A bivariate probit examination of financial and volunteer problems of non-profit sport clubs.
Coates, Dennis ; Wicker, Pamela ; Feiler, Svenja 等
Introduction
Non-profit organizations play a critical role in many Western
societies because they provide programs in areas that are not covered by
the state or the market. These organizations are particularly important
in heterogeneous societies where public and private organizations cover
mainstream interests and leave room for programs provided by
third-sector organizations (Anheier, 2010). Non-profit organizations are
the dominant type of organization within several social systems and
spheres of activity (Anheier, 2010), and also in the field of sport.
Many national sport systems in the European Union are organized as
third-sector systems with non-profit sport clubs as the organizational
base (Tokarski, Petry, Groll, & Mittag, 2009). The European
Commission actively supports this institutional model: It stated that
non-profit sport clubs should form the foundation of the European model
of sport (European Commission, 1999) and that it will support grassroots
sports (Commission of the European Communities [CEC], 2007b).
In order to adequately support clubs, key challenges need to be
identified (CEC, 2007a). Two key challenges that these organizations
face in almost all countries relate to the recruitment and retention of
volunteers and to the financial situation (e.g., Allison, 2001; Lasby
& Sperling, 2007; Taylor, Barrett, & Nichols, 2009). Governments
at all levels already support sport clubs through the provision of
public subsidies, in part because they appreciate the positive
externalities such as integration of youths and immigrants that are
produced by clubs (Lamprecht, Fischer, & Stamm, 2012) and are
considered beneficial for society, although these positive social
effects are also questioned by some researchers (e.g., Coalter, 2007).
In addition to government funding, clubs also seek funding from other
external stakeholders like sponsors and donors (e.g., Lasby &
Sperling, 2007; Taylor et al., 2009). Yet, the question is whether it is
reasonable for clubs to pursue funding from these external sources
because it may not necessarily benefit their overall financial
situation. Also, it may affect their human resources (volunteers) which
is another important resource of sport clubs (Enjolras, 2002; Lasby
& Sperling, 2007).
The purpose of this study is to examine the effect of external
funding on financial and volunteer problems of sport clubs and whether
both problems are interrelated. Specifically, this study advances the
following three research questions: (1) what influence does external
funding have on financial problems of sport clubs? (2) How does external
funding affect volunteer problems of clubs? And (3) are financial and
volunteer problems interrelated? Based on different theoretical
approaches, the effects of external funding on both problems are
conceptualized. Quantitative data from a survey of non-profit sport
clubs in Germany are used and a bivariate probit model is estimated to
analyze the research questions. This model allows examining the effect
of external funding on both types of problems, while also giving
information about the interrelatedness of the two problems. The findings
have implications for the management of non-profit sport clubs.
Theoretical Framework And Literature Review
Financial and Human Resources of Non-Profit Sport Clubs
Following the concept of club goods (Buchanan, 1965), sport clubs
are arrangements of members who share the same interests and pool their
resources (time and money) in order to provide programs (Heinemann,
1995). Thus, financial resources and human resources (volunteers)
represent the basic resources of non-profit organizations including
community sport clubs (Lasby & Sperling, 2007; Sharpe, 2006). This
distinguishes non-profit sport clubs from for-profit organizations that
typically do not rely on volunteers; they have to use financial
resources to employ paid staff.
When looking at the financial resources of non-profit sport clubs,
it must be noted that they have lower accounting standards than
for-profit companies (Sigloch, 2009). Typically, an overview of club
revenues and expenses within a certain period (typically one year) is
provided to the members at the members' meeting (also a statement
of club assets and liabilities). This relatively simple statement of
revenues and
expenses is also assessed in quantitative sport club surveys (e.g.,
Lamprecht et al., 2012). Importantly, words like income and revenues are
used synonymously in the literature meaning the money generated by the
club, while expenses, expenditure, or costs refer to the money spent by
the club (e.g., Enjolras, 2002; Lamprecht et al., 2012). This is
different to the accounting of for-profit organizations that distinguish
between revenues, earnings, cash flow etc.
Relationship Between Financial and Human Resources
There are indications in the literature that financial and human
resources are interrelated because they can be partially substituted
(Andreff, 2006). For example, in a tennis club, the sand courts have to
be cleaned and prepared before the beginning of each season. While this
task could be performed by professionals who get paid for the work, the
reality in most clubs is that members serve as volunteers and do the
work, a procedure which saves money for the club. This is one example of
how clubs are able to substitute voluntary work for financial
expenditure.
As stated previously, the substitutability is only partial, not
perfect. It is hardly possible to run a non-profit sport club solely
with human resources and without any financial resources. Sport clubs
need "to raise enough money to cover costs since the voluntary
sector cannot raise revenue through taxation as government can"
(Gratton, Liu, Ramchandani, & Wilson, 2012, p. 15). Research shows
that sport clubs need money for a variety of expenses including
compensation for coaches, costs of facilities and maintenance,
equipment, licences, dues to governing bodies, and fees for competitive
sport (Lamprecht et al., 2012). While it can certainly be argued that
some expenses can also be covered by volunteer work (like maintenance
work and coaching), other expenses like the last three are not
replaceable; they have to be paid and therefore, money is needed.
Importantly, the substitution of financial resources by human
resources is only possible when enough (qualified) volunteers are
available (and vice versa). Research shows that the recruitment and
retention of volunteers is one of the most pressing problems for sport
clubs across countries (Allison, 2001; Sharpe, 2006; Taylor et al.,
2009), indicating that the opportunities for substitution may be limited
in some clubs. Problems regarding financial resources are also prevalent
among clubs, although they may be less pressing than the need for
volunteers (Lasby & Sperling, 2007; Sharpe, 2006). Resource problems
of sport clubs have only been examined in an isolated manner, but it
would be interesting to see if they are somewhat interrelated as
conceptualized above. This relationship has not yet been investigated
empirically to a great extent. For example, research showed that sport
clubs with high revenues experience smaller volunteer problems, while
clubs with a high level of voluntary engagement perceive financial
problems to be greater (Wicker & Breuer, 2013). However, the latter
finding only applies to core volunteers (with a formal position), not to
sporadic volunteers. This finding also does not relate to the difficulty
of recruiting and retaining volunteers. Indeed, a high level of
engagement by the core volunteers may be a symptom of difficulty finding
sporadic volunteers. High engagement by core volunteers may also be
necessary because finances do not allow employment of paid staff. The
discussion above suggests reasons why financial and volunteer problems
may be positively or negatively correlated and is covered in the first
hypothesis:
H1: Financial problems and volunteer problems are correlated.
