I found this article on a University site , pity its only upto 2005 as we now have lots of new entrants since but a lot of it will still apply.
All credits to the author..
Love thy Neighbor, Love thy Kin:
Strategy and Bias in the Eurovision Song Contest
¤
Sofronis Clerides
Thanasis Stengos
May 2006
Abstract
The annual Eurovision Song Contest provides a setting where Europeans can express
their sentiments about other countries without regard to political sensitivities. Analyzing
voting data from the 25 contests between 1981-2005, we ¯nd strong evidence for the existence
of clusters of countries that systematically exchange votes regardless of the quality of their
entries. Cultural, geographic, economic and political factors are important determinants of
point exchanges. Factors such as order of appearance, language and gender are also impor-
tant. There is also a substantial host country e®ect. We ¯nd some evidence of reciprocity
but no evidence of strategic voting.
Keywords
: Eurovision, social networks, reciprocity.
JEL Classi¯cation
: Z13.
¤
We thank George Deltas for helpful comments and Adamos Adamou, Yianna-Maria Loizou and Gavriella
Panayiotou for enthusiastic research assistance. We are solely responsible for any errors.
Author a±liations and emails: Clerides: University of Cyprus and CEPR; s.clerides@ucy.ac.cy; Stengos:
University of Guelph and University of Cyprus; tstengos@uoguelph.ca. Corresponding author: Sofronis Clerides,
Department of Economics, University of Cyprus, P.O.Box 20537, CY-1678 Nicosia, Cyprus. Phone: +357-
22892450; fax: +357-22892432.
1 Introduction
One Saturday in May of every year millions of Europeans sit glued in front of their television
to watch one of the entertainment highlights of the year: the Eurovision Song Contest (ESC).
The contest has evolved from a humble, seven-country music festival that was ¯rst staged in
1956 to a glitzy extravaganza that is expected to include 40 participating countries in 2006 and
reach a potential audience of one billion people worldwide. Each country is represented in the
contest with one song and then votes for the best song among all other countries' entries. The
country-centered format of the ESC has turned it into a national contest where people root for
the country's entry in the same way that they root for their national football team.
For this reason, but also because musically the contest is mediocre at best, many people would
argue that the most exciting part of the ESC is not the singing but the voting. After all the songs
have been performed live on stage, the presenters get on the phone with a representative of each
participating country and the country's votes are read out. The procedure is suspenseful and
exciting. Eurovision bu®s are well aware of the fact that votes are not always cast on the basis
of a song's quality. Friendly countries always exchange votes, while no country expects to get
points from a country that it is not on good terms with, no matter the quality of the song. This
setup makes the ESC a fascinating experiment where each country can express its sentiments
about another without having to give regard to the political sensitivities and considerations that
usually accompany inter-governmental relations.
In our paper we use data from all contests between 1981-2005 to examine cultural, geographic,
political and economic factors as possible explanations of voting behavior that results in the
formation of voting blocks and social networks. Large and systematic di®erences in voting
patterns may re°ect, in addition to di®erences in tastes, some deeper sociological likes and
dislikes among nations. These systematic biases that go well beyond the aesthetic quality of
the song itself are captured by what we have identi¯ed as a±nity factors, that is variables that
measure how each country feels towards another country. We ¯nd that a±nity variables are
very important in the voting process. We also test for the presence and importance of other
1
attributes in the song delivery such as the order of appearance, the language of the song and the
gender of the performing artist, as well as whether there is a bene¯t for the country hosting the
competition. These variables also turn out to be quite important in explaining voting patterns.
Although a±nity explains a lot of vote exchanges, reciprocity was also found to be important in
the period before televoting was introduced. We do not, however, ¯nd any evidence of strategic
voting intended to increase the vote-giver's chances of winning. The econometric analysis is
conducted in two ways. We ¯rst analyze the ordered ranked preferences of each participant and
then we proceed to examine the scores that each participant gives to the other participating
countries. Since the score data are censored we use estimation methods that account for that.
The paper is organized as follows. In the next section we present a brief history of the
ESC. We then proceed to present an overview of the existing literature followed by a description
of the voting patterns that govern the contest. The description of the data, the econometric
methodology used and the results from our analysis for di®erent speci¯cations that we tried are
presented in section ¯ve. Finally, in the next section we o®er some concluding remarks.
