Development and enhancement of Longitudinal Education Outcomes (LEO) data

了解个人和社会从不同的教育和培训课程中受益的程度，对于政府衡量教育和技能投资至关重要。获取有关教育、培训和收入的丰富信息的数据，对于估算这些收益至关重要，而且要有足够大的样本来考虑不同群体之间的收益是否存在差异（如按社会经济背景）或国家的不同地区划分，对重要的政策决定至关重要，例如，对弱势群体或生活在“左翼”的人的教育和技能投资的程度，落后地区将有助于国家的“升级”。我们可以在英格兰获得这样的数据，即所谓的纵向教育成果（LEO）数据，它将教育记录、福利记录和税收记录联系在一起。例如，这些数据为了解个人和地区从高等教育中受益的程度提供了至关重要的新见解。但是，迄今为止，这些数据的访问仅限于相对少数的个人和组织。这些数据的可获得性越来越广泛，但作为低地球轨道一部分的数据集的数量和复杂性给新用户带来了巨大的障碍，因为这意味着他们必须投入大量时间来理解数据，然后才能有效地使用数据，这可能意味着一些重要的研究问题因此得不到解答。此外，如果我们能够纳入更多的信息，这些数据可能会更有用。例如，如果我们可以包括个人工作地点的信息-以及谁与谁一起工作-那么我们就可以了解在教育和培训方面的投资对人们的同事以及他们工作的企业有多大的好处。同样，如果我们能够将哪些人申请了大学，以及在哪里申请的信息联系起来，并将其与他们收到的录取通知书和他们去了哪里进行比较，我们就可以更多地了解个人偏好和大学决定在社会经济背景，教育选择和后来的结果之间产生明显的紧密联系方面的作用。我们的项目将填补这两个空白。具体而言，它将：1。通过以下方式加强现有的低地轨道数据：a.创建一组简化和一致的变量，总结数据中的重要信息，例如教育程度，就业和收入的衡量标准，研究人员可以使用这些变量来帮助他们开始分析。c.链接新的背景数据，例如关于个人居住地区的数据。为这些新创建的变量共享文档、代码和元数据（在一个。和B.）D.创建和运行在线论坛，通过该论坛，当前和潜在用户可以找到有关数据和未来发展的信息，并寻求其他用户的帮助。提供介绍性和高级培训活动，以建设使用数据的能力。链接新的数据，包括个人工作的地方，对于那些申请的人，他们的大学申请和提供的信息，以及：将这些数据纳入第1条所述的每个要素中。b.为现有的低地球轨道数据编制文件和一致的变量，即为这些新数据编制文件和一致的变量;纳入额外的相关背景数据（例如关于申请和就读高等教育机构的“质量”）;通过将信息纳入在线论坛和提供定制的培训活动和资源，提高使用这些新数据的意识和能力。开展新的研究，以证明这些新数据在解决重要的政策相关问题方面的价值，例如教育与企业生产力之间的联系，以及给予来自弱势背景的学生较低的大学入学机会的政策是否有效地改善了这些人的结果。

Understanding how much individuals and society benefit from different education and training courses is vital for governments weighing up investments in education and skills. Access to data with rich information on education, training and earnings is crucial to estimating these benefits, and having a large enough sample to consider whether the benefits vary across different groups (e.g. by socio-economic background) or different areas of the country is crucial in informing important policy decisions, such as the extent to which investment in education and skills for disadvantaged individuals or those living in 'left-behind' areas will help 'level up' the country.We have access to such data in England, known as the Longitudinal Education Outcomes (LEO) data, which links together education records, benefit records and tax records. These data have provided crucial new insight into how much individuals and areas benefit from higher education, for example. But, to date, access to these data has been restricted to a relatively small number of individuals and organisations. The data are becoming more widely available, but the number and complexity of the datasets included as part of LEO presents a substantial barrier to new users, as it means they have to invest a lot of time in understanding the data before they are able to use it effectively, and may mean some important research questions go unanswered as a result. Moreover, the data could be even more useful if we were able to incorporate additional information. For example, if we could include information on the places where individuals work - and who works with whom - then we could understand how much investment in education and training benefits people's colleagues, and the businesses in which they work. Similarly, if we were able to link in information about which individuals applied to university, and where, and compare this to the offers they received and where they went, we could understand more about the role of individual preferences and university decisions in generating the strong links evident between socio-economic background, education choices and later outcomes. Our project will fill both of these gaps. Specifically, it will:1. Enhance existing LEO data by:a. Creating a simplified and consistent set of variables summarising important pieces of information from the data, such as measures of educational attainment, employment and earnings, that researchers can use to help get them started with their analysis.b. Linking in new contextual data, such as about the areas in which individuals live.c. Sharing documentation, code and metadata for these newly created variables (in a. and b.)d. Creating and running an online forum through which current and potential users can find information about the data and future developments, and seek help from other users.e. Providing introductory and advanced training events to build capacity in use of the data.2. Link in new data, including on the places where individuals work and, for those who applied, information on their university applications and offers, and:a. Incorporate this data into each of the elements outlined under 1. for existing LEO data, i.e. produce documentation and consistent variables for these new data; merge in additional relevant contextual data (e.g. on the 'quality' of the higher education institutions applied for and attended); and build awareness and capacity in use of this new data by incorporating information into the online forum and providing bespoke training events and resources.b. Undertake new research to demonstrate the value of this new data in addressing important policy-relevant questions, such as on the link between education and business productivity, and whether policies which give lower university entry offers to students from more disadvantaged backgrounds are effective in improving outcomes for these individuals.