Effect of External Funding on Financial and Volunteer Problems
Following Kearns (2007), not only the total amount of revenues is
important for an organization, but also the origin of the revenues,
i.e., where the money comes from. In this study, we distinguish between
revenues from internal stakeholders (i.e., club members; abbreviated by
internal revenues) and revenues from external stakeholders (such as the
government, sponsors etc.; abbreviated by external revenues). In the
sport club context, typical internal revenues are revenues from
membership fees, admission fees, and service fees for members. External
revenues can be revenues from sponsors, public subsidies from various
institutions, and donations from external stakeholders (e.g., Vos,
Wicker, Breuer, & Scheerder, 2013).
Before looking at theoretical perspectives on the effects of
external funding, it must be noted that its importance differs among
clubs. Non-profit sport clubs have different goals and the importance of
external funding may differ among goals. Research reveals three central
goals of sport clubs referred to as competitive sports, mass sports, and
sociability (Nagel, 2008). It was also shown that competitive sport
requires higher financial expenses (Nagel, 2008). Typically, competitive
sport is already cross-subsidized within sport clubs (Heinemann, 1995)
in the sense that the revenues generated through (older) adults paying
higher membership fees while demanding mass sport programs are partially
transferred to competitive sport which is mainly practiced by youths and
younger adults. Oftentimes, additional money is needed and one option to
generate the required revenues is through increasing external revenues.
Research confirms that sport clubs providing competitive sport rely more
heavily on external funding sources like government subsidies,
sponsorship income, and other market income (Breuer & Feiler, 2013).
Since there is no single sport club theory available that fully
explains the relationship between financial and human resources, we
provide different theoretical angles in an effort to approach this
topic. There are at least four perspectives that inform the present
research regarding the effects of external funding on financial and
volunteer problems.
The first perspective is the legitimacy perspective. Theories from
the political sciences argue that the perceived importance and
legitimacy of an organization in the community plays a role (Chang &
Tuckman, 1994). Organizations may be highly accepted in their community
when they are able to attract external money from recognized
institutions. The likely consequence is then that other funding bodies
think of this organization as a fundable organization which means that
those organizations are also able to attract money from other
institutions. Thus, there is a phenomenon that "success breeds
success" (Kearns, 2007, p. 298) since revenues in one area crowd in
revenues in other areas (Anheier, 2010; Young, 2007). Thus, the
legitimacy perspective suggests that external revenues can be beneficial
to clubs because they are able to improve their social acceptance in the
community and crowd in more revenues. This crowd-in effect would lead to
a better financial situation.
However, the opposite effect is also possible, i.e., that public
funding crowds out private contributions to non-profit organizations, a
concern which is often expressed in the literature (Anheier, 2010). If
this was the case, organizations would be better off not to pursue
government funds if they over proportionately reduce revenues in other
areas, leading to a worse financial situation overall. However, the
evidence in this regard is not consistent with some studies supporting a
crowd-out effect and others finding no evidence for it (for an overview
see Tinkelman, 2010).
The second perspective refers to the controllability of revenues.
It is suggested that internal revenues such as membership fees are more
projectable by clubs for two reasons. First, membership fees are paid on
a regular basis, typically once per year. This means the club can plan
with those revenues. Second, the revenues from membership fees are split
into small pieces with each member paying a small proportion. Thus, the
club has some idea of the overall amount of money that will be generated
through membership fees. Even when a few members leave the club, the
overall amount of revenues from membership fees does not materially
change. Consequently, those internal revenues are projectable. On the
contrary, external revenues like public subsidies are difficult to
project. Also, they are all-or-nothing in nature meaning that a club
either receives public subsidies from the community or not. The
reception of subsidies is less projectable because it can change from
year to year due to changes in government (Froelich, 1999) or because of
the financial realities of many communities. Relying heavily on external
revenues increases the probability of financial problems because
immediate changes are less foreseeable than internal revenues and out of
the clubs' control. Although some arguments in favor of external
revenues can be advanced, the review of literature indicated that
external revenues may be more problematic for the financial situation of
sport clubs than internal revenues. This is covered in the second
hypothesis:
H2: External funding increases financial problems of sport clubs.
External funding may not only affect financial problems, but also
volunteer problems.
The third perspective is the resource dependence perspective.
According to the resource dependence theory, organizations seek scarce
resources from external stakeholders because they cannot provide the
resources themselves (Pfeffer & Salancik, 1978). In doing so, they
increase their dependence on external resource providers which can exert
power over the organization (Pfeffer & Salancik, 1978).
Organizations may lose their autonomy. Having the resource dependence
perspective in mind, Rushton and Brooks (2007) recommended that
non-profit organizations should not pursue every dollar of government
funding since it rarely comes without strings.
A Flemish study supports this assumption by examining the effect of
government funding on volunteers. It documents that sport clubs with a
high share of revenues from government subsidies were more likely to
adopt subsidy conditions regarding the qualification of staff including
volunteers (Vos et al., 2011). However, the overall government pressure
on sport clubs as a result of public funding was found to be relatively
low (Horch, 1994; Vos et al., 2011). Research also shows that government
funding is coupled with development plans. Clubs are required to submit
strategic plans when applying for public subsidies indicating that
subsidies are increasingly associated with conditions on their use
(Allison, 2001). The problem in this case is that volunteers experience
problems when tasks are too complex and time consuming (Andreff, 2006;
Sharpe, 2006), and this may result in difficulty in recruiting and
retaining volunteers. Taken together, the resource dependence
perspective suggests that clubs may be better off pursuing internal
revenues than external revenues, but the evidence is not consistent.
The fourth perspective is referred to as the organizational mission
perspective, which suggests that an organization's mission is
critical for the decision to volunteer. What has happened in recent
years is that non-profit organizations were not able to finance their
original mission-related services. Therefore, they have tried to
generate commercial income in other areas and have used it to finance
important mission-related services by means of cross-subsidization
(James & Young, 2007). This movement has been referred to as the
social enterprise movement (Young, 2007) which also has some downsides
(Froelich, 1999). It carries an inherent trade-off between the
organization acting as a private enterprise and having a social mission
(Weisbrod, 1998). Having said this, external funding may negatively
influence the volunteer situation of sport clubs.
Previous research has conceptualized that the origins of revenues
may influence the decision to volunteer (Wicker & Hallmann, 2013).
It is questionable whether volunteers want to give their time to
organizations that provide services in areas that are away from their
original mission and that the volunteers do not really support. However,
this assumption was not supported empirically in previous research:
Enjolras (2002) examined the effects of increasing commercialisation in
a study of sport clubs in Norway. He found that commercial revenues did
not reduce the level of voluntary work. However, the empirical evidence
in this regard is relatively limited, suggesting that more research is
needed. Based on the theoretical arguments provided above, the third
hypothesis is:
H3: External funding increases volunteer problems of sport clubs.