2 The Eurovision Song Contest
The (ESC) originates from a contest that ¯rst took place in 1956 under the name Eurovision
Grand Prix which, in turn, was modeled after the popular Italian San Remo Festival. The
¯rst contest took place in Lugano, Switzerland with only seven participants. The number of
participants has grown now to 39 in the 2005 competition on the 50th anniversary of the ESC.
The list of participants includes countries that do not belong geographically in Europe such as
Israel and Morocco, while many other non-European countries are petitioning to enter.
1
The contest is run by the European Broadcasting Union, an association that is mostly com-
prised of European national television broadcasters. Although the speci¯c rules have changed
over the years, the basic format of the competition remains unchanged. Each national broad-
caster submits an entry that will represent the country in the contest. The selection process is
1
Armenia is scheduled to be the 40th participant in 2006.
2
up to the broadcaster; the only restriction is it has to be an original song.
2 On the night of the
event the competing songs are performed live and then each country awards points to songs by
other countries. The country that earns the most points wins the contest and gets to host the
following year's event.
Irrespective of whether ESC actually contributes to the creation of better music in the
continent, the festival itself constitutes an example of a truly international forum where a country
can express an opinion about another country, free of political or economic considerations. The
awarding of points to songs goes beyond rewarding a \good" song, since in that case a good
song should receive the same number of votes from all other countries barring any major taste
di®erences. In addition to di®erences in tastes, some deeper sociological likes and dislikes among
nations that manifest themselves in systematic biased voting. If it were that all participants
were equally likely to produce good and bad songs then the distribution of votes from a certain
contestant to her fellow competitors should be more or less equal over time. Systematic biases
however, conceal the di®erent considerations beyond the aesthetic quality of the song itself that
enter the voting preferences of participant countries.
The basic structure of the voting system has been in place since 1975, whereby each voting
country awards points to ten other countries, itself not included. There have been various
changes over the years to the main format of the contest, the structure of the voting system, the
number of contestants and the character of the songs. The main changes that are important for
our analysis are as follows:
(i) In 1975 the current scoring system was introduced. Each country ranks the ten best songs.
The top-rated song gets 12 points, the second gets 10 and the next eight get 8, 7, 6, ..., 1.
(ii) An important change was the introduction of televoting. Up until 1997 each country's
votes were decided by a panel of experts. Televoting was allowed as an option in 1998 and
was soon adopted by all participating countries.
2
For many years there was also a restriction that the song had to be performed in one of the country's o±cial
languages. This was lifted because of complaints that it put some countries at a disadvantage, a view that is
supported by the results in this paper.
3
(iii) In order to accommodate more contestants the competition was split into two rounds in
2004. Countries with poor records in the contest have to compete in a qualifying round
which determines the songs that will compete in the main event. The countries involved
in the quali¯ers can vote in the ¯nal, but only those that reach the ¯nal can receive votes.
This allowed the total number of participants to increase from 24 in 2003 to 36 in 2004
and 39 in 2005.
3 Related literature
A network is a mechanism of exchanges between participants, whereby these exchanges may
represent information in the form of communication messages between workers (Gandal, King,
and Van Alstyne 2005) or scientists collaborating on a joint project (Newman 2003). In a typical
social network individuals are depicted as nodes and the links between the nodes represent
communication exchanges between these individuals. Over time new nodes and new links will
appear as new individuals enter the network and new collaborations are established. In the
context of the ESC a network can be established as exchange takes place between di®erent
countries in the form of points. The countries are the individual nodes and the connecting
links between these nodes represent the points exchanged between the countries. These edges or
connecting links can be directed, undirected, weighted and unweighted, depending on whether
these links are taking into account the direction of the vote from country A to country B or not,
as well as whether the number of points exchanged is accounted for in the depicted link or not.
The minimum degree of connectivity (number of connecting links) that a node (country) can
have in a given year of competition is ten, as each country assigns points to ten other countries.
The maximum degree is equal to the total number of the other contestants in that year because
the country can receive points from all other countries.
