THE IMPACT OF PRIZE MONEY PERFORMANCE AND ACCLAIM

A THESIS

Presented to

The Faculty of the Department of Economics and Business

The Colorado College

In Partial Fulfillment of the Requirements for the Degree

Bachelor of Arts

By

Colbert Heathcott

March 2015

THE IMPACT OF PRIZE MONEY ON MARATHON PERFORMANCE AND ACCLAIM

Colbert Heathcott

March 2015

Economics

Abstract

In the past century, marathon running has become a major phenomenon in society. As a result, race participation and frequency have increased in the over the past decade. With the increased growth of the sport, the amount of money and the overall economic impact of races have risen, causing event organizers and sponsors to face decisions involving race awards and funding. Using an OLS regression model, this study examines the impact of prize money on marathon performance and acclaim. Results reveal marathon running to be exempt from incentive theory, as athletes do not perform better as a result of increases in winning prize money. Prize money also has no significant impact on the popularity of marathon events. Other factors, such as marathon location and history, significantly affect the acclaim of a marathon event. A thorough understanding on the impact of prize money is necessary for the future of the growing sport of marathon running.

KEYWORDS: (Marathon, Incentive, Sports Marketing)

ON MY HONOR, I HAVE NEITHER GIVEN NOR RECEIVED UNAUTHORIZED AID ON THIS THESIS

Colbert Heathcott

Signature

Acknowledgments

I would like to thank Dr. Judy Laux for advising me throughout the process of this research. I would also like to thank Will Richmond for Monday dinners, morning laps in the pool, and forcing me to go to the library. Without my wonderful roommates, BD and Robyn, this process would not have been nearly as fun. Also, without the wonderful teaching of every professor in the Colorado College Economics and Business Department, this thesis could not have happened – Thank you all.

TABLE OF CONTENTS

ABSTRACT iii ACKNOWLEDGMENTS iv 1 INTRODUCTION 1

2 THEORY 4 2.1 Incentives and the Theory of Tournaments 5 2.2 Marketing the Running Industry 6

3 LITERATURE REVIEW 10 3.1 Prize Money in the Sports Economic Context 10 3.2 State of the Running Industry 12 3.3 Determinants of Marathon Participation 15 3.4 Determinants of Marathon Performance 16

4 DATA AND METHODOLOGY 19 4.1 The Performance Model 19 4.2 The Acclaim Model 22 4.3 Descriptive Statistics and Correlation Tests 23

5. RESULTS AND ANALYSIS 25 5.1 OLS Regression and Other Tests 26 5.2 Analysis – Performance Model 26 5.3 Analysis – Acclaim Model 27

6. CONCLUSION 29

7. REFERENCES 33

8. APPENDIX A 37

9. APPENDIX B 39

LIST OF TABLES

1. The Economic Impact of Five Major U.S. Marathon Events 14

2. Descriptive Statistics - Performance Model 24

3. Descriptive Statistics - Participation Model 24

LIST OF FIGURES

1. Number of participants in ten major U.S. in 2014 2

2. Prize distribution in five major U.S. marathon events 7

3 Correlation Matrix – Performance Model 25

4. Correlation Matrix – Participation Model 25

Introduction

Tracing back to 490 B.C., the marathon has evolved into a major facet of modern culture. As legend states that Pheidippides completed his legendary run from Marathon to Athens, and reportedly died at its conclusion, the feat of completing a run of such magnitude was born. Since that day, millions of people all around the world have attempted to run as far as Pheidippides. As a result, the marathon has become a common event in society and is growing more each passing year. In fact, in the past 37 years, individual marathon completions in the United States have increased by 2164% (Running

USA, 2014). Through the years, as a result of increases in participation, the marathon has become commercialized with sponsorship, publicity, and rewards while retaining its aura as a monumental accomplishment upon completion.

Although participation in the marathon has increased dramatically, the overall performance by individuals in the marathon has significantly declined in the United

States. In fact, the median finishing time of a marathon in the past 33 years has increased by 27:43 for males and 37:59 for females (Running USA, 2014). The slower median times of the marathon suggest a competitive decline in the event as a whole. With increased participation and commercialization, the race itself, for most participants, becomes more about finishing than winning.

As runners flock to the marathon, the differences between various marathon events must be noted. Marathons differ in many ways, yet every certified marathon covers the same distance of 26.2 miles. As these courses change, certain races cater to a niche market of competitors while others cater to the masses. For example, a marathon gaining

7000 feet of elevation is likely to produce dramatically different results and attract a

1 different crowd of participants than a marathon in a major city, such as . The following study investigates the determinants of winning race times and marathon participation, observing the different United States marathons over time. Two different models are employed in the study to examine the impact of prize money on performance and participation. The first model observes the determinants of winning times in ten major U.S. marathons over the course of their existence, specifically focusing on the impact that prize money has on individual performance. (See Figure 1.) The second model observes the determinants of race participation in 100 various U.S. marathon events, specifically focusing on the impact that prize money has on event acclaim.

45000 40000 35000 30000 25000 20000 15000 10000 5000 0

FIGURE 1. Bar graph showing the number of participants in ten major U.S. marathons in 2014. From “10 Biggest Marathons in the U.S.,” by Active Network, 2014.

In the field of all sport competition, the presence of prize money can significantly affect the outcome of an event. In sports economics research, the theory of tournaments states that the attainment of money through performance is a motivating factor for all

2 athletes. In terms of the marathon, this does not seem to be the case. Although prize money has increased overall in the past few decades, current research reveals that median finishing times have become slower. Since median times significantly differ from winning times, trends in the performance of marathon winners, as opposed to trends of middle-of-the-pack finishers, are necessary in researching the effectiveness of prize money. This study specifically determines whether prize money affects winning marathon times by observing changes in prize money between different marathons and hypothesizes that, in marathon competition, prize money at stake does not affect winning times. Despite the rapid growth of the sport and increases in prize money, winning marathon times have remained fairly constant in the past 30 years. Other variables such as altitude, weather, and race size are true determinants of marathon pacing. As a result, the sport of the marathon disproves the theory of tournaments.

