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RE: Re: st: Balance for PSM
From
Carlos Tendilla González <[email protected]>
To
<[email protected]>
Subject
RE: Re: st: Balance for PSM
Date
Tue, 3 Dec 2013 12:24:09 -0600
The argument I want to use (if it's possible) is that the ATE is positive no matter which Matching method is used.
-----Mensaje original-----
De: [email protected] [mailto:[email protected]] En nombre de Ariel Linden
Enviado el: martes, 03 de diciembre de 2013 09:42 a.m.
Para: [email protected]
Asunto: re: Re: st: Balance for PSM
Let me add to Jorge's advice here.
You provide no rationale for running several different matching algorithms.
What are you hoping to achieve?
As a general rule, researchers should give thought to the research design and evaluation techniques they intend to employ (a priori). It doesn't appear that you've done that.
Different matching algorithms will very likely elicit different results, depending on many factors, such as sample sizes actually used (i.e., number of matches vs using entire dataset with weights), choice of estimator (ATE, ATT, or ATC), and when using kernel weights, the choice of kernel and bandwidths. All of these issues (and several others) will likely lead you to different conclusions. So you would be expected to think about these issues ahead of time, rather than reviewing the results after the fact and choosing the approach that gave you the result you liked the best...
I suggest you read the following excellent papers:
Stuart, E.A. (2010) Matching methods for causal inference: a review and a look forward. Statistical Science, 25(1), 1-21.
Caliendo, M. Kopeinig, S. (2008) Some practical guidance for the implementation of propensity score matching. Journal of Economic Surveys, 22, 31-72.
I hope this helps,
Ariel
Date: Mon, 2 Dec 2013 16:26:59 -0500
From: =?ISO-8859-1?Q?Jorge_Eduardo_P=E9rez_P=E9rez?= <[email protected]>
Subject: Re: st: Balance for PSM
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
> | |
>
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