Sage Journals: Discover world-class research

Abstract

This article examines sport/discipline-specific market concentrations in the context of the eight Summer Olympic Games held between 1992 and 2020. Three market concentration-related debates underpin this study: (1) the number of competing firms; (2) market size; and (3) time. A normalized version of the Herfindahl-Hirschman Index (HHIN) is calculated for gold medal and medal concentrations for all 42 sports/disciplines. The analyses included panel data models and Pearson correlations. The main findings are that (1) market concentrations for (gold) medals within most sport/disciplines have not significantly declined over time; and (2) neither an increased number of competing National Olympic Committees (NOCs) nor increased market size is likely to have impacted market concentration within a sport/discipline. The findings advance the understanding of market concentration trends and have implications for elite sport practitioners including the International Olympic Committee (IOC) and International Federations (IFs) in terms of an evaluation of existing measures introduced to promote competitive balance, and NOCs in terms of resource distribution among sports/disciplines and prioritization strategies.

Keywords

Olympic Games market concentration evolution competing nations market size

Introduction

Since the seminal work of Smith (1776), there has been considerable debate over the ramifications associated with market concentration (McIntosh et al., 2005). In the financial sector, market concentration measures the extent to which market shares are concentrated between a small number of firms (Organisation for Economic Co-Operation and Development, OECD, 2019) and is a proxy for competition intensity (Menezes & Quiggin, 2012). A higher level of market concentration suggests a lower level of competition intensity.

At the Olympics, market concentration is reflected in the distribution of medals amongst nations. This medal tally is a well-publicized feature of the Olympics (Chen et al., 2018). The intensification of competition at the Olympic Games has been a dominant characteristic of elite sport in the last two decades (Houlihan & Zheng, 2013). However, the number of (gold) medals are finite and countries must compete for a share of the “medal market.” Whilst 206 nations/delegations participated in the Tokyo 2020 Summer Olympic Games (in 2021), only 65 nations won a gold medal (from the 340 available) and only 93 nations won any kind of medal (from the 1,080 medals available) (International Olympic Committee, IOC, 2022). The top 10 nations in the gold medal table acquired a 60% of all gold medals and 54% of all medals. Clearly, the Summer Olympic Games has a market concentration problem.

This research explores market concentration through a case study of the Summer Olympic Games. In this study, (gold) medal distributions for each of the 42 sports/disciplines for overall, male- and female-specific competitions between 1992 and 2020 are calculated. Premised on the market concentration literature and three key determinants of market concentration—(1) the number of competing firms (Tsaur & Wang, 2011); (2) market size (Raith, 2003); and (3) time (Klepper, 1996; Klepper & Graddy, 1990), this research is guided by four research questions:

What sports/disciplines have high (gold) medal concentrations?

Are these (gold) medal concentration levels changing over time?

What are the correlations between market concentration levels within a sport/discipline and the number of competing National Olympic Committees (NOCs) competing in this sport/discipline?

What are the correlations between market concentration levels within a sport/discipline and the number of events/(gold) medals in this sport/discipline?

Literature Review

The Concept of Market Concentration and Associated Impacts

Market concentration is the extent to which market shares are concentrated amongst a small number of firms (Organisation for Economic Co-Operation and Development, OECD, 2019). The concept measures market performance (Menezes & Quiggin, 2012; Sung, 2014). A high level of market concentration indicates a low level of competition intensity.

There is a general consensus that high market concentration is to the detriment of the competitive environment and consumer welfare (Dranove et al., 1993; Hovhannisyan et al., 2019; Mia, 2018; Sung, 2014). An increase in market concentration often deters new entrants to the market (Sung, 2014). Concentrated markets are also vulnerable to unethical behavior (Yuan et al., 2019) including tacit or explicit collusion (Ojima et al., 2018). Market concentration leads to market fragility and concomitantly financial crises (Bretschger et al., 2012). However, others consider that a high degree of concentration encourages relationship banking, realizing economies of scale and this reduces the likelihood of financial crises (Beck et al., 2006; Rajan, 1992).

Market Share Instability

Market share instability refers to periodic market share changes (Mazzucato & Semmler, 1999). Market share instability reflects the degree of competition or rivalry amongst competing firms (Das et al., 1993; Eckard, 1987; Mazzucato & Semmler, 1999; Sandler, 1988). Predicated on existing literature, three factors seem to be particularly important in explaining market share instability: (1) number of firms; (2) market size; and (3) time.

Number of Firms

Industrial evolution occurs when “new firms enter an industry, some exit; some firms grow, others decline” (Malerba & Orsenigo, 1996, p. 51). The number of competitors plays a significant role in determining market concentration levels (Meisel, 1981; Shen & Cheung, 2018; Tsaur & Wang, 2011). As Kwiecinä ski (2017) summarized, the number of companies competing in a market per se is an indicator of competition strength. Similarly, Meisel (1981) observed a positive correlation between the number of brands and the entry rate of new brands on the one hand, and brand share instability on the other. Shen and Cheung (2018) championed this positive correlation and asserted that competition is more intense when the number of competitors increases. Sung (2014, p. 3046) added that “the number of new entrants and their entry timing may influence market concentration.”

However, caution should be drawn because the competitiveness of new entrants affects market concentration. Menezes and Quiggin (2012, p. 713) concluded that “when competition is intense, most of the gains from extra competition are captured with the entry of a small number of firms and subsequent gains from entry are small.” There is a distinction between dominant firms and the “competitive fringe” (Organisation for Economic Co-Operation and Development, OECD, 2002) which comprises a number of non-dominant competitors including potential entrants. Dick’s (2007) research on banking in the US found that the market remained similarly concentrated, despite an increase in the number of fringe banks against a relative consistency in the number of dominant banks. Market share instability is higher when small firms are more innovative (Acs & Audretsch, 1987). In other words, there are occasions where new entrants fail to significantly erode the market shares of dominant competitors.

Market Size

Market size measures how big the market is and is closely associated with the concept of total addressable market which refers to “the combined revenues (or unit sales) of all companies in a specific market” (Cayenne Consulting, 2013). Contextualized in the Olympic Games, market size refers to the number of events of each sport/discipline and the number of (gold) medals generated in corresponding sports/disciplines.

Market size is another key factor that impacts on market concentration levels. Within the financial sector, an increase in market size results in market growth. Nelson (1960) and Shepherd (1964) argued that market growth reduces the level of industry concentration. Raith (2003) espoused their view by noting that the increase in the market size is likely to lead to an intensified market competition, because market expansion encourages the entry of new firms. Similarly, Pryor (1994) also concluded that the changes in concentration levels in manufacturing in the US is related to market growth. However, it is noteworthy that the supporting evidence for this association has traditionally been limited (Kamerschen, 1968; Pashigian, 1969).

Time

Time and evolution are noted in various studies on market concentration and competition (e.g., Lien & Foss, 2009; Lusch & Laczniak, 1987; Mia, 2018). Based on a life cycle perspective, Klepper and Graddy (1990) suggested that the competition in nascent, or emergent, industries tends to be fiercer compared to relatively more mature markets. In other words, there is a higher degree of market share instability in the early stages of an industry (Klepper, 1996).

Empirical support is somewhat more equivocal. For example, Autor et al. (2017) used 4-digit SIC classifications to examine the evolution of concentration across 676 industries between 1982 and 2012. Average industry concentrations trended upwards across all six sectors (i.e., service industries, manufacturing industries, wholesale sector, utilities, financial services, and retail industries). In contrast, the UK-based Social Market Foundation (SMF, 2017) found little evidence of increased concentration in the markets for cars, groceries, broadband, mobile telephony, fixed line telephony, electricity, gas, personal current accounts, credit cards, and mortgages. However, it remains opaque as to whether market concentration level increases or decreases with time (Organisation for Economic Co-Operation and Development, OECD, 2018). The OECD’s review of market concentration trends concluded that “it is extremely difficult to draw any conclusion on whether there has been a change in competitive intensity or not” (Organisation for Economic Co-Operation and Development, OECD, 2018, p. 10).

The literature review highlights uncertainty about whether time, market size, and number of competing firms constrain or facilitate market concentration. The sector-dependent nature of market concentration encourages studies in diverse industries, such as elite international sport where there is a death of market concentration studies.

Competitive Balance in Sport

Competitive balance is either static or dynamic (Doria & Nalebuff, 2021). Static measures examine balance within a single season (or a single event edition). Dynamic (or dominance) measures focus on changes to relative team performances across multiple seasons (or event editions).

