Salary Inequality and Performance in MLB
This paper will explore the effect of team chemistry on performance in Major League Baseball (MLB). In the 2000s, the Yankees were a team of great individual talent, however, their lack of team unity was noticeable. In the playoffs, when heart, guts, and team chemistry matter greatly, the Yankees fell short and were easily eliminated each year. It wasn’t until 2009, when the Yankees acquired jokester Nick Swisher, prankster AJ Burnett, and fun loving C.C. Sabathia that the chemistry of the team improved drastically. Shaving cream pies to the faces of players who got the game winning hit, and various other team pranks became the norm in the once tense Yankee clubhouse. They finally seemed to enjoy the company of each other and it resulted in their first World Championship in nine years. This paper will not analyze championships; nevertheless, it is clear that team chemistry has a significant positive effect on team performance. The issue is quantifying team chemistry. I postulated that the more equally distributed a team’s salary was, the better the players would get along. It seems probable that a team of five big ego-ed stars with big salaries and twenty players paid minimum wage (rookies or over the hill veterans) would have less chemistry than a team of twenty-five players of equal salary (and equal egos). I thus hypothesize that the more equal a team’s salary is distributed, the better it will perform.
If this study is able to show that salary inequality has an injurious effect on team performance in MLB, General Managers should work on lowering team payroll inequality in order to promote team chemistry. The remainder of this paper will review past relevant studies to give perspective, explain the theoretical model, define and give expected signs to the variables, explain the data, and analyze the regression results.
Mark Foley and Fred Smith of Davidson College did a similar study on the effect of salary inequality on team wins using data from 1985-2002. They used the Gini coefficient to measure salary inequality. The coefficient is a value between 0 - 1 that describes salary distribution. 0 describes a team with perfectly equal salary distribution while 1 describes a team with a perfectly unequally distributed salary. The Gini coefficient will be explained to a greater extent later. Foley and Smith found no consistent effect of salary inequality on team performance. Their only statistically significant negative coefficient for salary inequality did not produce a result that was economically significant. They thus concluded that their results weren’t consistent with a negative coefficient for the Gini variable.
R. Todd Jewell and David Molina found a conflicting conclusion in their 2004 paper on salary inequality on MLB performance. They used data from 1985-2000 and also measured the Gini coefficient against winning percentage. Their study found that salary inequality does have a significant negative effect (at the 1% level) on winning percentage.
Brandon M Avrutin and Paul M. Sommers of Middlebury College used data from 2001-2005 and found a positive relationship between inequality and performance. However, they found their results to be insignificant and concluded there to be no effect of salary inequality on wins.
These recent studies show conflicting conclusions on the effect of salary inequality on performance in MLB.
The effect of wage inequality on group performance is theoretically ambiguous. I believe salary inequality has a detrimental effect on the performance of a MLB team. This view is consistent with Akerlof and Yellen’s (1990) study on wage inequality. They argue that firm performance suffers if workers think the payroll is unfair. However, salary inequality has been theorized to be beneficial to a firm, or team, as well.
Cited: Akerlof, George and Janet Yellen (1990), “The Fair Wage-Effort Hypothesis and Unemployment,” Quarterly Journal of Economics, vol. 105, no. 2, May.
Heyman, Fredrik. "Pay Inequality and Firm Performance: Evidence from Matched Employer-Employee Data ." FIEF Working Paper Series 2002 (2002): 31-41. Web. 15 Apr 2010. <http://swopec.hhs.se/fiefwp/papers/WP186.pdf>
Jewell, Todd, and David Molina
Lewis, Michael. Moneyball. New York: W. W. Norton & Company Inc., 2003.
"MLB Team Stats." ESPN
Sommer, Paul, and Brandon Avrutin. "Work Incentives and Salary Distributions in Major League Baseball." Atlantic Economic Journal. 35.4 (2007): 509-510. Print.
"USA Today Salaries Databases." USA Today. N.p., 2010. Web. 16 Apr 2010.<http://content.usatoday.com/sports/baseball/salaries/teamdetail.aspx?year=2009&team=9>.
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