R/coronanet_government_response_data.R
coronanet_government_response_data.RdThis 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.
coronanet_government_response_data()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
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.
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()
res = coronanet_government_response_data()
#> Warning: One or more parsing issues, see `problems()` for details
head(res)
#> # A tibble: 6 × 65
#> record_id policy_id entry_type update_type update_level update_level_var
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 R_3FKlACY6Djr1… 2975738 new_entry NA NA NA
#> 2 R_2arWTemRbMLK… 7024226 new_entry NA NA NA
#> 3 R_1reUk0mSCc6q… 1850843 new_entry NA NA NA
#> 4 R_pMJfXPNXIYRV… 889484 new_entry NA NA NA
#> 5 R_1eJC2cHJL6SX… 769460 new_entry NA NA NA
#> 6 R_2y9GMTXGfnZh… 129322 new_entry NA NA NA
#> # … with 59 more variables: description <chr>, date_announced <date>,
#> # date_start <date>, date_end <date>, date_end_spec <chr>, country <chr>,
#> # iso3c <chr>, iso2c <chr>, init_country_level <chr>, domestic_policy <dbl>,
#> # province <chr>, ISO_L2 <chr>, city <chr>, type <chr>, type_sub_cat <chr>,
#> # type_new_admin_coop <chr>, type_vac_cat <chr>, type_vac_mix <chr>,
#> # type_vac_reg <chr>, type_vac_purchase <chr>, type_vac_group <chr>,
#> # type_vac_group_rank <dbl>, type_vac_who_pays <chr>, …
colnames(res)
#> [1] "record_id" "policy_id"
#> [3] "entry_type" "update_type"
#> [5] "update_level" "update_level_var"
#> [7] "description" "date_announced"
#> [9] "date_start" "date_end"
#> [11] "date_end_spec" "country"
#> [13] "iso3c" "iso2c"
#> [15] "init_country_level" "domestic_policy"
#> [17] "province" "ISO_L2"
#> [19] "city" "type"
#> [21] "type_sub_cat" "type_new_admin_coop"
#> [23] "type_vac_cat" "type_vac_mix"
#> [25] "type_vac_reg" "type_vac_purchase"
#> [27] "type_vac_group" "type_vac_group_rank"
#> [29] "type_vac_who_pays" "type_vac_dist_admin"
#> [31] "type_vac_loc" "type_vac_cost_num"
#> [33] "type_vac_cost_scale" "type_vac_cost_unit"
#> [35] "type_vac_cost_gov_perc" "type_vac_amt_num"
#> [37] "type_vac_amt_scale" "type_vac_amt_unit"
#> [39] "type_vac_amt_gov_perc" "type_text"
#> [41] "institution_cat" "institution_status"
#> [43] "institution_conditions" "target_init_same"
#> [45] "target_country" "target_geog_level"
#> [47] "target_region" "target_province"
#> [49] "target_city" "target_intl_org"
#> [51] "target_other" "target_who_what"
#> [53] "target_who_gen" "target_direction"
#> [55] "travel_mechanism" "compliance"
#> [57] "enforcer" "dist_index_high_est"
#> [59] "dist_index_med_est" "dist_index_low_est"
#> [61] "dist_index_country_rank" "pdf_link"
#> [63] "link" "date_updated"
#> [65] "recorded_date"
dplyr::glimpse(res)
#> Rows: 116,372
#> Columns: 65
#> $ record_id <chr> "R_3FKlACY6Djr1sjcNA", "R_2arWTemRbMLKdk0NA", …
#> $ policy_id <chr> "2975738", "7024226", "1850843", "889484", "76…
#> $ entry_type <chr> "new_entry", "new_entry", "new_entry", "new_en…
#> $ update_type <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ update_level <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ update_level_var <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ description <chr> "March 6, Afghanistan \"Measures have been tak…
#> $ date_announced <date> 2020-03-06, 2020-03-16, 2020-05-14, 2020-05-2…
#> $ date_start <date> 2020-03-06, 2020-03-16, 2020-05-14, 2020-05-2…
#> $ date_end <date> 2022-05-06, 2020-03-16, 2020-05-15, NA, 2020-…
#> $ date_end_spec <chr> "The policy has an unlimited time span (e.g. r…
#> $ country <chr> "Afghanistan", "Afghanistan", "Afghanistan", "…
#> $ iso3c <chr> "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG…
#> $ iso2c <chr> "AF", "AF", "AF", "AF", "AF", "AF", "AF", "AF"…
#> $ init_country_level <chr> "National", "National", "National", "National"…
#> $ domestic_policy <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
#> $ province <chr> NA, NA, NA, NA, NA, NA, "Herat", "Herat", "Her…
#> $ ISO_L2 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ city <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ type <chr> "Anti-Disinformation Measures", "Anti-Disinfor…
#> $ type_sub_cat <chr> NA, NA, NA, NA, NA, NA, "Preschool or childcar…
