THE PENNSYLVANIA STATE UNIVERSITY SCHREYER HONORS COLLEGE DEPARTMENT OF ECONOMICS ARE MAJOR LEAGUE BASEBALL PLAYERS PAID THEIR MARGINAL REVENUE PRODUCT? BRIAN SCHANZENBACH Spring 2014 A thesis submitted in partial fulfillment of the requirements for a baccalaureate degree in Economics with honors in Economics Reviewed and approved* by the following: Ed Coulson Professor of Economics Professor of Real Estate Economics Jeffrey and Cindy King Fellow in Real Estate Thesis Supervisor Russell Chuderewicz Senior Lecturer in Economics Honors Advisor * Signatures are on file in the Schreyer Honors College. i ABSTRACT This thesis studies whether Major League Baseball players are paid their marginal revenue product. In other words, the goal is to determine whether the players are paid the amount of money they make for their franchise. Major League Baseball wants a competitive league because a competitively balanced league makes the most amount of money. In order to have a competitively balanced league, the players must be paid their marginal revenue product. Thus, it is in the league’s best interest for the players to receive their marginal revenue product as compensation. In order to calculate the marginal revenue product, I ran winning regressions and attendance regressions. The winning regressions calculated what factors into a team’s winning percentage, while the attendance regressions calculated what factors into attendance at a ball game. The winning regressions gave me the marginal product and the attendance regressions gave me the marginal revenue. I was then able to calculate the marginal revenue product, as it is simply the marginal revenue multiplied by the marginal product. The next step was to regress the player salary on MRP, so that I could determine whether players are paid their marginal revenue product. I found that, on average, all Major League Baseball players are underpaid. Therefore, my recommendation to the league would be to increase the average salaries of each position accordingly to create a more competitively balanced league. ii TABLE OF CONTENTS List of Tables ........................................................................................................................... iii Acknowledgements .................................................................................................................. iv Chapter 1 Introduction ............................................................................................................. 1 Chapter 2 Literature Review .................................................................................................... 5 Chapter 3 Data and Methodology ............................................................................................ 13 Chapter 4 Results ..................................................................................................................... 22 Chapter 5 Conclusion ............................................................................................................... 28 Appendix A Data ............................................................................................................. 30 BIBLIOGRAPHY ............................................................................................................ 91 iii LIST OF TABLES Table 1. Winning Regression Data .......................................................................................... 17 Table 2. Attendance Regression Data ...................................................................................... 20 Table 3. Winning Regression ................................................................................................... 22 Table 4. Attendance Regression .............................................................................................. 23 Table 5. Salary and Marginal Revenue Product Regression Data ........................................... 26 Table 6. Determination of Whether a Player is Paid Their MRP ............................................ 27 Table 7. Team Data .................................................................................................................. 30 Table 8. City Population and Team Attendance Data .............................................................. 31 Table 9. Player Salary Data ..................................................................................................... 32 Table 10. Field Player Statistics Data ...................................................................................... 60 Table 11. Pitcher Statistics Data .............................................................................................. 75 iv ACKNOWLEDGEMENTS Professor Edward Coulson, For your advice throughout the completion of this thesis. Professor James Tybout, For your wisdom and help through the duration of the Honors Program in Economics. Chapter 1 Introduction Professional athletes are paid incredibly high salaries to play a game that is typically reserved for children. Some people believe athletes are paid far too much since their occupation is nothing more than entertainment. Others believe their salaries are perfectly just given the utility of entertainment for each individual can be substantial. The fact of the matter is professional sports franchises are cash cows. They make a huge amount of money, create many jobs, and are responsible for a great deal of cash flow, whether it is advertising campaigns during games or internal financing. The goal of this paper is to attempt to determine whether each position in major league baseball is paid, on average, the amount they make for their team. In a competitive market, as professional sports intend to be, a player will be paid the amount they make for their team given complete free agency. However, complete free agency does not exist because players sign contracts that are longer than one year. However, we will assume that when each player signs their contract they were provided with a salary equal to their marginal revenue product. If they are paid less than their marginal revenue product then they are underpaid, while if they are paid more they are overpaid. In order to go about solving this problem, first I did research to discover what work had already been completed in this area and what models were used. The major piece of literature was Scully’s paper on how performance influences salaries in Major League Baseball. He wrote the first major article on pay and performance in baseball and 2 his model has been used in various papers (Scully 1974). I based my model on Scully’s with a few changes. The other major literature I used was a thesis written by Brian Fields entitled “Estimating the Value of Major League Baseball Players.” His paper is an analysis similar to Scully’s paper, but uses more recent data (Fields 2001). My paper is similar as I am attempting to determine a player’s marginal revenue product to compare it to his salary. I will then be able to determine whether they are overpaid, underpaid, or paid at the margin by comparing these two values. However, there are a few major differences that make my study unique and interesting. First, my paper looks at the 2011 Major League Baseball season. Since it is an analysis of the 2011 season, it is the most recent analysis of this kind. This is important, as salaries, statistics, and franchise values have exploded in recent years. Second, my paper uses attendance to measure marginal revenue product. I believe that this will give a more accurate value because it is easier to measure attendance, since those numbers are readily available, while revenue numbers are not. However, there are a few problems with using attendance to measure marginal revenue product. First off, there are things that affect attendance that players do not have any control over. A few examples of these would be the weather, other events going on in the area, and promotional giveaways. I attempted to account for these as best I could in the regression model, but not everything was accounted for. Another major issue with using attendance is there is a maximum capacity at each stadium. There will be some bias when calculating the affect of different statistics on attendance because there are a finite number of fans capable of viewing the game. There may be more fans that want to see a given game, but we cannot account for that because they were unable to purchase tickets. 3 The paper will begin with a literature review of all relevant information that was used to write this thesis. That includes the two sources discussed above, as well as many other resources. The next step is the data work. I have run many regression models to determine the marginal product, marginal revenue, and affect of salary on MRP. I analyzed the data from the regressions to further understand which statistics matter and how much they matter. I analyze the data and explain the calculations. I then perform the marginal revenue product and salary calculations to get the numbers I will compare. Next, I compare those numbers to determine whether players are paid effectively or not. I then am able to determine which positions are overpaid and which are underpaid. The other interesting thing about this analysis is it is not simply a sports paper, but can be used to understand the labor market economically. It is difficult to measure the marginal revenue product of employees because so many factors do not have numbers associated with them. For example, the only true way to measure someone’s marginal