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Consumption changes associated with the Shanghai F1 Grand Prix are concentrated in the few days around the race. Accordingly, this paper examines publicly reported indicators for accommodation, transportation, commercial retail, food and leisure, and tourism and entertainment within three- to seven-day event windows. The dataset contains 46 observations from 2017-2019 and 2024-2026, each linked to a source URL, and 40 observations meet the criteria for the main regression.The remaining six cover periods longer than seven days, duplicate other indicators, report an extreme value for a single outlet, or cover fewer than three days, and are retained only in raw-data sheet for verification. Five early observations in the main sample are calculated from reported attendance figures.The Shanghai race was suspended from 2020 to 2023, those years are left blank. The regression uses l n ( 1 + g i 100 ) as the dependent variable, the regression includes a no-intercept industry dummy variables and applies HC3 standard errors. The estimated geometric mean increases are 74.59% for accommodation, 32.84% for transportation, 32.45% for commercial retail, 81.68% for food and leisure, and 17.85% for tourism and entertainment. Accommodation, transportation, commercial retail, and tourism and entertainment are statistically significant at the 5% level, whereas the food-and-leisure group has only three observations and a wide confidence interval. Thirty-four observations are either reported precisely or can be reconstructed from attendance counts. Monthly fixed-effects models for 2014-2025 and a Shanghai-Shenzhen comparison present a different picture: some local indicators rise during race week, but net effect on citywide monthly consumption is not stable.
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Shengdong Huang (2026) studied this question.
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