Printer Friendly

The economic choice of participation and time spent in physical activity and sport in Canada.

Background and Motivation

Physical activity (including sport participation) is essential to overall health. Regular physical activity can reduce the risk for many chronic diseases including breast and colon cancer, diabetes, stroke, heart disease, hypertension, and depression (World Health Organization, 2010). Given the importance of physical activity to overall health, promoting regular participation in physical activity and sport is a public health priority in many countries. In Canada, the country of interest in this study, the prevalence of meeting physical activity guidelines is improving but still remains low. A comparison of the results from the 1996-1997 National Population Health Survey (NPHS) with those from the 2005 Canadian Community Health Survey (CCHS) shows that Canadians who report at least moderately active leisure time physical activity rose from 43% to 52% (Gilmour, 2007). Based on data from the CCHS, the percentage of Canadians who reported being either physically active or moderately physically active rose a modest 2 percentage points, from 52% in 2005 to 54% in 2011 (Statistics Canada, 2012). Despite this improvement of participation rates based on self-reported data, an alarmingly low percentage (15%) of adult Canadians (over age 20) meet the guidelines for sufficient physical activity based on objective accelerometer data (Colley et al., 2011). (1) This statistic is a motivating factor for the recently renewed Canadian Sport Policy goal that both the number and diversity of Canadians participating in sport will increase over the timeframe of 2012-2022 (Canadian Sport Policy, 2012). Central to achieving such policy objectives is an understanding of the determinants of participation in sport and physical exercise. The objective of this study is to investigate how changes in key economic variables (income, wage, education, and occupation), individual characteristics (age and gender), and family structure (marital status and presence of children) affect individual decisions about participation and time spent in physical activity and sport.

Perhaps in response to the nearly global policy priority of encouraging regular exercise for health benefits, a large literature in health services research, public health, and, more recently, economics that examines physical activity and sport participation has emerged. The recent empirical literature in economics can be loosely placed into three categories: 1) analyses of the determinants of physical activity and sport (Brown & Roberts, 2011; Downward, 2007; Downward, Lera-Lopez, & Rasciute, 2011; Downward & Riordan, 2007; Eberth & Smith, 2010; Farrell & Shields, 2002; Garcia, Lera-Lopez, & Srnrez, 2011; Humphreys & Ruseski, 2007; Humphreys & Ruseski, 2011; Lera-Lopez & Rapun-Garate, 2007; Meltzer & Jena, 2010); 2) analyses of the impact of physical activity and sport on health-related factors like self-assessed health status, health outcomes, and health care utilization (Balia & Jones, 2008; Contoyannis & Jones, 2004; Costa-Font & Gil, 2005; Humphreys, McLeod, & Ruseski, 2014; Ruseski & Humphreys, 2011; Sari, 2009; Sarma, Devlin, Gilliland, Campbell, & Zaric, 2013); and 3) the impact of physical activity and sport participation on other factors, like labor market outcomes and happiness (Forrest & McHale, 2011; Huang & Humphreys, 2012; Kavetsos, 2011; Lechner, 2009; Lechner & Downward, 2013; Pawlowski, Breuer, & Leyva, 2011; Pawlowski, Downward, & Rasciute, 2011; Rasciute & Downward, 2010).

We restrict our attention to summarizing the findings from the first category of empirical studies since our study contributes to this category by developing additional evidence on the economic determinants of sport participation using data from the CCHS Cycle 1.1. These studies consistently find that participation in sport is affected by age, education, household income, household structure, and ethnicity. Most studies using cross-sectional data find that the probability of participation in any kind of activity declines with age. Education and income are consistently positively associated with participation in any type of activity.

Women and married people are less likely to participate than males and singles. The presence of children in the household has mixed effects depending on the type of activity and the dimension (participation or time spent) of physical activity studied. This pattern slightly changes when one moves from overall participation to specific activities. Also, the importance of income differs across studies. Findings with respect to employment status are mixed. For example, Downward (2007) finds that unemployed and part-time employed people are more likely to exercise than employed and full-time employed people, while Humphreys and Ruseski (2007) find that employed people are more likely to participate in physical activity and sport. (Downward et al. [2011] and Garcia et al. [2011] provide excellent summaries of the empirical evidence on the determinants of sport participation.)

An important economic factor that is not as widely studied is the opportunity cost of time. Exceptions include Anokye et al. (2011), Brown and Roberts (2011), Christian (2009), Garcia et al. (2011), Humphreys and Ruseski (2011), Kuvaja-Kollner et al. (2012), Maruyama and Yin (2012), Meltzer and Jena (2010), and Pawlowski et al. (2009). Pawlowski et al. and Anokye et al. examine the effect of travel time costs on participation while Christian studies the impact of commuting time costs on time spent in physical activity and other non-work activities. Kuvaja-Kollner et al. use labor market position (working or retired) as the opportunity cost of time in a study of the effect of the time cost of physical exercise on the amount of physical activity undertaken. Meltzer and Jena and Maruyama and Yin use income as a proxy for the opportunity cost of time to evaluate the effect of earnings on exercise intensity. Our paper adds to this limited group of studies by using information in the CCHS to construct a wage variable. The wage variable is used as a proxy for the opportunity cost of time in our study, which allows for a more direct evaluation of the effect of the opportunity cost of time on the frequency and duration of sport participation. Our findings about the effect of the opportunity cost of time extends the analyses in Brown and Roberts (2011), Garcia et al. (2011), and Humphreys and Ruseski (2011). Brown and Roberts and Garcia et al. also construct or derive wage variables as measures of the opportunity cost of time. Humphreys and Ruseski do not have the data to construct a wage variable and use education as a proxy for the opportunity cost of time.

We estimate a double hurdle model of the decisions to participate and time spent in leisure time sports activities. The theoretical framework motivating the empirical analysis is the economic model of participation and time spent in physical activity developed by Humphreys and Ruseski (2011). This model emphasizes that decisions about physical activity are made on two margins: the extensive margin governing the participation decision, and the intensive margin governing the time spent decision, conditional on participation. This distinction has implications for the way observed correlates of practicing sport affect the participation and time spent decisions.

