The Economic Multiplier Effect of Mega- Sporting Events: A Case Study of the Formula 1 Shanghai Grand Prix on Urban Tourism and Consumer Spend
DOI:
https://doi.org/10.61173/v0dpkx46Keywords:
Formula One Shanghai Grand Prix, Multiplier effects, Urban tourism, Consumer spendingAbstract
This study explored the economic multiplier effect of the Formula 1 (F1) Shanghai Grand Prix on the local tourism industry, filling the research gap in the literature on the economic multiplier effect of large-scale events. By combining Keynesian multiplier theory with empirical data (including official reports and responses to 209 questionnaires), the analysis results show that the 2024 event brought a direct economic impact of 1.406 billion yuan and an indirect output of 3.928 billion yuan, creating 9,857 jobs. The theoretical multiplier (3.12) is higher than the observed multiplier (2.793), because of the economic leakages such as international expenses (eg FIA licensing fees), imports, and cross-border expenditures which together account for 34.6% of the total economic impact. Although 78% of the participants were non-local tourists, which promoted the short-term tourism in Shanghai and the Yangtze River Delta region, significant challenges also emerged, including traffic congestion (reported by 69% of residents) and noise pollution. The survey results indicated that tourists were highly satisfied (88.7% willing to visit again), but also identified concerns about infrastructure and prices. In conclusion, the recommendations for sustained development include (1) minimizing leakages by strengthening local supply chains (2) increasing the marginal propensity to consume through targeted consumption vouchers; (3) promoting regular route usage (eg. car tourism); (4) introducing community compensation mechanisms. This study emphasises that safeguarding residents’ wellbeing alongside pursuing economic gains is essential for ensuring long-term viability. Limitations include the restricted sample size and limited accessibility of relevant data.