MICS6 data on foundational learning for 31 countries
This .do file prepares a dataset of foundational reading and numeracy outcomes for all countries that participated in the UNICEF MICS6 household survey's foundational skills module (as of February 2023). The raw data is open access and freely available from UNICEF at the country level (https://mics.unicef.org/surveys). The value-add of this code is to clean the data (which contain a number of non-systematic idiosyncrasies at the country level), build key learning outcomes variables, and create a merged dataset of country-level means (disaggregated by multiple demographic subgroups of interest) that easily allows for cross-country comparisons of learning.
This dataset may be of general interest since MICS6 is one of the largest internationally comparable datasets of children’s contemporary foundational learning outcomes. It is also presented in the particular interest of research transparency since it forms the basis for a joint UNESCO/RISE Programme website with data visualizations, policy simulations and data tools focused on learning trajectories (https://www.education-progress.org/en/articles/trajectories).
The dataset is representative of the following 31 countries as of Feb 2023 with over 400,000 children assessed (weighted total): Bangladesh Belarus CAR Chad DRC Ghana Guinea-Bissau Kiribati Kyrgyzstan Lesotho Madagascar Malawi Mongolia Nepal North Macedonia Pakistan Palestine Samoa Sao Tome & Principe Sierra Leone Suriname Thailand The Gambia Togo Tonga Tunisia Turkmenistan Turks and Caicos Islands Tuvalu Vietnam Zimbabwe
For a deep dive in the MICS6 survey and the variables in the dataset, this UNICEF manual is a good starting point: https://mics.unicef.org/files?job=W1siZiIsIjIwMjAvMDUvMTIvMTgvMjUvNDUvNzAxLzIwXzA1XzA4X01JQ1NfR3VpZGVib29rX2NvbXByZXNzZWQucGRmIl1d&sha=6d386818d588d05c
Note that a quick start “codebook” is also available on this Github site which glosses some of the key variables.
For those seeking to use the data to construct learning trajectories or run related policy simulations, a detailed description of the methodology used is available here: https://riseprogramme.org/publications/descriptive-learning-trajectories-and-policy-simulations-using-mics6-data