This model has been used to motivate the empirical analysis in some recent economic studies of sport participation and physical activity (Brown & Roberts, 2011; Eberth & Smith, 2010; Eisenberg & Okeke, 2009; Lera-Lopez & Rapun-Garate, 2007). Our study is primarily an extension of Humphreys and Ruseski (2011) but differs from it and adds to the larger existing literature in four important ways. First, it uses data from Canada rather than the United States. Canada differs from the United States with respect to national sport policy. Canada's federal Department of Canadian Heritage is concerned with sport participation at the grassroots level as is Sport Canada (Doherty & Clutterbuck, 2013). On the other hand, the United States has no federal counterpart and the promotion of widespread sport participation is left to state and local organizations (Ruseski & Razavilar, 2013). Second, it analyzes the participation and time spent decisions for specific sports rather than examining participation and time spent in any activity. An analysis of different sports and physical activities allows us to assess if and how the determinants of sports and physical activity participation vary across activities. Other studies that look at the frequency and/or duration in specific activities include Downward (2007), Downward and Riordan (2007), Eberth and Smith (2010), Farrell and Shields (2002), and Humphreys and Ruseski (2007). Third, the CCHS allows us to use wages, rather than some other proxy like education or income to measure the opportunity cost of time. Finally, the CCHS data reflect the fact that many Canadians choose not to participate in sport or be physically active. Our econometric approach is to account for the large number of zeros observed in the measures of physical activity by estimating a "full double hurdle" model (Jones, 2000) of participation and time spent practicing sport. The "full double hurdle" model allows for factors that affect participation and factors that affect time spent to have different signs and for correlation in the equation error terms. Most empirical studies of physical activity and sport participation do not employ this empirical approach. For example, Downward and Riordan (2007) and Humphreys and Ruseski (2007) both adopt the Heckman self-selection approach to estimate the participation and time spent equations. Humphreys and Ruseski estimate a Cragg model. (2) Garcia et al. (2011) first estimate a probit model for sport participation and then estimate a linear system of demand for sport and other leisure time activities using seemingly unrelated regression. Eberth and Smith (2010) use the "copula approach" to model the participation and time spent decisions.

We focus our analysis on seven sports and physical activities: walking, swimming, exercising at home, cycling, running, golfing, and weight lifting. We choose these sports because they have the highest participation rates. We find that individuals with higher income are more likely to participate in these activities but, conditional on participating, spend less time. This finding is important even though numerous studies have found a positive correlation between income and physical activity because it suggests that the income effect works differently on the extensive and intensive margins. Our model also shows that the effect of a change in the opportunity cost of time can be decomposed into an income and substitution effect just as a change in the price of a good can be decomposed in this manner. These effects work in opposite directions and are empirically testable. Our results generally suggest that the income effect of a change in the opportunity cost of time dominates the substitution effect. Our findings with respect to the effect of income and the opportunity cost of time are consistent with the findings of Humphreys and Ruseski (2011) using US data. These results can help inform the design of policy interventions aimed at increasing participation in physical activity. Finding a positive income effect on the extensive margin suggests that consumers will respond to economic incentives to initiate sports programs.

Theoretical Framework

The theoretical model motivating our econometric analysis is the economic model of participation and time spent in sport developed by Humphreys and Ruseski (2011), which is summarized here. In this model, the decision governing participation in sport and physical activity is a two-part decision. First an individual must decide to participate; for example, to go for a walk or go for a swim. Second, having made that decision, the individual must determine how much time to spend walking or swimming.

The mechanisms underlying these two separate, but related, decisions are not explicitly considered here but could potentially be handled in an extension to this model. Intuitively, the nature of the activity should influence how choices are made along the participation (or extensive) margin and the time spent (or intensive) margin. For example, playing a round of golf entails getting a tee time at a golf course and possibly coordinating with friends in order to have a foursome. The decision to play a round of golf is likely affected by different factors than the decision to go for a walk that essentially entails putting on walking shoes and stepping out the door.

Individuals maximize utility by allocating time to participation in sport and all other activities (such as sleeping, sedentary leisure, working for pay, and working at home) and purchasing a bundle of goods and services subject to time and budget constraints. The utility function is U(a, t, z) where a represents the individual's decision to participate in sport; t is the amount of time spent per episode of sport activity, conditional on participation; and z represents the individual's decision to engage in all other activities, including work.

The budget constraint is Y = [F.sub.a] + [c.sub.a] at + [c.sub.z]z where Y is money income; [F.sub.a] is the fixed cost of engaging in physical activity; [c.sub.a] is the variable cost associated with engaging in sports; and cz is the cost all other goods and services. The budget constraint includes both fixed and variable costs associated with participating in sports. The fixed costs are one-time costs incurred to participate in sports but do not depend on how many times the individuals participate, such as the yearly membership fee at a golf club. Variable costs are costs that depend on the amount of time or the number of times the individual engages in physical activity, such as a golf coach's fee.

The time constraint is [[T.sup.*] = at + [theta]z where [T.sup.*] is the time available for consumption activities such as sports and [theta] is time spent consuming z. [T.sup.*], t, and [theta] are measured in the same units such as hours. If T is the total time available for work and all other activities, then [T.sup.*] = T - h where h is time spent working. If individuals can choose the amount of hours they work, then h is endogenous and wage earnings w can be expressed as follows: wh = w(T - at - [theta]z) where wages are shown in terms of total time available (T) and time spent in activities other than work. Any time spent not working reduces earnings; thus, w can be viewed as the opportunity cost of time spent engaged in non-work activities. The full budget (or income) constraint is [y.sub.0] + wT = [F.sub.a] + [p.sub.a] at + [p.sub.z]z where [y.sub.0] is exogenous income; wT is potential income if individuals spend all of their time working; pa = [c.sub.a] + w is the full cost of participating in sports activities; and [p.sub.z] = [c.sub.z] + [theta]w is the full cost of participating in other activities. Notice that the full budget constraint includes w, the opportunity cost of time.

Consumers choose a, t, and z to maximize utility subject to the full budget constraint. Recall that z encompasses all other activities that an individual chooses to spend time doing including work in the labor force and work in the home. In this sense, work is part of the choice set for all individuals. The first order conditions describing the utility maximizing choices of a, t, and z and the comparative static analysis of the effect of changes in income and the opportunity cost of time (measured by wages) on sport participation decisions are provided in the technical appendix in Humphreys and Ruseski (2011). As is the case in any comparative static analysis, the effect of changes income and wages on the participation and time spent decisions are analyzed holding all other inputs and their respective prices constant.

Consider first the effect of a change in income on the participation and time spent decisions. The direction of the effect of a change in income on both the participation and time spent decisions is ambiguous. In both cases, it depends on the relationship between the marginal utility of participating (or time spent) in physical activity and the marginal utility from other non-leisure activities like meals or watching television. Next consider the effect of a change in the opportunity cost of time on the participation and time spent decisions. In this case, the comparative static expressions have two components that are analogous to, but not as straightforward as, the income and substitution effects of a change in the price of a market good. It is important to note that this income effect results from decomposing the overall effect of a wage change into the income and substitution effects of that change. It is different from the income effect arising from a change in income.

In the standard consumer theory model of product demand, individuals purchase products. An increase in the price of a good effectively decreases the consumer's real income and, therefore, purchasing power, so we expect the consumer to purchase less of the good. In addition, the income effect of the price change is greater as the importance of the good in the consumer's budget increases. Although similar, the income effect of a change in the opportunity cost of time is more complex because it involves a labor-leisure trade-off and because it has the opposite effect on the consumer's real income. This occurs because an increase in the opportunity cost (or price) of time means a higher wage and an increase in real income. If sport participation is a normal good, then we would expect the income effect of an increase in the opportunity cost of time to be positive because we would expect individuals to trade-off labor for leisure in response to an increase in real income. On the other hand, the substitution effect is negative, which means that the likelihood of participating in sport decreases as the opportunity cost of time increases. The comparative static predictions of the model are ambiguous because the substitution and income effect move in opposite directions. The effect of a change in the opportunity cost of time on both participation and time spent in sport is positive if the income effect dominates the substitution effect or negative if the substitution effect dominates the income effect.

