Dynamic Behavioural Selection in Youth Employability Programs

This repository contains the materials for the article “Dynamic Behavioural Selection in Youth Employability Programs”. The repository includes the Python code used to implement the Monte Carlo simulations, synthetic individual-level simulation data, calibration metadata, programme-exposure outcomes, expected and realised labour-market moments, period-specific dynamics, policy counterfactuals, and empirical prediction checks. The simulation generates a common synthetic population of 15,000 eligible individuals over eight programme periods. Take-up and continuation parameters are calibrated only in the rational benchmark and are subsequently held fixed across behavioural scenarios. The behavioural scenarios examine present bias, loss aversion, unfavourable belief distortions, heuristic substitution, and their combined effects.