Local Investment and National Impact: The Case of the Football World Cup 2006 in Germany1
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Local Investment and National Impact:
The Case of the Football World Cup 2006 in Germany1
by MARKUS KURSCHEIDT and BERND RAHMANN2
Abstract: A Football World Cup is a mega event with far-reaching socio-economic ef-
fects. The hosting country should therefore perform public project evaluation. Applying
cost-benefit analysis to the case of a Football World Cup 2006 in Germany raises the
analytical problem of treating at once two types of national impact: one of nation-wide
local investment in durable facilities (about ten stadiums) and the other of short-term con-
sumption during the event. A portfolio technique to overcome this difficulty and solutions
to specific data problems will be presented. We conclude with some political economic
issues emerging from social efficiency implications of the aggregated results.
JEL classification: H43, E62, D61
Keywords: Football World Cup, public project evaluation, cost-benefit analysis, portfolio
analysis, pareto-efficiency
This manuscript has been slightly revised and published as:
Kurscheidt, M. and B. Rahmann (1999), „Local Investment and National Impact: The
Case of the Football World Cup 2006 in Germany“, in: C. Jeanrenaud (ed), The
Economic Impact of Sport Events, Neuchâtel: Editions CIES, 79-108.
1 Introduction
A Football World Cup (FWC) is undoubtedly not only a sport competition but an international
event with far-reaching and multiple socio-economic effects for the hosting country. Apart
from the football and, more generally, the sport system, it has (external) impacts on tourism,
the environment and urban planning, the socio-cultural life, the individual and collective
psychic well-being in different respects, policy goals, and, of course, on the regional and
national economy (Rahmann et al. 1997; Ritchie 1984). Depending on the circumstances and
the efficiency of the event organization the net result of these effects may turn out to be as
well positive as negative. Therefore a country willing to apply for it should perform ex ante
public project evaluation. The approach has to take account of both qualitative and
quantitative effects in order to assess the overall consequences of actually hosting a Football
World Cup and to decide rationally whether to candidate or not. The "classical" method for so-
called complex decision situations facing projects with significant externalities is cost-benefit
analysis (CBA) (Boardman et al. 1996; Mühlenkamp 1994; Mishan 1988). CBA seems to be
1
The article is based on the results of a research project on socio-economic effects of the Football World
Cup 2006 in Germany (Rahmann et al. 1997).
2
University of Paderborn, Department of Economics, especially Public Finance, Warburger Str. 100, D-33098
Paderborn, Germany-2- the appropriate approach to evaluate an FWC because of its capability of treatingin an integrated methodological frameworkquantifiable direct and indirect impacts as well as "intangibles", i.e. effects that cannot or not easily be expressed in terms of money. Furthermore the quantitative part of the CBA has the advantage of reducing the multiple socio-economic impacts at different points of time to one aggregated figure that can be generally communicated: the net present value (NPV) in currency units.i CBA possesses not only an analysis structure that renders project impacts more transparent, and thereby helps improve the use of resources (public production efficiency), but it is flexible enough to provide also information on the interpersonal, intersectoral (in the sense of public versus private sector), interorganizational (private firms of different industries, public insti- tutions and jurisdictions etc.) or interregional distribution of costs and benefits (pareto-effi- ciency). The latter is an indispensable prerequisite for establishing an efficient financing that is capable of integrating the interests of different groups involved in the project.ii Although the evidence of the mentioned arguments among others point to the fact that CBA is the most appropriate approach to evaluateiii sport events (Burgan and Mules 1992; Maennig 1991; Thöni 1984) it is by no means a "methodological panacea". In actual practice, the analyst has to find a number of pragmatic solutions to theoretical and political economic problems that are well-known in the CBA literature, e.g. what kind of prices to apply, what discount rate to use and how to prevent institutional pressures or a misleading interpretation of the results in the political debate (Boardman et al. 1996; Caesar 1996; Drèze and Stern 1987). Especially in the quantitative part of CBA, most of the analytical difficulties are typical of economic impact studies irrespective of the chosen approach.iv Applying CBA to a Football World Cup necessitates first a deeper understanding and thorough characterization of the event from a socio-economic perspective. In the Anglo-Saxon literature of leisure and tourism researchalthough sometimes used in a slightly different waythe distinction of so-called hallmark, special and mega events has become widely accepted (Hall 1992 and 1989; Getz 1991; Ritchie 1984). All of these cate- gories are either regularly recurring or one-time events that are held for a certain, usually relatively short period of time at a defined place. Hallmark events are regional tourist attract- ions intended to enhance people’s awareness in a rather limited geographical area whereas special events have a national or even international scope and are linked to a certain political, cultural or sporting cause. Finally mega events are international events of universal scope that can be hosted anywhere in the world in exactly the same way and under the same rules, i.e.
