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From |
Austin Nichols <austinnichols@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Regression Discontinuity Design |

Date |
Fri, 7 Oct 2011 11:10:35 -0400 |

Nyasha Tirivayi <ntirivayi@gmail.com> : What do you mean, "baseline labour supply rates for the treated sample (68%) are lower than from the control group (57%)" fwiw, I see no evidence of a discontinuity: clear input T Community N HIVrate 1 1 103 22.5 1 2 120 22.6 1 3 122 22.5 1 4 129 20.3 0 5 124 18.5 0 6 140 20.4 0 7 126 18.5 0 8 138 23.9 end sc T HIVrate [aw=N] On Fri, Oct 7, 2011 at 10:15 AM, Nyasha Tirivayi <ntirivayi@gmail.com> wrote: > 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. 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. > > CommunityHouseholdsAdult 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 > > > > 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 >> * >> * For searches and help try: >> * http://www.stata.com/help.cgi?search >> * http://www.stata.com/support/statalist/faq >> * http://www.ats.ucla.edu/stat/stata/ >> > > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ > * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: Regression Discontinuity Design***From:*Nyasha Tirivayi <ntirivayi@gmail.com>

**References**:**st: Regression Discontinuity Design***From:*Nyasha Tirivayi <ntirivayi@gmail.com>

**Re: st: Regression Discontinuity Design***From:*Austin Nichols <austinnichols@gmail.com>

**Re: st: Regression Discontinuity Design***From:*Nyasha Tirivayi <ntirivayi@gmail.com>

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