I am using an unbalanced panel of 144 countries and 3300 obs to measure the effect of globalisation on inequality in developing countries. I am using the KOF measure of globalisation, avg years of education data and log gdp per capita as independent variables and Gini coefficients (from Frederick Solt's SWIID) as the dependent.
I am struggling to understand whether to use random or fixed effects in my model, and when one should use time and entity FE or simply entity.
The FE and RE outputs from STATA are:
Code:
. xtreg gini_disp kofgi educ ln_gdppc ln_gdppc2, fe rob Fixed-effects (within) regression Number of obs = 3,299 Group variable: countrycode Number of groups = 144 R-sq: Obs per group: within = 0.0405 min = 1 between = 0.0887 avg = 22.9 overall = 0.0870 max = 46 F(4,143) = 1.56 corr(u_i, Xb) = 0.1466 Prob > F = 0.1873 (Std. Err. adjusted for 144 clusters in countrycode) ------------------------------------------------------------------------------ | Robust gini_disp | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- kofgi | .0711179 .0365415 1.95 0.054 -.0011134 .1433492 educ | -.5249671 .3325533 -1.58 0.117 -1.182323 .1323885 ln_gdppc | 2.322239 1.694989 1.37 0.173 -1.028233 5.672712 ln_gdppc2 | -.1558884 .1132115 -1.38 0.171 -.3796727 .0678958 _cons | 32.9037 6.38667 5.15 0.000 20.27922 45.52818 -------------+---------------------------------------------------------------- sigma_u | 7.3203003 sigma_e | 1.6181866 rho | .95341139 (fraction of variance due to u_i) ------------------------------------------------------------------------------ . xtreg gini_disp kofgi educ ln_gdppc ln_gdppc2, re rob Random-effects GLS regression Number of obs = 3,299 Group variable: countrycode Number of groups = 144 R-sq: Obs per group: within = 0.0404 min = 1 between = 0.0900 avg = 22.9 overall = 0.0882 max = 46 Wald chi2(4) = 6.74 corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.1505 (Std. Err. adjusted for 144 clusters in countrycode) ------------------------------------------------------------------------------ | Robust gini_disp | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- kofgi | .0727446 .0352125 2.07 0.039 .0037294 .1417597 educ | -.5576193 .3121589 -1.79 0.074 -1.169439 .0542009 ln_gdppc | 2.33882 1.694603 1.38 0.168 -.9825407 5.66018 ln_gdppc2 | -.1549905 .1130468 -1.37 0.170 -.3765582 .0665771 _cons | 32.15156 6.363721 5.05 0.000 19.6789 44.62423 -------------+---------------------------------------------------------------- sigma_u | 7.0202524 sigma_e | 1.6181866 rho | .94954907 (fraction of variance due to u_i) ------------------------------------------------------------------------------
From the literature on the subject I have seen some papers with similar models that use a GMM model but I am not familiar with this estimator, if you could explain the merits of the models and when they should applied I would be enormously grateful!
Thanks for reading,
Nick Akam
Undergraduate Liberal Arts & Sciences, University of Birmingham, UK
P.s Apologies if this is not a well phrased/structured topic, it is my first time using this forum.
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