Method
Data Source
This study utilizes two waves of data from a German survey looking
at the situation of non-profit sport clubs in Germany. In the full
project, sport clubs are surveyed every two years with the first wave
starting in 2005. To date, four waves have been completed. In each wave,
the data are collected with an online survey. The clubs' email
addresses are provided by the 16 federal state sports confederations.
Germany is home to over 91,000 sport clubs which are well spread
throughout the country (DOSB, 2013). During the years, the number of
provided email addresses has constantly increased (from 18,085 in 2005
to 67,708 in 2011) indicating that more clubs have become online. The
clubs receive an invitation email with a link to the online
questionnaire, information about the project as well as anonymous and
confidential treatment of data. For this study, only data from the third
(2009) and fourth wave (2011) can be used because some of the relevant
questions were only asked in those waves. A balanced panel is created,
meaning that only those clubs which have participated in both waves are
in the dataset (n = 8,302). Since complete cases for financial variables
(clubs' revenues and expenses) in both years are needed for the
analysis, the final sample consists of n = 1,028 clubs, respectively n =
2,056 cases in the vertical panel.
Measures and Variables
An overview of the variables and the summary statistics are
provided in Table 1. In the online questionnaire, the club's board
was asked to assess the severity of various organizational problems on a
five-point Likert scale (from 1 = no problem to 5 = a very big problem).
The variables capturing financial (fin_problems) and volunteer problems
(vol_problems) were part of this list. For the current analysis, the
original five-point scale variables were recoded into dummy variables
with 1 capturing the previous categories big (4) and very big (5). This
recoding is necessary to address the main issue of this paper, whether
volunteer and financial problems tend to occur together or are unrelated
to one another. Moreover, it is, of course, likely that each respondent
has a different idea of the distinction between a big and a very big
problem. The current approach narrows the variation, but raises the
likelihood that each participant would agree that a problem exists.
In sport club research, financial and human resource problems are
typically assessed with similar problem scales (e.g., Lamprecht et al.,
2012; Lasby & Sperling, 2007). The validity and reliability of the
problem scale used in this study was checked. Cronbach's alpha for
the whole problem scale consisting of 16 items is 0.823, which is above
the suggested threshold of 0.8 (Tabachnick & Fidell, 2007) and can
thus be considered reliable. Regarding validity, the internal validity
of answers had been checked during the data cleaning phase. Moreover, it
was tested whether the perceived financial situation was correlated with
the ability of the club to at least break even, that is, cover all its
expenses from its revenues. The correlation analysis shows that the
better the clubs perceive their financial situation, the more likely
they break even (r = -0.078***). The validity of the volunteer questions
was confirmed in previous research (Wicker & Breuer, 2013).
In the survey, a subjective problem measure was preferred over an
objective measure to allow the comparability of clubs in Germany that
are heterogeneous in size. Using an objective financial measure such as
total revenues would be problematic because it does not contain
information about whether this is good or bad for the club. For example,
a certain amount of revenues (say 100,000 [euro]) could be a success for
some clubs, while other clubs would regard this figure as poor financial
performance.
A set of financial variables is included in the analysis consisting
of three external revenue categories (sponsor, subsidy, and ext_misc)
and one variable measuring internal revenue (int). Subsidy captures
public funding, while sponsor measures private/market funding (Vos et
al., 2013). In the survey, a total of 25 different income sources are
assessed; yet, the problem is that not all income sources can be clearly
assigned to external or internal revenues. One example in this regard is
income from donations where it is not clear whether they come from
members or non-members. Thus, only revenue categories with a clear
external or internal origin are used for the analysis. Table 1 shows the
respective revenue categories that are used for the construction of the
financial variables. The share of expenses on administrative personnel
(admin) serves as a proxy for paid staff. Controlling for paid staff is
critical because of possible tensions between volunteers and paid staff
working in the same organization (Shilbury & Ferkins, 2011).
The literature review showed that an organization's mission
and goals are critical to the examination of financial and volunteer
problems. Therefore, four club philosophy variables are included in the
analysis (Table 1). In the questionnaire, respondents were asked to
state the extent that the club's board agrees to a list of
statements on a fivepoint Likert scale (from 1 = do not agree at all to
5 = totally agree). Similar to the problem variables, the philosophy
variables are recoded into dummies with 1 capturing the categories agree
(4) and totally agree (5). The philosophy variable phil_stay indicates
whether the club wants to stay the way it is; research showed that some
clubs are lethargic organizations and resistant to changes (Thiel &
Mayer, 2009). Phil_strategy measures whether the club has a strategic
policy which may affect organizational problems (Wicker & Breuer,
2013). Given the social enterprise movement (Young, 2007), phil_service
captures whether the club considers itself as a service provider.
Phil_vol measures the extent to what the board agrees that the club
should only be run by volunteers, a philosophy that may affect
organizational problems.
Previous research examining strategy (Shilbury & Ferkins, 2011)
of sport organizations indicate that governance plays a critical role.
Therefore, a set of governance variables is included in this research
that control for the structure of decision-making and of executive
control (Table 1). This study also controls for organizational size
(members, sports, sportsq), elite sport (squad), and facilities
(pub_fac, own_fac) because these factors could also influence
organizational problems (Wicker & Breuer, 2013).
Econometric Model
One issue that we assess is the extent to which clubs experiencing
one type of problem are more likely to experience another type. Our
approach is to estimate a bivariate probit model which allows the
occurrence of the two problems to be correlated without imposing the
restriction that having one type of problem causes the other type, and
vice versa. Let yj* and yy* be the severity of the financial and
volunteer problems, respectively, as indicated in equations (1) and (2).
The true severity of the problems is not observed, but the survey
responses reveal if the respondent views the problems as severe or not.
The reflect any influences on the severity of the problem that are not
captured systematically in the [x.sub.1]. Such uncaptured influences may
include competence or incompetence of the club management, robustness of
the local economy, proximity to major metropolitan areas, proximity to
alternative clubs offering the same services, and characteristics of the
local population such as average age, presence of children, retirement
status, and employment status. If the respondent indicates the problems
are severe, then we observe a value of one, otherwise we observe a zero,
recorded as y1 and y2, respectively.
