通过公共土地处置进行国家建设？矩阵补全在反事实预测中的应用
State-Building through Public Land Disposal? An Application of Matrix Completion for Counterfactual Prediction
AI总结：
本文应用倾向性加权矩阵补全模型，估计宅地政策对美国边疆州的反事实影响，发现其显著降低州政府收支，并通过模拟和实证验证了方法的优越性。
摘要：
本文考察了宅地政策（即开放广阔边疆土地以供定居的政策）如何影响美国边疆州的发展。研究采用一种经处理倾向性加权的矩阵补全模型，用以估算在没有宅地政策情况下这些州的反事实规模。模拟研究表明，该方法在偏差和方差方面均低于其他估计器，尤其是在高复杂度情境下。实证分析显示，宅地政策显著且持续地降低了州政府支出和收入。这些发现与使用146万条土地专利记录进行的连续双重差分估计结果一致。本研究将矩阵补全方法扩展至纳入倾向性得分加权，用于面板数据中的因果效应估计，尤其是在交错处理情境下，通过提高长期政策影响评估的精确度，增强了政策评估的能力。
英文摘要：
This paper examines how homestead policies, which opened vast frontier lands for settlement, influenced the development of American frontier states. It uses a treatment propensity-weighted matrix completion model to estimate the counterfactual size of these states without homesteading. In simulation studies, the method shows lower bias and variance than other estimators, particularly in higher complexity scenarios. The empirical analysis reveals that homestead policies significantly and persistently reduced state government expenditure and revenue. These findings align with continuous difference-in-differences estimates using 1.46 million land patent records. This study's extension of the matrix completion method to include propensity score weighting for causal effect estimation in panel data, especially in staggered treatment contexts, enhances policy evaluation by improving the precision of long-term policy impact assessments.
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