Data and Code for Optimal Lockdown in a Commuting Network

Principal Investigator(s):  View help for Principal Investigator(s) Pablo Fajgelbaum, Princeton; Amit Khandelwal, Columbia GSB; Wookun Kim, Southern Methodist University; Cristiano Mantovani, Universitat Pompeu Fabra; Edouard Schaal, CREI

Version:  View help for Version V1

Name File Type Size Last Modified
  replication 03/26/2021 05:17:PM

Project Citation: 

Fajgelbaum, Pablo, Khandelwal, Amit, Kim, Wookun, Mantovani, Cristiano, and Schaal, Edouard. Data and Code for Optimal Lockdown in a Commuting Network. Nashville, TN: American Economic Association [publisher], 2021. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2021-11-23. https://doi.org/10.3886/E127341V1

Project Description

Summary:  View help for Summary
We study optimal dynamic lockdowns against Covid-19 within a commuting network. Our framework integrates canonical spatial epidemiology and trade models, and is applied to cities with varying initial viral spread: Seoul, Daegu and NYC-Metro. Spatial lockdowns achieve substantially smaller income losses than uniform lockdowns. In NYM and Daegu—with large initial shocks—the optimal lockdown restricts inflows to central districts before gradual relaxation, while in Seoul it imposes low temporal but large spatial variation. Actual commuting reductions were too weak in central locations in Daegu and NYM, and too strong across Seoul
.
Funding Sources:  View help for Funding Sources European Research Council (OPTNETSPACE - 804095); Spanish Ministry of Economy and Competitiveness (CEX2019-000915-S); Government of Catalonia (2017-SGR-1393)

Scope of Project

Subject Terms:  View help for Subject Terms commuting; covid; lockdown; network; economic geography; Seoul; Daegu; new york metropolitan area
JEL Classification:  View help for JEL Classification
      C60 Mathematical Methods; Programming Models; Mathematical and Simulation Modeling: General
      R38 Production Analysis and Firm Location: Government Policy
      R40 Transportation Economics: General
Geographic Coverage:  View help for Geographic Coverage Daegu, South Korea, Seoul, South Korea, New York Metropolitan Area, USA
Time Period(s):  View help for Time Period(s) 1/1/2020 – 4/30/2020
Collection Date(s):  View help for Collection Date(s) 3/15/2020 – 5/20/2020
Universe:  View help for Universe Districts in Seoul and Daegu, South Korea; Counties in New York Metropolitan Area, USA 
Data Type(s):  View help for Data Type(s) aggregate data; geographic information system (GIS) data; observational data; program source code

Methodology

Data Source:  View help for Data Source All the data sources are cited in the readme.
Unit(s) of Observation:  View help for Unit(s) of Observation Counties, USA, Districts, South Korea

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Published Versions

V1 [2021-11-23]

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