Professional team sports dominate competitive balance research (Gerrard & Kringstad, 2022; Owen & Owen, 2022; Runkel, 2022; Wagner et al., 2021). However, there is also a small literature related to competitive balance at recurring major sports events contested among nations (i.e., National Olympic Committees, NOCs) rather than clubs.

Otamendi and Doncel (2014) conducted a multi-edition study of medal distributions at the Winter Olympic Games between 1992 and 2010, identifying different medal concentrations between sports. Forrest et al. (2017) examined 15 individual sports at the Summer Olympic Games between 1992 and 2012, and concluded that medal distributions are less unequal in sports practiced in multi-sports venues. Weber et al. (2018) analyzed the competitive balance at the seven consecutive Winter Olympic Games and determined that biathlon and short track speed skating were the sports with the greatest fluctuations in competitive balance.

Ramchandani and Wilson (2014) concluded that the Commonwealth Games became significantly less competitive between 1930 and 1990 and that events contested by men only were more balanced compared to both women’s and mixed events. Zheng et al. (2019) observed no improvement in competitive balance at the Olympics, but they discerned some evidence that competitive balance has improved in male competitions.

Research Methods

Data Collection

A database of the 7,476 Summer Olympic medals awarded (2,381 gold, 2,379 silver, and 2,716 bronze) between 1992 and 2020 was created. All medal data were sourced from the official International Olympic Committee (IOC) website (International Olympic Committee, IOC, 2019, 2022). Any decision to withdraw and/or re-allocate medals after March 31, 2022 are not reflected in this database. Congruent with Forrest et al. (2017), Weber et al. (2016), Otamendi and Doncel (2014), and Zheng et al. (2019), the year 1992 was selected as the starting point because it was the first Olympic Games held in the post-Cold War era. The medal distribution patterns in the previous three Olympic Games had been significantly affected by boycotts involving major nations (the United States in 1980, the Soviet Union in 1984 and Cuba in 1988).

Data Analysis

Performance Measures

The study uses two most prevalent performance measures for each nation—the total number of gold medals, and medals. For mixed gender events, the weight or proportion of (gold) medals and points awarded to each gender is proportional to the ratio of male and female athletes of each nation (Zheng et al., 2018, 2019). For example, in mixed-doubles badminton and the Nacra 17 mixed in sailing, male and female athletes are each awarded half of the (gold) medals.

Market Concentration Indicator—(Normalized) Herfindahl-Hirschman Index

A normalized version of the Herfindahl-Hirschman Index (HHIN) is applied to represent medal distribution patterns and medal concentration rates. The HHI measure is pervasive in the market concentration literature (e.g., Dai et al., 2019; Gallagher et al., 2015; Hsieh & Lin, 2016; Kwiecinski, 2017; Mia, 2018; Pham, 2018; Shen & Cheung, 2018) and is used in studies of competitive balance in sport (Freestone & Manoli, 2017; Read et al., 2021; Rocke, 2019; Weber et al., 2016; Zheng et al., 2018). The HHI for each sport/discipline at a single Olympics is the sum of the squared market shares of each nation. The market share denotes each nation’s share of the medals awarded. Put simply, the HHI converts market shares into a reflection of market concentration (Sung, 2014).

HHIN takes precedence because the conventional HHI is very sensitive to changes in the number of participating nations, an issue that is adequately remedied by HHIN. HHIN was used in studies of medal distributions at the Commonwealth Games (Ramchandani & Wilson, 2014), Summer Olympic Games (Zheng et al., 2019), and in Olympic and World Championships table tennis competitions (Zheng et al., 2018).

The HHIN for each edition is calculated as follows:

HHIN = \frac{(HHI - \frac{1}{N})}{(1 - \frac{1}{N})}

In this study, N refers to the number of all participating nations in one sport/discipline, irrespective of medal-winning and non-medal-winning divide, consistent with Forrest et al. (2017) and Zheng et al. (2018, 2019). HHIN is calculated using each of the two performance indicators (gold medals and medals). The HHIN results range between 0 and 1. A high HHIN is the result of a great concentration of (gold) medal success among a small number of nations. An HHIN value of 1 indicates a monopolistic market (Hirschman, 1964; Scherer & Ross, 1990).

Concerning the thresholds, 42 sports/disciplines are stratified into three tiers of concentration levels used by the US Department of Justice and Federal Trade Commission: low (below 0.150), medium (0.150–0.249), and high (0.250 or above) (Chakrabarti et al., 2017; Mia, 2018; Shen & Cheung, 2018).

Research Questions 2, 3, and 4 were subject to regression studies and examined through panel data models that are structured by Olympic editions and sports/disciplines. In general, it can be assumed that sports/disciplines continued to perform well in particular countries that have already been successful in corresponding sports/disciplines, hence the market concentration level of certain Olympic Games was likely to be affected by the result of preceding Olympic Games. Therefore, the dynamic panel models which direct considerable attention to the impact of previous figures (or previous Olympic Games in this context) as the predictor on later Olympic Games were identified as compatible and effective. Arellano and Bond (1991) advocated the incorporation of heteroscedastic serial correlation into dynamic panel models. The formula for the dynamic panel model that covers 42 sports/disciplines and eight time periods (i.e., eight Summer Olympic Games from 1992 to 2020) is as follows:

$HHI N_{it} = X_{it} β + ρ HHI N_{it - 1} + α_{i} + u_{it}$ for t = 1, …, 7, and i = 1, …, 37

It is noteworthy that $X_{it}$ is a time-variant variable, including the number of available (gold) medals in each sport/discipline and number of competing NOCs in each sport/discipline. The subscript t is indicative of Olympic editions. For example, 1 represents Barcelona 1992 and 8 denotes Tokyo 2020. Within the formula, i refers to sports/disciplines (e.g., 1 = 3 × 3 basketball).

However, it is worth mentioning that the dynamic panel model mainly centers on the impact of previous Olympic Games on the subsequent Games. Therefore, it is mainly used to explore the time trend. Corresponding to Research Questions 3 and 4, Pearson correlations were also calculated between all the edition-specific HHIN Gold and HHIN Medals results for overall, male and female competitions on the one hand, and edition-specific numbers of competing nations and the number of (gold) medals of each of these 42 sports/disciplines respectively on the other. In these two cases, a negative correlation coefficient indicates that an increase (or decrease) in the number of competing nations in one sport/discipline or number of (gold) medals in each sport/discipline is associated with a decrease (or increase) in the (gold) medal market concentration levels.

Results

Concentration Analyses—Sports/Disciplines

Table 1 provides a comparison of (gold) medal concentration for each sport/discipline between 1992 and 2020. Considered collectively, excluding new Olympics sports that debuted in either Rio de Janeiro 2016 or Tokyo 2020 (e.g., rugby sevens and sport climbing) that deserved an allowance (somewhat inevitable high market concentrations), basketball, diving, and table tennis are the frequent offenders. In specific terms, the USA has won 14 of all 16 basketball Olympic gold medals during the period 1992 to 2020. Over the same period, China accounts for 78.6% (44/56) of all diving gold medals and more than 40% (72/168) of all diving medals produced between Barcelona 1992 and Tokyo 2020/2021. China’s dominance in table tennis is more hegemonic and striking. Between Barcelona 1992 and Tokyo 2020/2021, China has won 30 of the 33 Olympic gold medals in table tennis and its table tennis-specific medal proportion also reaches 53.4% (55/103). Edition-specific results for each sport/discipline are presented in “Supplemental Material.”

Table 1.

Gold Medal and Medal Concentration of Each Sport/Discipline: 1992 to 2020 Combined, HHIN, Rank and Categorization.

Table tennis is particularly noteworthy. Table tennis ranks (near) first across overall, male and female competitions, in terms of both gold medal and medal concentration levels. For the medal distribution of medals of any color, table tennis is the only sport with an overall high level of market concentration, which exceeds the 0.250 threshold used by the US Department of Justice and Federal Trade Commission. This illustrates that the lack of (gold) medal openness is a particularly thorny issue for table tennis, compared to all the other 41 Summer Olympic sports/disciplines (Zheng et al., 2018). In comparison, shooting, taekwondo, athletics especially women’s athletics, rowing, sailing, cycling particularly women’s cycling, and wrestling especially men’s wrestling present a relatively less concentrated (gold) medal distribution pattern among nations amid all 42 sports/disciplines. However, counter to athletics, as another extremely medal-intensive sport discipline and second richest source of Olympic (gold) medals, swimming, in particular men’s swimming, is an illustration of a high degree of (gold) medal market concentration, synonymous with low (gold) medal distribution openness among nations.

