Research question: The current study investigates the income elasticities and socio-economic determinants of direct and indirect sports expenditure categories by means of a log normal hurdle regression. Research methods: The data stem from a representative sample of 3005 Flemish families with school-aged children, gathered through a sports-specific survey. A log normal hurdle regression was used to calculate the determining factors and expenditure elasticities of expenditure on sports participation. Results and findings: The results indicate that income, education and the age of the youngest child are positively related to almost all sports expenditure categories, while the number of family members and degree of urbanisation are significant for only a number of the expenditure categories. The elasticity value of the direct sports expenses is smaller than is the case for indirect sports expenditure. Between the expenditure categories large differences exist, as relatively large elasticities are found for sports holidays, transport and sports food and drinks, as opposed to low values of sports events, sports club membership, entrance fees for sports infrastructure, sports camps, clothing, footwear and equipment. Implications: The fact that income significantly influences all expenditure categories demonstrates that further policy intervention is required to make sports consumption more accessible to lower income groups. Sports enterprises and policymakers need to be aware that negative income shifts have a more profound impact on the indirect expenditure categories, and that certain sports activities (e.g. participation events) are relatively more favoured by low-income groups than is the case for sports club membership
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Several studies show that logistics facilities have spread spatially from relatively concentrated clusters in the 1970s to geographically more decentralized patterns away from urban areas. The literature indicates that logistics costs are one of the major influences on changes in distribution structures, or locations and usage of logistics facilities. Quantitative modelling studies that aim to describe or predict these phenomena in relation to logistics costs are lacking, however. This is relevant to design more effective policies concerning spatial development, transport and infrastructure investments as well as for understanding environmental consequences of freight transport. The objective of this paper is to gain an understanding of the responsiveness of spatial logistics patterns to changes in these costs, using a quantitative model that links production and consumption points via distribution centers. The model is estimated to reproduce observed use of logistics facilities as well as related transport flows, for the case of the Netherlands. We apply the model to estimate the impacts of a number of scenarios on the spatial spreading of regional distribution activity, interregional vehicle movements and commodity flows. We estimate new cost elasticities, of the demand for trade and transport together, as well as specifically for the demand for the distribution facility services. The relatively low cost elasticity of transport services and high cost elasticity for the distribution services provide new insights for policy makers, relevant to understand the possible impacts of their policies on land use and freight flows.
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.