Fenn, Suleman, Efstathiou, and Johnson (2005) use complex dynamic networks to analyze
the voting patterns of the ESC in the period 1992 to 2003. The authors are able to uncover
nonlinear patterns that emerge over time that contradict the hypothesis that the ESC is a
4
random contest. In a random contest countries simply vote for the best song without any likes
or dislikes for the other contestants. If a country is equally capable of producing a good or
bad song as any other country that would generate a pattern over time that would not di®er
from a random number generator simulating the results of the competition over time. Fenn et
al (2005) ¯nd this is not the case and there are patterns that are not compatible with random
behavior. In social networks two nodes that are connected to a third one are more likely to be
linked together as well, as someone's acquaintances are more likely to know and communicate
with each other. In the context of ESC, the same phenomenon may occur in the form of \voting
blocks" within the contest where there are clustering e®ects that di®er from the pattern that
would arise in a \random contest" environment. Similar conclusions are reached by Gatherer
(2006) using an approach based on simulated Monte Carlo histories. In this case simulated
histories are compared with the actual history of the contest and allow for the revelation of
patterns of voting blocs. Collusive behavior is shown to exist especially with the entry of many
new contestants in the 1990s. However, the paper does not attempt to explain the reasons
behind the emergence of such blocs and why collusion takes place.
Doosje and Alexander (2005) examined the issue of reciprocity in voting behavior between
countries in the ESC and concluded that countries give on average more points to countries from
who they tend to receive higher scores. In other words reciprocity between participant countries
over time acts as a catalyst for vote clustering. Bornhorst, Ichino, Karl, and Winter (2004) use
experimental data to look at the impact of cultural diversity on agents' choices of partners as
well as on the outcomes of economic interactions. In a dynamic trust game environment, where
subjects were divided between cultural lines, northern and southern Europeans, they show the
existence of cultural biases in the way agents conduct their economic activities. In the context of
the game northerners seem to be culturally biased against southerners, as they perceive them as
less trustworthy, where trust is measured by the tendency to reciprocate by making a generous
payback for a transfer received. The results are not due to stereotyping, since they emerge and
are reinforced by repeated interactions between di®erent nationalities even when agents are not
characterized by strong stereotyping at the outset of the interaction. Other examples of trust
games in the economic literature are Fershtman and Gneezy (2001) who examined the issue
5
of trust between Ashkenazi (Jews of European descent) and Sephardi (Jews of Middle Eastern
origin) Israelis, in the context of an one shot game. In general similar conclusions are reached by
Guiso, Sapienza, and Zingales (2003) who ¯nd that trust di®erences between people of di®erent
countries a®ect the level of trade.
In the context of the ESC voting biases are the equivalent of transfer biases in the above
games. Cultural (trust) biases between countries (groups) would produce the same clustering
e®ects that are predicted by a non-random contest environment. In this paper, we use the work
of Fenn et al (2005) on the formation of voting networks over time as a starting point. Our
intention is to o®er statistical evidence as to the factors that help create these networks, by
identifying the variables that help explain the voting preferences of the participant countries.
Ginsburgh and Noury (2004) and Haan, Dijkstra, and Dijkstra (2005) also examined di®erent
factors that a®ected voting behavior in the ESC. Ginsburgh and Noury (2004) looked at the
possible e®ect of vote trading or logrolling as opposed to an index of quality of their song,
whereas Haan, Dijkstra, and Dijkstra (2005) looked at the possible di®erences of the judging
behavior of expert juries as compared to that of the general public using the e®ect of the order
of appearance on the outcome of the vote in data sets from the ¯nals of the ESC as well as
those from national ¯nals. In the ESC until 1998 voting was conducted by expert juries whereas
in national competitions the public was typically more involved in the song selection. The
¯ndings of Ginsburgh and Noury (2004) ¯nd some evidence that language a®ects voting but
other measures of culture are either statistically insigni¯cant or very close to zero numerically.
Haan, Dijkstra, and Dijkstra (2005) ¯nd evidence that order of appearance has an e®ect on voting
although this e®ect is smaller for expert juries. In our paper using a data set that includes all
voting records between participants in the ESC for the period 1981-2005 we examine the link
between cultural, geographical and economic factors as possible explanations of the behavior
that results in the formation of possible voting blocks and social networks.
6
4 Voting patterns
We have collected voting data from all Eurovision song contests from 1981 to 2005.
3 The number
of contestants ranged from 18 to 26; a total of 39 countries participated in the ESC in the period
under examination.4 There are a total of 656 country pairs that are observed between 1 and 25
times; only Spain, Sweden and the UK participated in all 25 contests.
The ¯rst thing to do is to look at the mean number of points awarded between di®erent
countries and look for country pairs that exhibit \abnormal" voting behavior. In order to set
a benchmark suppose that song quality is randomly determined every year. Each country gets
a draw from the same distribution so that expected song quality is the same for all countries.
