I'm currently working with panel data with five waves of data. Persons are nested within teams, so I'm treating team as level 3, person as level 2, and survey submission as level 1.
A person's race is constant, so in theory, Stata shouldn't let me ask for race to have a random slope at the person level. However, it does and the variance is quite large. In contrast the variance at the team level is pretty much nil. Here is the output. ExternalReference is the unique id of each person. Could someone explain what Stata is doing here? Also, what should I be doing to run this if I want to see if the effect of race varies at the team level? Should I enter it as a series of dummies and put the variable names after "team:" with/without an R or i prefix?

Code:
. mixed aghiw i.raceB || team: R.raceB || ExternalReference: R.raceB,  residuals(ar 1, t(wave))
Note: time gaps exist in the estimation data

Computing standard errors:

Mixed-effects ML regression                     Number of obs     =        391

-------------------------------------------------------------
                |     No. of       Observations per Group
 Group Variable |     Groups    Minimum    Average    Maximum
----------------+--------------------------------------------
           team |         14         18       27.9         40
   ExternalRe~e |         85          2        4.6          5
-------------------------------------------------------------

                                                Wald chi2(6)      =       2.60
Log likelihood = -350.29526                     Prob > chi2       =     0.8569

------------------------------------------------------------------------------
       aghiw |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       raceB |
   Hispanic  |   .0525582   .2350682     0.22   0.823    -.4081669    .5132833
      Black  |  -.0869336   .3276859    -0.27   0.791    -.7291862    .5553189
      Asian  |   .1653421   .1415124     1.17   0.243     -.112017    .4427012
Asian-White  |   .0855664   .2103184     0.41   0.684    -.3266502    .4977829
 M. Eastern  |   .2873001   .2822071     1.02   0.309    -.2658156    .8404158
      Other  |  -.1040551   .3185585    -0.33   0.744    -.7284184    .5203081
             |
       _cons |   3.566309   .0871464    40.92   0.000     3.395505    3.737113
------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
team: Identity               |
                var(R.raceB) |   1.34e-12   3.29e-12      1.09e-14    1.66e-10
-----------------------------+------------------------------------------------
ExternalRe~e: Identity       |
                var(R.raceB) |   .1950636    .048428      .1199086    .3173233
-----------------------------+------------------------------------------------
Residual: AR(1)              |
                         rho |   .2612881   .0875236       .083185    .4232158
                      var(e) |   .2894514   .0331079      .2313204    .3621908
------------------------------------------------------------------------------
LR test vs. linear model: chi2(3) = 127.98                Prob > chi2 = 0.0000

Note: LR test is conservative and provided only for reference.