In summary, the model motivating our empirical analysis (taken from Humphreys and Ruseski [2011]) describes consumers' decisions about participating in physical activity and sport and time spent for all other activities, including work. Decisions about sport participation are affected by changes in the relative price of time and market goods. The signs of comparative static expressions of the effect of changes in income and the opportunity cost of time on participation and time spent in sports activities cannot be theoretically determined. We now turn to empirically analyzing the effect of economic factors and individual characteristics on decisions about participating and time spent in sports and physical activities.

Econometric Analysis of Participation and Time Spent in Sports and Physical Activities

Data Description

We use data from the CCHS Cycle 1.1 Public Use Microdata File (PUMF) in the empirical analysis. The survey is a cross-sectional survey that includes information on health status, health care utilization, and health determinants for a nationally representative sample of Canadians. Until recently, the CCHS operated on a two-year cycle. The CCHS Cycle 1.1 was conducted between September 2000 and November 2001 and included persons aged 12 or older. Seasonal effects were eliminated by randomly dividing the sample to ensure that each month of the year was properly represented for each region of the country. (Statistics Canada, 2002). The survey includes data on leisure time physical activity (primarily sports activities), work-related physical activity, smoking and drinking habits, eating habits, chronic conditions, general health status, and health care utilization. The survey also includes data on demographic factors like age, gender, marital status, ethnicity, and household composition, and on economic factors like income and labor market participation. This makes the CCHS data an ideal setting for analyzing the effect of economic factors and individual characteristics on participation in sports.

The CCHS Cycle 1.1 survey included 130,880 people. The questions about participation in different physical activities specify leisure time. The basic physical activity question in the CCHS survey is:

Have you done any of the following in the past three months?--Walking for exercise, gardening, swimming, bicycling, popular or social dance, home exercises, ice hockey, ice skating, inline skating, jogging or running, golfing, exercise class or aerobics, downhill skiing, bowling, baseball or softball, tennis, weight training, fishing, volleyball, basketball, other or no activity.

Respondents could indicate up to three "other" leisure-time physical activities. We initially define participation in sports and physical activities using this survey question. The CCHS asks further questions about the number of times individuals participated in the various physical activities and how much time (in minutes) they spent per episode. The question asking about frequency of participation is:

In the past three months, how many times did you activity--e.g. walk for exercise?

The question about duration elicits an approximation of about how much time individuals spent on each episode of reported physical activity. The possible response categories are: 1 = 1 to 15 minutes; 2 = 16 to 30 minutes; 3 = 31 to 60 minutes; 4 = more than one hour. These data provide enough detail to construct an estimate of the total time spent participating in sports activities in the past three months. We constructed a measure of minutes spent per episode by setting each categorical response to the mid-point of the range as follows:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]

Table 1 contains summary statistics on frequency, participation rates, number of participation episodes, and minutes spent per episode for the sample of adults used in our econometric analysis. The analysis sample contains 99,322 observations after dropping respondents under the age of 18, accounting for missing values, and dropping observations with unrealistically high (greater than $500 per hour) or low (less than the minimum wage in the respondent's province) hourly wages.

Walking is by far the most frequent activity with 62.51% of the sample reporting walking for exercise at least once in the past three months. Participating in more than one of these physical activities is relatively common in the CCHS. Table 2 shows the number of activities that respondents reported participating in during the past three months. Approximately 63% of the sample reported participating in multiple activities. Participating in more than four activities is relatively uncommon.

Description of Sample and Variables

Since we are interested in examining the economic factors and individual characteristics that affect the decisions about participation and time spent in sports activities, we use a subsample of the CCHS Cycle 1.1 in our empirical analysis. First, Table 1 shows considerable heterogeneity in the types of physical activities individuals participated in, participation rates, number of episodes, and time spent per episode. An empirical analysis using aggregated data for all possible activities will mask potentially important variation in the effect of economic factors and individual characteristics on decisions about participation and time spent in sports and physical activities. It may well be the case the effect of income on the decision to run is different than its effect on the decision to ski since running entails few monetary costs while skiing is relatively expensive. Rather than make ad hoc decisions about how to group activities, we focus our empirical analysis on adult participation in seven of the most common activities that are clearly sports or physical activities: walking, swimming, cycling, running, home exercise, golf, and weight lifting.

Figure 1 shows the frequency of participation and average amount of time spent in the CCHS for these seven activities. The seven activities displayed in Figure 1 differ in important ways that might affect participation and time spent. About one third of the sample reported participating in at least one of these seven activities at least one time in the past three months. The number of times that each participant reported taking part in these activities exhibits quite a bit of variation. Home exercise incorporates a wide variety of exercise activities that can be done in the home, like running on a treadmill or doing yoga. Walkers and home exercisers participated the most frequently, and swimmers and golfers the least frequently. On the other hand, walkers and home exercisers spend less time per episode of activity than swimmers and golfers. These differences in frequency of participation and time spent likely reflect differences in the total cost of participation in each activity. Home exercise does not require leaving the house, and can be done in any weather. For most individuals, swimming and golfing require travel to a pool or golf course and paying a fee to participate, thereby raising the total cost of participation. Golfing is also time intensive as it takes a few hours to play 18 holes. Cycling, weight lifting, and running frequency falls in between these two extremes. Cycling and running require going outside and also require some equipment.

[FIGURE 1 OMITTED]

Figures 2 and 3 illustrate the frequency of participation distribution for the activities in this sample. For purposes of constructing these figures, walking, home exercise, and weight lifting are grouped together as physical activities and running, cycling, swimming, and golfing are grouped together as sports activities. Note that the distribution of frequency of participation reported in the sample shows considerable skew for the four sports activities. Most of participants report participating only a few times in the previous three months, but a small number of participants report very frequent participation. Taking swimming as an example, Figure 3 shows that 60% of the swimmers in the sample reported swimming 10 or fewer times in the past three months. A small number of swimmers report participating 60 to 90 times over the past three months, which corresponds to daily, or nearly daily participation. It is also possible that the frequency distributions of participation reflect some respondent recall bias since three months is a fairly long time period over which to remember how many times they participated in any one activity.

Table 3 summarizes the sample means of the economic and demographic characteristics of participants in each of the seven activities. The final column contains averages for the entire sample for comparison. We include age, sex, marital status, education, hourly wage, household income categories, and the presence of children under age 12 in the household as covariates in our statistical models. Both personal and household income are reported in ranges in the CCHS. The ranges in the survey are less than $15,000, between $15,000 and $30,000, between $30,000 and $50,000, between $50,000 and $80,000, and greater than $80,000. Following Ruhm (2005), the level of income for each individual is coded as the midpoint of the range reported, or 150% of the unbounded top range. We use personal income and hours worked per week to construct a wage variable that we use as a proxy for the opportunity cost of time.

