Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/251014 
Autor:innen: 
Erscheinungsjahr: 
2022
Schriftenreihe/Nr.: 
Working Paper No. WP 2022-04
Verlag: 
Federal Reserve Bank of Chicago, Chicago, IL
Zusammenfassung: 
I jointly use daily data on deaths and public transportation ridership in San Francisco in 1918-19 to estimate a model in which agents choose their level of economic activity based on perceived infection risk, modeled as a function of current and lagged infections or deaths. Agents' choices in turn affect the dynamics of the epidemic by reducing contacts in an otherwise standard SEIR model. Non-pharmaceutical interventions restrict agents' activity either as a tax or a bound. I estimate the parameters by maximum likelihood and use the best-fitting model to compute counterfactuals. San Francisco's intervention reduced deaths by a few percent only, and it was away from the Pareto frontier: an earlier and milder intervention would have done better. The behavioral feedback narrows the room for intervention compared to a model with unresponsive agents, and ill-timed interventions can worsen outcomes. Masks also had an effect on transmission rates.
Schlagwörter: 
1918 influenza epidemic
San Francisco
public transportation
non-pharmaceutical interventions
SIR macro model
policy evaluation
counterfactuals
JEL: 
H12
I18
I19
N12
R40
Persistent Identifier der Erstveröffentlichung: 
Dokumentart: 
Working Paper

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