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From |
Nyasha Tirivayi <ntirivayi@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Regression Discontinuity Design |

Date |
Fri, 7 Oct 2011 20:54:06 +0200 |

Hi Austin Thanks so much for the advice Regards Nyasha Tirivayi Maastricht University Netherlands On Fri, Oct 7, 2011 at 7:42 PM, Austin Nichols <austinnichols@gmail.com> wrote: > Nyasha Tirivayi <ntirivayi@gmail.com> > I said in my first reply: "There are IV methods one might use, > perhaps based on distance to clinic...." We would also need to know every > bit of information in your data, and what other data might be matched onto it, > to tell you what can be done. Perhaps the best approach is to recruit > a coauthor who can help you brainstorm another method. > > On Fri, Oct 7, 2011 at 11:28 AM, Nyasha Tirivayi <ntirivayi@gmail.com> wrote: >> Hi Austin >> >> I mean baseline employment rates (obtained retrospectively) are higher >> for control individuals than for treated individuals. What we were >> told by the program staff was that community selection was done based >> on HIV rates of above 22%. But as you can see, one treated community >> is below 22% and one control community is above 22%. >> >> If I cannot use RDD, what other methods can I use instead of >> propensity score matching? My outcome is labour supply measured as >> weekly hours, cross sectional data. >> >> May you kindly advise >> >> Nyasha Tirivayi >> Maastricht University >> Netherlands >> >> >> On Fri, Oct 7, 2011 at 5:10 PM, Austin Nichols <austinnichols@gmail.com> wrote: >>> 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/

**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>

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

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

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

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