Introduction
Physical inactivity is a major public health concern in England as it is associated with about 20 health conditions including coronary heart disease, cancer, diabetes and stroke [
1‐
4] and is rated among the top ten leading causes of death in high-income countries [
5,
6]. It may also lead to reduced psychological well-being and social interaction [
3] as well as increased absenteeism within the working population [
2]. Physical activity is multifaceted and includes a wide range of activities such as sports and exercise, housework as well as occupational activity [
6]. Only 40% of men and 28% of women in England are physically active i.e. they participate in physical activity of either moderate intensity for a minimum of 30 min on 5 days each week or vigorous intensity for a minimum of 20 min on 3 days each week [
7]. This study focuses on the sports and exercise component of physical activity, as it represents a planned aspect often aimed at attaining health benefits [
8] and, as such, can be relatively easily targeted by policies to increase activity. Also, it is subject to less measurement error since sports and exercise activities are usually premeditated and hence easier to recall by respondents [
9].
Policy aims at increasing activity not only among the inactive but also among those who are active but do not participate sufficiently often or with sufficient intensity. Understanding of the determinants of sports and exercise participation could help to identify target areas for policy and the achievement of such objectives. The challenge that public health practitioners face in securing adherence to sports and exercise might be attributed partially to inadequate understanding of the economic factors influencing the degree to which an individual participates and is willing to change their behaviour [
10‐
12]. Economic factors can play a key role in developing understanding of preferences of individuals, as the trade-off between scarce resources (time and money) by individuals is examined [
13,
14], and any efforts to improve lifestyle behaviour must incorporate individual interests in order to be effective [
15].
The available theoretical and empirical literature on the economics of participation in sports and exercise suggest the need to account for price (i.e. time and money price) and perceived benefits among other factors in explaining such behaviour [
11,
12,
16]. To date, there is a paucity of studies looking at this issue, mainly due to the lack of relevant data [
17]. A few studies [
11,
12,
16] have explored the effects of price but only partially, with attempts limited to either assessing the impact of time price alone, using proxies to capture the opportunity cost of time [
11,
12], or money price via the reduction in admission charges to exercise referral programs [
18]. To the best of our knowledge, no study has collected and analysed data on both price and perceived benefits of sports and exercise participation. This study originated as the outcome of our search for data on both price and perceived benefits in relation to physical activity [
19]. As no such data could be located, the best starting point was to collect similar data through a small scale survey [
20].
The aim of this study is to investigate how the demand for sports and exercise is influenced by price and perceived benefits. The objectives are twofold: (a) to estimate how much it cost people to participate in sports and exercise, and describe what the sources of price are and; (b) to assess the impact of price and perceived benefits on sports and exercise, given participation. The paper intends to inform policies aimed at encouraging people to be more active as well as a future research agenda.
Discussion
The findings show that people spend an average of £27.41 on sports and exercise per month and 19.8 min travelling, per occasion of participation. The money price of participation mostly included membership fees, entrance charges, and purchase of sports apparel, sports equipment and nutritional supplements. This is the only study to have shown that both high time and money prices (price per occasion) of sports and exercise may deter (although less than proportionately) participation in sports and exercise. Demand for sports and exercise was found to be price inelastic, except for meeting the recommended level of participation which was time price elastic. An explanation for this could be that meeting of recommended level requires more time and hence consumers tend to be more sensitive to extra time expenditure. Nonetheless it could also be a function of the sample as their activity levels (an average of 11 days), given intensity of participation, were close to the recommended level. Awareness of benefits promotes participation in sports and exercise and is associated with shifts in the demand curve.
Interestingly, money price appears influential only in the context of ‘number of days’ of participation. A plausible explanation is the offsetting responsiveness of frequency of participation and the duration per occasion of participation to changes in money price. Though money price is inversely related to the frequency of participation, it is positively related (elasticity = 0.43; p > 0.05) to the duration per occasion of participation. Nevertheless, the total effect of higher price on sports and exercise is negative because the negative correlation with frequency of participation overpowers the positive correlation with duration per occasion. For example, while a 10% higher price is associated with 2.5% fewer days, it results in a 0.3% more average time spent per occasion.
The findings on price and perceived benefits supported the predications of the economic theory underlying the empirical research. A priori expectations with respect to control variables were also met. These results were consistent across variant models, but a useful consideration is which of the models is superior? It is difficult discriminating between models given that they all showed good specification and fit and satisfied relevant statistical assumptions. However, if we consider the penalised log-likelihood (AIC and BIC) as a criterion, then the “total time model” appears to be the best model as it had the lowest AIC and BIC i.e. explained the most variation in sports and exercise. Nonetheless, given that all the models could be considered ‘valid’ it reasonable to argue that the choice of superior model should rather be based on which model is fit for purpose—what specification of demand is of interest.