[y.sup.*.sub.1] = [x.sub.1] [beta] + [[epsilon].sub.1],
[y.sup.*.sub.1] > 0 [??] [y.sub.1] = 1 (1)
[y.sup.*.sub.2] = [x.sub.2] [gamma] + [[epsilon].sub.2],
[y.sup.*.sub.2] > 0 [??] [y.sub.2] = 1 (2)
[[epsilon].sub.i] ~ N(0, 1)
E([[epsilon].sub.1] [[epsilon].sub.2]) = [rho]
Using the standard bivariate normal distribution, we have the joint
probability distribution
f([y.sub.1], [y.sub.2]) = 1/2[pi][[sigma].sub.1] [[sigma].sub.2]
[(1 - [[rho].sup.2]).sup..5] [e.sup.2] -z/(1 - [[rho].sup.2]) (3)
where
z = [([x.sub.1] [beta] - [[mu].sub.1]).sup.2]/[[sigma].sup.2.sub.1]
+ [([x.sub.2][gamma] - [[mu].sub.2]).sup.2]/ [[sigma].sup.2.sub.2] -
2[rho]([x.sub.1][beta] - [[mu].sub.1])([x.sub.2] [gamma] -
[[mu].sub.2])[[sigma].sub.1][[sigma].sub.2]
and [[mu].sub.j] are the mean of the financial and volunteer
problem variables, respectively, [[sigma].sub.j] = 1 by assumption of
the standard normal distribution, and [rho] is the correlation between
the unobserved determinants of financial and volunteer problems.
Estimation of p is a primary issue for this research as it provides
evidence on hypothesis 1. For generality, the model above allows
different determinants (x's) of the financial and volunteer
problems though this is not necessary.
In the model of sport club problems, the vector of explanatory
variables x includes measures of the revenues from different sources.
Since one outcome is the existence of financial problems, these revenue
variables might be correlated with the 81 and 82. Consequently, we treat
these revenue source variables and the administrative personnel cost as
potentially endogenous, and estimate the model above using the approach
of Smith and Blundell (1986). Each of the revenue sources and the
administrative cost share is explained in first stage regressions using
all the explanatory variables in the probit equations plus an array of
35 dummy variables indicating whether or not the club offers a specific
sport (e.g., soccer, tennis, swimming). For each of the first stage
regressions we conducted an F-test of joint significance of the sport
dummies. In each case one could reject the null hypothesis that all
coefficients are zero at least at the 5% level and usually with p-values
of < 0.001. The first stage equations are available upon request.
Following Smith and Blundell (1986), we construct estimated errors from
the first stage estimates and include these along with the observed
values of the revenue and administrative cost variables as regressors in
the bivariate probit equations. Under the null hypothesis of weak
exogeneity of the latter, the fitted error terms will have zero
coefficients. If the null hypothesis is rejected, then the estimates are
consistent, and more efficient than the standard IV approach of
including only the fitted values of the potentially endogenous
variables. We report bootstrapped standard errors to improve accuracy
over the asymptotic standard errors. Estimating the model with
club-fixed effects is problematic as there are only two observations per
club, but over 1,000 clubs. Including a year dummy does not change the
results reported below.
Results and Discussion
The results of the bivariate probit model are presented in Table 2.
The first pair of columns show the results when all revenue share and
administrative cost variables are treated as exogenous, the second pair
of columns show the results when those variables are treated as
endogenous. The test of the null hypothesis that the revenue and cost
variables are exogenous rejects the null. Consequently, the discussion
below is restricted to the results in the third and fourth columns.
Summarizing, the results show a significant positive correlation between
the errors in the financial and volunteer problems, that is, of p. In
other words, after controlling for systematic determinants of financial
and volunteer problems, our results indicate that clubs that have one
type of problem tend to have both financial and volunteer problems;
clubs that do a poor job of managing their finances tend also to do a
poor job of attracting and retaining volunteers. Thus, the first
hypothesis (H1) can be confirmed; in this case the evidence is that the
problems are positively correlated.
Variables reflecting club governance are not jointly significant,
and only two, the presence of a vice president in the volunteer equation
and multiple treasurers in the finance equation, are even weakly
individually significant. The presence of a vice president reduces the
likelihood of volunteer problems, while multiple treasurers reduce the
likelihood of financial problems. The evidence here is that club
governance structures, at least as indicated by the existence of
particular officers, is not a source of either financial or volunteer
problems. If club governance is considered more broadly to include the
philosophy of the organization, then governance is an important
deterrent of problems. The club philosophy variables are j ointly
significant, and five of eight are individually significant at least at
the 5% level and another is at the 10% level. The more important the
club philosophy is to the operation of the club, the less likely is the
club to have volunteer or financial problems. The results for strategy
are consistent with the findings of Wicker and Breuer (2013) which
indicate clubs with a strategic policy have fewer organizational
problems.
Clubs for which the survey respondent indicates a strong agreement
with the club staying as it is also are less likely to experience
problems. Thiel and Mayer (2009) describe clubs as lethargic
organizations, with no clear organizational objectives and a tendency to
promote management from within which hinders the clubs' ability to
change. Clearly, for Thiel and Mayer (2009), clubs choosing to stay the
same is indicative of problems. Our results, on the other hand, indicate
such clubs are less likely to report serious problems. Taylor and Ho
(2005) suggest that financial and human resources are barriers to change
but are, at the same time, explanations for resistance to change within
the club. The inertia of non-profits was also documented in other
sectors; organizational and environmental realities were interpreted
differently by board members and their decision-making was affected by
fear, tradition, and serendipity (Millesen & Martin, 2013).
Clubs with a strong service orientation have less severe financial
problems than clubs with less emphasis on service. This result may
indicate that some clubs follow the social enterprise movement (Young,
2007) in the sense that they regard themselves more as a service
provider and less of a club that serves the interests of its members.
Clubs provide services for which they charge fees and, thus, generate
commercial income. This is somewhat different from the original club
good (Buchanan, 1965) character of sport clubs where the idea is that
members pool their resources to produce programs of interest to them
all. Although this movement describes that clubs move away from their
original mission, research shows that service fees are an important
income source. For example, revenues from service fees were found to be
the income source of third greatest importance for sport organizations
in Canada (Lasby & Sperling, 2007). In light of this fact, it is
surprising that service orientation does not increase volunteer problems
(although the effect is not significant). Somewhat obviously, clubs that
believe the club should only be run by volunteers report fewer problems
with attracting and retaining volunteers.
Sources of revenue variables are jointly significant.
Interestingly, sponsorship money leads to both greater financial
problems and greater volunteer problems, while subsidy income only
increases volunteer problems. Thus, the second hypothesis (H2) is only
supported for sponsorship income, but is not supported for subsidy
income or miscellaneous external revenues. One explanation for this
finding may be that sponsorship money is more sensitive to changes in
the economy than are subsidies (Lindqvist, 2012). On the contrary,
government subsidies are actually considered a relatively stable income
source--if there are no changes in governments and funding decisions
(Froelich, 1999). Also, government funding was found to be less volatile
than other revenue sources like donations (Gronbjerg, 1991).
