Investigating the epidemiological and economic effects of a third-party certification policy for restaurants with COVID-19 prevention measures

About this repository

We publish the data and codes for analyses of the policy effects of the Yamanashi Green Zone Certification System, which was implemented as a countermeasure against COVID-19 infections in Yamanashi Prefecture since July 2020. The effects to be verified are (1) economic effects (changes in sales and the number of customers per restaurant) and (2) infection prevention effects (changes in the number of new cases of COVID-19 infection) resulting from the spread of the Green Zone Certification in restaurants in Yamanashi Prefecture.

01_admin

• An outline of the folder structure for 02_bring, 03_build, and 04_analyze.
• Master code (Run.R)

02_bring

All raw data used for the analyses is stored in this folder.

• Google_mobility
  Human mobility data for each facility
• Pop
  Population data in each prefecture
• Vresas
  Data on views of restraunt's websites and intra/inter prefectural human mobility
• Weather
  Data on average temperature and rainfall
• Covid_cases
  Data on new infection cases in each prefecture
• Tests
  Data on COVID-19 tests in each prefecture
• Stayhome_rate
  Data on changes in the amount of human mobility compared to normal human mobility
• Dummy_vars
  Data on dummy variables of gathering restriction and school closure

03_build

This folder processes the raw data and makes them available for analyses in 04_analyze.

• GZlist
  Number of GZ-certified places by city in Yamanashi Prefecture, and by facility

• GZ_covid
  Dataset on number of the GZ-certified restaurants and new infections cases by date with main control variables

• Controls
  Cleaned data on number of COVID-19 tests, population, and infectious mobility from 47 prefectures in Japan into prefectures concerned

• weekSIR
  Dataset combined with 3 built data above by week

• Postas
  Dataset for analyzing economic effects (sales and number of customers)
  Dataset for analyzing prevention effects (merged with weekSIR dataset)

• Counterfactual
  Dataset to create the non-intervention scenario plots

• Robust_check
  Dataset to implement robustness check (stayhome-rate, mobility, restraunts' views, school closure and gathering restriction)

04_analyze

This folder performs regression analyses and data visualization by using the built data in 03_build.

• GZlist
  Visualizing the cumulative number of the GZ-certified restaurants

• Vresas
  Performing regression analyses on restraunt information views online and human mobility

• Google Mobility
  Performing regression analyses on human mobility in each facility

• Counterfactual
  Visualizing the non-intervention scenario in terms of economic effects and infection prevention effects

• Postas
  Performing regression analyses on sales and the number of customers per restaurant

• Covid
  Performing regression analyses on the new COVID-19 cases

• Stayhome_rate
  Performing regression analyses on the stay-home rate

• Summary_stat
  Creating a table of summary statistics of key variables

05_report

This folder creates Supplementary Information document.