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The optimality of age-based lockdown policies.
Giammetti, Raffaele; Papi, Luca; Teobaldelli, Désirée; Ticchi, Davide.
  • Giammetti R; Marche Polytechnic University, Department of Economics and Social Sciences, Piazzale Martelli 8, 60121 Ancona, Italy.
  • Papi L; Marche Polytechnic University, Department of Economics and Social Sciences, Piazzale Martelli 8, 60121 Ancona, Italy.
  • Teobaldelli D; University of Urbino, Department of Law, Via Matteotti 1, 61029 Urbino, Italy.
  • Ticchi D; Marche Polytechnic University, Department of Economics and Social Sciences, Piazzale Martelli 8, 60121 Ancona, Italy.
J Policy Model ; 44(3): 722-738, 2022.
Article in English | MEDLINE | ID: covidwho-1945805
ABSTRACT
This paper studies an age-based lockdown that keeps over-60 workers at home as policy response to COVID-19 pandemic in a sample of thirty countries of the European single market. Three main policy issues are addressed, and the results can be summarized as follows. First, age-based lockdown policies are associated with limited output losses and, therefore, are an efficient strategy to limit the spread of the virus in a pandemic, especially in presence of strong age-dependent fatality rates. Second, lockdown policies generate substantial spillover effects; hence, international policy coordination avoiding that too many countries are in lockdown contemporaneously or that such coordination takes place across the countries with the highest integration of over-60 workers along GVCs may be helpful in reducing disruptions. Third, non-targeted lockdowns are much more costly than age-based ones; therefore, other things equal, age-based policies should always be preferred to non-targeted ones. Our analysis also suggests that, in our sample, the over-60 workers are relatively more numerous in sectors where the value added and the integration in GVCs is lower; this feature should be kept in mind in the design of other policies as it might play an important role.
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Full text: Available Collection: International databases Database: MEDLINE Language: English Journal: J Policy Model Year: 2022 Document Type: Article Affiliation country: J.jpolmod.2022.05.001

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Full text: Available Collection: International databases Database: MEDLINE Language: English Journal: J Policy Model Year: 2022 Document Type: Article Affiliation country: J.jpolmod.2022.05.001