Rural China under the COVID‐19 pandemic: Differentiated impacts, rural–urban inequality and agro‐industrialization

We use field data collected in a village in northern China to examine the impacts of the COVID‐19 pandemic on rural economy and livelihoods. The lockdown effectively protected the village from the pandemic, resulting in zero infection. The economic impacts were mostly negative but differentiated acr...

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Bibliographic Details
Main Authors: ZHANG, Qian Forrest, HU, Zhanping
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2021
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Online Access:https://ink.library.smu.edu.sg/soss_research/3433
https://ink.library.smu.edu.sg/context/soss_research/article/4690/viewcontent/joac.12425.pdf
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Institution: Singapore Management University
Language: English
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Summary:We use field data collected in a village in northern China to examine the impacts of the COVID‐19 pandemic on rural economy and livelihoods. The lockdown effectively protected the village from the pandemic, resulting in zero infection. The economic impacts were mostly negative but differentiated across economic sectors and livelihood strategies; some gained from the business opportunities arising from the pandemic. Wage loss for migrant workers was the most common negative impact but lasted less than 2 months. Overall, rural China has escaped the worst impacts of the pandemic found in other developing countries. We argue that the structure of inequalities in today's China, now primarily based on market differentiations, has become far more complex than the simplistic rural–urban dichotomy based on the household registration (hukou) status can possibly explain. Rather than exacerbating the hukou‐based rural–urban inequality, the true impacts of the pandemic on rural China are more likely to result from the reforms of the agri‐food system. The suspected link between the new pathogen and the food system has created a misinformed urgency for the government to accelerate the industrialization of the food system, which we think can backfire and increase the likelihood of the next outbreak.