DOI: 10.6084/m9.figshare.31320961.v1
title: UNESCO Teacher Attrition Data

description: <b>Data source: </b>Administrative data from schools and human resources records on educational personnel as per UNESCO’s Institute for Statistics Education Survey database. Dataset adjusted for years of 2014-2024 with only latest reporting year for each country to be included in final visualisation. Countries that do not have data in UNESCO’s database have been removed. The data includes both public and private educational institutions for both sexes.<b>Calculation method: </b>The number of leavers is estimated by subtracting the number of teachers in year t from those in year t-1 and adding the number of new entrants to the teaching workforce in year t. The attrition rate is the number of leavers expressed as a percentage of the total number of teachers in year t-1.<b>Interpretation:</b><b> </b>A high value indicates high levels of teacher turnover which can be disruptive for the learning of students. Where teachers teach for 30-40 years, the attrition rate will be well below 5%. Attrition rates above 10% indicate that the average teaching career lasts only 10 years.<b>License: </b>The data is licensed under the [Creative Commons Attribution-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-sa/4.0/) license.<b>UIS Data API: </b>https://api.uis.unesco.org/api/public/documentation/ <b>UNESCO Terms and Conditions of use:</b> https://databrowser.uis.unesco.org/terms-and-conditions<b>Glossary information:</b> https://databrowser.uis.unesco.org/resources/glossary/3192Note: Part-time teachers and teachers who teach multi-grade classes are included in the data and may impact the precision of the data. This graph does not provide reasons as to why teachers leave the profession, just the percentage.METHODOLOGY:1. Access UNESCO data browser: https://databrowser.uis.unesco.org/browser2. Search ‘teacher attrition’ in indicator search bar3. Select following indicator: <b>Teacher</b><b> </b><b>attrition</b><b> </b><b>rate from secondary education, both sexes (%)</b>4. Adjust time range to 2014-2024 (2025 data incomplete)5. Adjust table to ‘comfortable’ for easy viewing6. Turn on ‘Show datapoint metadata’7. Download filtered data – choose format – Excel – tick yes to ‘Additional info to include’ for ‘Metadata’ and ‘Footnotes’8. Open in Excel9. Create a copy of data called ‘AltData’ (Altered Data)10. Create ‘Notes page’ with metadata information<br><b>In AltData Sheet:</b>11. Remove duplicate country data (removal of ‘China, Hong Kong Special Administrative Region’ and ‘China, Macao Kong Special Administrative Region’ to just include ‘China’)12. Change geounit codes to full country names (for easy viewing)<br>https://api.uis.unesco.org/api/public/documentation/operations/listGeoUnits<br>13. Remove country data with 0 Value (Burundi 2016 [completely removes country from dataset], Dominica 2022, Jordan 2020, Monaco 2020, Montserrat, 2018, Tokelau 2020-2021 [completely removes country from dataset])14. Column ‘qualifier’ includes estimate data from UNESCO for countries Monaco 2018, Palestine 2016 and Rwanda 2019 (<b>UIS_EST</b>). Column ‘qualifier’ deleted.15. Column ‘magnitude’ deleted – no data present.16. Delete data that is not most recent i.e if 2018 and 2019 data is present for country, delete 2018 (Only including most recent value in final data visualisation (“Latest Year”)17. Round up data to two decimal places (Format cells - number - two decimal places)18. Highlight all columns: Sort - by value - lowest to highest19. Upload data to data storing/sharing program: Figshare<br>