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RE: Re: st: RE: Difference in Difference vs. Fixed Effects
From
"Hussein, Mustafa (Mustafa Hussien)" <[email protected]>
To
"[email protected]" <[email protected]>
Subject
RE: Re: st: RE: Difference in Difference vs. Fixed Effects
Date
Fri, 4 Oct 2013 18:18:20 +0000
Joreg,
I was just about to suggest that you use factor variables instead of the "did" term you made yourself, as Ariel just pointed out. The fact that "treatment" gets omitted in your fixed effects makes the coefficient estimate of your "did" problematic, since "did" is essentially an interaction term whose main effect, "treatment", was dropped. Another question, what does your "treatment" variable denote? if it's collinear with your DID setup, why was not it dropped when you ran DID-only FE regression? Is it collinear with any of your other x variables?
I hope this helps
Very Best,
Mustafa
________________________________________
From: [email protected] [[email protected]] on behalf of Ariel Linden, DrPH [[email protected]]
Sent: Friday, October 04, 2013 12:53 PM
To: [email protected]
Subject: Re: Re: st: RE: Difference in Difference vs. Fixed Effects
Joerg,
I've read through your post a couple times and I still don't understand what
you're asking. Please correct me if I am wrong but you get similar results
across all models, all indicating no treatment effect (based on the
covariate labeled "did"). You then ask why do you get different results when
you add additional covariates? Why would you think this is a problem with
the statistical model and not due to influences of the other covariates?
Also, as a matter of capitalizing on Stata's factor variable notation, you
should consider rerunning your regression as follows:
. regress gender_decision i.treatment##i.followup
This will allow you to run -margins- to get additional useful information,
such as:
. margins treatment, over(followup)
. margins treatment#followup, pwcompare(effects)
Ariel
Date: Thu, 3 Oct 2013 10:55:03 +0200
From: Joerg Lang <[email protected]>
Subject: Re: st: RE: Difference in Difference vs. Fixed Effects
I am sorry! This shouldn't have been sent in such a format. Here is
now the edited version of the tables. As you can see, they are -
despite some minor changes in the standard - pretty much the same when
there are no other covariates included. However, including these
covariates leads to more different results. If there is anyone that
could help me explaining what is either wrong with my Stata command or
point me in the right direction for the interpretiation that would be
really helpful.
The did denotes the difference in differece estimator and is thus the
one of interest. The treatment dummy is only included in the xtreg for
better "comparison". Obviously, one could have also construcet a
treatment dummy that varies between the time periods, i.e. 0 for both
treatment and control grouop in the baseline period and 1 for the
treatment group in the followup while 0 for the control group in the
followup. However, leaving the did estimator should yield the same
result since this is an interaction of treatment and time and thus 0
for both groups in the baseline period since time is 0. The value is 1
for the treatment group in the followup since both variables are then
1 but 0 for the case of the control group in the followup since the
control groups value is 0.
reg gender_decision did treatment followup, cluster (h1)
Linear regression Number of obs =
636
F( 3,
317) = 5.77
Prob >
F = 0.0007
R-squared = 0.0233
Root
MSE = .3456
(Std. Err. adjusted for
318 clusters in h1)
-
----------------------------------------------------------------------------
--
Robust
gender_dec~n | Coef. Std. Err. t P>|t| [95%
Conf. Interval]
-
-------------+--------------------------------------------------------------
--
did | .0913309 .0678396 1.35 0.179
- -.0421419 .2248037
treatment | -.0164835 .0396981 -0.42 0.678
- -.0945886 .0616215
followup | .0864469 .0292938 2.95 0.003
.028812 .1440818
_cons | .2387057 .0157684 15.14 0.000
.2076819 .2697296
-
----------------------------------------------------------------------------
--
xtreg gender_decision treatment did followup, fe vce(cluster h1)
