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Lock-down or let-it-flow: a spatial analysis of Covid-19 on Chinese economy

This paper studies the impacts of the outbreak of the Covid-19 and the lock-down policy on the Chinese economy. Combining the changes of daily migration flow data, industrial outputs data, population census data, and a spatial equilibrium model, we quantify the impact of the lock-down policy on the spatial spread of Coronavirus as well as regional economic outputs. We first show that the lock-down policy significantly reduces the economic outputs of cities that lost a massive number of migrant workers as well as the spread of Coronavirus in these larger cities. We then build a spatial equilibrium model featuring migration with heterogeneous skills, endogenous medical constraints, and endogenous virus spreading paths. The model combines the Gravity model in Ahlfeldt et al. (2015) and the SIR model in epidemic studies to quantify the welfare tradeoff from different policies. Our quantitative results indicate that a very restrictive lock-down policy generates a massive economic loss initially comparing to what we call the let-it-flow policy, which almost does not restrain population flow. However, the let-it-flow policy causes much more persistent economic damage comparing to the lock-down policy. Therefore, the welfare gain from the lock-down policy depends on the medical constraints and the SIR characteristics of the virus. Feeding the current population flow, medical conditions, and virus characteristics data into the model, we show that the lock-down policy is superior to let-it-flow policy in China. Our quantitative results also indicate that there are distributional effects among workers with different skills, and there is room for potential welfare improvement from a counterfactual partial lockdown policy.

Lead investigator:

Minjie Deng

Affiliation:

University of Rochester

Primary topic:

Recession & recovery

Secondary topic:

Jobs, work, pay & benefits

Region of data collection:

Asia and Oceania

Country of data collection

China

Status of data collection

Complete

Type of data being collected:

Publicly available

Unit of real-time data collection

Region/State

Start date

1/2019

End date

4/2020

Frequency

Daily