The COVID-19 Effective In-Person Learning database is a balanced panel of about 70,000 public and private U.S. schools containing weekly estimates of school-level Effective In-Person Learning (EIPL). EIPL measures the extent to which the general student population is exposed to traditional in-person learning through the school being either fully opened or partially opened while offering hybrid learning. The COVID-19 Effective In-Person Learning database contains sampling weights to ensure representativeness and school identifiers that allow users to match schools from the database to administrative and survey data of the National Center for Education Statistics. The Effective In-Person Learning database is also available in more aggregated formats, namely at the school district, county, CBSA and state levels. Full details about the data construction process are presented in the paper "[School Closures and Effective In-Person Learning during COVID-19][1]". A ReadMe.pdf file available in the "Files" section of this repository provides a dictionary of variables contained in the database. All data files are Stata-13 datasets. Last update: June 13, 2023. If you have trouble accessing the database, please email lale.etienne@uqam.ca. [1]: https://doi.org/10.1016/j.econedurev.2023.102422