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What Affects Perceived Health Risk Attitude During the Pandemic: Evidence From Migration and Dining Behavior in China
Xiaoguang Chen1, William A V Clark2, Jingye Shi1
1Research Institute of Economics and Management, Southwestern University of Finance and Economics, Chengdu, China.
Dining out behavior in China during the COVID-19 pandemic revealed individual risk attitudes toward health threats. This behavior significantly predicted consumption and highlighted confidence in domestic safety measures, contrasting with concerns about international travel.
Area of Science:
- Behavioral Economics
- Public Health
- Epidemiology
Background:
- The COVID-19 pandemic induced significant behavioral shifts impacting work, leisure, and economic activities.
- Changes in social behaviors, like dining out, have substantial economic and employment implications.
Purpose of the Study:
- To investigate how individuals in China responded to pandemic-related health risks by examining dining-out decisions.
- To assess dining-out behavior as a predictor of economic recovery and individual risk perception.
Main Methods:
- Analysis of dining-out behavior as a proxy for health risk attitude during the pandemic.
- Comparison of dining-out behavior with traditional financial risk measures.
- Examination of the influence of domestic and international migration patterns on dining-out decisions.
Main Results:
- Dining out behavior was a more significant predictor of pandemic health risk attitude and future consumption than financial risk measures.
- Positive association between domestic in-migration and dining out, indicating confidence in internal safety.
- Negative association between international migration/port city status and dining out, reflecting heightened virus risk perception.
Conclusions:
- Dining out behavior serves as a valuable indicator of public response to health risks and economic recovery.
- Government policies, particularly border controls, were crucial for economic resilience.
- Understanding city and household characteristics is key to analyzing pandemic-related behavioral changes.
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