Each country gives out a total of 58 (= 12 + 10 + 8 + 7 + 6 + 5 + 4 + 3 + 2 + 1) points and also
expects to receive, on average 58 points. In a contest with N participants, the expected number
of points received per participant is 58=(N ¡ 1). Given that the median contest size is 23 and
assuming random song quality, we expect that each country will receive from each other roughly
58=22 = 2:64 points.
In order to compare this with our data we calculated the mean number of points awarded
between the members of each pair in our sample. In Table 1 we present the pairs with the highest
and lowest averages; we excluded pairs that appear fewer than three times. Column (4) reports
the mean number of points awarded from country A to country B while column (5) reports
the mean number of points awarded in the opposite direction. We see that many countries
systematically give to speci¯c other countries many more points than the 2.64 we expect on
average. At the same time, those countries also receive a lot of points from the countries they
give to. The correlation between columns (4) and (5) is .8757. Give and thou shalt receive?
Perhaps.
In reality, of course, song quality is not random. Some countries have stronger musical
3
The data are readily available at the o±cial ESC website (http://www.eurovision.tv) as well as various
websites maintained by ESC a¯cionados (e.g. http://www.kolumbus.fi/jarpen/ and http://www.esctoday.
com).
4
For the years 2004 and 2005 we use the countries that participated in the ¯nal round only.
7
Table 1: Pairs with the highest and lowest point exchanges
Mean points awarded \Overgiving"
Country A Country B Obs. A to B B to A all to A all to B A to B B to A
(1) (2) (3) (4) (5) (6) (7) (8) (9)
I love you:
Cyprus Greece 18 10.9 10.1 2.2 2.2 7.9 8.7
Slovakia Malta 3 11.3 6.7 0.3 4.4 6.4 6.9
Ukraine Russia 3 8.7 7.3 2.9 2.6 4.4 6.1
Estonia Latvia 4 9.3 8.5 4.5 3.4 4.0 5.9
Finland Estonia 5 8.4 4.4 0.6 2.7 3.8 5.7
Croatia FYRMacedonia 5 6.6 8.0 2.8 1.0 5.2 5.6
Turkey Bosnia 10 6.5 4.6 2.8 1.4 1.8 5.1
Croatia Bosnia 11 6.5 6.1 2.3 1.4 3.8 5.1
Denmark Sweden 20 8.2 6.1 3.0 3.8 3.1 4.4
Slovenia Croatia 9 7.1 5.0 1.5 2.8 3.5 4.3
Romania FYRMacedonia 5 5.0 5.2 1.7 1.1 3.5 3.9
Germany Poland 8 5.8 4.3 2.9 1.9 1.4 3.9
Estonia Sweden 9 7.0 5.9 3.4 3.6 2.5 3.4
Norway Sweden 24 7.1 4.7 2.5 3.7 2.2 3.4
Norway Denmark 19 6.6 3.9 2.3 3.2 1.6 3.4
Cyprus Serbia 12 6.6 4.7 2.6 3.4 2.1 3.2
Romania Russia 6 5.3 5.0 1.9 3.3 3.1 2.0
Croatia Malta 13 6.3 4.8 2.6 3.3 2.2 3.0
Sweden Iceland 17 4.9 5.5 3.7 1.9 1.8 3.0
Romania Greece 6 6.0 3.8 1.8 3.2 2.0 2.8
I love you not:
Romania Latvia 4 0.0 0.0 2.9 4.4 -2.9 -4.4
Croatia Sweden 13 0.5 0.5 2.8 3.8 -2.3 -3.3
Turkey Latvia 5 1.2 0.0 3.1 3.6 -3.1 -2.4
Denmark Croatia 8 0.0 1.5 3.7 2.8 -2.2 -2.8
Malta France 15 0.2 2.0 3.7 2.9 -1.7 -2.7
Correlations: .8757 -.0328 .9527
8
traditions or more mature entertainment industries and are able to consistently produce above
average songs. In columns (6) and (7) we present the mean number of points received by each
country from all other countries. To ensure comparability we took this mean over contests that
included both countries. Looking at the ¯rst entry in the table, in the 18 contests that both
Cyprus and Greece competed, each country averaged 2.2 points. Yet Cyprus gave Greece an
average of 10.9 points and received an average of 10.1. The di®erence between 10.9 and 2.2 is
the \overgiving" from Cyprus to Greece after controlling (crudely) for song quality. Cyprus has
given to Greece an average of 8.7 points more than the average Greece has received from all
other countries. Greece and Cyprus are the most extreme example, but it is clear from the table
that overgiving occurs between other countries also. In the bottom part of the table we see pairs
of countries that do not give many points to each other. There are sone notable di®erences here
also although they are not as large because 2.64 is much closer to zero than it is to 12. Note that
controlling for quality has actually increased the correlation to a striking .9527. Going beyond
simply looking at country pairs, just reading out loud the names of the countries that appear
in Table 1 gives some clues of more interesting behavior. Countries in Scandinavia, the Balkans
and eastern Europe dominate the table. Croatia appears six times, Sweden ¯ve, Romania four,
Estonia and Latvia three. Lots of points are exchanged within three clusters: nordic countries;
balkan countries; and countries hailing from the former Soviet Union.