-3- independently of the exact location, local culture, political and economic system. The event it- self communicates certain world-wide accepted values and norms. These characteristics are sometimes referred to by the notion "footloose industry". There are only four types of events that fit in this exclusive category: Olympic Summer and Winter Games, world fairs and Football World Cups (Ritchie 1984). Mega events have in common that, world-wide, they take place regularly but from the perspective of one single country they are a unique or singular event because the opportunity to host it more than once cannot be planned for a foreseeable period of time. This implicates for a CBA that there is virtually no alternative to a mega event (Steiner and Thöni 1995), i.e. the project under examination can only be evaluated relative to the status quo (Boardman et al. 1996; Maennig 1991; Hanusch 1987). Therefore it is not possible to apply the concept of opportunity costs in the usual way by measuring costs in terms of forgone utility of an alternative use of resources.v Costs have to be interpreted as violations and benefits as achievements of goals that are set prior to a CBA. The more societal goals are positively affected or even achieved by the project the more favorable are the results of the evaluation. There is another implication on the temporal dimension: Effects of singular events should be differentiated not only in short-term and long-term impacts as usually done in economic analysis but as welldepending on their temporal occurrencein effects of the pre-event, present, and post-event phase. There are some specific features of an FWC distinguishing it decisively from other mega events, especially from Olympic Games. First, an FWC focuses on only one sport and, hence, has a different attraction potential from Olympics (Messing and Müller 1995; Foucard and Torrenti 1991; Pyo, Cook and Howell 1988)vi. Second, its duration is significantly longer than at the Olympic Games (2 weeks vs. 4 weeks). Finally, the most important difference is the regulation of the sporting competition. There have to be about ten high-standard stadiumsvii dispersed across the whole hosting country to run a match schedule with 32 teams and 64 matches organized in two rounds of 16 days each with temporally decreasing and geo- graphically increasing intensity, i.e. in the first round 48 group matches in all stadiums and in the second round 16 play-off matches only in the bigger arenas (FIFA 1995 and 1996a). From an economic perspective, an FWC has at once local and regional effects at several locations across the country as well as an aggregated national impact. In contrast to other mega events whose effects are much more locally bound, for an FWC two types of national impacts therefore must be considered: one of nation-wide but specific local investment in durable facilities (especially stadiums), i.e. the national impact of (nationally endogenous)viii local
-4- action, and the other of an aggregated short-term rise of consumption (especially of foreign tourists) during the event, i.e. the national impact of an exogenous impulse. Both of these impacts can as well be located and analyzed on the regional and local level where they actually first occur. But this would necessitate thorough regional analyses for each of the ten locations which, at present, cannot be done since these locations are not yet nominated. Moreover, it is the whole country, not a single region that hosts the event. Thus, for an overall evaluation, the national perspective has to be adopted in order to, first of all, decide whether actually to candidate. Once this decision is made and the exact locations are known, regional analyses must follow to assess the impacts on the disaggregated level.ix The article presents the main results of an ex ante CBA of a possible Football World Cup 2006 in Germany. The focus is set on the analytical difficulties encountered in the quantitative part of the CBA (for its analysis structure and the qualitative part see Rahmann et al. 1997). It is structured as follows: Section 2 identifies the major economic determinants of the net benefits of an FWC and develops a portfolio technique to overcome the main analytical problem of simulating a set of ten locations in order to build a model of the FWC 2006. Section 3, then, presents the structure and data of a computer simulation to forecast the probable impact and discusses the aggregated results. Section 4 concludes with some institutional and political economic issues that arise from conditions for allocative (pareto-)efficiency. 2 The Model: Simulation of Locations and Estimation Design Ex ante CBA always faces the difficulty of dealing with uncertainty in some way or another. Depending on the requirements of prognostic precision and the length of the period to be fore- casted, uncertainty appears to be a serious problem. In the case of FWC 2006, there are two distinct elements of uncertainty: first, the exact set of ten locations is yet unknown and will be a matter of a politically delicate and thereby unforeseeable decision-making (specific element of uncertainty), and, second, the long period of nine years that is still to go until the event may be held causes well-known difficulties of economic forecast (general element of uncertainty). To cope with the latter statistics, econometrics and related fields suggest a variety of useful methods.x Additionally, there is always the possibility of leaving out especially uncertain aspects by assuming them away. This is always a second or even third choice "solution" to the problem of uncertainty, but nevertheless, a feasible and sometimes unavoidable one, even though often criticized by practitioners as being too abstract. It is for example useful to hold the overall economic environment constant to exclude unpredictable structural shifts from the analysis.
-5- As to the issue of uncertainty, the predominant concern in a CBA of a Football World Cup is a specific one: the modeling of a meaningful set of locations because investments in (sport) infrastructure cannot be analyzed independently of the spatial dimension. What is actually needed therefore, is a "simulation of space"xi. The aim of this is to deduce systematically built scenarios of potential locations, i.e. standardized categories of spatial economic constellations whose implications then, can be valuated in terms of money. This has to be approached in several successive steps. First: identifying the main economic determinants of local investments. Second: deriving contingencies of spatial economic constellations by operationalizing these determinants and combining them in a portfolio matrix. Third: grouping these constellations yields standardized scenarios which then, can be aggregated by building hypotheses on their distribution in potential sets of locations. The specific element of uncertainty will thus be reduced to the evaluation of net benefit effects of standardized scenarios. The first step necessitates an analysis of the demand side as well as the supply side impacts of a local investment project in (sport) infrastructure. Since an FWC is a singular event it can be expected that the long-term net benefit effects of investments in durable facilities has an important impact on the overall result of the CBA. Therefore, its determinants must be carefully analyzed. The following can be identified: • the need of investment, depending on the existing infrastructure endowment and on (main- ly) local preferences, i.e. the politically desired improvement of this infrastructure and non- sport, basically yield interests of the—not necessarily local—private sector; • the utilization of the new or renovated facilities both during and especially after the event which, at present, can only be evaluated by the expected demand for the event itself (that will be discussed later) and the estimation of the actual and future potential of regional demand for sport facilities. • the financing, i.e. the interpersonal distribution of subsequent burdens (especially between the public and private sector) and the absolute amount of capital charges; The need of investment first of all, leads to short-term investment expenditures which are treated as costs in the CBA structure. (The long-term effect of investments is determined by the induced subsequent capital charges in the post-event phase.) It has a negative effect on the net benefit in the pre-event phase. Since the preferences of the public and private sector which also affect the need of investment differ certainly significantly between locations they cannot
-6-
be standardized. Hence the level of existing infrastructure has to be taken as a proxy for the
actual need of investment.
The utilization of the facilities is a very problematic variable because ex ante, it cannot be ob-
served. It must be approximated by an estimation of the potential of demand for the location’s
(sport) infrastructure. It is however, a variable that is very difficult to operationalize. This is
mainly due to the variety of its influencing elements. Both general characteristics of the spatial
demand structures (e.g. population density, hinterland, purchasing power, and local propensity
to consume etc.) and specific determinants of the demand for leisure activities in general and
specifically for sport facilities (e.g. competition in the leisure sector, demand for football and
other sports etc.) play an important role. Although these elements are certainly strongly
interrelated it is not possible to identify a good proxy among them that could represent a main
determinant. Though not very precise, potential of demand is better kept as proxy. But, in
contrast to this lack of precision, its impact on net benefits can be clearly identified as a
positive, long-term one.
demand side supply side
deter-
minants
potential of demand need of investment
- population density
- local attraction/hinterland non-sport
- purchasing power/ (yield) interests
elements
propensity to consume (existing)
- leisure facilities/competition infrastructure politically
- demand for football/other sports endowment desired
- location/quality of stadium improvement of
- general demand for events infrastructure
utilization investment
expenditure
impact
(efficient)
financing
time
long- (+) short-
term term
NET _
effect on net benefit
+ BENEFIT
Figure 1. Analysis of the Main Economic Determinants of a Football World Cup
How to deal with the financing is less clear. First: Note that the financing problem cannot be
solved in a standardized way, i.e. independently of the very specific characteristics of the
investment project under examination and of the politico-economic constellation of the local
jurisdiction (Rahmann et al. 1997). Second: On the one hand, financing is closely linked to—-7-
often even wrongly perceived as being identical with—capital charges that are mainly deter-
mined by the initial investment, but on the other hand, it is literally inevitable for any
investment project. The notion of financing has to be seen in a broader conceptual framework.
