usual and actual hours at work during the reference week. As a statutory holiday, Good Friday normally results in reduced hours at work. The April reference week in 2019 included Good Friday, such that using 2019 as a bench- mark would result in understating the negative impacts of COVID-19 on hours worked. The April reference week for 2018 is preferred because in 2018 the reference week did not include Good Friday. We considered other options that combined multiple years of data but decided a simple and transparent comparator was best. With this in mind, the main estimates we provide for a COVID-19 impact on outcomes (Y) represent a difference- in-differences estimate, measured as COVID-19 Impact = (YApril2020 - YFeb2020 ) / YFeb 2020 -(YAprilzong - YFebzons ) / YFebams- The difference is generally presented as a percentage dif- ference in outcome Y. The double differences represent the changes in 2020 over and above February-April changes that we would expect to normally occur over those months as proxied by the 2018 realizations. Our sample represents individuals aged 20-64 years, residing in the Canadian provinces but not the territories. The results presented show the COVID-19 impact across a variety of interesting splits of the data. We consider how losses in hours and employment are distributed across individuals by socio-economic characteristics such as age, gender, age of children in the home, and education. We also consider job characteristics, such as occupation and industry, job tenure, and whether a person is paid hourly or is salaried. Finally, we consider the earnings quartile from which workers are laid off