Because of its stance in society as a mass cultural phenomenon, marathons are exempt from the standard sports economic theories involving compensation as motivation. The beauty of running as a sport is its inclusivity, as anyone can participate.

Although some races have required qualifying times, most do not, allowing anyone to register. The inclusivity of marathons is visibly apparent when professional runners are lined up at the same starting line as non-professionals and each is running the same distance on the same course. Not many other single participant sport competitions offering prize money, such as or tournaments, share the same inclusivity in their competitors. Along with the impact of prize money on performance, this study also examines the impact of prize money on race participation, hypothesizing that increases in prize money lead to increases in the demand to participate. As the running industry

3 grows, the races get bigger, and the marathon phenomenon continues to build, an understanding of the impact of prize money should be useful in race planning and marketing strategy.

This thesis includes four sections following this introduction. The first describes the theory of tournaments and the effectiveness of incentives in detail. Next, an explanation of the marketing sector of the running specialty industry is provided. The following section then consists of a literature review that provides a foundation for the research methods employed in the study. Using existing literature and sports economic theory, this section describes the effect of prize money in the sports industry, portrays the impact of the growing running industry and its present economic trends, and defines the determinants of marathon acclaim and individual marathon performance. The section concludes by stating the differences between the marathon and other sporting events, suggesting why this research is necessary. Next, the data collected and the regression models used for analysis are described, and the final section reveals the results of the study, discusses limitations, and offers avenues for further research.

Theory

The theory discussed in this section is necessary to determine the potential impact of prize money on marathon performance and acclaim. To determine the effectiveness of prize money, performance and enrollment trends in the marathon must be analyzed. All in all, theory suggests a significant positive correlation between prize incentives and athletic performance. Concepts in event marketing also suggest a positive correlation between available funding and event participation. The section is divided into two focuses:

4 1) The first will discuss the economic theory of incentives and the theory of tournaments in a sports economics context.

2) The second will discuss the growth of the running industry and the marketing theory behind event acclaim.

Incentives and the Theory of Tournaments

In economic theory, people are driven to perform by incentives. Simply put, incentives are a motivating force. In many cases, an incentive is a tangible award, such as a cash bonus or a free vacation. Neurologically, when one receives an award, dopamine is released in the brain, which produces a positive feeling. Due to the nature of the brain and dopamine, it is in human nature to possess a motivation to attain awards.

In the workplace, managers use incentives to help get things done and provide competition between workers. If workers are competing to receive an incentive, their effort will change depending on the change in utility from gaining or failing to receive the incentive. Utility, in an economic context, is the amount of satisfaction one receives from consuming a good or service in an economy. Utility varies on an individual basis, which explains why some individuals are more motivated to work for certain incentives than others.

Incentives are important because in the simplest form, they explain why people do what they do. With the placement of incentives, people can be persuaded to change habits and make decisions that they normally wouldn’t. For example, a college student likely has no interest in grading accounting homework for pleasure. If you offer the college student $9 an hour to grade, the incentive of money will likely persuade the student to take the job.

5 In the field of professional sports, incentives factor into performance efforts. In major sports (e.g., , football, and ) performance-based incentives are offered along the lines of individual statistical accomplishments, such as hitting 40 home runs in a season, and team accomplishments, such as winning a championship. In professional running, competitors are rewarded for winning races with prize money and other awards. Theoretically, professional runners respond to incentives the same way as other professional athletes and everyday workers.

Basic incentive theory is described further by the theory of tournaments in a sports economic context. For example, a tournament with two competitors involves a winning prize and a small consolation prize for the loser. As the difference between losing and winning increases, the incentive to win also increases. As the incentive to win increases, the investment of the competitor increases as well. Based on this theory, an increased spread of prizes will result in greater effort of competitors. Thus, in a marathon event, runners will try harder based on the gain or loss in rewards. Typically, most major marathon events offering prize money include significant differences between winning and consolation prizes. (See Figure 2). The current study examines the power of incentives on marathon performance in relation to the theory of tournaments, specifically tracking the changes in prize money in different races over time.

Marketing the Running Industry

Through the years, the culture of running has grown tremendously, and along with the growth of running, the marketing associated with the industry has developed. Back in the mid 1900s, only a few running specialty brands existed, and the concept of a running specialty store was foreign. In the present, many brands exist, stores are plentiful, and

6 run marketing becomes relevant. To understand the marketing behind the running industry, one must address the brands, the products, the consumer, and the events.

FIGURE 2. Bar graph showing the prize distribution in five major U.S. marathon events.

160,000

140,000

120,000

1st Place 100,000 2nd Place 80,000 3rd Place

60,000 4th Place 5th Place 40,000

20,000

0 NYC Boston Chicago Honolulu Los Angeles

Although running culture was firmly rooted in brands such as Nike and in the 1970s, many more running-oriented brands are thriving in the present. Promoters of each of these brands use different marketing strategy to make themselves known in the industry. For example, Nike advertises a “Just Do It” campaign, while encourages runners to “Find Your Strong.” Nike’s message involves the mentality of getting things done and achieving goals, while Saucony’s message is more about the strength an individual can find within him or herself through participation in running. As a result, each brand attracts a different type of customer. Due to the motivation-based strategy of Nike, runners choosing the brand tend to be goal-oriented and driven. With

7 Saucony’s more universal message, runners choosing the brand tend to be focused on personal growth by means of the sport. Both of these brands represent major figures in the industry and function well with differing strategies.

Over time, specialty products related to running are evolving to meet consumer interests and industrial innovation. Many companies produce the same shoes every year, making only minor changes to color and fit. In most cases, these decisions are simply made on the basis of sales and keeping up with the competition. Because of the minor changes, many brands develop a cult-like following of people who repeatedly purchase the newest edition of the shoe every year.

