To Pay or Not to Pay? The Effects of Monetary Compensation on Volunteers in Sports Clubs

Abstract

Previous research has shown that monetary compensation can crowd out intrinsic motivation for various activities. However, the existing evidence regarding voluntary work is limited and has produced inconclusive findings in the past. This study aims to address this research gap by investigating how monetary compensation affects the supply of voluntary work in the context of volunteer coaches in sports clubs in Germany. A propensity score matching approach is employed to consider a potential selection bias. The results show no evidence for a crowding-out phenomenon and instead indicate positive effects on the supplied hours and numerous items measuring the retention and recruitment of volunteers. The findings are robust when analyzing the sample based on whether the motivation leans more toward intrinsic or extrinsic.

Keywords

monetary compensation volunteer coaches crowding-out effect retention recruitment

Introduction

Many organizations in the non-profit sector rely heavily on volunteers as a key human resource component. In fact, 55% (15 million) of all employees in the non-profit sector in Europe qualify as volunteer workers (Salamon & Sokolowski, 2018). However, as the number of non-profit organizations (NPOs) has increased and as other social and economic factors have shaped volunteerism to reduce the availability of long-term, committed volunteers, voluntary work has become a scarce resource (Lorente-Ayala et al., 2019).

Consequently, NPOs must understand the factors driving individuals’ decisions to supply voluntary work hours and the extent to which organizational actions can influence this decision. The supply of voluntary work, however, is not only defined by the number of hours supplied but also by the retention and recruitment of these workers. Both these aspects represent key challenges for NPOs in all non-profit sectors including health (Chatio & Akweongo, 2017), education (Devaney et al., 2015), human services (Jamison, 2003), and sports (Cuskelly, 2004).

According to standard economic theory, one effective measure to increase the supply of voluntary work is the provision of monetary compensation, which may positively affect supply by decreasing the opportunity costs of individuals (Menchik & Weisbrod, 1987). In the past, different payment schemes rewarding voluntary work were introduced in various voluntary work fields including for firefighters (De Lisi et al., 2006), health emergency workers (Volunteering Australia, 2020), and sports clubs (German Volunteering, 2021).

However, a large body of research has revealed that volunteers have a different motivational mix than fully compensated workers (e.g., Liao-Troth, 2001) and that intrinsic motivation plays an important role in an individual’s decision to supply voluntary work (e.g., Hoye et al., 2008). Intrinsic motivation occurs when an individual does not seek additional benefits other than participating in the activity itself (Deci, 1971). Examples include a feeling of self-efficacy or the simple joy derived from participating in an activity. For extrinsic motivation, the activity serves as an instrument through which to receive different byproducts of that activity (Deci et al., 2008). Such motivations might involve acquiring a new skill or gaining public recognition. Previous research has explored the dynamics between monetary compensation and intrinsic motivation, shedding light on both crowding-out and crowding-in effects across various behaviors such as blood donations or civic duty (Frey & Jegen, 2001). More recent studies have continued this exploration, providing further insights and nuanced understandings of these effects in diverse contexts (Georgellis et al., 2011; List et al., 2018).

In the context of this study, the question arises of how the supply of voluntary work might be affected in situations where organizations have discretion over the amount of monetary compensation available.

Previous studies investigating this relationship in the past (Carpenter & Myers, 2010; Fiorillo, 2011; Frey & Goette, 1999) have produced inconclusive findings. Therefore, this study aims to add new empirical evidence to this small body of research. As such, the research focuses on the effect of monetary compensation for voluntary workers in German sports clubs, specifically on voluntary coaches. Within this context, the study contributes to the existing literature in multiple ways. First, in contrast to previous work, this study investigates not only the effect on the number of hours of volunteer work but also the relationship between monetary compensation and the retention and recruitment of volunteers. This represents two important dimensions of the future supply of voluntary work. Moreover, this study examines whether the effects of monetary compensation differ for those mainly motivated by intrinsic factors compared to those motivated mainly extrinsically in their supply of voluntary work. Third, when estimating the effects of monetary compensation, previous studies have not accounted for the problem of self-selection, meaning that it is likely not random if a volunteer receives monetary compensation. Finally, this study is the first to focus on the effect of monetary compensation on the supply of voluntary work in non-profit sports clubs. This approach could lead to new insights into the crowding-out phenomenon, as existing studies have shown that the conditions around the behavior are likely to determine whether a crowding-in or crowding-out effect occurs (Frey & Jegen, 2001). The context of sports clubs is especially relevant because in Europe, these clubs, as one of the most important suppliers of sports opportunities to the population (Breuer et al., 2015), rely heavily on voluntary work. However, over the last few years, sports clubs in various countries have reported a decline in the number of volunteers (Australian Sports Foundation, 2021; Seippel et al., 2020; Sport England, 2019). Previous research demonstrated the important role of intrinsic motivation for volunteers in sports clubs (Emrich et al., 2014). Thus, the question arises of how different actions might affect the supply of voluntary work.

