Article Title
Salary Dispersion and Team Performance in Major League Baseball: A Quantile Regression Analysis
Abstract
This paper investigates the relationship between salary dispersion and team performance in Major League Baseball. Player salary data is collected to calculate each team’s annual Gini coefficient from 1998-2016, which is used to represent a team’s level of wage inequality in a given year. The study incorporates a fixed and random effects model, and distinguishes itself from previous research by employing multiple quantile regressions to analyze how the impact of salary dispersion differs depending on a team’s performance level. The results find that the fixed effects model is preferred, and that there is consistently a negative relationship between wage differentials and team performance across all models used. Further, the quantile regressions completed reveal that salary dispersion has a greater impact on lower performing teams than it does for higher performing teams. This finding profoundly adds to existing research because it indicates that the relationship between salary dispersion and team performance is not uniform. This helps to advance the application of the regression results, where MLB teams can use these results to adjust their player selection and compensation decisions more effectively depending on which performance quantile their team falls within.
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