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Re: st: pool cross-section survey data


From   "Stas Kolenikov" <skolenik@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: pool cross-section survey data
Date   Thu, 9 Oct 2008 12:19:52 -0500

As long as you don't have panel data, you can forget about almost any
econometrically fancy things you can find in Wooldridge. (Not that
those things are not making sense -- on the contrary, that's an
excellent book, the one I would recommend as a first reading for a
huge number of topics). What you seem to need to concentrate on is
correct specification of the complex survey designs, and if you have
treatments whose effects you are trying to assess, you would need to
make sure you have proper randomization into control and treatment
groups -- or get into a terrible mess with matching estimators.

I had a recollection that either Kish or Cochran had some sections on
repeated surveys, but I cannot find those in the tables of contents.
May be I am mixing this up with Korn & Graubard
(http://www.citeulike.org/user/ctacmo/article/553280) who do have this
stuff in chapters 7-8. Strongly recommended, a great book for a
thorough intro reading. Chambers & Skinner
(http://www.citeulike.org/user/ctacmo/article/716022) have several
chapters on longitudinal data, and Skinner and Vieira had a recent
paper in Survey Methodology
(http://www.citeulike.org/user/ctacmo/article/2862653).

I had a working paper on related topics available from Carolina
Population Center website
(https://www.cpc.unc.edu/measure/publications/pdf/wp-05-87.pdf). There
should be a little bit of discussion on how to specify the design
options in Stata. If samples are taken independently, then I would
specify the year/wave as super-strata. If your samples were clustered,
but not stratified, then your starting point would be something like

svyset psuXyear [pw=weight in each wave], strata(year)

And if your surveys were stratified, to begin with, then it would become

svyset psuXyear [pw=weight in each wave], strata(waveXoriginal_strata)

where -X- stands for interaction along the lines of:

egen psuXyear = group(psu year)

Then you could estimate your treatment effect through

svy : means whatever , over(year)

or

xi: svy : reg response i.year other controls

If people could opt out of the treatment, or there was partial
compliance with it, then you are in real trouble. I don't think those
issues have been developed well enough in technical literature,
although Steven S (or Austin N, or somebody out there!!!) can have
more information about the topic. I would probably have more trust in
instrumental variables estimators than in matching estimators, as the
former are smoother, so svy-appropriate inferential procedures are
easier to be applied towards them (-svy: ivregress- should work right
away, for instance).

On 10/9/08, Ana Gabriela Guerrero Serdan <ag_guerreroserdan@yahoo.com> wrote:
> Dear Steven,
>
>  Yes, PSUs were randomly selected in each survey. One survey design was done in two stages the others in three stages. However, the sampling frame is the same and based on the census.
>
>  I want to see if outcomes (Yi e.g. school/health) do change over time for peple that are living in some areas (dt) that are exposed to a certain treatment. So in the main issue I am looking for is the effect of residing in a certain region at a certain time on outcomes (assuming there is no migration).
>
>  I am also wondering if I would need to aggregate variables to a higher level maybe cohort or district?  because I do not have panel data but repeated cross section surveys.
>
>  How do I deal with the difference of the sample designs?
>
>  regards,
>  Gaby
>
>  --- On Tue, 10/7/08, Steven Samuels <sjhsamuels@earthlink.net> wrote:
>
>  > From: Steven Samuels <sjhsamuels@earthlink.net>
>  > Subject: Re: st: pool cross-section survey data
>  > To: statalist@hsphsun2.harvard.edu
>  > Date: Tuesday, October 7, 2008, 2:07 PM
>
> > You might find useful some of the advice at
>  > http://www.stata.com/
>  > statalist/archive/2007-11/msg00216.html.
>  >
>  > You probably need a -survey- enabled analysis, or at least
>  > one that
>  > can handle weights and clustering.  To advise you further,
>  > we would
>  > need details of the survey design (strata, stages, units at
>  > each
>  > stage, weights).  Of particular interest: were primary
>  > sampling units
>  > (PSUs) selected anew at each survey? Also, what exactly is
>  > the goal
>  > of your analysis?  The suffix "dt" in your
>  > equation suggests to me
>  > that you want to look at changes.
>  >
>  > -Steve
>  >
>  > On Oct 7, 2008, at 1:47 PM, Clive Nicholas wrote:
>  >
>  > > Gaby Guerrero Serdan wrote:
>  > >
>  > >> I wonder if you could point me out on readings and
>  > on the main
>  > >> issues when trying to pool two or three
>  > independent cross-
>  > >> sectional surveys. N is large and T is small. The
>  > data is not
>  > >> panel in the sense that I do not observe the same
>  > individuals in
>  > >> the three surveys but they are representative at
>  > the provincial
>  > >> and urban/rural areas.
>  > >>
>  > >> I am trying to see if I can model something like
>  > this:
>  > >>
>  > >> Yidt= a + b Xidt + c Zt + dPidt + u
>  > >>
>  > >> where Xit are characteristics that might varied
>  > over time for each
>  > >> individual. Z is specific time for all
>  > individuals. P is dummy for
>  > >> individuals treated in region d and time t.
>  > >>
>  > >>  I have been reading the Wooldrige on
>  > cross-sectional and panel
>  > >> data but would like to know if you know of any
>  > other sources or
>  > >> have in mind any applied examples and/or
>  > econometric problems you
>  > >> may encounter.
>  > >
>  > > John Micklewright's chapter on analysing pooled
>  > cross-sectional data
>  > > in Dale and Davies (1994) might be a very useful
>  > starting point for
>  > > you.
>  > >
>  > > --
>  > > Clive Nicholas
>  > >
>  > > [Please DO NOT mail me personally here, but at
>  > > <clivenicholas@hotmail.com>. Please respond to
>  > contributions I make in
>  > > a list thread here. Thanks!]
>  > >
>  > > Dale A and Davies RB (1994) Analysing Social and
>  > Political Change: A
>  > > Casebook of Methods, London: Sage.
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-- 
Stas Kolenikov, also found at http://stas.kolenikov.name
Small print: I use this email account for mailing lists only.
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