Theoretical Framework and Literature Review

Research on the Supply of Voluntary Work

In general, volunteering is considered a service with the distinct characteristic that no monetary compensation is paid to the individuals providing it. Hence, NPOs in various fields (e.g., health, education, and sports) tend to rely heavily on voluntary work (Salamon & Sokolowski, 2018). However, as volunteers are becoming a scarce resource, a substantial body of research has investigated factors determining the supply of voluntary work (Grizzle, 2015; Taniguchi, 2012), with a considerable strain of literature specifically focusing on sports clubs (Schlesinger & Nagel, 2013; Wicker, 2017). Studies have shown that individual socio-demographic characteristics such as age, migration background, and education (e.g., Grizzle, 2015; Hallmann & Dickson, 2017), as well as job-related determinants such as job-related income and working hours (Dawson & Downward, 2013; Schlesinger & Nagel, 2013; Taniguchi, 2012), significantly affect an individual’s decision to volunteer and how many hours of voluntary work to supply. According to Freeman (1997), differences in those characteristics can be explained by their correlation with the opportunity costs of providing voluntary work. Here, higher opportunity costs lead to a lower supply.

In addition to individual characteristics, previous research has also indicated that the organizational context must be considered, particularly regarding the retention and recruitment of volunteers (e.g., Clary et al., 1992; Wicker, 2017). In their review article, Studer and von Schnurbein (2013) identified three main clusters of relevant organizational factors: organizational attitudes toward volunteers and the organization’s embedded values, structural features that limit the action space of volunteers, and practices and instruments of volunteer management. For example, organizational attitudes toward volunteers refer to their positioning with respect to paid staff or aspects related to organizational identity. Applied to sports clubs, Cuskelly (2004) identified numerous potential conflicts between paid staff and volunteers, noting a disempowerment of volunteers, which could negatively influence the retention process. Structural features that limit the action space of volunteers represent, for example, the field of activity or the amount of bureaucracy. Swierzy et al. (2018) demonstrated that various dimensions of organizational capacity (financial, structural) positively affect voluntary engagement in sports clubs. On the other hand, Harris et al. (2009) showed that higher levels of bureaucracy have a deterring effect when recruiting new volunteers. The practices and instruments of volunteer management mainly refer to actions that improve the retention and recruitment of volunteers, including active recruitment via email (Cnaan & Cascio, 1998) or retention through volunteer development (Cuskelly, 2004). The monetary compensation of volunteers also fits in this category, as it can be considered an organizational instrument to improve the appeal of the volunteering activity.

Motivation for Voluntary Work

The motivational foundation of volunteering has been the subject of numerous studies in various fields (e.g., Clary et al., 1998; Finkelstein, 2009; Ziemek, 2006). The Volunteer Function Inventory (VFI) developed by Clary et al. (1998) is the most prominent conceptual framework in this regard (Chacón et al., 2017). The VFI is based on functional theory, meaning that although a volunteering activity seems identical for all individuals, the individual motivations driving engagement can be completely different. To measure volunteer motives, Clary et al. (1998) developed a 30-item scale and identified the following six motivational functions: Values, understanding, social, career, protective, and enhancement. Existing research has verified the concept in multiple contexts, such as sports events (e.g., Dunn et al., 2016) or youth sports (e.g., Kim et al., 2010), and demonstrated that a mix of motivations drives an individual’s decision to engage in volunteering. Other studies have verified the multidimensionality of volunteer motivation for other scales based on the VFI. For example, in the context of community sports clubs, Hoye et al. (2008) used four factors to describe volunteers’ motivation (altruistic value, personal development, community concern, and social adjustment).

With research demonstrating that volunteering can be considered both an altruistic and egoistic activity (e.g., Phillips & Phillips, 2011), the understanding of intrinsic and extrinsic motivation and their interplay is crucial. Amabile (1993) argues for a synergistic model where intrinsic and extrinsic motivations coexist and amplify each other’s impact. This idea of “motivational synergy” underscores that individuals can be driven by a mix of motivations, which are neither strictly binary nor oppositional.

Based on this perspective, Finkelstein (2009) approached the motives from the VFI and categorized them not as isolated entities but as potential areas where multiple motivations might interact. The motives of values, understanding, social, protection, and enhancement belong to the internal/intrinsic group, as the individual benefits from the activity itself. Previous studies confirmed the positive impact of intrinsic motivation on the probability of volunteering (e.g., Cappellari & Turati, 2004), voluntary work hours (e.g., Finkelstein, 2009), and retention of volunteers (e.g., Wu et al., 2016). The motive of career fits more into the external/extrinsic category, as a benefit that is separable from engaging in the volunteering activity is acquired. The benefit can occur by increasing an individual’s human capital, which is then valued by the labor market, such as an increase in job-related income (Hackl et al., 2007), or refer to material rewards or a form of monetary compensation. Finkelstein (2009) emphasized that individuals cannot be described as either intrinsically or extrinsically motivated and instead should be categorized based on their dominating orientation.

In the context of sports coaches, only a few studies have investigated the motives of volunteer sports coaches. Jowett (2008) found that both intrinsic and extrinsic motives significantly contribute to the job satisfaction of sports coaches. Busser and Carruthers (2010) applied the VFI to investigate volunteer youth coaches. Their results showed that the motive of values, a mainly intrinsic motivation, was the dominating motivational factor. However, the motive of a career as a proxy for extrinsic motivation ranked second.

Crowding-Out Theory

Motivation crowding-out theory states that monetary incentives or similar extrinsic rewards could crowd out the intrinsic motivation to undertake an activity. In the existing literature, the motivation crowding phenomenon mediates between economic models and psychological theories (Frey & Jegen, 2001). From an economist’s perspective, it works in opposition to the relative price effect, which states that participation in an activity increases if the price (wage) for the activity increases. As such, the role of extrinsic motivation is questioned. Based on economic theory, it can either occur because of a change in preferences, meaning that intrinsic motivation is replaced by extrinsic motivation, or because of a change in the perceived nature of the performed activity. However, social psychologists attribute this effect to the role of intrinsic motivation. According to psychological theory, the effect of an external reward on intrinsic motivation can result in either impaired self-determination, meaning that intrinsic motivation is substituted by external control, or by impaired self-esteem if, due to external rewards, individuals are no longer able to demonstrate their intrinsic motivation (Deci, 1971). It is important to note that Amabile (1993) posits that this interplay between intrinsic and extrinsic motivation should not be perceived as strictly binary; rather, they can also coexist in a complementary manner without necessarily undermining one another.