#> $ type_new_admin_coop <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ type_vac_cat <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ type_vac_mix <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ type_vac_reg <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ type_vac_purchase <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ type_vac_group <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ type_vac_group_rank <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ type_vac_who_pays <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ type_vac_dist_admin <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ type_vac_loc <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ type_vac_cost_num <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ type_vac_cost_scale <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ type_vac_cost_unit <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ type_vac_cost_gov_perc <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ type_vac_amt_num <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ type_vac_amt_scale <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ type_vac_amt_unit <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ type_vac_amt_gov_perc <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ type_text <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ institution_cat <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ institution_status <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, "Primary S…
#> $ institution_conditions <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ target_init_same <chr> "Yes", "Yes", "Yes", "No", "Yes", "Yes", "Yes"…
#> $ target_country <chr> "Afghanistan", "Afghanistan", "Afghanistan", "…
#> $ target_geog_level <chr> "One or more countries, but not all countries"…
#> $ target_region <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ target_province <chr> NA, NA, NA, "Balkh; Kabul; Nangarhar; Kunar; P…
#> $ target_city <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ target_intl_org <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ target_other <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ target_who_what <chr> "All Residents (Citizen Residents + Foreign Re…
#> $ target_who_gen <chr> "No special population targeted", "No special …
#> $ target_direction <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ travel_mechanism <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ compliance <chr> "Voluntary/Recommended but No Penalties", "Man…
#> $ enforcer <chr> "Ministry/Department of Health,Other (Please s…
#> $ dist_index_high_est <dbl> 52.91608, 52.68646, 84.67092, 85.83168, 85.831…
#> $ dist_index_med_est <dbl> 44.30324, 45.55852, 77.76448, 79.82725, 79.827…
#> $ dist_index_low_est <dbl> 34.85850, 36.72021, 70.08431, 73.13763, 73.137…
#> $ dist_index_country_rank <dbl> 123, 60, 125, 131, 131, 135, 121, 121, 121, 10…
#> $ pdf_link <chr> "https://tummgmt.eu.qualtrics.com/Q/File.php?F…
#> $ link <chr> "https://www.etilaatroz.com/94246/fears-rumors…
#> $ date_updated <date> 2021-08-15, 2021-08-15, 2021-12-11, 2021-08-1…
#> $ recorded_date <dttm> 2021-08-15 16:53:01, 2021-08-15 16:57:33, 202…
summary(res)
#> record_id policy_id entry_type update_type
#> Length:116372 Length:116372 Length:116372 Length:116372
#> Class :character Class :character Class :character Class :character
#> Mode :character Mode :character Mode :character Mode :character
#>
#>
#>
#>
#> update_level update_level_var description date_announced
#> Length:116372 Length:116372 Length:116372 Min. :2019-12-31
#> Class :character Class :character Class :character 1st Qu.:2020-03-30
#> Mode :character Mode :character Mode :character Median :2020-05-25
#> Mean :2020-07-20
#> 3rd Qu.:2020-10-07
#> Max. :2022-12-28
#> NA's :18
#> date_start date_end date_end_spec
#> Min. :2019-12-31 Min. :2020-01-01 Length:116372
#> 1st Qu.:2020-03-31 1st Qu.:2020-05-03 Class :character
#> Median :2020-05-28 Median :2020-07-17 Mode :character
#> Mean :2020-07-22 Mean :2020-09-28
#> 3rd Qu.:2020-10-09 3rd Qu.:2020-12-21
#> Max. :2024-05-29 Max. :2027-07-01
#> NA's :47747
#> country iso3c iso2c init_country_level
