Do public subsidies and facility coverage matter for voluntary sports clubs?

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Sport Management Review xxx (2019) xxx–xxx

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Sport Management Review journal homepage: www.elsevier.com/locate/smr

Do public subsidies and facility coverage matter for voluntary sports clubs? Karsten Elmose-Østerlund* , Evald Bundgård Iversen Centre for Sports, Health and Civil Society, Research unit for Movement, Culture and Society, Department of Sports Science and Clinical Biomechanics, University of Southern Denmark, Campusvej 55, 5230 Odense M, Denmark

A R T I C L E I N F O

A B S T R A C T

Article history: Received 6 December 2018 Received in revised form 26 March 2019 Accepted 26 March 2019 Available online xxx

In many countries, voluntary sports clubs are the main recipients of public subsidies regarding the provision of recreational sport, and the economically most important level of government is usually the local government (i.e. the municipalities). The purpose of this article is to examine whether and how municipal framework conditions matter for sports clubs. Inspired by previous studies, we examine the role of direct and indirect public subsidies as well as facility coverage. The authors conduct analyses on a general level (for all sports) and specifically for five sports (badminton, football, gymnastics, handball and swimming). The differentiation between sports allows us to examine how municipal framework conditions might be associated differently with density and participation across sports. In the statistical analyses, the authors apply data collected among all 98 Danish municipalities. The results show that the municipal levels of direct and indirect subsidies are weakly correlated with both the density of and participation in sports clubs, while stronger correlations can be identified with regard to facility coverage. The analyses for the five sports reveal that the supply of relevant facility types is – with few exceptions – positively correlated with sports club density and participation levels within each sport. Thus, facility coverage seems to play a more important role for sports clubs than the levels of indirect and particularly direct subsidies. © 2019 Sport Management Association of Australia and New Zealand. Published by Elsevier Ltd. All rights reserved.

Keywords: Sports policy Sports management Sports participation Public funding Sports facilities Municipalities

1. Introduction In many countries, voluntary sports clubs are the main recipients of public subsidies regarding the provision of recreational sport. A number of different political objectives are often attached to granting subsidies to sports clubs. Examples are: to increase sports participation, improve public health, and develop social capital (Green, 2006; Hoye & Nicholson, 2008). Across countries, the economically most important level of government for sports clubs is the local government (e.g. the municipalities). Significant differences in the level of public funding for sports clubs exist across different municipalities (Ibsen et al., 2016; Thøgersen & Iversen, 2014). Hence, sports clubs are provided with different municipal framework conditions under which they manage their activities and, ultimately, fulfil their mission to provide affordable sports activities for their members. As a result, it is interesting to examine empirically how different municipal framework conditions might matter for sports clubs. In the article, we examine Danish municipalities and sports clubs, but as

* Corresponding author. E-mail addresses: [email protected] (K. Elmose-Østerlund), [email protected] (E.B. Iversen). https://doi.org/10.1016/j.smr.2019.03.007 1441-3523/© 2019 Sport Management Association of Australia and New Zealand. Published by Elsevier Ltd. All rights reserved.

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the role of the local government in many western countries are similar with regard to subsidising sports clubs (Iversen & Cuskelly, 2015), the findings are likely to be relevant to other national contexts as well. The municipal framework conditions provided for sports clubs consists of public subsidies and facility coverage. Public subsidies for sports clubs can be divided into two main categories: direct and indirect subsidies. Direct subsidies are given directly to the sports clubs typically with the aim of keeping the cost of joining sports clubs low, thereby increasing the possibility for more people to join sports clubs. Indirect subsidies are public expenses that are used to facilitate the operation of sports clubs. A relevant illustration of this is the substantial amounts of public subsidies invested by municipalities in developing, building and maintaining a well-functioning physical framework for the sports activities in sports clubs. Related to the indirect public subsidies provided for sports clubs is the facility coverage in a municipality. Municipalities often grant sports clubs with prioritised and cheap access to, for example, green outdoor spaces and indoor sports facilities such as sports halls and swimming pools. As a result, the facility coverage in a municipality plays a central role for the opportunities to provide sports clubs with good conditions. Without a sufficient facility coverage, the opportunities of sports clubs to offer sports activities to their members could potentially be reduced. In general, the indirect subsidies granted to sports clubs are of a more substantial size than the direct subsidies (Bergsgard, Borodulin, Fahlen, Høyer-Kruse, & Iversen, 2017; Iversen & Cuskelly, 2015). The question of how municipal framework conditions matter for sports clubs could potentially be examined by applying several different measures for the success of sports clubs in a municipality. Two central measures that feed into discussions in the international literature on this topic are sports club density and participation. The density of sports clubs in a municipality can serve as a measure for the ability of sports clubs to offer a broad range of sports to the citizens in the municipality. Even though these sports clubs might provide similar activities (e.g. several clubs might offer football) and the clubs differ with regard to how many people they have the capacity to cater for, club density remains relevant as an indicator for whether people in a municipality have different (local) sports offers to choose from. Participation is another indicator for the success of sports clubs in a municipality. A high level of sports club participation in a municipality can serve as an indication that sports clubs have been more successful in catering for the citizens in the local community. In this article, we use data collected from different databases with information about Danish municipalities and sports clubs to answer the following research question: how do direct and indirect municipal subsidies as well as facility coverage relate to the density of and participation in sports clubs? We combine a general analysis for all sports with a specific analysis for five selected sports (badminton, football, gymnastics, handball and swimming). This allows us to examine whether differences in municipal framework conditions are associated differently with density and participation according to the sport provided in the clubs. This knowledge can provide a more nuanced input into discussions about how sports policies can help foster participation in sports clubs. 2. Literature review The interaction between municipalities and sports clubs with regard to resources has recently received some scholarly attention. For example, Feiler, Wicker, and Breuer (2018) has assessed whether the engagement of sports clubs in different areas promoted by the government is financially rewarded. The results indicate that the fulfilment of funding conditions is rewarded in different ways. Some policy regulations are reflected in the receipt of subsidies, others are not. Competitive sport and youth promotion activities are financially supported while health sport is not. The role of public subsidies for sport clubs has also been studied by Fahlén (2017), who has assessed how a performance measurement system initiated by the public sector resulted in end users (i.e. sports clubs) gaming the system. More specifically, the clubs focused on activities that would maximise financial return and arranged activities in a manner that suited the system rather than the logic of the sport. An example of this is dividing teams in two when they are above 30 participants, as this is the maximum number of participants eligible for subsidisation in one team. By doing so, the club can increase its subsidy. In this article, we focus specifically on how municipal framework conditions might matter for sports club density and participation. This theme has received some attention in the literature. First, some scholars have primarily focused on how participation in physical activity seems to be influenced by differences in the municipal framework conditions. For example, Hallmann, Wicker, Breuer, and Schönherr (2012) showed how participation in sport was influenced by the coverage of sports facilities provided by the municipalities. According to their study, the coverage of sports facilities was a predictor of participation in sport. Their findings indicate both correlations that could be expected and correlations that seem harder to explain. For example, sports fields had a significant positive correlation with football and coverage of park area had a positive relationship with participation in swimming. Dallmeyer, Wicker, and Breuer (2017) also found that indirect public subsidies to sports clubs via public expenditures on swimming pools and sports facilities had a significant effect on participation in physical activity. However, general funding to sports promotion (primary direct public subsidies to sports clubs) did not have any significant positive effect on participation in physical activity. Lera-López, Wicker, and Downward (2015) assessed to which extent national spending on health and education might have any influence on participation in physical activity. They found that national spending on health and education had a significant positive effect on participation in physical activity. Another example of a study that assessed how overarching policies might influence participation in physical activity is Wicker, Downward, and Lera-López (2017) who explored the link between differences in the quality of regional government and participation in physical activity. The quality of government was assessed based on items regarding the quality of regional government (for example, user surveys of the quality of public education, health care and the police force in their region), impartiality (when regional public entities deliver their services)