Interestingly, greater reliance on subsidies is associated with
more volunteer problems. Consequently, the third hypothesis (H3) can be
confirmed for sponsorship and subsidy income, but not for miscellaneous
external revenues. Contrary to Enjolras (2002), who did not find
evidence that government funding crowds out voluntary work, we find that
government subsidies increase volunteer problems. It is likely that this
finding has to do with the institutional pressure and expectations
associated with government subsidies (Rushton & Brooks, 2007) that
may negatively affect voluntary work. For example, volunteers may be
forced to implement policies, as in the UK (Nichols et al., 2005), or be
required to have specific qualifications, as in Belgium, although the
government pressure was found to be relatively low (Vos et al., 2011).
Another explanation that could be advanced is the bureaucratic
application process for government subsidies which may be too complex
and time-consuming for volunteers (Andreff, 2006), thus leading to
higher volunteer problems in clubs. The negative effect of
administrative personnel costs supports this assumption; clubs employing
paid staff that is responsible for complex tasks like writing subsidy
applications reported fewer volunteer problems.
The evidence strongly suggests that sponsorship revenue share is
endogenous in both the volunteer and financial problems equations, while
administrative personnel costs are endogenous in the volunteer problems
equation. Failure to account for the endogeneity of sponsorship revenues
underestimates the influence of sponsorship revenues on both volunteer
and financial problems. The subsidy, internal, and external revenue
source shares do not appear to be endogenous. Consequently, we
re-estimated the model treating only sponsorship share and
administrative personnel costs as endogenous. Results of this estimation
are reported in Table 3.
Estimates from this specification are not substantively different
than those of Table 2, though generally they are a bit stronger. For
example, subsidy income is a significant determinant (at the 5% level)
of volunteer problems in Table 3, but it was significant only at the 10%
level in Table 2 when it is treated as endogenous. Moreover, the
coefficient point estimate is 0.030 in Table 2, but it is only 0.007 in
Table 3, essentially the same as when all revenue shares and
administrative cost shares were exogenous in Table 2. Additionally, in
Table 3, six of the eight club philosophy variables are significant at
the 5% level and five of the six are significant at the 1% level.
To get a better sense of the influence of sponsorship on the
prevalence of volunteer and financial problems, we computed the change
in the probability of a problem given different changes in the
sponsorship share. For example, we first computed the probability if the
club had no sponsorship funds; about 70% of the observations in our
sample are in this situation to begin with. In other words, setting
sponsorship to zero for all observations, we computed the value of
[x.sub.1]b or [X.sub.2]g, where b and g are the estimated values of
[beta] and [gamma], respectively, and inserted these values into the
standard normal cumulative distribution function. We then computed the
probability using the mean value of sponsorship share for each
observation and at the 75th percentile of the sponsorship share. We then
found the difference between the probability with no sponsorship revenue
and each of these two alternative levels. Additionally, we determined
the change in the probability of each type of problem if the club had
its sponsorship share rise, or fall, by one standard deviation of the
sample sponsorship share. A large fraction of the clubs had no
sponsorship revenues, meaning we could not reduce their sponsorship
share. Consequently, we compute probability differences both on the full
sample and on the sample of only those clubs that received sponsorship
financing. The sample averages of the changes in probabilities are
reported in Table 4.
The key implication from Table 4 is that one standard deviation
increases or decreases in the share of revenues gathered from
sponsorship can have substantial effects on the likelihood of both
financial and volunteer problems. Since clubs without sponsorship
revenues could not have those revenues reduced, these clubs all have no
impact of reducing sponsorship income, and bias the mean effect toward
zero. Dropping those observations from the computation, the effects are
larger. For example, reducing the club's share by one standard
deviation, about 5.8 percentage points in the full sample, will reduce
the likelihood of financial problems by 9.7 percentage points in the
full sample and by 11.6 percentage points in the sample restricted to
sponsorship recipients. For volunteer problems the figures are 8.8 and
9.0 percentage points, respectively. Raising sponsorship share by one
standard deviation raises the likelihood of financial problems by 18.8
and 20.4 percentage points, and the likelihood of volunteer problems by
9.2 percentage points in both the full and the restricted samples.
Conclusion
This study examined the effect of external funding on financial and
volunteer problems among German sport clubs. The results of a bivariate
probit model show that both types of problems are interrelated. Clubs
relying on sponsorship revenues report higher financial and volunteer
problems, while government subsidies only increase volunteer problems.
If clubs want to improve their financial situation and are in a position
to choose between different income sources, it can be recommended that
clubs prefer subsidies over sponsorship income. If the European Union
wants to support grassroots sports as it was stated in the White Paper
on Sport (CEC, 2007b), then the provision of subsidies would be one
option. Moreover, volunteer and financial problems are significantly
affected by the club's governance and philosophy indicating that
having a strong philosophy and a capable board reduces both types of
problems. Specifically, clubs should develop a strategic policy and
consider having multiple treasurers who control each other.
This study has some limitations that represent avenues for future
research. First, only two years of panel data have been available for
this research since some variables were missing in earlier waves of this
survey. Future research may extend the examination to more years of
data. Second, this research looked at the sample as a whole including
all types of sports. Future research should examine whether there are
differences among clubs providing different sports with regard to the
role of external funding.
The findings of this study should be generalizable to other
non-profit sport club systems which have a similar structure (for an
overview see Hallmann & Petry, 2013). This may be particularly the
case for sport clubs in Western Europe that were found to have a similar
financial structure despite different policy systems (Vos et al., 2013).
While this study primarily contributes to the body of research on
financing non-profits, the finding that clubs relying heavily on
sponsorship income experience bigger financial problems may also be
relevant to major football clubs, both inside and outside of Germany.
With Financial Fairplay being introduced by the UEFA, and the motivation
to bring financial sanity to professional football, the question arises
whether there is a connection to the disparate revenue sources,
particularly sponsorship. This may be another avenue for future studies.
References
Allison, M. (2001). Sports clubs in Scotland. Edinburgh, UK:
SportScotland.
Andreff, W. (2006). Voluntary work in sport. In W. Andreff & S.
Szymanski (Eds.), Handbook on the economics of sport (pp. 219-224).
Cheltenham, UK: Edward Elgar.
Anheier, H. K. (2010). Nonprofit organizations. Theory, management,
policy. London: Routledge.