Time

Table 2 displays the results for dynamic panel model analyses (Arellano & Bond, 1991) and explores whether and to what extent (gold) medal concentration levels have changed from 1992 to 2020. More detailed HHIN results for each sport/discipline at each of the seven Olympic Games can be found in “Supplemental Material.”

Table 2.

Results for Dynamic Panel Model Analyses for Research Question 2.

		Gold medals			All medals
	Variable	B	z	p	B	z	p
Overall	HHIN_t−1	−.004	−.03	.976	−.028	−.27	.783
	#(Gold)Medals	−.026	−2.57	.010	−.003	−3.07	.002
	#nations	.001	−.88	.381	.001	.052	.603
	(cons)	.710	5.99	.000	.234	4.94	.000
Male	HHIN_t−1	.037	.43	.664	−.232	−1.50	.133
	#(Gold)Medals	−.019	−1.85	.065	−.003	−2.04	.041
	#nations	−.001	−.080	.424	.001	1.86	.063
	(cons)	.671	7.25	.000	.250	5.00	.000
Female	HHIN_t−1	−.025	−.014	.891	−.160	−.97	.330
	#(Gold)Medals	−.081	−2.69	.003	−.005	−2.43	.015
	#nations	.001	.45	.653	−.001	−.47	.638
	(cons)	.978	5.40	.000	.338	5.17	.000

Note. The significance is set at the 5% level.

According to Table 2, an insignificant negative coefficient applies to all analyses, irrespective of genders and the gold medal/medal distinction, with the only exception of gold medals for male competitions that is characterized by an insignificant positive coefficient. To elaborate, the coefficients of HHIN_t−1 in the cases of both gold medals (B = −.004, p = .976) and medals of any color (B = −.028, p = .783) are insignificantly negative, indicative of a statistically insignificant decrease in market concentration levels.

Male- and female-specific regression analyses were characterized by a similar trend, measured by medals of any color. More specifically, the coefficient results for medals in the case of male analyses were −.232 and −.160 respectively, and both were insignificant. While the result for female-specific gold medals was consistent (i.e., an insignificant decrease in market concentration levels), male competitions presented a counter pattern, demonstrated by an insignificantly tendency toward a more concentrated gold medal market (B = .037, p = .664).

In brief, from the perspective of dynamic panel model-based regression studies which incorporated the results for each of the 42 Summer Olympic sports/disciplines, the market concentration level for both gold medals and medals has not significantly changed from Barcelona 1992 to Tokyo 2020, irrespective of overall and gender-specific competitions. However, it is noteworthy that the pattern that characterizes gold medal distribution for males is in contrast with all five of the other indicators (i.e., gold medals for overall and female competitions, and medals of any color that all share a statistically insignificant decline in market concentration levels with the passage of time).

Number of Competing NOCs in Each Sport/Discipline

Table 3 summarizes Pearson correlations between the number of competing NOCs in each sport/discipline and any changes in (gold) medal concentration for most sports/disciplines.

Table 3.

Correlations Between the Number of Competing Nations and Market Concentration Trends at the Summer Olympic Games from 1992 to 2020.

Sport/discipline		Overall		Male		Female
Sport/discipline		HHIN gold	HHIN medals	HHIN gold	HHIN medals	HHIN gold	HHIN medals
3 × 3 Basketball	Corr.	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.
3 × 3 Basketball	p	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.
Archery	Corr.	.637	.338	−.025	−.073	−.057	−.199
Archery	p	.089	.413	.953	.864	.893	.637
Artistic Gymnastics	Corr.	−.531	−.525	−.227	−.509	.165	−.015
Artistic Gymnastics	p	.175	.181	.589	.198	.696	.971
Athletics	Corr.	−.558	−.502	−.400	−.398	−.234	−.314
Athletics	p	.151	.205	.326	.329	.576	.449
Badminton	Corr.	.041	−.001	.328	.001	−.227	−.436
Badminton	p	.923	.999	.428	.999	.589	.280
Baseball	Corr.	N.A.	1.000**	N.A.	1.000**	N.A.	N.A.
Baseball	p	N.A.	.000	N.A.	.000	N.A.	N.A.
Basketball	Corr.	.537	−.039	N.A.	N.A.	N.A.	1.000**
Basketball	p	.170	.928	N.A.	N.A.	N.A.	.000
Beach Volleyball	Corr.	.006	−.131	N.A.	.268	N.A.	−.345
Beach Volleyball	p	.991	.780	N.A.	.562	N.A.	.449
Boxing	Corr.	−.232	−.190	−.294	−.451	−.999*	−.939
Boxing	p	.581	.652	.480	.263	.028	.224
Canoeing Slalom	Corr.	−.098	−.002	−.106	.369	−.736*	−.354
Canoeing Slalom	p	.817	.996	.803	.368	.037	.390
Canoeing Sprint	Corr.	−.820*	−.842**	−.753*	−.523	−.336	−.375
Canoeing Sprint	p	.013	.009	.031	.184	.416	.360
Cycling	Corr.	.254	.128	.673	−.556	−.877**	−.701
Cycling	p	.544	.763	.068	.153	.004	.053
Diving	Corr.	−.535	.128	.110	.333	−.720*	−.491
Diving	p	.172	.763	.796	.420	.044	.216
Equestrian	Corr.	−.180	.090	.403	.593	−.077	.418
Equestrian	p	.671	.833	.322	.121	.856	.302
Fencing	Corr.	.444	.437	.125	.514	−.930**	−.286
Fencing	p	.271	.279	.768	.193	.001	.492
Football	Corr.	−.826*	−.729*	N.A.	N.A.	N.A.	.996**
Football	p	.012	.040	N.A.	N.A.	N.A.	.000
Golf	Corr.	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.
Golf	p	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.
Handball	Corr.	.086	.273	N.A.	N.A.	N.A.	.993**
Handball	p	.840	.513	N.A.	N.A.	N.A.	.000
Hockey	Corr.	.994**	−.243	N.A.	N.A.	N.A.	.993**
Hockey	p	.000	.562	N.A.	N.A.	N.A.	.000
Judo	Corr.	.091	.042	.193	.688	.236	−.150
Judo	p	.830	.920	.646	.059	.574	.723
Karate	Corr.	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.
Karate	p	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.
Modern Pentathlon	Corr.	.213	−.241	N.A.	−.310	N.A.	−.758
Modern Pentathlon	p	.613	.565	N.A.	.456	N.A.	.081
Rhythmic Gymnastics	Corr.	.330	.458	N.A.	N.A.	N.A.	N.A.
Rhythmic Gymnastics	p	.425	.254	N.A.	N.A.	N.A.	N.A.
Rowing	Corr.	−.357	−.427	−.027	−.365	−.664	−.665
Rowing	p	.385	.291	.950	.375	.072	.072
Rugby Sevens	Corr.	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.
Rugby Sevens	p	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.
Sailing	Corr.	−.097	−.084	−.348	−.154	−.379	−.232
Sailing	p	.820	.844	.398	.715	.355	.581
Shooting	Corr.	−.728*	−.075	−.228	−.245	−.007	.233
Shooting	p	.040	.859	.588	.558	.987	.578
Skateboarding	Corr.	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.
Skateboarding	p	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.
Softball	Corr.	N.A.	1.000**	N.A.	N.A.	N.A.	1.000**
Softball	p	N.A.	.000	N.A.	N.A.	N.A.	.000
Sport Climbing	Corr.	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.
Sport Climbing	p	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.
Surfing	Corr.	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.
Surfing	p	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.
Swimming	Corr.	.051	.097	.179	.146	.138	−.171
Swimming	p	.905	.819	.671	.731	.745	.685
Synchronized Swimming	Corr.	−.037	.502	N.A.	N.A.	N.A.	N.A.
Synchronized Swimming	p	.932	.205	N.A.	N.A.	N.A.	N.A.
Table Tennis	Corr.	.363	−.084	.457	.091	−.396	−.503
Table Tennis	p	.376	.843	.255	.831	.331	.204
Taekwondo	Corr.	−.139	−.216	.250	−.134	−.225	−.269
Taekwondo	p	.793	.681	.633	.800	.669	.607
Tennis	Corr.	.021	.144	−.204	−.329	.319	.360
Tennis	p	.600	.734	.628	.426	.441	.381
Trampoline	Corr.	−.180	−.061	N.A.	.387	N.A.	−.186
Trampoline	p	.733	.909	N.A.	.448	N.A.	.724
Triathlon	Corr.	−.105	.705	−.101	.046	−.350	−.410
Triathlon	p	.843	.118	.849	.931	.496	.420
Volleyball	Corr.	.994**	−.229	N.A.	N.A.	N.A.	1.000**
Volleyball	p	.000	.585	N.A.	N.A.	N.A.	.000
Water Polo	Corr.	−.700	−.501	N.A.	N.A.	N.A.	.974**
Water Polo	p	.053	.206	N.A.	N.A.	N.A.	.001
Weightlifting	Corr.	−.624	−.610	−.577	.074	.027	−.404
Weightlifting	p	.098	.109	.134	.861	.960	.426
Wrestling	Corr.	−.786*	−.296	−.547	.012	.845	−.800
Wrestling	p	.021	.476	.161	.978	.072	.104

