Generation COVID and Social Mobility: Evidence and Policy

我们的目标是从绝对和相对两个方面全面介绍这一流行病在培育更具社会流动性的社会方面所面临的挑战。那些从事低质量工作、长期失业、或未能达到在教育、培训或就业方面取得下一步工作所需的考试成绩的年轻人，在有生之年不太可能超越父母的经济地位。他们面临着比前几代人更糟糕的前景。我们的重点是年轻人生活的这些方面，尤其是政策如何通过防止新冠肺炎这一代受影响最严重的群体在教育和劳动力市场上留下“伤疤”，来防止‘富人’和‘穷人’之间出现更大的鸿沟。实证分析的起点是展示儿童的教育环境是如何受到疫情的影响的，以及从家里学习的机会如何因社会经济地位而异。我们将记录不同个人在父母时间投入、金钱投入(如在线辅导)和在线教育资源获取方面的差异，这些差异因社会经济背景而异。我们还将探讨父母失业和劳动力市场状况如何塑造家庭学习环境。面对经济困难、健康状况恶化造成的更大压力，或者由于拥有关键工人身份而增加工作量的父母，不太可能像能够在家工作的收入较高的父母那样为孩子提供更多的时间和支持。我们的目标是量化这些投入如何结合起来产生适销对路的技能，并讨论弥补投入下降的政策，并确保错过关键教学时间的儿童的才华不会得不到满足。经济和教育困难是齐头并进的。在学校不能发挥自己的潜能不仅会关闭高等教育的大门，也会关闭最理想的工作和早期职业培训机会的大门。早期机会的匮乏可能会导致以后的生活机会有限。除了教育，我们还将重点关注疫情如何影响那些进入劳动力市场的人和那些处于职业生涯早期的人。鉴于我们对社会流动性的关注，我们将看看来自不同背景的个人在封锁期间如何产生不同的劳动力市场结果。随着封锁的结束和向正常运转的劳动力市场过渡的开始，我们将讨论这些结果可能对个人意味着什么。我们将再次提出一系列建议，并根据我们的调查结果评估其有效性。这些政策建议旨在防止当前的经济不平等因新冠肺炎而进一步加剧。特别是，我们将考虑(我们自己和其他人的)证据，看看围绕学校和大学招生、职业培训和再技能的政策，以及更一般的劳动力市场政策，如工作保障。为了实现上述目标，我们将使用一系列跟踪年轻人年龄的数据集。这些数据使我们能够将家庭环境与出生队列的劳动力市场结果联系起来。我们将使用的三个数据集--了解社会、千年队列研究和下一步--也包含或将包含关注新冠肺炎大流行如何影响研究参与者的模块。为了解市民对我们建议的政策的支持程度，我们亦会进行一项调查，询问受访者对我们建议的再分配政策的意见。

Our aim is to provide a comprehensive picture of the challenges the pandemic presents in terms of fostering a more socially mobile society in absolute and relative terms. Young people who fall into low quality jobs, long-term unemployment, or who fail to achieve the exam grades needed to pursue the next steps in education, training or employment, are unlikely to rise above the economic status of their parents during their lifetime. They face the prospect of faring worse than previous generations. Our focus is on these aspects of young people's lives and, in particular, on how policy can prevent an even greater chasm opening between the 'haves' and the 'have nots' by preventing educational and labour market 'scarring' of the hardest hit members of the COVID-19 generation.The starting point of the empirical analysis is to show how children's educational environment has been affected by the pandemic and how opportunities for learning from home differ by socioeconomic status. We will document how differences across individuals in parental time inputs, monetary inputs (such as online tutoring), and access to online schooling resources, differ by socioeconomic background. We will also explore how parental job loss and labour market status have shaped the home learning environment. Parents facing financial hardship, greater stresses caused by failing health, or increased workload due to having key worker status, are unlikely to be able to provide as much time and support for their children as higher earning parents who are able to work from home. We aim to quantify how these inputs combine to produce marketable skills and discuss policies to remedy the falls in inputs and ensure that the talent of children who have missed out on key instruction time does not go unfulfilled. Economic and educational hardship go hand in hand. Failing to reach one's potential at school can close the door not only to higher education, but also to the most desirable jobs and early career training opportunities. A dearth of opportunities early on can lead to limited opportunities later in life. In addition to education, we will focus on how the pandemic has affected those entering the labour market and those early in their careers. Given our focus on social mobility, we will look at how individuals from different backgrounds have had different labour market outcomes during lockdown. We will discuss what these outcomes are likely to mean for individuals as the lockdown ends and the transition towards a functioning labour market begins. Once again, we will present a series of proposals, and assess their efficacy, in light of our findings. The policy proposals are aimed at preventing current economic inequalities being further exacerbated by COVID-19. In particular, we will consider evidence (of our own and from others) to look at policies surrounding school and university admissions, vocational training and reskilling, and more general labour market policies such as job guarantees. In order to carry out the above, we will make use of a range of datasets that follow young people as they age. These data allow us to link home environments with labour market outcomes for birth cohorts. The three datasets that we will use - Understanding Society, the Millennium Cohort Study, and Next Steps - also contain, or will contain, modules looking at how the Covid-19 pandemic has affected study participants. In order to gauge the extent to which the polices we propose are supported by the public, we will also carry out a survey asking participants of their views on the redistributive polices that we propose.