Over the years, many studies from both fields have investigated this phenomenon via numerous laboratory and field experiments. In their review of the existing literature on the crowding-out effect, Frey and Jegen (2001) concluded that crowding effects on intrinsic motivation have been empirically proven to exist in numerous contexts but that it depends on different conditions if the effect dominates the relative price effect or if a crowding-in effect can be observed. Deci et al. (1999) conducted a meta-analysis of evidence from social psychological experiments. They found that tangible rewards have a clear negative effect on intrinsic motivation, but only if they are expected and not contingent on-task behavior. They concluded that external rewards can control individuals’ behavior but undermine self-regulation, and consequently, individuals rely less on themselves for motivation.

In a real-life context, field evidence from the field of economics confirmed a crowding-out effect, for example, in the context of charitable giving (Chao, 2017), siting of locally unwanted projects (Frey & Oberholzer-Gee, 1997), public services (Houston, 2006), and pay for performance schemes in the public sector (Weibel et al., 2010). However, the effect of a reward is determined by the nature of the task and the importance of intrinsic motivation when performing an activity.

Crowding-Out Theory and Voluntary Work Supply

As mentioned, intrinsic motivation can be considered a key factor in determining the supply of voluntary work (Cappellari & Turati, 2004; Finkelstein, 2009), particularly in sports clubs (Emrich et al., 2014). Hence, a potential crowding-out effect can be expected when external rewards for volunteering activities are provided.

One of the first studies on the effect of monetary compensation on voluntary labor was by Frey and Goette (1999). They described the decision to supply voluntary work as a comparison of costs and benefits. They found indications of a crowding-out effect, with volunteers in Switzerland providing fewer hours of voluntary work if they were monetarily compensated. The study did not specifically focus on a specific field of voluntary work and also found that increasing monetary compensation increases the supply thereof, although in a diminishing marginal manner. Cappellari and Turati (2004) obtained similar results in their study on volunteers in Italy. However, their results were field-specific. Carpenter and Myers (2010) focused on volunteer firefighters, showing that extrinsic incentives increased the probability of a call response. However, the effect was canceled out if the firefighters had specific image concerns. Finally, Fiorillo (2011) found no evidence of a crowding-out effect when looking at volunteer supply in Italy. The study was not field-specific and identified evidence for the relative price effect, since monetary compensation had a positive effect on the number of hours volunteered.

This small body of research provides several avenues for future research, which this study aims to address. First, previous studies only looked at the hours volunteered as a measure of voluntary work supply. Bearing the importance of voluntary work for many NPOs in mind, the recruitment and retention of volunteers are vital for organizations across sectors (Breuer et al., 2012; Clary et al., 1992) and particularly important in the future supply of voluntary work. Second, existing studies only used a proxy for intrinsic and extrinsic motivation and could not assess whether there was a difference in motivation between volunteers who received compensation and those who did not. With previous research demonstrating the important role of both motivational components in voluntary work supply (Cappellari & Turati, 2004), a detailed understanding of how they affect the role of monetary compensation is needed. Finally, previous research has not considered that it is not random if a volunteer worker receives monetary compensation. Consequently, a potential selection bias could affect the estimated effects of monetary compensation on voluntary work supply.

Method

Research Context and Data Collection

The research context of this study is Germany, where, according to the German Olympic Sports Confederation (DOSB, 2021), about 88,000 non-profit sports clubs exist. Non-profit sports clubs in Germany, as in many other European countries and overseas (e.g., Canada and New Zealand), form the basis of the sport system. Non-profit sports clubs are considered the main pillar of mass sports provision. They offer affordable sports programs to various population groups, including elite, competitive, recreational, and grassroots sports (Breuer et al., 2015; Enjolras, 2002). Sports clubs fulfill important societal functions such as health promotion, social integration, education, and democracy promotion (Nagel et al., 2020a). The structure of sports clubs in Germany is heterogeneous, particularly regarding club size in terms of members and the sports offered (Feiler & Breuer, 2020).

This study is based on the seventh wave of the Sport Development Report (SDR), a nationwide longitudinal study of German sports clubs. From the beginning of the seventh wave, that is, 2017/2018, in addition to clubs, different sports club stakeholders, including voluntary coaches, were also surveyed. In the past, data from various waves of the SDR have been used to study various topics, including the financial aspects of sports clubs (e.g., Feiler et al., 2015, 2019) various issues related to volunteering (Ibsen et al., 2019; Nagel et al., 2020b), organizational problems (Coates et al., 2014; Wicker & Breuer, 2013), and human capital formation in relation to voluntary coaching (Breuer et al., 2021).

The survey period was from the beginning of March until the beginning of May 2018. Overall, data from a sample of n = 6,752 coaches from n = 2,352 sports clubs were collected (Breuer & Feiler, 2020b). Respondents were asked if they provide coaching in the role of a volunteer, part-time worker, or full-time worker. Given the focus on voluntary work, coaches working part or full-time were not considered for the underlying study.