Without the consumer, the running specialty industry would not have grown so rapidly and would not continue to grow. With modern running specialty marketing, consumers are persuaded to purchase gear that they never believed they needed in the past. Back in the early stages of running, a typical runner only needed a shirt, shorts, stopwatch, and shoes. Now, thanks to marketing and specialty retail, runners are encouraged to have GPS watches, spandex, multiple pairs of shoes, and many other accessories that did not exist in the early stages of running as a sport.

With brands, products, and consumers, actual running events serve as an inclusive venue for all aspects of running specialty. Specifically, brands use events as a venue for recognition and product marketing. All major races in the United States have a sponsor, and the sponsor is seen and heard throughout the race experience. Hypothetically, if a runner has a positive racing experience, the runner will then feel positively towards the sponsoring brand. With regard to the actual product, many brands will use events as a venue to get their product tested and noticed. At many big races, different brands will set

8 up booths and give away samples of products they are selling. The personal interaction with the race, product, brand, and the consumers serves as an effective marketing strategy in the event sector.

The growth of the running industry and associated marketing warrant an investigation. In terms of prize money in marathon racing, increased sponsorship may lead to a change in prize distribution and amount, resulting in possible increases in participation. Branding marathon-related products attracts consumers, so an understanding of incentives on competition involving the consumer sets the tone for future marketing development. In theory, events with increased funding and sponsorship presence should make more money than events with less funding and should also draw more consumer attention. Increases in participation result from a higher demand to participate in the event, which is driven by money being put into the event by supporting organizations. This study examines whether prize money is an effective determinant of participation at major marathon events, as prize money may be representative of overall marketing efforts.

All in all, incentive theory and marketing provide insight into a thorough grasp of the marathon event. In terms of incentives, individual performance in the marathon relates to the underlying factors that serve as motivational tools in the racing environment. In terms of marketing, the growing running industry and its economic implications serve as a framework for recognizing the importance of the marathon event in society. The theoretical implications described above are backed by literature in the field of sports economics, marketing, and physiology, as evidenced by the following review of literature.

9

Literature Review

In the previous section, we explored sports economic theory and the basis of marathon marketing. After concluding that athletes are motivated by incentives in terms of performance efforts, we now find that the literature reveals conflicting results. The literature also provides a firmer grasp on the current state of the running industry and the determinants of marathon acclaim. Many factors affect an athlete’s level of performance and motivation to participate in certain events. For most competitive sports, existing research suggests a relationship between rewards and performance in certain athletic venues. In research specific to running, evidence reveals a lack of significant correlation between incentivized rewards and performance efforts. The following literature review examines research regarding prize money in the context of sports, the current economic impact of the running specialty industry, determinants of marathon acclaim, and determinants of overall marathon performance.

Prize Money in the Sports Economics Context

In most professional sports, athletes are paid their perceived value and have the opportunity to make a higher income through performance-related bonuses. Specifically, a contract year phenomenon in major sports involves the performance of athletes and their contract situations. According to a study on price motivation in professional sports,

Major League Baseball players perform better in their contract years in hopes of receiving more money in their next contract (Konstantatos, 2009). In the season after they receive a new contract, however, results show a statistical decline in performance.

Similar studies addressing the contract year phenomenon have also been conducted with the National Basketball Association (White & Sheldon, 2014). Due to the lack of

10 motivation following a new contract, both studies suggest that managers be cautious of a decline in performance when structuring new contracts.

In less-known sports, research reveals individual motivation for prize money and the effect on performance efforts. In a tournament setting, Dodgeball is a growing minor sport that has experienced increases in prize money and sponsorships in the past decade

(Beaton, 2014). With this growth, more adults are competing in tournaments with prize money awarded to the champions. This is evident in the fictional movie “Dodgeball: A

True Underdog Story” (2004) when Vince Vaughn’s character, Peter La Fleur, creates a dodgeball team in hopes of making $50,000 to keep his gym, Average Joes, in business.

Another growing minor sport, Ultimate Frisbee, is gaining sponsor recognition and athlete participation with increases in prize money as a result. In Ultimate Frisbee, prize money has become commonplace and athletes no longer play for free (Sludge Online,

2013). As a result, teams such as Spikes Peak, based in Colorado Springs, travel to various tournaments across the country in hopes of winning prize money (Norman,

2014).

During actual competition, the theory of tournaments predicts that a worker’s effort depends on the difference between winning and losing prizes (Connelly et al., 2014).

Specifically the theory of tournaments is revealed in Arabian horse racing. During competition, jockeys typically increase their efforts in the second half of races when the amount of prize money lost by dropping a place is greater (Lynch 2005). In horse racing, however, the marginal difference between the first and second place suggests higher competition than marathon running as a whole. In most major marathons, the time between first and second place ranges from a few seconds to over ten minutes; in horse

11 racing, the difference in finishing time between first and second place typically is less than one second, depending on the length of the race. With a major gap between the winner and last place participants, the theory of tournaments likely does not apply to most popular distance running events in the same way that it applies to horse racing.

Much has been written in theoretical literature testing the theory of tournaments, and results are mixed. In professional road racing, the relationship between prize money at risk and finishing time is examined using a data set containing performance information from USA Track and Field certified courses of various distances in 1994

(Lynch & Zax, 2000). Using fixed effects controls for runner ability, results reveal a weak relationship between prize money at risk and finishing time. Although races with large prizes had faster finishing times, results reveal the faster finishing times were apparent because races with greater prizes attract faster runners. In contrast to the theory of tournaments, the study reveals that prizes available do not encourage all runners to run faster.

Although sports economic theory reveals the potential effectiveness of incentivized performance, the presence of conflicting results in empirical research exhibits a lack of clarity in the sport of marathon running. The following section provides a framework for understanding the current position of the running industry. With this understanding, we will continue to analyze the effectiveness of prize money in the marathon spectrum.

