Testing the Determinants of Income Distribution in Major League Baseball

Article excerpt


Income of professional athletes is a frequent topic of discussion of fans, sportscasters, and the general public. Economists have also increasingly been attracted to this topic, because the distribution of income of these players has both policy and business implications. As a consequence, the report of Major League Baseball's (MLB) Blue Ribbon Panel, formed in 1998 by MLB commissioner Bud Selig, received quite a lot of attention. This panel was charged with describing and explaining the economic condition of MLB. Comprised of such dignitaries as former Federal Reserve Chairman Paul Volker, Senator George Mitchell, Yale University President Richard Levin, and columnist George Will, this panel reported that team payrolls have become increasingly disparate; the gap between "rich" and "poor" teams is not only wide but is growing (Levin et al. 2000). A case in point is the fact that the salary of the highest-paid player in the 2000 season (Los Angeles' Kevin Brown at $15.7 million) was 95% of the entire payroll of the poorest team, the Minnesota Twins. The effect, according to the panel's report, is a dramatic decline in parity and competitiveness of MLB. Since 1994, a team in the top payroll quartile has won every World Series game. In 1999, the teams with the five largest payrolls had an average winning percentage of 0.557, whereas the five poorest teams had a comparable figure of 0.444. The report discusses various recommendations that may narrow this gap, leading to what one might call "convergence" in team payrolls.

This article examines the distribution of income in MLB for 1985 to 2000. We revive (and extend) a technique first suggested by Thurow (1970) and show how it can be used to test for time trends versus cross-sectional demographic and economic aspects influencing income distribution. Thurow (1970) discusses the impact of various measures on median income. In the present case, by calculating the Gini coefficient, we can discuss the impact that the explanatory variables have on income inequality. In the context of MLB, we attempt to shed light on the reasons why teams have greater or lesser payroll disparity. We leave the question of the effect of between-team payroll inequality on team wins and team revenues for future research.

The issue of whether to use an inequality measure that is based on a particular distribution or one that is distribution-free has been widely discussed in the income distribution literature (Silber 1999). Ryu and Slottje (1999), in advocating the use of parametric distributions, point to several benefits of estimating the Lorenz curve in this manner. These benefits include the ability to summarize thousands of observation points with a few parameters, the ability to estimate the density function at any point, an enhanced ability to construct inequality measures, and the ability to formulate possible "laws" that would otherwise not be possible to detect. However, there are problems associated with the use of a parametric Lorenz curve, such as the choice of a suitable distribution. The problem is compounded by the fact that income data are often grouped and have an open-ended highest income category, making it difficult to obtain accurate estimates. One advantage of using professional sports data is the fact that the salary is available per individual and is not grouped; consequently, an actual number is available for the highest income.

This study analyzes payroll inequality in a professional sport by team. That is, for each year we calculate a measure of inequality for each MLB team and consider the characteristic differences in within-team payroll distributions. Our study is not, however, the first to consider income inequality within professional team sports. Depken (2000) and Jewell and Molina (2004) find that MLB teams with greater wage disparity have fewer wins. Furthermore, Sommers (1998) finds a negative relationship between team success and within-team payroll inequality using National Hockey League data. …


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