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ORIGINAL ARTICLE

How to Raise Voluntary Giving for Nonprofit Sports Clubs: An Analysis of Factors Influencing Donations

Svenja Feiler • Pamela Wicker • Christoph Breuer

Published online: 8 August 2014

� International Society for Third-Sector Research and The Johns Hopkins University 2014

Abstract Nonprofit sports clubs generate revenue from a variety of sources. One of the main income categories is donations. Previous research only analyzed the

amount of money generated through donations, but not the influencing factors. The

purpose of this study is to investigate determinants of donations for nonprofit sports

clubs. The study is based on the public goods theory (Weisbrod in ‘‘The economics

of nonprofit institutions.’’ ‘‘Studies in structure and policy.’’ Oxford University

Press, New York, pp 21–44, 1986) and the contract failure theory (Hansmann in

Yale Law J 89(5):835–902, 1980) and makes use of an unbalanced panel data set

from a nationwide online survey of nonprofit sports clubs in Germany (n = 41,343).

The results show that particularly the provision of elite sport and the promotion of

young talents positively influence the reception of donations. Moreover, sports clubs

caring for social aspects, companionship, and conviviality as core values are able to

generate higher revenues from donations. The same applies to clubs employing paid

staff. Contrary, a commercial orientation was found to have a negative effect.

Résumé Les clubs sportifs à but non lucratif génèrent des revenus à partir de sources diverses. Parmi celles-ci, les dons constituent l’un des principaux postes de

revenus. Les études existantes n’analysent que les sommes générées par les dons,

sans considérer les facteurs d’influence. Cette étude vise à rechercher les détermi-

nants des dons faits à des clubs sportifs à but non lucratif. Elle est basée sur la

théorie des biens collectifs (Weisbrod 1986) et sur la théorie de l’échec du contrat

(Hansmann 1980). Elle exploite les données d’un panel non compensé provenant

d’une enquête réalisée en ligne au niveau national et portant sur les clubs sportifs à

but non lucratif en Allemagne (n = 41,343). Les résultats indiquent que les faits de

proposer un sport d’élite et de promouvoir les jeunes talents influencent

S. Feiler (&) � P. Wicker � C. Breuer Institute of Sport Economics and Sport Management, German Sport University Cologne,

Am Sportpark Muengersdorf 6, 50933 Cologne, Germany

e-mail: s.feiler@dshs-koeln.de

123

Voluntas (2015) 26:1219–1239

DOI 10.1007/s11266-014-9489-3

particulièrement et de manière positive les dons. De plus, les clubs sportifs attachés

aux aspects sociaux, à la camaraderie et à la convivialité génèrent davantage de

revenus par le don. Il en va de même pour les clubs qui emploient du personnel

rémunéré. À l’inverse, l’orientation commerciale se révèle avoir un effet négatif.

Zusammenfassung Die Einnahmen gemeinnütziger Sportvereine stammen aus verschiedenen Quellen. Eine der wichtigsten Einnahmekategorien sind Spenden.

Bisherige Forschungen analysierten lediglich den durch Spenden erzielten Geld-

betrag, nicht jedoch die Einflussfaktoren. Zweck dieser Studie ist die Untersuchung

der Determinanten von Spenden an gemeinnützige Sportvereine. Die Studie beruht

auf der Theorie der öffentlichen Güter (Weisbrod 1986) und der Theorie des

Vertragsversagens (Hansmann 1980) und verwertet unbalancierte Paneldaten aus

einer landesweiten Online-Befragung von gemeinnützigen Sportvereinen in Deu-

tschland (n = 41,343). Die Ergebnisse zeigen, dass sich insbesondere das Angebot

von Spitzensport und die Förderung von Nachwuchstalenten positiv auf den Erhalt

von Spenden auswirken. Darüber hinaus können Sportvereine, die großen Wert auf

soziale Aspekte, Gemeinschaft und Geselligkeit legen, höhere Spendeneinnahmen

erzielen. Gleiches gilt für Vereine, die über bezahlte Mitarbeiter verfügen. Da-

hingegen stellte man fest, dass sich eine kommerzielle Orientierung negativ

auswirkt.

Resumen Los clubes deportivos sin ánimo de lucro generan ingresos a partir de una serie de fuentes. Una de las principales categorı́as de ingresos son las donaci-

ones. Investigaciones previas sólo analizaron el importe del dinero generado med-

iante donaciones, pero no los factores que influyen. El propósito del presente estudio

es investigar los determinantes de las donaciones para los clubes deportivos sin

ánimo de lucro. El estudio se basa en la teorı́a de los bienes públicos (Weisbrod

1986) y en la teorı́a de los fallos de contrato (Hansmann 1980) y hace uso de un

conjunto de datos de panel desequilibrados de una encuesta online a nivel nacional

de clubes deportivos sin ánimo de lucro en Alemania (n = 41,343). Los resultados

muestran que en particular la provisión de deporte de élite y la promoción de

talentos juveniles influyen positivamente en la recepción de donaciones. Asimismo,

los clubes deportivos a los que les importan los aspectos sociales, el compañerismo,

y la buena convivencia como valores fundamentales pueden generar mayores in-

gresos de las donaciones. Los mismo se aplica a los clubes que emplean a personal

pagado. Por el contrario, se encontró que una orientación comercial tiene un efecto

negativo.

Keywords Nonprofit finance � Income sources � Nonprofit sports organizations

Introduction

Nonprofit organizations are characterized as private organizations supplying public

goods and mixed goods with private and public components (Anheier 2005;

Weisbrod and Dominguez 1986). Moreover, goods with positive externalities are

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produced by nonprofit organizations (Rooney 2007) which contribute to the welfare

of society (Gratton and Taylor 2000). These characteristics also apply to nonprofit

sports clubs which are the main pillar of mass sport provision in many European

countries as well as overseas (e.g., Enjolras 2002; Lasby and Sperling 2007; Vos

et al. 2012). Nonprofit sports clubs are concerned with offering affordable sports

opportunities which are available to a wide range of the population. The clubs are

‘‘thereby promoting the idea of sport for all’’ (Enjolras 2002, p. 353). Moreover, the

intention of nonprofit sports clubs is to offer a sports supply which is welfare

oriented and produces social benefits (Vos et al. 2012). A major factor for the

existence of nonprofit sports clubs, apart from the voluntary work accomplished in

the clubs, is their financial health since a financially secured situation is vital for the

clubs’ overall success in fulfilling their broader mission (Allison 2001; Young

2007). This is particularly important since prior research showed that other types of

nonprofit organizations have more financial resources at their disposal than sports

organizations which makes the latter potentially financially vulnerable (Lasby and

Sperling 2007). Despite the importance of a stable financial basis, reaching and

keeping such a situation is a key challenge to many nonprofit sports clubs in

Western Europe (Lamprecht et al. 2012; SRA (2013).

