RE: st: Instrumental Variables approach using -xt- for three-level growth model

[David Torres <writeon4truth2@msn.com>]

Thanks, Billy,

I'd actually found the Hoxby article.  Now to read the others.

As far as using Stata for analysis, do you have any advice?  Can you point to anything similar to the xtivreg command for a three-level growth model with random coefficients?  Once I have the instrument(s), my challenge will then be setting up the model properly in Stata or HLM7.

Diego

--

David Diego Torres, PhD
NSF SBE Postdoctoral Research Fellow
Houston Education Research Consortium
Rice University
6100 Main Street, MS-28
Houston, TX 77005
Phone: 713-348-2984
Email: ddtorres at rice dot edu


----------------------------------------
> Subject: Re: st: Instrumental Variables approach using -xt- for three-level growth model
> From: william@williambuchanan.net
> Date: Mon, 6 May 2013 10:02:24 -0700
> To: statalist@hsphsun2.harvard.edu
>
> Hi David,
>
> Here are the other references:
>
> Hoxby, C. M. (2000). Does Competition Among Public Shools Benefit Students And Taxpayers. American Economic Review, 90(5). pp. 1209-1238.
> Hastings, J. S., Kane, T. J., & Staiger, D. O. (2005). Parental Preferences and School Competition: Evidence from a Public School Choice Program. NBER Working Paper Series #11805.
> Hastings, J. S., Kane, T. J., & Staiger, D. O. (2006). Preferences and Heterogeneous Treatment Effects in a Public School Choice Lottery. NBER Working Paper Series #12145.
>
> I'm pretty sure the Hastings et al papers have been published elsewhere, but that will at least get you the relevant information to move forward with things.
>
> HTH,
> Billy
>
>
>
> On May 6, 2013, at 9:49 AM, Austin Nichols <austinnichols@gmail.com> wrote:
>
> > David Torres <writeon4truth2@msn.com>:
> > On Hoxby, see
> > http://gsppi.berkeley.edu/faculty/jrothstein/hoxby/documentation-for-hoxby-comment
> > and note that school choice is affected by parental income, education,
> > and employment,
> > but so are test scores, so these are not valid instruments.
> >
> > On Mon, May 6, 2013 at 12:39 PM, David Torres <writeon4truth2@msn.com> wrote:
> >> Hi, Billy,
> >>
> >> I'd very much appreciate the full citations of the articles/papers you mentioned.
> >>
> >> Thanks,
> >> Diego
> >>
> >> --
> >>
> >> David Diego Torres, PhD
> >> NSF SBE Postdoctoral Research Fellow
> >> Houston Education Research Consortium
> >> Rice University
> >> 6100 Main Street, MS-28
> >> Houston, TX 77005
> >> Phone: 713-348-2984
> >> Email: ddtorres at rice dot edu
> >>
> >>
> >> ----------------------------------------
> >>> Subject: Re: st: Instrumental Variables approach using -xt- for three-level growth model
> >>> From: william@williambuchanan.net
> >>> Date: Mon, 6 May 2013 07:52:55 -0700
> >>> To: statalist@hsphsun2.harvard.edu
> >>>
> >>> Hi David,
> >>>
> >>> I don't have access to the full citations at the moment, but there is a paper from Caroline Hoxby that used natural boundaries (e.g., rivers) as a school choice instrument. More recently, Justine Hastings along with Tom Kane and some other folks have done a fair amount of modeling of school choice in the Charlotte-Mecklinburg school district. I know both of the papers I am thinking of are available via NBER and can send full citations later if you'd like. Do you have good parental education/income data? One of the things that Hastings found was that school choice decisions were a function of parental income, education, and employment (e.g., parents with higher incomes tended to choose schools that were higher performing regardless of location while parents with lower incomes tended to choose schools that were closer to the home). If nothing else, those papers could give you a good idea of how the issue has been approached in the past.
> >>>
> >>> HTH,
> >>> Billy
> >>>
> >>> Sent from my iPhone
> >>>
> >>> On May 6, 2013, at 7:33, David Torres <writeon4truth2@msn.com> wrote:
> >>>
