# athlete incomes February 3, 2022 on Chris Howey's blog

Notes from reading A quantitative method for benchmarking fair income distribution by Thitithep Sitthiyot and Kanyarat Holasut1.

The purpose of this paper is to attempt to find how countries income distribution compares to what is fair. Fair being hard to define. As they note in the paper, some fairness is attributed throughout the world to reward being related to effort or contribution. The authors chose athletics to be their baseline benchmark.

Unfortunately, the 75 countries they used for their assesesment all seem to be outside the G20, so the actual conclusion didn’t interest me. However, there was some neat data related to sport salaries which I found interesting.

(pg 16) Table 1. The descriptive statistics of the athletes’ salaries from 11 professional sports. The unit of currency is the United States dollar except for the EPL where the unit of currency is the Pound Sterling.

Sport Mean Median Minimum Maximum Std Dev # players
WNBA 73,190 59,718 2,723 127,500 32,589 151
EPL 2,695,133 1,976,000 0 19,500,000 2,489,890 523
NFL 4,682,534 3,000,000 831,349 30,700,000 4,525,065 1,000
NHL 2,618,049 1,237,500 675,000 12,500,000 2,396,904 1,000
MLB 7,985,791 5,000,000 583,500 37,666,666 7,708,701 481
NBA 7,600,037 3,500,000 208,509 40,231,758 8,767,208 517
PGA 1,235,495 838,030 5,910 9,684,006 1,433,077 264
LPGA 39,064 21,380 4,015 313,272 58,022 77
MLS 409,288 175,135 56,250 7,200,000 718,621 658
ATP 37,474 1,084 54 3,915,011 158,294 1,070
WTA 27,520 625 37 2,916,508 122,838 968

(pg 18) Table 4. The values of the Gini index for the athletes’ salaries and the salary shares by quintile (in decimals) for each type of the 11 professional sports. The 2 conditions are also included which are perfect equality and perfect inequality where the Gini index for the athletes’ salaries takes the values of 0 and 1, respectively.

Sport Gini Q5 Q4 Q3 Q2 Q1 Multi2
Equality 0.000 0.200 0.200 0.200 0.200 0.200 1.00
WNBA 0.247 0.323 0.248 0.199 0.148 0.081 3.05
EPL 0.447 0.480 0.244 0.151 0.084 0.041 10.90
NFL 0.468 0.509 0.228 0.139 0.080 0.045 10.40
NHL 0.469 0.507 0.231 0.140 0.079 0.043 10.90
MLB 0.495 0.511 0.256 0.141 0.066 0.026 19.04
NBA 0.557 0.571 0.243 0.116 0.048 0.021 26.52
PGA 0.560 0.569 0.252 0.117 0.045 0.018 31.11
LPGA 0.603 0.631 0.205 0.094 0.043 0.027 23.33
MLS 0.604 0.637 0.195 0.092 0.046 0.030 20.13
ATP 0.870 0.954 0.040 0.003 0.002 0.001 870.00
WTA 0.873 0.957 0.038 0.003 0.002 0.001 873.00
Inequality 1.000 1.000 0.000 0.000 0.000 0.000 Inf

The takeaway I got from this is the the more individual ability has to alter the chances for success, the higher the bias to inequal pay. Makes sense, in tennis or golf, individuals win championships (big money) or make due with much lesser consolation prizes. Sports that are team, but big names have higher influence also have higher pay disparities. Caps, indvidual or team, push down the disparities in pay between indviduals.

Seeing that “Multi” column makes me think CEO pay should be under 50x that of the lowest paid, probably closer to 25x at most.

1. Thitithep Sitthiyot and Kanyarat Holasut (2022). A quantitative method for benchmarking fair income distribution arXiv arXiv:2201.00917 [econ.GN] ↩︎

2. I also added an additional “Multi” column which shows how much more income the top quintile receives compared to the bottom quintile. ↩︎