State of the Running Industry

The running industry has grown tremendously in the past few decades and is continuing to grow in the present. Research indicates more than 13.4 million U.S. adults are avid runners, defined as running 100 or more times a year (Williams, 2007). Of that

12 group, over 8 million runners compete in organized road races. Since 1997, the number of marathons in the U.S. has grown 29% (to over 400 races) and participation has increased by 35% (to over 470,000 marathon runners) (Running USA, 2014). The

Boston Marathon and ING City Marathon are the most well-known marathons with 22,000 and 40,000 participants in 2011, respectively. Other major marathons in the

United States include the Bank of America , Marine Corps Marathon, and Honolulu Marathon, among others. These major marathons take place in big cities throughout the country that serve as a tourist destination alongside the purpose of the marathon event.

As marathon races have become major tourist attractions, they are recognized as more than niche sporting events. Marathons have a lasting economic impact that stimulates the local economy of their location. (See Table 1). In professional sports, many cities compete over franchises in hopes of bringing in more revenue. For the marathon, cities act in a similar manner. The economic impact of a marathon is measured by changes in economic activity in an area as a result of the marathon event.

The growth of the event reveals that cities are embracing these marathons for the associated economic upswing. A marathon benefits a city by bringing people in to see it, showcasing what the city has to offer, and bringing people back in the future (Georgiadis,

2010). With all of these benefits, the number of marathons has increased and will continue to increase in the future.

13 TABLE 1

The Economic Impact of Five Major U.S. Marathon Events

Marathon Economic Impact

ING Marathon $250 million

Honolulu Marathon $100 million

Boston Marathon $95 million

Bank of America Chicago Marathon $80 million

Marine Corps Marathon – Washington, D.C. $20 million

Adapted from “Chapter 48: Marathons” by R.K. Miller and K. Washington, 2009, Sports Marketing, p. 211.

Along with hosting cities, many sponsors benefit from supporting marathon events.

In the event, marathoners serve as pre-qualified customers; sponsors receive interest and commonality, rather than having to attract consumers on their own, through more standard marketing activities. Following a marathon event, attributes such as brand recognition and trust increase, as does interest in doing business with sponsors and the probability of recommending sponsored products and services to others. In most cases, marathon finishers are a sought-after customer: they’re highly educated, high earners, and in most cases, travel and spend more on hospitality (Georgiadis, 2010). Although most sponsors decline to reveal the return on investment, an increase of marathon sponsorships in the past few decades indicates a worthwhile relationship between sponsors and race organizers.

With the rise of nation-wide marathon running, the professional running community in the U.S. has developed considerably in the past decade. Following a

14 domestic performance decline in the late 1990s, more resources have become available for long distance runners, encouraging them to pursue professional running careers.

Specific training centers around the country serve as a venue for athletes to focus on running full time. These centers provide income, coaching, physical therapy, and many other services to professional distance runners (RunPro, 2014). As these training centers become more established, running performance and participation will continue to improve in the United States.

Determinants of Marathon Participation

The motivation for individual participation in marathon events is rooted in a variety of factors. With the development of marathon running, event organizers strive to market certain factors that, in hopes of gaining acclaim, present their brand in different ways than others. As different styles of races take place throughout the world, race organizers emphasize event image and customer satisfaction, both vital in developing brand popularity (Sung, Byon, and Baker, 2014). Potential marathon runners choose certain races over others, warranting an investigation of the determinants of individual decision- making regarding the marathon event.

Marathon races are being recognized as more than niche sporting events; they have become major tourist attractions. When runners choose a marathon to run, they may make their race decision based on where they would like to travel. Every year more than eight million runners compete in and finish races. Many of these runners travel for the events, often bringing along family members or friends (Miller and Washington, 2008).

Event organizers throughout the world take advantage of the growing marathon tourism industry by creating races of their own. Race organizers strive to market the

15 differentiating factors of their race in hopes of increasing the demand for their product

(Weber, 2005). For example, the ING Miami Marathon incorporates the social media hashtag #miamifamous into its marketing campaign to differentiate itself from other marathon events by the terms of the societal undertones involving the city of Miami (The

Miami Marathon, 2015).

The historical significance of the marathon event serves as a framework for each future marathon to build on the prestige set by its predecessors. Since the 1976 New

York City Marathon, the first “urban tour” marathon, the sport has exploded on a global basis (Burfoot, 2007). Before this explosion, marathons were limited largely to the

Olympics, Boston, and a few other races. As an Olympic event deeply rooted in Greek tradition, the marathon continues to draw in participants year after year due to its history.

Participants likely choose certain races over others based on their prestige, which is a result of how long each race has existed and whether significant achievements, such as world records or Olympic qualifications, have occurred at the race (Roll, 2014). All in all, literature reveals that tourism and prestige are significant determinants of marathon participation.

Determinants of Marathon Performance

Marathon performance always differs, as races have different features and participants and take place at different points in time. Literature suggests that environmental factors, among others, play a role in determining marathon performance.

Recent studies examine the effect of altitude on marathon race times using a data set of

16 popular marathons at various elevations (Lara, Salinero, & Coso, 2014). Results suggest that races in cities at higher altitude produce slower times than cities closer to sea

16 level. Along with altitude, temperature significantly affects marathon performance.

Studies in the physiological effects of weather determine that temperatures between

38.9°F and 43.2°F are ideal for marathon running, but ideal temperatures vary in every runner (Hutchinson, 2014). Races at temperatures outside the ideal range produce slower times than those within the ideal range. Specifically, runners with less experience and preparation tend to struggle more in heat than veteran marathon competitors.

Along with overall race variables involving the environment, the difficulty of any course can be measured by the overall elevation profile of the course. Flatter courses typically produce faster times than those that contain many uphill and downhill climbs

(Davis, 2010). Physically, these climbs are taxing on the body and weaken the competitor. Studies on the topic reveal that running hills requires different muscle power factors and rely on higher volume of oxygen levels more than running over flat distances

(Paavolainen, Nummela, and Rusko, 2000).

The fact that marathons lack uniformity, except for the 26.2 mile distance covered by participants, reveals itself in the differing number of participants in various races throughout the country. For example, the data set in the Performance Model in this study involves marathons ranging from 8,166 finishers to 43,660 finishers (Active Network,

2014). A difference in the number of participants affects race performance in many ways. In bigger races, runners have more people to pace alongside and with whom to share the experience. Benefits to group running include ease in pacing, improved motivation, and a higher ceiling for enjoyment (Robbins, 2009). In a less crowded field, runners may be missing some of the benefits they would receive in a larger race.

