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RE: st: Balance for PSM
From
Carlos Tendilla González <[email protected]>
To
<[email protected]>
Subject
RE: st: Balance for PSM
Date
Tue, 3 Dec 2013 09:18:46 -0600
Thank you very much for the observations.
Regards,
-----Mensaje original-----
De: [email protected] [mailto:[email protected]] En nombre de Jorge Eduardo Pérez Pérez
Enviado el: lunes, 02 de diciembre de 2013 03:28 p.m.
Para: [email protected]
Asunto: Re: st: Balance for PSM
I meant
" You have few controls"
--------------------------------------------
Jorge Eduardo Pérez Pérez
Graduate Student
Department of Economics
Brown University
On Mon, Dec 2, 2013 at 4:26 PM, Jorge Eduardo Pérez Pérez <[email protected]> wrote:
> Carlos,
>
> You are not supposed to send attachments to Statalist. I did not open it.
>
> You are also supposed to say that psmatch2 is an user written command from SSC.
>
> Having said that, you may want to rethink your problem. Do you think
> that informality is as good as randomly assigned to workers after
> controlling for the limited set of covariates you have? I think not:
> you lack quite few controls. Are workers within some industries more
> likely to be informal than others? Are workers in different cities
> more likely to be informal than others? I could go on and on, but this
> is the Stata list, not the economics one.
>
> Your results show that your covariates are not balanced in either your
> unmatched or matched sample, with the exception of gender which seems
> balanced according to the t-test (which has it's own problems, see
> http://imai.princeton.edu/research/files/matchse.pdf) . So you need to
> redefine your model before estimating ATE or ATT before proceeding
> with the matching.
>
> It seems that what you ran was a nearest neighbour matching. Radius
> matching can be more computationally demanding, but before buying a
> new computer I would change the propensity score specification, make
> sure I have balance, and then start obtaining matching estimates. And
> before doing that, I would think about whether propensity score
> matching is the right tool to use.
>
> Regards, Jorge Pérez.
> --------------------------------------------
> Jorge Eduardo Pérez Pérez
> Graduate Student
> Department of Economics
> Brown University
>
>
> On Mon, Dec 2, 2013 at 3:27 PM, Carlos Tendilla González
> <[email protected]> wrote:
>> Hi,
>>
>> I am using Stata 13. I am doing a study about Informality and its effect on Wage. The data base contains information about employees and their work status, and also some personal characteristics (age, sex, state, civil status and others).
>>
>> I have to perform the Propensity Score Matching for NN, Startification, Radius and Kernel Matching. I started doing a PS Match using psmatch2.ado, and the results I had were (also available in attached):
>>
>> . pstest familiar casado hombre edad edad2 escolaridad escolar2
>> edadsexo, raw t(totalformal) . probit totalformal familiar casado
>> hombre edad edad2 escolaridad escolar2 edadsexo . predict double ps .
>> psmatch2 totalformal, outcome (lsalhora) pscore(ps) ate . pstest
>> familiar casado hombre edad edad2 escolaridad escolar2 edadsexo, both
>>
>> ------------------------------------------------------------------------------
>> Unmatched | Mean %reduct | t-test
>> Variable Matched | Treated Control %bias |bias| | t p>|t|
>> --------------------------+----------------------------------+-------
>> --------------------------+----------------------------------+-------
>> --------------------------+----------------------------------+--
>> familiar Unmatched | .47932 .29533 38.5 | 59.46 0.000
>> Matched | .47932 .48352 -0.9 97.7 | -61.65 0.000
>> | |
>> casado Unmatched | .545 .37322 35.0 | 54.35 0.000
>> Matched | .545 .54642 -0.3 99.2 | -55.63 0.000
>> | |
>> hombre Unmatched | .6161 .62242 -1.3 | -2.03 0.043
>> Matched | .6161 .62591 -2.0 -55.0 | -0.86 0.390
>> | |
>> edad Unmatched | 35.085 31.907 26.9 | 42.43 0.000
>> Matched | 35.085 34.781 2.6 90.4 | -38.79 0.000
>> | |
>> edad2 Unmatched | 1348.2 1179.4 19.4 | 30.44 0.000
>> Matched | 1348.2 1322.7 2.9 84.9 | -27.09 0.000
>> | |
>> escolaridad Unmatched | 11.209 7.9337 57.0 | 88.51 0.000
>> Matched | 11.209 11.156 0.9 98.4 | -95.41 0.000
>> | |
>> escolar2 Unmatched | 159.96 94.585 14.8 | 22.94 0.000
>> Matched | 159.96 156.09 0.9 94.1 | -27.02 0.000
>> | |
>> edadsexo Unmatched | 21.85 19.616 11.9 | 18.39 0.000
>> Matched | 21.85 22.111 -1.4 88.3 | -18.50 0.000
>> | |
>> ---------------------------------------------------------------------
>> ---------
>>
>> I thought the results were ok, since the bias in all cases is less
>> than 5%. But then I tried to run a Radius Matching doing the same
>> steps I did before, but this time including radius in the command
>>
>> . psmatch2 totalformal, outcome (lsalhora) pscore(ps) ate radius
>>
>> The issue I had is that Stata never ended processing the command after 24 hrs. So then I tried to use pscore.ado and Stata reported that the Sample does not Satisfies the Balance condition so I have to redefine the model to achieve balance.
>>
>> In conclusion I have 2 questions:
>>
>> 1) The first results I had with psmatch2.ado were wrong (unbalanced)?
>> 2) If the answer is no, do I have to get a better PC to process Radius Matching with psmatch2.ado?
>> 3) If the answer is yes, why psmatch2.ado worked without Radius and did not worked with Radius?
>> 4) Is it possible that my sample is not good for PSM?
>>
>> Thanks and regards.
>>
>>
>>
>>
>>
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>>
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