note: treatment omitted because of collinearity
Fixed-effects (within) regression Number of obs =
636
Group variable: h1 Number of groups
= 318
R-sq: within = 0.0465 Obs per group: min =
2
between = 0.0016
avg = 2.0
overall = 0.0230
max = 2
F(2,317)
= 8.59
corr(u_i, Xb) = -0.0067 Prob > F =
0.0002
(Std. Err. adjusted for 318 clusters in
h1)
-
----------------------------------------------------------------------------
--
| Robust
gender_dec~n | Coef. Std. Err. t P>|t|
[95% Conf. Interval]
-
-------------+--------------------------------------------------------------
--
treatment | 0 (omitted)
did | .0913309 .067786 1.35 0.179
- -.0420364 .2246982
followup | .0864469 .0292707 2.95 0.003
.0288575 .1440363
_cons | .2363732 .0132882 17.79 0.000
.210229 .2625174
-
-------------+--------------------------------------------------------------
--
sigma_u | .25124096
sigma_e | .33511619
rho | .35982364 (fraction of variance due to u_i)
-
----------------------------------------------------------------------------
--
With more controls:
reg gender_decision did treatment followup log_hhsize h16_hoh
h16_hohsp hohdum_edu_no_imp sphohdum
> _edu_no_imp asset_Index_imp women_groupmember log_hexp, cluster (h1)
Linear regression Number of obs =
636
F( 11,
317) = 2.64
Prob > F
= 0.0030
R-squared = 0.0379
Root MSE
= .34518
(Std. Err. adjusted for 318
clusters in h1)
-
----------------------------------------------------------------------------
---------
| Robust
gender_decision | Coef. Std. Err. t P>|t|
[95% Conf. Interval]
-
--------------------+-------------------------------------------------------
---------
did | .0775325 .0689252 1.12
0.261 -.0580762 .2131412
treatment | -.0422406 .0423164 -1.00
0.319 -.125497 .0410158
followup | .0816601 .0315261 2.59
0.010 .0196332 .143687
log_hhsize | -.0050365 .0308 -0.16 0.870
-.0656347 .0555617
h16_hoh | .0002403 .0019649 0.12 0.903
-.0036256 .0041061
h16_hohsp | -.000707 .0023795 -0.30 0.767
-.0053886 .0039746
hohdum_edu_no_imp | .015657 .0364541 0.43 0.668
- -.0560656 .0873795
s phohdum_edu_no_imp | .0712825 .0410581 1.74 0.084
- -.0094982 .1520633
asset_Index_imp | .0650232 .0761579 0.85 0.394
- -.0848155 .214862
women_groupmember | .0290404 .03097 0.94 0.349
- -.0318924 .0899731
log_hexp | .0190211 .0207127 0.92
0.359 -.0217307 .0597728
_cons | .0026582 .2046481 0.01
0.990 -.3999819 .4052984
-
----------------------------------------------------------------------------
---------
xtreg gender_decision treatment did followup log_hhsize h16_hoh
h16_hohsp hohdum_edu_no_imp sphohd
> um_edu_no_imp asset_Index_imp women_groupmember log_hexp, fe vce(cluster
h1)
note: treatment omitted because of collinearity
Fixed-effects (within) regression Number of obs =
636
Group variable: h1 Number of groups =
318
R-sq: within = 0.0723 Obs per group: min =
2
between = 0.0005 avg =
2.0
overall = 0.0140 max =
2
F(10,317) =
2.80
corr(u_i, Xb) = -0.2809 Prob > F =
0.0024
(Std. Err. adjusted for 318
clusters in h1)
-
----------------------------------------------------------------------------
---------
| Robust
gender_decision | Coef. Std. Err. t P>|t|
[95% Conf. Interval]
-
--------------------+-------------------------------------------------------
---------
treatment | 0 (omitted)
did | .0944656 .0685701 1.38
0.169 -.0404444 .2293756
followup | .0637825 .0348781 1.83
0.068 -.0048393 .1324043
log_hhsize | -.0601096 .0553426 -1.09 0.278
-.1689948 .0487756
h16_hoh | -.0014844 .0055086 -0.27 0.788
-.0123225 .0093537
h16_hohsp | .005337 .0047125 1.13 0.258
-.0039347 .0146088
hohdum_edu_no_imp | .1778034 .0849698 2.09 0.037
.0106275 .3449793
sphohdum_edu_no_imp | .0998635 .0816447 1.22 0.222
- -.0607705 .2604975
asset_Index_imp | .0307611 .1463719 0.21 0.834
- -.257222 .3187443
women_groupmember | .0098729 .0503457 0.20 0.845
- -.0891811 .1089268
log_hexp | -.0025051 .0278105 -0.09 0.928
-.0572215 .0522113
_cons | .1998801 .3670395 0.54
0.586 -.5222612 .9220215
-
--------------------+-------------------------------------------------------
---------
sigma_u | .26714979
sigma_e | .33481165
rho | .38900009 (fraction of variance due to u_i)
-
----------------------------------------------------------------------------
---------
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