Breuer, C., & Feiler, S. (2013). Finanzielle Situation und
okonomische Bedeutung des Vereinssports. [Financial situation and
economic importance of club sports]. In C. Breuer (Ed.),
Sportentwicklungsbericht 2011/2012--Analyse zur Situation der
Sportvereine in Deutschland (pp. 48-71). Cologne: Sportverlag Straufi.
Buchanan, J.M. (1965). An economic theory of clubs. Economica,
32(125), 1-14. Chang, C. F., & Tuckman, H. P. (1994). Revenue
diversification among non-profits. Voluntas, 5, 273-290.
Coalter, F. (2007). A wider social role for sport. London:
Routledge.
Commission of the European Communities (CEC) (2007a). Action plan
"Pierre de Coubertin." Accompanying document to the white
paper on sport. Brussels: European Union.
Commission of the European Communities (CEC) (2007b). White paper
on sport. Brussels: European Union.
DOSB (2013). Bestandserhebung 2012. Retrieved from
http://www.dosb.de/fileadmin/sharepoint/Materialien%20%7B82A97D74-
2687-4A29-9C16- 4232BAC7DC73%7D/Bestandserhebung_2012.pdf
Enjolras, B. (2002). The commercialization of voluntary sport
organizations in Norway. Nonprofit and Voluntary Sector Quarterly,
31(3), 352-376.
European Commission (1999). The European model of sport. Brussels:
European Union.
Froelich, K. A. (1999). Diversification of revenue strategies:
Evolving resource dependence in nonprofit organizations. Nonprofit and
Voluntary Sector Quarterly, 28, 246-268.
Gratton, C., Liu, D., Ramchandani, G., & Wilson, D. (2012). The
global economics of sport. London: Routledge.
Gronbjerg, K. A. (1991). How nonprofit human service organizations
manage their funding sources: Key findings and policy implications.
Nonprofit Management and Leadership, 2(2), 159-175.
Hallmann, K., & Petry, K. (Eds.) (2013). Comparative sport
development. Heidelberg: Springer.
Heinemann, K. (1995). Einfuhrung in die Okonomie des Sports.
[Introduction to the economics of sport]. Schorndorf: Hofmann.
Horch, H-D. (1994). Does government financing have a detrimental
effect on the autonomy of voluntary associations? Evidence from German
sport clubs. International Review for the Sociology of Sport, 29,
269-282.
James, E., & Young, D. R. (2007). Fee income and commercial
ventures. In D. R. Young (Ed.), Financing nonprofits (pp. 93-120).
Plymouth: AltaMira Press.
Kearns, K. (2007). Income portfolios. In D. R. Young (Ed.),
Financing nonprofits (pp. 291-314). Plymouth: AltaMira Press.
Lindqvist, K. (2012). Museum finances: Challenges beyond economic
crises. Museum Management and Curatorship, 27(1), 1-15.
Lamprecht, M., Fischer, A., & Stamm, H. P. (2012). Die
Schweizer Sportvereine: Strukturen, Leistungen, Herausforderungen.
[Swiss sports clubs: structures, benefits, challenges]. Zurich: Seismo.
Lasby, D., & Sperling, J. (2007). Understanding the capacity of
Ontario sports and recreation organizations. Toronto, Ontario: Imagine
Canada.
Millesen, J. L., & Martin, E. C. (2013). Community foundation
strategy: Doing good and the moderating effects of fear, tradition, and
serendipity. Nonprofit and Voluntary Sector Quarterly (in press).
doi:10.1177/0899764013486195
Nagel, S. (2008). Goals of sports clubs. European Journal for Sport
and Society, 5(2), 121-141.
Nichols, G., Taylor, P., James, M., Holmes, K., King, L., &
Garrett, R. (2005). Pressures on the UK voluntary sport sector.
Voluntas, 16(1), 33-50.
Pfeffer, J., & Salancik, G. R. (1978). The external control of
organizations: A resource dependence perspective. New York: Harper and
Row.
Rushton, M., & Brooks, A.C. (2007). Government funding of
nonprofit organizations. In D. R. Young (Ed.), Financing nonprofits (pp.
69-92). Plymouth: AltaMira Press.
Sharpe, E. K. (2006). Resources at the grassroots of recreation:
Organizational capacity and quality of experience in a community sport
organization. Leisure Sciences, 28, 385-401.
Shilbury, D., & Ferkins, L. (2011). Professionalisation, sport
governance, and strategic capability. Managing Leisure, 16, 108-127.
Sigloch, J. (2009). Rechnungslegung. [Accounting]. In C. Breuer
& A. Thiel (Eds.), Handbuch Sportmanagement (p. 195-215).
Schorndorf: Hofmann.
Smith, R.T., & Blundell, R.W. (1986). An exogeneity test for a
simultaneous equations Tobit model with an application to labor supply.
Econometrica, 54(3), 679-685.
Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate
statistics. Boston, MA: Allyn & Bacon.
Taylor, P., Barrett, D., & Nichols, G. (2009). Survey of sports
clubs 2009. London: CCPR.
Taylor, T., & Ho, C. (2005). Global human resource management
influences on local sport organisations. International Journal of Sport
Management and Marketing, 1(1/2), 110-126.
Thiel, A., & Mayer, J. (2009). Characteristics of voluntary
sports clubs management: A sociological perspective. European Sport
Management Quarterly, 9(1), 81-98.
Tinkelman, D. (2010). Revenue interactions: Crowding out, crowding
in, or neither? In B. A. Seaman & D. R. Young (Eds.), Handbook of
research on nonprofit economics and management (pp. 18-41). Northampton,
MA: Edward Elgar.
Tokarski, W., Petry, K., Groll, M., & Mittag, J. (2009). A
perfect match? Sport in the European Union. Aachen: Meyer & Meyer.
Vos, S., Breesch, D., Kesenne, S., Van Hoecke, J., Vanreusel, B.,
& Scheerder, J. (2011). Governmental subsidies and coercive
isomorphism. Evidence from sports clubs and their resource dependencies.
European Journal for Sport and Society, 8(4), 257-280.
Vos, S., Wicker, P., Breuer, C., & Scheerder, J. (2013). Sports
policy systems in regulated Rhineland welfare states: Similarities and
differences in financial structures of sports clubs? International
Journal of Sport Policy and Politics, 5, 55-71.
Weisbrod, B. A. (1998). The nonprofit mission and its financing.
Journal of Policy Analysis and Management, 17, 165-174.
Wicker, P., & Breuer, C. (2013). Understanding the importance
of organizational resources to explain organizational problems: Evidence
from non-profit sport clubs in Germany. Voluntas, 24, 461-484.
