Re: st: Regression Discontinuity Design

[Nyasha Tirivayi <ntirivayi@gmail.com>]

Hi Austin

Thank you so much for the response. I am trying to estimate the impact
of a social program on intrahousehold labour supply. Hence I have
labour supply data at individual level. In total I have 474
individuals from 200 treated households (residing in 4 treated
communities) and 532 individuals from 200 control households (residing
in 4 control communities).

I had initially done propensity score matching (PSM). However baseline
labour supply rates for the treated sample (68%) are lower than from
the control group (57%). Once comment I have received is that the
possibility of differential trends in labor market outcomes across
program and non-program communities implies that any observed
differences are not reliable measures of the effects of the food
program.   Hence journal reviewers are concerned about the possibility
of unobservables and suggested a regression discontinuity approach (if
possible) or within community estimates.

So if I cannot use the RD approach what alternatives to PSM to deal
with unobservables in cross sectional data?

The study design is summarized below:

Community         Households        Adult Individuals    Community HIV rate
Treated
1                             50                    103                    22.5
2                             50                    120                    22.6
3                            50                      122                    22.5
4                            50                      129
      20.3
Control
1                            50                      124
      18.5
2                            50                       140
       20.4
3                            50                       126
       18.5
4                            50                       138
       23.9

Regards

N.Tirivayi
Maastricht University
Netherlands



On Fri, Oct 7, 2011 at 1:55 PM, Austin Nichols <austinnichols@gmail.com> wrote:
> Nyasha Tirivayi <ntirivayi@gmail.com>
> 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 <ntirivayi@gmail.com> 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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