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Re: st: Randomly picking observations based on a certain condition

From   Nikhil Srivastava <>
Subject   Re: st: Randomly picking observations based on a certain condition
Date   Wed, 13 Apr 2011 14:02:12 -0700

I am not trying to actually sample households. As I wrote in my rely
to Nick,I am trying look at the effectiveness of a transfer program
targeted to adults of a household which has a certain exclusion error.
The exclusion error that we are assuming is that 1 percent of eligible
participants within each expenditure quintile do not receive the
benefits. In my sample within the first quintile 1 percent of the
total adults comes to around 100. Thus for the first quintile I need
to randomly assign non-beneficiary status to households so that the
total number of adults for these households comes to 100. Similarly I
have to pick randomly 1 percent of adults for each quintile and assign
them non-beneficiary status. In my previous mail I used the number 100
as an example. Thanks


On Wed, Apr 13, 2011 at 1:06 PM, Joerg Luedicke
<> wrote:
> On Wed, Apr 13, 2011 at 3:17 PM, Nikhil Srivastava
> <> wrote:
>> Hi,
>> I have a dataset at the household level which contains the expenditure
>> details of a sample of households. The dataset also records the number
>> of adults within each household. I have divided this dataset into 5
>> quintiles based on the level of expenditure. Now I need to randomly
>> select a set of observations within each quintile so that the sum of
>> the adults for those observations comes to 100. Could somebody please
>> help me in writing a code for this part?
>> I would really appreciate any help in this regard. Thanks
> Do I understand that right, you want to sample households, and within
> each quintile of household expenditure, the number of household
> members among sampled households is supposed to add up to 100? Why
> would you do that? Why not just taking a random sample of households
> or a stratified sample with respect to household size, if that is a
> concern. That way, you would at least have a clear picture of the
> population you are targeting, whereas in the other case, this picture
> becomes pretty blurry, no?
> J.
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