Importing Human Capital: The Effects of a Foreign Football Manager on Seasonal Results
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Importing Human Capital: The
Effects of a Foreign Football
Manager on Seasonal Results
This empirical study investigates the effects of hiring
a foreign football manager on club performance. We
investigate the effects on seasonal results using both
BY:
a fixed effects OLS and a random effects ordered
probit model. Ignoring the omitted variables bias, we
find evidence that foreign managers have a positive ALEXANDER
effect on performance.This effect disappears, however,
once the panel structure in the data is taken into SCHRAM
account. We do find that, conditional on performance,
the probability of getting the sack is higher for foreign
managers than for local managers using a random
effects probit model.
MSC-LEVEL | ECONOMETRICS
1. Introduction
In modern organizations, managers are responsible for the day-to-day
running of business. In this way, managers play a crucial role in organizational
success or failure. However, measuring the quality of a manager’s work
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can be difficult. There are several reasons underlying this lack of empirical
evidence on managerial quality: (1) private firms are not required to
reveal internal data, and (2) many organizations are complex entities,
where it is difficult to isolate the influence of a manager on organizational
performance.An exception to this observation is the sports industry, which
offers a more suitable environment to investigate manager quality. First,
data are widely available on the output (results) of the manager’s work.
Second, shirking seems unlikely in the sports industry, since club owners
and directors are able to observe the production process every time a
match is played, whereas this process might be much more complex in
other industries. Finally, in most sports, the number of managers is limited
(often to only one) and responsibilities are clearly defined, which simplifies
the isolation of any particular manager’s output. All this leads to a relatively
clear measurement of managerial performance, and therefore to higher
chance of being sacked after poor performance. In turn, this lowers the
opportunity to shirk. Third, firms in sports (clubs) are identical in several
aspects: they produce the same output, compete under the same rules,
and so on. They only differ in size, for which one can control in statistical
analyses, and have different owners and managers.
This paper contributes to the literature by investigating the effect of
appointing a foreign football manager on seasonal results in European
football. In theory, organizations only import employees from abroad if
it increases human capital, which in turn increases performance. The
choice for a foreign employee is costly (Bauer and Kunze, 2004). First,
obtaining a working permit could cause difficulties, although this is not
likely to be the case in European football since most managers are from
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countries within the European Union. However, there 2.2 Explanatory variables
are more specific difficulties, such as language problems The most important explanatory variable in this study
and socio-cultural differences. For these reasons, is the dummy variable foreign indicating whether or
organizations are likely to only hire foreign workers not the manager is from abroad. There are quite some
if their qualifications outweigh these issues. Highly differences between countries in our dataset when it
skilled workers tend to be costly, and importing a comes to the percentage of clubs starting the season
foreign manager is no exception. Therefore we expect with a foreign manager. For instance, the percentage of
that a club will only appoint a foreign manager if he foreign managers is far below the European average of
is expected to significantly increase club performance. 20.6% in Croatia (3.4%), the Czech Republic (3.3%), Italy
Note that we consider the case where a club starts (7.2%) and the Netherlands (7.9%), while in countries
the season with a foreign manager. Thus the number of such as Greece (48.1%) and Russia (42.5%) almost half
points and final league ranking are attributed to starting of the managers is from abroad. Notice that a manager
the season with a foreign or domestic manager, even who works at a club for several seasons is counted
though the manager may have been fired during the for each season he starts. For instance, Arsene Wenger
season. was the manager of FC Arsenal in every season in our
dataset, which means Wenger is observed nine times.
2. Data and variables Also, it is important to note that for these numbers,
Data is collected from http://www.transfermarkt.de/. managers from England, Northern-Ireland, Scotland
League results are collected for 24 countries for the and Wales (Great Britain) are considered as domestic
seasons 2005-06 until 2013-14 for winter leagues in each of those countries. The percentage of foreign
and 2006 until 2013 for summer leagues. For each managers is reasonably stable across seasons for
of the seasons, we also collected information on the winter leagues. For summer leagues, there appears to
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manager(s) and players of the clubs. Combining these be a decline in the percentage of foreign managers after
with league results leads to our final dataset of 2,956 the 2010 campaign.
observations.