Nonprofit sports clubs can be described as ‘‘community-based economy

voluntary organizations’’ (Enjolras 2002, p. 356) as they receive a combination of

public, voluntary, and market resources. This means that nonprofit sports clubs, like

nonprofit organizations in general (Grønbjerg 1991), are dependent on a wide range

of different income sources. This requires them to pay attention to their total

revenues, but also to the composition of their income portfolio since interactions

between different revenue categories (crowd-out and crowd-in effects) might exist

(Kearns 2007; Young 2007). The diverse revenue sources are among others

membership and admission fees, public subsidies, service-fees from nonmembers,

and sponsorship income (cf., Wicker et al. 2012). Additionally, an important

revenue source among nonprofit organizations in general is donations (Okten and

Weisbrod 2000; Rooney 2007). Different characteristics of nonprofit organizations,

particularly the nondistribution constraint, lead to the assumption that nonprofits are

more trustworthy (Hansmann 1987) which in turn makes potential donors more

willing to donate to nonprofits since the money will most likely be used for the

proposed purpose (James 1990). Also, in nonprofit sports clubs, donations are one of

the main sources of income, for example, in Canada (Lasby and Sperling 2007) and

Germany (Wicker et al. 2012), which makes this revenue source an important one

for the clubs.

Although different studies exist which have analyzed the characteristics of

individual donors to nonprofit organizations (e.g., Khanna and Sandler 2000; Okten

and Weisbrod 2000) as well as crowding-out effects of public subsidies on

donations (e.g., Payne 1998; Steinberg 1991), no focus has so far been put on factors

influencing donations from an organizational perspective. Since donations as an

income source for nonprofit organizations have been found to be more volatile than

other income sources such as public subsidies (Grønbjerg 1991), it seems

particularly important to detect which factors have an impact on the reception of

donations for nonprofit sports clubs to secure this important revenue source. Thus,

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this study advances the main research question: Which clubs are more likely to

generate revenues form donations than others? The findings help nonprofit sports

clubs to secure receiving donations and thereby have implications for the sports

clubs’ management. The study adds to the body of research on nonprofit finance in

sports.

Literature Review

The literature on financing nonprofits is widespread and has focused to a large part

on funding sources, the income mix, and revenue diversification (e.g., Chang and

Tuckman 1994; Fischer et al. 2011; Frumkin and Keating 2011). Moreover, various

studies have analyzed main revenue categories of ‘‘pure’’ nonprofit institutions

(Weisbrod 2004, p. 42), namely donations and public subsidies. On the individual

level, demographic and economic factors of donors such as age, income, and

educational level were investigated and found to be positively correlated with

individual giving (for an overview see Rooney 2007). Moreover, research

concentrated on the behavior of people and organizations and investigated motives

for donating to nonprofits (e.g., Ashley et al. 2010; Cordes and Sansing 2007). In

this context, particularly, the concept of altruism plays an important role to explain

individual giving behavior (Rose-Ackerman 1996). However, not all donors are

pure altruists as there are many other motives for charitable giving (Andreoni 1990).

It was found that donors prefer to pay for programmatic expenses, but not for

overhead costs (Rooney 2007), which shows that supporting the key product of

nonprofit organizations plays an important role for donors. This most likely applies

in situations of impure altruism where people donating also receive private benefits

from the contribution (Andreoni 1989).

Apart from individual motives for donating, determinants of donations in

different organizational forms of nonprofits have been investigated, for example, in

UK charities (Khanna and Sandler 2000) and different organizational types of

nonprofits in the USA, using economic variables such as price and other income

sources as determinants of donations (Okten and Weisbrod 2000). Alike the named

studies, further research investigated possible interactions, so-called crowd-out and

crowd-in effects, between donations and other revenue categories, e.g., public

subsidies and commercial income (e.g., Andreoni and Payne 2011; Herman and

Rendina 2001; Khanna and Sandler 2000; Payne 1998; Sokolowski 2013; Wicker

et al. 2012). The various studies on interactions between public subsidies and

donations come to different results, finding both crowd-out (Andreoni and Payne

2011; Kingma 1989; Payne 1998) as well as crowd-in effects (Khanna and Sandler

2000; Sokolowski 2013; Wicker et al. 2012). These divergent effects have recently

been confirmed by Sokolowski (2013) who concludes that the relationship between

donations and public funding is a very complex one. With regard to commercial

income and donations, an American case study looked at donors’ reactions to

commercial activities of nonprofits. The study showed that only a small part of the

donors cared about the nonprofit being involved in commercial activities. However,

if people did care about such activities, they mostly only approved commercial

action if it was used to advance the mission of the organization (Herman and

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Rendina 2001). In the sports context, a study conducted among Norwegian sports

clubs investigated crowding-out between commercial income and public grants as

well as voluntary resources (Enjolras 2002). The author found that neither public

funding nor voluntary work is crowded-out by commercial activities. However,

Enjolras (2002) put no focus on interactions between donations and other income

categories. This has been investigated by Wicker et al. (2012) who find crowd-in

effects between donations and subsidies.

As described above, a large stream of research in the field of economics and

finances of nonprofit organizations deals with questions of crowding-out and

crowding-in effects. Andreoni and Payne (2011) investigated interactions between

donations and public subsidies and put a special focus on fundraising. The authors

found that public grants crowd-out donations particularly due to reduced fundraising

activities. Like the study by Andreoni and Payne (2011), research focusing on

donations for nonprofits frequently concentrates on fundraising activities to acquire

donations. This is particularly true for studies conducted in the USA and the UK

(e.g., Marudas and Jacobs 2004; Okten and Weisbrod 2000; Weisbrod and

Dominguez 1986). However, fundraising activities in nonprofit sports clubs are

rather unusual which is documented by the fact that expenses for fundraising

activities are not even surveyed in different sports club studies (e.g., Breuer and

Wicker 2011; Lamprecht et al. 2012). This also applies to the underlying study

which makes it impossible to investigate fundraising expenses as a determinant of

donations. Thereby, the relevance for investigating determinants of donations for

nonprofit sports clubs is once more stressed as the clubs are in different positions

than other nonprofits which receive money through excessive fundraising and have

more financial resources at their disposal (Gumulka et al. 2005; Lasby and Sperling

2007). Nonprofit sports clubs on the other hand often have to deal with scarce

human and financial resources that foster organizational problems (Wicker and

Breuer 2013). The financial situation of sports clubs has been found to be a

challenge for clubs worldwide (e.g., Gumulka et al. 2005; Lasby and Sperling

2007). Allison (2001) detected that sports clubs are oftentimes financially

underdeveloped which is reflected by 41 % of the surveyed clubs stating to have

financial difficulties. In a recent British survey on sports clubs, it is reported that

52 % of the clubs see a challenge in accessing funding in the next 2 years and 48 %

find a challenge in generating sufficient income. For 41 % of the clubs, keeping

financial sustainability is found to be an issue (SRA 2013).