A helpful way of displaying voting alliances is through the use of network graphs. In Figure
1 we display such a graph.
5 Countries were arranged in approximate geographical position with
such adjustments made to make the patterns clearer and keep the graph size small. A connecting
link from country A to country B was drawn if country A gave to country B at least 6.1 points
(the mean plus two standard deviations) on average over all contests they both participated in.
From the ¯gure we can discern certain network clusters that mimic the geographic positions of
the countries involved. These separate clusters include the nordic group of countries (Sweden,
Finland, Norway, Denmark, Iceland and Estonia); the former Soviet republics (Estonia, Latvia,
Lithuania, Moldova, Russia, Ukraine); and the former Yugoslav republics (Bosnia-Herzegovina,
5
The graph was created using the NetDraw program which is part of the UCINET package. We are grateful
to Neil Gandal for pointing us to this literature.
9
Croatia, FYR Macedonia, Slovenia, Serbia & Montenegro).
Figure 1: Network graph depicting overgiving
5 Econometric analysis
5.1 Conceptual framework
The basic problem faced by each country is to identify and rank the ten best songs in a contest.
Our basic modeling assumption is that this decision will depend on two factors: the
a±nity that
each country feels towards each other country and the perceived quality of each song. The latter
can be further decomposed into objective quality that relates to observable song attributes and
subjective quality
that relates to a country's idiosyncratic preferences for a certain type of song.
10
In order to be concrete, let aij denote the a±nity between country i and country j. The
quality of country j's entry as perceived by country i is denoted by qij = µj+"ij , where µj denotes
objective song attributes and "ij denotes country i's idiosyncratic preference for country j's song.
The overall valuation of each song amounts to a mapping from these three factors to an one-
dimensional index vij = f(aij ; µj ; "ij ). The songs are then ranked: the song j with the highest
v
ij
is ranked 1st (RANKij = 1), the next one 2nd (RANKij = 2), and so on up to the 10th
song. The remaining songs are not ranked. RANKij is then translated into points POINTSij
as described above. Thus variable
POINTSij takes values in f0; 1; 2; 3; 4; 5; 6; 7; 8; 10; 12g and
variable RANKij takes values in f1; 2; 3; 4; 5; 6; 7; 8; 9; 10; :g, where a dot \." signi¯es entries that
are not ranked. RANKij is the natural dependent variable to use but is also cumbersome and
di±cult to interpret. For that reason we will rely mostly on speci¯cations that use POINTSij
as the dependent variable.
We parameterize a±nity and objective quality as
aij = Pl ¯l xijl and µj = Pm °m wjm
respectively. Assuming a linear speci¯cation for
f() yields the following expression for a song's
valuation:
v
ij
=
L
Xl=1
¯
l xijl
+
M
Xm=1
°
m wjm
+ "ij : (1)
5.2 Explanatory variables
Our goal is to determine the factors the determine a±nity
aij and perceived quality qij and
the relative importance of each in determining the outcome of the voting process. A useful
point of reference is the gravity model that has been used extensively in the international trade
literature to model trade °ows between countries.6 In a typical speci¯cation the variable to
be explained is the amount of exports from country A to country B. Explanatory variables
include size, geographic proximity and variables that aim to capture cultural, religious and
political links between the two countries; in short, variables that capture a±nity. In our case
the dependent variable will be the number of points given by country A to country B (or the
6
Anderson (1979) provides a theoretical justi¯cation for the gravity model; Leamer and Levinsohn (1995)
review the empirical literature.