17 The ratio of male to female competitors differs in every race and serves as a determinant of overall marathon performance. Males historically have recorded faster marathon finishing times than females; thus, races with more males than females are likely to produce faster overall results than races with more females. In professional distance running, women’s races tend to be less competitive than men’s races, even with prize money and/or prestige at stake (Frick, 2011). In terms of the top tier of competitors, a race with a male-dominated field may suggest an increased level of competitiveness, resulting in a faster winning time.

Altogether, the literature describes various trends involving the marathon as an event and as an industry. In terms of athletic performance, the literature reveals a positive response between incentives and athletic performance in certain sport venues.

Research specific to running indicates a tendency for faster runners to compete in races with prize money. Despite these findings, no significant correlation between prize money and improved winning times is present in the results (Lynch & Zax, 2000). The marathon industry has grown significantly over the past few decades, and this growth has resulted in major economic impacts. Because of the potential economic effect, event organizers have created many different kinds of marathons throughout the world (Sung,

Byon, and Baker, 2014). Potential marathon runners choose certain races for various reasons, such as tourism and race prestige. Race organizers create races and market marathon events with tourism and prestige in mind, among other factors. Lastly, marathon performance is dependent on many variables, such as weather, altitude, sex, and race size. With a thorough base in past literature, the following sections introduce

18 two models that explore the impact of prize money on trends in marathon performance and the demand for marathon participation.

Data and Methodology

To evaluate the hypothesis that prize money affects marathon acclaim, yet has no impact on winning marathon performances, the current study utilizes two regression models. These models include data regarding various potential determinants of marathon performance and event acclaim. In the following section, each model is presented along with the expected results.

The Performance Model

The first model in the study, the Performance Model, implements an Ordinary Least

Squares (OLS) regression to analyze the impact of prize money on winning marathon times. For this model, the data set for the analysis will consist of the ten current largest

United States marathons over the span of their existence. The full model is expressed as follows: WINTIME = βo + β1PRIZE + β2MALE + β3WTHR + β4ALT + β5ELEVCH +

β6QUAL + β7SIZE, where: WINTIME = winning marathon time (in minutes), PRIZE = prize money (in dollars), MALE = percentage of male competitors in the total race field,

WTHR = weather conditions (0 = Not ideal, 1 = Ideal), ALT = altitude of hosting city (in feet), ELEVCH = change in elevation over the marathon distance (in feet), QUAL = qualifying time standard (0 = if not present, 1 = if present), SIZE = total number of competitors in each race. Each of these is described in more detail in the following paragraphs.

The dependent variable is the winning time of each marathon observed. The data were extracted from race result archives in each marathon’s online database. This

19 variable, described in minutes, takes on values from 123 to 175. Note that the lowest value in the data set, ’s victory (which resulted in a $150,000 prize), is much faster than the highest value in the data set, John McDermott’s victory at the first ever Boston Marathon in 1897 (which resulted in no prize money).

The first independent variable, PRIZE, measures how much prize money was available to the winner of each marathon. This variable does not include additional time- related bonuses that some races may have given out, as those data were unavailable.

Some races in the data set, such as the Marine Corps Marathon and Walt Disney

Marathon, have never offered prize money in their history. Others, such as the Boston

Marathon and , have offered prize money greater than $100,000 for a significant part of their existence. Recall that the hypothesis predicts an insignificant coefficient for this variable, disproving the theory of tournaments in marathon running.

The second independent variable, MALE, measures the percentage of male participants in each marathon. With this variable defined as a percentage of total participants, in theory, it should result in a negative coefficient, as races with male runners are more competitive (in terms of completion times) than those with a higher percentage of female runners. The third independent variable, WTHR, measures the weather conditions on the day of each race. If the race took place in ideal conditions, the variable takes on a value of 1. If the race took place in non-ideal conditions, the variable takes on a value of 0. An ideal condition for marathon running takes place in temperatures between 38.9°F and 43.2°F. Along with researching online weather archives, data were backed up by post-race press reports for each marathon event. These

20 press reports frequently mention weather and account for factors such as extreme humidity and wind. The WTHR variable involves a combination of both data sources, covering most factors regarding weather and performance. As runners tend to perform better in ideal weather conditions, the model predicts a negative coefficient.

The following two variables in the model involve environmental factors that likely affect performance. In the model, altitude (ALT) is measured by the elevation, in feet, of the city of the hosting marathon. A positive coefficient is predicted by the model, as distance runners tend to perform better in lower altitude races. To account for course difficulty, ELEVCH is a variable measuring the change in elevation over the course of each marathon. In theory, courses with a lot of overall elevation change are more difficult than relatively flat courses. Because of this theory, the model predicts a positive coefficient.

The sixth variable, QUAL, is a dummy variable measuring whether each marathon has a qualifying standard time for entry. A value of “1” will signify that the race has a qualifying time, with the value of “0” meaning the opposite. The model predicts a negative coefficient, as races with qualifying standards are likely more competitive. In the data set implemented, the Boston Marathon is the only race with a qualifying time standard. Over the 313 marathons observed in total, 33 past Boston Marathon events with corresponding qualified standards are included. The seventh and final variable,

SIZE, is a measure of how many runners participate in each race. The model predicts a negative coefficient for this variable, as leading runners may be more motivated in a larger field.

21 The Acclaim Model

The second model in this study, the Acclaim Model, uses data from 100 marathons in 50 different states in 2014. The purpose of the Acclaim Model is to determine the impact of prize money on the overall fame and reputation of a marathon event. The dependent variable, ACCLAIM, measures the scope of the overall desirability behind consumer preferences in a marathon event. Backed by research on the running industry and marathon participation, this study assigns each marathon a value from 1 to 10 based on its desirability and prestige. For example, the Boston Marathon is given a “10” as it is very difficult to gain entry into this race, arguably the most well known race in the country. On the opposite end of the spectrum, the Wicked Marathon in Wamego, Kansas is given a “1” based on its poorly designed website, minimal history, and low online ratings (Marathon Guide Online, 2014). The creation of the ACCLAIM variable serves as a quantifiable definition of current societal recognition of the 100 marathon events throughout the United States observed in this study. (See Appendix B for details.)