Despite the existing financial problems of nonprofit sports clubs, the literature

review shows that there is a lack of research in terms of investigating drivers behind

the various income sources that a sports club receives. However, to secure the

revenues for the clubs, it is important to know which clubs are more likely to receive

donations than other clubs. Since donations are one of the most important revenue

sources of nonprofit sports clubs, this study aims at beginning to close the gap in the

literature by investigating determinants of donations for nonprofit sports clubs in

Germany from an organizational point of view.

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Theoretical Framework

This study is based on economic theories of nonprofit organizations. Particularly,

two approaches which, according to Hansmann (1987) as well as Ben-Ner and Gui

(2003), can be regarded as complementary, build the theoretical framework: first,

the public goods theory of the nonprofit sector which explains the existence of

nonprofit organizations based on failure scenarios (Weisbrod 1986); second, the

contract failure theory which is based on information asymmetries and the

nondistribution constraint (Hansmann 1980). Both theories serve not only to explain

the existence of the nonprofit sector, but also give justification for why nonprofit

organizations receive donations.

Public Goods Theory

According to the public goods theory which has originally been developed by

Weisbrod (1986), nonprofit organizations produce public (or collective) goods

(Steinberg 2006) and exist due to market failure and government failure (Weisbrod

1986). A market failure situation arises when a private market ‘‘fails to cater

adequately for the full effects of the market on the welfare of society’’ (Gratton et al.

2012, p. 22). In such a situation, the government comes into play to compensate the

underprovision of the public good. However, if also governments fail to provide an

adequate level of public goods, nonprofits are able to satisfy heterogeneous demand.

In this case, following the public goods theory, ‘‘nonprofit organizations provide

public goods through donor support’’ (Anheier 2005, p. 123). The reasoning for

charitable giving is that donors want to secure the collective output of the nonprofit

(Kingma 1997). Thus, the theory serves to generally explain donors’ contributions

to nonprofit organizations.

Weisbrod’s theory (1986) in its original form puts a focus on nonprofit

organizations with an output of pure public goods. However, this theory has been

expanded to nonprofit organizations which produce mixed goods with public and

private components as well as goods with positive externalities (for an overview see

Kingma 1997). Nonprofit sports clubs can be described as such organizations.

Pertaining to the public goods aspect, the clubs are beneficial to society by

producing collective goods such as national sporting success which foster civic

pride (Gratton and Taylor 2000). The production of national sporting success is only

possible due to nonprofit sports clubs: they form the basis for elite sport in Germany,

and without the clubs, no squad athletes could arise. Thus, according to the public

goods theory, donors are willing to give money to nonprofit sports clubs to keep the

output of the public good ‘‘national sporting success’’ at an adequate level.

Following this argumentation, the first hypothesis is derived:

H1 Being involved in elite sports and talent promotion as a nonprofit sports club has a positive impact on the reception of donations.

Apart from national sporting success, nonprofit sports clubs fulfill further

important societal functions and contribute to the social welfare of a nation

(Lamprecht et al. 2012). The output of the sports clubs includes goods with positive

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externalities such as youth promotion, integration, crime prevention, and health

(Handy and Brudney 2007; Ulseth 2004; Vos et al. 2012). Pertaining to such

externalities, Preston (1988, p. 496) assumes that organizations which generate

‘‘higher social benefits will receive more donations.’’ Social benefits can affect

different population groups, e.g., children and adolescents, older people, and people

with a migration background. Therefore, the second hypothesis is formulated as

follows:

H2 Caring for the youth, for migrants, and for the elderly positively influences the reception of donations.

A further positive effect of nonprofit organizations is the creation of social capital

(Steinberg 2006). Nonprofit organizations are able to create ‘‘a lively and pleasant

social environment’’ (Ben-Ner and Gui 2003, p. 7) which has the character of a

collective good. The creation of social capital by community sports organizations

and voluntary sports clubs has been documented in various studies (e.g., Coalter

2007; Doherty and Misener 2008; Vos et al. 2012). Moreover, sports clubs

particularly put high value on social integration and aim at creating an atmosphere

of community, companionship, and conviviality (Lamprecht et al. 2012; Ulseth

2004). These aspects are covered in the third hypothesis:

H3 Caring for core social values positively influences the reception of donations.

Contract Failure Theory

In addition to the public goods theory, a further approach to explain the existence of

nonprofit organizations and the behavior of people donating to nonprofits is the

contract failure theory, also known as trust-related theory (Hansmann 1980).

Hansmann (1987) partly criticizes the public goods theory in its original form as he

states that some services of nonprofits are ‘‘difficult to characterize as public goods

in the usual sense’’ (Hansmann 1987, p. 29). Thereby, the rationale that nonprofits

rather than for-profit organizations fulfill the demand for such goods is unclear. In

response to these shortcomings he argues that nonprofit organizations rather exist in

the marketplace due to information asymmetries and contract failure. ‘‘Contract

failure occurs when the customer does not have sufficient information to evaluate

the quality or competitive value of goods and services available in the marketplace’’

(Grønbjerg 1993, p. 18). Moreover, the nondistribution constraint does not allow for

enrichment of staff as it prevents ‘‘excessive executive compensation and self-

serving dealings’’ (Ben-Ner and Gui 2003, p. 5). Thereby, nonprofits are more

trustworthy than for-profits in situations of information asymmetries. In other

words, nonprofit organizations are ‘‘less prone to contract failure than for-profit

organizations because they cannot gain from misleading customers’’ (Young and

Steinberg 1995, p. 35). This is particularly important for potential donors since they

are assured that their given money cannot be misused for enrichment of staff.

