The Persistent Effects of Peru's Mining MITA: Double Machine Learning Approach
秘鲁矿业米塔制度的持久影响：双重机器学习方法AI 解读 ↓
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秘鲁矿业米塔制度的持久影响：双重机器学习方法
- 研究问题
- 殖民时期米塔强制劳动制度对现代经济结果的长期影响有多大，其影响是否随地理位置和地区特征发生变化？
- 方法与思路
- 作者复现利用米塔边界的断点回归研究，并引入双重机器学习的部分线性回归与交互回归模型处理高维协变量。
- 材料陈述的贡献
- 摘要称估计结果显示更强且空间异质的负面遗产，交互回归结果提示靠近波托西及地区因素可能加剧影响。
- 阅读关注点
- 应核对 DML 与原断点设计的识别关系、共同支持、空间相关处理，以及异质性结果究竟对应何种因果参数。
- 阅读边界
- 这是预印本，同行评审或期刊接收未核实；摘要未交代扩展模型的识别条件，机器学习灵活性本身不能保证因果识别。
原文摘要
This study examines the long-term economic impact of the colonial Mita system in Peru, building on Melissa Dell's foundational work on the enduring effects of forced labor institutions. The Mita, imposed by the Spanish colonial authorities from 1573 to 1812, required indigenous communities within a designated boundary to supply labor to mines, primarily near Potosi. Dell's original regression discontinuity design (RDD) analysis, leveraging the Mita boundary to estimate the Mita's legacy on modern economic outcomes, indicates that regions subjected to the Mita exhibit lower household consumption levels and higher rates of child stunting. In this paper, I replicate Dell's results and extend this analysis. I apply Double Machine Learning (DML) methods--the Partially Linear Regression (PLR) model and the Interactive Regression Model (IRM)--to further investigate the Mita's effects. DML allows for the inclusion of high-dimensional covariates and enables more flexible, non-linear modeling of treatment effects, potentially capturing complex relationships that a polynomial-based approach may overlook. While the PLR model provides some additional flexibility, the IRM model allows for fully heterogeneous treatment effects, offering a nuanced perspective on the Mita's impact across regions and district characteristics. My findings suggest that the Mita's economic legacy is more substantial and spatially heterogeneous than originally estimated. The IRM results reveal that proximity to Potosi and other district-specific factors intensify the Mita's adverse impact, suggesting a deeper persistence of regional economic inequality. These findings underscore that machine learning addresses the realistic non-linearity present in complex, real-world systems. By modeling hypothetical counterfactuals more accurately, DML enhances my ability to estimate the true causal impact of historical interventions.