On the individual level, the dataset provides sociodemographic information and several details on the working conditions of the volunteer coaches. Furthermore, the dataset is enriched by organizational data of the sports clubs employing the coaches. Therefore, the final sample used for the underlying study was n = 1,163 coaches from n = 751 sports clubs.

Measures and Variables

Since the study focused on the role of monetary compensation for voluntary coaches, the treatment variable was measured by an item asking about the payment coaches received in 2017 from the sports clubs. In Germany, by law, as of the corresponding year 2017, this monetary compensation cannot exceed €2,400 per year if the coaches want to retain their status as volunteers. If, however, the compensation exceeds the scope of regulation, the work performed by volunteers could also be considered a form of undeclared work (Vos et al., 2012). Therefore, the underlying study only included coaches who conducted their activity voluntarily and did not receive compensation of more than €2,400 per year. Thus, the treatment variable measures whether or not voluntary coaches received a payment up to this limit.

The key outcomes were assessed by either single or multiple items. Starting with the amount of supplied voluntary work, a measure indicating the hours per week was used, which is aligned with previous research on voluntary work supply (e.g., Cappellari & Turati, 2004; Fiorillo, 2011). Regarding the recruitment dimension, information on the likelihood that a coach recommends the coaching job or the club to others was obtained, as previous research identified both as key factors in the recruiting process (Lee et al., 2016). The dimension of volunteer retention was assessed by the willingness to quit volunteering and indirectly by measuring an individual’s satisfaction with volunteer work, a crucial driver of decisions to continue volunteering (Garner & Garner, 2011). All four items of the recruitment and retention dimension were measured on a scale of 0–10.

To identify individuals’ different motives for supplying voluntary work, the study employed a scale by Hoye et al. (2008). The scale is based on Wang (2004) and distinguishes between the four dimensions of altruistic value, personal development, community concern, and social adjustment. Each dimension was measured by three or four single items. For more details on the items, see Hoye et al. (2008). Participants rated the extent to which they agreed with statements fitting the respective dimensions on a 7-point Likert-type scale. The rating scale ranges from 1 = Strongly disagree to 7 = Strongly agree. To identify if the main drivers for individuals are extrinsic or intrinsic motives, we followed Finkelstein (2009) and categorized the dimension of personal development as extrinsic motivation because an outcome outside of the volunteering activity is required. We used the other three dimensions to measure intrinsic motivation, wherein individuals gain benefits from the activity itself. For both motivation measures, an additive index was created. The Cronbach’s α of the intrinsic motivation index was 0.842, and the extrinsic motivation index was 0.770. In line with Finkelstein (2009), individuals were categorized as either mainly intrinsically or mainly extrinsically motivated based on a relative measure. This relative measure of motivation was calculated by dividing the additive value of intrinsic motivation by the additive value of extrinsic motivation. If the relative measure is greater than 1, it is assumed that the intrinsic component predominates. If the relative measure is smaller than 1, the extrinsic component is considered predominating.

In addition, based on previous research on an individual’s decision to supply voluntary work, numerous control variables reflecting sociodemographic and coaching job characteristics were considered (e.g., Schlesinger & Nagel, 2013). On the sociodemographic level, characteristics such as age, nationality, gender, general working hours, and household (HH) size were added. Furthermore, a variable indicating whether the coach’s highest education is at least the a-level (i.e., university qualifying degree; yes/no), whether the coaches have children aged under 14 years (yes/no), overall satisfaction with their health (measured on a scale from 0 to 10), and their net income outside their voluntary work was obtained. Since it can be expected that coaching job characteristics influence all outcome variables, additional control variables reflecting the tenure of the coach in the club, whether he or she is a club member (yes/no), or has an additional function in the club (yes/no) were also considered. Moreover, specifically regarding the coaching job, variables measuring whether the coach is only coaching adults, has a coaching license, or does not have any specific qualifications to coach (all yes/no) were included in the empirical analysis. Finally, information on the coaching field was used. Therefore, the coaches indicated on a predefined list the main field in which they coach at a sports club. The options included leisure (e.g., sports for children and the elderly), fitness, health, and specific sports (competitive and non-competitive).

The existing literature has also demonstrated that voluntary engagement is influenced by macro-level factors (Hallmann & Dickson, 2017; Schlesinger & Nagel, 2013). Hence, at the club level, measures indicating whether a club has financial problems (yes/no), the number of club members, whether the club offers multiple sports, and the size of the club’s community were considered.

Empirical Strategy

Table 1 shows substantial differences in the sociodemographic, coaching job-related, and club-specific characteristics between monetarily compensated and non-compensated coaches. This indicates that selection into the two groups is not completely random and that the differences are systematic.

Table 1.

Descriptive Statistics (n = 1,163).

