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Re: st: Regression Discontinuity Design


From   Nyasha Tirivayi <ntirivayi@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Regression Discontinuity Design
Date   Fri, 7 Oct 2011 16:57:54 +0200

Dear Ariel

Quick answers to your questions:
a) X variable for assignment is HIV prevalence rate at community level
b) cut off is 22%
c) yes I am also worried n=8 at community level is not sufficient

My outcome is labour supply for individuals in 200 treated households
(residing in 4 chosen communities) and 200 control households
(residing in 4 control communities). However does this still seem as
randomization at community level if program placement was non-random
i.e. they specifically targeted communities with higher HIV rates
(above 22%). Household recruitment was not randomized either.

In that case can I use multilevel modelling? I had done propensity
score matching, but reviewers feel there are unobservables I am
overlooking. So with cross sectional data, what other methods can I
plausibly use?

Kindly advise

Regards

Nyasha Tirivayi
Maastricht University
Netherlands

On Fri, Oct 7, 2011 at 4:33 PM, Ariel Linden, DrPH
<ariel.linden@gmail.com> wrote:
> 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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