Maternal Health Behaviours and their Children's Education, Health, and Economic Outcomes

我们的目标是使用数量因果方法来探索三种母亲健康行为在子宫和围产期对儿童教育(特别是GCSE)结果的重要性。我们特别关注因果影响-因为可能会出现不可观察到的混淆。这一点很重要，因为这项工作的政策相关性将取决于对已清除混杂的不可观察因素的影响的估计。该项目组合旨在对三种重要健康行为行为对具有政策相关性的教育结果的影响进行高质量的因果估计。这项工作将发展评估方法的技能，以开启卫生经济学的职业生涯，并支持博士后研究议程。该学生将探索：千年队列研究(MCS)，其中的数据收集自受孕；以及两项英国年轻人纵向研究，LSYPE1和2(跟踪13岁以上的儿童)。监管人员对这些数据有着长期的经验--允许快速发展。我们的目标是产生几个学术影响：我们希望启动发达国家卫生经济学家对斋月更广泛影响的研究。我们的目标是促进发展经济主义者探索遵守斋月。我们希望，通过我们对集合识别的使用，将促使其他研究人员在“工具变量”方法(IV)将不起作用的情况下使用它(这是很少得到承认的)。我们希望，通过在丰富的数据集上应用“机器学习”(ML)方法，我们将向其他母乳喂养研究人员展示它的有效性。我们将量化研究不足的因果关系对与两种常见的健康行为相关的结果的影响，我们还将增加一种新的行为(斋月)。这些方法非常适合手头的问题，而且数据质量很高。因此，学生身份有望为重要的政策问题提供新的证据。我们将探索比文献中通常描述的更广泛的儿童结果-对特殊教育需求(D)状态的影响，非认知特征(尽职尽责，控制点等，以及耐心和风险态度)；KS2，KS3，GCSE，和水平的影响；以及大学结果，25岁时的收入和就业，25岁时的幸福和心理健康，志愿服务和慈善捐赠，出生体重，早产，母乳喂养建立和停止，儿童发育测试，儿童健康问题，行为问题，......等等。

Our aim is to use quantitative causal methods to explore the importance of three maternal healthbehaviours, in utero and peri-natal, on educational (especially GCSE) outcomes for the children.We specifically focus on causal effects - because of the possibility of unobservable confounding.This Is important because the policy relevance of the work will depend on having estimates thathave been purged of the effects of confounding unobservable factors. The portfolio aims to featurehigh quality causal estimates of the effect of three important health behaviours behaviours oneducational outcomes of policy relevance. The work will develop skills in evaluation methods tolaunch a career in health economics and support a post-doc research agenda.The student will explore: the Millennium Cohort Study (MCS) where data has been collected fromconception; and the two Longitudinal Studies of Young People in England, LSYPE1 and 2 (that followchildren beyond 13+). The supervisors have long experience of these data - to allow rapid progress.We aim to have several academic impacts: we hope to kickstart research on the wider effects ofRamadan by developed world health economists. And we aim to promote development economiststo explore compliance with Ramadan. We hope that by our use of set identification will prompt itsuse by other researchers where the "Instrumental Variables" method (IV) would be underpowered(something seldom acknowledged). And we hope that by applying "Machine Learning" (ML)methods on rich datasets we will demonstrate its usefulness to other researchers of breastfeeding. We will be quantifying under-researched causal effects on outcomes associated with two welltroddenhealth behaviours, and we will add a new one (Ramadan). The methods are well-suited tothe problems at hand and the data is of high quality. So the studentship promises new evidencewell-matched to important policy issues. We will explore a much wider range of outcomes for thechildren than has usually featured in the literature - effects on SEN(D) status, non-cognitive traits(conscientiousness, locus of control etc as well as patience and risk attitudes); KS2, KS3, GCSE, andA-level effects; and university outcomes, earnings and employment at 25, well-being and mentalhealth at age 25, volunteering and charitable giving, birthweight, prematurity, breastfeedingestablishment and cessation, child development tests, child health problems; behaviouralproblems, .......and more.