#> Length:116372 Length:116372 Length:116372 Length:116372
#> Class :character Class :character Class :character Class :character
#> Mode :character Mode :character Mode :character Mode :character
#>
#>
#>
#>
#> domestic_policy province ISO_L2 city
#> Min. :0.0000 Length:116372 Length:116372 Length:116372
#> 1st Qu.:0.0000 Class :character Class :character Class :character
#> Median :1.0000 Mode :character Mode :character Mode :character
#> Mean :0.7214
#> 3rd Qu.:1.0000
#> Max. :1.0000
#>
#> type type_sub_cat type_new_admin_coop type_vac_cat
#> Length:116372 Length:116372 Length:116372 Length:116372
#> Class :character Class :character Class :character Class :character
#> Mode :character Mode :character Mode :character Mode :character
#>
#>
#>
#>
#> type_vac_mix type_vac_reg type_vac_purchase type_vac_group
#> Length:116372 Length:116372 Length:116372 Length:116372
#> Class :character Class :character Class :character Class :character
#> Mode :character Mode :character Mode :character Mode :character
#>
#>
#>
#>
#> type_vac_group_rank type_vac_who_pays type_vac_dist_admin type_vac_loc
#> Min. :1.00 Length:116372 Length:116372 Length:116372
#> 1st Qu.:1.00 Class :character Class :character Class :character
#> Median :1.00 Mode :character Mode :character Mode :character
#> Mean :1.91
#> 3rd Qu.:2.00
#> Max. :9.00
#> NA's :115946
#> type_vac_cost_num type_vac_cost_scale type_vac_cost_unit
#> Min. : 0 Length:116372 Length:116372
#> 1st Qu.: 5 Class :character Class :character
#> Median : 88 Mode :character Mode :character
#> Mean : 1644373
#> 3rd Qu.: 200
#> Max. :110000000
#> NA's :116304
#> type_vac_cost_gov_perc type_vac_amt_num type_vac_amt_scale
#> Min. : 0 Min. : 0.1 Length:116372
#> 1st Qu.: 100 1st Qu.: 7.2 Class :character
#> Median : 100 Median : 46.8 Mode :character
#> Mean : 2348 Mean : 8028.9
#> 3rd Qu.: 100 3rd Qu.: 399.4
#> Max. :100100 Max. :940800.0
#> NA's :116239 NA's :115905
#> type_vac_amt_unit type_vac_amt_gov_perc type_text institution_cat
#> Length:116372 Min. : 0.00 Length:116372 Length:116372
#> Class :character 1st Qu.:100.00 Class :character Class :character
#> Mode :character Median :100.00 Mode :character Mode :character
#> Mean : 93.73
#> 3rd Qu.:100.00
#> Max. :100.00
#> NA's :116069
#> institution_status institution_conditions target_init_same
#> Length:116372 Length:116372 Length:116372
#> Class :character Class :character Class :character
#> Mode :character Mode :character Mode :character
#>
#>
#>
#>
#> target_country target_geog_level target_region target_province
#> Length:116372 Length:116372 Length:116372 Length:116372
#> Class :character Class :character Class :character Class :character
#> Mode :character Mode :character Mode :character Mode :character
#>
#>
#>
#>
#> target_city target_intl_org target_other target_who_what
#> Length:116372 Length:116372 Mode:logical Length:116372
#> Class :character Class :character NA's:116372 Class :character
#> Mode :character Mode :character Mode :character
#>
#>
#>
#>
#> target_who_gen target_direction travel_mechanism compliance
#> Length:116372 Length:116372 Length:116372 Length:116372
#> Class :character Class :character Class :character Class :character
#> Mode :character Mode :character Mode :character Mode :character
#>
#>
#>
#>
#> enforcer dist_index_high_est dist_index_med_est dist_index_low_est
#> Length:116372 Min. :13.30 Min. : 9.893 Min. : 3.146
#> Class :character 1st Qu.:58.81 1st Qu.:55.665 1st Qu.:52.541
#> Mode :character Median :68.84 Median :65.608 Median :62.358
#> Mean :67.18 Mean :63.903 Mean :60.529
#> 3rd Qu.:77.40 3rd Qu.:73.925 3rd Qu.:70.428
#> Max. :98.75 Max. :98.298 Max. :97.683
#> NA's :23041 NA's :23041 NA's :23041
#> dist_index_country_rank pdf_link link
#> Min. : 1.0 Length:116372 Length:116372
#> 1st Qu.: 61.0 Class :character Class :character
#> Median :101.0 Mode :character Mode :character
#> Mean : 91.6
#> 3rd Qu.:126.0
#> Max. :139.0
#> NA's :23041
#> date_updated recorded_date
#> Min. :2020-03-28 Min. :2020-03-28 06:37:36.00
#> 1st Qu.:2020-12-16 1st Qu.:2020-12-16 04:05:48.75
#> Median :2021-05-19 Median :2021-05-19 09:38:35.50
#> Mean :2021-04-29 Mean :2021-04-29 15:59:23.93
#> 3rd Qu.:2021-09-15 3rd Qu.:2021-09-15 00:37:48.00
#> Max. :2022-04-30 Max. :2022-04-30 23:40:59.00
#>