SOEPpapers on Multidisciplinary Panel Data Research 705
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