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and corruption (based on the users’ perception of how widespread corruption is with the different public services in the region). Wicker et al. (2017) identified a positive and significant association between the quality of regional government and participation in physical activity. Hallmann, Wicker, Breuer, and Schüttoff (2011) investigated the possible relation between the supply of sports facilities and sport participation across different sizes of municipalities. They found that in the metropolitan city, the supply of swimming pools had a positive and significant effect on sport participation. In the medium-sized municipalities it was the supply of sports fields that had a positive and significant effect on sport participation. Second, other researchers have examined the effect of the municipal framework conditions on both participation in physical activity and participation in sports clubs. A recent Dutch study found that the aggregate level of direct and indirect public subsidies granted from municipalities to sport was positively correlated with both the levels of participation in physical activity and the sports club participation rates among youth (6–17 years old). However, the same study found no or only negligible effects on participation rates among adults (25–75 years old) (Hoekman, Breedveld, & Kraaykamp, 2017). Also, Wicker, Hallmann, and Breuer (2013) showed that the facility coverage did influence participation in physical activity as well as in sports clubs. However, their results were somewhat mixed. As an example, they found participation in physical activity to be positively and significantly correlated with the coverage of swimming pools, but to be significantly and negatively correlated with the coverage of gymnastics/dance rooms. Regarding the participation in sports clubs, they also found mixed results as the area of sports fields revealed a positive and significant correlation with sports club participation. But they also found a negative and significant correlation between coverage of park area and gymnastics/dance halls on the one hand and sports club participation on the other. Third, some studies have focused solely on the role of municipal framework conditions for sports club participation. Eime et al. (2017) assessed the link between facility coverage and sports club participation. A condition for using the data was that the included sports would not be identified in publicly available research output. Therefore, we do not know which types of sports were under scrutiny, but the researchers found that for three of four sports, facility coverage was significantly and positively correlated with club membership. O’Reilly, Berger, Hernandez, Parent, and Séguin (2015) showed how differences in the urban sportscape were associated with the number of sports club members using the sports facilities. The urban sportscape was defined as the coverage of sports facilities (for example, sports halls and swimming pools) as well as the supporting infrastructure (for example, sports clubs and coaching). The results presented by O’Reilly et al. (2015) indicated that the urban sportscape was positively correlated with sports club participation. Pilgaard and Nielsen (2017) did a similar study and found a positive and significant correlation between the level of aggregated direct and indirect public subsidies on sports club participation. However, they did not identify any statistically significant correlation between facility coverage and sports club participation. Fourth, a study in a Danish context has assessed the significance of municipal framework conditions and coverage of sports facilities for both the density of sports clubs and sports club participation. In Thøgersen and Iversen (2014), facility coverage was significantly and positively correlated with sports club participation and sports club density. However, they found no effect on the level of direct public subsidies to sports clubs on the one hand and sports club density and sports club participation on the other (Thøgersen & Iversen, 2014). In sum, a number of different input and output measures have been used in the studies previously cited. On the input side, municipal framework conditions have been operationalised as municipal funding for sport in general (Hoekman et al., 2017), municipal funding for sports clubs (Pilgaard & Nielsen, 2017; Thøgersen & Iversen, 2014), coverage of sports facilities (Eime et al., 2017; Hallmann et al., 2011,2012; O’Reilly et al., 2015; Pilgaard & Nielsen, 2017; Thøgersen & Iversen, 2014; Wicker et al., 2013), policy programmes (Hoekman et al., 2017), expenditure to promote sports participation as well as health, environment, and education (Dallmeyer et al., 2017; Lera-López et al., 2015) and the quality of the regional government (Wicker et al., 2017). On the output side, studies have applied different measures relevant for sports clubs, such as participation in physical activity (Dallmeyer et al., 2017; Hallmann et al., 2011, 2012; Hoekman et al., 2017; Lera-López et al., 2015; Wicker et al., 2013, 2017), participation in sports clubs (Eime et al., 2017; Hoekman et al., 2017; O’Reilly et al., 2015; Wicker et al., 2013), and sports club density (Thøgersen & Iversen, 2014). In this article, we draw on data that include three different input categories (direct public subsidies, indirect public subsidies and coverage of sports facilities) and three different output measurements (density of sports clubs, sports club participation among children and young people below the age of 25 years and sports club participation among adults aged 25 years or above). We differentiate the input measurements regarding facility coverage by distinguishing between six facility types, while the general output measurements are supplemented by specific measurements for density and participation within five selected sports. Our approach targets some of the gaps in the literature. For example, Wicker et al. (2013) argue that it is important that future studies assess which facilities are important for participation as all sports facilities are not equally important. Also, Hoekman, Breedveld, and Kraaykamp (2016) point to a gap in understanding the alleged interdependence between the presence and variety of sports facilities on the one hand and sports participation on the other. 3. Conceptual development In this section, we describe the theoretical model of viable sports clubs developed by Breuer (2005) and contextualise it to the research question posed in this article as well the national and local framework for sports clubs in Denmark. Based on this conceptual development, we conclude by posing some hypotheses regarding the statistical analyses.