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2.1 Dependent variables
The first dependent variable under consideration
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is a dummy variable sack which equals zero if the
manager who started the season also finished it and
one otherwise. Since a manager who under performs
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is likely to be sacked by the board and successful
managers are assumed to at least finish the season Table 1: Descriptive statistics of the explanatory variables
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before considering a move to another (bigger) club, we
assume that all managers that leave before the end of In addition to the dummy variable foreign, this paper
the season have been sacked. Both for domestic and considers the following explanatory variables. Besides
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foreign managers, the percentage of sacked managers the nationality of each manager, we also know his age.
is close to 50%, with the percentage of sacked foreign Almost all managers in football start their managing
managers being slightly higher. career after a career as a player. A playing career
Second, we measure performance by results per normally ends somewhere around the age of 35,
season. We take two measures of team performance: followed by a short period as youth- or assistant
MSC-LEVEL VEL
(1) the total number of points earned during the season trainer during which courses must be followed at
and (2) the position of the club in the final ranking of the the nation’s football association. Table 1 shows the
league. Since every season takes on its own course, the descriptive statistics of age, which are in line with this
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Recommended
total number of points does not always reflect the same career path. We use age as a proxy for experience and
rate of success. For instance, when Martin Jol managed expect experience of the manager to have a positive
to collect 85 points in the 2009-10 Dutch Eredivisie effect on performance.
campaign, AFC Ajax finished second behind FC Twente To control for the different size of clubs, we need
(86 points). In all four seasons thereafter, manager Frank a proxy for each club’s financial status. Since data on
de Boer was able to become champion with fewer than finances in football are not widely available, we consider
85 points. This shows that final league ranking may be the aggregate market value, as given by Transfermarkt.
a better measure of success than the total number of This market value is the sum of the market values
points earned. Obviously, the final league rank of a team of the players in every season. The market value of a
is correlated with the total number of points earned. player is determined by various factors: performance,
Since a lower number of points yields a lower final expected transfer sum, medial focus on the player
league ranking, the correlation is negative (-0.8086). and talent status. Based on these factors, the experts
30of Transfermarkt continuously discuss market values 3.1Random effects probit
of nearly all professional football players around the For binary outcome data the dependent variable y
world. Since football is the core business of a club, takes one of two values. In our case, manager i gets the
we assume that most clubs’ main investments aim at sack in season t with probability and gets to keep his
strengthening the squad and thus increasing the market job with probability 1- :
value of the selection. For better interpretation, we use
total market value divided by 1,000,000 as a control
variable and of course we expect the market value to
have a positive impact on performance.
We also include the variance of the market value
of the players in each selection. This allows us to Because our data has both a cross-sectional and a
investigate whether or not a club is better off investing temporal structure we also use the natural extension
in a few highly valued players or in a selection of players of the probit model for panel data: the random effects
of roughly the same value. This issue is fiercely debated probit model. We consider
by many experts. For example, Real Madrid’s president
Florentino Perez introduced a transfer strategy called
Zidanes y Pavones when he first took control of the club
in 2000. The strategy was to sign one major superstar where is the standard normal cdf. The random
per year (for instance Zinedine Zidane in 2001) and effects MLE assumes that the individual effects are
promote youth players to fill up the remainder of the normally distributed, with Using
selection (Francisco Pavon was also added in 2001). random effects we assume that the individual specific
Initially the Zidanes y Pavones strategy was successful, effects are uncorrelated with the explanatory variables.
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with Real winning the Spanish Primera Division in 2000- It maximizes the panel-level likelihood with respect
01 and 2002-03 and claiming the UEFA Champions to and
League in 2001-021. However, subsequent seasons
showed limited success on the pitch, with Real failing
to win any trophy for three seasons after the 2002-03 (1)
ECONOMETRICS
campaign.