Since investment expenditures have always to be financed in some way or another the
important question is rather how to finance efficiently. Relative to inefficient financing, an
efficient one has a positive effect on net benefits whereas capital charges of course, exercise a
negative influence.
Figure 1 summarizes this analysis of the main economic determinants of a Football World
Cup.
POTENTIAL OF DEMAND
short-term: B < C, I. short-term: B ≈ C, II. short-term: B > C, III.
high investment medium investment low investment
high
long-term: B > C, long-term: B > C, long-term: B > C,
high utilization high utilization high utilization
short-term: B < C, IV. short-term: B ≈ C, V. short-term: B > C, VI.
high investment medium investment low investment
medium
long-term: B ≈ C, long-term: B ≈ C, long-term: B ≈ C,
medium utilization medium utilization medium utilization
short-term: B < C, VII. short-term: B ≈ C, VIII. short-term: B > C, IX.
high investment medium investment low investment
low
long-term: B < C, long-term: B < C, long-term: B < C,
low utilization low utilization low utilization
low medium high
where: B = benefits, C = costs (SPORT) INFRASTRUCTURE ENDOWMENT
Figure 2. Nine-Field-Matrix of Locations
The two characteristics of spatial economic structure, potential of demand and (sport) infra-
structure endowment, can be combined with each other in a portfolio matrix. Since, ex ante,
the financing cannot be standardized it is left out in this simulation of space. But nevertheless,
it is possible to categorize at least certain approaches to the financing problem by analyzing
the expected net benefits of the scenarios. In order to derive meaningful standardized locations
it is sufficient to ascribe three levels to each of the two characteristics: (1) low, (2) medium,
(3) high. This leads to the nine-field-portfolio shown in figure 2. For each of these fields, the-8-
expected net benefit effect can be assessed both in the short and the long run according to the
level of investment and utilization respectively.
Before constructing scenarios, it is first necessary to sort out fields that are not plausible in a
meaningful set of locations. Field IX. can be omitted since it is not realistic to find a huge
stadium and, for instance, an airport in a rural area with a typically poor regional demand.
Field VII. should be dropped because it is (rationally) not desirable to consider locations that
lead clearly to a negative outcome. There are seven fields left which can be arranged in groups
in order to reduce complexity. Categorizing these fields according to their net effect of short-
term and long-term impacts yields four distinct scenarios as listed in table 1:
SCENARIOS NET BENEFIT EFFECT FINANCING
scenario 1 • positive net benefit effect (B > C) • low investment
(III.) • certain post-event utilization • differentiated financing recommended
• low short-term, but high medium-/long-term (e.g. ppp), purely private financing
expenditure effect possible
scenario 2 • net benefit effect tends to be positive (B > C) • low up to medium investment
(II., VI.) • sufficient up to certain post-event utilization • differentiated financing recommended
• low short-term, but fairly high medium-/long- (e.g. ppp), purely private financing
term expenditure effect tends to be possible
scenario 3 • net benefit effect uncertain, tends to be zero • medium up to high investment
(I., V.) (B ≈ C) • differentiated financing necessary (e.g.
• uncertain up to sufficient post-event utilization ppp) and tends to be possible
• high short-term expenditure effect
scenario 4 • net benefit effect tends to be negative (B < C) • medium up to high investment
(IV., VIII.) • insufficient up to uncertain post-event • differentiated financing on principle
utilization necessary (e.g. ppp), but does not tend
• high short-term expenditure effect to be possible
with ppp = public-private-partnership
Table 1. Scenarios of Potential Locations
In short scenario 1 leads to the best result with certain net benefits, scenario 2 generates a
good result with probable net benefits, whereas the outcome of scenario 3 is uncertain and
finally, scenario 4 yields a bad result with probable net costs. It should be kept in mind that
political and private preferences could shift the position of their location in this scheme. If for
instance, a location with a high level of infrastructure decides for any reason to build a new
stadium instead of renovating the old one it places itself more to the left of the portfolio (e.g. a
shift from field II. to I.).
Ten locations are necessary to actually perform a Football World Cup. Therefore assumptions
have to be made on the distribution of these scenarios in hypothetical sets of potential
locations. This can be done in an objective way by assuming a mean hypothesis based on a
stylized normal distribution, i.e. 2-3-3-2 beginning with scenario 1 up to scenario 4. It shall be-9- called hypothesis II. Since this approach is rather technical two additional hypotheses can be derived by systematically altering the normal distribution for one position in each scenario. This yields a maximum hypothesis I. with a positively shifted distribution (i.e. 3-4-2-1) and, accordingly, a minimum hypothesis III. with a negatively shifted distribution (i.e. 1-2-4-3). Having hereby modeled possible sets of locations, now, money values have to be ascribed to each scenario in order to actually compute the aggregated outcome of the hypotheses. Thus far, only solutions to the specific element of uncertainty have been discussed. When valuating costs and benefits in terms of money, one has to cope with the general element of uncertainty which consists in predicting future values of the variables under examination. As mentioned above, statistics and related fields provide a number of appropriate forecasting methods. Evaluating a Football World Cup raises the difficulty that there is not much information available on historically observed frequencies of—even crucial—variables. Hence one cannot derive objective probabilities to calculate, for example: expected values. The only solution therefore is to rely on subjective assessments which, of course, do usually not have a great confidence. As a consequence, a certain spread of the true value around the estimate has to be systematically implemented in the estimation design.xii This can be done with worst- and best- case analysis. This method consists of building upper and lower bounds—if technically fea- siblexiii—for each estimate. (Note that these ranges of values are no statistical confidence intervals.) When computing the model, the right combination of these upper and lower values yields two aggregated results, one for best-case assumptions, and one for worst-case assump- tions. All outcomes in between these extremes represent the realistic, or (most) probable range of net benefits. In addition a (partial) sensitivity analysis can be performed to consider the consequences of possible variations of assumptions on the overall results (for the sensitivity analysis of the CBA at hand see Rahmann et al. 1997). With regard to the issue of probability, Boardman et al. (1996, p.201) point out "that if the ranges really are plausible, then the probability of actually realizing net benefits as extreme as either the worst or the best case gets very small as the number of parameters gets large". Having considered three hypothetical sets of locations so far, the model contains already upper (hypothesis I.) and lower (hypothesis III.) bounds for distributions of scenarios. As shown in table 2, the expected net benefits of HI. are therefore supposed to exceed the result of HII. which again should realize a higher outcome as HIII.. Since the latter is the minimum hypothesis it will then be of special interest to investigate whether it yields positive or negative net benefits. Introducing upper and lower bounds by valuing each variable in terms of
- 10 -
money, thus, leads to six results for net benefits. In a next step, the net benefits are discounted
for time to find net present values. Hence six highly aggregated figures will be given for each
period of a certain time horizon. Finally three cases can be distinguished and analyzed: a best
case, a worst case, and a base case which is in fact a range of outcomes. It is most probable
that the true result will be situated somewhere in this range.
