Consider the following regression model$$y_{it}=\beta_{1}M_{i}+\beta_{2}F_{i}+x_{it}'\gamma+\epsilon_{it}$$ where the LHS is some individual specific, time varying regressand the RHS variables consist of a vector of covariates $x_{it}'$ and 2 dummy variables for the sex of the individual. Notice that I have not included a constant to prevent multicollinearity. It is clear that if $x_{it}$ was not present, an OLS regression of the LHS on the RHS would result in the estiamtes representing conditional means by groups. For instance:$$\hat{\beta_{1}}=E[y_{it}|M=1]$$
This can be readily done by hand. My question is how does the inclusion of $x_{it}$ affect the procedure/interpretation? What would be the interpretation of $\beta_{1}$ in this case? Thanks!