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.
⇒ This article is also available on gemini.