Now that we have defined the dependent variable, the model is as follows:

ACCLAIM = βo + β1PRIZE + β2WREC + β3YEARS + β4DEST + β5DIFF, where:

PRIZE = total prize purse available (in dollars), WREC = number of past world record performances, YEARS = how many years each race has existed, DEST = whether the race takes place in an ideal tourist destination or not (0 = not ideal, 1 = ideal), and DIFF = whether the course is relatively difficult or not (0 = not relatively difficult, 1 = relatively difficult). Each of these is described in more detail in the following paragraphs.

The first independent variable, PRIZE, measures the total prize purse available at each marathon. The total prize purse is defined by how much money is awarded to all

22 participants of each race. The model predicts a positive coefficient, as races offering more money generate higher demand for potential participants. The second independent variable, WREC, measures how many world records have been set in the history of each marathon. With world record performances, a marathon is likely to build prestige and increase in demand. The model predicts a positive coefficient for this variable.

The third independent variable, YEARS, measures how many years each race has taken place. The model predicts a positive coefficient, as races with a longer history garner more prestige and respect. The fourth independent variable, DEST (a dummy variable), measures whether each marathon takes place in an ideal tourist destination or not. DEST takes on a value of 1 if the marathon is located in an ideal tourist destination, such as Miami, Florida. DEST takes on a value of 0 if the marathon is located in a remote tourist destination, such as Fargo, North Dakota, and a positive coefficient is predicted.

The fifth and final independent variable in the Acclaim Model, DIFF, measures the course difficulty in each marathon. Each race is given a “1” if it takes place on a notoriously difficult course. If a race takes place on a course that is not difficult relative to others, it is given a value of “0.” Data describing difficulty were extracted from many online sources relating to each specific marathon. For DIFF, the model does not predict a positive or negative coefficient, as difficult courses are popular to some highly competitive runners but not to all potential marathon participants.

Descriptive Statistics and Correlation Test

Table 2 summarizes the descriptive statistics for the Performance Model, and Table

3 summarizes those for the Acclaim Model. The binary variables for the Performance

23 Model (WTHR and QUAL) and the Acclaim Model (DEST and DIFF) are not included in the summary statistic tables, because the calculations measure the percent change in the dependent variable when those binary variables are present. For any binary variable, the minimum is always 0 and the maximum is always 1; therefore, averages and standard deviations do not provide useful information.

A glance at the correlation coefficients of the independent variables (see Figure 3) reveals one potentially troublesome source of multicollinearity in the Performance

Model. correlation occurs between PRIZE and SIZE at .7635, which is above the maximum level of acceptance. A glance at the correlation coefficients of independent variables for the Acclaim Model (see Figure 4) reveals a high multicollinearity between WREC and PRIZE; a world record performance is most deserving of a larger prize purse. With these two exceptions, multicollinearinity does not appear to be a problem. The following section offers the regression results.

Table 2 Descriptive Statistics - Performance Model (n=313) Variable Mean Std. Dev. Min Max WINTIME (minutes) 133.70 5.05 123.03 175.16 PRIZE (dollars) 24386.58 40417.74 0.00 150000.00 MALE (percentage) 59.28 8.64 43.45 100.00 ALT (feet) 264.35 226.18 19.00 702.00 ELEVCH (feet) 1441.06 805.41 50.00 2145.00 SIZE (# of participants) 11494.53 10952.52 10.00 50740.00

Table 3 Descriptive Statistics - Participation Model (n=100) Variable Mean Std. Dev. Min Max ACCLAIM 4.61 2.86 1 10 PRIZE (dollars) 29559.20 113250.90 0 80600 YEARS (number of) 15.91 17.02 1 118 WREC (number of) 0.06 0.37 0 3

24 Figure 3 Correlation Matrix – Performance Model PRIZE MALE WTHR ALT ELEVCH QUAL SIZE PRIZE SIZE 1 MALE -0.0314 1 WEATH 0.0293 0.0153 1 ALT -0.0095 0.2123 -0.0594 1 ELEVCH -0.0722 0.3647 0.0686 0.0207 1 QUAL 0.2360 -0.0686 0.0054 -0.1875 0.242 1 SIZE 0.7619 -0.1389 0.0394 -0.0129 -0.2909 0.0391 1

Figure 4 Correlation Matrix – Acclaim Model PRIZE YEARS WREC DEST DIFF PRIZE 1 YEARS 0.6217 1 WREC 0.8119 .3701 1 DEST 0.3214 .3875 .2671 1 DIFF 0.2314 .1416 .2075 .1736 1

Results and Analysis

This section presents the results of the regression runs for the two models. Recall that in the performance model, we have six independent variables, all expected to be significantly correlated to WINTIME and one independent variable, PRIZE, predicted to be insignificant, if the theory of tournaments does not hold. In the Acclaim Model, we have five independent variables expected to be significantly correlated with ACCLAIM

(a measure of marathon popularity and desirability). Specifically, the impact of prize money on the independent variables will be observed with the hypothesis that prize money has an insignificant impact on winning times and a positively significant impact on race acclaim. The inclusion of these variables is based on a sizable body of work already in existence on the subject; however, none of the literature has considered this particular question.

25 OLS Regression and Other Tests

An ordinary least squares (OLS) regression was implemented in both the

Performance Model and the Participation model. On top of running an OLS regression, this study will check for heteroscedasticity, as it is fairly common in cross sectional studies. If the model is heteroscedastic, robust standard errors will be included. This study will also check for omitted variable bias in each model. Lastly, this study will analyze the R-squared value of each model and the significance of the included variables.