[FIGURE 2 OMITTED]

[FIGURE 3 OMITTED]

Walkers, swimmers, and home exercisers contain more females; cyclists, golfers, weight lifters, and runners contain more males. Participants tend to be younger than the general population. Runners and weight lifters are the youngest groups of participants, and home exercisers the oldest. The percentage of individuals with a college degree in all of the sports and physical activities is higher than that of the general population. Similarly, individuals in white collar jobs (reported occupation "management," "professional," "technical," or "administrative") comprise a larger proportion of participants in physical activities than in the general population. The reported personal and household incomes are above the sample average for all activities. Employment rates among participants is higher than the general population. The hourly wage of participants in all activities is higher than overall sample average hourly wage.

Econometric Methods

Table 2 shows that 82% of the individuals in the sample report participating in physical activity in the previous three months. Of the 99,322 individuals in our sample, 81,552 were physically active in the previous three months and 17,770 were not. Both the indicator variable for participation in physical activity and sport and the variable for the amount of time spent in physical activity in the previous month contain a large number of zeros. Our econometric analysis of participation in sport must account for the large number of zeros observed in the data.

We assume that the zeros present in the data represent "genuine zeros" as discussed in Jones (2000), meaning that the observed non-participation in physical activity in this sample is the result of the utility maximizing choices of sampled individuals. The alternative explanation for the zeros is non-observable response that occurs due to censoring. In the case of participation in physical activity, censoring would take place if the time period considered in the survey instrument was so short that some individuals who would normally participate did not participate during that time period. We assume that the three-month time period referred to in the CCHS is long enough to avoid the non-observable response problem.

Both Jones (2000) and Amemiya (1984) discuss the appropriate econometric techniques for dealing with zeros that are the result of utility maximizing decisions in survey data but use different terminology. We adopt the terminology used by Jones where "genuine zeros" call for either a two-part model, when the participation and intensity decisions are independent, or a hurdle model when they are related. The key difference between a two-part model and a hurdle model is that the participation and time spent equations are estimated separately in a two-part model but simultaneously in a hurdle model. Hurdle models are further differentiated based on assumptions about dependence of the error terms. A double hurdle model with dependence, also known as the Cragg model (Cragg, 1971), allows for factors that affect participation and factors that affect time spent to have different signs but assumes independence of the error terms. A double hurdle model with dependence, sometimes referred to as the "full double hurdle model," allows for factors that affect participation and factors that affect time spent to have different signs and for correlation in the equation error terms. This means that the unobservable factors affecting participation and time spent in physical activity can be correlated. We first estimate the parameters of the full double hurdle model using data from the CCHS. The parameters of this model can be estimated using a standard maximum likelihood approach under the assumption that the equation error terms are drawn from a normal distribution. We test the hypothesis of independent equation errors ([H.sub.0] : p = 0). The results of the Wald test indicate that the equation error terms of the participation and time spent equations are not correlated for running, home exercise, swimming, and weight lifting. The double hurdle models for these activities are then re-estimated assuming that the equation errors are independent.

A technical issue in estimating double hurdle models is the use of exclusion restrictions to identify the model. Exclusion restrictions are not strictly required for identification but including the same set of explanatory variables in both equations of the model creates difficulties in identifying the parameters and obtaining convergence. Imposing exclusion restrictions can help to improve identification. There are examples of double hurdle models estimated with and without exclusion restrictions in the literature. An exclusion restriction means excluding one or more variables from the time spent equation that appear in the participation equation. However, since the double hurdle model is not an instrumental variables estimator, the excluded variables should not be viewed as instruments for participation. No theoretical guidance exists to aid in the determination of exclusion restrictions. We use two variables, an indicator variable for individuals who walk to work, and an indicator variable for individuals who reported that their health status improved significantly or somewhat significantly over the past year, to identify the participation decision. With respect to walking to work, the intuition is that getting exercise through walking to work will likely influence the first hurdle decision of participation in additional leisure time physical activity. Somebody who walks to work may consider that to be sufficient physical activity and may choose not to engage in other physical activities. However, once the first hurdle is passed, walking to work may not influence the decision about how much time to spend per episode of physical activity. Similarly, a person who experienced improvement in health status may be more likely to decide to participate in physical activity but the improved health status may not influence the time spent decision. We also estimated the double hurdle models using only improvement in health status as an exclusion restriction and achieved convergence with qualitatively similar results.

Results and Discussion

The estimation results for the participation equation are shown by physical activity and sport in Table 4. The results for the time spent equation are presented in Table 5. The tables contain parameter estimates and asymptotic z-statistics for a two-tailed test of the null hypothesis that the parameter is equal to zero. We included province-specific effects in the models but do not report the results in Tables 4 and 5. (3)

The model identifies two economic factors, income and the opportunity cost of time as measured by hourly wage, as potentially important determinants of participation and time spent in physical activity. We begin by discussing our empirical findings about the effect of changes in income and the opportunity cost of time on participation and time spent in physical activity. We then turn our attention to our empirical findings about the effect of individual characteristics like age, sex, education, and family structure on these decisions.

Effect of Income on Participation and Time Spent

Our findings with respect to the effect of income on participation and time spent are mixed. Tables 4 and 5 show that the parameter estimates on income are mainly positive and significant in the participation equation but are mainly negative and significant in the time spent equation. We find that individuals with higher income are likely to participate in swimming, golfing, weight lifting, and running but income does not affect decisions about walking, home exercise, or cycling. A different story emerges when evaluating the effect of income on time spent. Time spent walking, exercising at home, golfing, weight lifting, and running decreases with income but income does not affect time spent cycling and swimming. Regardless of activity, the magnitude of the effect of income on time spent is not large. (4)

Our findings with respect to the effect of income on participation and time spent highlight the importance of recognizing that decisions about participation in sports and physical activity are made on two margins: the participation margin, and the time spent margin. The empirical results indicate that the effect of a change in income on participation is positive but negative on the optimal amount of time spent engaged in physical activity. This finding is important for informing policy because it suggests that consumers will respond differently to economic incentives to be physically active depending on whether the participation or time spent margin is targeted by the incentive.

We do not interpret our results as causal evidence of the effect of income on physical activity because, due to data limitations, we have not addressed the potential endogeneity of income in the model. Most previous studies of physical activity do not treat income as a potentially endogenous variable in the empirical analysis. This is likely the case because it is difficult to find suitable instruments for income in survey data. Three recent exceptions include Lechner (2009), Humphreys and Ruseski (2011), and Kosteas (2012). Lechner and Kosteas use propensity score matching to account for the endogeneity between participation in physical activity, including sport and earnings, while Humphreys and Ruseski take an instrumental variables approach. Our results, uncorrected for endogeneity, are consistent with the endogeneity corrected results in Humphreys and Ruseski (2011) where a change in income has a positive effect on participation but a negative effect on time spent. Given this consistency in results across the two studies, we do not believe that the findings about the effect of income on physical activity in this study are spurious.

Effect of Opportunity Cost of Time on Participation and Time Spent

Referring again to Tables 4 and 5, we turn next to the results for the hourly wage, which is a measure of the opportunity cost of time. Recall from the model that the effect of a change in the opportunity cost (or price) of time can be decomposed into an income and substitution effect. The income effect arising from this price change decomposition is different from the income effect arising from a change in earnings. A higher opportunity cost of time makes non-work related activities more costly and reduces the amount of time spent participating in those activities; therefore, reducing time spent participating in physical activities as the hourly wage increases indicates that the substitution effect dominates the income effect. Conversely, a positive relationship between hourly wage and time spent in physical activity is suggestive of a dominating income effect. Participation in sports entails at least some monetary costs and people with higher incomes have greater financial means to participate.