Wicker, P., & Hallmann, K. (2013). A multi-level framework for
investigating the engagement of sport volunteers. European Sport
Management Quarterly, 13(1), 110-139.
Young, D. R. (2007). Why study nonprofit finance? In D. R. Young
(Ed.), Financing nonprofits (pp. 3-20). Plymouth: AltaMira Press.
Dennis Coates (1), Pamela Wicker (2), Svenja Feiler (2), and
Christoph Breuer (2)
(1) University of Maryland, Baltimore County
(2) German Sport University Cologne
Dennis Coates is a professor of economics in the Department of
Economics. His research interests focus on the effects of stadiums and
professional sports on local economies.
Pamela Wicker is a senior lecturer in the Institute of Sport
Economics and Sport Management and an associate professor (adjunct) at
Griffith University, Australia. Her main research interests are
financing and capacity of non-profit sport clubs, economics of sport
consumer behavior, and spectator sport economics.
Svenja Feiler is a researcher and PhD student in the Institute of
Sport Economics and Sport Management. Her research interests include
financing and development of nonprofit sport clubs, sport tourism and
event management, and sport participation.
Christoph Breuer is a professor and head of the Institute of Sport
Economics and Sport Management as well as the Vice Chancellor for
Resources. His main research interests are the development of non-profit
sport clubs, effectiveness of sponsorships, and determinants of sport
participation.
Authors' Note
The authors would like to thank the Federal Institute for Sports
Sciences (BISp), the German Olympic Sports Confederation (DOSB), and the
16 federal state sports confederations (LSBs) for supporting the
research into sport clubs in Germany (Sport Development Report).
Table 1: Overview of variables and summary statistics
Variable Description
Dependent variables (1 = big/very big/existential,
0 = medium, small, no problem)
fin_problems Financial situation of the club
vol_problems Recruitment/retention of volunteers
Financial variables (as shares of total revenues
respectively expenses)
sponsor Revenues from sponsorship (=jerseys/
equipment, boards, broadcasting rights,
advertisement)
subsidy Revenues from subsidies (=from sport
organizations, state, community/
district, EU, other)
ext_misc Miscellaneous external revenues
(=fund management, service fees from
non-members, credits)
int Internal revenues (=membership fees,
admission fees, service fees from members)
admin Expenditure on administrative personnel
Club philosophy (1 = agree/totally agree)
phil_stay Our club wants to stay the way it is
phil_strategy Our club has a strategic policy
phil_service Our club considers itself a service
provider in sports
phil_vol Our club should be run exclusively
by volunteers
Governance (1 = yes)
pres Club has one president
multi_pres Club has multiple presidents
vicepres Club has one vice president
multi_vice Club has multiple vice presidents
execdir Club has an executive director
treas Club has one treasurer
multi_treas Club has multiple treasurers
Controls
members Total number of members in the club
squad Club has squad athletes (1 = yes)
sports Number of sports provided by the club
sportsq Sports squared
pub_fac Club uses public sport facilities (1 = yes)
own_fac Club possesses its own facilities (1 = yes)
Variable Scale Mean SD
Dependent variables (1 Dependent variables (1 = big/very
big/existential, 0 = big/existential, 0 =
medium, small, no problmedium, small, no problem)
fin_problems Dummy 0.142 0.349
vol_problems Dummy 0.414 0.493
Financial variables (as shares of total revenues
respectively expenses)
sponsor Metric 2.402 5.877
subsidy Metric 9.441 11.446
ext_misc Metric 2.006 6.688
int Metric 58.504 26.868
admin Metric 2.823 12.069
Club philosophy (1 = agree/totally agree)
phil_stay Dummy 0.497 0.500
phil_strategy Dummy 0.543 0.498
phil_service Dummy 0.614 0.487
phil_vol Dummy 0.808 0.394
Governance (1 = yes)
pres Dummy 0.963 0.190
multi_pres Dummy 0.021 0.143
vicepres Dummy 0.829 0.377
multi_vice Dummy OO O O 0.268
execdir Dummy 0.198 0.399
treas Dummy 0.930 0.255
multi_treas Dummy 0.059 0.235
Controls
members Metric 326.949 674.43
squad Dummy 0.107 0.309
sports Metric 3.399 4.152
sportsq Metric 28.784 85.964
pub_fac Dummy 0.644 0.479
own_fac Dummy 0.434 0.496
Coates, Wicker, Feiler, Breuer
Table 2: Bivariate probit model-volunteer and financial problems
Shares exogenous
Variables vol_problems fin_problems
sponsor 0.00610 0.0160 ***
(1.196) (2.796)
subsidy 0.00664 ** -0.00218
(2.402) (-0.645)
int -0.00220 * -0.00336 **
(-1.769) (-2.200)
ext_misc 0.00133 0.00257
(0.306) (0.513)
admin -0.000303 -0.00329
(-0.116) (-0.942)
sponsor_err
subsidy_err
int_err
ext_misc_err
admin_err
members -1.31e-05 -0.000170
(-0.203) (-1.313)
squad -0.213 ** 0.0402
(-2.198) (0.343)
sports 0.0122 -0.0292
(0.757) (-1.458)
sportsq -0.000529 0.00184 *
(-0.704) (1.877)
pres -0.168 -0.539 **
(-0.714) (-2.112)
multi_pres -0.335 -0.376
(-1.109) (-1.094)
vicepres -0.180 -0.0593
(-1.594) (-0.416)
multi_vice 0.0487 -0.000258
(0.320) (-0.00137)
execdir 0.148 * 0.0776
(1.763) (0.726)
treas -0.0365 -0.00211
(-0.295) (-0.0138)
multi_treas -0.219* -0.445 **
(-1.714) (-2.315)
pub_fac 0.185 *** 0.0642
(2.699) (0.747)
own_fac 0.0728 0.299 ***
(1.142) (3.722)
phil_stay -0.266 *** -0.380 ***
(-4.452) (-5.015)
phil_strategy -0.275 *** -0.141 *
(-4.643) (-1.904)
phil_service -0.00712 -0.144 *
(-0.116) (-1.897)
phil_vol -0.132 * -0.104
(-1.660) (-1.069)
Constant 0.538 * -0.0152
(1.890) (-0.0466)
Error correlation 0.369 ***
(7.662)
Observations 2,048 2,048
Shares endogenous
Variables vol_problems fin_problems
sponsor 0.0355 * 0.140 ***
(1.800) (4.929)
subsidy 0.0301 * -0.0233
(1.796) (-0.974)
int -0.00363 0.00278
(-0.655) (0.394)
ext_misc -0.00537 0.0617
(-0.151) (1.569)
admin -0.0264 ** -0.00319
(-2.363) (-0.208)
sponsor_err -0.0336 * -0.135 ***
(-1.732) (-4.689)
subsidy_err -0.0239 0.0218
(-1.416) (0.925)
int_err 0.00190 -0.00525
(0.345) (-0.742)
ext_misc_err 0.00732 -0.0600
(0.203) (-1.562)
admin_err 0.0280 ** -0.000497
(2.512) (-0.0333)
members -1.86e-05 -0.000243
(-0.266) (-1.406)
squad -0.200 * 0.00431
(-1.871) (0.0344)
sports 0.000684 -0.00721
(0.0264) (-0.179)
sportsq 0.000585 0.00165
(0.471) (0.753)
pres -0.0830 -0.314
(-0.363) (-1.004)
multi_pres -0.305 -0.435
(-0.907) (-0.997)
vicepres -0.209 * -0.177
(-1.709) (-1.060)
multi_vice -0.0247 -0.265
(-0.149) (-1.214)
execdir 0.0842 -0.0465
(0.958) (-0.407)
treas -0.0900 0.0420
(-0.673) (0.253)
multi_treas -0.180 -0.440*
(-1.066) (-1.718)
pub_fac 0.102 0.0305
(1.444) (0.292)
own_fac 0.0793 0.237 **
(0.780) (2.041)
phil_stay -0.231 *** -0.336 ***
(-3.764) (-4.341)
phil_strategy -0.282 *** -0.163*
(-4.146) (-1.949)
phil_service -0.0410 -0.236 ***
(-0.620) (-2.940)
phil_vol -0.238 ** -0.0620
(-2.496) (-0.443)
Constant 0.536 -0.829
(0.978) (-1.197)
Error correlation 0.367 ***
(7.525)
Observations 2,048 2,048
Note: z-statistics in parentheses; *** p < 0.01, ** p < 0.05, * p <
0.1; the number of observations is reduced due to missing values for
the problem variables.
