coronanet_government_response_data: CoronaNet government policy response database

In seandavi/sars2pack: COVID-19 data resources and analysis tools

CoronaNet government policy response database

Description

This dataset contains variables from the CoronaNet government response project, representing national and sub-national policy event data from more than 140 countries since January 1st, 2020. The data include source links, descriptions, targets (i.e. other countries), the type and level of enforcement, and a comprehensive set of policy types.

Usage

coronanet_government_response_data()

Value

• record_id Unique identifier for each policy record

• entry_type Whether the record is new, meaning no restriction had been in place before, or an update (restriction was in place but changed). Corrections are corrections to previous entries.

• event_description A short description of the policy change

• type The category of the policy

• country The country initiating the policy

• init_country_level Whether the policy came from the national level or a sub-national unit

• index_prov The ID of the sub-national unit

• target_country Which foreign country a policy is targeted at (i.e. travel policies)

• target_geog_level Whether the target of the policy is a country as a whole or a sub-national unit of that country

• target_who_what Who the policy is targeted at

• recorded_date When the record was entered into our data

• target_direction Whether a travel-related policy affects people coming in (Inbound) or leaving (Outbound)

• travel_mechanism If a travel policy, what kind of transportation it affects

• compliance Whether the policy is voluntary or mandatory

• enforcer What unit in the country is responsible for enforcement

• date_announced When the policy goes into effect

• link A link to at least one source for the policy

• ISO_A3 3-digit ISO country codes

• ISO_A2 2-digit ISO country codes

• severity_index_5perc 5% posterior low estimate (i.e. lower bound of uncertainty interval) for severity index

• severity_index_median posterior median estimate (point estimate) for severity index, which comes from a Bayesian latent variable model aggregating across policy types to measure country-level policy severity (see paper on our website)

• severity_index_5perc 95% posterior high estimate (i.e. upper bound of uncertainty interval) for severity index

Source

• https://coronanet-project.org/download

References

Cheng, Cindy, Joan Barcelo, Allison Hartnett, Robert Kubinec, and Luca Messerschmidt. 2020. “Coronanet: A Dyadic Dataset of Government Responses to the COVID-19 Pandemic.” SocArXiv. April 12. doi:10.31235/osf.io/dkvxy.

• https://coronanet-project.org/working_papers

See Also

Other data-import: acaps_government_measures_data(), acaps_secondary_impact_data(), apple_mobility_data(), beoutbreakprepared_data(), cci_us_vaccine_data(), cdc_aggregated_projections(), cdc_excess_deaths(), cdc_social_vulnerability_index(), coronadatascraper_data(), cov_glue_lineage_data(), cov_glue_newick_data(), cov_glue_snp_lineage(), covidtracker_data(), descartes_mobility_data(), ecdc_data(), econ_tracker_consumer_spending, econ_tracker_employment, econ_tracker_unemp_data, economist_excess_deaths(), financial_times_excess_deaths(), google_mobility_data(), government_policy_timeline(), jhu_data(), jhu_us_data(), kff_icu_beds(), nytimes_county_data(), oecd_unemployment_data(), owid_data(), param_estimates_published(), test_and_trace_data(), us_county_geo_details(), us_county_health_rankings(), us_healthcare_capacity(), us_hospital_details(), us_state_distancing_policy(), usa_facts_data(), who_cases()

Other NPI: acaps_government_measures_data(), government_policy_timeline(), us_state_distancing_policy()

Examples

res = coronanet_government_response_data()
head(res)
colnames(res)
dplyr::glimpse(res)
summary(res)

seandavi/sars2pack documentation built on May 13, 2022, 3:41 p.m.