Variable	Control (n = 503)		Min	Max	Treated (n = 660)		Min	Max
Variable	Mean	SD	Min	Max	Mean	SD	Min	Max
Supply of voluntary work
Weekly coaching hours	3.859	3.345	1	36	4.360	6.292	1	150
Retention
Coaching satisfaction	7.659	1.780	0	10	7.926	1.553	0	10
Intention to quit	3.705	3.016	0	10	3.422	2.848	0	10
Recruitment
Recommend coaching	7.659	2.066	0	10	7.777	2.097	0	10
Recommend club	8.677	1.812	0	10	8.623	1.820	0	10
Socio-demographics
Age	45.883	12.572	18	82	43.714	13.552	19	89
German	0.972	0.165	0	1	0.965	0.184	0	1
Female	0.268	0.444	0	1	0.386	0.487	0	1
Working hours	35.165	14.392	0	65	33.829	13.850	0	80
HH size	3.016	1.726	1	10	2.789	1.226	1	9
A-level min.	0.475	0.500	0	1	0.580	0.494	0	1
Child u. 14	0.334	0.472	0	1	0.291	0.455	0	1
Health satisfaction	6.702	2.502	0	10	7.027	2.204	0	10
Monthly income
Monthly income <€500	0.048	0.213	0	1	0.091	0.288	0	1
Monthly income €501–€1,500	0.155	0.362	0	1	0.229	0.420	0	1
Monthly income €1,501–€2,500	0.282	0.451	0	1	0.314	0.464	0	1
Monthly income €2,501–€3,500	0.298	0.458	0	1	0.206	0.405	0	1
Monthly income €3,501–€4,500	0.131	0.338	0	1	0.115	0.319	0	1
Monthly income >€4,500	0.086	0.280	0	1	0.045	0.208	0	1
Coach characteristics
Relative intrinsic motivation	1.080	0.271	0.48	2.5	1.072	0.344	0.46	6.1
Intrinsic motivation index	5.491	0.893	1.80	7	5.421	0.913	2.5	7
Extrinsic motivation index	5.287	1.130	1.75	7	5.315	1.181	1	7
Club tenure	9.601	9.476	0	54	11.768	9.982	0.5	49
Club member	0.986	0.117	0	1	0.945	0.227	0	1
Add. function in club	0.634	0.482	0	1	0.558	0.497	0	1
Coaching adults	0.535	0.499	0	1	0.495	0.500	0	1
Coaching license	0.535	0.499	0	1	0.767	0.423	0	1
No specific qualification	0.320	0.467	0	1	0.162	0.369	0	1
Coaching field
Leisure	0.177	0.382	0	1	0.261	0.439	0	1
Fitness	0.085	0.280	0	1	0.115	0.319	0	1
Health	0.058	0.233	0	1	0.112	0.316	0	1
Sport-specific	0.662	0.473	0	1	0.638	0.481	0	1
Club characteristics
Club financial prob	.137	0.344	0	1	0.102	0.302	0	1
Club members	474.541	572.994	6	4,134	1,003.058	1,291.066	7	10,904
Club multi. sports	1.517	0.500	0	1	1.698	0.459	1	2
Club community size	1.807	0.941	1	4	2.062	1.101	1	4

Note. HH = household size.

Hence, a simple comparison between compensated and non-compensated coaches would lead to biased results. Instead, to identify the effect of monetary compensation, a quasi-experimental design approximating the observational data produced by random experiments was chosen for the purposes of this study (Rubin, 2008). Random experiments use a random assignment of treatment to guarantee a “balance” of individual characteristics. In non-experimental studies, researchers must posit an assignment mechanism. To address the research question of this study, conducting a randomized experiment was not feasible. Thus, the study adopted a quasi-experimental design for the coaches receiving monetary compensation, who represented the treatment group, and all other coaches comprised the control group. Then, for each individual, the propensity score of receiving the treatment (monetary compensation) conditional on individual characteristics was estimated. The propensity score can be defined as the probability of receiving the treatment (Rosenbaum & Rubin, 1983). To generate propensity scores, we estimated a logit model with the treatment assignment (monetarily compensated = 1, not monetarily compensated = 0) as a dependent variable, which was regressed on individual, coaching job, and sports club characteristics (Table 2). This procedure enables researchers to reduce multi-dimensional individual characteristics to a single-dimension score.

Table 2.

Logit Models for Estimating Propensity Scores for Monetary Compensation.

Variable	Full sample	Mainly intrinsic	Mainly extrinsic
Socio-demographics
Age	−0.016** (0.008)	−0.039*** (0.013)	−0.016 (0.012)
German	−0.177 (0.413)	−0.504 (0.696)	−0.105 (0.591)
Female	−0.081 (0.196)	−0.663** (0.331)	−0.133 (0.294)
Working hours	0.003 (0.006)	−0.003 (0.010)	0.005 (0.010)
HH size	−0.130* (0.069)	−0.087 (0.102)	−0.212** (0.106)
A-level min.	0.338** (0.157)	0.406* (0.240)	0.326 (0.252)
Child u. 14	0.106 (0.193)	0.220 (0.287)	−0.124 (0.308)
Health satisfaction	0.035 (0.031)	−0.020 (0.047)	0.120** (0.052)
Monthly income
Monthly income <€500	0.734 (0.480)	0.679 (0.764)	1.036 (0.746)
Monthly income €501–€1,500	0.724* (0.371)	0.157 (0.544)	1.324** (0.614)
Monthly income €1,501–€2,500	0.580* (0.327)	0.412 (0.481)	0.766 (0.534)
Monthly income €2,501–€3,500	0.074 (0.318)	−0.106 (0.459)	0.320 (0.531)
Monthly income €3,501–€4,500	0.145 (0.346)	−0.389 (0.495)	0.950 (0.583)
Coach characteristics
Rel. intrinsic motivation	−0.398* (0.237)	−	−
Club tenure	0.041*** (0.009)	0.063*** (0.015)	0.037** (0.015)
Club member	−1.195** (0.504)	−0.658 (0.830)	−1.646** (0.747)
Add. function in club	−0.104 (0.161)	0.009 (0.252)	−0.213 (0.251)
Coaching adults	−0.042 (0.172)	−0.266 (0.277)	0.258 (0.269)
Coaching license	1.315*** (0.247)	2.054*** (0.418)	0.901** (0.366)
No specific qualification	0.181 (0.266)	0.776* (0.429)	−0.252 (0.410)
Coaching field
Leisure	0.403* (0.212)	0.304 (0.325)	0.529 (0.346)
Fitness	0.038 (0.283)	0.204 (0.525)	−0.077 (0.398)
Health	0.512 (0.329)	0.065 (0.526)	1.384*** (0.535)
Sport-specific	0.238 (0.201)	−0.115 (0.320)	0.233 (0.315)
Club characteristics
Club financial prob.	−0.271 (0.231)	−0.399 (0.365)	−0.319 (0.370)
Club members	0.001*** (0.000)	0.001*** (0.000)	0.001*** (0.000)
Club multi. sports	0.132 (0.177)	−0.213 (0.277)	0.249 (0.277)
Club community size	0.079 (0.085)	−0.109 (0.130)	0.279** (0.138)
Constant	−0.329 (1.156)	1.559 (1.827)	−1.869 (1.745)
Observations	1,163	568	568
R²	0.221	0.268	0.289

