{"@context":"http://schema.org","@type":"Dataset","@id":"https://doi.org/10.13130/RD_UNIMI/3NPROL","identifier":"https://doi.org/10.13130/RD_UNIMI/3NPROL","name":"Replication Data for: Policy-taking styles: a typology and an empirical application to anti-Covid policies","creator":[{"name":"Giuliani, Marco","affiliation":"University of Milano","@id":"https://orcid.org/0000-0002-6927-7177","identifier":"https://orcid.org/0000-0002-6927-7177"}],"author":[{"name":"Giuliani, Marco","affiliation":"University of Milano","@id":"https://orcid.org/0000-0002-6927-7177","identifier":"https://orcid.org/0000-0002-6927-7177"}],"datePublished":"2023-03-19","dateModified":"2023-03-19","version":"1","description":["Several studies have investigated the variety of governance strategies adopted by European countries to cope with the Covid-19 pandemic. Some nations relied on a more liberal approach, based on recommendations and a lack of mandatory constraints; others trusted more top-down regulations and longlasting restrictions. The feasibility and success of the different strategies also depend on the way in which policy-takers react. The article uses this exemplary policy case to propose a novel theoretical framework which maps the variety of policy-taking styles applying March and Olsen’s (2006) logics of conditionality and appropriateness. Using mobility data, it then employs the new typology to explore the diverse styles adopted by policy-takers reacting to anti-Covid workplace regulations in 29 European countries. The categories proposed can be applied also in different contexts, especially where policy success crucially depends on countless individual behaviours, and policymakers need to choose the most effective mix of enforcement tools."],"keywords":["Social Sciences","Policy style","Policy taking","Compliance","Covid-19"],"citation":[{"@type":"CreativeWork","text":"Giuliani M. (2023). Policy-taking styles: a typology and an empirical application to anti-Covid policies, \"Journal of European Public Policy\", DOI:10.1080/13501763.2023.2188891","@id":"https://doi.org/10.1080/13501763.2023.2188891","identifier":"https://doi.org/10.1080/13501763.2023.2188891"}],"license":{"@type":"Dataset","text":"CC0","url":"https://creativecommons.org/publicdomain/zero/1.0/"},"includedInDataCatalog":{"@type":"DataCatalog","name":"UNIMI Dataverse","url":"https://dataverse.unimi.it"},"publisher":{"@type":"Organization","name":"UNIMI Dataverse"},"provider":{"@type":"Organization","name":"UNIMI Dataverse"},"distribution":[{"@type":"DataDownload","name":"Eu29 mobility plot.tab","fileFormat":"text/tab-separated-values","contentSize":76127,"description":"Stata dataset for descriptive graphs","@id":"https://doi.org/10.13130/RD_UNIMI/3NPROL/LQ6LHO","identifier":"https://doi.org/10.13130/RD_UNIMI/3NPROL/LQ6LHO","contentUrl":"https://dataverse.unimi.it/api/access/datafile/17398"},{"@type":"DataDownload","name":"Eu29 style do.do","fileFormat":"application/x-stata-syntax","contentSize":4743,"description":"Stata syntax","@id":"https://doi.org/10.13130/RD_UNIMI/3NPROL/B8IA3X","identifier":"https://doi.org/10.13130/RD_UNIMI/3NPROL/B8IA3X","contentUrl":"https://dataverse.unimi.it/api/access/datafile/17397"},{"@type":"DataDownload","name":"Eu29 style mobility.tab","fileFormat":"text/tab-separated-values","contentSize":4304787,"description":"Stata main dataset","@id":"https://doi.org/10.13130/RD_UNIMI/3NPROL/LDYYAJ","identifier":"https://doi.org/10.13130/RD_UNIMI/3NPROL/LDYYAJ","contentUrl":"https://dataverse.unimi.it/api/access/datafile/17399"},{"@type":"DataDownload","name":"Eu29 style plot.tab","fileFormat":"text/tab-separated-values","contentSize":5160,"description":"Stata dataset for final plot","@id":"https://doi.org/10.13130/RD_UNIMI/3NPROL/9WANCY","identifier":"https://doi.org/10.13130/RD_UNIMI/3NPROL/9WANCY","contentUrl":"https://dataverse.unimi.it/api/access/datafile/17396"}]}