Table 3: Bivariate probit model--sponsorship and administrative
costs shares endogenous
Sponsorship and administrative
personnel shares endogenous
Variables vol_problems
sponsor 0.0422 (2.600) ***
subsidy 0.00690 (2.357) **
int -0.00194 (-1.561)
ext_misc 0.00201 (0.390)
admin 0.0243 (-2.350) **
spons_err 0.0405 (-2.546) **
admin_err 0.0259 (2.467) **
members -2.23e-05 (-0.301)
squad -0.177 (-1.676) *
sports 0.0191 (0.838)
sportsq 1.88e-05 (0.0155)
pres -0.0816 (-0.368)
multi_pres -0.325 (-1.187)
vicepres -0.202 (-1.762) *
multi_vice 0.00737 (0.0450)
execdir 0.0809 (0.875)
treas -0.0437 (-0.354)
multi_treas -0.182 (-1.422)
pub_fac 0.145 (2.032) **
own_fac 0.0665 (0.958)
phil_stay 0.245 (-4.554) ***
phil_strategy 0.270 (-4.490) ***
phil_service -0.0296 (-0.490)
phil_vol 0.238 (-2.992) ***
Constant 0.479 (1.577)
Error correlation
Observations 2,048
Sponsorship and administrative
personnel shares endogenous
Variables fin_problems
sponsor 0.119 (5.460) ***
subsidy -0.00179 (-0.459)
int -0.00198 (-1.190)
ext_misc 0.00299 (0.572)
admin 0.000523 (0.0371)
spons_err -0.114 (-5.201) ***
admin_err -0.00398 (-0.298)
members -0.000236 (-1.518)
squad -0.0136 (-0.115)
sports -0.0325 (-1.179)
sportsq 0.00238 (1.236)
pres -0.340 (-1.102)
multi_pres -0.361 (-0.993)
vicepres -0.171 (-1.165)
multi_vice -0.276 (-1.336)
execdir -0.0243 (-0.229)
treas -0.0214 (-0.121)
multi_treas -0.390 (-1.717) *
pub_fac -0.00863 (-0.0930)
own_fac 0.275 (3.255) ***
phil_stay -0.332 (-4.100) ***
phil_strategy -0.180 (-2.239) **
phil_service -0.229 (-2.785) ***
phil_vol -0.0855 (-0.746)
Constant -0.376 (-1.066)
Error correlation 0.363 (7.793) ***
Observations 2,048
Note: z-statistics in parentheses; *** p < 0.01, ** p < 0.05, * p <
0.1; the number of observations is reduced due to missing values for
the problem variables.
Table 4: Probability of changes in problems as sponsorship share
changes
Obs. Mean SD
Full sample
Financial problems
None to mean 2048 0.0541 0.0300
None to 75th% 2048 0.0339 0.0195
Actual + one St. Dev. 2048 0.1877 0.0611
Actual--one St. Dev. 2048 -0.0972 0.0583
Volunteer problems
None to mean 2048 0.0365 0.0054
None to 75th% 2048 0.0239 0.0036
Actual + one St. Dev. 2048 0.0922 0.0087
Actual--one St. Dev. 2048 -0.0876 0.0117
Restricted sample
(only clubs with sponsorship income)
Financial problems
None to mean 624 0.0352 0.0297
None to 75th% 624 0.0219 0.0190
Actual + one St. Dev. 624 0.2041 0.0603
Actual--one St. Dev. 624 -0.1159 0.0642
Volunteer problems
None to mean 624 0.0345 0.0074
None to 75th% 624 0.0226 0.0049
Actual + one St. Dev. 624 0.0917 0.0084
Actual--one St. Dev. 624 -0.0903 0.0098
Min Max
Full sample
Financial problems
None to mean 0.0000 0.1140
None to 75th% 0.0000 0.0752
Actual + one St. Dev. 0.0000 0.2742
Actual--one St. Dev. -0.2731 0.0000
Volunteer problems
None to mean 0.0000 0.0404
None to 75th% 0.0000 0.0266
Actual + one St. Dev. 0.0000 0.0986
Actual--one St. Dev. -0.0986 0.0000
Restricted sample
(only clubs with sponsorship income)
Financial problems
None to mean 0.0000 0.1138
None to 75th% 0.0000 0.0748
Actual + one St. Dev. 0.0024 0.2742
Actual--one St. Dev. -0.2731 -0.0002
Volunteer problems
None to mean 0.0034 0.0404
None to 75th% 0.0022 0.0266
Actual + one St. Dev. 0.0496 0.0986
Actual--one St. Dev. -0.0986 -0.0411
Note: The number of observations is reduced due to missing
values for the problem variables.