Thereby, the contract failure theory provides a rationale for nonprofits receiving

donations.

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Pertaining to nonprofit sports clubs, the contract failure theory leads to certain

assumptions. First, the clubs receive donations because people giving money trust

the clubs to use the money thoughtfully and for the proposed purpose. As described

in the literature review, research has shown that donors prefer to give their money to

finance programs of nonprofit institutions, but not overhead costs (Rooney 2007).

For nonprofit sports clubs, this would mean that the clubs receive donations mainly

for their core product, i.e., sports offers, but not for administrative expenses, e.g.,

paid staff. Thus, if nonprofit sports clubs only rely on volunteers and have no

professional structures, i.e., paid staff, donors would be more willing to donate to

this kind of clubs. Hence, the fourth hypothesis is derived:

H4 Employment of paid staff in nonprofit sports clubs negatively influences the reception of donations.

Going along with donors trusting nonprofits that their money is used for the

proposed purpose, i.e., the core product of nonprofit sports clubs, the study of

Enjolras (2002) has to be considered. He states that voluntary sports organizations

are not very professionalized, but they are expected to become more and more

commercialized. If nonprofit organizations increase their commercial activities, this

might lead to a decrease in donations. That is, if donors regard increased

commercial activities as a failure in reaching the organizational mission, then they

might have an aversion to commercial activities and cut their donations. Based on

the contract failure theory and the assumptions of Enjolras (2002), the last

hypothesis is formulated as follows:

H5 Being commercially oriented as a nonprofit sports club has a negative impact on the reception of donations.

Method

Data Collection

This study is based on primary data from the Sport Development Report which is a

nationwide online survey of nonprofit sports clubs in Germany. The project is

financed by the Federal Institute of Sports Sciences (BISp), the German Olympic

Sports Confederation (DOSB), and the 16 regional sports confederations of

Germany. The project started in 2005 with the first wave and has until now

continued to wave four being finalized and wave five just being in the works. Thus,

the project is designed as a panel study with the clubs being surveyed every 2 years.

The sports confederations of all 16 federal states in Germany provide the email

addresses of the clubs. From the existing 91,000 clubs in Germany (DOSB 2012), an

increasing number could be reached via email over the years. In 2005, the number

of valid email addresses amounted to 18,085, in 2007 the number grew to 37,206,

further to 58,069 in 2009 and in 2011, in the fourth wave, 67,708 email addresses

were provided by the confederations. In all conducted waves, the clubs received an

invitation email containing a personalized link to the online questionnaire. Each

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survey period lasts for approximately 3 months. Analogous to the provided email

addresses, the sample sizes have increased over the years (2005: n = 3,731; 2007:

n = 13,068; 2009: n = 19,345; 2011: n = 21,998).

The survey questionnaire typically consists of a set of core questions (e.g.,

members, sports offerings, organizational problems, volunteers, finances) and some

additional questions that address current issues (e.g., demographic change, doping,

cooperation with schools, migrant integration, paid staff, club philosophy). For this

paper, only data from the third (2009) and fourth wave (2011) have been used

because the relevant club philosophy questions were only asked in those waves.

Thus, data from the third and fourth wave were pooled in one data set, creating an

unbalanced panel data set with two measuring points. The pooled data set rather

than a cross-sectional data set from one of the waves was chosen to obtain a larger

sample size and thereby get more precise estimators and test statistics (Wooldridge

2013). Overall, the pooled data set consists of n = 41,343 cases but due to missing

values the number of cases included in the analyses amounts to n = 8,680 for

models 1a, 1b, 3a, and 3b and to n = 6,391 for models 2a and 2b.

Measures and Variables

The variables that have been used for the analyses are displayed in Table 1. The

clubs have been asked to state whether they receive donations (dummy_donations).

Moreover, they were asked to give the amount of money they received from

donations. For this study, the total logged donations were used (LN_donations).

Using the natural logarithm instead of the total values is common in financial

studies (e.g., Carroll and Stater 2009). Moreover, the share of donations in relation

to total revenues (share_donations) was integrated. The three described variables

serve as the dependent variables in this study.

To answer the overall research question and the stated hypotheses, various

independent variables were integrated in the models. To give an answer to

hypothesis one, two variables are included. The first variable is an objective

measure and asks whether the club has squad athletes at its disposal (squad_ath-

letes). The second variable is one item of the club philosophy which is measured on

a five-point Likert’s scale (from 1 = do not agree at all to 5 = totally agree). Items

of the club philosophy display the goals and mission of the sports clubs and have

previously been used as subjective measures in different sports clubs studies (e.g.,

Wicker et al. 2014). The item applied here asks to what extent the club is engaged in

the promotion of young talent (phil_youngtalent). Both variables are related to elite

sports since the existence of squad athletes and the promotion of young talent are

necessary conditions for a club being involved in elite sports.

The second hypothesis is examined with four more items of the club philosophy,

namely to what extent the club is engaged in youth work (phil_youth), to what

extent the club offers sports for people with a migration background (phil_migra-

tion) and for older people (phil_elderly), and to what extent the club is committed to

the health sport sector (phil_health). The first three variables clearly show the

relation to the three groups which are addressed in the second hypothesis. The

variable phil_health is included since it is assumed that health sport programs are

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particularly suitable for older people and therefore additionally serves for measuring

‘‘caring for the elderly.’’ In addition to the named subjective measures, the share of

children and adolescents in relation to all members (share_youth) and the share of

seniors, i.e., people older than 60, in relation to all members (share_elderly) are

added as objective measures for testing hypothesis two. It is assumed that clubs with

Table 1 Overview of variables

Variable Description Scale

Dependent variables

dummy_donations Revenues from donations (1 = yes, 0 = no) Dummy

LN_donations Natural logarithm of donations Metric

share_donations Share of donations relative to total revenues Metric

Independent variables

squad_athletes Club has squad athletes (1 = yes, 0 = no) Dummy

phil_youngtalent Our club is highly engaged in the promotion of young talent (1 = do not

agree at all to 5 = totally agree)

Ordinal

share_youth Share of children and adolescents in relation to all members (in %) Metric

phil_youth Our club is highly engaged in youth work (1 = do not agree at all to

5 = totally agree)

Ordinal

phil_migration Our club offers sports for people with a migration background (1 = do

not agree at all to 5 = totally agree)

Ordinal

share_elderly Share of seniors (over 60) in relation to all members (in %) Metric

phil_elderly Our club offers sports for older people (1 = do not agree at all to

5 = totally agree)