For most sports/disciplines, there are no statistically significant correlations between the number of competing NOCs in a sport/discipline and any changes in (gold) medal concentration, overall and gender-specific analyses combined. Nevertheless, there are some exceptions. A significant and positive correlation was identified for HHIN Medals (Corr. = 1.000, p = .000) for overall/men’s baseball, HHIN Medals (Corr. = 1.000, p = .000) for women’s basketball, HHIN Medals (Corr. = .996, p = .000) for women’s football, HHIN Gold (Corr. = .993, p = .000) for overall handball and HHIN Medals (Corr. = .993, p = .000) for women’s handball, HHIN Gold (Corr. = .994, p = .000) for overall hockey and HHIN Medals (Corr. = .993, p = .000) for women’s hockey, HHIN Medals (Corr. = 1.000, p = .000) for overall/women’s softball, HHIN Gold (Corr. = .994, p = .000) for overall volleyball and HHIN Medals (Corr. = 1.000, p = .000) for women’s volleyball, and HHIN Medals (Corr. = .974, p = .001) for women’s water polo.

In comparison, a significant and negative correlation is found in HHIN Gold (Corr. = −.999, p = .028) for women’s boxing, HHIN Gold (Corr. = −.736, p = .037) for women’s canoeing slalom, HHIN Gold (Corr. = −.820, p = .013) and HHIN Medals (Corr. = −.842, p = .009) for overall canoeing sprint, HHIN Gold (Corr. = −.753, p = .031) for men’s canoeing sprint, HHIN Gold (Corr. = −.877, p = .004) for women’s cycling, HHIN Gold (Corr. = −.720, p = .044) for women’s diving, HHIN Gold (Corr. = −.930, p = .001) for women’s fencing, HHIN Gold (Corr. = −.826, p = .012) and HHIN Medals (Corr. = −.729, p = .040) for overall football, HHIN Gold (Corr. = −.728, p = .040) for overall shooting, and HHIN Gold (Corr. = −.786, p = .021) for overall wrestling.

An in-depth scrutiny of these significant negative correlations revealed case-dependent features. For example, the number of female canoeing slalom events had been consistent between Barcelona 1992 and Rio de Janeiro 2016, but the number of events/gold medals increased from one to two at the most recent Tokyo 2020/2021 Olympic Games, which enabled more NOCs to share the gold medal triumph in women’s canoeing slalom at a single Olympic Games. Coinciding with the increase in the number of events, the number of participating NOCs in women’s canoeing slalom also rose from 21 in one event in 2012 and 2016 to more than 25 in two events at Tokyo 2020.

The impact of the increased number of events (i.e., market size expansion within one sport) on the correlation between an increase in the number of participating NOCs in a sport/discipline and a lower degree of market concentration level in this sport/discipline also applied to HHIN Gold for women’s boxing. More specifically, women’s boxing was not officially included in the Olympic Games until London 2012 and only three events were included at London 2012 and Rio de Janeiro 2016 respectively. However, the number of female boxing events, synonymous women’s boxing gold medals at a single Olympic Games, reached a new high and increased to five (i.e., a 40% increase) at Tokyo 2020/2021. The number of participating nations in Olympic women’s boxing also increased, but the five gold medals were won by five different NOCs in Tokyo. These factors combined to explain the significant negative correlation between the change in participating NOCs in a sport/discipline and the change in (gold) medal market concentration levels in one sport/discipline, at least for female events.

Women’s cycling and women’s fencing, which have welcomed an increase in both the number of events and the number of participating NOCs over the same 29-year period, acts as two additional illustrations. Fundamentally, at least in women’s boxing, women’s cycling, women’s fencing and women’s canoeing slalom, the IOC and respective international federations’ (IFs) promotion of gender equality bore fruit in competitive balance terms and enabled more NOCs to be both qualified and able to secure a gold medal market share.

In comparison, women’s diving provides a counter example. the number of participating NOCs in women’s diving slightly decreased from 1992 to 2020/2021, but the number of events doubled in Sydney 2000 (from two to four), and has been stable since then. However, China has won all the female diving gold medals since Beijing 2008, which explained the negative correlation that was observed in HHIN Gold for women’s diving. However, counter to the aforementioned examples of women’s cycling, women’s fencing, women’s canoeing slalom, and women’s boxing, women’s diving is described as an increasingly concentrated gold medal market that is attributable to one country’s establishment of a hegemonic dominance, accompanied by the decrease in the number of participating NOCs in women’s diving on the Olympic stage.

Since the inclusion of women’s football at Atalanta 1996, the number of football gold medals and the number of football medals at a single Olympic Games have remained unchanged. With the exceptions of Beijing 2008 (Brazil) and Rio de Janeiro 2016 (Germany), the six football medals (men’s and women’s combined) were evenly distributed among six different nations/regions at each single Olympic Games between 1992 and 2020/2021. However, the number of participating nations in football gradually increased from approximately 21 in 1996, 2000, 2004, and 2008, to 23 or 24 in 2012, 2016, and 2020/2021. Therefore, the negative correlation between an increase in the number of competing NOCs in football and a modest decrease in football-specific medal market concentration levels for the overall competition that combined men’s and women’s competitions was observed.

The statistically significant correlation in the case of HHIN Gold for overall wrestling can be ascribed to the fluctuated pattern in terms of the trend in the number of participating nations (i.e., approximately 60 in 1992, 75 in 1996, 55 in 2000, and 67 in 2016) and the fact that when the number of wrestling-specific (overall) participating NOCs were low (55 in 2000 and 59 in 2008), its gold medal concentration levels were high (i.e., the highest concentration level in 2008 and the second highest in 2000), indicative of a small number of NOCs’ ability to dominate the gold medal pool. This pattern is also applicable to HHIN Gold for overall shooting and for overall and male canoeing sprint where high gold medal concentration rates took place when the number of participating NOCs in respective sports/disciplines (male and female combined) were relatively low.

In brief, for overall competitions, significant p values do not apply to 23 out of the 35 statistically valid sports/disciplines for HHIN Gold, and only two out of the total 35 valid sports/disciplines demonstrate statistically significant negative correlations between the number of competing nations in a sport/discipline and changes in overall medal market concentration levels in corresponding sport/discipline from 1992 to 2020 respectively.

Market Size—The Number of (Gold) Medals in Each Sport/Discipline

Table 4 summarizes the Pearson correlations between the number of (gold) medals in a sport/discipline and market concentration for (gold) medals in corresponding sport/discipline.

Table 4.

Correlations Between the Number of (Gold) Medals/Events and Market Concentration Trends at the Summer Olympic Games from 1992 to 2020.

