An Algorithmic Approach to Reducing Gender Discrimination: Evidence from a Large-Scale Experiment on Tracking Decisions

We study how to equalize students’ educational opportunities in a tracked high school system. We will use a randomized control trial in around 400 Italian middle schools to test two conceptually different ways to close gender gaps in high school track recommendations. To help teachers predict future performance, our first treatment provides teachers with a simple algorithmic recommendation that indicates whether students are likely to excel in the more rigorous scientific high school tracks. To address behavioral biases and awareness issues, our second treatment provides teachers with real-time feedback on the diversity of their track recommendations compared to an algorithmic benchmark, which they can then adjust before communicating their recommendations to students. We hypothesize that the first intervention will lead to fairer track recommendations if teacher biases are driven by inaccurate mental models of how individual students will perform in high school. Meanwhile, the second intervention will lead to fairer recommendations if teachers are best able to correct behavioral biases when considering the aggregate diversity of their track recommendations.