Ordinal

phil_health Our club is committed to the health sport sector (1 = do not agree at all

to 5 = totally agree)

Ordinal

exp_events Expenditure for nonsports-related events (1 = yes, 0 = no) Dummy

phil_conviviality Our club sets high value on companionship and conviviality (1 = do not

agree at all to 5 = totally agree)

Ordinal

exp_admin Administrative costs and administrative personnel (1 = yes, 0 = no) Dummy

phil_volunteers Our club should be run exclusively by volunteers (1 = do not agree at all

to 5 = totally agree)

Ordinal

phil_commercial Our club follows the sports supply of commercial sports providers

(1 = do not agree at all to 5 = totally agree)

Ordinal

Control variables

own_facilities Club is in possession of own sport facilities (1 = yes, 0 = no) Dummy

public_facilities Club uses public facilities (1 = yes, 0 = no) Dummy

members Total number of members in the club Metric

members_sq Members squared Metric

sports Total number of sports provided by the club Metric

sports_sq Sports squared Metric

sport Type of sport provided by the club (ten most frequent sports: gymnastics,

football, volleyball, table tennis, tennis, track and field, shooting,

badminton, equestrian, dancing; 1 = yes, 0 = no)

Dummy

year Year of survey (2009 = 0; 2011 = 1) Dummy

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a higher share of youth and seniors have special offers for these two target groups

and thereby particularly care about them.

Transmitting social values and focusing on community and conviviality is

captured in hypothesis three and measured with two variables. It is assumed that

clubs which organize, next to the sport offerings, also events which are not related to

sports, but rather to social aspects (e.g., Christmas celebrations, carnival) are able to

receive higher amounts of donations (cf., Preston 1988). Thus, the first variable

measures whether the club has expenses for nonsport-related events (exp_events).

Moreover, a club philosophy item once again serves as a subjective measure by

stating to what extent the club sets value on companionship and conviviality

(phil_conviviality).

Professional structures in nonprofit sports clubs are assumed to negatively

influence the reception of donations (H4). This is measured by two variables: First,

with the objective measure whether clubs have expenditures for administrative costs

and administrative personnel as a proxy for paid staff (exp_admin). Second, with a

subjective measure from the club philosophy, namely in how far the club agrees to

run the club exclusively by volunteers (phil_volunteers). Alike professional

structures, commercial activities are expected to have a negative impact on

donations (H5). This last hypothesis shall be answered by making use of the club

philosophy item ‘‘Our club follows the sports supply of commercial sports

providers’’ (phil_commercial).

In addition to the described independent variables, further control variables are

included. Since the infrastructure of the club might play a role for donors because

adequate sport offerings and social gatherings are only possible if adequate facilities

exist, measures for the possession of own sport facilities (own_facilities) and the use

of public facilities (public_facilities) are included. Moreover, since research has

shown that organizational size is an important measure in sports clubs studies (e.g.,

Koski 1995), this study controls for club size, measured by total members

(members) as well as its squared term (members_sq). In addition to membership

numbers, the number of sports offered by the club (sports) and again the squared

term (sports_sq) is included. The reason for including the squared terms is to

capture quadratic effects of size in terms of members and sports that have been

documented in previous research on sports clubs (e.g., Wicker et al. 2014). In the

three models labeled with a ‘‘b’’ as appendix, instead of the two variables sports and

sports_sq, the ten most frequent named sports from the survey are used for the

analyses. The variables are included in the form of dummies to control for possible

sport-specific effects. The study further controls for the year of the survey (year) to

capture effects that might be ascribed to certain events (e.g., financial crisis)

happening in the years of the surveys.

Data Analysis

Before starting the analyses, the pooled data set was created by matching the data

and integrating waves three and four into one vertical panel data set. Only variables

that had been surveyed in the same way in both waves were integrated in the

analyses. First, descriptive statistics were computed to give an overview of the

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means and standard deviations of the included variables. To answer the overall

research question, different regression analyses were run. Six regression models

with three different dependent variables relating to donations were estimated to

check the robustness of the models. The regression models are of the following

general form:

donations = b0 ? b1 squad_athletes ? b2 phil_youngtalent ? b3 share_ youth ? b4 phil_youth ? b5 phil_migration ? b6 share_elderly ? b7 phil_elder- ly ? b8 phil_health ? b9 exp_events ? b10 phil_conviviality ? b11 exp_ admin ? b12 phil_volunteers ? b13 phil_commercial ? b14 own_facilities ? b15 public_facilities ? b16 members ? b17 members_sq ? b18 sports ? b19 sports_sq ? b20 year ?e.

In models 1b, 2b, and 3b, the variables sports and sports_sq were replaced by the

ten sport dummy variables to avoid collinearity issues. The first two estimation

models (1a and 1b) are logistic regressions with the dependent variable

dummy_donations. In a first step, the odds ratios were estimated and supplemented

by the marginal effects in a second step. The marginal effects were computed as

they give more precise information about the probability of receiving donations.

Models 1a and 1b are reported with robust standard errors. Models 2a and 2b are

log-linear regression models, whereas models 3a and 3b are linear regression

models (OLS models). Heteroskedasticity was tested for by using the Breusch-

Pagan test . For models 2a and 2b with the dependent variable LN_donations, the

null hypothesis of homoskedasticity could be confirmed, whereas for models 3a and

3b, it was rejected. Thus, models 3a and 3b with the dependent variable

share_donations were estimated with robust standard errors (White 1980). Although

pooled data with two measuring points were used for the analyses, no typical panel

data methods (fixed or random effects) were applied. This is due to the fact that

many clubs have only participated in one of the two waves which would have led to

a large loss of clubs. Thus, it was decided to treat the data as pooled cross sections

despite the drawback of possible unobserved heterogeneity. Also, it does not seem

feasible to add thousands of coefficients to the regression equation (e.g., in a fixed-

effects procedure).

Results and Discussion

The summary statistics are displayed in Table 2. The table shows that 71 % of the

clubs receive donations and that revenues from donations make up on average 8.8 %

of all revenues the clubs receive. These results underline the importance of

donations for nonprofit sports clubs. Squad athletes are present in 12.4 % of the

clubs which allows the clubs to offer elite sport. One-fourth of the members are

children and adolescents, and 17.3 % are older than 60. Nearly half of the clubs

(48.7 %) have expenditures for nonsport events and 59.1 % employ paid staff.