DISTRIBUTION OF SCENARIOS FOR 10 LOCATIONS
SCENARIOS HYPOTHESIS I. HYPOTHESIS II. HYPOTHESIS III.
maximum mean minimum
scenario 1 (III.) 3 2 1
scenario 2 (II., VI.) 4 3 2
scenario 3 (I., V.) 2 3 4
scenario 4 (IV., VIII.) 1 2 3
expected NBIII.≤ 0 or
NET BENEFIT NBI.>NBII. NBI.>NBII.>NBIII. NBIII.≥ 0 (?)
estimation interval HI. estimation interval HII. estimation interval HIII.
VALUATION upper lower upper lower upper lower
bound HI.u bound HI.l bound HII.u bound HII.l bound HIII.u bound HIII.l
NET PRESENT VALUE NPV (HI.u) NPV(HI.l) NPV(HII.u) NPV(HII.l) NPV(HIII.u) NPV(HIII.l)
best- worst-
CASES case realistic or (most) probable range case
Table 2. Hypotheses and Estimation Design
3 Computing the Model
3.1 Data Inputs and Dynamics of the Model
At present there is a lack of detailed data that is needed to actually compute the model. This is
basically due to two aspects. On the one hand, the locations are yet unknown and even the
candidates which have declared their interest in participating have not yet decided their exact
investment planning. Thus up to now, it is not possible to gather data from empirical work. On
the other hand, there are no research results available on the expenditure behavior of foreign
visitors of Football World Cups. Therefore one has to rely, firstly on some data from the past
that can be found in the literature, secondly on experiences from the practice, and thirdly, on
plausible assumptions. As time passes by, the data of the model will probably have to be
slightly adjusted when there is more detailed information available.
By now, only the following variables that can be valuated with sufficient precision are con-
sidered in the model:
• costs induced by the need of investment during the pre-event phase (which also determine
the subsequent capital charges);- 11 - • net benefit returns on the utilization of the sport infrastructure during the post-event phase (revenue from rents minus operating costs and capital charges including interests and amor- tization); • benefits from foreign tourist expenditure during the present phase; • benefits from multiplier effects of investment and consumption expenditures; • benefits from the budget surplus of the Local Organization Committee (LOC) (revenues minus expenses). As mentioned above, it makes sense to hold the general economic setting constant. Therefore, the following potential influences are omitted: • inflation and effects of relative price changes induced by the event itself; • influences of foreign exchange rates and the balance of payment; • influences of changes in the tax system; • any structural shifts like the expected introduction of the euro; • any unpredictable exogenous influences (political, economic, meteorological ones etc.); • business cycle influences (capacity utilization, unemployment rate etc.) and the development of productivity. The absence of inflation in the model implicates that all money values (given in Deutschmarks) are based on 1996 prices. Since it would be certainly insufficient to focus on the effects close to the present phase a time horizon of 15 periods is assumed, starting in the year 2000 with the FIFA decision on the hosting country to include the pre-event phase, and thus ending in 2015. Choosing an appropriate discount rate is always a difficult task in CBA. Here, a rate of 4 percent is applied. This value is a compromise between the method of constant discounting as usual practice in CBA and recent findings in the literature on time preference that suggest non- constant discounting as an appropriate method (Harvey 1994; Cropper, Ayede, and Port- ney 1992; Loewenstein and Prelec 1992). An attitude towards time was found which appeared to be common for all individuals: As the time horizon lengthens the rate of time preference decreases. Comparing the graphs of discounting functions of both methods shows that a constant rate of 4 percent is a good mean. It also meets the requirement of another typical (investment oriented) approach to the discounting problem. 4 percent is approximately equal to the present interest rate for long-term bonds on the German capital market less the present inflation rate. Since the variables in the CBA at hand are calculated with real values this method would be consistent with the general approach of the analysis. Furthermore note that
- 12 -
the aggregated results of a singular event like an FWC with only one peak of effects in the
present phase are less sensitive to the choice of discount rate than investment projects with net
benefit flows that fluctuate over a long period.
Assumptions on investment costs are based on estimations of several feasibility studies which
have been made for stadium construction projects in Germany. Only those costs are
considered that are plausible for meeting the technical requirements of the FIFA. Possible
investment costs for general traffic infrastructure are left out because, at present, they cannot,
with sufficient precision, be predicted and ascribed to the event. The values are ascribed to the
scenarios according to their horizontal position (i.e. with given demand conditions) in the
portfolio. Thus investment costs for both scenario 3 and 4 are estimated between 100 and 300
million DM, whereas scenario 2 is assessed at 50 up to 120 million DM and, finally,
scenario 1 at 20 up to 80 million DM. Table 3 quotes the total investment costs for the
different hypothesis, i.e. the result of the multiplication with the scenario distributions.
HYPOTHESIS I. HYPOTHESIS II. HYPOTHESIS III.
upper bound lower bound upper bound lower bound upper bound lower bound
total investment
(in million DM) 1620 560 2020 690 2420 820
Table 3. Total Investment Costs
According to usual practice, the payments of these costs are not assumed to be effected at
once. Therefore in the model, they are distributed in equal amounts over three periods be-
ginning in 2003.xiv This implies that the construction works are well managed so that they can
be supposed to be finished at the end of 2005.
