get_policy_scores {CoronaNetR}R Documentation

Download Policy Intensity Scores


This function accesses the latest policy intensity scores showing the level of COVID-19 policy-making activity in a given country with measurement error.


  countries = "All",
  type = "All",
  from = "2019-12-31",
  to = "2021-07-01",
  time_out = FALSE



Specify a specific country to query By default all countries.


Specify a specific index to query. By default all types.


The beginning time period in YYYY-MM-DD format.


The end time period in YYYY-MM-DD format. At present the index goes until 04-29-2021.


Whether to set a 5-second time-out on the API call. Beyond 5 seconds, the function will return an empty data-frame. Only useful for complying with CRAN submission requirements. Default is FALSE.


Use this function to access the latest policy intensity scores for six types: ,

  1. Business

  2. Health Monitoring

  3. Health Resources

  4. Masks

  5. Schools

  6. Social Distancing

By default, all six indices for all countries are downloaded, running from the index start at January 1, 2020 until April 29, 2021. The indices are periodically updated with new data as the CoronaNet project continues coding policies and integrating external datasets.

The scores are Normally-distributed with a mean of 0. It is possible to re-scale the scores but we recommend using the default scale to preserve relationships between units and time points.

You can read more about the index construction and evaluation in our working paper.


Kubinec, Robert; Barcelo, Joan; Goldzsmidt, Rafael; Grujic, Vanja; Model, Timothy; Schenk, Caress; Cheng, Cindy; Hale, Thomas; Spencer Hartnett, Allison; Messerschmidt, Luca; Petherick, Anna, and Thorvaldsdottir, Svanhildur. "Cross-National Measures of the Intensity of COVID-19 Public Health Policies." SocArchiv (2022).

Because the indices were produced with a measurement model, they include measurement error. The most likely estimate is in the med_estimate column, and the uncertainty interval high/low are in the low_estimate\codehigh_estimate columns. The SD_estimate column has the standard deviation of the measurement error. These measurement error estimates can be used either in a model that explicitly incorporates the SD of the measurement error (so-called errors-in-variables models) or by using the low_estimate and high_estimate scores as robustness checks. The default for estimation and modeling should be the most likely med_estimate column.


A data frame with one row per policy intensity score per country


# Download policy intensity scores for Japan and China

japan_scores <- get_policy_scores(countries=c("Japan","China"),

[Package CoronaNetR version 0.3.0 Index]