regression Discontinuity Design
Date   Fri, 7 Oct 2011 10:33:40 -0400

Hi Nyasha,

It seems like you've got several different things going on here at once. The
RD design can be thought of as an observational study equivalent of an RCT
(where the cutoff represents the randomization). If we think about it in
those simple terms, then I'd ask you this: (a) what is the X variable that
you'd be using for assignment, (b) what would be the cutoff, and (c) do you
think that a N=8 is reasonable?

It is not clear from your description what either (a) or (b) is, but I can
certainly say without any hesitation that N=8 is not sufficient.

An excellent recent article for you to read on the RD design is: Lee, D.S.,
Lemieux, T. (2010) Regression discontinuity designs in econometrics. Journal
of Economic Literature 48, 281-355.

Without getting into too deep of a methodological discussion, it seems to me
that if you already have randomization at the community level, you should
consider hierarchical/multi-level modeling to tease out whatever effect you
are looking for.

Ariel 

 

Date: Fri, 7 Oct 2011 00:39:17 +0200
From: Nyasha Tirivayi <ntirivayi@gmail.com>
Subject: st: Regression Discontinuity Design

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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