Dear Stata-listers,

I have a three-level mixed model and I am struggling on the specific interpretation of the coefficients.
I have observations nested within individuals, further nested within firms. I am trying to understand whether a policy's effectiveness varies with age. The data are in a three-wave panel with two years between each wave. The panel is quite unbalanced with many people appearing only once.

A simplified version of my model and output is below. This is using test data, due to data access rules, so the results may not make a lot of sense. I will also add that I have calculated marginal effects which help me interpret the model practically, but I am unclear precisely how to interpret the regression coefficients with respect to the levels (i.e., within and between person). I have seen other posts on this issue but am still unsure with regard to my own model. I would appreciate any help.

Code:
mixed y c.age##i.policy i.year || firm: || person: age

Mixed-effects ML regression                     Number of obs     =     21,398

-------------------------------------------------------------
                |     No. of       Observations per Group
 Group Variable |     Groups    Minimum    Average    Maximum
----------------+--------------------------------------------
             firm |      6,502          1        3.3         19
        person |     17,582          1        1.2          3
-------------------------------------------------------------

                                                Wald chi2(5)      =       3.19
Log likelihood = -23030.701                     Prob > chi2       =     0.6707

---------------------------------------------------------------------------------
            y |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
            age |   .0000896   .0015416     0.06   0.954     -.002932    .0031111
                   |
         policy |
         1 Yes  |  -.1205814   .0992169    -1.22   0.224    -.3150429    .0738802
                   |
policy#c.age |
         1 Yes  |   .0024477   .0021021     1.16   0.244    -.0016723    .0065676
                   |
           year |
          2000  |  -.0004633   .0119253    -0.04   0.969    -.0238365    .0229099
          2002  |   -.002491   .0126007    -0.20   0.843     -.027188    .0222059
                |
          _cons |   3.002878   .0716159    41.93   0.000     2.862513    3.143243
---------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
firm: Identity          |
                   sd(_cons) |   1.35e-06          .             .           .
-----------------------------+------------------------------------------------
person: Independent         |
                     sd(age) |   6.83e-06          .             .           .
                   sd(_cons) |    .000022          .             .           .
-----------------------------+------------------------------------------------
                sd(Residual) |   .7098958          .             .           .
------------------------------------------------------------------------------
LR test vs. linear model: chi2(3) = 0.00                  Prob > chi2 = 1.0000

Note: LR test is conservative and provided only for reference.
I realize that the coefficients are not statistically significant, however this is because I can only share results of test data here. I am not sure why I am not getting CIs or SEs for the variance components, but I assume this is also a test data issue as this problem is not there with the real data. Assuming the random slopes and intercepts are statistically significant from zero, how do I interpret for example the effect of age? Does this mean that taking the average person, when they are 1 year older than their average age in the panel they have a 0.0000896 higher value of y on average (again ignoring statistical significance)?