Regarding the club philosophy items, the highest value is reached for the statement

that the club sets high value on companionship and conviviality (M = 4.295),

directly followed by offering sports for people with a migration background

(M = 4.291). These results underpin that nonprofit sports clubs particularly care for

1230 Voluntas (2015) 26:1219–1239

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social values and social integration and are in accordance with prior studies (cf.,

Ulseth 2004). The aim to run the club only by volunteers also reaches a relatively

high average value (M = 4.220) which applies to offering sports for the elderly

(M = 4.108) and being involved in youth work (M = 4.027) as well. Being

committed to the health sport sector reaches an average of M = 3.032. The results

further show that being engaged in young talent promotion (M = 2.768) and

following the supply of commercial sports providers (M = 2.054) reach lower

values. Thus, the majority of nonprofit sports clubs clearly want to distance

themselves from for-profit sports providers. Roughly half of all clubs are in

possession of own sport facilities, and 60.8 % also use public facilities. A club

averagely consists of 358 members, and the number of sports offered on average

amounts to 3.4. The most often provided types of sports are gymnastics (30.4 %)

and soccer (28.5 %).

The results of the regression analyses are displayed Table 3 for the logistic

regressions and Table 4 for the log-linear and linear regression models. Very clear

and robust results are obtained relating to the first hypothesis which states that being

involved in elite sports and talent promotion has a positive impact on the reception

of donations. This hypothesis can be confirmed since all six models show positive

effects for the two variables which were used to test the hypothesis. The effects of

the variable squad_athletes are significant in all models but one (model 3a), and the

effects for the variable phil_youngtalent are even significant in all six models. The

marginal effects in the logistic regression models 1a and 1b show that if a club turns

from not having squad athletes to having squad athletes, the probability of receiving

donations rises by 4.9 %, respectively, 5.6 %. Moreover, having squad athletes and

being engaged in the promotion of young talent not only increase the probability of

receiving donations, but also positively influence the amount of donations a club

receives as well as the share of donations relative to all revenues of the clubs. Thus,

the results confirm that promoting young talent and having squad athletes lead to the

public good of national sporting success and thereby civic pride (Gratton and Taylor

2000). This is reflected by people donating to nonprofit sports clubs which offer elite

sports. The donors are giving money because they aim at securing the level of the

public good output of the club in the form of national sporting success (cf., Anheier

2005; Kingma 1997).

Pertaining to the second hypothesis which argues that sports clubs are more likely

to receive donations if they particularly care for the youth, for migrants, and for the

elderly cannot be confirmed in all parts. Therefore, the results for the three target

groups addressed in H2 are discussed successively. Regarding the youth, one of the

two variables employed to test this part of the hypothesis, namely the subjective

measure phil_youth, displays positive and significant results in all six models. This

shows that donors value clubs which aim at caring for young people because

positive externalities such as youth promotion and crime prevention can arise

(Handy and Brudney 2007; Preston 1988). On the other hand, the share of youth in

relation to all members of the club shows positive, but not significant results for

models 1a, 1b, 2a, and 2b. However, significant but negative coefficients are

reported for models 3a and 3b with the dependent variable share_donations. This

means that the share of youth within a club does not have an impact on the reception

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and the level of donations, but negatively influences the share of donations relative

to total revenues. The latter could be explained by complex relationships between

income categories, as found by Sokolowski (2013). Clubs with a higher share of

youth might, for example, receive more public subsidies which would in turn lead to

higher shares of subsidies and thereby lower shares of other income categories like

donations. Another explanation could be that donors of sports clubs are at the same

time members of the respective club. Thus, it can be assumed that they are not

Table 2 Descriptive statistics

Variable Mean SD

dummy_donations 0.710 0.453

LN_donations 7.197 1.750

share_donations 8.819 13.129

squad_athletes 0.124 0.330

phil_youngtalent 2.768 1.265

share_youth 25.796 20.763

phil_youth 4.027 1.172

phil_migration 4.291 0.896

share_elderly 17.348 17.139

phil_elderly 4.108 1.090

phil_health 3.032 1.292

exp_events 0.487 0.499

phil_conviviality 4.295 0.827

exp_admin 0.591 0.492

phil_volunteers 4.220 1.034

phil_commercial 2.054 1.014

own_facilities 0.510 0.499

public_facilities 0.608 0.488

members 358.37 1,147.49

members_sq 1,445,113 93,000,000

sports 3.394 4.117

sports_sq 28.465 78.024

badminton 0.100 0.300

football (soccer) 0.285 0.451

track and field 0.126 0.332

equestrian 0.093 0.291

shooting 0.107 0.309

dancing 0.093 0.290

tennis 0.137 0.343

table tennis 0.164 0.370

gymnastics 0.304 0.460

volleyball 0.166 0.372

year 0.532 0.499

1232 Voluntas (2015) 26:1219–1239

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giving with pure altruism, but rather because they also receive private benefit from

donating to the club (Andreoni 1989; Kingma 1997). From prior research on

demographic attributes of donors (in this case age), it can be derived that children