11
rank assigned to it). Our choice of explanatory variables was guided by the gravity equation
literature. One notable di®erence is that we use trade °ows (the dependent variable in the
gravity equation) as an independent variable to capture the closeness of economic relations
between the two countries. We de¯ned two trade variables;
EXPORTS for the pair (A,B)
is the percentage of country A's exports that went to country B in 1994; TOTTRADE is
the sum of the values of the EXPORTS variables for the two countries in each pair; that is,
TOTTRADE
ij
= TOTTRADEji = EXPORTSij + EXPORTSji. TOTTRADE captures
overall importance of trade between the two countries while EXPORTS captures asymmetric
e®ects that may arise from exports as opposed to imports.7
In order to capture geographic closeness we created the variable
PROXIMITY, de¯ned as
the negative of the log-distance between airports in capital cities unless the countries have a
common border in which case PROXIMITY was set to zero. In that case we capture the pure
neighborhood e®ect that is not contaminated by a case where the capitals are far apart in
distance, yet the countries have common borders. In order to capture the e®ect of a common
language we created two dummy variables. MAINLANG takes the value of 1 if the main language
of the two countries is the same (e.g. Germany and Austria, Greece and Cyprus). ANYLANG
takes the value of 1 if the two countries have any one common language, even if it is not the main
one (e.g. Italy and Switzerland get a 0 for
MAINLANG and a 1 for ANYLANG). The variable
ANYRELIG
was de¯ned in the same way as ANYLANG to capture similarities in religious
beliefs. Colonial links are identi¯ed with two variables. COLONY identi¯es colonizer-colony
pairs (e.g. UK and Malta) and COLONIZER °ags countries that had a common colonizer (e.g.
Cyprus and Malta).8
We also collected data on several observable characteristics that may be thought to impact
a song's perceived quality. One thing we can
not measure is pure artistic quality.9 What we can
7
We selected an indicative year in order to reduce the data collection burden. It should be a su±ciently good
proxy for our purposes. Trade data came from the IMF's Direction of Trade Statistics database.
8
All variables except ANYRELIG were obtained from the CEPII database (http://www.cepii.fr/
anglaisgraph/bdd/distances.htm). Clair, Gaulier, Mayer, and Zignago (2006) provide a detailed expla-
nation of variable construction. Information on religious a±liation was obtained from the CIA Factbook
(http://www.cia.gov/cia/publications/factbook).
9
A cynic would say that such a measure would take the value of zero for all entries and thus would not be
identi¯ed.
12
do is test various conjectures that have been circulating in Eurovision circles.
10 For example, it
is widely thought that the language that the song is written in is important; speci¯cally, songs
in \strange" languages are at a disadvantage. We construct two dummy variables, ENGLISH
and
FRENCH to identify songs written in those two languages. Female performers are said to
do better than males; the dummy variables SOLOMALE and SOLOFEMALE aim to capture
that e®ect. We also include a variable named DUET to capture di®erential e®ects of duets
versus larger groups. The song's order of performance is also said to be important. This is
captured in the variable POSITION, which takes values from 1 to 26. Finally, the host country
typically does better than average, so we include the dummy HOST to identify the host. These
are variables that can be construed as packaging e®ects of the song delivery. The ¯rst three
are variables where the performing country has a choice in a®ecting the delivery of its song by
choosing the artist and the language of delivery, whereas the last three variables capture the
exogenous characteristics of the song delivery which are beyond the performer's control. Table
2 provides descriptive statistics for all the variables used in the analysis.
5.3 Estimation and base results
Our data are essentially an unbalanced panel of partial rankings by each participant of all other
participants. Such data are not common in economics and we will draw on techniques developed
in sociology in order to analyze them.
11 An observation in our data is uniquely de¯ned by the
triple giver-receiver-year. We note that every country pair appears twice in each year. For
example, the pair (A,B) appears once when A gives to B and once when B gives to A. Thus in
a contest with Nt participants we have Nt ¢ (Nt ¡ 1) observations. A total of 656 country pairs
appear in all; the total number of observations we work with is 12,151.
The rank-ordered logit speci¯cation, also known in the literature as exploded logit and as
the Plackett-Luce model (Marden 1995), is the appropriate method to use in the context of a
contest with many participants, where some of these participants will be ranked. The method
10
The webmaster of http://www.kolumbus.fi/jarpen/ lists some of these conjectures and provides supporting
evidence using data from winning entries only.
11
See Allison and Christakis (1994) and Marden (1995).
13
Table 2: Descriptive statistics
Variable Mean Std dev. Min. Max.