Since the exact financing actually cannot be standardized for all locations one is forced to
simulate it in order to valuate the capital charges.xv These estimates are just a global number
to take account of subsequent burdens. It is assumed that all investments are entirely debt-
financed. In practice, it is a rule of thumb that big investment projects should be amortized at
least after a period of 20 years. For the financing simulation, this requirement is taken as a
basis for each scenario. This can be done by assuming a constant annuity per period that is
calculated by a percentage of 9.5 of the initial investment. As the according interest rate for
loans amounts to about 7 percent at present the annuity hence includes a rate of amortization
of 2.5 percent in the first period of annuity payment. Subsequently, this rate rises over time as
the rate of interest payments on the residual debt declines. Table 4 resumes, among other
variables, the resulting capital charges by scenario, and table 5 by hypothesis. In the model,- 13 -
the annuity payments start in period 6, i.e. 2006, since the completion of construction works
are assumed in 2005.
NET BENEFIT RETURNS PER PERIOD (in million DM)
scenario 1 scenario 2 scenario 3 scenario 4
upper lower upper lower upper lower upper lower
bound bound bound bound bound bound bound bound
benefit returns 9.6 6.4 9.6 5.0 9.6 5.0 6.4 1.5
operating costs 6.4 9.6 6.4 9.6 6.4 9.6 6.4 9.6
capital charges 1.9 7.6 4.75 11.4 9.5 28.5 9.5 28.5
total costs 8.3 17.2 11.15 21 15.9 38.1 15.9 38.1
net benefit
returns 1.3 -10.8 -1.55 -16 -6.3 -33.1 -9.5 -36.6
Table 4. Net Benefit Returns per Period by Scenario
TOTAL NET BENEFIT RETURNS PER PERIOD (in million DM)
HYPOTHESIS I. HYPOTHESIS II. HYPOTHESIS III.
upper bound lower bound upper bound lower bound upper bound lower bound
benefit returns 92.8 50.7 89.6 45.8 86.4 40.9
operating costs 64 96 64 96 64 96
capital charges 53.2 153.9 65.55 191.9 77.9 229.9
total costs 117.2 249.9 129.55 287.9 141.9 325.9
total net benefit
returns -24.4 -199.2 -39.95 -242.1 -55.5 -285
Table 5. Total Net Benefit Returns per Period by Hypothesis
Operating costs (fix and variable) depend on the efficiency of the management of the sport
facilities. According to experiences from the practice, they can be approximately assessed by
calculating 10 percent of the initial investment at the upper bound of scenario 1 which
amounts to 80 million DM. Then a lower bound can be built for an efficient management at 8
percent and an upper bound for an inefficient one at 12 percent. This yields operating costs per
period of 6.4 up to 9.6 million DM which are ascribed to all scenarios. The reasoning for this
assessment is that scenario 1 represents the industry leader in the market of sport facilities.
Thus it is the benchmark for the other suppliers. It can therefore be assumed that they seek to
be as good as scenario 1, at least, with respect to their management since they obviously
cannot change the position of their location in the above portfolio. Differences in operating
costs are certainly low among scenarios because fix costs of stadiums typically are high. In
fact, big sport facilities can be categorized as natural monopolies (Késenne and Butzen 1987).
Hence the operating costs can be assumed to be almost equal in all scenarios.
In order to valuate the periodic benefit returns on the utilization of the sport infrastructure
during the post-event phase, again, scenario 1 is taken as benchmark. Note that, at present,
most of the stadiums in Germany do not even cover operating costs not to mention capital- 14 - charges. Since scenario 1 is the industry leader it is supposed to realize benefit returns in terms of revenue on rents that potentially exceed operating costs and—at least under best-case assumptions—capital charges as well. To guarantee such an outcome in scenario 1 the same estimation interval as for the operating costs, i.e. 6.4 up to 9.6 million DM, is a plausible assessment. According to the vertical position in the above portfolio, money values that take account of the risk on the demand side are ascribed to the scenarios. If a scenario includes fields with lower demand levels than scenario 1 the upper or lower bounds are reduced according to plausibility. For the exact values see table 4. Of course in the model, both operating costs and benefit returns also start in 2006. A rise in domestic consumption that could be induced during the realization of the Football World Cup is not considered since the net effect for the German economy would only be positive if the national marginal propensity to consume increases at least for a short period. Ex ante, this is of course not predictable. Thus only the spending of foreign visitors is taken into account. First of all, the expected number of tourists from abroad has to be estimated. This is done by assuming a plausible capacity utilization of the stadiums (75 up to 90 percent) which is applied to an estimation of expected total capacity in 2006 and, then, weighted by a hypothetical match schedule (i.e. the distribution of matches across stadiums). Finally assu- ming a percentage of tickets sold abroad among the total number of tickets (32 percent bought by foreign tourists and 0.36 percent by journalists) provides an estimate of the number of "foreign tickets" and journalist's tickets.xvi. Given the high world-wide interest in information on the FWC, journalists are assumed to be predominantly foreigners. (An estimation for parti- cipants, i.e. teams, officials and referees, is dropped because of—among other reasons—the relatively low economic importance of their spending.) Note that this is no estimate for the actual number of foreigners since it was calculated by predicting ticket sales. In order to valuate total tourist expenditure the spending per "foreign ticket" and journalist's ticket has to be assessed. As above mentioned, there is few reliable information available on the consumption behavior of foreign tourists of Football World Cups. The only solution is therefore to make plausible assumptions on the spending of a representative foreign "FWC-tourist". Basically, four variables seem to determine the expenditure per foreign visitor: (1) the disposable income; (2) the distance of the country of origin and, thereby, the traveling expenses that depend on the means of transportation; (3) the number of matches visited; and closely related to the latter, (4) the time spent in the hosting country. However it should be recognized that there is certainly a
- 15 - wide spread of possible spending behaviors, considering for instance the following extremes: At the lower bound, there could be a European visitor coming from an adjacent country by car to attend only one match in a stadium near to the frontier and then driving home immediately after the match. At the upper bound, there could be a South American visitor who travels with a Lufthansa airplane and spends the entire 4 weeks of the World Cup in Germany to attend a maximum number of matches and stays in a first class hotel. This demonstrates that an assessment of the average behavior of a "FWC-tourist" is of course uncertain and should be cautious. It is therefore assumed that a representative foreign visitor spends 10 days in Germany to attend 3 matches. His total expenditures for traveling, tickets, food, lodging, shopping etc. may amount to 3600 DM and hence, the average spending per "foreign ticket" is estimated at 1200 DM. Upper and lower bounds can be set by building an interval of ± 20% around this base assumption. Applying this estimate to the number of journalist's tickets as well yields a total tourist expenditure of 813 million DM up to 1460 million DM. The intermediate results of this calculation can be found in table 6. These expenditures occur in period 6. The multipliers used in the model are only applied to the investment and tourist expenditure. They have been estimated for the investments by a comprehensive econometric (business cycle) model of the German economy (multiplier for public investment in infrastructure) and for tourist expenditure by an input-output model (multiplier for the restaurant and hotel industry). Both of which were computed by the Rheinisch-Westfälisches Institut für Wirtschaftsforschung (RWI). The investment multiplier is a dynamic one whose effect lasts for three periods but decreases over time. Since only the additionally induced income can be counted as benefit the value of the multiplier in the first period has to be reduced by one. Otherwise the total amount of investment expenditure would be both considered as costs and benefits in the structure of the CBA. The consequence would be that investment costs vanished immediately which, of course, does not make sense. In contrast to that, the multiplier effect in the successive periods can be entirely counted as benefit since, then, the induced income is in fact additional. The multiplier for tourist expenditure is static (calculated for only one period) and can also be entirely considered as benefits because of the net injection character of foreigners' spending for the German economy. Both multiplier effects occur in the successive period of the according expenditure.