Table 3 Summary of logistic regression models

Variable Model 1a: dummy_donations Model 1b: dummy_donations

Odds z Marginal

effects

Odds z Marginal

effects

Constant 0.208 -6.56*** 0.210 -6.43***

squad_athletes 1.362 3.05** 0.049 1.442 3.62*** 0.056

phil_youngtalent 1.152 5.17*** 0.024 1.155 5.24*** 0.024

share_youth 1.002 0.98 0.000 1.003 1.58 0.001

phil_youth 1.258 7.54*** 0.039 1.269 7.81*** 0.040

phil_migration 1.063 2.02* 0.010 1.044 1.40 0.007

share_elderly 1.003 2.02* 0.001 1.006 3.17** 0.001

phil_elderly 0.882 -4.51*** -0.021 0.918 -2.94** -0.014

phil_health 0.922 -3.28** -0.014 0.939 -2.53* -0.010

exp_events 1.517 7.61*** 0.070 1.504 7.42*** 0.068

phil_conviviality 1.082 2.36* 0.013 1.062 1.78 0.010

exp_admin 1.697 9.54*** 0.092 1.739 9.89*** 0.095

phil_volunteers 1.025 0.80 0.004 1.017 0.54 0.003

phil_commercial 0.934 -2.28* -0.011 0.923 -2.69** -0.013

own_facilities 2.176 12.10*** 0.129 2.222 11.80*** 0.131

public_facilities 1.483 6.12*** 0.068 1.384 4.77*** 0.055

members 1.001 5.02*** 0.000 1.001 4.23*** 0.000

members_sq 0.999 -5.30*** -0.000 0.999 -4.44*** -0.000

sports 1.090 3.93*** 0.015 – – –

sports_sq 0.995 -4.51*** -0.001 – – –

badminton – – – 1.059 0.46 0.009

football (soccer) – – – 1.831 7.06*** 0.092

track and field – – – 1.564 3.48** 0.067

equestrian – – – 0.939 -0.53 -0.011

shooting – – – 0.892 -1.21 -0.020

dancing – – – 0.895 -0.98 -0.019

tennis – – – 0.886 -1.20 -0.021

table tennis – – – 1.238 2.32* 0.034

gymnastics – – – 0.892 -1.25 -0.019

volleyball – – – 0.869 -1.42 -0.024

year 0.900 -1.95 -0.018 0.897 -2.00* -0.018

Pseudo R 2

0.146 0.153

Wald chi 2

1,073.40 1,112.95

p \0.001*** \0.001***

* p \ 0.05; ** p \ 0.01; *** p \ 0.001; robust standard errors reported

Voluntas (2015) 26:1219–1239 1233

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and adolescents are less likely to be donors (Rooney 2007). A higher share of youths

within the clubs would, in situations of impure altruism, consequently lead to lower

shares of donations.

Pertaining to the second part of H2, caring for migrants, the results of the six

models again do show ambiguous effects. The variable phil_migration was applied

to test this part of H2. In model 1a, a positive and significant effect can be detected

which shows that caring for migrants positively influences the reception of

donations. Although the coefficient for this variable is also positive in model 1b, it is

not significant. This might be ascribed to the inclusion of the sport dummies in this

Table 4 Summary of OLS regression models

Variable Model 2a: LN_donations Model 2b: LN_donations Model 3a: share_donations Model 3b: share_donations

Coeff. t Coeff. t Coeff. t Coeff. t

Constant 6.046 35.78*** 5.918 34.92*** 8.383 6.33*** 7.426 5.59***

squad_athletes 0.276 4.75*** 0.372 6.44*** 0.929 1.95 1.231 2.58*

phil_youngtalent 0.151 8.27*** 0.174 9.61*** 0.791 5.37*** 0.823 5.53***

share_youth 0.001 0.82 0.001 0.73 – 0.033 – 2.87** – 0.034 – 2.85**

phil_youth 0.176 7.82*** 0.174 7.87*** 0.621 3.55*** 0.649 3.70***

phil_migration – 0.087 – 3.76*** – 0.101 – 4.37*** – 0.121 – 0.68 – 0.164 – 0.93

share_elderly – 0.004 – 2.64** 0.000 0.04 – 0.058 – 5.95*** – 0.042 – 4.23***

phil_elderly – 0.161 – 8.24*** – 0.083 – 4.13*** – 1.188 – 6.96*** – 0.810 – 4.60***

phil_health – 0.043 – 2.36* – 0.028 – 1.58 0.013 0.09 0.042 0.30

exp_events 0.159 4.23*** 0.156 4.20*** 0.291 1.03 0.182 0.65

phil_conviviality 0.077 3.23** 0.049 2.09* 0.554 3.08** 0.446 2.48*

exp_admin 0.185 4.55*** 0.200 5.02*** 0.349 1.12 0.413 1.34

phil_volunteers – 0.070 – 3.42** – 0.082 – 4.08*** 0.288 1.73 0.211 1.28

phil_commercial – 0.044 – 2.13* – 0.074 – 3.67*** – 0.349 – 2.18* – 0.432 – 2.73**

own_facilities 0.499 12.20*** 0.417 9.80*** – 0.156 – 0.50 – 0.084 – 0.26

public_facilities 0.215 4.67*** 0.156 3.29** 1.533 4.36*** 1.212 3.33**

members 0.001 19.07*** 0.001 19.17*** – 0.002 – 5.29*** – 0.002 – 6.08***

members_sq – 0.000 – 15.39*** – 0.000 – 14.79*** 0.000 3.48** 0.000 3.98***

sports 0.082 6.71*** – – 0.040 0.47 – –

sports_sq – 0.004 – 7.09*** – – – 0.001 – 0.29 – –

badminton – – – 0.036 – 0.53 – – – 0.435 – 0.88

football (soccer) – – 0.711 14.49*** – – 3.395 8.49***

track and field – – 0.036 0.57 – – 1.801 3.41**

equestrian – – 0.386 4.71*** – – 1.777 2.47*

shooting – – – 0.264 – 4.10*** – – – 1.212 – 2.67**

dancing – – – 0.122 – 1.82 – – – 1.765 – 4.28***

tennis – – 0.057 1.03 – – – 1.972 – 5.41***

table tennis – – 0.088 1.62 – – 1.064 2.62**

gymnastics – – – 0.002 – 0.04 – – – 1.482 – 3.19**

volleyball – – – 0.081 – 1.32 – – – 0.603 – 1.41

year 0.032 0.87 0.031 0.84 – 0.478 – 1.69 – 0.479 – 1.71

R2 350.0730.0703.0382.0

F 31.9125.9114.20112.721

p < 0.001*** < 0.001*** < 0.001*** < 0.001***

* p < 0.05; ** p < 0.01; *** p < 0.001; displayed are the unstandardized coefficients; robust standard errors reported in models 3a and 3b

1234 Voluntas (2015) 26:1219–1239

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model. The dummy variable for soccer in model 1b shows a positive and highly

significant effect, meaning that clubs providing soccer are more likely to receive

donations. The probability to receive donations increases by 9.2 % when the club

offers soccer. This effect could overlap the migration effect since the share of

migrants in soccer clubs is averagely higher than in clubs without soccer offerings

(Stahl et al. 2011). On the contrary to models 1a and 1b, caring for migrants has

negative and significant effects in models 2a and 2b. Thus, donors seem to value the

inclusion of migrants in the clubs, but they are not willing to spend more money.

This finding is only partly in accordance with Preston (1988) who states that

organizations that produce social benefits receive higher levels of donations. The

level of donations due to integrating migrants does not rise according to the

underlying study, but the probability of receiving donations at all goes up.