POINTS 2.677 3.675 0 12
EXPORTS 0.028 0.051 0 0.389
TOTTRADE 0.056 0.077 0 0.448
ANYRELIG 0.358 0.479 0 1
ANYLANG 0.222 0.415 0 1
PROXIMITY -6.71 2.16 -8.56 0
POSITION 11.94 6.70 1 26
COLONY 0.046 0.208 0 1
COLONIZER 0.001 0.097 0 1
HOST 0.042 0.200 0 1
ENGLISH 0.271 0.444 0 1
FRENCH 0.12 0.325 0 1
SOLOMALE 0.267 0.442 0 1
SOLOFEMALE 0.441 0.496 0 1
DUET 0.126 0.332 0 1
NORDIC 0.033 0.180 0 1
SOVIET 0.005 0.073 0 1
YUGOSLAV 0.007 0.084 0 1
PASTGIVING 0.515 2.091 -7.33 11.9
PASTGIVSQ 4.639 11.55 0 142.1
was proposed in the econometrics literature by Beggs, Cardell, and Hausman (1981) and further
re¯ned by Hausman and Ruud (1987) who also coined the term rank-ordered logit. Note that
there is more than one ranked participant as voting does not simply pick the best among the
contestants but also ranks the rest in the group of alternatives. This is di®erent than the standard
ordered logit or probit speci¯cations, where one chooses one among many alternatives. The rank-
ordered logit has a sequential interpretation. One ¯rst chooses the best among all participants
(assigns the maximum 12 points). Next, the voting country selects the best alternative among
the remaining alternatives and the process continues in that way. The decisions at each stage of
the decision making process are described by a standard ordered probit or logit model.
12 Note
that the model also accounts for censoring as only the top ten alternatives are ranked among
more than twice as many contestants. The method of estimation is maximum likelihood (Beggs,
Cardell, and Hausman 1981).13
12
Ginsburgh and Noury (2004) used a ordered probit model as a way of checking the robustness of their results,
whereas Haan, Dijkstra, and Dijkstra (2005) used linear OLS methods to analyze the rankings.
13
The implementation of the maximum likelihood estimator is obtained as the partial likelihood estimator of
an appropriate Cox regression model for waiting time (Allison and Christakis 1994). In this case, higher values
of an alternative is equivalent to a higher hazard rate of failure. In other words a higher stated preference is
14
A unique aspect of our data is that we have repeated rankings by the same contestants. The
entries that each contestant has to rank di®er from year to year but also have common elements
(the a±nity aspect). We are not aware of any previous work that uses the rank-ordered logit
in a similar setting. In order to keep the likelihood function tractable we make the simplifying
assumption that there is no link between rating countries in di®erent years. In other words,
France in 1985 is a di®erent country than France in 1986. This means that we can not fully
exploit the panel aspect of our data. The basic model that we estimate is
RANK
ijt
= f(xij;wjt; µ) (2)
where vector xij includes variables that capture a±nity between countries i and j, wjt includes
characteristics of entry j and µ is the parameter vector to be estimated. Note that xij has no t
subscript as all our a±nity variables are time-invariant.
The rank-ordered logit has the disadvantage that the results do not have a natural inter-
pretation. For this reason we also estimated an alternative speci¯cation using
POINTSit as
the dependent variable. We treat POINTSit as a continuous variable and apply the Tobit
correction to account for censoring. The basic speci¯cation is:14
POINTS
ijt
= ® + x0ij¯ + w0jt° + "ijt (3)
In Table 3 we present the results of our base speci¯cation using four methods: the rank-
ordered logit, the simple OLS panel, the ¯xed-e®ects panel tobit and the random e®ects panel
tobit. Overall, the set of a±nity variables are important in explaining voting behavior, in the
tobit speci¯cations and less so in the ranked logit. In the random e®ects panel tobit, EXPORTS,
TOTTRADE
, ANYLANG, and MAINRELIG are all statistically signi¯cant and positively a®ect
the votes that one country gives to another. In the other speci¯cations TOTTRADE, ANY-
represented by a shorter waiting time until failure.
14
Ginsburgh and Noury (2004) used the observation of the pair indexed by the giving country as that of the
dependent variable, but included the observation where the same country is a receiver as a regressor to capture
possible voting exchange or reciprocity among the members of the pair. That introduces endogeneity in the
estimation that needs to be accounted for by the use of instrumental variables.
