Note there might not be perfect collinearity between variables $x_i$ and $a_i$ as $cor(x_i,a_i)\neq 1 \lor -1$, but still perfect collinearity between $x_i$ and $a_i$ conditional $z_i$ i.e. it is possible $cor(x_i,a_i)|z=1 \lor-1$.
Additionally, note that regression might not be run on your whole sample if there are missing observations. If you regress $y_i$ on, $x_i$, $a_i$, $b_i$ and $c_i$ regression, even without any 'if' condition, will only be run over observations where none of the variable misses an observation. So if you have 300 observations, $y_i$ is available for all 300, $x_i$ has some N/A so it is only available for 250 observations, $a_i$ for 260, $b_i$ for 207 and $c_i$ for 265, the regression will only be run over sample composed of observations where for observation $i$ only if $y_i,x_i, a_i, b_i, c_i \neq N/A$ at the same time.
In addition to the explanation in first paragraph again $cor(x_i,a_i) \neq 1 \lor -1$ but at the same time $cor(x_i,a_i) |y_i,x_i, a_i, b_i, c_i \neq N/A= 1 \lor -1$.
As to what to do about this it depends on what is actually causing the problem which you have to investigate first.
Is it the fact that you want to run regression only if RaceEth=1? Then remove that condition or one of the variables with perfect multicollinearity. If variables are perfectly collinear you don't get any more information from regression by controlling for the additional variable since the coefficient would be either exactly same or inverse which is something you can check via correlation between the variables.
If its issues with observations try to collect larger sample.
If you can't get more data or drop variables try to pivot your topic. I hope you didn't fall into common trap of many students writing nearly whole thesis without first checking if the regressions can be run given the data you have. If you didn't made this mistake you should be able to talk to your thesis advisor about pivoting the topic due to insufficient data to answer the current research question. Most professors will not mind at the early stages of thesis writing process because they expect some ideas might have data issues that make them infeasible.
If you made the mistake of already writing everything out and you are at a late stage of thesis writing process you should still talk to your supervisor. Your supervisor might be able to accept output that is subpar taking into account data issues but even if accepted it will most likely be still reflected in grading.