Higher Education Peer Effects and Contextual Admission Policy

这建立在我的LEO HE关于“课程”对学生财务结果的因果关系的研究之上。这个数字显示了我们最近对30岁毕业生（相对于参考科目-历史）的（平均）收入差异的估计，控制了高等教育之前的教育程度。在这些方面，不同学科之间存在很大差异，数据显示，收入最低的学科与非大学毕业生的收入非常接近。该研究的弱点在于，虽然它控制了学生的先前成绩，但它没有控制其他同学的先前成绩，即忽略了同伴效应。同伴效应的估计受到“反射问题”的影响，即群体的平均表现会影响组成该群体的个人的表现（Manski, REStuds 1993）。这个问题的解决方案是了解学生的参考群体是如何形成的——在“同伴的同伴”方法中进行操作（Angrist Labour Economics, 2014）。我用这种方法在Mendolia等人（OxEconPapers 2018）中对英国中学的同伴效应（使用LEO的NPD成分）进行了建模——它依赖于了解哪些中学生上过哪些小学。我们可以在高等教育的背景下使用这个想法，因为LEO告诉我们谁去了哪个高等教育做了什么，以及他们在随后的收入方面做得有多好。我们知道每个毕业生的高等教育同行是谁，他们在16-18岁时就读于哪所学校，以及他们的表现如何。这将是有史以来第一次将“同行的同行”方法应用于高等教育学生，并将形成学生的“就业市场”论文。感谢学生办公室，我们知道了高等教育机构的“准入和参与计划”。超过25%的高等教育机构实行情境录取（CA）。GRADE提供了UCAS的信息，这对于模拟反事实很重要，哪个学生去了哪里做了什么，它也显示了他们申请去了哪里，但没有去。CA会影响谁去哪里，改变一个人得到的同伴，从而改变一个人的结果。第二篇论文将是一个重要的（合著的）延伸，它将告诉我们，一门课程增加了多少劳动力市场“价值”，而不依赖于同侪组合的质量。有了这个，我们可以模拟任何数量的CA的影响，允许其复杂的溢出效应。这是一个具有挑战性的问题，“集合识别”（即估计因果效应的界限）是解决这个问题的最先进的文献。这篇最终的论文将会成为一种影响深远的工具。

This builds on my LEO HE research on the causal effect of "courses" on student financial outcomes. This figure shows our recent estimates of the (average) earnings differentials of graduates (relative to the reference subject - History) at age 30, controlling for pre-HE educational attainment. There is wide variation across subjects in these, and the earnings of the lowest subjects are very close to non-graduates in the data. The weakness of the research is that, while it controls for the prior achievement of the student, it does not control for the prior achievement of fellow students - i.e. it ignores peer effects. The estimation of peer effects suffers from "the reflection problem" whereby the average performance of a group influences the performance of the individuals that comprise that group (Manski, REStuds 1993). The solution to this problem is to have an idea of how students' reference groups form - operationalised in the "peers of peers" method (Angrist Labour Economics, 2014). I used this method to model peer effects in English secondary schools (using the NPD component of LEO) in Mendolia et al (OxEconPapers 2018) - it relied on knowing which secondary school students had attended which primary schools. We can use this idea in this HE context since LEO tells us who went to which HEI to do what, and how well they did in terms of subsequent earnings. And we know, who each graduate's HE peers were and which school they attended at 16-18, and how well they did. This would be the first ever application of the peers of peers method to HE students a would form the student's "job-market" paper. Thanks to the Office for Students, we know HE institutional "access and participation plans". Over 25% of HEIs practice contextual admission (CA). GRADE provides UCAS information, important for modelling the counterfactual, on which student went where to do what, it also shows where they also applied to go, but didn't. CA affects who goes where and changes the peers one gets, which changes one's outcomes. This second paper would be a significant (co-authored) extension that would tell us how much labour market "value" a course added, independently of the quality of the mix of peers. Armed with this we could simulate the effects of any amount of CA allowing for its complex spillover effects. This is challenging problem and "set identification" (ie estimating bounds on causal effects) is the state-of-the-art literature to tackle it. This final paper would be a tool that would power impact.