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3.1. The theoretical model of viable sports clubs The sports club is an organisation that has several interactions with the external environment. Sports clubs interact with sponsors, different types of organisations and, in many cases most importantly, they interact with a municipality. Municipalities are deeply involved in providing direct and indirect subsidies to sports clubs (Ibsen et al., 2016; Iversen & Cuskelly, 2015). This interaction is very important for sports clubs, as sports clubs often rely on resources from the external environment. To be able to attract resources, sports clubs need to understand the context in which they are situated and understand how they can attract external resources (Pffeffer & Salancik, 2003; Wicker & Breuer, 2015). For example, how they can maximise the amount of direct and indirect subsidies granted to the sports club by the municipality. By adapting to these circumstances, the sports club might be able to attract the necessary external resources (Lawrence & Lorsch, 1969, as cited in Nagel et al., 2015). Besides the external resources, the sports club is also dependent on providers of internal resources such as voluntary efforts, social networks and membership fees. These considerations are illustrated in a model of the ‘viable sports club’ developed by Breuer (2005). According to this approach to a sports club, the sports club can only survive if the sports club has the resources it needs, and the sports club will only have the resources it needs if it is able to adapt to the external environment. According to the model, the input to sports clubs is differentiated between internal and external providers of resources in sports clubs (Breuer, 2005; Nagel et al., 2015). Both internal and external sources might influence the output of the sports clubs, which can be defined as producing certain sports club goods at a reasonable price for the benefit of the members of the sports club (Nagel et al., 2015). As illustrated in Fig. 1, the idea with the model of the viable sports club is that differences in the level of external resources provided for sports clubs in different municipalities will influence the level of output from sports clubs. In municipalities that provide higher levels of external resources, it is expected that sports clubs can provide higher levels of output compared to sports clubs in municipalities that provide lower levels of external resources. It would be relevant to also consider to which extent differences in the level of sports clubs’ internal resources might influence the output. However, such data do not exist in a Danish context and, therefore, in this article, we do not investigate any differences in internal resources across different sports clubs. Clearly, this is a limitation of our study. We have indicated this limitation in the figure by drawing a line across the arrow between the input from internal providers and the total input. We acknowledge this limitation, but we do argue that when the input from the external provider – i.e. the municipality – is higher, it seems reasonable to generally assume that the total input is likely to increase as well, and this will, according to our model, result in an increased output. 3.2. The national and local framework for sports clubs in Denmark To be able to formulate hypotheses, it is relevant to shortly present the municipal framework conditions for sports clubs in Denmark. In Denmark, the Leisure Act makes it mandatory for municipalities to subsidise the activities provided by democratically-organised sports clubs. According to the Leisure Act, direct and indirect municipal funding should give priority to members under the age of 25 years (Ibsen, Østerlund, & Laub, 2015, 2016). The publicly-owned sports facilities are to be provided by municipalities to sports clubs for free or with the payment of a minor fee. The use of own or rented sports facilities by sports clubs is to be subsidised by municipalities (when used for providing activities for members under 25 years) with 2/3 of the cost. However, research has shown that municipalities implement the law differently. Most (2/3) of the municipalities pay more than this in subsidy and only four out of ten municipalities charge a fee for the use of public facilities. Furthermore, more than half of the municipalities (53%) also subsidise activities for sports club members above 25 years of age (Thøgersen, 2017).

Fig. 1. The model of the ‘viable sports club’ developed by Breuer (2005), here adapted from Nagel et al. (2015).

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3.3. Hypotheses We draw on the description of the theoretical model and the Danish system for subsidising sports clubs to formulate hypotheses regarding the role of municipal framework conditions for sports clubs. Regarding the density of sports clubs (the number of clubs per inhabitant in a municipality), our hypothesis is that the amount of external resources that is provided by a municipality matters for the number of clubs that exist in that municipality. With more external resources present, it is likely to be easier to start and run a club. As for the participation in sports clubs (the percentage of the inhabitants in a municipality that are members of a sports club), we differentiate between two groups. The first group consists of children and young people (below the age of 25 years), while the second group includes adults (aged 25 years or above). Overall, we expect that higher levels of external resources imply that sports clubs can devote more resources to increase sports club participation by, for instance, developing new sports activities, recruiting more volunteers and/or hiring paid staff. Therefore, we hypothesise that the density of sports clubs and the participation in sports clubs among both children and young people as well as adults is positively associated with the level of direct subsidies (H1), the level of indirect subsidies (H2) and the facility coverage (H3) provided by the municipalities. We examine the role of municipal framework conditions for the density of and participation in sports clubs in general, but we also differentiate the results according to specific sports. Hence, we conduct each analysis on all sports clubs in the municipality and on five selected sports: badminton, football, gymnastics, handball and swimming. These five sports were selected because they are all among the largest in terms of both the density of clubs and participation in clubs at the same time as they require specific programming of facilities or specialised facilities of different kinds to be able to offer their activities to the members. As different sports are dependent on different types of facilities, we pose different hypotheses to how the coverage of specific types of sports facilities might influence the density of and participation in sports clubs. In the analysis, we differentiate the coverage of sports facilities into six categories: small sports halls, medium-sized sports halls, large sports halls, swimming halls, natural grass pitches and artificial grass pitches. As a result, we supplement hypothesis H3, stating that there is a positive association between the supply of sports facilities and the density of and participation in sports clubs in general, by posing specific hypotheses regarding the role of different types of sports facilities for the density of and participation in sports clubs offering each of the five selected sports. Badminton is most often played in large sports halls. The smaller types of sports halls are also in many cases suitable for playing badminton, but they are rarely used for this purpose by sports clubs. Therefore, we hypothesise that the coverage of large sports halls (H3a) is positively associated with the density of and participation in badminton clubs. Football is primarily an outdoor activity played on both natural and artificial grass pitches. In Denmark, natural grass pitches far outnumber artificial ones, so, clearly, natural grass pitches are more important in terms of the overall density of and participation in sports clubs. Nevertheless, we hypothesise that the coverage of both natural grass pitches (H3b) and artificial grass pitches (H3c) are positively associated with the density of and participation in football clubs. Gymnastics is an indoor activity that is offered in all sizes of sports halls included in the analysis. However, the medium-sized and large sports halls cater for more people and make the use of more specialised equipment for gymnastics possible. Thus, we hypothesise that the coverage of medium-sized sports halls (H3d) and large sports halls (H3e) is positively associated with the density of and participation in gymnastics clubs. Handball is almost exclusively played in large sports halls due to the requirements of the court size and, thus, we expect that the coverage of large sports halls (H3f) is positively associated with the density of and participation in handball clubs. Finally, and obviously, swimming clubs are dependent on swimming halls to be able to offer swimming activities to their members. Thus, we expect that the coverage of swimming halls (H3g) is positively associated with the density of and participation in swimming clubs. Due to the prioritisation of facilities and activities for children and young people under the age of 25 years within the legislation in Denmark (the Leisure Act), we hypothesise (H4) that the level of external resources granted from the municipalities will be more strongly correlated with sports club participation among children and young people below the age of 25 years compared to adults aged 25 or above. Finally, we include three control variables at the municipal level that could be significantly correlated with the (in) dependent variables: the number of inhabitants (Lundåsen, 2005; Thøgersen & Ibsen, 2008), the tax base (Dawson & Downward, 2013; Downward & Rasciute, 2010), and the proportion of citizens from non-Western countries (Eime, Harvey, Charity, & Payne, 2016; Rowe & Champion, 2000). 4. Data and methods In this article, we use data collected from all 98 municipalities in Denmark. The data concerning the municipalities stem from an online collection of data from four databases: (a) data from Statistics Denmark on the direct and indirect municipal subsidies for sports clubs (Statistics Denmark, 2018); (b) data from the Ministry for Economic Affairs and the Interior on the municipalities (the number of inhabitants, the tax base and the share of inhabitants from non-Western countries) (Ministry for Economic Affairs & the Interior, 2018); (c) data from the Danish Sport Facility Database on the number of different types of sports facilities (sports halls, swimming halls and grass pitches) in each municipality (Danish Sport Facility Database, 2018), and (d) data from the Central Register of Associations on the number of sports clubs and members registered within the three largest national sports organisations (NSO’s) in Denmark (Central Register of Associations, 2018). From the Central Register

