Do consumers care about doping scandals in sports? - Evidence from TV broadcasts of the Tour de France in Denmark - College of the Holy Cross
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Do consumers care about doping scandals in
sports? – Evidence from TV broadcasts of the
Tour de France in Denmark
Arne Feddersen∗
University of Southern Denmark
June 2020
Abstract
This study analyzes the demand for TV broadcasts of the Tour de
France in Denmark. The average number of TV viewers is estimated by
means of an OLS regression. The observation period is 1993–2015. The
main focus of this study is a test of the hypothesis that the behavior
of sports consumers will be (negatively) affected by doping scandals.
The results are mainly consistent with existing research. Stage charac-
teristics and patriotism are the main determinants of TV viewership,
while the weather showed the expected impact. However, in contrast
to other studies, no evidence for a (short-term) impact of doping on
demand can be found.
Keywords: TV demand, Cycling, Tour de France, Doping, Denmark
JEL-Classification: Z20, D12
∗
Niels Bohrs Vej 9, 6700 Esbjerg, Denmark, phone: +45-6550-1579, e-mail:
af@sam.sdu.dk. I would like to thank Andreas Christian Mikkelsen for his valuable re-
search assistance.
11 Introduction
Broadcasts of sporting events – no matter whether it is football, boxing, ski
jumping, cycling, or Formula One – attract the largest TV audiences around
the world. However, so far, the empirical evidence on the demand determi-
nants of TV audiences is limited, although growing during the last couple of
years. This is somewhat surprising since there are more than 120 scholarly pa-
pers, which analyzed the determinants of stadium attendance. Of the existing
papers on the determinants of TV broadcasts of sporting competitions the
vast majority analyze TV audiences in sports like soccer (e.g., Alavy, Gaskell,
Leach, & Szymanski, 2010; Feddersen & Rott, 2011; Nüesch & Franck, 2009)
and American football (e.g., Paul & Weinbach, 2007; Tainsky, 2010). Some
few analysis can be found regarding (slightly) less popular sports exists like
tennis (Konjer, Meier, & Wedeking, 2017) or cycling (Rodríguez-Gutiérrez,
Pérez, Puente, & Rodríguez, 2015; Van Reeth, 2013).
This paper analyzes the determinant of demand for TV broadcasts of the
Tour de France in Denmark. This study contributes to the existing literature
on the determinants of demand for TV broadcasts in two ways. First, it
analyses a sports outside of European football and the North American Major
Leagues and thus contributes to the literature on TV demand for cycling
broadcasts. Second, it is only the third paper which addresses explicitly the
question whether doping scandals (negatively) affect the behavior of sports
consumers in the form of demand for TV broadcasts. According to Cisyk
(2020), TV audiences seems to be a very good proxy for demand for sports,
since the reaction of the consumers can be observed immediately and without
2a lag (compared, e.g., to ticket sales) and the size of the TV audience as well
as the economic significance is much larger then, e.g., stadium attendance.
Generally, due to the frequency and prominence of doping scandals, cy-
cling appears to be an interesting sports to study the impact of these doping
scandals on consumer behavior. Additionally, Denmark seems to be a good
case study. During the sample period, TV broadcasts of the Tour de France
reached an average market share of approx. 62% and a population share of
approx. 7%. According to Van Reeth (2013), Denmark has the second highest
population share of watching cycling in the world after the Belgian region of
Flanders and even ahead of traditional cycling nations such as France, Italy,
the Netherlands, and Spain. Furthermore, Danish cyclist have been relatively
success during the observation period with Bjarne Riis winning the Tour de
France in 1996 as well as deeply involved in doping scandals.
The remainder of this paper is organized as follows. Section 2 provides a
review of the existing literature on the determinants of demand for broadcasts
of sporting events. The third section introduces the empirical model, while
section 4 describes the variables and data sources. In section 5 the findings
of the empirical analysis are highlighted and discussed. The final section
concludes.
2 Literature Review
The literature regarding the determinants of demand for sports has – for a
long time – been dominated by studies of game attendance at sporting events
(for comprehensive reviews of 80+ studies, see Borland & MacDonald, 2003;
3García Villar & Rodríguez Guerrero, 2009). However, lately the number of
empirical analysis of the determinants of demand for sports broadcasts has
increased. The first study, which addressed the topic of television demand
for live sports, was the study of Forrest, Simmons, and Buraimo (2005).
This study was followed, inter alia, by Johnsen and Solvoll (2007), Paul and
Weinbach (2007), Buraimo and Simmons (2009), Nüesch and Franck (2009)
Di Domizio (2010), Alavy et al. (2010), and Feddersen and Rott (2011).
