US Women's Soccer Salary Discrimination

Last week a judge threw out the US Women's Soccer Salary Discrimination part of their lawsuit against US Soccer.  As I pondered last year, the judge seemed to view the way the US Men's and US Women's salaries are paid under a collective bargaining agreement as being different.  Thus, the judge seems to have ruled that US Soccer is not paying the US Men and US Women differently because they are discriminating against female soccer players, but rather the salaries between the two groups are different because the two groups have chosen different ways to be paid.

Since the ways they are paid are different, then it's not salary discrimination.  I am not convinced that is always true, but it would be substantially more difficult to show that salary discrimination occurs; unfortunately for the US Women's Soccer players.

2011-2019 MLB Team Payroll Inequality

Yesterday, I took a look at competitive balance in MLB and found that MLB competitiveness was declining this season as compared to last season.  So now, what I want to look at is the level of team payroll inequality using team payroll data.

In the chart below is the MLB Team Payroll Gini coefficients from 2011 to 2019.  As you can see, team payroll is highly unequal.  Want to know how to calculate the Gini coefficient?  Look here.

U.S. Women's Soccer Salary Discrimination Suit

In March of this year, members of the US Women's Soccer team filed a lawsuit alleging that women's soccer team are being discriminated against in terms of their salary.   The legal definition of gender discrimination is different from the economic definition.  In economics, we think about how salaries are determined based on two concepts:  production and revenue.  Let's take each in turn.  There is no debate that the US Soccer women's team is more productive than the US Soccer men's team.  US Soccer women's team has been one of the premier teams in international play, with the women's team winning the 2015 World Cup.  The second is revenue and until recently, not much was publicly know regarding revenue.  Now that has changed; ticket sales revenue between the two programs has been very similar, with the women's program now generating more ticket sales revenue than the men's program.   So economically, since the women's program is superior in performance and now slightly better in terms of revenue, then the women's players should be paid more than the men's player's in a competitive market.  

US Soccer denied gender discrimination.  US Soccer stated, "that no pay comparison can be made between the women’s players, who are paid in guaranteed salaries and benefits, and men’s players, who are paid in appearance fees." 

Ok, so the two groups are paid differently and that difference is their argument as to why the salaries are different.  I can understand that different salary structures yield different salaries, but it is unclear as to why the men's program and the women's program have different salary structures.  It could be that the men's players have a substantial substitute salary options as compared to women's players; and as such the women's players chose the guaranteed salaries option as opposed to the appearance fee option chosen by the men's players. 

What is going to happen?  Who knows.  While I am not a lawyer, I think the women's lawsuit has an uphill battle, since their salaries are based on a collective bargaining agreement. 

MLB Team Payroll Inequality

Yesterday, I took a look at competitive balance in MLB and found that while the leagues overall have been improving in terms of their competitiveness, the AL this past season did not.  So now, what I want to look at is the level of team payroll inequality using team payroll data from www.spotrac.com.

In the chart below is the MLB Team Payroll Gini coefficients from 2011 to 2018.  As you can see, team payroll is highly unequal.  Want to know how to calculate the Gini coefficient?  Look here.

 

So, if MLB team payroll is unequal as we can see from the Gini coefficients above, what is the relationship between payroll and performance?  That I will address tomorrow.

NBA Salary Inequality for the 2016-17 Season

Now that the NBA playoffs are well underway, I thought I would come back to looking at salary inequality among NBA players.  As I wrote earlier, NBA salary inequality had been improving over the last few years.  So I again I collected the NBA salary data from ESPN and calculated the Gini coefficient for the league as a whole.  For the most recent season, we see that salary inequality has taken a step backwards, with the Gini coefficient now 0.5519 and is worse than the US Gini coefficient for 2014 of 0.480.

NBA Salary Income Inequality since 2005

Now that the NBA season is well underway, I thought I would look at how equal (or unequal) NBA salaries are over the last few years.  To do that I collected the NBA salary data from ESPN and calculated the Gini coefficient for the league as a whole.  Results are listed in the table below by season, the number of players (n), the Gini coefficient and the total salary for all players in the league.

Season	n		Gini		Total
2005-2006	394		0.4917		$1,619,154,926
2006-2007	409		0.5006		$1,697,883,876
2007-2008	415		0.5161		$1,817,073,513
2008-2009	413		0.5053		$2,002,564,767
2009-2010	428		0.5260		$1,972,670,023
2010-2011	450		0.5366		$1,964,987,905
2011-2012	501		0.5500		$2,013,125,239
2012-2013	541		0.5601		$2,062,073,682
2013-2014	391		0.5409		$1,786,979,523
2014-2015	431		0.5294		$1,903,052,600
2015-2016	407		0.5017		$2,126,853,179

As you can see the NBA has been fairly unequal in the distribution of salary among its members, but over the last few years, salary inequality has been decreasing in part reflecting the increase in average salary over the last few years.  For comparison purposes, the US Gini coefficient for 2014 is 0.480.  Below is a chart of the Gini coefficient in the NBA since the 2005-2006 season.

