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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) - ---------------------------------------------------------------------------- --------- * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**RE: Re: st: RE: Difference in Difference vs. Fixed Effects***From:*"Hussein, Mustafa (Mustafa Hussien)" <[email protected]>

**References**:**Re: Re: st: RE: Difference in Difference vs. Fixed Effects***From:*"Ariel Linden, DrPH" <[email protected]>

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