Sport/discipline		Overall		Male		Female
		HHIN gold	HHIN medals	HHIN gold	HHIN medals	HHIN gold	HHIN medals
3 × 3 Basketball	Corr.	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.
3 × 3 Basketball	p	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.
Archery	Corr.	.162	−.605	−.186	−.473	.143	−.451
Archery	p	.702	.112	.658	.237	.736	.262
Artistic Gymnastic	Corr.	.448	.307	.330	.729*	−.209	−.347
Artistic Gymnastic	p	.266	.459	.424	.040	.620	.074
Athletics	Corr.	−.505	−.281	−.006	−.058	−.007	.575
Athletics	p	.202	.500	.988	.892	.987	.736
Badminton	Corr.	−.018	−.017	.031	.165	.308	.134
Badminton	p	.966	.967	.941	.696	.458	.752
Baseball	Corr.	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.
Baseball	p	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.
Basketball	Corr.	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.
Basketball	p	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.
Beach Volleyball	Corr.	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.
Beach Volleyball	p	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.
Boxing	Corr.	−.327	−.597	−.106	−.791**	−1.000**	.908
Boxing	p	.430	.118	.803	.019	.006	.202
Canoeing Slalom	Corr.	N.A.	N.A.	−.351	−.685	−1.000**	.315
Canoeing Slalom	p	N.A.	N.A.	.394	.061	.000	.447
Canoeing Sprint	Corr.	N.A.	−.404	−.021	.250	−.292	.454
Canoeing Sprint	p	N.A.	.321	.961	.550	.482	.252
Cycling	Corr.	−.672	−.759*	−.439	.739*	−.732*	−.889*
Cycling	p	.068	.029	.276	.036	.039	.027
Diving	Corr.	.165	−.537	.396	−.718*	−.321	.478
Diving	p	.697	.170	.332	.045	.439	.230
Equestrian	Corr.	N.A.	N.A.	−.825*	−.047	.189	.709*
Equestrian	p	N.A.	N.A.	.012	.913	.654	.049
Fencing	Corr.	−.434	−.295	.440	.222	−.781*	.067
Fencing	p	.283	.478	.275	.597	.022	.550
Football	Corr.	−1.000**	−.890**	N.A.	N.A.	N.A.	N.A.
Football	p	.000	.003	N.A.	N.A.	N.A.	N.A.
Golf	Corr.	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.
Golf	p	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.
Handball	Corr.	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.
Handball	p	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.
Hockey	Corr.	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.
Hockey	p	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.
Judo	Corr.	.729*	.287	.918**	.024	.365	.204
Judo	p	.040	.491	.001	.955	.374	.628
Karate	Corr.	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.
Karate	p	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.
Modern Pentathlon	Corr.	−.488	−.732*	N.A.	−.338	N.A.	N.A.
Modern Pentathlon	p	.220	.039	N.A.	.413	N.A.	N.A.
Rhythmic Gymnastics	Corr.	−.218	−.824*	N.A.	N.A.	−.218	−.824*
Rhythmic Gymnastics	p	.604	.012	N.A.	N.A.	.604	.012
Rowing	Corr.	N.A.	N.A.	.371	.243	−.361	.141
Rowing	p	N.A.	N.A.	.365	.562	.379	.740
Rugby Seven	Corr.	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.
Rugby Seven	p	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.
Sailing	Corr.	−.118	−.252	−.333	−.041	−.723*	−.048
Sailing	p	.781	.547	.420	.924	.043	.375
Shooting	Corr.	−.839**	−.174	−.504	.355	−.457	−.583
Shooting	p	.009	.681	.203	.388	.255	.470
Skateboarding	Corr.	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.
Skateboarding	p	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.
Softball	Corr.	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.
Softball	p	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.
Sport Climbing	Corr.	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.
Sport Climbing	p	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.
Surfing	Corr.	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.
Surfing	p	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.
Swimming	Corr.	−.170	−.140	−.207	−.480	.208	.385
Swimming	p	.687	.741	.624	.228	.621	.828
Synchronized Swimming	Corr.	−.756*	.558	N.A.	N.A.	−.756*	.558
Synchronized Swimming	p	.030	.151	N.A.	N.A.	.030	.151
Table Tennis	Corr.	−.405	−.629	−.266	−.741*	−1.000**	−.451
Table Tennis	p	.320	.095	.525	.036	.000	.261
Taekwondo	Corr.	N.A.	−.740	N.A.	−.688	N.A.	−.843*
Taekwondo	p	N.A.	.092	N.A.	.131	N.A.	.035
Tennis	Corr.	−.440	−.455	−.592	−.302	−.599	−.488
Tennis	p	.275	.257	.122	.467	.116	.220
Trampoline	Corr.	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.
Trampoline	p	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.
Triathlon	Corr.	−1.000**	−.289	−1.000**	−.655	−1.000**	−.456
Triathlon	p	.000	.579	.000	.158	.000	.364
Volleyball	Corr.	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.
Volleyball	p	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.
Water Polo	Corr.	−1.000**	−.908**	N.A.	N.A.	N.A.	N.A.
Water Polo	p	.000	.002	N.A.	N.A.	N.A.	N.A.
Weightlifting	Corr.	−.470	−.784*	−.276	.532	N.A.	.189
Weightlifting	p	.240	.021	.508	.175	N.A.	.757
Wrestling	Corr.	−.578	−.611	−.349	.088	.238	−.667
Wrestling	p	.133	.107	.397	.836	.700	.142

Significant negative correlations were observed in HHIN Medals (Corr. = −.791, p = .019) for men’s boxing, HHIN Gold (Corr. = −1.000, p = .006) for women’s boxing, HHIN Gold (Corr. = −1.000, p = .000) for women’s canoeing slalom, HHIN Medals (Corr. = −.759, p = .029) for overall cycling, HHIN Gold (Corr. = −.732, p = .039) and HHIN Medals (Corr. = −.889, p = .027) for women’s cycling, HHIN Medals (Corr. = −.718, p = .045) for men’s diving, HHIN Gold (Corr. = −.825, p = .012) for men’s equestrian, HHIN Gold for women’s fencing (Corr. = −.781, p = .022), HHIN Gold (Corr. = −1.000, p = .000) and HHIN Medals (Corr. = −.890, p = .003) for overall football, HHIN Medals (Corr. = −.732, p = .039) for modern pentathlon, HHIN Medals (Corr. = −.824, p = .012) for overall/women’s rhythmic gymnastics, HHIN Gold (Corr. = −.723, p = .043) for women’s sailing, HHIN Gold for overall shooting (Corr. = −.839, p = .009), HHIN Gold for overall/women’s synchronized swimming (Corr. = −.756, p = .030), HHIN Medals (Corr. = −.741, p = .036) for men’s table tennis and HHIN Gold (Corr. = −1.000, p = .000) for women’s table tennis, HHIN Medals (Corr. = −.843, p = .035) for women’s taekwondo, HHIN Gold (Corr. = −1.000, p = .000) for overall, male and female triathlon competitions, HHIN Gold (Corr. = −1.000, p = .000) and HHIN Medals (Corr. = −.908, p = .002) for overall water polo and HHIN Medals (Corr. = −.784, p = .021) for overall weightlifting. To interpret, an increase in the number of events/(gold) medals in a sport/discipline have resulted in a significantly decreased (gold) medal concentration levels and promoted competitive balance.

However, a few counter examples merit the attention and warranted an ad hoc scrutiny. In specific terms, a significant positive correlation is applicable to HHIN Medals (Corr. = .729, p = .040) for men’s artistic gymnastics, HHIN Medals (Corr. = .739, p = .036) for men’s cycling, HHIN Medals (Corr. = .709, p = .049) for women’s equestrian, and HHIN Gold (HHIN Medals (Corr. = .729, p = .004) for overall judo and HHIN Gold HHIN Medals (Corr. = .918, p = .001) for men’s judo. In these cases, an increase in the number of (gold) medals (i.e., the number of events in most cases) positively impacts market concentration level, leading to an increased (gold) medal market concentration, synonymous with deteriorated (gold) medal competitive balance among competing nations.

Significant changes in market concentration levels are not a feature for eight of all 27 statistically valid (i.e., experiencing changes in the number of events/medals) sports/disciplines, irrespective of overall, male and female competitions.

Discussion

This research examined market concentration through a study of Summer Olympic (gold) medal distributions from 1992 to 2020, specific to overall, male and female competitions, covering each of the 42 Summer Olympic sports/disciplines. The main findings of this article are that (1) table tennis represents the highest level of market concentrations for medals, followed by basketball and diving that are confronted with a similar market concentration issue; (2) in the majority of sports/disciplines, female sports/disciplines are more likely to have high levels of market concentration; (3) market concentrations for (gold) medals within one sports/disciplines have not significantly changed over time; (4) an increased number of competing firms (nations) in one sport/discipline is unlikely to impact market concentration within a sport/discipline; and (5) evidence for correlations between an increased market size (availability of medals in a sport/discipline) and market concentration is divergent, sport-specific and can be gender-dependent.

Theoretical Implications

The concept of market concentration underpins the analyses. More specifically, this study is structured around three debates in the market concentration literature.

Time

Collectively, this study provided some evidence from all 42 sports/disciplines particularly non-medal-limited sports/disciplines that fails to support, if not entirely contradicts, Klepper and Graddy’s (1990) finding that concentration level increases with time as the industry matures or Klepper’s (1996) assertion that industries (sports competitions) demonstrate a higher degree of intensification of competition in the early rather than later stages. This also warrants cautions in making any generalized conclusions from single industry studies to other industries (Organisation for Economic Co-Operation and Development, OECD, 2018).