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of Associations database, it was also possible to extract figures at an aggregated level across the five sports in our analysis: badminton, football, gymnastics, handball and swimming. The information in the first three databases uses data reported by municipalities in a uniform manner at an annual rate. Hence, the figures are the most reliable available data about municipalities and the coverage of facilities available. The data about sports clubs and the number of members that stem from the Central Register of Associations database is also the most reliable information available. From the Central Register of Associations, we calculate club density as the sum of single sport clubs and the number of branches in multisport clubs. Regarding membership numbers, the Central Register of Associations database takes membership overlaps between branches within multisport clubs as well as membership overlaps of clubs between the three NSO’s into account. However, it is worth mentioning that not all sports clubs in Denmark are members of one of the three largest NSO’s, and that a small proportion of Danish sports clubs are, therefore, not included. Figures from a study on sports clubs broadly estimate that around one in ten sports clubs is not a member of one of the three largest NSO’s. These clubs are typically smaller clubs that offer activities that are less facility-intensive (e.g. running and cycling clubs) (Ibsen, 2006). As a result, the estimated nine out of ten Danish sports clubs included in Central Register of Associations database is likely to be more dependent on specialised facilities than the clubs not included. Should this have any impact on our findings, it is most likely to increase the likelihood that facility coverage does play a significant role for sports clubs. 4.1. Data analysis We conducted linear OLS regression analyses to allow for generalisations about the correlations between municipal framework conditions on the one hand and the density of and participation in sports clubs on the other. We started the analysis by including information from the entire population of Danish municipalities. Checks regarding violations of the assumptions of linear regression analysis (absence of influential outliers, linearity, absence of strong multicollinearity and homogeneity of variance) were then performed. When conducting these checks, we discovered that the three smallest island municipalities (all with less than 4000 inhabitants) represented significant and influential outliers to the general tendencies in the other 95 municipalities. Thus, they were excluded from the statistical analyses for this article, which, therefore, builds on information from the remaining 95 municipalities. No other serious violations of the assumptions of linear regression were identified. 4.2. Dependent and independent variables The dependent variables were constructed by combining the information about sports clubs from the Central Register of Associations database with information about the number of inhabitants in each municipality from the Statistics Denmark database. The data was recoded into three measures that were applied as dependent variables: one measure for density and two measures for participation. Regarding density, the number of sports clubs in each municipality was divided by the number of inhabitants (in thousands) to provide a relative measure that could be compared across municipalities. As for participation, a distinction was made between people below 25 years of age and people aged 25 years or above. This differentiation was made because it is guiding for the municipal provision of public subsidies and facilities for sports clubs (see Section 3.2). For each of these age groups, the number of sports club memberships in each municipality was divided by the number of inhabitants yielding two measures that were comparable across municipalities. Within these three categories of independent variables, variables were constructed both at the general level (for all sports clubs in each municipality) and within the five selected sports (badminton, football, gymnastics, handball and swimming). Descriptive information about the dependent variables is available in Table 1. Turning to the independent variables, two measures for municipal funding for sports clubs were constructed using information from the Statistics Denmark database. The first was a measure for the direct municipal subsidies to clubs. In the calculation, the municipal account numbers in which the direct municipal funding is coming from is included (account numbers: 3.38.70, 3.38.73 and 3.38.75) (Statistics Denmark, 2018). The second measure was for the indirect municipal funding for sports clubs in the form of expenses to operate and subsidise sports facilities. In the calculation, the municipal account numbers with a relevance for sports facilities was included as well (account numbers: 0.32.31, 0.32.35, 3.22.18 and 3.38.74; Statistics Denmark, 2018). Only 75% of the subsidies on all municipal accounts starting with 3.38 were included as only 75% of the total costs reported within these accounts are – on average – related to sport (Breddeidrætsudvalget, 2009). As the level of direct and particularly indirect subsidies can fluctuate on a year-to-year basis, a five-year average of the yearly operating costs (from 2013 to 2017) was chosen to give a more precise account of the input side by evening out differences across the years. Furthermore, both measures were divided by the number of inhabitants to yield comparative measures across municipalities and the amounts were recalculated from DKK to Euro. Regarding facility coverage, we extracted information from the Danish Sport Facility Database on each municipality about the coverage of the three types of facilities that were most relevant for sports clubs: sports halls, swimming halls and grass pitches. We further used the subdivisions in the database for sports halls into three sizes: small (less than 300 square metres), medium-sized (300–799 square metres) and large (800+ square metres). Similarly, the grass pitches were differentiated into natural and artificial grass pitches. To construct comparable measures for facility coverage, the absolute number of facilities within each of the six categories was divided by the number of inhabitants (in thousands) in each municipality.

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Table 1 Descriptive statistics for the dependent variables included in the analysis. Dependent variables

Density of sports clubs Number of sports clubs per 1000 inhabitants - Badminton

Average (Std. deviation)

Nmunicipalities

4.05 (1.42) 0.30 (0.15)

95

- Football

0.36 (0.15)

- Gymnastics

0.32 (0.16)

- Handball

0.17 (0.11)

- Swimming

0.12 (0.07)

Participation in sports clubs % sports club members (under 25 years) - Badminton

72.93 (16.76) 2.78 (0.98)

- Football

15.77 (3.52)

- Gymnastics

13.67 (4.39)

- Handball

5.52 (3.01)

- Swimming

10.80 (5.41)

% sports club members (25+ years) - Badminton

95

33.50 (8.27) 95 2.26 (0.74)

- Football

2.16 (0.67)

- Gymnastics

2.87 (1.23)

- Handball

0.59 (0.28)

- Swimming

1.48 (1.39)

Finally, we also included variables describing the municipal context from the Ministry for Economic Affairs and the Interior database. These include the number of inhabitants (in thousands), the tax base (converted into thousands of Euros) and the share of inhabitants from non-Western countries (in % of the population in each municipality). These were included as control variables in the statistical analysis. Descriptive information about all the independent variables is available in Table 2.

Table 2 Descriptive statistics for the independent variables included in the analysis. Independent variables

Average (Std. deviation)

Nmunicipalities

Municipal subsidies and facilities Direct municipal subsidies 2013–17 (£ per inhab.) Indirect municipal subsidies 2013–17 (£ per inhab.) Small sports halls per 1000 inhab. Medium-sized sports halls per 1000 inhab. Large sports halls per 1000 inhab. Swimming halls per 1000 inhab. Natural grass pitches per 1000 inhab. Artificial grass pitches per 1000 inhab.