Comparing the results on price to the few studies [
11,
12,
16] that had explored the effects of price on sports and exercise unsurprisingly reveals slight differences. Humphreys and Ruseski [
11,
12] showed a positive impact of time price while Tai et al. [
18] found no effect for money price. Differences in measurement of price between these studies and ours could explain why the two sets of findings vary. Time price were measured via proxies in the literature [
11,
12] while money price were specified as only entrance charges [
18].
A question could be raised about the completeness of the perceived benefits covered in our study. One could contend that people would expect additional benefits from physical activity participation other than these benefits. Although we could argue that the list of benefits in the questionnaire were fairly complete as they were consistent with those in the literature [
19], we further explored the availability of additional benefits by asking the respondents in the illustrative survey: ‘
Are/is there any other benefit(s) not mentioned on the card that you think participation in sports or exercise activities could help you gain?’ and the possible responses were
yes or
no. If yes, respondents were probed to list those benefit(s). Twenty-seven per cent (
n = 16) of respondents answered yes and mentioned benefits, but these were found to be comparable with the list of benefits already provided in the questionnaire. There is, however, a need to verify the reliability and validity of this finding because the question was not pretested.
This study is exploratory and several caveats justify some caution in interpretation. A first set of limitations reflect on the impact of using cross-sectional data. While we have emulated usual practice in the estimations of price elasticity with cross-section data [
31,
32], time-series data would be more useful. Using cross-sectional data tends to lead to higher price elasticities because a cross-section model assumes consumers have already responded to changes in prices and are at their long term equilibrium [
33]. Long-run elasticities are higher than the short-run elasticities because the long run offers more time for consumers to adjust to price changes. Related to this, it is also possible that temporal trends in control variables (e.g. income, age) that might impact on price elasticity of demand are under represented in cross-sectional data. We are also aware some of the variation in prices may be due to differences between consumer rather than supply conditions. This may be caused, for example, by differences in opportunity cost of time and marginal price/benefits of information search for prices [
34]. If unaccounted for, as in this study, such price variations could lead to bias and misleading elasticities [
35]. Future work could consider the use of a hedonic price equation to adjust for such price variations, by regressing global price for sports and exercise on a set of factors including purchase characteristics to predict an ‘adjusted price’.
A second set of limitations relates to the measurement of variables. First, the indicators of physical activity participation were measured via self-reported questionnaire. The use of self-reports to measure physical activity can be fraught with overestimation or problems with recall [
25]. Alternative approaches such as use of objective measurements like accelerometers were not feasible within the constraints of this study. It is also important to note that a more complete approach to the analysis would have been to adjust for other types of physical activity (e.g. occupational activity) as participation levels in those activities may impact on sports and exercise behaviour [
16]. Data limitations, however, did not allow such an investigation and future research should address this. Second, the specification of some of control variables may have influenced the robustness of estimates. For example, the low influence of income might have occurred because income had to be operationalised as a binary to ensure enough observations in categories.
A third set of limitations concerns the nature of the sample. The small sample size meant we could not account for selection bias. It is also logical to expect that the university sample may, for example, have higher levels of physical activity compared with the general population due to unobservable factors such as easier access to sporting facilities [
27]—a confounding effect that may be more profound in our study given Brunel University’s sporting excellence.
Although of questionable generalisability, the findings do indicate the potential for generating policy relevant information on demand-side incentives. For example, mass media campaigns could promote awareness about nonhealth benefits of participation in sports and exercise. The price elasticity of demand suggests any price changes would need to be large to encourage participation in sports and exercise. Assuming our data were generalisable and the estimates robust, we could demonstrate the potential impact of alternative policies. For example, consider two money price reduction policies (e.g. vouchers) aimed at encouraging the current number of days on which sports and exercise are undertaken: policy ‘A’ aims at a 25% reduction and policy ‘B’ a 100% reduction in price. Predictions based on the ‘number of days’ model indicate that the 25% lower price could result in people doing an additional half a day of sports and exercise and the latter two and half additional days. Only the 100% price reduction, people could meet the recommended level of participation (13 days), given intensity.
This was an exploratory study with limited resources, and therefore, we recommend that future studies should determine the robustness of the findings using a large nationally representative sample from England. Future analysis ought to add an extra indicator of sports and exercise—participate or not. Although that indicator is unlikely to be influenced by price of participation, it enables potential selection bias to be accounted for. Another potential consideration is to supplement self-report data on sports and exercise with objective data in order to minimise recall bias.
This paper provides a first estimate of how demand for sports and exercise in England is correlated with time and money price and perceived benefits of participation. However, it would be important to get better evidence in order to understand these relationships and provide varied policy options to encourage participation in sports and exercise.