Number of Competing Firms

The majority of the analyses of the present study invalidate the argument made by Meisel (1981), Tsaur and Wang (2011), and Shen and Cheung (2018) that the increase in the number of competitors raises the competition intensity and subsequently reduces the level of market concentration. Again, the limited competitiveness of most competitive fringe nations including new entrants, despite the burgeoning of competing firms (new participating nations) was a main culprit for the limited evidence of a negative correlation between the number of competing firms in a sport/discipline and market concentration.

Prime examples illustrating the importance of new entrants’ lack of competitiveness include women’s artistic gymnastics and swimming (overall). The number of participating NOCs in women’s artistic gymnastics increased from 24 in 1992 to 50 in 2016, and from 92 in 1992 to 164 in 2016 in swimming (overall). Yet, neither sport had any increase in (gold) medal concentration levels over this period. The ability for market stability to remain unchanged, despite a notable increase in competing firms, reflects the inability of new firms to displace incumbent firms with a large market share. For women’s artistic gymnastics, only three to four nations share the six gold medals at each Olympics and no more than seven nations win at least one of the 18 medals each time. Moreover, the US women’s gymnasts further expanded their “territory.” While the number of medal-winning nations slightly increased at the most recent Tokyo 2020 Summer Olympic Games, the USA remained the most successful female gymnastics nation and the dominance of a very small number of nations remained consistent.

A similar trajectory applies to the relatively slow increase in the number of (gold) medal winning nations in swimming. It appears that the large number of new entrants has been offset by the USA’s ever-increasing (gold) medal success. While the USA’s gold medal market was eroded and Australia performed to a very high standard, reaching a record high number of gold medals in swimming at Tokyo 2020, the number of medal-winning nations remained unchanged compared to Rio de Janeiro 2016 (i.e., 21 nations), while the number of gold medal winning swimming nations even decreased from 14 in 2016 to 12 in 2020. This trend was indicative of the inability of the vast majority of competing nations including most new entrants to win medals, providing a powerful explanation for the lack of significant decrease in market concentration levels in many sports/disciplines, in spite of the ever-rising number of competitors. These findings reinforce the cautionary words of Acs and Audretsch (1987), Dick (2007), and Menezes and Quiggin (2012) who encouraged researchers and policy-makers alike to consider the capacity or competitiveness of new firms rather than focusing on the absolute number of competing firms/new entrants.

Market Size

A considerable number of sports/disciplines do not provide evidence of a link between increased market size and a decreased market concentration level which was identified by Nelson (1960), Shepherd (1964), and Raith (2003). The findings resonate with the view of Kamerschen (1968) and Pashigian (1969) on the limited evidence supporting the association between market size and market concentration levels and the attention to the context-specific nature of any association. Yet, there are some exceptions. Sports/disciplines with a relatively medium to large market size including cycling (overall medals and female gold medals and medals of any color), diving (male medals), fencing (female gold), sailing (female medals), shooting (overall gold), and weightlifting (overall medals) demonstrate a significant negative correlation between the increased market size in each of the related sports/disciplines and market concentration levels in these sports/disciplines. The women’s cycling case underlines the close interrelationship between and combined effects of time, market size and the number of competing nations including the competitiveness of non-dominant fringe nations including new entrants in affecting the market concentration levels and trends in an industry (sport/disciplines).

The number of shooting events increased from 13 in 1992, to 15 in 1996 and 17 in 2000 and 2004, but decreased to 15 in 2008, 2012, 2016, and 2020. The number of competing firms (NOCs) has kept increasing despite a slight decrease at the Rio de Janeiro 2016 Summer Olympic Games, while the number of gold medal market share possession nations demonstrates a camel-shaped curve, largely consistent with the changes in the market size, which was key to the negative correlation observed in shooting (overall gold). An additional factor which may be instrumental in demystifying the positive impact of the changes in the market size on eroding market concentration resides in the International Shooting Sport Federation’s (ISSF) new rule that excludes qualification results in the final results and the adoption of an elimination competition format in the finals at Rio de Janeiro 2016. The most immediate impact was that China, the most successful shooting nation at Sydney 2000, Athens 2004, and Beijing 2008, underwent a significant decline in gold medal winning capabilities (from 4 in 2004 and 5 in 2008, to 2 in 2012 and 1 in 2016). While Chinese shooters won seven medals at Rio de Janeiro 2016, only one was gold. However, if qualification results were included, then Chinese shooters should have won another three gold medals. The case of shooting illustrates the role deliberate policy intervention (rule changes) plays in affecting market concentration levels.

A few significant positive correlations, synonymous with an increased market concentration level derived from an increase in the number of events/number of (gold) medals in one sport/discipline, were discovered in a small number of sports, specific to certain indicators for certain competitions. Judo is an illustrative example, characterized by a significant positive correlation in both HHIN Gold and HHIN Medals for overall competitions from 1992 to 2020. A closer examination reveals the secrets. To illustrate, the number of judo events had been consistent in being 14 between Barcelona 1992 and Rio de Janeiro 2016, but an additional event (i.e., mixed team) and concomitantly four additional medals (including a gold medal) were added at Tokyo 2020, raising the number of judo events from 14 to 15. However, as the host nation, Japanese judokas won 12 medals including gold medals, marking an unprecedented level of success. Therefore, a combination of slightly increased number of events and medals on the one hand, and more skewed pattern of (gold) medal distribution pattern, spurred the aforementioned significant positive correlations. Nevertheless, the case of judo is atypical and rare, if not unique, amid all sports/disciplines.

However, the value of this paper transcends confirming, nullifying, or comparing with existing market concentration literature. This study highlights four key issues relevant to the understanding of market concentration. First, there is the interrelation between the number of competing firms within a sport/discipline, market size of a sport/discipline, and time, and their combined effects on market concentration levels should not be overlooked. Second, there is the issue of new entrants being insufficiently capable of challenging powerful incumbents. Third, the nuanced nature of each industry (sport/discipline) warrants caution in extending single industry studies to other industries. The fourth issue is the role of policy intervention, rule changes, and deliberate restrictive measures to promote an increased distribution of medals.

An additional academic contribution of this study resides in the nuanced insights into each of the 42 Summer Olympic sports/disciplines and the concomitant sport-to-sport comparisons, combined with an emphasis on the market concentration literature as the theoretical underpinning, which are not features of any existing sport competitive balance research.

Practical Implications

In practical terms, first, a nuanced understanding of (gold) medal distributions will enable the IOC to identify sports/disciplines with high levels of market concentration and pursue counter-balancing strategies, working in collaboration with corresponding IFs. The IOCs and at least some of the IFs are keen to ensure “an exciting sporting event that reflects genuine global competition” (Weber et al., 2016, p. 400). The dearth of significant deterioration of market concentration following the regression studies, the lack of impact of an increase in the number of competing firms and in the market size may provide IOC and IFs with a sobering reminder to re-examine the competitive balance issue and medal distribution patterns, the morale of the majority of non-medal winning nations, and to consider whether the increase in the number of competing NOCs and events in many sports/disciplines actually results in the decrease in the market concentration levels or benefits dominant NOCs.

From the perspective of NOCs most notably the vast majority of non-dominant nations, this research provides rich and detailed data that can help NOCs make a more informed decision regarding their medal strategy, prioritization, and elite sport direction. For example, in addition to seizing the opportunity of an ever-decentralizing Olympic (gold) medal market, the relatively unconcentrated market segments of heavy sports including taekwondo and men’s wrestling, shooting and athletics, and new Olympic sports are worthy of critical consideration for a range of non-dominant NOCs. Thailand, Chinese Taipei, Vietnam, and Hong Kong’s notable Olympic (gold) medal success in (women’s) weightlifting and taekwondo, shooting (for Vietnam) and fencing and karate (for Hong Kong) in recent years may spur the neighboring NOC—Singapore to reconsider its prioritization strategy, which is characterized by an ever-rising ambition but very sporadic Olympic medal achievements and by the marginal positions of taekwondo, weightlifting, shooting and new Olympic sports on the government policy agenda (Team Singapore, 2021).