The hourly wage is generally positive and significant in both the participation and time spent equations, suggesting a dominating income effect. The effect of a change hourly wage on time spent differs depending on the activity. It is positive and significant for cycling, swimming, golfing, and running; negative and significant for walking; and insignificant for home exercise and weight lifting. Regardless of sign and significance, the effect of a change in the hourly wage on time spent is small.

It is possible that some of the effect of the opportunity cost of time on the participation and time spent decisions is reflected in the education and white collar job variables. A positive relationship between income and education has been widely documented in the economics literature. Evidence shows that more educated people tend to have higher paying (and probably white collar) jobs and higher hourly wages and, therefore, higher opportunity costs of time. We allow education to have a nonlinear effect on participation and time spent in sport by including two indicator variables. Education College is an indicator variable that is equal to 1 if the individual completed college and 0 otherwise and Education--High School is an indicator variable that is equal to 1 if the individual graduated from high school and 0 otherwise. Graduating from college has a strong positive effect on the participation decision across all activities except running. People in white collar jobs are less likely to participate in all of the activities. Completing high school is not an important factor in explaining the participation decision. Conditional on participation, occupation and education have mainly strong positive effects on time spent. People with white collar jobs spend between 4.7 and 33.5 minutes more per week engaged in sport and physical activity than people in other types of jobs. People with a high school or college education spend between 9 and 43 minutes more per week playing sports or being physically active than people with less than a high school education. If occupation and education are picking up an opportunity cost of time effect, then these results, together with the hourly wage results, provide further evidence of a dominating income effect.

Effect of Individual Characteristics and Family Structure on Participation and Time Spent We turn next to the influence of age, sex, marital status, and the presence of young children in the household on sport and physical activity. The effect of age on participation differs across sports and physical activities, but conditional on participation, time spent tends to decline with age. The decrease in time spent varies across the sports and physical activities, ranging from a decrease of 9 minutes over three months for home exercisers to 69 minutes for weight lifters. In the participation equation, older people are more likely to walk, exercise at home, swim, cycle, and lift weights but are less likely to run. Golfers are different in that age does not affect either the participation or time spent decisions. These results further highlight the importance of distinguishing between the participation (extensive) and time spent (intensive) margins when evaluating the effect of age on being physically active. Most cross-sectional studies that examine the effect of age on only the time spent in physical activity find a negative relationship between age and participation. Treating the decision as a two-part decision suggests that the mechanisms underlying the relationship between age and physical activity are complex.

Distinguishing between decisions on the extensive and intensive margins furthers our understanding of the mechanism underlying the effect of sex on physical activity A positive association between being male and physical activity has been found in many studies. Our results indicate that the effect of sex on physical activity behavior is more complex than men simply being more physically active than women. On the extensive margin, we find that women are more likely to participate in all of the sports and physical activities except for golf. The positive association between being male and physical activity occurs on the intensive margin, but only for some sports and activities. Women spend more time walking, exercising at home, and swimming than men. Men spend more time cycling, golfing, lifting weights, and running. These results are largely consistent with the results in Humphreys and Ruseski (2007). They find that males are more likely to participate in activities that take more time like group sports and outdoor recreation activities whereas females are more likely to engage in less time consuming activities.

The effect of family structure on sport participation and physical activity are measured by marital status and the presence of young children in the household. The effect of being married and having young children in the household suggests that family structure plays an important role in decisions about sport participation and physical activity, particularly on the intensive margin. Again, golfers appear to be different. Married people are more likely to play golf and spend more time playing than single people. Otherwise, marriage does not affect the participation decision. Marriage does play a role in the time spent decision. Married people spend less time in home exercise, cycling, lifting weights, and running but more time swimming and golfing than single people.

The effect of having young children in the household varies across sports and activities. We do not find a relationship between having young children and participation for walking, exercising at home, cycling, or swimming but we find that people with young children are less likely to golf, lift weights, and run. Conditional on participation, people with young children spend more time cycling and swimming but less time in the other activities. These results indicate that married couples and households with young children have different demands on their time and different opportunity costs of time than unmarried and childless people. The increase in time spent in cycling and swimming when there are young children in the house is not surprising since these are common activities for families to do together.

Summary and Policy Implications

This research examines participation and time spent in seven common sports and physical activities: walking, home exercise, cycling, swimming, golfing, weight lifting, and running by empirically examining testable implications from our consumer choice model. A number of interesting conclusions emerge from the analysis. Our findings about the effect of income on participation and time spent are mixed. Income does not have an effect on participation or time spent across all activities. However, patterns do emerge among the statistically significant variables. When significant, people with higher income are more likely to participate but, conditional on participation, spend less time. Using the wage rate as a proxy of the opportunity cost of time, we find some evidence that the income effect dominates the substitution effect as the opportunity cost of time increases.

With respect to the income, age, and sex variables, we find it is important to recognize that decisions about participation in sports and physical activity are made on two different margins: the participation margin and the time spent margin. Cross-sectional studies that are based on single equation models consistently find a positive relationship between income and participation, that older people spend less time engaged in physical activity, and that males are more likely to participate than females. Our results are consistent with these results but the new insight here is establishing where the link is occurring. In the case of income, we find that people with higher income are more likely to choose to participate in sport and physical activity but, conditional on participation, devote less time participating. The positive relationship between income and physical activity is occurring in the participation decision rather than the time spent equation. Similarly, we find that the effect of age on participation differs across sports and physical activities, but that time spent declines with age, suggesting that the well-documented negative relationship between age and participation is occurring the time spent, rather than the participation equation. These results suggest that programs aimed at increasing participation in older populations and encouraging continued participation over the life cycle might be particularly effective. Finally, we find that, with the exception of golf, women are more likely to participate in all of the sports and physical activities but that the effect of sex on time spent differs across sports.

Distinguishing between the participation and time spent margins is also important in examining the effect of family structure on physical activity decisions. We find that, with the exception of golf, marriage does not affect the participation decision. Being married does have an effect on the amount of time spent but the effect differs across activities. Married people spend less time in home exercise, cycling, lifting weights, and running but spend more time swimming and playing golf than single people. The effect of having young children on participation and time spent is also complex. We find that people with young children are less likely to play golf, lift weights, and run; however, conditional on participation, people with young children spend more time participating in family- oriented activities like riding bikes and swimming. These results provide further evidence that policy interventions designed to target these sub-populations are likely to be more effective than a "one size fits all" policy.

References

Amemiya, T. (1984). Tobit model: A survey. Journal of Econometrics, 24, 3-61.

Anokye, N., Pokhrel, S., Buxton, M., & Fox-Rushby, J. (2012). The demand for sports and exercise: Results from an illustrative survey. The European Journal of Health Economics, 13, 277-287. Balia, S., & Jones, A. (2008). Mortality, lifestyle and socio-economic status. Journal of Health Economics, 27, 1-26.

