Planning support schoool admission: Towards Equal opportunity for all - A case study in Florida
Lydia Prieto Leon is a PhD student in the department of Urban and Regional Planning and Geo-Information Management. (Co)Supervisors are prof.dr.ir. M.F.A.M. van Maarseveen and dr. J. Flacke from the faculty of Geo-Information Science and Earth Observation and dr. J. Aguero-Valverde from the Univesity of Costa Rica.
Inequalities in education are one of the major challenges of our time. It affects millions of children around the globe, but especially the most vulnerable and marginalized groups. Public school choice has become a key policy tool to battle inequality in access to schools. Despite improvements in both, the quality and availability of educational resources, it has been recognized that the school admission policy plays a crucial role in equally distributing opportunities. One of the major obstacles to achieving equitable quality education is the absence of a robust measurement toolset, i.e., a thermometer of fairness in the assignment practices for new schooling opportunities. The primary objective of this thesis is, therefore, to provide planning support tools to facilitate assessing the fairness of the admission policy. Using school admission data, we draw upon a case study from a specialty (magnet) middle school choice program, in a large, highly diverse, and representative school district in Florida, US, which had faced strong segregation issues in the past. Therefore, the results are based on empirical evidence. Three specific objectives were addressed.
The first objective focuses on the demand side, aiming to model student preferences, to determine the set of factors that influence how applicants choose among specialized public school choice programs. Our experiments using the case study data reveal that Rank-Ordered Models (ROL) and Revealed Preferences (RP) data generate smaller standard errors in the estimations and deliver greater accuracy to identify choice behaviour patterns when compared with standard discrete choice models.
The second objective focuses on the supply side, by analysing the admission criteria used by the school district to choose the students, and by measuring the inequality of opportunity, i.e., how closely an existing admission policy matches the equality of opportunity principle. Remarkably, our analysis opens up transparency, demonstrating that the Human Opportunity Index (HOI) methodology is capable to capture disparities in the selection, and the Shapley decomposition is able to determine the marginal contribution of each applicant’s life circumstances to Inequality of Opportunity (IOP).
The third objective focuses on matching, aiming to develop a methodological framework that supports the definition of different scenarios and guides the assessment of school admission strategies. The framework has three main components: ranking, matching, and evaluating. The ranking component incorporates statistical techniques that capture the preferences from both sides of the market (students and schools) and uses a multicriteria decision analysis model to structure the decision-making process. The matching component is based on computational techniques that solve the student assignment problem by implementing several matching algorithms. The evaluating component is supported by a simulation engine that performs the allocation for several different scenarios, and a measuring toolset that determines ‘how good’ is the assignment from the point of view of the students and the schools.
This study contributed to the discussion on how to monitor progress toward detecting inequalities in access to quality basic education. We revealed important constraints faced by school policy-makers when planning for school admission; we integrated views from three sides of the market -the supply, the demand, and the matching-; and we reflected on what the challenges are for reviewing and communicating these results. Although the HOI was established to place emphasis on detecting unfair access to opportunities, this study confirmed that the index is driven much more robustly by the coverage term than by the inequality term. This limits the usability of the index since schools are easily not able to remove capacity constraints.
To conclude, the research proposed a methodological framework that remains valid for its application in similar case studies in the future, in the US and beyond. The evaluation component of the framework, which is supported by the HOI methodology needs further research. Other fairness metrics should be incorporated that overcome the HOI limitations, and that might provide further insights into the analysis of inequalities in education.