The main research question of this paper is the impact of doping scan-
dals on the demand for TV braodcasts. And while there is a larger group
of papers discussing doping and especially the reasons for doping from an
economic perspective by means of micro economic or game theoretical mod-
els (Breivik, 1992; Eber, 2008; Haugen, 2004; Maennig, 2002), only very few
papers actually have looked into the effect of doping and doping scandals
on demand for sports products (e.g., tickets or TV broadcasts). This is to
some extent surprising, since one of the argumentation in the fight against
doping (besides different health related issues) very often is a severe loss in
fan interest. So far, mainly surveys have been have been employed to analyze
the effect of doping on consumer behavior (Abeza, O’Reilly, Prior, Huybers,
& Mazanov, 2020; Engelberg, Moston, & Skinner, 2012; Solberg, Hanstad, &
Thøring, 2010). Notable analyses of revealed preferences are the papers by
Van Reeth (2013), Van Reeth (2019), and Cisyk (2020), which are strongly
related to this analysis.. These three papers analyze the impact of doping
scandals on the demand for TV broadcasts of cycling or baseball. Addition-
ally, Cisyk and Courty (2017) studies the demand effect of doping with re-
spect to stadium attendance in Major League Baseball. A comparable study
4for cycling, however, is not possible since most of the event is non-ticketed.
3 Empirical Model
The empirical model is based on existing literature regarding the demand for
sports (see Borland & MacDonald, 2003; García Villar & Rodríguez Guerrero,
2009, for comprehensive reviews) as well as the growing sports economics
literature which analyses the determinants of demand for TV broadcast of
sporting events. Of course, the most influential papers with respect to this
empirical analysis are the four papers on the demand for TV broadcasts of
cycling events (Rodríguez-Gutiérrez & Fernández-Blanco, 2017; Rodríguez-
Gutiérrez et al., 2015; Van Reeth, 2013, 2019).
Inspired by the literature five categories of variables have been identified:
stage type, state of the progress of the competition, national hero, day of the
week, and weather. All variables in this model are all available ex ante (i.e.,
prior to the start of a stage). Equation 1 can be formalized as follows:
T Vit =β0 + T Y P Eit β1 + ST AT Eit β2 + N AT ION ALit β3 +
(1)
β4 Dopingit + DOWit β5 + W EAT HERit β6 + γt + it .
T Vit is the dependent variable and represents the number of TV viewers
of stage i in year t displayed in 1,000 viewer. In order to provide better read-
ability, the described independent variables are grouped in vectors. T Y P Eit
is a vector of five dummy variables consisting of five dummy variables the
stage type. The dummy variables in this set take the value of one if the stage
is either a flat stage, a mountain stage, a hilly stage, an individual time trial,
5a team time trial, or a prolog and zero otherwise. Flat stage is the reference
category.
ST AT Eit is a vector consisting of three variables depicting the state of the
progression of a Tour de France. The first variable, 1st Stageit , is one if stage
i is the first stage of an edition of the Tour de France and zero otherwise.
The next variable (LastStageit ) is capturing the effect of the tour d’honneur
(lap of honor) as well as the prestigious finish at Champs-Élysées and takes
the value of one if stage i is the last stage of the Tour de France in year t and
zero otherwise. Finally, StageN umberit contains the running number of the
stage within one edition of the Tour de France, which is included in order to
capture a trend in suspense during the course of this Tour.
The variables within the vector N AT ION ALit should capture effects of
a national hero or national pride (e.g., with respect to a Danish team par-
ticipating in the Tour de France). First, Danishriderinyellowit is a dummy
variable which takes the value of one if a Danish rider is in the lead, and thus
is wearing the yellow jersey, at the start of stage i in year t. It is expected
that this has a strong positive effect on Danish demand for TV broadcasts
of this stage. Second, Danishriderwonthepreviousstageit is one if a Danish
rider was victorious at the previous stage.
The variable of main interest in this analysis is Dopingit , which is intended
to capture any effect of doping scandals on Danish TV audience. It takes the
value of one if a rider has been caught using PED during the Tour de France
in year t and has been expelled or withdrawn from the competition in year
t. Furthermore, the variable remains one for the remaining stages of year t’s
Tour de France. If not doping case has been occurred in year t up to stage i,
6the value of Dopingit is zero.
DOWit consist of two dummy variables that should capture day-of-the-
week effects. W eekday (W eekend) takes the value of 1 if stage i was held
on a weekday (weekend), where W eekday is the reference category. The
vector W EAT HERit consists of the variable T emperatureit and Rainit .
