This is the first blog post in a series designed to help implementers, policymakers, and funders unpack the different types of evaluations relevant for “AI for Good” applications. Stay tuned for forthcoming posts providing a deeper dive on each of the evaluation levels.

This post discusses an evaluation design by researchers at J-PAL North America and Pursuit to measure the impact of the Pursuit's sectoral employment program, showing how randomization can be integrated into the application process without compromising enrollment preferences.