摘要翻译：
延期接受算法由于其策略证明性而成为一种流行的学校分配机制。然而，随着应用成本的增加，策略证明失效，导致识别问题。在本文中，我通过开发一个新的阈值秩设置来解决这个识别问题，该阈值秩设置将整个秩顺序列表建模为一步效用最大化问题。我应用这个框架研究智利的学生作业。本文的主要贡献有三个方面。我开发了一个递归算法来计算我的一步决策模型的可能性。部分识别是通过将外部价值和期望的接纳概率纳入线性成本框架来解决的。实证应用表明，虽然学校距离是学校选择的一个重要变量，但学生能力对高学术分数学校的排名至关重要。研究结果表明，旨在提高学生能力的辅导等政策干预措施有助于增加低收入、低能力学生在智利优质学校的代表性。
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英文标题：
《Preference Estimation in Deferred Acceptance with Partial School
Rankings》
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作者：
Shanjukta Nath
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最新提交年份：
2020
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分类信息：

一级分类：Economics 经济学
二级分类：General Economics 一般经济学
分类描述：General methodological, applied, and empirical contributions to economics.
对经济学的一般方法、应用和经验贡献。
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一级分类：Quantitative Finance 数量金融学
二级分类：Economics 经济学
分类描述：q-fin.EC is an alias for econ.GN. Economics, including micro and macro economics, international economics, theory of the firm, labor economics, and other economic topics outside finance
q-fin.ec是econ.gn的别名。经济学，包括微观和宏观经济学、国际经济学、企业理论、劳动经济学和其他金融以外的经济专题
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英文摘要：
The Deferred Acceptance algorithm is a popular school allocation mechanism thanks to its strategy proofness. However, with application costs, strategy proofness fails, leading to an identification problem. In this paper, I address this identification problem by developing a new Threshold Rank setting that models the entire rank order list as a one-step utility maximization problem. I apply this framework to study student assignments in Chile. There are three critical contributions of the paper. I develop a recursive algorithm to compute the likelihood of my one-step decision model. Partial identification is addressed by incorporating the outside value and the expected probability of admission into a linear cost framework. The empirical application reveals that although school proximity is a vital variable in school choice, student ability is critical for ranking high academic score schools. The results suggest that policy interventions such as tutoring aimed at improving student ability can help increase the representation of low-income low-ability students in better quality schools in Chile.
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