10.75 (5.64) 77.81 (23.93) 0.43 (0.15) 0.12 (0.08) 0.33 (0.14) 0.08 (0.04) 0.37 (0.18) 0.06 (0.02)

95 95 95 95 95 95 95 95

Municipality characteristics Inhab. in municipality (in thousands) Tax base per inhabitant (in 1000 £) % citizens from non-Western countries

60.42 (71.11) 24.94 (5.06) 3.96 (1.76)

95 95 95

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5. Results The results from the conducted linear OLS regression analyses are presented in three tables, one for each of the three categories of dependent variables included: (a) the density of sports clubs (Table 3), (b) the percentage of sports club members below 25 years of age (Table 4), and (c) the percentage of sports club members aged 25 years or above (Table 5). In each table, the results are included for six dependent variables containing the general figures for all sports clubs in the municipalities and the specific figures from within the five selected sports (badminton, football, gymnastics, handball and swimming). 5.1. Density of sports clubs When examining the significance of municipal framework conditions for the density of sports clubs, we find that, for sports clubs in general, there are no statistically significant correlations between direct or indirect public subsidies on the one hand and the density of clubs on the other. Within the analysis of the specific sports, we find significant correlations between direct subsidies and the density of badminton clubs and between the indirect public subsidies and the density of gymnastics clubs. However, contrary to our expectations, the correlations are negative. Thus, the analysis does not provide any support for hypothesis H1 or H2 stating that in municipalities with higher levels of direct and indirect public subsidies, there is a higher density of sports clubs. Regarding the supply of facilities, we find that the supply of large sports halls and natural grass pitches is positively correlated with the density of sports clubs in general, which partly confirms our general hypothesis (H3). Within the specific sports, our hypotheses are also mainly confirmed. The density of badminton, gymnastics and handball clubs is positively correlated with the number of large sports halls, which confirms hypotheses H3a, H3e and H3f. However, hypothesis H3d cannot be confirmed, since no significant correlation exist between the coverage of medium-sized sports halls and the density of gymnastics clubs. For football, a positive correlation exists between the density of clubs and the number of natural Table 3 The results from the linear OLS regression analysis using the density of sports clubs (number of clubs per 1000 inhabitants in each municipality) as the dependent variable. Density of sports clubs All sports clubs B Municipal subsidies and facilities Direct municipal 0.005 subsidies 2013–17 (£ per inhab.) 0.003 Indirect municipal subsidies 2013–17 (£ per inhab.) Small sports halls per 0.019 1000 inhab. Medium-sized sports 0.807 halls per 1000 inhab. Large sports halls per 3.184*** 1000 inhab. 3.188 Swimming halls per 1000 inhab. Natural grass pitches 2.982*** per 1000 inhab. Artificial grass pitches 1.921 per 1000 inhab.

β

3.388*** 0.809 95

β

B 0.022

0.042

0.002

0.003**

0.000

0.003

Gymnastics clubs β

B

Handball clubs β

B <0.001

B

β

<0.001

0.015

0.002

0.056

0.074

-<0.001

0.004

0.001*

0.103*

<0.001

0.029

<0.001

0.028

0.002

0.022

0.022

0.013

0.012

0.020

0.028

0.018

0.042

0.038

0.020

0.011

0.008

0.209**

0.249**

0.237** 0.393

0.319***

0.339***

0.314***

0.040

0.010

0.378***

0.356***

0.418***

0.034

0.212

0.035

<0.001

0.062

0.005**

0.152**

0.005

0.058

0.099

0.048

0.221**

0.500***

0.431*** 0.316***

0.421*** 0.134**

0.284**

0.098

0.125

0.029

0.140**

0.503***

0.287***

0.314***

0.040

0.108

0.581*** 0.687*** 0.281*** 0.043

0.007

0.001*** 0.435*** 0.001

0.008

0.003 0.746 95

0.008

Swimming clubs

0.005

0.055

0.269*** 0.841 95

β

0.104** -<0.001

0.105

0.085

Football clubs B

0.045

Municipality characteristics Inhab. in municipality 0.001 0.051 (in thousands) 0.046** 0.162** Tax base per inhabitant (in 1000 £) % citizens from non0.122* 0.098* Western countries Model characteristics Constant Adjusted R2 Nmunicipalities

Badminton clubs

0.036

0.090

0.394**

0.307*** 0.186***

0.250

0.038

0.003

<0.001

0.050

<0.001

0.005**

0.145

0.002

0.105

0.003

0.031

0.007

0.115

0.256*** 0.810 95

0.028 0.768 95

0.001

0.085

0.046

<0.001 0.001

0.003

0.017

0.045 0.069

0.075

0.017 0.464 95

Note: *P < 0.1; **P < 0.05; ***P < 0.01. B: non-standardised regression coefficient. β: standardised regression coefficient.

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Table 4 The results from the linear OLS regression analysis using the participation in sports clubs among inhabitants below the age of 25 years (number of club memberships divided by the number of inhabitants below the age of 25 years in each municipality) as the dependent variable. Sports club participation of children and young people (below the age of 25 years) All sports clubs B Municipal subsidies and facilities 0.330 Direct municipal subsidies 2013–17 (£ per inhab.) 0.126** Indirect municipal subsidies 2013–17 (£ per inhab.) Small sports halls per 6.793 1000 inhab. Medium-sized sports 26.958 halls per 1000 inhab. Large sports halls per 65.108*** 1000 inhab. Swimming halls per 39.392 1000 inhab. Natural grass pitches 1.102 per 1000 inhab. 7.548 Artificial grass pitches per 1000 inhab.

β

Badminton clubs β

B

0.111

0.181**

0.061

0.015

0.002

0.087

0.041

Football clubs B

β

0.032

0.051

0.005

0.004

0.027

0.004

β

B

0.006

0.070

0.131

0.027

0.029

0.024

0.018

0.139

0.078**

0.346**

2.832*

0.143*

2.153

0.060

0.150

0.006

0.178

1.586

0.128

3.274

0.074

14.716*** 0.266***

0.551***

3.178***

0.458***

6.202*

0.250*

3.357

0.089

1.050

0.041

15.376

0.166

0.012

0.610

0.111

5.605*

0.286*

0.011

5.721

0.145

23.473* 0.167*

0.502 0.351 95

6.407* 0.388 95

0.057

0.168

0.179

Swimming clubs

β

0.089

0.127

Handball clubs B

0.577

Municipality characteristics 0.006 Inhab. in 0.024 -<0.001 0.012 0.003 municipality (in thousands) 0.754** 0.227** 0.068*** 0.348*** 0.117 Tax base per inhabitant (in 1000 £) % citizens from non0.452*** 0.153** 0.274** 0.358 4.320*** Western countries Model characteristics Constant 31.802** Adjusted R2 0.651 Nmunicipalities 95

Gymnastics clubs

0.006

0.108

1.712

0.045

11.036*** 0.519***

0.046

6.933

0.182

26.822

0.188

0.051

6.344*

0.259*

2.009

0.120

6.229

9.290

0.053

0.601

0.005

14.366

0.004

0.089

0.004

0.099

0.661**

0.143

0.114

0.264**

9.403** 0.44 95

0.073

3.108

5.874

0.009

5,780

β

B

0.083

0.139

0.434**

0.253**

1.938 0.48 95

0.242*

0.747*

0.207 0.066

0.053

0.226*

0.242*

0.099 0.165 95

Note: *P < 0.1; **P < 0.05; ***P < 0.01. B: non-standardised regression coefficient. β: standardised regression coefficient.