- 16 -
ES TIMATION IN TERVALS
VARIABLES (TIME t) UPPER BOUND LOWER BOUND
investment scenario 1 80 million DM 20 million DM
costs scenario 2 120 million DM 50 million DM
scenario 3 300 million DM 100 million DM
(at 1
3
in t =3 to 5) scenario 4 300 million DM 100 million DM
benefits on scenario 1 9.6 million DM 6.4 million DM
investment in scenario 2 9.6 million DM 5.0 million DM
sport facilities scenario 3 9.6 million DM 5.0 million DM
(from t =6 to t =15) scenario 4 6.4 million DM 1.5 million DM
hypotheses HI. HII. HIII.
scenario scenario 1 3 2 1
distribution scenario 2 4 3 2
scenario 3 2 3 4
(from t =0 to t=15) scenario 4 1 2 3
operating costs (from t =6 to t =15) (for all scenarios) 9.6 million DM (for all scenarios) 6.4 million DM
capital charges (from t =6 to t =15) (for all scenarios) 9.5 percent of investment as annuity (interest + repayment)
capacity utilization 90 percent 75 percent
total tickets 3140812 2617330
tickets per match 49.075 40896
"foreign tickets" (32 percent) 1005060 837546
journalist's tickets (0.36 percent) 11307 9422
expenditure per “foreign ticket” 1440 DM 960 DM
total tourist expenditure in t = 6: 1.463.57 million DM in t = 6: 813.09 million DM
surplus of LOC (in t = 6) 150 million DM 0 DM
multi- for time: in t = 4: in t = 5: in t = 6: in t = 4: in t = 5: in t = 6:
pliers investment expenditures 1.1* 1 0.8 0.4* 0.2 0.2
for tourist expenditure in t = 7: 2.45 in t = 7: 2
discount rate 4 percent
*
The value of the multiplier is reduced by one.
Table 6. Data Inputs to the Model and Dynamic Structure
At present the costs and benefits in terms of expenses and revenues that accrue from the activi-
ties of the Local Organization Committee (LOC) can hardly be predicted with sufficient preci-
sion because they depend on legal regulations and contract bargaining (e.g. the distribution of
revenues from TV rights between the FIFA and the LOC which will account certainly for a
considerable part of the total budget). Therefore in the model, a more or less conservative
estimate (0 up to 150 million DM) is made for the expected surplus of the LOC's budget which
can be assessed according to the experiences from the past.
To sum up the data inputs to the model they can be categorized as follows:
• effects related to the construction and operation of durable facilities
(1) scenario distribution; (2) investment costs; (3) operating costs; (4) capital charges;
(5) benefits on investment
• effects related to the realization of the FWC
(6) tourist expenditure; (7) budget surplus of the Local Organization Committee- 17 - • effects related to micro- and macroeconomic relationships (8) multipliers; (9) discount rate Hence there are 9 main variables that represent 3 sources of effects. Table 6 resumes all values for these variables and the periods of their occurrence. 3.2 Aggregated Results Having in mind that as to the data base, there is a twofold problem of uncertainty the aggre- gate result nevertheless provides some useful and interesting insights. Figure 3 depicts the discounted net benefits per period. Six graphs are shown since two aggregated numbers for each of the three hypotheses were computed, one upper and one lower bound result. The general shape of the graphs is consistent with the expected pattern. During the pre-event phase, the investment costs first generate negative results which are soon recouped by the multiplier induced positive income effects. Shortly before and after the present phase, there is a considerable peak of positive economic effects lasting about 3 periods which, then, fall down to adopt a steady development. Finally the post-event phase is determined—both in the pessimistic and the optimistic cases—by constant deficits accruing from the operation of sport facilities. Furthermore, as also expected, multiplier effects play an important role. Note that over time, the income effects induced by investment multipliers penetrate by three successive and overlapping waves into the entire economy. This is the reason for the early positive trend of the aggregated impact. The multiplier effects strengthen the expenditure injection and shift it at least one period into the future. But on the other hand, once they have run out the impulse declines very quickly. Furthermore, it is—apart from considerably higher financing burdens in the post-event phase—the difference between the intensities of this impulse that decisively distinguishes the outcome at the upper and lower bound. Figure 4 shows the net present values over time, i.e. the cumulated, discounted net benefits per period. Because of this aggregation, the graphs reflect the previous development of the in- duced periodic effects. Figure 4 depicts the development of net present values up to every point of time. It is also possible to evaluate the Football World Cup 2006 if a shorter time horizon than 15 periods is considered. However the net present values are able to show the differential between the levels of the aggregated results for the optimistic and pessimistic forecast.