◦
T emperatureit is the mean temperature in C and Rainit is the total pre-
cipitation in millimeters. Both weather variables contain values for the day
of the broadcast of stage i in year t and are taken from a weather station in
the city of Odense, which is – geographically – located very central within
Denmark. Data source is the web-service www.weatherunderground.com. Fi-
nally, year fixed effect (γt ) have been included in order to capture any time
invariant effects.
4 Data and Descriptive Statistics
According to Van Reeth (2013), empirical analyses of TV viewership are usu-
ally based on two different measures: (a) average number of people watching
a sports broadcast; (b) the percentage share of viewers watching the sports
broadcast. The majority of articles are using average number of viewership
(e.g., Bergmann & Schreyer, 2019; Buraimo & Simmons, 2009; Feddersen
& Rott, 2011; Forrest et al., 2005; Gasparetto & Barajas, 2020; Humphreys
& Pérez, 2019; Johnsen & Solvoll, 2007), while fewer studies are based on
the share of viewers (e.g., Alavy et al., 2010; Di Domizio, 2010; Nüesch &
Franck, 2009). Furthermore, Van Reeth (2013) introduces a third measure
to the sports economics literature, the peak audience. This measures the
7maximum number of viewers at any given moment. Since the measure peak
audience is rarely available, it has been not been used much in the literature
(Van Reeth, 2013, p. 44) and won’t – due to non-availability – be used in
this analysis too.
The dependent variable in this analysis is average viewership of Tour de
France broadcasts in Denmark between 1993 and 2015. In Denmark, televi-
sion ratings are collected by Kantar Media, which uses a representative panel
of 1,200 Danish households to estimate the nationwide television ratings. The
analysis in this study is based on the average number of TV viewers with
an age of 3 or older (in 1,000). Thereby, a person is considered as a viewer
for any given TV program if this person has watched at least 10 consecutive
minutes of this program (Kantar Gallup, 2019).
TV broadcasts of stages of the Tour de France are popular in Denmark.
As it can be seen in Table 1, the overall number of viewers of the broadcasts
is about 370,000 or 7.1% of the Danish population of approx. 5.8 million
(Danmarks Statistik, 2020). The minimum was about 38,000 viewer and the
maximum 1.2 million viewer. Due to the schedulung duting the afternoon,
the broadcasts reach a high market share (i.e., share of the viewers watching
the broadcasts of the Tour de France out of all people watching TV at the
same time) of 62% on average with a standard deviation of 15.5%. The max-
imum was a market share of 91.5%. Comparing the number of viewers to the
overall population of Denmark, the population share is 7% on average with
a minimum of just 1% and a maximum of 23.5% .
As described above, it can be assumed that the stage type as well as
the day of the week will show a significant effect on the TV viewership.
8Table 1: Summary Statistics 1
Mean Std. Dev. Min. Max.
Viewer 366,223 164,845 37,700 1,170,500
Market share 61.56 15.48 5.70 91.40
Population share 7.10 3.25 0.70 23.50
Thus, in order to present a first indication of the connection between the
determinants and TV viewership, Table 2 highlights the descriptive statistics
for the different stage types as well as for weekday and weekend stages.
Table 2: Descriptive Statistics
Obs. Mean Std. Dev. Min. Max.
Overall 447 366,223 164,845 37,700 1,170.500
Mountain 114 413,237 163,330 141,300 899,000
Hilly 64 377,380 122,602 221,700 775,900
Time Trial 47 433,555 224,520 51,200 1,101,900
Team Time Trial 12 280,200 74,663 148,300 425,800
Flat 210 327,148 153,049 37,700 1,170,500
Weekend 162 409,308 202,798 51,200 1,170,500
Weekday 285 341,733 133,038 37,700 830,100
As expected and in line with similar calculations from different countries,
the different stage types show noticeably different descriptive statistics. The
popular mountain and individual time trial stages have a mean viewership of
approx. 415,000 and 435,000, while ranging from 140,000 to 900,000 viewer for
the mountain stages and from 50,000 to 1.2 million viewer for the individual
time trials. Flat stages, which are the majority in this dataset, I Have an
average viewership of approx. 330,000 viewer. Interestingly, team time trials,
which are beloved by the organizers due to their marketing value, show the
lowest interest from the TV viewers with an average audience of ”just” 280,000
9viewers. Additionally, stages scheduled on a weekend show a higher average
TV audience (approx. 410,000) than stages scheduled on a weekday (approx.
340,000).
Figure 1 shows a scatter plot of all 447 stages in chronological order.