grass pitches as expected in hypothesis H3b, but, contrary to the expectation in hypothesis H3c, no relation is found between the coverage of artificial grass pitches and club density. As expected in hypothesis H3g, we also find a positive correlation between the density of swimming halls in a municipality and the density of swimming clubs. Even though most of the findings regarding the significance of the supply of sports facilities for the density of clubs are as expected, we also find significant results for facilities that are irrelevant for the selected sports. Consequently, we find positive and significant correlations between the supply of large sports halls and club density, and positive and significant correlations also exist between the supply of natural grass pitches and club density, except for swimming clubs. 5.2. Sports club participation for children and young people (below the age of 25 years) Turning to the role of municipal framework conditions for the participation in sports clubs of children and young people under the age of 25 years, we find a somewhat similar pattern to the one identified regarding density. The role of direct and indirect subsidies is once again limited. We do, however, find a positive correlation between the size of the indirect municipal subsidies and the participation in sports clubs. The same is specifically true for the participation in swimming clubs. These results are as expected, indicating that higher levels of municipal operating costs and subsidies given to sports facilities are positively correlated with sports club participation. This is, however, only the case for indirect subsidies, and not for direct subsidies, even though the direct funds are mainly given to clubs according the number of members below the age of 25 years. In sum, the results provide some support for the hypothesised role of indirect subsidies (H2) for the participation of children and young people in general and in swimming clubs, while there is no support for the hypothesised role of direct public subsidies (H1). As for the role of the supply of facilities for the participation of children and young people, we find that, overall, the only category of facilities that is positively correlated with participation in sports clubs in general is large sports halls, which only partly confirms hypothesis H3. Turning to the results from the specific sports, we once again find several correlations that are

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Table 5 The results from the linear OLS regression analysis using the participation in sports clubs among inhabitants aged 25 years or above (number of club memberships divided by the number of inhabitants in each municipality aged 25 years or above) as the dependent variable. Sports club participation of adults (aged 25 years or above) All sports clubs B Municipal subsidies and facilities 0.180 Direct municipal subsidies 2013-17 (£ per inhab.) 0.052 Indirect municipal subsidies 2013-17 (£ per inhab.) Small sports halls per 9.362 1000 inhab. 0.214 Medium-sized sports halls per 1000 inhab. Large sports halls per 28.090*** 1000 inhab. 2.645 Swimming halls per 1000 inhab. Natural grass pitches 3.987 per 1000 inhab. Artificial grass pitches 10.398 per 1000 inhab. Municipality characteristics Inhab. in municipality 0.025* (in thousands) Tax base per inhabitant 0.056 (in 1000 £) 1.220* % citizens from nonWestern countries Model characteristics Constant Adjusted R2 Nmunicipalities

25.145** 0.123 95

β

Badminton clubs β

B

0.123

0.151

0.031**

Football clubs β

B 0.239** 0.027**

Gymnastics clubs Handball clubs β

B

B

β

B

β

0.030

0.223**

0.030

0.138

0.001

0.041

0.164

0.003** 0.269** 0.004

0.062

0.007*

0.218*

0.009***

0.331***

0.008

0.155

0.342

0.071

0.548

0.124

0.543

0.067 0.166

0.652

0.070

1.666*

0.197*

2.533

0.162

0.482*** 1.211

0.233

0.407

0.086

0.522

0.060

1.293

0.073

0.172 0.002

0.012

4.527**

0.234**

0.087

0.363

0.089

0.799

0.214

0.031

6.882**

0.234**

2.463

0.092

0.219*

-<0.001

0.002

0.005***

0.580***

0.034

0.020

0.140

0.110**

0.264

0.259*

2.493*** 0.271 95

0.039*** 0.003

1.555** 0.425 95

Swimming clubs

0.297*** 0.007

2.269 0.502

0.069

0.615* 1.353

0.094 0.312* 0.184

0.402

0.258

3.097

0.062 0.893

0.080

0.003

0.180

0.166

0.043

0.175

0.074

4.704*** 0.078 95

0.073

0.332

0.090

0.105

0.001

1.750*

0.190*

0.838

0.048

1.574

0.160

9.432**

0.257**

0.059

0.008

4.538

0.081

0.006*** 0.314***

0.011

0.195

0.013

0.046

0.023

0.142

0.155

0.195

0.312 0.238 95

1.173 0.139 95

Note: *P < 0.1; **P < 0.05; ***P < 0.01. B: non-standardised regression coefficient. β: standardised regression coefficient.

as expected. Positive correlations exist between the supply of large sports halls and the participation in badminton as expected in hypothesis H3a. The participation in gymnastics is positively associated with the coverage of medium-sized sports halls, which confirms hypothesis H3d, but not H3e that hypothesised a positive association also with the coverage of large sports halls. As expected in hypothesis H3f, participation in handball is positively correlated with the supply of large sports halls, but, surprisingly, also with the supply of small sports halls. Also, as expected, the supply of natural and artificial grass pitches is positively correlated with participation in football clubs, which confirms hypotheses H3b and H3c. Contrary to the expectation in hypothesis H3g, no significant correlation exists between the supply of swimming halls and the participation in swimming clubs among children and young people. 5.3. Sports club participation for adults (aged 25 years or above) When examining the significance of municipal framework conditions for sports club participation among adults aged 25 years or above, we find limited support for the importance of direct and indirect municipal subsidies for sports clubs. Significant and positive correlations do exist between direct and indirect municipal subsidies and adult participation in football clubs. At the same time, participation in badminton and handball clubs is higher in municipalities with higher indirect subsidies. Contrary to our hypotheses, a negative correlation exists between the levels of direct municipal subsidies and adult participation in badminton clubs. In sum, the results provide limited support for the hypothesised role of direct subsidies (H1) and indirect subsidies (H2) for the participation of adults, because no general and significant correlations exist. Only within football, we find some support for hypothesis H1, while some support for hypothesis H2 exist in bad minton, football and handball. Regarding facility coverage, we do find some results that are in accordance with our hypotheses. The number of large sports halls is positively correlated with adult participation in sports clubs in general, which partly confirms hypothesis H3. As expected, the supply of swimming halls is also positively correlated with adult participation in swimming clubs, which confirms hypothesis H3g. Apart from these correlations, we also find a few correlations that are not as expected. For