Note. Displayed are the coefficients; standard errors in parentheses. Reference category is: job income > 4,501. HH = household size.

p < .1, **p < .05, ***p < .01.

In the second step, the propensity score was used to match observations from the control and treatment group, and the average treatment effect on the treated (ATT) was calculated. Thereby, we measured differences in the outcomes of matched individuals from the control and treatment groups based on their propensity scores. Since the matched observations are balanced in terms of individual characteristics, the differences in outcomes can be attributed to the treatment. As a matching approach, we chose radius matching with a bias adjustment (Lechner et al., 2011). Radius matching matches individuals in the control and treatment groups based on a specific radius around an individual’s propensity score. In contrast to nearest neighbor matching, for example, it considers all available observations within a certain radius when constructing matches. Huber et al. (2013) demonstrated that radius matching outperforms other matching techniques such as nearest neighbor or kernel matching (matching with specific weights).

When applying propensity score matching, two critical assumptions must be considered. First, considerable overlap in the range of propensity scores between individuals in the control and treatment groups must be ensured. Overall, in the full sample, n = 1,118 observations were so-called on support, meaning that a match exists, and n = 39 were excluded (off support), indicating an overall satisfying overlap across the different propensity scores. Second, the conditional independence assumption (CIA) has to hold. The CIA states that the outcomes are independent of the treatment or control group allocation given the considered individual characteristics. The assumption cannot be tested. However, given the rich dataset including observable characteristics of the individuals, coaching job, and club level, the majority of the potential selection bias is likely removed.

To identify if the relationship between monetary compensation and the different outcome measures varies with the predominating motives, the sample was split into mainly intrinsic and mainly extrinsic motivation groups. In the mainly intrinsic motivation sample, n = 498 participants were on support, and n = 67 were off support. In the mainly extrinsic motivation sample, the corresponding numbers were n = 537 and n = 28, respectively.

Results

Descriptive Results

Table 1 provides the descriptive statistics for the control and treatment groups. On average, individuals in the control group who did not receive monetary compensation supplied a total of 3.859 hours of voluntary work per week. In comparison, the treatment group of individuals receiving monetary compensation provided 4.360 hours of voluntary work per week. Regarding the measures reflecting the retention of volunteers on a 10-point scale, the mean in the control group was lower than that in the treatment group for satisfaction with coaching (7.659 vs. 7.926). Furthermore, intention to quit was higher in the control group (3.705 vs. 3.422). Regarding recruitment, the control group had a lower score for recommending coaching (7.659 vs. 7.777) but a slightly higher score for recommending the club (8.677 vs. 8.623). The descriptives of the relative intrinsic motivation measure show for the control group a mean of 1.080, indicating a slight predominance of intrinsic motivation. The treatment group had a slightly lower mean of 1.072, still suggesting a small dominance of intrinsic motivation.

In addition, the descriptive results indicated considerable differences in the sociodemographic characteristics of both groups. For example, in the treatment group, more people were female and had a-levels (i.e., university qualifying degree) as the highest educational degree. Regarding coach characteristics, participants in the treatment group had, on average, a longer tenure in the club and were more likely to have a coaching license. On the other hand, coaches in the control group were more likely to have an additional function within the club and no specific coaching qualification.

Finally, a descriptive comparison of the club characteristics of both groups showed that coaches in the treatment group were working in a club with, on average, more than twice as many members as the clubs of coaches in the control group. Moreover, clubs of coaches in the treatment group were less likely to have financial problems and more likely to provide multiple sports. In addition, the size of the clubs’ community was slightly larger in the treatment group.

Propensity Score Estimation

The descriptive statistics reveal a selectivity bias, as specific characteristics on the individual, coaching, and sports club levels appear to influence the probability that volunteer coaches receive monetary compensation. To correct this bias, a matching approach was applied wherein, first, the propensity score of receiving monetary compensation was estimated.

Table 2 shows the regression results of the respective logit models for the full sample and the sample split as mainly intrinsic or extrinsic motivation. As the results do not differ much between the three models, the focus will be on the full sample.

In total, the pseudo-R² indicates that in the full sample model, 22.1% of the variance in treatment assignment is explained by the included variables. For the split samples, these values are even slightly higher. The results further show a handful of individual characteristics significantly influencing the probability of volunteer coaches receiving monetary compensation. Younger individuals with higher education and a lower income are more likely to be monetarily compensated. In addition, tenure in the club and having a coaching license have a positive effect, whereas being a club member reduces the likelihood of monetary compensation (i.e., an indicator of a potential coproduction motive). Finally, club size in terms of members positively affects the probability of monetary compensation.