> >>>> Thanks for you response, Austin.
> >>>>
> >>>> To be clear, though, I am using data from a large school district and am using as my outcome scores on the Stanford 10 math and reading assessments, so no plausible values. My explanatory variable of interest is whether a child's parents exercised choice to send the child to a school other than the zoned school (0 = no, 1 = yes). It's this explanatory variable that I'm focusing on as one that needs to be instrumented, given the differential likelihood of exercising choice deriving from parents of various education levels, density of neighborhood, etc. I'm only using the 2008 kindergarten cohort and am following these children across five years. The boundaries remain constant across the years, as far as I can tell.
> >>>>
> >>>> As an aside, I do have addresses for all families in the district and will be geocoding these soon. I then plan to use the -vincenty- command to calculate the distance to the nearest school (which will also be geocoded) to which a student is not zoned. This will be included as an additional control.
> >>>>
> >>>> --
> >>>>
> >>>> David Diego Torres, PhD
> >>>> NSF SBE Postdoctoral Research Fellow
> >>>> Houston Education Research Consortium
> >>>> Rice University
> >>>> 6100 Main Street, MS-28
> >>>> Houston, TX 77005
> >>>> Phone: 713-348-2984
> >>>> Email: ddtorres at rice dot edu
> >>>>
> >>>>
> >>>> ----------------------------------------
> >>>>> Date: Mon, 6 May 2013 10:18:40 -0400
> >>>>> Subject: Re: st: Instrumental Variables approach using -xt- for three-level growth model
> >>>>> From: austinnichols@gmail.com
> >>>>> To: statalist@hsphsun2.harvard.edu
> >>>>>
> >>>>> David Torres <writeon4truth2@msn.com>
> >>>>> You don't specify, but I assume you have 5 plausible values drawn from
> >>>>> a posterior distribution on achievement from some standardized test as
> >>>>> your outcome variable, and school type attended as your explanatory
> >>>>> variable of interest--none of the variables you cite satisfy an
> >>>>> exclusion restriction, as all would be expected to have direct impacts
> >>>>> on achievement. If you do in fact have 5 plausible values as your
> >>>>> outcome measure, you should not be using -xtmixed- IMHO but perhaps
> >>>>> -mi- since these are 5 imputations for one observation, not 5
> >>>>> observations. If you have 5 plausible values at two points in time,
> >>>>> you now have 25 imputations, and so on. You should get similar results
> >>>>> if you just average across the 5 imputations, but no identical of
> >>>>> course. The more important challenge is finding good instruments for
> >>>>> school type attended. Do you know where people live relative to the
> >>>>> boundary of the "zoned school area" so you can compute that distance
> >>>>> precisely? Do boundaries change over time?
> >>>>>
> >>>>> On Sun, May 5, 2013 at 4:57 PM, David Torres <writeon4truth2@msn.com> wrote:
> >>>>>> Hey, all,
> >>>>>>
> >>>>>> I would like to utilize the instrumental variables approach with hierarchical data that has repeated measures (five) of students nested within schools. I'd prefer to do this in HLM7, but I'm not sure if the program allows for this more complex model setup. Anyway, I know Stata has the -xtivreg- command. Is there something similar for mixed effects models, i.e., -xtivmixed- perhaps? If not, how can I accomplish my aim using what's available in Stata. (I do have the Rabe-Hesketh & Skrondal texts.)
> >>>>>>
> >>>>>> I am instrumenting for the likelihood of parents exercising choice to send their children to a school outside the zoned school area. Obviously, self-selection is an issue here, but proxies that might explain the likelihood of choice are, and this list is by no means exhaustive, number of older children in the household, maternal education, or percent of persons over 18 with a bachelor's degree...or something like that. HELP.
> >>>>>
> >
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