Dataset Information

The COVID-19 pandemic and unemployment: Evidence from mobile phone data from China.

ABSTRACT: Based on mobile phone records for 71 million users and location tracking information for one million users over almost three years, this study examines the labor market impacts of the COVID-19 pandemic in China's Guangdong province, whose GDP is larger than that of all but the top 12 countries in the world. Using a standard difference-in-differences framework, our analysis shows dramatic and protracted effects of the pandemic on the labor market: it increased unemployment by 72% and unemployment benefits claims by 57% even after the full reopening in 2020 relative to their levels in the same period in 2019. The impact was also highly heterogeneous, with women, workers older than 40, and migrants being more affected. Cities that rely more on export or that have a higher share of the hospitality industry in GDP but a lower share of the finance and healthcare industries experienced a more pronounced increase in unemployment. The lingering impact likely reflects the global transmission of the pandemic's effects through the supply chain and trade channels.

SUBMITTER: Li T

PROVIDER: S-EPMC9988403 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature

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Publications

The COVID-19 pandemic and unemployment: Evidence from mobile phone data from China.

Li Teng T Barwick Panle Jia PJ Deng Yongheng Y Huang Xinfei X Li Shanjun S

Journal of urban economics 20230307

Based on mobile phone records for 71 million users and location tracking information for one million users over almost three years, this study examines the labor market impacts of the COVID-19 pandemic in China's Guangdong province, whose GDP is larger than that of all but the top 12 countries in the world. Using a standard difference-in-differences framework, our analysis shows dramatic and protracted effects of the pandemic on the labor market: it increased unemployment by 72% and unemployment ...[more]

PMID: 36910460

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Project description:BackgroundRestricting human mobility is an effective strategy used to control disease spread. However, whether mobility restriction is a proportional response to control the ongoing COVID-19 pandemic is unclear. We aimed to develop a model that can quantify the potential effects of various intracity mobility restrictions on the spread of COVID-19.MethodsIn this modelling study, we used anonymous and aggregated mobile phone sightings data to build a susceptible-exposed-infectious-recovered transmission model for COVID-19 based on the city of Shenzhen, China. We simulated how disease spread changed when we varied the type and magnitude of mobility restrictions in different transmission scenarios, with variables such as the basic reproductive number (R 0), length of infectious period, and the number of initial cases.Findings331 COVID-19 cases distributed across the ten regions of Shenzhen were reported on Feb 7, 2020. In our basic scenario (R 0 of 2·68), mobility reduction of 20-60% within the city had a notable effect on controlling COVID-19 spread: a flattening of the peak number of cases by 33% (95% UI 21-42) and delay to the peak number by 2 weeks with a 20% restriction, 66% (48-75) reduction and 4 week delay with a 40% restriction, and 91% (79-95) reduction and 14 week delay with a 60% restriction. The effects of mobility restriction were increased when combined with reductions of 25% or 50% in transmissibility of the virus. In specific analyses of mobility restrictions for individuals with symptomatic infections and for high-risk regions, these measures also had substantial effects on reducing the spread of COVID-19. For example, the peak of the epidemic was delayed by 2 weeks if the proportion of individuals with symptomatic infections who could move freely was maintained at 20%, and by 4 weeks if two high-risk regions were locked down. The simulation results were also affected by various transmission parameters.InterpretationOur model shows the effects of various types and magnitudes of mobility restrictions on controlling COVID-19 outbreaks at the city level in Shenzhen, China. The model could help policy makers to establish the optimal combinations of mobility restrictions during the COVID-19 pandemic, especially to assess the potential positive effects of mobility restriction on public health in view of the potential negative economic and societal effects.FundingGuangdong Medical Science Fund, and National Natural Science Foundation of China.

Dataset Information

The COVID-19 pandemic and unemployment: Evidence from mobile phone data from China.

Publications

The COVID-19 pandemic and unemployment: Evidence from mobile phone data from China.

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