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badminton, a negative correlation exists between the number of swimming halls and adult participation, while the correlation with artificial grass pitches is positive. The supply of medium-sized sports halls and adult participation in football clubs is negatively correlated, which is also the case between the supply of small sports halls and participation in swimming clubs. 5.4. Explanatory power of the statistical models Finally, if we turn to the explanatory power of the statistical models, we find that the general models regarding the density of sports clubs (adjusted R2 = 0.809) and the participation among children and young people (adjusted R2 = 0.651) explain a large amount of the variation in the dependent variables, while for participation among adults, the explanatory power of the general model is significantly lower (adjusted R2 = 0.123). The same general pattern exists when looking at the adjusted R2 values for the five selected sports. Relative to each other, the figures generally show that the municipal framework conditions are less important for swimming clubs than for badminton, football, gymnastics and handball clubs. These findings seem to confirm hypothesis H4 stating that the municipal framework conditions play a stronger role for the participation of children and young people than for the participation of adults. 6. Discussion and conclusion With this article, we set out to examine whether and how municipal framework conditions matter for the density of and participation in sports clubs and, thereby, contribute to the literature that examine the role of direct and indirect public subsidies as well as facility coverage for sports clubs. By conducting the analyses both on a more general level (for all sports) and specifically for five sports (badminton, football, gymnastics, handball and swimming), we addressed research gaps in the current body of literature. The inclusion in the statistical models of data for different sports allowed us to examine how municipal framework conditions were associated differently with the density of and participation in sports clubs according to the sports offered by the clubs. The results of the statistical analyses generally showed that the levels of direct and indirect subsidies from municipalities to sports clubs were only weakly correlated with both the density of sports clubs and the participation in sports clubs among children and young people (below the age of 25 years) as well as adults (aged 25 years or above). In most of the statistical models, the correlations were, furthermore, statistically insignificant. Instead, the analyses generally underlined the importance of the facility coverage for both the density of and participation in sports clubs. When looking at the results for the five selected sports, they generally revealed that the supply of the facility types that were relevant for each of the specific sports was also – with a few exceptions – positively correlated with both sports club density and participation levels within the specific sports. 6.1. The role of direct and indirect public subsidies for sports clubs The modest and most often statistically insignificant correlations between direct and indirect public subsidies on the one hand and sports club density and participation on the other identified in this study is similar to the findings of Hoekman et al. (2017); Pilgaard and Nielsen (2017), as well as Thøgersen and Iversen (2014). In Hoekman et al. (2017), statistically significant effects were only identified for the participation of children and young people (aged 6–17 years), whereas the effects concerning adult (aged 25–75 years) participation were insignificant. This differentiation seems to fit with the results in this study, where indirect public subsidies were found to be significantly correlated with the participation of children and young people (below the age of 25 years), but not with the participation of adults (aged 25 years or above). Thus, there is some indication that if public subsidies are relevant for sports clubs, it is mainly the indirect subsidies that should be in focus and these seem to primarily correlate with the participation of children and young people. One potential explanation for the limited role that direct public subsidies seem to play for sports clubs is that they generally make up a relatively small proportion of the total revenues in clubs. In Denmark they make up on average 15% of the total revenues, and in countries such as the Netherlands, England, and Germany, they make up less than 10% (Breuer et al., 2017). In a Danish context, most of the revenues in sports clubs come from membership fees, club events and sponsors (Ibsen et al., 2015). Thus, in general, the direct public subsidies do not seem to be of a sufficient size to be essential for decisions on whether to start up or close down sports clubs (sports club density) or for the ability of clubs to recruit and retain members (sports club participation). However, it should be noted that the value of indirect subsidies that come with the municipallysubsidised provision of sports facilities was not included in the calculation of the overall revenues of clubs. Furthermore, a word of caution seems appropriate since the general tendencies might not reflect the situation of all clubs. When differentiating clubs according different sports, as has been done with regard to both sports club density and participation in this article, there is, however, no clear pattern that allows us to conclude that the direct or indirect public subsidies are generally more important for clubs that offer one sport rather than the other (based on our differentiation between badminton, football, gymnastics, handball and swimming clubs). The modest correlations between direct and indirect public subsidies on the one hand and sports club density and participation on the other could also have to do with the fact that in Denmark both the density of and participation in sports clubs is relatively high (European Commission, 2018). There could potentially be a saturation effect making it more difficult to increase participation with public subsidies (direct or indirect). Specifically, regarding indirect public subsidies, studies

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have shown that only a minority of Danish sports clubs (less than 10%) are unhappy with the access and nearness to sports facilities as well as the condition of the facilities (Elmose-Østerlund, Pedersen, & Ibsen, 2015). This positive view could indicate that the sports facilities are generally well-maintained. Measurements of the condition of 500 sports facilities in 23 of the 98 Danish municipalities support this interpretation by concluding that the facilities are generally in good repair (Høyer-Kruse, Iversen, & Forsberg, 2019). As a result, the last five years of indirect public subsidies are perhaps less important for clubs, but a longer period with low spending on the maintenance and operation of sports facilities could lead to a more general decline in satisfaction and, thereby, possibly also in the density of and participation in clubs. 6.2. The role of facility coverage for sports clubs In our study, facility coverage was generally found to be more strongly and consistently correlated with sports club density and participation than direct and indirect public subsidies. The importance of facility coverage is relatively wellestablished in the literature (Eime et al., 2017; Hallmann et al., 2012; O’Reilly et al., 2015; Thøgersen & Iversen, 2014; Wicker et al., 2013). Though these studies support the overall hypothesis that the supply of facilities is positively correlated with sports club density and participation, they do not find all facilities to be equally important, which is also the case in our study. In the general analysis, we found the supply of large sports halls and natural grass pitches to be positively correlated with sports club density, while only the supply of large sports halls was positively correlated with sports club participation among children and young people (below 25 years of age) and adults (aged 25 years or above). A plausible explanation for this finding is that these facility types are the ones that are most frequently used by sports clubs and, at the same time, can accommodate most participants. This is parallel to the finding of Wicker et al. (2013) that the supply of sports fields was the only facility type that was positively correlated with the overall participation in sports clubs. Turning to the sport-specific analyses, our results mainly confirmed our hypothesis that the supply of the facilities most relevant for the specific sports would be positively correlated with sports club density and participation within each sport. Similar results were identified by Eime et al. (2017) and Hallmann et al. (2012). We did, however, also identify significant correlations with facility types that are irrelevant for the selected sports. In the analysis regarding the density of sports clubs, we generally found that positive correlations existed with the supply of large sports halls for all sports and with the supply of natural grass pitches for all sports except swimming. In the analysis regarding participation, examples of similar unanticipated correlations were identified (e.g. a positive correlation between the supply of large sports halls and participation in football among children and young people). Similarly, our analyses yielded examples of results in which the expected correlation between the supply of a relevant sports facility and the participation in a specific sport could not be identified (e.g. the supply of swimming halls was not significantly correlated with participation in swimming for children and young people, and the supply of natural or artificial grass pitches was not significantly correlated with participation in football for adults). It is difficult to provide substantive explanations for the (lack of) correlations described above. However, part of the explanation is likely that different facility types are often co-located, as also suggested by Hallmann et al. (2012). A prominent example from the Danish context is that sports halls are often co-located – and share changing rooms and cafeteria – with natural and/or artificial grass pitches. Furthermore, it is quite normal for swimming halls to be located in connection to or near other types of sports facilities. For this reason, the somewhat surprising results reported above could reflect that it is difficult to isolate the effect of each facility type in the regression analyses. Other studies have struggled with the same challenge (e.g. Hallmann et al., 2012; Wicker et al., 2013). Furthermore, it is an underlying condition for the statistical analysis that the direction of causality run from facility coverage to the density of and participation in sports clubs. However, the alternative direction of causality could potentially be equally relevant. Research into Danish sports facilities in 23 of 98 municipalities shows how sports clubs are the dominant user group in the sports facilities as they occupy the sports facilities in 83% of the time between 4 pm and 10 pm (HøyerKruse, Iversen, & Forsberg, 2017). Since sports clubs are the primary user group in most of the types of sports facilities included in this study, we can expect that municipalities will seek to adjust the number of available facilities to the demand from sports clubs. As a result, the lack of a correlation between the coverage of a certain facility and the density of and participation in sports clubs that use this type of facility could indicate that the municipality has not yet adjusted the supply of that facility type to the demand from sports clubs and users. 6.3. Differences in the role of municipal framework conditions When departing from the differences in the explanatory power of our statistical models, we generally found higher variance explained in the general models concerning the density of sports clubs, and the participation among children and young people, than in the models concerning the participation among adults. As suggested in Section 6.2, the explanation for the close correlation between facility coverage and club density is likely that municipalities seek to adjust the facility coverage to the demand from sports clubs. The stronger role that municipal framework conditions seem to play for participation among children and young people than among adults is in line with the purposes in the legislation – the Leisure Act – in which municipalities are only obliged to provide public subsidies and sports facilities for activities in sports clubs for people younger than 25 years of age. Furthermore, we know from sports participation studies of the Danish population that children and young people practice sport in ‘classic’ facility types such as sports halls and grass pitches to a much higher