- 18 -
3500
pre-event phase post-event phase
HI.g
3000
discounted net benefits per period (million DM, basis 96)
HI.u
decline of the HII.g
impulse HII.u
2500
HIII.g
HIII.u
2000
optimistic cases
1500 pessimistic cases
1000
steadiness of effects
investments
500
0
2 3 4 5 6 7 8 9 10 11 12 13 14 15
-500
multiplier effects
-1000
time
Figure 3. Discounted Net Benefits Per Period For a Simulated Financing
6000
short-term effects long-term effects
5000
HI.g
HI.u
net present value (million DM, basis 96)
4000 HII.g
HII.u
HIII.g
persistence of the
3000 HIII.u previous development
risk
2000
optimistic cases
1000
0
2 3 4 5 6 7 8 9 10 11 12 13 14 15
-1000
pessimistic cases
-2000
time horizon
Figure 4. Net Present Values For a Simulated Financing
First under upper bound assumptions, all hypotheses—even in the long run—yield a fairly
high gain for the national economy which amounts to about 5 billion DM. This overall out-
come is not significantly reduced by the subsequent burdens of the financing. Furthermore as- 19 - can be seen in table 7, the differences between the results of the hypotheses is almost negli- gible. Note that the "bad" hypothesis III. that causes the highest investments represents the best case. This is due to the ambivalent character of the investments. On the one hand they are costs and on the other hand, they induce benefits by their multiplier effects. It depends on which of these effects prevails to evaluate the net impact of investments. TIME NET PRESENT VALUES (in million DM) HORI- HYPOTHESIS I. HYPOTHESIS II. HYPOTHESIS III. ZON upper bound lower bound upper bound lower bound upper bound lower bound 4 -149.99 -757.02 -184.81 -943.93 -219.63 -1130.85 5 18.78 -934.55 23.14 -1165.30 27.50 -1396.06 6 1702.55 -107.97 1793.93 -288.33 1885.32 -468.69 7 4664.21 1140.56 4803.05 968.13 4941.89 795.70 8 4755.49 1073.92 4908.31 889.63 5061.12 705.34 12 4690.78 545.58 4802.35 247.50 4913.91 -50.58 13 4676.12 425.94 4778.35 102.10 4880.58 -221.74 14 4662.03 310.91 4755.28 -37.71 4848.53 -386.32 15 4648.48 200.30 4733.10 -172.13 4817.71 -544.57 Table 7. Selected Net Present Values For a Simulated Financing Second under lower bound assumptions, the initial impulse of the present phase is by far weaker (about 800 up to nearly 1150 million DM) than for the optimistic forecast and the financing costs have a much higher weight. This can be seen during the post-event phase which is marked by a clearly negative slope of the graphs. The capital charges show an increasing importance because a considerable spread between the hypotheses can be observed. This effect differentiates them decisively. Hypothesis III. realizes a negative result after a time horizon of 11 years and hypothesis II. after 13 years whereas hypothesis I. is the only one which is able to yield at least a small gain of about 200 million DM. For an optimistic decision-maker expecting upper bound conditions there is no doubt. The Football World Cup 2006 will be beneficial to Germany and therefore serious efforts should be undertaken to get the event. Similarly for all intermediate conditions as well, a positive conclusion can be drawn. In contrast to this, an pessimistic decision-maker would be more cautious. The hypotheses II. and III. should be avoided in favor of hypothesis I. The latter evaluation would only have to be revised if either a comparable mega event could be held e.g. in 2012—which is not very likely—or the time horizon of 15 years proved to be too long and e.g. 10 years were considered to be sufficient. Apart from these considerations, the organizers are potentially able to exercise a significantly positive influence on the overall outcome by efficiently utilizing strategic variables. This can be shown by sensitivity analysis (Rahmann et al. 1997). Especially the sale of tickets abroad and the spending of foreign visitors could be
- 20 - enhanced by appropriate measures, for instance, innovative distribution channels for ticket sales or attractive offers of goods and services near to the stadiums where the matches take place. On the other hand, a control of investment costs is more difficult to manage for the organizers since they depend predominantly on local political decisions. Nevertheless organizers should make appeals at local decision-makers and undertake efforts in their information policy. Finally and the most powerful strategic tool for organizers of a Football World Cup is the choice of locations for the matches. Here the net present values in figure 4 reveal an important point. The area between the optimistic and pessimistic forecast can be interpreted as risk. Thus risk avers organizers should definitely choose hypothesis I. (or an even better set, e.g. 4-4-1-1) because it represents the less risky possible set of locations in the model. 4 Conclusion Both active sports and passive sport-related activity have a multi-dimensional nature. In order to judge sporting activity in its overall repercussions on society and the economy a broad evaluation approach is needed. CBA seems to be a fruitful methodology in the field of sports in general and specifically for the analysis of sport events. Partial economic analysis like e.g. the input-output approach is only able (and conceived) to shed light on distinct single effects on main economic variables and sectors, once the decision for a certain impact has been made. As the case of the Football World Cup 2006 in Germany demonstrated, CBA encounters in its quantitative part not only the general problems of economic forecast and impact analysis but additional evaluation difficulties that are specific to sports and—again more specific—football tournaments. The problem of choosing a set of about ten locations to actually perform a Football World Cup poses an analytical challenge. In this paper, a portfolio approach and scenario technique were presented to cope with this problem. But it still remains the challenge of interpreting and decomposing the aggregated result. Here a new distribution problem arises that is basically due to the above-mentioned twofold national impact. Given the large number of individuals and as well public as private organizations that are affected by the FWC in one or another way and intensity, the conditions of pareto- optimality demand a complex compensation system. If this point is neglected there might well be winners and losers regardless of the aggregated overall net benefit for the economy, i.e. a suboptimal social welfare effect. Thus a multi-dimensional compensation is needed: • intertemporal: compensating for future burdens related to the financing of sports infra- structure by saving benefits accruing from the present phase of the event;
- 21 -
• interregional: compensating for losses of some local jurisdictions by profits of others;
• interfunctional: compensating for societal dysfunctions in non-economic fields by appro-
priate public measures (e.g. socio-pedagogical and ecological programs and so on), i.e.
extending economic benefits (in terms of money) to a greater variety of political and social
goals, especially in a federal state, this means among other things grants-in-aid and
vertically corporate financing;
• intersectoral: compensating the public sector for outlays in (sports) infrastructure supply by
integrating financial contributions of the positively affected industries of the private sector
in financing models of a public-private-partnership type.