From a dynamic perspective, a number of nations’ newfound achievements and (greater) (gold) medal successes in sports/disciplines which demonstrates a (near-)significant decrease in market concentration levels, ranging from Brazil, Azerbaijan, and New Zealand in canoeing sprint, China and Colombia in women’s cycling, and Malaysia and Italy in diving, to Argentina and Belgium in men’s hockey, Puerto Rico in women’s tennis, the USA and South Africa in triathlon, and Serbia and Iran (expansion) in wrestling, may prompt other nations to consider whether the trends in these sports/disciplines may represent future market opportunities. Additionally, while a significant change has not been observed in weightlifting, a number of Latin American nations’ (e.g., Colombia, Venezuela, and Dominican Republic) deliberate promotion of and recent success in weightlifting may impact the market concentration level in this sport in the near future. Nevertheless, there are context-specific domestic factors (e.g., resources, tradition in certain sports, and specific elite sport objectives) for nations to gauge as well.

Conclusions

This paper offers rich and sport/discipline-specific insights into the market concentration issue at the Summer Olympics. However, two issues are noteworthy. First, the degree of concentration and balance is relative. This research focuses on competing nations. For example, while both rowing and canoeing sprint are representative of relatively low or significantly decreased concentration levels, most of the dominant market-share holders are relatively wealthier Western nations particularly from Europe. Accordingly, regional and continental analyses are encouraged in the future as complementary reflections of market concentration and medal distribution patterns.

Second, there are other factors such as but not restricted to geography, economic capabilities, political regime, the IOC and IFs’ incentives, these international peak bodies’ decisions to include or remove certain sports, disciplines and events, rule changes, the degree of professionalization of a sport/discipline, athlete immigration and naturalization, boycotts and doping and anti-doping practice that also have an impact on NOCs’ prioritization strategies and Olympic (medal) performances that are not unrelated in market concentration level terms in each sport/discipline. It is impossible to identify a complete list of contributory factors to elite sport success and Olympic (gold) medal market concentration levels. Nevertheless, while this article focuses on three factors that are well-publicized in the marketing literature (i.e., time, market size, and the number of competitors) and the word limit policy invalidates any attempt to include all the aforementioned factors which are irrelevant to the four central research questions of this manuscript that revolves around time, market size, and the number of competitors in one sport/discipline, researchers may consider exploring the impact of the factors presented at the start of this paragraph through empirical and evidence-based studies in the future.

Supplemental Material

sj-docx-1-sgo-10.1177_21582440241244972 – Supplemental material for Market Share Instability and Market Concentration: A Sport/Discipline-Specific Study of the Summer Olympic Games 1992–2020

Supplemental material, sj-docx-1-sgo-10.1177_21582440241244972 for Market Share Instability and Market Concentration: A Sport/Discipline-Specific Study of the Summer Olympic Games 1992–2020 by Jinming Zheng, Taeyeon Oh, Geoff Dickson, David P. Hedlund, Tao Zhong and Veerle De Bosscher in SAGE Open

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Jinming Zheng

Taeyeon Oh

David P. Hedlund

Tao Zhong

Data Availability Statement

The data supporting the findings of this study are available upon request from the corresponding author.

Supplemental Material

Supplemental material for this article is available online.

References

Acs

Z. J.

Audretsch

D. B.

(1987). Innovation, market structure, and firm size. The Review of Economics and Statistics, 69(4), 567–574. https://doi.org/10.2307/1935950

Arellano

Bond

(1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies, 58(2), 277–297. https://doi.org/10.2307/2297968

Autor

Dorn

Katz

L. F.

Patterson

Van Reenen

(2017). Concentrating on the fall of the labor share. American Economic Review: Papers & Proceedings, 107(5), 180–185. https://doi.org/10.1257/aer.p20171102

Beck

Demirgüç-Kunt

Levine

(2006). Bank concentration, competition, and crises: First results. Journal of Banking & Finance, 30(5), 1581–1603. https://doi.org/10.1016/j.jbankfin.2005.05.010

Bretschger

Kappel

Werner

(2012). Market concentration and the likelihood of financial crises. Journal of Banking & Finance, 36(12), 3336–3345. https://doi.org/10.1016/j.jbankfin.2012.07.016

Cayenne Consulting. (2013). Understanding market size, or demystifying TAM, SAM and SOM. https://www.caycon.com/blog/2013/10/understanding-market-size-or-demystifying-tam-sam-and-som/

Chakrabarti

Hsieh

Y.-T.

Chang

(2017). Cross-border mergers and market concentration in a vertically related industry: Theory and evidence. The Journal of International Trade & Economic Development, 26(1), 111–130. https://doi.org/10.1080/09638199.2016.1220971

Chen

Zheng

Dickson

(2018). An organizational life cycle approach to exploring the elite sport legacy of Summer Olympic host nations: The cases of China (Beijing 2008) and Australia (Sydney 2000). The International Journal of the History of Sport, 35(12–13), 1276–1305. https://doi.org/10.1080/09523367.2019.1583646

Dai

Guo

Luo

(2019). Market concentration measurement, administrative monopoly effect and efficiency improvement: Empirical data from China civil aviation industry 2001–2015. Applied Economics, 51(34), 3758–3769. https://doi.org/10.1080/00036846.2019.1584381

10.

Das

B. J.

Chappell

W. F.

Shughartii

W. F.

(1993). Advertising, competition and market share instability. Applied Economics, 25(11), 1409–1412. https://doi.org/10.1080/00036849300000145

11.

Dick

A. A.

(2007). Market size, service quality, and competition in banking. Journal of Money, Credit and Banking, 39(1), 49–81. https://doi.org/10.1111/j.0022-2879.2007.00003.x

12.

Doria

Nalebuff

(2021). Measuring competitive balance in sports. Journal of Quantitative Analysis in Sports, 17(1), 29–46. https://doi.org/10.1515/jqas-2020-0006

13.

Dranove

Shanley

White

W. D.

(1993). Price and concentration in hospital markets: The switch from patient-driven to payer-driven competition. The Journal of Law & Economics, 36(1), 179–204. https://doi.org/10.1086/467270

14.

Eckard

E. W.

(1987). Advertising, competition, and market share instability. The Journal of Business, 60(4), 539–552.

15.

Forrest

McHale

I. G.

Sanz

Tena

J. D.

(2017). An analysis of country medal shares in individual sports at the Olympics. European Sport Management Quarterly, 17(2), 117–131. https://doi.org/10.1080/16184742.2016.1248463

16.

Freestone

C. J.

Manoli

A. E.

(2017). Financial fair play and competitive balance in the premier league. Sport, Business and Management: An International Journal, 7(2), 175–196. https://doi.org/10.1108/SBM-10-2016-0058

17.

Gallagher

D. R.

Ignatieva

McCulloch

(2015). Industry concentration, excess returns and innovation in Australia. Accounting and Finance, 55(2), 443–466. https://doi.org/10.1111/acfi.12074

18.

Gerrard

Kringstad

(2022). The multi-dimensionality of competitive balance: Evidence from European football. Sport, Business and Management: An International Journal, 12(4), 382–402. https://doi.org/10.1108/SBM-04-2021-0054

19.

Hirschman

A. O.

(1964). The paternity of an index. The American Economic Review, 54(5), 761.

20.

Houlihan

Zheng

(2013). The Olympics and elite sport policy: Where will it all end? The International Journal of the History of Sport, 30(4), 338–355. https://doi.org/10.1080/09523367.2013.765726

21.

Hovhannisyan

Cho

Bozic

(2019). The relationship between price and retail concentration: Evidence from the US food industry. European Review of Agricultural Economics, 46(2), 319–345. https://doi.org/10.1093/erae/jby026

22.

Hsieh

Lin

S. M.

(2016). Exploring the market concentration and the market dominance of international tourist hotels in Taiwan. Journal of Statistics and Management Systems, 19(2), 285–301. https://doi.org/10.1080/09720510.2015.1103449

23.

International Olympic Committee (IOC). (2019). Results. https://www.olympic.org/olympic-results

24.

International Olympic Committee (IOC). (2022). Tokyo 2020 medal table. https://olympics.com/en/olympic-games/tokyo-2020/medals

25.

Kamerschen

D. R.

(1968). Market growth and industry concentration. Journal of the American Statistical Association, 63(321), 228–241. https://doi.org/10.2307/2283843

26.

Klepper

(1996). Entry, exit, growth, and innovation over the product life cycle. The American Economic Review, 86(3), 562–583.

27.

Klepper

Graddy

(1990). The evolution of new industries and the determinants of market structure. The RAND Journal of Economics, 21(1), 27–44. https://doi.org/10.2307/2555491

28.