The last part of hypothesis two addresses the elderly. The three variables used to

test this part of H2 show nearly consistent results. The philosophy item that measures

the level of sports offers for the elderly shows negative and significant results in all six

models. Pertaining to providing health sport offers, also negative and significant

results are detected for models 1a, 1b, and 2a. Significant negative results are found

for the share of older people in relation to all members for models 2a, 3a, and 3b. The

only positive and significant results are found for the share of the elderly in models 1a

and 1b. The latter finding could again be explained by donors being members at the

same time. Since donating is positively correlated with age (Rooney 2007), a higher

share of older people would explain these results. The negative effects on the other

hand suggest that caring for older people and offering health sports programs do not

lead to receiving higher amounts of donations. This could be due to the fact that

particularly health sport is not yet regarded a core product of nonprofit sports clubs

since commercial sport providers offer such programs as well. Thereby, the rationale

for supporting nonprofit sports clubs might not be given for donors (Hansmann 1987).

Overall, hypothesis two can only partially be supported: Caring for the youth is

predominantly found to have a positive impact on receiving donations, whereas

offering sports offer for the elderly rather shows negative effects. The effects for

aiming at integrating migrants are mixed.

Hypothesis three is clearly supported by the results of all six models. Both

variables, having expenditures for nonsport events as well as putting high value on

companionship and conviviality, show positive and predominantly significant

results. Particularly, staging nonsport events show highly significant effects for

receiving donations and also for the amount of donations. The marginal effects in

the logistic regression models show that turning from not staging nonsport events to

staging such events, the probability of receiving donations rises by 7 %. Moreover,

donors value the attitude of clubs to care for social values by aiming at providing an

atmosphere of companionship and conviviality. These findings are in accordance

with prior research (Lamprecht et al. 2012; Ulseth 2004) and theoretical

assumptions (Ben-Ner and Gui 2003; Steinberg 2006).

Hypothesis four (H4) that states that employing paid staff has a negative impact

on donations has to be rejected. The results of the two incorporated variables to test

this hypothesis show the direct opposite of what was expected: Having adminis-

trative expenses as a proxy for paid staff is positive and significant in four of six

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models. On the other hand, the philosophy item stating that the club should only be

run by volunteers only shows significant, but negative results in models 2a and 2b.

Interestingly, employing paid staff in nonprofit sports clubs has a positive impact on

receiving donations. This means that donors do not mistrust the clubs when

employing paid staff, but they seem to value the professional structures. This could

be due to the fact that professionally run clubs are more likely to follow the main

goals and mission of the club which is typically valued by donors, as prior research

has shown (Herman and Rendina 2001).

The last hypothesis is that being commercially oriented has a negative impact on

the reception of donations for nonprofit sports clubs. The six models show

consistent results in regard to H5: The variable measuring the level of commercial

orientation of the clubs shows negative and significant coefficients in each model.

Thus, H5 can be confirmed and demonstrates that donors value clubs which

concentrate on reaching their original goals and mission (Enjolras 2002; Herman

and Rendina 2001), namely providing affordable sports offers and caring for social

benefits (Vos et al. 2012). However, it needs to be considered that a certain level of

commercialization (e.g., revenues from sales, sponsoring) can also help to cross-

subsidize the key products of nonprofit organizations (Enjolras 2002).

Apart from the independent variables used to test the five hypotheses, the

integrated control variables show some interesting results. Possessing own facilities

and using public facilities positively influence the reception of donations as well as

the level of donations. This implies that donors value an adequate infrastructure of

the clubs. Moreover, size plays a role in regard to receiving donations. This applies

both to club size as well as number of sports provided. Larger clubs and clubs with a

bigger number of sports offerings are more likely to receive donations. However,

the square terms in models 1a and 2a show that there is a saturation effect, meaning

that at a certain level, donations are not growing any more with increasing size. The

sport dummies show mixed effects over the three models. However, soccer has a

positive effect in all three models indicating that clubs which have soccer offerings

are more likely to receive donations. This might be ascribed to soccer being the

most popular sport in Germany. However, also track and field, table tennis as well

as equestrian sports show positive effects in two models. On the other hand,

shooting clubs are less likely to receive donations as indicated by the results of

models 2b and 3b. It could be that shooting clubs are regarded as less trustworthy.

This assumption is supported by another study which finds that shooting clubs have

generally bigger problems to cooperate with schools since the sport of shooting is

regarded as not adequate for such cooperations (Breuer and Feiler 2013).

Conclusion

This study investigated factors influencing donations for nonprofit sports clubs in

Germany using an unbalanced panel data set. Previous research in the field of

nonprofit finance has mainly concentrated on various income categories and

possible interaction effects. However, no research has so far examined factors

influencing donations in the field of nonprofit sports clubs. Thus, this study advances

1236 Voluntas (2015) 26:1219–1239

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the literature in the field of nonprofit sports organizations with regard to financial

issues. The results show that particularly the provision of elite sport and the

promotion of young talents have a positive impact on receiving donations.

Moreover, sports clubs caring for social aspects, companionship, and conviviality as

core values are able to generate higher levels of donations. The same is true for

professionally run clubs that employ paid staff. On the other hand, a commercial

orientation was found to be a negative factor.

The findings of this study allow deriving some implications for the management of

nonprofit sports clubs. To generate donations, the clubs should increase the level of

young talent recruitment and promotion and should try to qualify athletes to become

members of a squad. A certain level of professionalization, i.e., not only relying on

voluntary work but also employing paid staff, raises the probability of receiving

money from potential donors. Moreover, apart from focusing on sport-related

offerings, concentrating on core values of nonprofit sports clubs, i.e., social inclusion

and social capital (Vos et al. 2012), help the clubs to expand the level of donations. On

the other hand, in the light of receiving donations, clubs should avoid to become

increasingly commercialized since donors seem to fear that the clubs could thereby

lose their focus on the main club mission. Nevertheless, revenues from commercial

activities can also be used to cross-subsidize the main product of the clubs.

This leads to possible directions for future research. In regard to the ongoing

commercialization of the nonprofit sector as described by Weisbrod (1998), it would

be interesting to investigate how a commercial orientation affects other income

sources of nonprofit sports clubs and whether interaction effects exist. Moreover, the

limitations of this study can also guide the way to future research. This study was

not designed as a longitudinal study and therefore has to deal with the shortcomings

of cross-sectional data and OLS regressions. However, since the Sport Development

Report has a panel design, it will be possible to apply panel data methods as soon as

the next waves are finished. Apart from Germany, it would be interesting to

investigate determinants of donations for nonprofit sports clubs in other countries

with similar sport structures to test the generalizability of the results.

Acknowledgments 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 sports clubs in Germany (Sport Development Report).

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