[论文解读] Student Flow Modeling for School Decongestion via Stochastic Gravity Estimation and Constrained Spatial Allocation

Sebastian Felipe R. Bundoc, Paula Joy B. Martinez+2 more|arXiv (Cornell University)|Feb 20, 2026

School Choice and Performance被引用 0

一句话总结

该论文基于分散的菲律宾教育数据构建随机引力模型，以模拟补贴与容量政策如何重新配置学生并缓解公立学校拥挤的问题。

ABSTRACT

School congestion, where student enrollment exceeds school capacity, is a major challenge in low- and middle-income countries. It highly impacts learning outcomes and deepens inequities in education. While subsidy programs that transfer students from public to private schools offer a mechanism to alleviate congestion without capital-intensive construction, they often underperform due to fragmented data systems that hinder effective implementation. The Philippine Educational Service Contracting program, one of the world's largest educational subsidy programs, exemplifies these challenges, falling short of its goal to decongest public schools. This prevents the science-based and data-driven analyses needed to understand what shapes student enrollment flows, particularly how families respond to economic incentives and spatial constraints. We introduce a computational framework for modeling student flow patterns and simulating policy scenarios. By synthesizing heterogeneous government data across nearly 3,000 institutions, we employ a stochastic gravity model estimated via negative binomial regression to derive behavioral elasticities for distance, net tuition cost, and socioeconomic determinants. These elasticities inform a doubly constrained spatial allocation mechanism that simulates student redistribution under varying subsidy amounts while respecting both origin candidate pools and destination slot capacities. We find that geographic proximity constrains school choice four times more strongly than tuition cost and that slot capacity, not subsidy amounts, is the binding constraint. Our work demonstrates that subsidy programs alone cannot resolve systemic overcrowding, and computational modeling can empower education policymakers to make equitable, data-driven decisions by revealing the structural constraints that shape effective resource allocation, even when resources are limited.

研究动机与目标

• 协调碎片化的行政数据以绘制跨数千所学校的国家级学生流网络。
• 估计驱动学校选择的距离、净成本与社会经济因素的行为弹性。
• 开发双约束分配机制，在容量限制下模拟政策情景。
• 识别约束条件和反事实，为数据驱动的公平资源分配提供依据。

提出的方法

• 使用通过负二项回归估计的随机重力模型构建起源-目标学生流。
• 估计距离（d_ij）与净成本（c_ij）以及起点/目的地社会经济因素的弹性。
• 使用固定效应控制时间不变的起点与目的地因素。
• 应用带有候选受益池与目的地名额的双重约束空间分配机制来模拟政策情景。
• 通过改变 subsidy 金额并在容量约束下评估缓解拥挤的情况进行对照性仿真。
• 对多次随机处理顺序进行蒙特卡罗聚合，以量化分配不确定性。

Figure 1. Overview of the computational policy framework for student redistribution. Institutional data from multiple national government agencies and open-source geospatial datasets are unified to represent the national school network. A stochastic gravity model estimated via negative binomial regr

实验结果

研究问题

• RQ1 地理距离、净学费成本与社会经济因素如何影响菲律宾EMS/ESC受益流动？
• RQ2 在补贴方案下，近邻性与价格的重要性相对如何影响私立学校入学？
• RQ3 哪些结构性约束（容量 vs。 补贴）决定在不同政策情景下的缓解潜力？
• RQ4 如何通过约束分配框架量化潜在的缓解程度并识别未被开发的容量路径？
• RQ5 观察到的补贴名额是否有效缓解拥挤的公立学校，还是容量才是约束？

主要发现

• 地理近距离对学校选择的影响力约为学费成本的四倍，决定了入学流向。
• 距离弹性约为 -0.45，净成本弹性约为 -0.11，表明在邻近私立选项稀缺时，补贴本身影响有限。
• 更高的ESC评分与受益人数量的减少相关，表明价格/可用性与容量动态对 uptake 的影响超出评分本身。
• 起点 LGU 收入对补贴使用呈负相关，显示对低收入市镇的定向覆盖效果。
• 目的地 LGU 收入对入学呈负相关，暗示富裕地区的隐性成本影响参与。
• 离散参数确认存在显著过度离散性，验证使用 Negative Binomial Regression 相对于 Poisson 更合适。