Analysis – The Performance Model

Before beginning to analyze the results, we must address several other potential problems. Using White’s test for heteroscedasticity, the data reveals a high Chi-squared value of 99.37. (See the Appendix A for details on this and the following results.) To account for heteroscedasticity, the model is tested with standard errors. The possibility of omitted variable bias is explored by conducting a Ramsey RESET test using powers of the fitted values of WINTIME. The RESET test reveals an F-value of 9.03, which is not alarmingly high but reveals that omitted variable bias may be a concern. Other factors, such as the training regimen of each individual winner, are likely determinants of winning marathon times. Unfortunately, that data would be very difficult to collect given the data set present in this study.

The regression results reveal an R-squared value of 0.4362, meaning that the coefficients of the independent variables explain 43.62% of the variation in winning marathon times. OLS results reveal that the hypothesis cannot be rejected, as PRIZE is statistically insignificant with a t-value of -.66. Other independent variables, such as

WTHR, ALT, QUAL, and SIZE, are statistically significant with a negative

26 coefficient, as predicted. Specifically, for this sample, winning race times that took place in ideal conditions are 1.36 minutes faster than those in non-ideal conditions. For every decrease in feet of altitude, winning times are .0075 minutes faster. The Boston races with qualifying standards result in winning times 3.604 minutes faster than those without qualifying standards. For every increase of 1 participant in the race field, winning times are .0002 minutes faster. The independent variables ELEVCH and MALE are insignificant in the model. ELEVCH is likely insignificant because it accounts for downhill and uphill climbs, which may cancel each other out depending on each specific course. A surprising result in the regression is the insignificance of the MALE variable. Theory argues that races with more male competitors will be more competitive and achieve faster winning times. For the current study this is not the case.

The Performance Model satisfies the purpose of the current study by revealing determinants of marathon performance and shedding light on the complicated past research on the theory of tournaments. As research in sports economics regarding incentivized motivation in performance is a mixed bag, the Performance Model reveals that prize money does not have a significant impact on performance efforts in marathon running. Although the insignificance of prize money is not present in all sports, the motivations of long-distance runners warrants further research. Clearly, this study concludes that most competitive marathon runners are not in it for the money.

Analysis – The Acclaim Model

As with the previous model, before beginning to analyze the results, one must address several potential problems. Using White’s test for heteroscedasticity, the data reveal a high Chi-squared value of 66.06. Similar to the Performance Model, this value is

27 not a surprise, as heteroscedasticity is fairly common in cross sectional studies. To account for heteroscedasticity, the Acclaim Model is also tested with robust standard errors. As before, the possibility of omitted variable bias is explored by conducting a

Ramsey RESET test using powers of the fitted values of ACCLAIM. The RESET test reveals an F-value of 14.2, which is not alarmingly high but reveals that omitted variable bias may be a concern. Other factors, such as the overall marketing effort in each event, are likely determinants of the demand for marathon participation, but no such measure was available in the current study.

The regression results reveal an R-squared value of 0.3771, meaning that the coefficients of the independent variables explain 37.71% of the variation in marathon acclaim. OLS results reject the hypothesis, as PRIZE is statistically insignificant at the

95% confidence level with a t-value of .94. Although the coefficient is positive, the t- value is not high enough to reject the null hypothesis. Other independent variables, such as YEARS and DEST, were statistically significant with a positive coefficient, as predicted. According to these variables, for every additional year a marathon has existed, its demand will rise by .0445 within the scale of 1 to 10 that measures acclaim in the model. If a marathon takes place at an ideal tourist destination, its demand will increase by 1.876 within the scale. The remaining independent variables, WREC and

DIFF, are statistically insignificant. Since world records are a rare occurrence, the insignificance of WREC is not a surprise. Difficulty, as predicted, is statistically insignificant, which may warrant further investigation on consumer preferences in course difficulty.

28 The Acclaim Model provides insights into the determinants of marathon popularity.

Above all determinants, race organizers must note the significance of years and destination. For a race to gain acclaim, it must have continuity throughout the years. The location of a race also appears to be an important factor of popularity as marathons draw many tourists throughout the world. The model rejects the hypothesis that prize money is significantly related to race acclaim. All in all, the acclaim model reveals that prize money should not be the primary focus of overall marketing efforts.

Conclusion

This analysis has addressed several objectives. On a theoretical level, the study examines the effect of incentives on physical performance, the theory of tournaments, marathon marketing, and trends in marathon performance. Literature reveals mixed results on the theory of tournaments, warranting further investigation. The literature section also highlights the current state of the running industry and reveals determinants of marathon performance and acclaim.

This study designs and investigates the power of two models, the Performance

Model and the Acclaim Model. The Performance Model tests for determinants of winning marathon times. In terms of performance incentives, the model aims to investigate whether the sport of marathon running is exempt from sports economic theory, specifically the theory of tournaments. The Acclaim Model tests for determinants of marathon desirability. In terms of marketing theory, the study aims to investigate whether total prize money is a determinant of event acclaim. Both models analyze two important aspects of the marathon running phenomena: performance and participation.

29 The results of the Performance Model indicate that we can reject the hypothesis that prize money has a significant impact on winning marathon performances. This result reveals that the theory of tournaments is not present in this study of marathon running.

Along with the prize money, total elevation change and percentage of male competitors show no significance in the model. The Performance Model reveals that weather, altitude of the hosting city, qualifying standards, and race size are significant determinants of winning race times.

The Acclaim Model suggests that we can reject the hypothesis that prize money is significantly correlated with race acclaim. Other variables, such as years (how many years a race has taken place) and destination (a binary measuring whether a race takes place at an ideal tourist destination) were positively correlated with race acclaim, as predicted. Along with prize money, world record history and course difficulty demonstrated insignificant correlation to race acclaim.

While the investigation offered many insights, this study could be improved in multiple ways. First, omitted variable bias is present in both models. To correct for this problem in the Performance Model, variables involving the training routine of marathon victors (to account for individual performance capabilities) and the amount of prize money awarded to the 2nd place finisher (to further validate conclusions about the validity of the theory of tournaments) should be added to the model. To correct for omitted variable bias in the Acclaim Model, a variable measuring total marketing effort may better serve as an indication of acclaim than the variables used. Each data set could also improve with the addition of more marathon events and their corresponding data.

