This paper analyses the income effect of the participation in elite sports. To quantify the average difference in the monthly net income of former elite athletes and non-athletes we estimate sample average treatment effect scores (SATT) by using covariate nearest-neighbour matching (CVM). While our treatment group consists of formerly funded top-level athletes, the control group of non-athletes is drawn from the SOEP database. Matching takes place by socio-demographic variables as well as measures of personal qualities and attitudes. On average, former athletes receive higher incomes than similar non-athletes. The income premium for former team sports and male athletes is even higher. Comparing the income of former female athletes with male non-athletes, we find that the participation in elite sports closes the gender-wage gap. Our results are robust to variations in the specification and statistically as well economically significant.
funding of elite sports nearest-neighbor matching job success gender-wage gap