We also control for the average age of the players.
Since most managers will try to find a balance between where is the standard normal cdf. There is no
experienced players and youngsters, the distribution is closed-form solution to the log-likelihood of model
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peaked around the average of 24.4, as can be seen in (1), but Stata is able to compute it numerically.
Table 1. Including this variable will show whether it is Unfortunately, no fixed effects probit estimator exists,
beneficial for a club to have an above average age of the as discussed by Greene, Han and Schmidt (2002). Fixed
selection or not. We also include the variance of the effects might be more appropriate, since the fixed
age of the players to see if clubs are better off with a effect assumption is that the individual specific effects
for readers |of| XXx-level
balanced or unbalanced selection with regards to the are correlated with the explanatory variables, which in
age of the players. In football, it is generally believed our case could be true. For instance, the total market
that a selection should consist of a mix of talented value of the selection could be correlated with the
(younger) and experienced (older) players, which financial capabilities of a club.
suggests a positive relationship between this variance
and team performance. 3.2 Fixed Effects
By observing changes in the dependent variable over MSC-LEVEL
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3. Model specifications time, it is possible to control for the omitted variable
Recommended
Many of the models used in this study are extensively bias without observing all relevant variables. This
described in Cameron and Trivedi (2005). Estimation of the controls for omitted variables that differ between cases
models is done using Stata. For all our dependent variables but are constant over time, known as fixed effects. Our
(sack, points and position), we first estimate the coefficients of fixed effects model is given by
our explanatory variables by standard OLS. One potential
problem with an OLS approach is the possibility of correlated
errors, which would violate standard assumptions for the
model. In our case, errors might be correlated between (2)
observations of the same club and observations from the
same league.This is dealt with in Section 3.4. Furthermore, where the individual-specific effects measure
OLS is not appropriate in our case since the nature of the unobserved heterogeneity that are possibly correlated
data calls for more sophisticated methods. with the regressors. The fixed effects esimator is
1
With Zidane scoring the winning goal in the final against Bayer Leverkusen.
31estimated by subtracting the time-averaged model where is the standard normal cdf. Again, there is
from the original model. The no closed-form solution to the likelihood function, but
estimator is given by Stata computes it numerically using a C-point Gauss-
Hermite quadrature approximation.
3.4 (Non-nested) Two-way clustering
In order to conduct accurate statistical inference, it is
important to estimate the standard errors correctly,
which can be estimated by OLS. We are mainly as argued by Cameron, Gelbach and Miller (2011). The
interested in the coefficients of . Interpretation of main potential problem is the possibility of correlated
the estimated coefficients is similar to OLS. Model errors. Our data asks for two-way clustering since
(2) incorporates possible correlation of the errors at errors are likely to be non-independent at both across-
the club level, but again we also cluster the errors at a section level and a temporal level: non-independent
league level (see Section 3.4). over both clubs and seasons per league. For leagues,
points (and thus position) are always gathered at
3.3 Random effects ordered probit the expense of another club in the same league,
Lastly, one could argue that the final league ranking hence errors will be correlated within leagues. Each
is a natural ordering of alternatives, which calls for a observation belongs to his own group of observations
model that takes into account this ordering, such as a per club and to a group of clubs in
random effects ordered probit model. We estimate the the same season per league
coefficients of our explanatory variable position using For our two-way clustering, the variance estimator
a panel data approach by including random effects in uses those elements of with where
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the ordered probit model. For a detailed study of the the and the observation share a cluster in one
random effects ordered probit model, see Crouchley or both of the dimensions. Now we can estimate
and Boes (1995). The starting point of the model is
ECONOMETRICS
where is an N x N indicator matrix with
where is not observed, the added random effects entry equal to one if the ith and jth observation share
are independent and identically distributed N(0; 2) and a cluster and zero otherwise. Since Stata allows one
errors uit are independent of i. We do observe position, to calculate cluster-robust standard errors for one-
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which is given by : way clustering, we use the following decomposition
of taken from Cameron, Gelbach and Miller
(2001): , where is an
N x N indicator matrix with ijth entry equal to one if
the ith and jth observation belong to the same cluster
for readers |of| XXx-level
an N x N indicator matrix with
entry equal to one if the and observation belong
to the same cluster , and
is an N x N indicator matrix with entry equal
where the ’s represent the thresholds. We can derive to one if the and observation belong to the
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the probability of observing outcome j for response same cluster and the same cluster
as and zero otherwise. Now we get
Recommended
where is the standard normal cdf. The random Which leads to
effects MLE is very similar to our random effects probit
model given in Section 3.1, but now we maximize the
panel-level log-likelihood with respect to , and
thresholds :
( )
our two-way cluster-robust variance matrix.
