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Protective Policy Index (PPI) global dataset of origins and stringency of COVID 19 mitigation policies

dc.contributor.authorShvetsova, O
dc.contributor.authorZhirnov, A
dc.contributor.authorAdeel, AB
dc.contributor.authorBayar, MC
dc.contributor.authorBayrali, OG
dc.contributor.authorCatalano, M
dc.contributor.authorCatalano, O
dc.contributor.authorChu, H
dc.contributor.authorGiannelli, F
dc.contributor.authorMuftuoglu, E
dc.contributor.authorRosenberg, D
dc.contributor.authorSeyis, D
dc.contributor.authorSkopyk, B
dc.contributor.authorVanDusky-Allen, J
dc.contributor.authorZhao, T
dc.date.accessioned2022-06-17T13:58:07Z
dc.date.issued2022-06-16
dc.date.updated2022-06-17T12:41:37Z
dc.description.abstractWe have developed and made accessible for multidisciplinary audience a unique global dataset of the behavior of political actors during the COVID-19 pandemic as measured by their policy-making efforts to protect their publics. The dataset presents consistently coded cross-national data at subnational and national levels on the daily level of stringency of public health policies by level of government overall and within specific policy categories, and reports branches of government that adopted these policies. The data on these public mandates of protective behaviors is collected from media announcements and government publications. The dataset allows comparisons of governments’ policy efforts and timing across the world and can serve as a source of information on policy determinants of pandemic outcomes–both societal and possibly medical.en_GB
dc.identifier.citationVol. 9, article 319en_GB
dc.identifier.doihttps://doi.org/10.1038/s41597-022-01437-9
dc.identifier.urihttp://hdl.handle.net/10871/129973
dc.identifierORCID: 0000-0002-2978-8239 (Zhirnov, Andrei)
dc.language.isoenen_GB
dc.publisherNature Researchen_GB
dc.relation.urlhttps://github.com/COVID-policy-response-lab/PPI-dataen_GB
dc.relation.urlhttps://www.openicpsr.org/openicpsr/project/123401en_GB
dc.rights© The Author(s) 2022. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.en_GB
dc.titleProtective Policy Index (PPI) global dataset of origins and stringency of COVID 19 mitigation policiesen_GB
dc.typeArticleen_GB
dc.date.available2022-06-17T13:58:07Z
exeter.article-number319
dc.descriptionThis the final version. Available on open access from Nature Research via the DOI in this recorden_GB
dc.descriptionData Records: We have created a Github repository (https://github.com/COVID-policy-response-lab/PPI-data) to store the datasets with the Public Health Protective Policy Index and its components. A copy of the included datafiles, as described below, was deposited with openICPSR15. It presently requires creating an account with the depository. Data access is free. Data location is at https://www.openicpsr.org/openicpsr/project/123401. The datasets are stored as csv files with five types of layouts. “PPI_country_m1.csv” is a file with country-level aggregates of region-level PPIs, computed using method 1, and their components. Each row corresponds to a country-date. The rows are identified using the country name (cname), numeric and 2-letter ISO 3166-1 codes (isocode and isoabbr respectively), as well as a date variable. The names of the policy variables contain four components: the name of the broader category, the name of the category, the level of issuing government (“nat” refers to the national policies, “reg” refers to the subnational policies, and “tot” refers to the combination of national and subnational policies), as well as suffix “ave”. For example, the average Total PPI is denoted as “ppi.all.tot.ave”, and the average stringency of the closures of air borders by the national government is denoted as “borders.air_bord.nat.ave”. See the codebook for the complete list of variables. “PPI_country_m2.csv” is a file with country-level aggregates of region-level PPIs, computed using method 2, and their components. The identifying variables and the naming convention for the policy variables is the same as in “PPI_country_m1.csv”, with the addition of suffix “0.2” at the end of the policy variable names. “PPI_regions_XX_m1.csv” (replace XX with the 2-letter ISO 3166-1 country codes) are country-specific files with region-specific PPIs, computed using method 1, and their components. The identifying variables include the numeric and 2-letter ISO 3166-1 codes of the country (isocode and isoabbr respectively), the name of the region (state_province), its ISO 3166-2 code (iso_state), as well as a date variable. The names of the policy variables contain three components: the name of the broader category, the name of the category, and the level of issuing government (“nat” refers to the national policies, “reg” refers to the subnational policies, and “tot” refers to the combination of national and subnational policies). For example, the average Total PPI is denoted as “ppi.all.tot”, and the stringency of the closures of air borders by the national government is denoted as “borders.air_bord.nat”. “PPI_regions_XX_m2.csv” (replace XX with the 2-letter ISO 3166-1 country codes are country-specific files with region-specific PPIs, computed using method 2, and their components. The identifying variables and the naming convention for the policy variables is the same as in “PPI_regions_XX_m1.csv”, with the addition of the suffix “0.2” at the end of the policy variable names. “changes_regions_m1.csv” is an auxiliary file that describes the changes in the policy states, as recorded in the “PPI_regions_XX_m1.csv” files. Each row in this file corresponds to a change in a value of a policy state variable in a region and of a specific government level. The case identifying variables include the name of the country (cname), the numeric and 2-letter ISO 3166-1 code of the country (isocode and isoabbr, respectively), the name of the region (state_province) and its ISO 3166-2 code, date, policy dimension, and a marker of policies issued by a regional government (subnational). Among others, the attributes included in this file include the branch of the government (branch) and the date when the change was announced (report_date).en_GB
dc.descriptionCode availability; The code used to produce our calculations is available at https://github.com/COVID-policy-response-lab/PPI-dataen_GB
dc.identifier.eissn2052-4463
dc.identifier.journalScientific Dataen_GB
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_GB
dcterms.dateAccepted2022-05-18
rioxxterms.versionVoRen_GB
rioxxterms.licenseref.startdate2022-06-16
rioxxterms.typeJournal Article/Reviewen_GB
refterms.dateFCD2022-06-17T13:54:52Z
refterms.versionFCDVoR
refterms.dateFOA2022-06-17T13:58:13Z
refterms.panelCen_GB
refterms.dateFirstOnline2022-06-16


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© The Author(s) 2022. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
Except where otherwise noted, this item's licence is described as © The Author(s) 2022. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.