(This is my first time posting something on StataList, so if my formatting or explanations are wrong or missing elements, please let me know, and I will make any changes accordingly.)
I am using Stata 15

I have recently encountered a problem while running my Random-Effects Model.
I am using an unbalanced Panel Data Set which consists of time-invariant and time-variant variables. The time-invariant variables are cultural dimensions and the time-variant variables are economic, population and infrastructure ones (GDP, Gini coefffient, foreign population etc.) My dependent variable is TTT_tot. My control variables are NE, _ios and _gp.
I have N=20 groups/observational units in my sample, and T=64.

I have calculated three models with the following do.file-code:
Code:
reg TTT_tot `eco_list' `cul_list' `infr_list' `pop_list' NE _ios _gp, vce(robust)
xtset CountryCode
xtreg TTT_tot `eco_list' `cul_list' `infr_list' `pop_list' NE _ios _gp ,fe vce(robust)
xtreg TTT_tot `eco_list' `cul_list' `infr_list' `pop_list' NE _ios _gp ,re vce(robust)
I want to compare a standard OLS regression, the Random-Effects Model and a Fixed-Effects model with one another, to be able to compare their standard errors and sigma u and sigma e values. However, I constantly receive a 0 for my sigma u value in my Random-Effects model.

Code:
Random-effects GLS regression                   Number of obs     =     11,934
Group variable: CountryCode                     Number of groups  =         19

R-sq:                                           Obs per group:
     within  = 0.0541                                         min =        594
     between = 1.0000                                         avg =      628.1
     overall = 0.1528                                         max =        630

                                                Wald chi2(13)     =          .
corr(u_i, X)   = 0 (assumed)                    Prob > chi2       =          .

                                             (Std. Err. adjusted for 19 clusters in CountryCode)
------------------------------------------------------------------------------------------------
                               |               Robust
                       TTT_tot |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------------------+----------------------------------------------------------------
                           GDP |   2.53e-06   4.09e-06     0.62   0.536    -5.48e-06    .0000105
                          Gini |  -.0559253   .5105896    -0.11   0.913    -1.056663    .9448119
           net_national_income |   -.001845   .0017765    -1.04   0.299     -.005327    .0016369
        UncertaintyAvoidanceSP |  -2.461559   .2853507    -8.63   0.000    -3.020836   -1.902281
           FutureOrientationSP |  -4.779695    .704769    -6.78   0.000    -6.161016   -3.398373
               PowerDistanceSP |  -5.717074   .2371044   -24.11   0.000     -6.18179   -5.252358
  CollectivismISPInstitutional |   17.74914     .52927    33.54   0.000     16.71179    18.78649
           HumaneOrientationSP |  -6.138548   .0818562   -74.99   0.000    -6.298983   -5.978113
      PerformanceOrientationSP |  -7.951868   .4913199   -16.18   0.000    -8.914837   -6.988899
    CollectivismIISPIngroupCol |  -1.273744   .7499475    -1.70   0.089    -2.743615    .1961257
        GenderEgalitarianismSP |   .5030384   .5760547     0.87   0.383    -.6260081    1.632085
               AssertivenessSP |   11.80073   .3703147    31.87   0.000     11.07492    12.52653
        UncertaintyAvoidanceSV |  -9.892615   .6663491   -14.85   0.000    -11.19864   -8.586595
           FutureOrientationSV |   1.941818   .2248855     8.63   0.000     1.501051    2.382586
               PowerDistanceSV |   11.56595   .7878502    14.68   0.000     10.02179    13.11011
  CollectivismISVInstitutional |   19.87028   .9030751    22.00   0.000     18.10028    21.64027
           HumaneOrientationSV |   8.966052    .961623     9.32   0.000     7.081306     10.8508
      PerformanceOrientationSV |  -2.439227    .112894   -21.61   0.000    -2.660495   -2.217959
    CollectivismIISVIngroupCol |   1.623323   .7085817     2.29   0.022     .2345288    3.012118
        GenderEgalitarianismSV |  -8.887995   .3053794   -29.10   0.000    -9.486528   -8.289462
               AssertivenessSV |   8.362968    .623689    13.41   0.000      7.14056    9.585376
        Broadband_subscription |   .0118887   .0081043     1.47   0.142    -.0039954    .0277728
mobile_broadband_subscriptions |  -.0003924   .0004245    -0.92   0.355    -.0012243    .0004396
                     urban_pop |   .5537966   1.768498     0.31   0.754    -2.912395    4.019988
              new_foreign_pop_ |   .2852685   .9709759     0.29   0.769    -1.617809    2.188346
        new_foreign_pop_growth |  -.0539973   .1212795    -0.45   0.656    -.2917008    .1837061
                  edu_post_sec |   2.001969   1.610598     1.24   0.214    -1.154745    5.158683
                      edu_tert |   .2519419   .7611614     0.33   0.741    -1.239907    1.743791
                            NE |  -6.031001   1.565617    -3.85   0.000    -9.099554   -2.962448
                          _ios |   .0218838   .0709499     0.31   0.758    -.1171756    .1609431
                           _gp |   .0341277   .0900629     0.38   0.705    -.1423923    .2106476
                         _cons |  -102.1462   12.21292    -8.36   0.000    -126.0831   -78.20936
-------------------------------+----------------------------------------------------------------
                       sigma_u |          0
                       sigma_e |  12.622343
                           rho |          0   (fraction of variance due to u_i)
------------------------------------------------------------------------------------------------
Here we can see the 0 value for sigma_u. Normally this would mean that the RE-model is degenerated. But this seems weird, since the RE model results in more statistically significant results, with better standard errors compared to my OLS regression results.

I have furthermore conducted a Breusch and Pagan Langrangian Multiplier test to determine whether I can even use a Random-Effects Model. My Prob > chibar2 equals 1, which seems peculiar to me.


All the best,
Johanna