The purpose of the matching approach is to balance characteristics to decrease the bias on observable characteristics. Table 3 reports the overall measures of imbalance in characteristics before and after the matching. After the radius matching, the mean bias, an indicator of the average bias due to unbalanced characteristics, was significantly reduced and below the threshold of 10% in the full sample model and in both split samples. The share of observations lost to common support, meaning that no match exists, was quite small, with only 3.4% in the full sample and 12.7% and 8.8% in the mainly intrinsic and mainly extrinsic motivation samples, respectively.

Table 3.

Individual Characteristics Balancing Before and After Matching.

Radius matching estimator	Logit pseudo R² Before	Logit pseudo R² After	Mean bias (%)Before	Mean bias (%)After	Lost to common support (%)After
Full sample	0.229	0.064	11.6	5.7	0.034
Mainly intrinsic	0.274	0.108	12.5	8.2	0.127
Mainly extrinsic	0.285	0.103	13.9	6.8	0.088

Note. The pseudo-R² stems from the logit estimations of the propensity scores and indicates how well the individual characteristics explain variations in treatment.

Matching Results

Table 4 shows the average treatment effect on the treated (ATT) for the different outcomes. Regarding the supply of working hours, the results demonstrate a positive effect of monetary compensation of 0.666 hours per week, representing a 17.2% increase in the control sample’s average working hours per week. An effect of 0.633 hours per week was observed in the mainly intrinsic sample. In the mainly extrinsic sample, the effect increases to 0.852. Regarding the retention of volunteers, the findings indicate positive effects: on a scale from 0–10, coaching satisfaction increased in the full sample by 0.645 (+8.4%) and in both the mainly intrinsic and extrinsic samples (0.600 and 0.347, respectively).

Table 4.

Estimated ATT After Radius Matching.

Outcomes	Full sample (n = 1,118)			Mainly intrinsic motivation (n = 498)			Mainly extrinsic motivation (n = 537)
Outcomes	ATT	Treated	Controls	ATT	Treated	Controls	ATT	Treated	Controls
Supply of voluntary work
Weekly coaching hours	0.666*	4.293	3.628	0.633**	3.999	3.337	0.852***	4.088	3.234
Retention
Coaching satisfaction	0.645***	7.947	7.302	0.600***	7.960	7.356	0.457**	7.986	7.529
Intention to quit	−0.792***	3.381	4.173	−0.584**	3.552	4.135	−0.689*	3.033	3.723
Recruitment
Recommend coaching	0.538***	7.791	7.252	0.254	7.804	7.550	0.506	7.841	7.335
Recommend club	0.280*	8.656	8.376	0.364**	8.790	8.426	0.684***	8.668	7.983

Note. ATT = average treatment effect on the treated.

***

p < 0.01. **p < 0.05. *p < 0.1.

We found similar results for the intention to quit the volunteer coaching job: across all three samples, the effect was significant and negative. The findings reveal for the full sample a significant decline of −0.792 (−21.3%) and in the mainly intrinsic and extrinsic samples decreases of −0.584 and −0.689. Finally, in the recruitment dimension, we found the positive effects of monetary compensation. However, the effects were slightly smaller compared to the first two outcomes in the full sample, as a significant increase in recommending coaching by 0.538 (+7.0%) and recommending the club by 0.280 (+3.2%) was observed. A significant effect for recommending coaching was not observed in the two subsamples. For recommending the club, we found a considerably higher effect of 0.684 in the mainly extrinsic-motivated sample than the mainly intrinsic-motivated sample (0.364).

Discussion and Conclusion

The crowding-out effect of monetary compensation in the context of intrinsic motivation contrasts fundamental economic theories. Consequently, a vast body of research has investigated this phenomenon. However, the existing studies examining it in the context of voluntary work, an area where intrinsic motivation has been identified as a key driver of the decision to participate, have produced inconclusive findings (e.g., Hoye et al., 2008).

This study focused on the effect of monetary compensation on volunteer coaches in sports clubs in Germany. Similar to the study of Fiorillo (2011), the results did not indicate a crowding-out effect, but found evidence of an increased supply of volunteer work hours when the coach receives monetary compensation. According to these results, in this sense, voluntary work is not different from regular work, as an increase in price leads to an increase in supply (relative price effect). This result is also aligned with existing research that argued that both types of motivation can coexist at the same time and that mainly intrinsically motivated volunteers still respond to extrinsic rewards (Amabile, 1993; Bruno & Fiorillo, 2012). In addition, Carpenter and Myers (2010) showed the effect of monetary compensation varies depending on the image concern of volunteers; presumably, receiving monetary compensation does not create any form of conflict for volunteer coaches. This argument may be true, because the investigated monetary compensation of up to €2,400 per year is labeled by law as monetary compensation in the form of a lump sum and not a payment. This ensures that coaches can retain their status as volunteers.

