Abstract
This paper examines the gender wage gap in professional sports using a pooled cross-section of professional tennis players across the years 2011-2017. The dependent variable is the prize money earned by the top fifty male and top fifty female ranked tennis players throughout the world. This prize money is measured in 2017 real dollar value. The independent variables include: number of tournaments played, age, rank differentiation, gender, country and WTA/ATP score. Gender inequality is measured by determining the wage gap shown through the mean prize money earned by the professional tennis players from 2011-2017. While prize money for men and women has recently become equal in the Grand Slam tournaments, there is evidence to show that women's prize money is considerably lower in the less-publicized tournaments. Results of the ordinary least squares (OLS) regressions suggest that there is evidence for a gender-related pay disparity in professional tennis due to a number of statistically significant variables including WTA/ATP score (+), age (+), country (+) and the gender (-) and year (+) dummies.
TopIntroduction
Feminism is the general belief that women and men should be treated equally (Marcus, 2015). In spite of its simple principles and goals, such as the “Me too Movement” against sexual harassment and sexual assault, there are many who argue that feminism has no relevance in today’s society and that gender inequality exists but may be less pervasive. When looking objectively at the gender inequality issues that persist in our society, it is undeniable that women still face discrimination, especially regarding employment and objectification. While it is indisputable that women have advanced in society, there is still much progress to be made.
Throughout world history, gender has been correlated with economic discrimination, which suggests that discrimination will also transcend into the world of modern professional athletics. Professional sports are an important aspect of current American pop-culture. Many view professional sports as a means of enjoyable leisure activity and a time for fans to come together with a common interest. Even though sports indiscriminately reach the masses, where viewers are concerned, America fails to look at the economic discrimination among athletes regarding gender that prominently exists in professional sports today.
The topic of economic discrimination in professional sports has been examined previously, (Buysse, 2004; Hanson, 2012), so comparing the results among multiple sports to identify which sports exhibit the most gender inequality is not a difficult task. Substantial information (Kahn, 1991; Paserman, 2007) exists that relates to gender inequality in salary and prize money earned in professional sports, specifically tennis. Due to the popularity of professional tennis, in addition to the fact that there have been recent strides in reducing the sex pay gap in the sport, tennis has become an interesting case study to attempt to parse out the true discrimination that professional female athletes may face relative to their male counterparts.
Since there have been positive movements at a macro level for professional tennis in diminishing the sex pay gap, individuals may believe that the issue has been solved. However, Flake, Dufur, and Moore (2013) found that while prize money for men and women is equal in prestigious events such as the Grand Slams, which include Wimbledon, the US Open, Australian Open, and French Open, women’s prize money is considerably lower in many of the less-publicized tournaments. This creates financial barriers for players who compete in their remaining tournaments for the season and for those that do not qualify for the Slams. The prize money earned by the female winners of the four tennis majors being equal to that of the male champions was not always the case. The US Open was the first major to offer equal pay in 1973, championed by Billie Jean King. Approximately 28 years later, the Australian Open granted men and women champions equal prize money too. The French Open and Wimbledon soon followed suit in 2006 and 2007, respectively.
A snapshot into the economic discrimination that Flake et al. (2013) exposed in his work showcases the many barriers that professional women tennis players face today, even though the media often portrays tennis as an equal sport. In examining the prize money won per tournament played by each athlete in 2018, roughly 70% of the world’s top 100 men still earned more than women of the same ranking (WTA, 2018; ATP, 2018). Past male tennis athletes have spoken up and claimed that men earn the higher prize money since they attract more income and viewers; however, data suggest viewing figures are not based on gender but more so on the individual competing in the tournament. This trend was supported by factual evidence when, in 2010-2014, the women’s US Open final drew a larger audience in America than the men’s final. The reason for this is simple. Serena Williams, one of the most irrefutably popular athletes in any sport, competed in four of those US Open finals.
Key Terms in this Chapter
Chow Test: Indicates if the regression coefficients are different for split data sets. Basically, it tests whether one regression line or two separate regression lines best fit two different groups in a data set.
Discrimination: The unjust or prejudicial treatment of different categories of people or things, especially on the grounds of race, age, or sex.
Agency: A thing or person that acts to produce a particular result.
Regression: A measure of the relationship between the mean value of one variable (e.g., output) and corresponding values of other variables.
Multicollinearity: A state of very high intercorrelations or inter-associations among the independent variables.
Cross-Sectional: A type of data collected by observing many subjects (such as individuals, firms, countries, or regions) at the same point of time or without regard to differences in time.
Dummy Variable: A numeric variable that represents categorical data such as gender, race, political affiliation, etc.
t-Statistics: Used in a t-test when deciding to support or reject the null hypothesis.
Statistical Significance: The claim that a result from data generated by testing or experimentation is not likely to occur randomly or by chance; instead, it is likely to be attributable to a specific cause.
Descriptive Statistics: Brief descriptive coefficients that summarize a given data set, which can be either a representation of the entire or a sample of a population.
Heteroskedasticity: Refers to the circumstance in which the variability of a variable is unequal across the range of values of a second variable that predicts it.