Hi everyone!
I have a question regarding year dummy variables. I am trying to investigate the impact of Trade liberalisation on female labour force participation rate in 14 developing Asian countries and am therefore running a Fixed Effects regression. The first regression I run is without any year dummy variables and solely includes the Control Variables and the explanatory variable. The code is shown below.
This gave me the following result where TO is significant at the 5% level.
However, when I add a dummy variable for each year (minus 1) that is in my sample, i.e, from 1990 to 2018, the coefficient on TO becomes insignificant.
This is also the case for some other variables that I am using as explanatory variables in separate regressions for the same research.
Does anyone have any insight on why this might be the outcome and what it implies in terms of the yearly trends and the relationship between the explanatory and outcome variables?
Additionally I also wanted to confirm whether it is necessary/helpful to add year dummy variables when one is using a Fixed Effects model?
Thank you!
I have a question regarding year dummy variables. I am trying to investigate the impact of Trade liberalisation on female labour force participation rate in 14 developing Asian countries and am therefore running a Fixed Effects regression. The first regression I run is without any year dummy variables and solely includes the Control Variables and the explanatory variable. The code is shown below.
Code:
xtreg FLFPR FR FUR GDPpc TO, fe robust
Code:
Fixed-effects (within) regression Number of obs = 403 Group variable: CountryNum Number of groups = 14 R-sq: Obs per group: within = 0.0876 min = 26 between = 0.2218 avg = 28.8 overall = 0.1360 max = 29 F(4,13) = 2.91 corr(u_i, Xb) = -0.4371 Prob > F = 0.0639 (Std. Err. adjusted for 14 clusters in CountryNum) ------------------------------------------------------------------------------ | Robust FLFPR | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- FR | -.7364388 .7314433 -1.01 0.332 -2.316626 .8437484 FUR | .083618 .2047825 0.41 0.690 -.3587877 .5260237 GDPpc | .0001044 .0000646 1.62 0.130 -.0000352 .000244 TO | -.0282203 .0121169 -2.33 0.037 -.0543972 -.0020434 _cons | 54.80075 3.269936 16.76 0.000 47.73648 61.86501 -------------+---------------------------------------------------------------- sigma_u | 19.497251 sigma_e | 2.4764401 rho | .98412337 (fraction of variance due to u_i) ------------------------------------------------------------------------------
Code:
xtreg FLFPR FR FUR GDPpc TO y1 y2 y3 y4 y5 y6 y7 y8 y9 y10 y11 y12 y13 y14 y15 y16 y17 y18 y19 y20 y21 y22 y23 y24 y25 y26 y27 y28, fe robust
Code:
Fixed-effects (within) regression Number of obs = 403 Group variable: CountryNum Number of groups = 14 R-sq: Obs per group: within = 0.1396 min = 26 between = 0.0039 avg = 28.8 overall = 0.0003 max = 29 F(13,13) = . corr(u_i, Xb) = -0.1276 Prob > F = . (Std. Err. adjusted for 14 clusters in CountryNum) ------------------------------------------------------------------------------ | Robust FLFPR | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- FR | -1.708764 .96501 -1.77 0.100 -3.793541 .3760136 FUR | .0074997 .1827171 0.04 0.968 -.3872366 .402236 GDPpc | .0001995 .0000496 4.02 0.001 .0000923 .0003066 TO | -.0247951 .0168391 -1.47 0.165 -.0611737 .0115836 y1 | 3.389236 2.208276 1.53 0.149 -1.381454 8.159926 y2 | 3.233488 2.046978 1.58 0.138 -1.18874 7.655715 y3 | 2.842947 1.78705 1.59 0.136 -1.017741 6.703634 y4 | 2.85432 1.773783 1.61 0.132 -.9777045 6.686345 y5 | 2.955971 1.664716 1.78 0.099 -.6404285 6.552371 y6 | 2.5787 1.498052 1.72 0.109 -.6576454 5.815045 y7 | 2.636164 1.457976 1.81 0.094 -.5136015 5.78593 y8 | 2.565584 1.569233 1.63 0.126 -.8245376 5.955705 y9 | 3.053849 1.642985 1.86 0.086 -.4956048 6.603303 y10 | 2.601952 1.472541 1.77 0.101 -.5792797 5.783184 y11 | 2.50133 1.517437 1.65 0.123 -.7768944 5.779554 y12 | 2.761161 1.551812 1.78 0.099 -.5913241 6.113647 y13 | 2.263491 1.601462 1.41 0.181 -1.196258 5.723241 y14 | 2.026965 1.640635 1.24 0.239 -1.517411 5.571341 y15 | 1.769197 1.743178 1.01 0.329 -1.99671 5.535105 y16 | 1.714858 1.81088 0.95 0.361 -2.19731 5.627027 y17 | 1.45597 1.706395 0.85 0.409 -2.230472 5.142411 y18 | 1.589595 1.560558 1.02 0.327 -1.781786 4.960976 y19 | 1.325755 1.502858 0.88 0.394 -1.920973 4.572483 y20 | 1.340527 1.423183 0.94 0.363 -1.734073 4.415128 y21 | 1.286046 1.29986 0.99 0.341 -1.522132 4.094223 y22 | 1.401393 1.278901 1.10 0.293 -1.361506 4.164291 y23 | .9731538 1.114448 0.87 0.398 -1.434464 3.380772 y24 | .4656153 .7664366 0.61 0.554 -1.19017 2.121401 y25 | .2296892 .6472401 0.35 0.728 -1.168588 1.627966 y26 | .3651207 .6224359 0.59 0.568 -.9795703 1.709812 y27 | .2646698 .4753703 0.56 0.587 -.7623053 1.291645 y28 | .3260597 .3253869 1.00 0.335 -.376896 1.029015 _cons | 55.61924 3.822775 14.55 0.000 47.36063 63.87784 -------------+---------------------------------------------------------------- sigma_u | 19.066549 sigma_e | 2.4973914 rho | .98313287 (fraction of variance due to u_i) ------------------------------------------------------------------------------
Does anyone have any insight on why this might be the outcome and what it implies in terms of the yearly trends and the relationship between the explanatory and outcome variables?
Additionally I also wanted to confirm whether it is necessary/helpful to add year dummy variables when one is using a Fixed Effects model?
Thank you!
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