Although both a linear trend (dashed black line) and a non-parametric trend
based on the lowess comand in Stata 14 (solid black line) do not reveal a
noticeable trend in the TV viewership, two areas of higher TV demand can
be seen approximately between 1995 and 1998 (observations 50 and 125) and
2003 and 2011 (observations 215 and 375). The first peak occurred during
the best times of the career of Bjarne Riis, who won the Tour de France in
1996 and finished 3rd, 5th, and 7th in 1995, 1993, and 1997 respectively. The
second peak, which happened between 2003 and 2011, is very likely linked
to the sporting success of the Danish team ”Professional Cycling Denmark”
with Bjarne Riis as team manager. This team was sponsored by and operated
under the name of the US-American IT service company CSC (2001–2008)
and the Danish investment bank Saxo Bank (2009–2013). Although the team
did not have a Danish contender for the general classification of the Tour
de France, the Italian rider Ivan Basso and the Luxembourgian rider Andy
Schleck were prospects to win the general classification. Basso finshed 11th,
8th, 3rd, and second between 2002 and 2005, while Schleck finished 12th and
2nd in 2008 and 2009. Schleck finished the Tour de France 2010 on the second
place trailing to Alberto Contador by 39 seconds, but he got awarded the
first place retroactively by the Court of Arbitration for Sport (CAS) after
the initial winner Alberto Contador was convicted of the use of performance
enhancing drugs in February 2012. Additionally, the team consisted of many
10Danish riders and was likely the main driver of the higher TV demand during
this time period. Additionally, the Danish rider Michael Rasmussen won the
mountains classification (polka dot jersey) and one mountain stage at the
Tour de France each in 2005 and 2006, while finishing 7th and 17th in the
general classification. In 2007, Rasmussen was leading the Tour de France
after winning stage 8 and 16, but he was withdrawn from the race and fired
by his Dutch team ”Rabobank” for violations of internal rules as he missed
a couple of dopiing tests and lied about his whereabouts.
1500
TV audience (in 1,000 viewer)
1000
500
0
0 50 100 150 200 250 300 350 400 450
Stage
Figure 1: TV audience – 1993 to 2015
Figure 2 illustrates the number of TV viewers by stage number in order
to highlight the possibility of an increasing trend in the viewership due to
the increasing suspense over the course of the three weeks of the Tour de
France. It is widely assumed that the suspense will increase building up to
the final stages, were normally important mountain stages/finishes and time
trials are scheduled in order to provide/maintain the suspense with respect
111500
TV audience (in 1,000 viewer)
1000
500
0
0 5 10 15 20
Stage
Figure 2: TV audience – by stages
to the general classification. Both the linear trend (dashed line) and the non-
parametric locally weighted trend (solid line) show a clear upward trend over
the course of the Tour de France.
5 Results
The model displayed in Equation 1 is estimated using OLS. The results are
shown in Table 3. The dependent variable is the absolut number of viewers
older (age 3 and older) .
The results for TV broadcasts of the Tour de France in Denmark is
mostly in line with the results in the existing literature and expectations.
Corresponding with the results of the other papers on the demand for cy-
cling broadcasts (Rodríguez-Gutiérrez & Fernández-Blanco, 2017; Rodríguez-
Gutiérrez et al., 2015; Van Reeth, 2013, 2019), it can be seen that the type
12Table 3: Regression Results
Viewer in 1,000 OLS
Constant 266.684∗∗∗
(46.564)
Mountain 61.063∗∗∗
(13.216)
Hilly 8.006
(15.079)
Time Trial 63.804∗∗∗
(19.413)
Team Time Trial 36.756
(29.840)
Prolog 143.080∗∗∗
(46.978)
1st Stage -65.578∗
(37.656)
Last Stage 135.427∗∗∗
(28.007)
Stage Number 7.568∗∗∗
(1.093)
Danish rider in yellow 120.941∗∗∗
(30.510)
Danish rider won previous stage 18.006
(29.236)
Doping -39.457
(34.090)
Weekend 46.180∗∗∗
(10.795)
Temperature -5.310∗∗
(2.201)
Rain 17.249∗
(9.984)
N 447
R2 0.675
adj. R2 0.648
∗ ∗∗ ∗∗∗
Notes: p < 0.05, p < 0.01, p < 0.001.
13of the stage has a significant influence. Compared to the reference category
(flat stage), a mountain stage has 61,062 additional viewer. Individual time
trials show an effect of similar magnitude with 63,804 viewer. A prolog, which
is a short individual time trial (normally shorter than 10 km) that is occa-
sionally held at the start of the Tour de France, attracts 143,080 additional
viewer. This relatively high number compared to a flat stage but also to
mountain stages and individual time trials is very likely caused by the time
of the broadcast. A prolog — if included in the route – is normally scheduled
on a Saturday evening and, thus, the variable might capture also time-of-
broadcast effect in addition to the attractiveness of the short time trial.