It must be stressed that this distribution aims at allocative efficiency and not at some concept
of social justice. Suppose for example the possible situation that the potentially loosing local
authorities might get aware of their high risk of future losses and withdraw their candidature
as a location of the FWC. If then, there are not enough locations anymore that are willing to
participate the potentially winning local jurisdictions will have a strong incentive to re-
establish the necessary number of locations by compensating the potential losers in order to
avoid foregone benefits of renouncing the event as a whole. Solutions for multi-dimensional
compensation are not trivial and pose serious theoretical and practical questions, like for
instance: What organizational structures have to be chosen in the presence of opportunistic
behavior? How to cope with phenomena like moral hazard and adverse selection? How to
manage the related public choice processes? These issues of institutional and political econo-
mics might be a fruitful field for further research in economics of sport.
Notes
i
The numéraire must not necessarily be money and is not just a matter of convention (Brekke 1997). But in
CBA practice money is traditionally the first choice of numéraire. Undoubtedly it is the one that can be best
interpersonally communicated.
ii
For details on financing models for sport facilities see Rahmann et al. (1997).
iii
The notion of evaluation has to be emphasized because CBA faces, in its quantitative part, the same
problems of measurement as other methods that focus mainly on short-term economic impacts (see Burgan
and Mules 1992). It even uses the same techniques like e.g. multiplier analysis and sometimes input-output
analysis. The essential difference is that—by applying the mechanics of discounting—CBA treats at once
long-term as well as short-term effects and that it distinguishes costs, i.e. burdens, from benefits, i.e.
chances or opportunities. Therefore CBA provides a clearer picture of all types of project repercussions
than other approaches. In fact it is this feature of CBA that allows actual evaluation.
iv
See the contributions of Davidson and Késenne in this volume.
v
In theory even the status quo includes infinite (less one) alternatives. Given the problem of bounded
rationality in practice, these alternatives cannot be considered in the strict sense of the concept of
opportunity costs.
vi
Up to now, there is no systematic empirical research that has been done on consumer behavior of tourists on
Football World Cups. Foucard and Torrenti (1991) just try to theoretically categorize differences to the- 22 -
demand for Olympics. But significant work has been done on the empirical determinants of the demand for
football matches in several countries (see for Germany: Lehmann and Weigand 1997; for England: Peel and
Thomas 1992; for Belgium: Janssens and Késenne 1987; for Scotland: Jennet 1984; and for a survey of
such studies see Cairns, Jennet and Sloane 1986). The most striking result—among other determining
variables like stars in the teams, weather and, of course, income—is the (meanwhile famous in economics
of sport) so-called uncertainty of outcome that has statistically significant influence on the demand for
football (and other team sport) matches. But all this empirical research has been done on league sports.
Given the high potential attraction of an international football championship simply due to its uniqueness, it
could well be possible that other variables play an important role at an FWC. Further research is needed on
this issue.
vii
The next FWC 1998 in France will be the first to be held with 32 participating teams. Formerly the number
of teams has been 52 (since the FWC 1982 in Spain) or even less (in 1974 and 1978 38 teams, in 1962 to
1970 32 teams). The French Organization Committee (CFO) distributes the resulting 64 matches on ten
locations (Hopquin 1997; FIFA 1996a). This number can be taken as a reference for future FWC’s.
However, all stadiums have to meet the high technical standards set by the FIFA (for a technical guideline
see FIFA 1996b and DFB n.d.).
viii
In general variables set by political action are regarded as exogenous in economic analysis. From a broader
socio-economic (or institutional economics) perspective that takes political parameters explicitly into
account, it can be conceived as endogenous in a national politico-economic equilibrium approach. In this
terminology, effects that are driven by determinants coming from abroad can be considered as exogenous to
the national politico-economic system.
ix
Of course each region willing to candidate as a location faces the same decision situation as the country.
Prior to its candidature, it should as well perform project evaluation. But the aggregation of regional results
to the national impact is much more complex. This can only be done when there is more information and
less uncertainty on the involved locations.
x
Especially when objective probabilities can be observed, decision theory (e.g. decision trees, expected value
analysis etc.), game theory, statistical inference (e.g. probability distributions, hypothesis testing etc.), time
series analysis etc. provide a number of tools to deal with uncertainty. For a survey of such methods that are
applied in CBA see Boardman et al. (1996), Mishan, (1988), Hanusch (1987).
xi
The "simulation of space" is done in a simple way by using a portfolio approach. It could be objected that
the modeling of economic behavior in space is a far more complex task. But note that it is still an
insufficiently solved problem in urban and regional economics, even in very complex models. Thus a
simple model that considers the main aspects is sufficient for the problem at hand.
xii
Note that the notion estimation here is not equivalent to the concept of estimation in statistical inference
theory. Since there is poor information on ex post frequencies one cannot rely on samples and thereby it is
not possible to test statistical significance or to build confidence intervals. In fact for these reasons, the
notion "subjective assessment" is more appropriate. For simplicity estimation here is used synonymously.
xiii
Sometimes the programming of the software application becomes quite arduous when there are many
variables with upper and lower bounds to consider. Depending on the calculation objectives, the number of
combinations of these values can rise significantly. Therefore in order to save time and money (Performing
CBA should as well be subject to cost-benefit considerations!), it is often better to drop upper and lower
bounds for some variables and to check the impact of variations in assumptions later in a sensitivity
analysis.
xiv
Of course this is not realistic for minor renovation works. In favor of a consistent logic of the calculation,
this distribution of payments is applied to all scenarios.
xv
It can be recommended to first compute the model without simulating the financing because it should be
kept in mind that, on the one hand there is definitely no standardized solution to the financing problem and,
on the other hand the financing represents a variable which has to be optimized. Thus, it is preferable to first
evaluate the outcome without financing and then the simulation can be introduced in order to analyze the
changes of the results. This is actually done in Rahmann et al. (1997).
xvi
The estimation of expected total capacity is based on a list of the German football association (DFB), the
capacity utilization can be estimated by comparing ex post data of the FIFA and the percentage assessment
of tourists and journalists has been made by the French Organization Committee for the World Cup 1998.You can also read
