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

ABSTRACT

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.

KEYWORDS:

• Agent-based modeling
• complex systems
• complexity
• empirical calibration
• school choice
• school segregation

Acknowledgments

This paper is part of the Computational Modeling of Primary School Segregation (COMPASS) project which is funded by the Dutch Inspectorate of Education and the City of Amsterdam. The third author acknowledges financial support by the Netherlands Organization for Scientific Research (NWO) under the 2018 ORA grant ToRealSim (464.18.112) and the research program Sustainable Cooperation – Roadmaps to Resilient Societies (SCOOP) funded by NWO and the Dutch Ministry of Education, Culture and Science (OCW) in its 2017 Gravitation Program (grant number 024.003.025). The work was supported by the Ministerie van Onderwijs, Cultuur en Wetenschap [024.003.025]; Nederlandse Organisatie voor Wetenschappelijk Onderzoek [464.18.112]; the Dutch Inspectorate of Education and the City of Amsterdam.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

¹ More specifically, 120% of Q1 and Q5 households are generated per neighborhood. Every time the model is started, 100% out of the initial 120% is randomly sampled without replacement. This results in the simulation being executed with the actual known number of households (i.e. 100%), but with some residential variation.

² https://gitlab.computationalscience.nl/edignum/compassproject.