A data-driven agent-based model of primary school segregation in Amsterdam

Theoretical agent-based models of residential and school choice have shown that substantial segregation can emerge as an (unintended) consequence of interactions between individual households and feedback mechanisms, despite households being relatively tolerant. However, for school choice, existing models have mostly been highly stylized, leaving open whether they are relevant for understanding school segregation in concrete empirical settings. To bridge this gap, this study develops an empirically calibrated agent-based model focusing on primary school choice in Amsterdam. Consistent with existing models, results show that substantial school segregation emerges when schools are chosen based on a trade-off between composition and distance, and also when households are relatively tolerant. Additionally, findings of (hypothetical) policy simulations suggest that it is important to understand which preferences for school composition and distance households have and how these interact. We find that the effects of policies aiming to reduce school segregation through geographical restricting mechanisms are highly dependent on those interacting preferences. Also, we assessed the contribution of residential segregation to school segregation. Our findings may have implications for methodologies aiming to estimate school choice preferences, such as discrete choice models, as these methodologies do not explicitly control for implications of these interactions and feedback mechanisms, which might lead to incorrect inference.