The purpose of this article is to explore the determining factors of household expenditures on sports participation. Due to a relatively large amount of zero-expenditures, simple regression methods are not suited. Because of methodological reasons, the two-step Heckman approach is used over the Tobit approach and the Double Hurdle approach. The participation decision (spend money or not) is influenced by sports participation of the parents, family income, education, sports club membership, and sports frequency. Determining factors of the intensity decision (amount of money that is spent on sports participation) are family income, sports participation of parents during their youth, sports club membership, sports frequency, age of youngest child, and household size. Moreover, the results indicate that a two-stage approach is needed because it gives a more in-depth insight in the household spending behavior. For example, higher educated households more often spend money on sports participation. However, this research demonstrates that once higher educated households have decided to spend money on sports participation, the amount of money spent does not differ from lower educated households.
Given the recent economic crisis and the risen poverty rates, sports managers need to get insight in the effect of income and other socio-economic determinants on the household time and money that is spent on sports participation. By means of a Tobit regression, this study analyses the magnitude of the income effect for the thirteen most practiced sports by households in Flanders (the Dutch speaking part of Belgium), which are soccer, swimming, dance, cycling, running, fitness, tennis, horse riding, winter sports, martial arts, volleyball, walking and basketball. The results demonstrate that income has a positive effect on both time and money expenditure on sports participation, although differences are found between the 13 sports activities. For example, the effect of income on time and money expenditure is relatively high for sports activities like running and winter sports, while it is lower for other sports such as fitness, horse riding, walking and swimming. Commercial enterprises can use the results of this study to identify which sports to focus on, and how they will organise their segmentation process. For government, the results demonstrate which barriers prevent people from taking part in specific sports activities, based upon which they should evaluate their policy decisions.
It is unclear to what extent self-employed choose to become self-employed. This study aimed to compare the health care expenditures-as a proxy for health-of self-employed individuals in the year before they started their business, to that of employees. Differences by sex, age, and industry were studied. In total, 5,741,457 individuals aged 25-65 years who were listed in the tax data between 2010 and 2015 with data on their health insurance claims were included. Self-employed and employees were stratified according to sex, age, household position, personal income, region, and industry for each of the years covered. Weighted linear regression was used to compare health care expenditures in the preceding (year x-1) between self-employed and employees (in year x). Compared with employees, expenditures for hospital care, pharmaceutical care and mental health care were lower among self-employed in the year before they started their business. Differences were most pronounced for men, individuals ≥40 years and those working in the industry and energy sector, construction, financial institutions, and government and care. We conclude that healthy individuals are overrepresented among the self-employed, which is more pronounced in certain subgroups. Further qualitative research is needed to investigate the reasons why these subgroups are more likely to choose to become self-employed.
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