2014 NFL Income Inequality by Team

Last week I blogged about income inequality for the 2014 NFL season and showed that the NFL is more unequal in terms of income distribution than the US as a whole was in 2010 (closest year to this past season).  So I thought that I would follow up on this by looking at income inequality among the 32 NFL teams in 2014, again using the active roster data.  I am using the Gini coefficient (step-by-step guide to make this calculation) which is a measure of income inequality.

Below are the teams regular season winning percent, points scored and points surrendered, their payroll and relative payroll and lastly the team Gini coefficient for 2014.   San Diego was the least unequal in terms of income inequality and the Baltimore Ravens were the most unequal for 2014.

Not surprisingly, there is no statistically significant relationship between income inequality (Gini coefficient) and regular season winning percent.

Team	WinPct	PF	PA	Payroll	Relative Payroll	Gini
San Diego Chargers	0.563	348	348	87162147	0.8372	0.5255
Philadelphia Eagles	0.625	474	400	119105381	1.1440	0.5280
Indianapolis Colts	0.688	458	369	92940781	0.8927	0.5319
New York Jets	0.25	283	401	94454268	0.9073	0.5381
Washington Redskins	0.25	301	438	91755213	0.8813	0.5504
Cincinnati Bengals	0.656	365	344	107297822	1.0306	0.5512
Buffalo Bills	0.563	343	289	116442414	1.1185	0.5534
Detroit Lions	0.688	321	282	111226441	1.0684	0.5607
Dallas Cowboys	0.75	467	352	90952114	0.8736	0.5638
Miami Dolphins	0.5	388	373	105571107	1.0140	0.5641
Tennessee Titans	0.125	254	438	95962330	0.9217	0.5660
Jacksonville Jaguars	0.188	249	412	85737794	0.8235	0.5729
San Francisco 49ers	0.5	306	340	99854068	0.9591	0.5751
Carolina Panthers	0.469	339	374	89034415	0.8552	0.5779
Green Bay Packers	0.75	486	348	129562715	1.2445	0.5821
Chicago Bears	0.313	319	442	86877698	0.8345	0.5839
Pittsburgh Steelers	0.688	436	368	124378270	1.1947	0.5858
New England Patriots	0.75	468	313	110887895	1.0651	0.5858
Oakland Raiders	0.188	253	452	92244836	0.8860	0.5901
Houston Texans	0.563	372	307	94874026	0.9113	0.5981
Minnesota Vikings	0.438	325	343	94282411	0.9056	0.6028
Arizona Cardinals	0.688	310	299	96202750	0.9241	0.6031
St. Louis Rams	0.375	324	354	106781395	1.0257	0.6038
New York Giants	0.375	380	400	90112983	0.8656	0.6043
Kansas City Chiefs	0.563	353	281	110797441	1.0642	0.6048
Seattle Seahawks	0.75	394	254	119423389	1.1471	0.6130
New Orleans Saints	0.438	401	424	96329917	0.9253	0.6169
Denver Broncos	0.75	482	354	130851372	1.2569	0.6218
Tampa Bay Buccaneers	0.125	277	410	99527806	0.9560	0.6283
Atlanta Falcons	0.375	381	417	131809795	1.2661	0.6317
Cleveland Browns	0.438	299	337	105183505	1.0103	0.6389
Baltimore Ravens	0.625	409	302	123867781	1.1898	0.6577

Tomorrow, I hope to blog about income inequality by player position for 2014.  If not, I will get to this within the coming days.

2014 NFL Income Inequality

In July I blogged about MLS income inequality and one reader asked me about the NFL.  My response, was basically - cool idea; I don't know; have not done it.  Now I have.  I am using the Gini coefficient (step-by-step guide to make this calculation) which is a measure of income inequality.  The Gini coefficient is bound between zero (complete equality) and one (complete inequality).  For comparison purposes, the Gini coefficient for the US in 2010 is 0.411 (or 41.1 for the Gini Index, which is the Gini coefficient multiplied by 100).