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extent than adults who more frequently use other types of facilities, including outdoor facilities such as parks (Pilgaard & Rask, 2016). 6.4. Implications regarding the funding structure for sport This article has several implications for the funding structure for sport. First, this study revealed how direct public subsidies for sports clubs was not positively correlated with either the density of or participation in sports clubs. As mentioned previously, these funds also make up a relatively small proportion of the overall revenues of clubs. Therefore, it seems reasonable to conclude that long term investments in creating a sufficient sports infrastructure is more important for sports clubs than direct subsidies, and that the former should be the main priority in funding structures for sport. In general, sports clubs seem capable of generating enough income through membership fees, events and the like to manage their dayto-day operation. Exceptions to this general observation is likely to exist, but these cannot be identified from this study and require more specific data (see suggestions in Section 6.5). Second, the results from this study suggests that the density of and participation in sports clubs could be increased by building more sports facilities relevant for sports clubs. However, such a claim does not consider the nature of our study in which we identify correlations rather than causal relationships. As mentioned in Section 6.2, municipalities are likely to adjust the supply of sports facilities to the demand from sports clubs. Where this is the case, sports clubs are unlikely to experience that a lack of facilities either threatens their existence or prevents them from recruiting more members. Also, an analysis of the utilisation of sports halls in 23 of 98 Danish municipalities has shown that between 4 and 10 pm on weekdays, only 61% of the capacity was in use (Høyer-Kruse et al., 2019). The fact that less than one in ten Danish sports clubs are unhappy with the access, nearness and condition of sports facilities (Elmose-Østerlund et al., 2015) supports this general remark. Thus, this study can inform discussions on local funding structures for sport by bringing forth evidence that facility coverage is positively correlated with the density of and participation in sports clubs both generally and within most of the examined sports. However, whether more facilities can foster more clubs and higher participation will depend on the current supply and condition of sports facilities in a municipality. Types of studies that can bring about such knowledge are suggested in Section 6.5. Third, this study has shown how municipal framework conditions play a stronger role for participation of children and young people than adults. This finding feeds into political discussions about how to prioritise the public subsidies for sport. In Denmark, the Leisure Act (see Section 3.2) describes minimum standards for municipal support to sports clubs that provide activities for children and young people, while the same minimum standards do not apply to activities for adults. In practice, most municipalities also fund activities for adults, but, according to the results from this study, not with the same impact measured by club density and participation. A plausible explanation for this finding is that self-organised sports activities are more popular than activities in sports clubs among adults (Pilgaard & Rask, 2016). This raises the question of whether the best way to increase the physical activity level among adults is to invest more funds into municipal infrastructure targeted at activities in sports clubs, or to increase spending on infrastructure targeted at self-organised activities. Finally, it is not a given that municipalities should target their subsidies to sports clubs provided that the political aim is to increase the physical activity levels in the population. European research has shown how Danish sports clubs have both strengths and weaknesses in comparison with other European sports clubs. The Danish clubs are relatively successful in integrating women/girls and the elderly (65+ years), but less successful in integrating people with a disability and people with a migration background (Breuer et al., 2017). This might be due to the nature of the funding structure for sports clubs in Denmark, where few specific requirements are attached to the funding (e.g. regarding the provision of sports offers for socially vulnerable population groups). This equips the sports clubs with a high degree of autonomy in deciding how to utilise the public subsidies (Ibsen, 2017). Further, Danish sports clubs are the dominant user group in the sports facilities, and over time it has been difficult for other actors to get access to the facilities (Forsberg & Iversen, 2019). Hence, if the municipalities want to increase physical activity levels, building more sports facilities targeting sports clubs and increasing direct and indirect funding for clubs, might not be the most obvious way forward. We address ways forward for this type of research in Section 6.5. 6.5. Limitations and future studies In this study, we were able to include measures for the density of and participation in sports clubs (dependent variables) both for all sports clubs and in selected sports. At the same time, we were able to differentiate facility coverage (independent variables) into six categories of facilities that were expected to be the most relevant facility types for sports clubs. This level of detail in both dependent and independent variables is clearly a strength of the study, since it allowed us to examine whether certain aspects of the framework conditions (e.g. type of subsidy or facility) were more strongly associated with the density of and participation in sports clubs within some sports than others. It was, however, not possible to include variables with similar nuances as measures for direct and indirect public subsidies (independent variables). Thus, it was not possible for us to examine, for instance, whether large municipal investments in swimming halls in the last five years or high levels of direct subsidies specifically for swimming clubs would correlate positively with club density and participation levels in swimming. In short, our study could have benefitted from greater differentiation in the independent variables used as

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measures for direct and indirect public subsidies for sports clubs. Analyses with such data could provide researchers and policy makers with more specific information about how public subsidies can be used most effectively to increase sports club participation – in general and within specific sports. With our data, we were unable to combine information about the external inputs (i.e. the municipal framework conditions) into sports clubs with information about the internal input (e.g. from membership fees and events). This is clearly a limitation of our study, as the level and distribution of internal inputs are likely to differ significantly between clubs, and by not taking these variations into account in the statistical analyses, we are unable to show whether some clubs compensate for lower levels of public subsidies by increasing the internal inputs. Also, including data about internal inputs would allow for analyses on the relative importance of the two types of inputs – in general and within specific sports. This information could help policy makers in differentiating public subsidies according to the need of sports clubs within different sports. A more general limitation is related to cross-sectional nature of this study. The study is not well-suited to provide information about the causal mechanisms behind the identified correlations. We have drawn on theories and studies that have helped us develop hypotheses and explain our findings, but to fully grasp not only which aspects of municipal framework conditions are relevant for sports clubs, but also how and in which causal chain, other types of studies are needed. One option could be to combine information about developments in municipal framework conditions and changes over time with information about the development regarding the density of and participation in sports clubs over the same period to examine whether there is, in fact, a causal relationship. Also, case studies of sports clubs within different sports could help inform researchers and policy makers about how municipal framework conditions matter for sports clubs within different sports. In connection to this, it could be relevant for future studies to include a broader range of facilities and examine effects not only for sports clubs and members, but also self-organised sports participants. Declarations of interest None. References Bergsgard, N. A., Borodulin, K., Fahlen, J., Høyer-Kruse, J., & Iversen, E. B. (2017). National structures for building and managing sport facilities: A comparative analysis of the Nordic countries. Sport in Society, 22(4), 1–15. 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