15
Table 3: Results from the base speci¯cation
Rank-ordered Pooled Pooled Panel
Variables logit OLS tobit tobit
EXPORTS -0.079 2.707
¤¤ 6.278¤¤ 4.979¤
(0.666) (0.978) (1.972) (2.226)
TOTTRADE 0.986
y 1.035 2.208 5.106¤¤
(0.598) (0.716) (1.465) (1.621)
MAINRELIG 0.191
¤¤ 0.447¤¤ 0.842¤¤ 0.614¤¤
(0.038) (0.075) (0.154) (0.167)
ANYLANG 0.076 0.454
¤¤ 0.781¤¤ 1.042¤¤
(0.065) (0.143) (0.284) (0.318)
PROXIMITY 0.015 0.050
¤ 0.074y 0.072y
(0.009) (0.020) (0.040) (0.043)
COLONY 0.292
¤¤ 1.055¤¤ 1.780¤¤ 1.609¤¤
(0.072) (0.164) (0.324) (0.364)
COLONIZER 0.493
¤¤ 1.146¤¤ 2.350¤¤ 2.623¤¤
(0.130) (0.337) (0.662) (0.718)
HOST 0.518
¤¤ 1.711¤¤ 3.117¤¤ 2.573¤¤
(0.061) (0.164) (0.319) (0.298)
ENGLISH 0.512
¤¤ 0.692¤¤ 1.441¤¤ 0.963¤¤
(0.048) (0.077) (0.157) (0.156)
FRENCH 0.045 0.119 0.356 0.919
¤¤
(0.047) (0.106) (0.219) (0.232)
SOLOMALE -0.132
¤¤ -0.207¤ -0.617¤¤ -0.581¤¤
(0.046) (0.102) (0.215) (0.201)
SOLOFEMALE 0.213
¤¤ 0.510¤¤ 1.012¤¤ 1.104¤¤
(0.043) (0.095) (0.197) (0.184)
DUET 0.244
¤¤ 0.765¤¤ 1.501¤¤ 1.278¤¤
(0.051) (0.122) (0.250) (0.235)
POSITION 0.014
¤¤ 0.029¤¤ 0.054¤¤ 0.052¤¤
(0.002) (0.005) (0.010) (0.009)
INTERCEPT 1.744
¤¤ -2.246¤¤ -0.431
(0.190) (0.392) (0.409)
Obs. 12,175 12,175 12,175 12,175
ln
L -15,502 -22,876 -23,232
Â
2 584.5 635.5 553.0
¹ R2 0.053
½
0.139
(s.e.) (0.008)
Standard errors in parentheses. Signi¯cance levels:
y : 10%, ¤ : 5%, ¤¤ : 1%.
16
LANG
, and MAINRELIG variables are signi¯cant although the e®ect of TOTTRADE is not
strong. The e®ect both of COLONY and COLONIZER is quite strong in all the speci¯cations
considered. This is of course in line with the argument that past colonies shared a common
cultural a±nity that plays a very strong role in a®ecting exchange behavior expressed in the
formation of networks as argued by Fenn et al (2005). PROXIMITY also has a positive impact
that is statistically signi¯cant but less so than in the case of the other variables. This suggests
that geographic proximity is less important than cultural factors.
From Table 3 it is also evident that the factors that a®ect the presentation of the song
(packaging e®ects) are also very important. If the language of performance is ENGLISH, the
song has a de¯nite advantage over a performance in another language. What we found striking
is the fact that the order of appearance is very important in how a song is judged. Participants
that perform later on in the contest do signi¯cantly better on average than those who perform
¯rst, even though the order of appearance is determined randomly, con¯rming the ¯ndings of
Haan, Dijkstra, and Dijkstra (2005) and Bruine de Bruin (2005). One possible argument for this
result is that presence of a \fading memory e®ect", where jurors seem to better remember the
performance of the later contestants and fail to recall as well those who have performed earlier.
The host country receives about one and a half extra points from each participant.15 We think
that at least two factors contribute to this large impact. One is that the host is rewarded for
going through the trouble of \putting up the show". The second is that the host country invests
more heavily in the contest because it wants to make a strong showing on its home turf.16 A
similar host country e®ect was obtained by Bernard and Busse (2004) in their study of medals
won in Olympic Games. Finally, the presence of female artists gives an advantage to the song
of the country in question by more than one point on average, indicating a certain bias of jurors
or of the voting public towards female performers. Duets also seem to be better appreciated by
voters than larger groups.
The results are qualitatively very similar across speci¯cations. In the remainder of the
15
In the case of the tobit speci¯cations the marginal e®ects are calculated as the coe±cient estimate times the
proportion of the noncensored observations.
16
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