Brown, H., & Roberts, J. (2011). Exercising choice: The economic determinants of physical activity behaviour of an employed population. Social Science and Medicine, 73, 383-390. Canadian Sport Policy (2012). Canadian sport policy 2012. Technical report.

Christian, T. J. (2009). Opportunity costs surrounding exercise and dietary behaviors: Quantifying trade-offs between commuting time and health-related activities. Social Science Research Network.

Colley, R. C., Garriguet, D., Janssen, I., Craig, C. L., Clarke, J., & Tremblay, M. S. (2011). Physical activity of Canadian adults: Accelerometer results from the 2007 to 2009 Canadian health measures survey. Health Rep, 22, 7-14.

Contoyannis, P., & Jones, A. (2004). Socio-economic status, health and lifestyle. Journal of Health Economics, 23, 965-995.

Costa-Font, J., & Gil, J. (2005). Obesity and the incidence of chronic disease in Spain: A seemingly unrelated probit approach. Economics and Human Biology, 3, 188-214.

Cragg, J. (1971). Some statistical models for limited dependent variables with application to the demand for durable goods. Econometrica, 39, 829-844.

Doherty, A., & Clutterbuck, R. (2013). Chapter 24: Canada. In K. Hallmann & K. Petry (Eds.), Comparative sport development: Systems, particiaption andpublicpolicy (pp. 323-342). New York, NY: Springer.

Downward, P. (2007). Exploring the economic choice to participate in sport: Results from the 2002 General Household Survey. International Review of Applied Economics, 21, 633-653.

Downward, P., Lera-Lopez, F., & Rasciute, S. (2011). The zero-inflated ordered probit approach to modelling sports participation. Economic Modelling, 28, 2469-2477.

Downward, R, & Riordan, J. (2007). Social interactions and the demand for sport: An economic analysis. Contemporary Economic Policy, 25, 518-537.

Eberth, B., & Smith, M. (2010). Modelling the participation decision and duration of sporting activity in Scotland. Economic modelling, 27, 822-834.

Eisenberg, D., & Okeke, E. (2009). Too cold for a jog? Weather, exercise, and socioeconomic status. The B.E. Journal of Economic Analysis & Policy, 9, 1-30.

Farrell, L., & Shields, M. A. (2002). Investigating the economic and demographic determinants of sporting participation in England. Journal of the Royal Statistical Society (Series B), 165, 335-348.

Forrest, D., & McHale, I. (2011). Subjective well-being and engagement in sport: Evidence from England. In P. Rodriguez, S. Kesenne, & B. R. Humphreys (Eds.), The economics of sport, health and happiness: The promotion of well-being through sporting activities (pp. 184-199). Cheltenham, UK: Edward Elgar Publishing.

Garcia, J., Lera-Lopez, F., & Suarez, M. (2011). Estimation of a structural model of the determinants of the time spent on physical activity and sport. Journal of Sports Economics, 12, 515-537.

Gilmour, H. (2007). Physically active Canadians. Technical Report 3. Statistics Canada.

Huang, H., & Humphreys, B. (2012). Sports participation and happiness: Evidence from US microdata. Journal of Economic Psychology, 33, 776-793.

Humphreys, B., & Ruseski, J. (2007). Participation in physical activity and government spending on parks and recreation. Contemporary Economic Policy, 25, 538-552.

Humphreys, B. R., McLeod, L., & Ruseski, J. E. (2014). Physical activity and health outcomes: Evidence from Canada. Health Economics, 23, 33-54.

Humphreys, B. R., & Ruseski, J. E. (2011). An economic analysis of participation and time spent in physical activity. The BE Journal of Economic Analysis & Policy, 11, 1-47.

Jones, A. M. (2000). Health econometrics. Handbook of health economics, 1, 265-344.

Kavetsos, G. (2011). Physical activity and subjective well-being: An empirical analysis. In P. Rodriguez, S. Kesenne, & B. R. Humphreys (Eds.), The economics of sport, health and happiness: The promotion of well-being through sporting activities (pp. 213-222). Cheltenham, U K : Edward Elgar Publishing.

Kosteas, V. (2012). The effect of exercise on earnings: Evidence from the NLSY. Journal of Labor Research, 33, 225-250.

Kuvaja-Kollner, V., Valtonen, H., Komulainen, P., Hassinen, M., & Rauramaa, R. (2013). The impact of time cost of physical exercise on health outcomes by older adults: The DR's EXTRA Study. The European Journal of Health Economics, 14, 471-479.

Lechner, M. (2009). Long-run labour market and health effects of individual sports activities. Journal of Health Economics, 28, 839-854.

Lechner, M., & Downward, P. (2013). Heterogeneous sports participation and labour market outcomes in England. IZA Discussion Paper 7690, Bonn.

Lera-Lopez, F., & Rapun-Garate, M. (2007). The demand for sport: Sport consumption and participation models. Journal of Sport Management, 21, 103-122.

Maruyama, S., & Yin, Q. (2012). The opportunity cost of exercise: Do higher-earning Australians exercise longer, harder, or both? Health Policy, 106, 187-194.

Meltzer, D., & Jena, A. (2010). The economics of intense exercise. Journal of Health Economics, 29, 347-352.

Pawlowski, T., Breuer, C., & Leyva, J. (2011). Sport opportunities and local well-being: Is sport a local amenity? In P. Rodrguez, S. Kesenne, & B. R. Humphreys (Eds.), The economics of sport, health and happiness: The promotion of well-being through sporting activities (pp. 223-241). Cheltenham, UK: Edward Elgar Publishing.

Pawlowski, T., Breuer, C., Wicker, P., & Poupaux, S. (2009). Travel time spending behavior in recreational sports--An econometric approach with management implications. European Sport Management Quarterly, 9, 215-242.

Pawlowski, T., Downward, P., & Rasciute, S. (2011). Subjective well-being in European countries on the age-specific impact of physical activity. European Review of Aging and Physical Activity, 8, 93-102.

Rasciute,S., & Downward, P. (2010). Health or happiness? What is the impact of physical activity on the individual? Kyklos, 63, 256-270.

Ruhm, C. (2005). Healthy living in hard times. Journal of Health Economics, 24, 341-363.

Ruseski, J., & Humphreys, B. (2011). Participation in physical activity and health outcomes: Evidence from the Canadian Community Health Survey. In P. Rodrguez, S. Kesenne, & B. R. Humphreys (Eds.), The economics of sport, health and happiness: The promotion of wellbeing through sporting activities (pp. 7-32). Cheltenham, UK: Edward Elgar Publishing.

Ruseski, J. E., & Razavilar, N. (2013). United States. In K. Hallmann & K. Petry (Eds.), Comparative sport development: Systems, participation and public policy (pp. 311-322). New Yo r k, NY: Springer.

Sari, N. (2009). Physical inactivity and its impact on healthcare utilization. Health Economics, 18, 885-901.

Sarma, S., Devlin, R. A., Gilliland, J., Campbell, M. K., & Zaric, G. S. (2013). The effect of leisure-time physical activity on obesity, diabetes, high bp and heart disease among Canadians: Evidence from 2000/01 to 2005/06. Canadian Centre for Health Economics Working Paper #2013-01.