Since regular stages are almost exclusively held between approx. 10 a.m. and
6 p.m., a variable that captures any time-of-broadcast effect, which can be
found for many other sports broadcasts (e.g., Feddersen & Rott, 2011), has
not been included in the model due to issues with multicollinearity. All three
coefficients are significant at the 1% level. Hilly stages and team time trials
revealed to be not significantly different from zero. This might be surprising
especially with respect to the effect of team time trials, since both the orga-
nizers, sponsors and TV networks are normally advertising this special form
– probably because of the advertisement effect and the spectacular images.
However, due to the questionable sporting value of these team time trials,
TV viewers seem not to value these more than flat stages.
Broadcasts of the first stage have about 65,000 viewer less, while the last
stage attracts 135,000 additional viewer. The coefficient for the first stage
dummy is only significant at the 10% level and the coefficient for the last
stage dummy is at the 1% level. The last stage has some specific traditions.
14First, it is an unwritten law that the leader of the Tour de France will not
be attacked during these, so-called, tour d’honneur. Second, the first part
of this stage – prior to reach the city limits of Paris – is characterized by
celebrations and drolleries of the cyclists. Third, the final part of the last
stage is held in the city center of Paris in laps between Arc de Triomphe and
Place de la Concorde with the finish line on the the Champs-Élysées. This
is stages very often ends in a mass sprint as it is the last possibility for a
stage win for some teams and a prestigious win for any sprinter. Due to these
characteristics the last stage is relatively popular among TV viewer.
The number of the stage has a significantly positive effect on the TV
audience providing evidence for an increasing trend of suspense and interest
over the course of the Tour de France. This means that every stage attracts
about 7,500 viewer more than the previous stage. This might not seem to be
a big amount, however this means that due to this increasing trend additional
150,000 viewer watch the last stage compared to the first one.
The evidence regarding the effect of patriotism is mixed. While a Danish
rider in the lead of the general classification has a strong and significantly
positive effect on Danish TV audience (approx. 120,000 additional viewer
if a Dane is wearing the yellow jersey at the start of a stage), there is no
significant effect if a Danish rider won the previous stage.
The main variable of interest in this study is the variable capturing the
impact of doping scandals on the demand for TV broadcasts of the Tour
de France in Denmark. The coefficient of this variable is not significantly
different from zero and, thus, no evidence that TV viewer dislike doping
and change their consumption of TV broadcasts as a consequence. These
15results are mostly in line with findings in previous study (Van Reeth, 2013,
2019). Additionally, long-term impacts of doping scandals might have been
captured by the year fixed effects as doping scandals, which occurred outside
of the Tour de France, and especially retroactively voided Tour wins could
have negatively impacted the interest in and demand for cycling in Denmark.
However, the year fixed effects do not show any trend or pattern and, if at
all, they reveal positive year effects after the big doping scandals of 2006 to
2008.
The findings with respect to the day of the week and the weather variable
are in line with the majority of studies of TV viewership of sports broadcasts
not only in cycling. Stages held during the weekend have a significantly higher
viewership (plus 46,000) compared to a stage on a weekday. Temperature
has a negative effect on the TV demand for Tour de France broadcasts,
which is in line both with previous empirical findings and economics theory.
As temperatures increase, the opportunity costs of watching television also
increase. Finally, an additional millimeter of precipitation will increase the
TV audience by 17,249 viewer – however, the coefficient is ”only” significant
at the 10% level.
6 Conclusion
This study analyzes the demand for TV broadcasts of the Tour de France
in Denmark between 1993 and 2015. In line with the literature, the results
of the OLS regression indicate that stage characteristics and patriotism are
the main determinants of TV broadcasts of sporting events. It can be shown
16that mountain stages and individual time trials boost the TV audience signif-
icantly. A prolog, probably due to the scheduling during (access) prime time,
also increases the number of TV viewer significantly. The key finding of this
paper is that no evidence could be found that doping scandals, which occur
during the Tour de France, have an impact on the behavior of consumers of
TV broadcasts.
Our findings regarding the (short-term) impact of doping scandals are
similar to those by Van Reeth (2013). However, this study also postulates
the existence of a statistically significant long-term effect of doping on the
demand for TV broadcasts. Additionally, the findings in this study are contra-
dicting the findings by Cisyk (2020), who conclude that their results support
the hypothesis that consumers care about doping in sports.
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