Fixed Effects Model Interpretation $$\ln(\text{pay})_{itj}= \alpha_{ij} + \beta\ln(\text{performance})_{jt} + \gamma X_{ijt} + T_t + ε_{ijt}$$
This fixed effects model was used to investigate the link between pay-performance throughout the corporate hierarchy. It controls for employee-firm match fixed effects ($\alpha_{ij}$). $i=$ employee, $j=$ firm, and $t=$ time.

How would the coefficients on performance from this table be interpreted.
You have a log-log model, so the coefficients on performance can be interpreted as the elasticity of your outcome (performnce) with respect to the regressor of interest (level in the corporate heirarchy).
This table only shows the coefficient of interest for four separate regressions. For each of these job levels (CEO, other board, white-collar, all other), a 1% increase in the 'total expected ex ante pay' leads to a x-% increase in 'total shareholder returns'.
For example, based on the results reported in this table, a 1% increase in total ex-ante CEO pay is associated with a 0.22% increase in total shareholder returns, a 1% increase in total ex-ante 'other board executive' pay is associated with a 0.21% increase in total shareholder returns, etc.
All four coeficients are statistically significant at the 1% level (which you can confirm by looking up the values of each coefficient's t-statistic, which is just the coefficient value divided by the standard error).