Statistics Canada (2012). CANSIM Table 105-0501, Health indicator profile. Technical report. Tremblay, M. S., Warburton, D. E., Janssen, I., Paterson, D. H., Latimer, A. E., Rhodes, R. E.,... Dugan, M. (2011). New Canadian physical activity guidelines. Applied Physiology, Nutrition, and Metabolism, 36, 36-46.

World Health Organization (2010). Physical inactivity: A global public health problem. Retrieved from http://www.who.int/dietphysicalactivity/factsheet_inactivity/en/

Endnotes

(1) Canada's physical activity guidelines state that adults aged 18-64 years should accumulate at least 150 minutes of moderate- to-vigorous-intensity aerobic physical activity per week, in bouts of 10 minutes or more to achieve health benefits (Tremblay et al., 2011). These guidelines are different from the definitions of active and moderately active in the CCHS.

(2) The Cragg model, proposed by Cragg (1971), is a double hurdle model that allows for the same factors (for example, education) that affect both participation and time spent to have different signs but assumes independence of the equation error terms.

(3) The full set of results is available from the authors on request.

(4) Although participation in hockey is low (only 2.97% of the sample), we did estimate a model for hockey because it is a high profile sport in Canada. We find that the effect of changes in income and wages are positive and statistically significant in both the participation and time spent decisions.

Brad R. Humphreys [1] and Jane E. Ruseski [1]

[1] West Virginia University

Brad. R. Humphreys, PhD, is an associate professor of economics. His research interests include the economic impact of professional sports teams and facilities, the effect of regulations on intercollegiate athletics, and the economic determinants of sport participation.

Jane E. Ruseski, PhD, is an associate professor of economics. Much of her current research studies the socioeconomic determinants of health and (un)healthy behaviors, the effect of health behaviors on outcomes, and the mechanisms underlying health behaviors.
Table 1. Distribution of Physical Activities

Activity          Frequency     Participation
                                    Rate

Walking             62082          62.51%
Gardening           42165          42.45%
Home Exercise       21718          21.87%
Cycling             16211          16.32%
Swimming            16112          16.22%
Dancing             14105          14.20%
Golf                10558          10.63%
Fishing              9324           9.39%
Weight Lifting       8923           8.98%
Running              7748           7.80%
Other 1              7652           7.70%
Other 2              7138           7.18%
Bowling              6711           6.75%
Aerobics             5902           5.94%
Softball             3854           3.88%
Skating              3729           3.75%
Inline Skating       3307           3.33%
Hockey               2947           2.97%
Skiing               2572           2.59%
Volleyball           2469           2.49%
Basketball           2438           2.45%
Tennis               2180           2.19%
Other 3              1274           1.28%

Activity             Times       Minutes Spent
                  Participated    per Episode

Walking              46.08           37.57
Gardening            24.51           53.09
Home Exercise        39.78           25.88
Cycling              20.11           43.86
Swimming             14.03           45.63
Dancing               6.93           64.93
Golf                 10.69           73.76
Fishing               7.65           72.59
Weight Lifting       30.20           41.41
Running              23.58           33.03
Other 1              19.33           62.72
Other 2              20.22           63.80
Bowling               6.06           70.51
Aerobics             23.20           50.03
Softball             10.03           69.17
Skating               6.52           55.30
Inline Skating       10.61           49.52
Hockey               13.24           67.38
Skiing                6.14           72.83
Volleyball            7.45           63.78
Basketball           10.16           53.25
Tennis                9.47           62.37
Other 3              19.32           62.62

# of Observations 99,322

Table 2. Distribution of Number of Activities

Number of Activities     Frequency     Percent

0                          17,770       17.89
1                          19,206       19.34
2                          18,801       18.93
3                          14,618       14.72
4                          9,961        10.03
5                          6,647         6.51
6                          4,252         4.28
7                          2,878         2.90
8                          1,916         1.93
9                          1,287         1.30
10                          827          0.83
11                          584          0.59
12                          288          0.29
13                          204          0.21
14                          120          0.12
15                           71          0.07
16                           34          0.03
17                           23          0.02
18                           11          0.01
19                           4           0.00

# of Observations          99,322

Table 3: Summary Statistics on Participants

                                            Home
Variable                     Walkers     Exercisers    Cyclists

% Male                        40.61%       38.68%       54.74%
Age                            49.4         47.6         41.9
% Married                     59.94%       58.87%       62.06%
% HS Graduate                 18.02%       17.78%       17.12%
% College Graduate            49.87%       54.83%       59.35%
% Employed                    61.93%       65.36%       80.12%
% in "White Collar" Jobs      34.26%       38.22%       44.28%
Hours Worked                   40.0         40.0         41.1
Personal Income (000s)        32.582       34.145       40.007
Household Income (000s)       53.114       53.302       56.455
Hourly Wage                    14.2         15.3         19.0
% Young Children              23.4%        24.7%        33.3%
Participants                  62,080       21,717       16,211

                                                        Weight
Variable                     Swimmers     Golfers      Lifters

% Male                        43.88%       68.21%       56.76%
Age                            42.0         44.7         38.0
% Married                     64.82%       67.28%       54.86%
% HS Graduate                 18.32%       19.98%       17.01%
% College Graduate            61.08%       60.87%       65.00%
% Employed                    78.16%       81.01%       87.92%
% in "White Collar" Jobs      44.66%       46.71%       52.36%
Hours Worked                   40.8         44.2         42.2
Personal Income (000s)        38.895       48.606       44.639
Household Income (000s)       56.069       54.147       54.285
Hourly Wage                    18.4         20.9         21.2
% Young Children              38.8%        25.9%        28.1%
Participants                  16,108       10,558       8,923

Variable                     Runners      Overall

% Male                        57.55%       46.16%
Age                            36.4         50.1
% Married                     57.50%       59.94%
% HS Graduate                 16.30%       18.07%
% College Graduate            65.78%       46.32%
% Employed                    90.17%       56.86%
% in "White Collar" Jobs      53.70%       30.76%
Hours Worked                   42.2         41.0
Personal Income (000s)        45.140       32.002
Household Income (000s)       52.929       52.488
Hourly Wage                    21.7         12.7
% Young Children              33.0%        23.3%
Participants                  7,748        99,322

Notes: % Young Children: children under age 12 in household; Hourly
wage for employed persons only