Stata is able to compute all three elements of our
cluster-robust variance matrix given by seperately.
324 Results Wenger (born in Strasbourg, France). Wenger is the
manager of (the London-based team) FC Arsenal for all
4.1Probability of getting the sack nine seasons in our data. Figure 1 shows the probability
First we estimate whether the probability of getting the of Arsene Wenger getting the sack for each season
sack is different for foreign managers than for domestic of the English Premiership. It shows both the actual
managers. Results are given in Table 2. Insignificant estimated probability (Wenger is a foreign manager)
coefficients of seasonal dummies are not given in the and the hypothetical estimated probability (if Wenger
table, but are included in the models. Note that the had been a domestic manager). It shows that in each of
standard errors are robust since we clustered over the seasons, the probability of losing his job is higher
clubs. for Wenger the foreigner.
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Figure 1: The probability of FC Arsenal’s manager Arsene Wenger
getting the sack per season.
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4.2 Dependent variable points
The second dependent variable we consider is the
number of points earned during the regular season. As
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can be seen in Table 3, the OLS coefficient of foreign
is positive, but insignificant (95% confidence interval
[-0.707 ; 2.987]). As expected, the age of the manager
shows an increasing relationship with points, although
Table 2: Estimation results on dependent variable sack small (0.061) and also insignificant. Raising the average
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age of the selection by one year has a negative effect
The sign of the coefficient for is as expected in both of approximately one point. Of course, this does not
models: more points decreases the probability of getting suggest that a club should lower the average age of
red. Remarkably, the market value of the selection plays the players indefinitely. It only suggests that a club is
no significant role in our OLS model, while it is highly better off with a below average age of the selection.
significant and positive in our RE probit model, where
the sign is as expected: a higher market value of the
The variance of age is insignificant, so the OLS model
provides no evidence to support the idea that a MSC-LEVEL
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selection seems to lead to a higher probability of getting selection should consist of a mixture of talented and
Recommended
the sack conditional on the number of points. More experienced players. Increasing the market value of
importantly, our results show that foreign managers the selection by 1,000,000, which can be done by both
are more likely to be fired. Note that in the RE probit training the current players and buying new ones, gives
model, the interpretation of the coefficients is not as an expected increase of 0.138 points.
straightforward as in standard OLS. Although we can
interpret the sign and significance of a coefficient the
same way, we cannot directly interpret its magnitude.
However, we can predict the probability of a manager
getting the sack conditional on the number of points
earned that season. To illustrate the fragile position
of a foreign manager, we highlight the case of Arsene
33of a club. Note that the lower the league ranking,
the higher the sporting performance (champions are
number 1). Therefore, the model suggests that starting
the season with a foreign manager results in a better
league ranking of 0.931 positions. The model further
suggests that foreign managers who start the season
are expected to finish approximately one place higher
in the final league ranking. Both the variance of the
market value and the age of the players are insignificant.
All other explanatory variables are highly significant
and show the same relationship to performance as they
did in Section 4.2: a positive effect of the age of the
manager, a negative impact of higher average age of the
selection and, of course, a positive impact of the total
market value of the selection.