Specifically, what I have done is taken every active player pay (not the amount attributed to the salary cap) listed on the website for the 2014 season and measured the amount of income inequality.  I have not included players on injured reserve or players that were given a contract but then dropped from the team.  Here is the link for Arizona in 2014, follow the dropdown menu for the other teams.  Once all the players have been included in the dataset, I have a total of 1688 NFL players in 2014.  Total payroll among those players was $3.331 billion.  For all the active players, the Gini coefficient was 0.5942, which is quite a bit more unequal than the US in 2010.

UPDATE:  (09/16/2015:  US Gini coefficient for 2014 is 0.480.)

NCAA Football and Income Inequality

Recently, I have been researching NCAA bowl eligible football revenues, so given that  tonight is the start of the college football season for bowl eligible teams I thought that I would look at college football income inequality.  Specifically, I am using data from the Equity in Athletics database from 2007 to 2013 college football seasons.  (The 2014 season is not currently available).

Here I am going to report the team with the smallest amount of football revenue and the amount, the team with the largest amount of football revenue and the amount and the Gini coefficient for that season.  Please note that I have not adjusted for inflation in the revenue numbers presented below.  For comparison purposes, the Gini coefficient for the US in 2010 is 0.411 (or 41.1 for the Gini Index, which is the Gini coefficient multiplied by 100).  As you can see in the table below, NCAA football is slightly more unequal in terms of revenues than the US was in 2010.

Season		Team		Min. Revenue		Team		Max. Revenue		Gini
2007		Buffalo		$1,882,475		Texas		$72,952,397		0.4564
2008		Akron		$1,577,189		Texas		$87,583,986		0.4516
2009		Florida Atlantic		$2,280,834		Texas		$93,942,815		0.4515
2010		Louisiana-Monroe		$2,953,497		Texas		$95,749,684		0.4465
2011		Louisiana-Monroe		$3,588,964		Texas		$103,813,684		0.4381
2012		Louisiana-Monroe		$3,277,727		Texas		$109,400,688		0.4418
2013		Central Mich.		$3,899,929		Texas		$112,508,162		0.4448

We also notice that the University of Texas has the highest amount of revenue during this entire time period and that CUSA, Mid American or Sun Belt conference teams have had the lowest total football revenues during this time period.

2015 MLS Position Income Inequality

Today, I want to finish measuring income inequality in MLS by looking at how salaries are distributed by player position.  Taking the data from MLSPU, I have found for each player (except one) their position and then evaluated the level of income inequality by each position.  For players that are listed at multiple positions I have included them for each position.

Last year the position with the most equal salary were goal keepers and the most unequal were forwards.  This season goal keepers are still the most equal, but now midfielders are the most unequal.  Here is the Gini coefficients for this season using MLSPU's data.

Pos	Base Salary		Guaranteed Comp.
D	0.4137		0.4183
F	0.6844		0.6772
GK	0.3457		0.3518
M	0.7108		0.7109

As you can see midfielder are twice as unequal as goal keepers.  Defenders are close to goal keepers and forwards are close to midfielders for the league as a whole.

Prior posts on 2015 MLS Income Inequality:
MLS Team Income Inequality
MLS Overall Income Inequality

2015 MLS Team Income Inequality

Yesterday's I examined income inequality in Major League Soccer for the current season in terms of all players listed on the Major League Soccer Players Union salary release and I noted that income inequality has been increasing during the past three seasons.  Today, I want to look at the level of income inequality at the team level.

So looking at each MLS club here are the measures of income inequality for the 2015 season using the data from the MLSPU in the table below.

Team		Base Salary Gini		Guaranteed. Compensation Gini
CHI		0.564		0.560
CLB		0.342		0.371
COL		0.459		0.463
DAL		0.346		0.337
DC		0.377		0.394
HOU		0.436		0.448
KC		0.460		0.450
LA		0.777		0.774
MTL		0.367		0.368
NE		0.619		0.625
NY		0.419		0.450
NYCFC		0.777		0.772
ORL		0.740		0.739
PHI		0.422		0.420
POR		0.495		0.485
RSL		0.409		0.414
SEA		0.713		0.732
SJ		0.476		0.483
TOR		0.784		0.790
VAN		0.509		0.503

From the table above notice that TOR (Toronto) has the most amount of salary inequality followed closely by LA, NYCFC, ORL and SEA.  In terms of income equality, CLB (Columbus) is the most equal followed by DAL, MTL and DC.

Over the last few seasons here are two charts of team Gini coefficients (excluding NYCFC).  The first chart is team by team Gini coefficients for Base Salary.

The second is team by team Gini coefficients for Guaranteed Compensation.

While there has been some variation in how equal (or unequal) salaries are distributed among MLS clubs, overall many teams have similar levels of Gini coefficients; most likely due to long term contracts for relatively high paid MLS players.