Table 4. Parameter Estimates and z-statistics-Participation Equation

                                              Home
Variable                     Walkers       Exercisers      Cyclists

Age                         0.0131 ***     0.0181 ***     0.0203 ***
                              (8.37)         (7.06)         (4.23)
Male                        -0.532 ***     -0.731 ***     -0.874 ***
                             (-12.89)       (-10.22)       (-6.57)
Married                      -0.0121         0.0302         -0.064
                             (-0.31)         (0.41)        (-0.51)
Wage                          0.807        0.302 ***      0.347 ***
                              (0.88)         (8.68)         (8.41)
Household Income (000)       0.00067         0.0017       -0.000139
                              (1.34)         (1.96)        (-0.14)
White Collar Job            -2.417 ***     -1.941 ***     -1.947 ***
                             (-23.11)       (-12.43)       (-9.42)
Education--College          0.210 ***      0.477 ***       0.302 **
                              (4.9)          (6.1)          (3.19)
Education--High School       -0.0141        0.216 *         0.147
                             (-0.28)         (2.31)         (1.27)
Young Children               -0.0802        -0.0661         -0.109
                             (-1.36)        (-0.65)        (-0.97)
Improvement in Health       0.560 ***      0.820 ***       0.370 **
  Status                      (8.74)         (7.1)          (3.1)
Walk to Work                4.040 ***        4.454        2.775 ***
                              (5.2)          (1.06)         (6.06)
Participants                  62,080         21,717         16,211
Log likelihood              -596497.6      -222800.9      -168473.6
                              -0.08          0.029          0.216
Wald Test                   15.84 ***         0.38        16.91 ***

Variable                     Swimmers       Golfers

Age                          0.0147 *      -0.000584
                              (2.42)        (-0.29)
Male                        -0.768 ***      -0.0352
                             (-7.66)        (-0.60)
Married                      -0.0386       0.425 ***
                             (-0.31)         (8.22)
Wage                        0.487 ***      0.544 ***
                              (3.35)         (4.19)
Household Income (000)      0.00362 **    0.00453 ***
                              (3.27)         (6.76)
White Collar Job              -1.849       -1.987 ***
                             (-11.09)       (-9.00)
Education--College          0.444 ***      0.707 ***
                              (4.15)         (12.1)
Education--High School       0.301 *       0.501 ***
                              (2.5)          (7.15)
Young Children                0.0216       -0.464 ***
                              (0.19)        (-4.75)
Improvement in Health       0.418 ***        0.0182
  Status                      (3.49)         (0.26)
Walk to Work                1.892 ***      0.609 ***
                              (4.92)        (11.77)
Participants                  16,108         10,558
Log likelihood              -162617.9      -112042.7
                              0.045          0.0491
Wald Test                      0.94        174.02 ***

                              Weight
Variable                     Lifters        Runners

Age                         0.00810 *     -0.0149 ***
                              (2.21)        (-3.43)
Male                        -0.386 ***     -0.401 ***
                             (-4.82)        (-3.40)
Married                       -0.167         0.177
                             (-1.80)         (1.56)
Wage                        0.0807 ***     0.149 ***
                              (8.72)         (7.31)
Household Income (000)      0.00223 **     0.00245 *
                              (3.28)         (2.5)
White Collar Job            -0.344 **      -1.027 ***
                             (-2.92)        (-5.94)
Education--College           0.190 *         0.188
                              (2.21)         (1.72)
Education--High School        -0.025         0.0172
                             (-0.25)         (0.13)
Young Children               -0.188 *       -0.228 *
                             (-2.42)        (-2.13)
Improvement in Health       0.935 ***      0.460 ***
  Status                     (10.59)         (4.03)
Walk to Work                0.678 ***      1.335 ***
                              (9.43)         (9.84)
Participants                  8,923          7,748
Log likelihood               -98010.5       -82862.6
                              0.028          -0.023
Wald Test                      0.23           0.1

Participation: Indicator variable for participation in sport or
physical activity

* p <0.05; ** p <0.01; *** p <0.001

Table 5. Parameter Estimates and z-statistics-Time Spent Equation

                                             Home
Variable                    Walkers       Exercisers      Cyclists

Age                         1.780 **      -9.357 ***     -30.20 ***
                             (2.67)        (-14.58)       (-36.22)
Male                       -441.7 ***     -261.9 ***     553.7 ***
                            (-25.08)       (-14.44)       (27.26)
Married                      -4.269       -93.37 ***     -65.22 **
                            (-0.23)        (-4.98)        (-2.99)
Wage                       -1.655 ***      -0.0443       3.418 ***
                            (-3.57)        (-0.10)         (6.99)
Household Income (000)     -0.663 ***     -0.725 ***       -0.25
                            (-3.42)        (-3.72)        (-1.18)
White Collar Job             5.638        110.2 ***      181.6 ***
                             (0.28)         (5.29)         (8.12)
Education--College         216.4 ***      294.4 ***      270.8 ***
                            (10.29)        (13.41)        (10.83)
Education--High School     132.7 ***      107.4 ***        55.25
                             (5.2)          (4.04)         (1.82)
Young Children             -294.2 ***     -119.1 ***     82.36 ***
                            (-13.05)       (-5.24)         (3.43)
Participants                 62,080         21,717         16,211
Log likelihood             -596497.6      -222800.9      -168473.6
P                            -0.08          0.029          0.216
Wald Test                  15.84 ***         0.38        16.91 ***

Variable                    Swimmers       Golfers

Age                        -17.18 ***        0.52
                            (-20.27)        (0.54)
Male                       -99.05 ***     929.0 ***
                            (-6.54)        (39.31)
Married                     56.38 **      165.6 ***
                             (3.18)         (6.69)
Wage                       2.229 ***      2.827 ***
                             (5.68)         (5.85)
Household Income (000)       -0.278       -1.855 ***
                            (-1.58)        (-8.40)
White Collar Job           56.70 ***      241.2 ***
                             (3.38)        (10.29)
Education--College         355.0 ***      361.7 ***
                            (15.17)        (12.44)
Education--High School     177.1 ***      336.2 ***
                             (6.78)         (9.86)
Young Children             323.5 ***      -231.9 ***
                            (17.88)        (-8.67)
Participants                 16,108         10,558
Log likelihood             -162617.9      -112042.7
P                            0.045          0.0491
Wald Test                     0.94        174.02 ***

                             Weight
Variable                    Lifters        Runners

Age                        -68.93 ***     -50.03 ***
                            (-34.56)       (-29.56)
Male                       831.2 ***      505.6 ***
                            (17.43)        (17.43)
Married                    -261.9 ***     -118.7 ***
                            (-5.10)        (-3.62)
Wage                         1.185        3.465 ***
                             (1.27)         (4.97)
Household Income (000)     -2.561 ***     -2.787 ***
                            (-6.36)        (-10.15)
White Collar Job           391.2 ***      401.8 ***
                             (8.07)        (13.66)
Education--College         513.9 ***      408.4 ***
                             (8.52)        (10.78)
Education--High School     250.2 ***          80
                             (3.52)         (1.77)
Young Children             -375.8 ***     -114.6 ***
                            (-7.49)        (-3.65)
Participants                 8,923          7,748
Log likelihood              -98010.5       -82862.6
P                            0.028          -0.023
Wald Test                     0.23           0.1

Time spent: exercise minutes last three months

* p <0.05; ** p<0.01; *** P<0.001
COPYRIGHT 2015 Fitness Information Technology Inc.
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2015 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Author:Humphreys, Brad R.; Ruseski, Jane E.
Publication:International Journal of Sport Finance
Article Type:Report
Date:May 1, 2015
Words:10526
Previous Article:A tale of three cities: intra-game ratings in winning, losing, and neutral markets.
Next Article:Testing profitability in the NBA season wins total betting market.
Topics:

Terms of use | Copyright © 2018 Farlex, Inc. | Feedback | For webmasters