In the RE ordered probit model, only the total
market value and the variance of the age of the players
in the selection prove to have significant effects on the
final league ranking. Therefore, the model teaches us
that starting the season with a foreign manager does
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Table 3: Estimation results on dependent variable points
not have the desired effect on final league ranking
Lastly, the variance of the market value plays a highly while investing in the market value of your selection
significant role. The effect seems small, but since the is key in being successful. Again, the results show that a
variance of the market value in a selection is very higher variance of the age of the players in a selection
large, the coefficient strongly suggests that increasing has a negative effect on performance: a club is better
MSC-LEVEL | ECONOMETRICS
the variance of the market value of the selection has a off with a selection balanced in age than having a
significant negative effect on performance. Therefore, it mixture of talented and experienced players. All in all,
appears that a club is better off investing in an evenly our analyses show that modeling the data correctly is
balanced selection when it comes to player values. crucial in estimating the effect of a foreign manager on
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Second, we use the panel structure of the data to performance.
estimate the coefficients using our fixed effects model.
The sign of foreign has changed but remains insignificant.
The same holds for age manager. Remarkable is the
change in sign of age selection from negative to positive:
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apparently, an above average age of the players improves
the performance instead of lowering it. The sign and
significance of Var [age selection] shows that increasing
the variance of the age of the players in the selection
has a negative effect on points. Hence clubs are better
off with a selection where players are roughly the same
age, which contradicts common belief that a selection
should contain of a mixture of talented and experienced
players. Surprisingly, both the total market value and its
variance do not have a significant effect. The coeffcient
of market value however still suggests a positive effect
and only slightly misses the 10% significance (p-value
of 0.103) mark. A careful reader notices the loss of 94
observations, which were dropped because they are
singleton groups.
4.3 Dependent variable position
As can be seen in Table 4, OLS shows a significant Table 4: Estimation results on dependent variable position
negative effect of foreign on the final league ranking
345 Conclusions
This empirical study shows the importance of taking into
account an often ignored phenomenon in econometric
analysis: the omitted variables bias. We investigate the effect
of foreign managers on the seasonal results of a club. If we
ignore the existence of the bias, we find evidence that foreign
managers have a positive effect on both the number of points
and the final league ranking. However, once we use the panel
structure of the data to correct for omitted variables, these
effects disappear. We find that characteristics of the selection
such as total market value, average age and the variance of
the age of the players are more important to succeed than
managers attributes such as his nationality and age. This study
does not offer insights as to whether foreign managers are
better able to improve players quality than domestic managers
are, which will reflect in the total market value of the selection
and hence increase performance. Further research must be
done to exploit this possibility.
One result that stands even after incorporating the panel
structure of the data is that the probability of getting the sack
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(conditional on the number of points) is higher for foreign
managers. This result shows that clubs do not take into
account the fact that appointing a foreign manager is costly ABOUT
in deciding whether or not to fire the manager. It seems that
board members follow economic theory and consider the THE AUTHOR
MSC-LEVEL | ECONOMETRICS
costs of appointing a foreign manager as sunk costs when
deciding on whether or not to fire him.
Alexander Schram
Alexander Schram recently left
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Bauer, T. K. and Kunze, A. (2004). The demand for high-skilled Econometrics. Currently, Alexander
workers and immigration policy. Technical report, IZA Discussion is working as a Business Analyst at
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an independent consultancy firm
Recommended for readers of XXx-level
Boes, S. (2007). Three essays on the econometric analysis of specialized in public transportation
discrete dependent variables. Universitat Zurich, Zurich. and sports. This article summarizes
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Cameron, A. C., Gelbach, J. B., and Miller, D. L. (2011). Robust
wrote under the supervision of dr.
inference with multiway clustering. Journal of Business &
Hans van Ophem.
Economic Statistics, 29(2).
Cameron, A. C. and Trivedi, P. K. (2005). Microeconometrics:
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