In addition to the provided working hours, the study covered two other important dimensions of voluntary work supply: retention and recruitment. Again, the findings do not indicate any form of crowding-out effect and instead demonstrate positive effects across all items measuring the two dimensions. Concerning the literature on the retention of volunteers, previous studies focused on factors such as learning and development opportunities (Newton et al., 2014) or training in general (Jamison, 2003). This study adds new evidence that from an organizational perspective, providing monetary compensation can be considered an effective instrument, as coaching satisfaction and intention to quit can be positively affected. The effect on coaching satisfaction was even higher for coaches who indicated they were mainly intrinsically motivated than for those who reported a mainly extrinsic motivation. One explanation could be that even mainly intrinsically motivated coaches expect to receive a minimum compensation to cover the costs arising through the coaching activity (e.g., travel expenses). This argument can be supported by the fact that although material aspects including receiving money or paying lower membership fees rated lowest among the motives coaches indicated, average satisfaction with payment and tax reliefs was still low (Breuer & Feiler, 2020a).

A similar logic applies to the literature on the recruitment of volunteers. This stream of literature has focused on volunteer management practices (Devaney et al., 2015) and is now extended by new evidence on the effect of organizational action in the form of monetary compensation on recruitment activities (e.g., recommending the organization) by existing volunteers. This finding aligns with results from a study by Hager and Brudney (2011), who showed that organizations, when becoming active, can overcome problems pertaining to the recruitment of volunteers.

The results were robust for the two types of motives. However, the differentiation between mainly intrinsic and extrinsic motivation reveals that monetary compensation is particularly effective in reducing the intention to quit and increasing the likelihood of recommending the club when it comes to mainly extrinsically motivated coaches. This result demonstrates that these volunteers value such actions by organizations because they meet their understanding of volunteering as an instrumental activity (Deci et al., 2008).

From a methodological perspective, it was shown that when studying the effect of monetary compensation using observational data, a simple comparison of compensated and non-compensated individuals was insufficient, as a selection bias was identified. The monetary compensation of sports coaches is not random but depends on individual and club-specific characteristics. The findings at the individual level show that qualifications, as a form of human capital, are valued by sports clubs, which is aligned with the existing literature (e.g., Wicker et al., 2016).

Important practical implications were derived, as the results showed that sports clubs should consider providing monetary compensation to their volunteer workers in their efforts to affect the supply of voluntary work positively. Through this, the clubs do not have to be worried about crowding out intrinsic motivation, as the overall effects clearly show a dominating relative price effect. The additional coaching hours could be used to set up new training groups, which could attract new members and thereby generate new income from membership fees. It can also be concluded that clubs would benefit from a compensation scheme not only in terms of the volunteer hours supplied but also in terms of retaining existing volunteers and recruitment efforts by existing volunteers. Previous studies have shown that sports clubs in various countries are experiencing a decline in volunteers in general. Across different sports systems, volunteer coaches represent an important contributor to the community, particularly in youth sports (Harman & Doherty, 2014; Seippel et al., 2020). Hence, the results are not limited to the context of Germany. However, existing regulations must be followed when designing extrinsic incentive schemes. Otherwise, volunteers would become undeclared workers, which could potentially change the relationship between monetary compensation and the supply of work (Vos et al., 2012). Moreover, it must be considered that other organizational characteristics could moderate the effectiveness of extrinsic rewards, as existing studies have demonstrated that different organizational characteristics influence various volunteer outcomes (Greenfield et al., 2016; Penner & Finkelstein, 1998; Schlesinger & Nagel, 2013).

The study has a few limitations, which could guide future research. While this study specifically examines the crowding-out effect of monetary compensation, it is important to note that external and internal motivators can also lead to crowding-in effects under certain circumstances. Since this study was based on cross-sectional data, a potential reverse causality problem cannot be ruled out. However, the questions were designed in a way that the outcomes refer to the current year, whereas the question regarding the monetary compensation is based on the previous year. This should minimize the bias. Nevertheless, panel data would represent a valuable addition to the research in this regard. Panel data would also help to identify whether monetary compensation potentially affects the future supply of voluntary work in terms of the actual number of volunteer coaches leaving or quitting. Furthermore, observations over time would help to check whether monetary compensation might affect motivation, which would bias our results. To verify that this is not the case, the same analyses were run with the relative motivation measures as outcomes, and no significant effects were found. These results are available upon request.

Given the reliance on propensity score matching, omitted variables affecting the treatment status could bias the results. For example, for volunteer coaches who experience different managerial oversight, the response to direction and management could vary significantly (e.g., Nesbit et al., 2018). Hence, the absence of data on managerial or organizational context poses an avenue for further research.

Finally, the context of volunteer coaches in sports clubs is specific as, for example, the same service is provided by volunteers and paid workers, sometimes even within the same organization. Furthermore, the underlying motivation driving the decision to volunteer might be unique, with previous research demonstrating that many motives stem from an affiliation with the sports club, such as when children are active in the club (Nichols et al., 2019). More research in other fields of voluntary work is needed, especially because previous research has demonstrated that the predominating motives of volunteers change depending on the field (Bruno & Fiorillo, 2012).

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Federal Institute of Sport Science (BISp), the German Olympic Sports Confederation (DOSB), and the 16 Federal State Sports Confederations (LSBs) (grant number ZMVI4-081802/17-26).

ORCID iD

Sören Dallmeyer

Author Biographies

Sören Dallmeyer is a senior researcher and lecturer at the Institute of Sport Economics and Sport Management. His research interests include topics of health and labor economics in the context of physical activity.

Christoph Breuer is a full professor of sport management at German Sport University Cologne (W3-level). His main areas of research are sports clubs, sponsorship, and elite sports.

Svenja Feiler is a researcher at the Institute of Sport Economics and Sport Management. Her research focuses on non-profit organizations, in particular sports clubs.

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