Kwiecinski

(2017). Measures of competitive intensity – Analysis based on literature review. Journal of Management and Business Administration (Central Europe), 25(1), 53–77. https://doi.org/10.7206/jmba.ce.2450-7814.189

29.

Lien

L. B.

Foss

N. J.

(2009). The determinants of industry concentration: Two new empirical regularities. Managerial and Decision Economics, 30(8), 503–511. https://doi.org/10.1002/mde.1466

30.

Lusch

R. F.

Laczniak

G. R.

(1987). The evolving marketing concept, competitive intensity and organizational performance. Journal of the Academy of Marketing Science, 15(3), 1–11. https://doi.org/10.1007/BF02722166

31.

Malerba

Orsenigo

(1996). The dynamics and evolution of industries. Industrial and Corporate Change, 5(1), 51–87. https://doi.org/10.1093/icc/5.1.51

32.

Mazzucato

Semmler

(1999). Market share instability and stock price volatility during the industry life-cycle: The US automobile industry. Journal of Evolutionary Economics, 9(1), 67–96. https://doi.org/10.1007/s001910050075

33.

McIntosh

De Janvry

Sadoulet

(2005). How rising competition among microfinance institutions affects incumbent lenders. The Economic Journal, 116(506), 987–1004. https://doi.org/10.1111/j.1468-0297.2005.01028.x

34.

Meisel

J. B.

(1981). Entry, multiple-brand firms and market share instability. The Journal of Industrial Economics, 29(4), 375–384. https://doi.org/10.2307/2098252

35.

Menezes

F. M.

Quiggin

(2012). More competitors or more competition? Market concentration and the intensity of competition. Economics Letters, 117(3), 712–714. https://doi.org/10.1016/j.econlet.2011.11.001

36.

Mia

M. A.

(2018). Evolution of market concentration and competition in the microfinance industry of Bangladesh. International Journal of Social Economics, 45(6), 989–1007. https://doi.org/10.1108/IJSE-10-2016-0281

37.

Nelson

(1960). Market growth, company diversification and product concentration 1947–1954. Journal of the American Statistical Association, 55(292), 640–649. https://doi.org/10.2307/2281587

38.

Ojima

Shino

Ueda

(2018). Retailer market concentration, buyer-size discounts and inflation dynamics. The Japanese Economic Review, 69(1), 101–127. https://doi.org/10.1111/jere.12150

39.

Organisation for Economic Co-Operation and Development (OECD). (2002). Glossary of statistical terms: Dominant firm. https://stats.oecd.org/glossary/detail.asp?ID=3199

40.

Organisation for Economic Co-Operation and Development (OECD). (2018). Market concentration. https://one.oecd.org/document/DAF/COMP/WD(2018)46/en/pdf

41.

Organisation for Economic Co-Operation and Development (OECD). (2019). Market concentration. https://www.oecd.org/daf/competition/market-concentration.htm

42.

Otamendi

Doncel

L. M.

(2014). Medal shares in Winter Olympic Games by sport: Socioeconomic analysis after Vancouver 2010. Social Science Quarterly, 95(2), 598–614. https://doi.org/10.1111/ssqu.12055

43.

Owen

P. D.

Owen

C. A.

(2022). Simulation evidence on Herfindahl-Hirschman measures of competitive balance in professional sports leagues. Journal of the Operational Research Society, 73(2), 285–300. https://doi.org/10.1080/01605682.2020.1835449

44.

Pashigian

(1969). The effects of market size on concentration. International Economic Review, 10(3), 291–314. https://doi.org/10.2307/2525645

45.

Pham

T. A. T.

(2018). Industry concentration, firm efficiency and average stock returns: Evidence from Australia. Asia-Pacific Financial Markets, 25(3), 221–247. https://doi.org/10.1007/s10690-018-9246-5

46.

Pryor

F. L.

(1994). The evolution of competition in U.S. manufacturing. Review of Industrial Organization, 9(6), 695–715. https://doi.org/10.1007/BF01026580

47.

Raith

(2003). Competition, risk and managerial incentives. American Economic Review, 93(4), 1425–1436. https://doi.org/10.1257/000282803769206395

48.

Rajan

R. G.

(1992). Insiders and outsiders: The choice between informed and arm’s-length debt. The Journal of Finance, 47(4), 1367–1400. https://doi.org/10.1111/j.1540-6261.1992.tb04662.x

49.

Ramchandani

Wilson

(2014). Historical and contemporary trends in competitive balance in the Commonwealth Games. International Journal of Sport Science, 10(35), 75–88. https://doi.org/10.5232/ricyde2011.02405

50.

Read

Smith

A. C. T.

Skinner

(2021). A comparative analysis of competitive balance between a closed and an open league in rugby league. Journal of Sports Economics, 22(8), 871–892. https://doi.org/10.1177/15270025211022243

51.

Rocke

K. D.

(2019). Competitive balance within CONCACAF: A longitudinal and comparative descriptive review of the seasons 2002/2003–2017/2018. Managing Sport and Leisure, 24(6), 445–460. https://doi.org/10.1080/23750472.2019.1685403

52.

Runkel

(2022). Player mobility and competitive balance regulation in professional sports leagues. Journal of Sports Economics, 23(4), 479–500. https://doi.org/10.1177/15270025211059526

53.

Sandler

R. D.

(1988). Market share instability in commercial airline markets and the impact of deregulation. The Journal of Industrial Economics, 36(3), 327–335. https://doi.org/10.2307/2098471

54.

Scherer

F. M.

Ross

D. R.

(1990). Industrial market structure and economic performance. Houghton Mifflin.

55.

Shen

Cheung

S. Q.

(2018). How forming joint ventures may Affect market concentration in construction industry? International Journal of Construction Management, 18(2), 151–162. https://doi.org/10.1080/15623599.2017.1301026

56.

Shepherd

W. G.

(1964). Trends of concentration in American manufacturing industries, 1947–1958. The Review of Economics and Statistics, 46(2), 200–212. https://doi.org/10.2307/1928186

57.

Smith

(1776). The wealth of nations. W. Strahan and T. Cadell.

58.

Social Market Foundation (SMF). (2017). Concentration, not competition: The state of UK consumer markets. http://www.smf.co.uk/publications/concentration-not-competition-state-uk-consumer-markets/

59.

Sung

(2014). Market concentration and competition in OECD mobile telecommunications markets. Applied Economics, 46(25), 3037–3048. https://doi.org/10.1080/00036846.2014.920480

60.

Team Singapore. (2021). Team Singapore athletes at Tokyo 2020. https://www.teamsingapore.sg/major-games/olympics/tokyo-2020/athletes

61.

Tsaur

S.-H.

Wang

C.-H.

(2011). Personal ties, reciprocity, competitive intensity, and performance of the strategic alliances in Taiwan’s travel industry. The Service Industries Journal, 31(6), 911–928. https://doi.org/10.1080/02642060903078735

62.

Wagner

Preuss

Könecke

(2021). A central element of Europe’s football ecosystem: Competitive intensity in the “big five”. Sustainability (Switzerland), 13(6), 1–20. https://doi.org/10.3390/su13063097

63.

Weber

A. C.

De Bosscher

Kempf

(2018). Positioning in Olympic Winter sports: Analysing national prioritisation of funding and success in eight nations. European Sport Management Quarterly, 18(1), 8–24. https://doi.org/10.1080/16184742.2017.1336782

64.

Weber

A. C.

Kempf

Shibli

De Bosscher

(2016). Measuring competition in the Olympic Winter Games 1992–2014 using economic indices. Managing Sport and Leisure, 21(6), 399–420. https://doi.org/10.1080/23750472.2017.1304232

65.

Yuan

Zhou

Zhan

(2019). Product market competition, market munificence and firms’ unethical behavior. Chinese Management Studies, 13(2), 468–488. https://doi.org/10.1108/CMS-06-2018-0569

66.

Zheng

Dickson

De Bosscher

(2019). Competitive balance and medal distributions at the Summer Olympic Games 1992–2016: Overall and gender-specific analyses. Managing Sport and Leisure, 24(1–3), 45–58. https://doi.org/10.1080/23750472.2019.1583076

67.

Zheng

Kim

Dickson

De Bosscher

(2018). Competitive balance trends in elite table tennis: The Olympic Games and World Championships 1988–2016. Journal of Sports Sciences, 36(23), 2675–2683. https://doi.org/10.1080/02640414.2017.1375174

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.11 MB