30 Despite these shortcomings, this study succeeds in revealing determinants of winning marathon times and marathon acclaim, findings that assume some importance because they reveal key factors in the currently growing running industry. With the knowledge of prize money’s insignificant impact on performance and acclaim, for example, race organizers and sponsors can rethink their marketing strategy. Rather than giving a large sum to the winner of a marathon, the event organizer might decide participation would be increased by giving more money to other finishers or using the money to pay appearance fees for highly regarded professional athletes. Perhaps to enhance race acclaim, organizers should focus less on funding prize money and spend more on a broader range of marketing efforts.

All in all, this study examines the effectiveness of prize money in marathon running. The Performance Model reveals that the incentives of earning prize money in a race have an insignificant effect on performance. To build on the understanding of prize money, the study introduces the Acclaim Model to provide insight into consumer marketing. In this model, the prize variable served as a proxy for overall marketing efforts. In theory, events with more prize money are targeted towards a wider spectrum of potential marathon participants. Events with high acclaim, in theory, target diverse consumers; thus, events with more prize money will be more desirable for the average marathon runner than others. In contrast to this theory, however, results reveal that prize money is an insignificant determinant of race acclaim. The major takeaway is that marketing efforts leading to increased acclaim do not necessarily coincide with the total prize purse awarded to participants in a marathon event. Sponsors would be well-advised to broaden their marketing efforts accordingly.

31

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35 Appendix A

Heteroscedasticity Test – Performance Model

White’s test for Ho: homoscedasticity

against Ha: unrestricted heteroscedasticity

Chi2(31) = 99.37 Prob > Chi2 = 0.0000

Omitted Variable Test – Performance Model

Ramsey RESET test using powers of the fitted values of WINTIME Ho: model has no omitted variables

F (3,302) = 9.03 Prob > F = 0.000

OLS Regression – Performance Model

. regress WINTIME PRIZE MALE WTHR ALT ELEVCH QUAL SIZE, robust

Linear regression Number of obs = 313 F( 7, 305) = 42.96 Prob > F = 0.0000 R-squared = 0.4362 Root MSE = 3.8417

Robust WINTIME Coef. Std. Err. t P>|t| [95% Conf. Interval]

PRIZE -4.50e-06 6.78e-06 -0.66 0.507 -.0000178 8.84e-06 MALE .120496 .0746638 1.61 0.108 -.0264254 .2674174 WTHR -1.361569 .4582062 -2.97 0.003 -2.263214 -.4599235 ALT -.0074295 .0010982 -6.77 0.000 -.0095905 -.0052685 ELEVCH .0004129 .0003415 1.21 0.228 -.0002592 .0010849 QUAL -3.604547 .764007 -4.72 0.000 -5.107939 -2.101155 SIZE -.0002025 .0000266 -7.61 0.000 -.0002549 -.0001502 _cons 131.7702 3.83777 34.34 0.000 124.2183 139.322

36 Appendix A continued

Heteroscedasticity Test – Acclaim Model

White’s test for Ho: homoscedasticity against Ha: unrestricted heteroscedasticity

2 Chi (31) = 66.06 2 Prob > Chi = 0.0000

Omitted Variable Test – Acclaim Model

Ramsey RESET test using powers of the fitted values of WINTIME Ho: model has no omitted variables

F (3,302) = 14.2 Prob > F = 0.000

OLS Regression – Acclaim Model

. regress ACCLAIM PRIZE YEARS WREC DEST DIFF, robust

Linear regression Number of obs = 100 F( 5, 94) = 10.21 Prob > F = 0.0000 R-squared = 0.3771 Root MSE = 2.3165

Robust ACCLAIM Coef. Std. Err. t P>|t| [95% Conf. Interval]

PRIZE 7.17e-06 7.64e-06 0.94 0.351 -8.01e-06 .0000223 YEARS .0445301 .0178083 2.50 0.014 .0091714 .0798889 WREC -.8042912 1.573883 -0.51 0.611 -3.929272 2.32069 DEST 1.876642 .5553197 3.38 0.001 .7740414 2.979242 DIFF .1500616 .4761714 0.32 0.753 -.7953878 1.095511 _cons 3.174151 .3756239 8.45 0.000 2.428341 3.919961

37 Appendix B

Marathon Ratings for the Acclaim Model

Note – all variables either count for “1” (indicated with an X) or “0” (indicated by a blank.) The values are constructed from the website www.marathonguide.com, my personal experience working in the running specialty industry for five years, and each race’s individual website and social media platforms.

• EXC : A marathon is exclusive, EXC, if it is difficult to enter due to qualifying

standards, a lottery, or an occupancy limit.

• COR : COR is a measure of course prestige, based on online ratings and comments

made by past participants.

• SPO: SPO is representative of the presence of sponsors at each event, based on

advertisements, press releases, and social media.

• FAN: FAN is representative of the fan presence at each marathon event. For

example, the Pikes Peak marathon is not given an “X” for fan presence because it

takes place on a 14,000 feet mountain, while the Boston Marathon is given an

“X,” because it takes place in a large city where many fans participate in cheering

on participants.

• ORG: ORG is representative of overall event organization, based on how highly the

event organization was ranked through www.marathonguide.com ratings.

• WEB: WEB is representative of the quality of each marathon’s website, based on

personal knowledge and opinion.

38 • LOC: LOC measures whether the marathon event is a core part of the

corresponding city’s social structure and economy, based on online comments,

data on economic impacts (for select races), and personal knowledge of the sport.

• POP: POP is a measure of the local running population in the area where the race

takes place. The “X” values were constructed through knowledge of the running

industry and internet research on running clubs in each area.

• MAK: MAK is representative of the overall marketing effort that went into each

marathon event, determined by observing advertisements, press releases, and all

forms of social media.

• EVE: EVE is representative of the presence of events corresponding with the

marathon races, including expos, concerts, and food venues, among others.

39