Creating research-ready data by linking Census data to ASHE
Creating research-ready data by linking Census data to ASHE
批准号:
ES/Z502893/1
负责人:
John Forth
金额:
$16.69万
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2024
资助国家:
英国
项目状态:
未结题
起止时间:
2024 至 --
中文摘要
该项目将创建、评估、记录和支持研究就绪数据集的使用,该数据集是通过将人口普查的个人数据与工时和收入纵向年度调查(ASHE)的员工记录联系起来而产生的。这将为研究和政策界提供一个独特的强大的纵向数据集,可以对个人和家庭特征、工作特征和雇主对劳动力市场结果的影响产生新的见解。英国人口普查提供了大量关于人口中个人的信息,但不收集收入数据,限制了这些数据对劳动力市场研究的价值。ASHE是一项年度小组调查,基于1%的英国员工工作样本,收集关于员工收入和带薪时间的详细和高度可靠的信息。然而,调查几乎没有收集到员工的个人特征(仅限于性别、年龄和居住地点)。该项目将基于这些数据集的各自优势,为英国收入和工作时间的研究提供最大的纵向数据集。在ADR资助的工资和就业动态战略影响项目(WED)中,提议团队与国家统计局(ONS)合作,将ASHE的个人记录与2011年英格兰和威尔士人口普查(CEW11)的个人记录联系起来。这使得2011年英格兰和威尔士员工保持的ASHE记录中,约四分之三(74%)的个人特征数据被添加到人口普查数据中。ASHE-CEW11关联的数据集已经使政府和学术界的研究人员能够探索种族、残疾、教育、出生国家和家庭环境等因素如何影响个人的工资水平和加薪幅度。然而,如果将链接扩展到最近的人口普查,数据集的价值将显著提高。在拟议的项目中,我们将与英国国家统计局合作,使用一种新的和改进的链接协议(国家统计局的参考数据管理框架,RDMF),将2021年英格兰和威尔士人口普查的个人记录与纵向ASHE联系起来。RDMF还将用于将ASHE重新链接到2011年英格兰和威尔士人口普查,生成跨越至少十年的纵向ASHE-人口普查数据集。将对ASHE和人口普查之间的联系进行质量评估和记录。该项目将在培训和用户支持计划的支持下,专注于生成可用于研究的数据。由此产生的纵向Ahe-Ccount数据集将代表着英国劳动力市场研究数据基础设施的显著改进,提供比现有家庭调查目前可用的更可靠的收入数据、更大的样本和更好的雇主信息。该数据集将通过使用人口普查2021提供对员工体验的新见解,使政策制定者能够做出更明智的决策,从而支持该计划的目标。
英文摘要
The project will create, assess, document and support use of a research-ready dataset, generated by linking personal data from the Census to employee records from the longitudinal Annual Survey of Hours and Earnings (ASHE). This will provide the research and policy communities with a uniquely powerful longitudinal dataset, which can create new insights into the influence of personal and household characteristics, job characteristics and employers on labour market outcomes.The UK censuses provide a wealth of information on individuals in the population but do not collect data on earnings, limiting their value for labour market research. ASHE is an annual panel survey based on a 1% sample of employee jobs in the UK, collecting detailed and highly-reliable information on employees' earnings and paid hours. However, the survey collects few personal characteristics for the employee (restricted to sex, age and residential location). The project will build on the respective strengths of these datasets to provide the largest longitudinal dataset for research on earnings and hours in the UK.In the ADR-funded Wage and Employment Dynamics Strategic Impact Project (WED), the proposing team partnered with the Office for National Statistics (ONS) to link person records from ASHE to person records from the 2011 Census for England and Wales (CEW11). This has added Census data on personal characteristics to around three-quarters (74%) of the 2011 ASHE records held by employees in England and Wales. The ASHE-CEW11 linked dataset is already enabling researchers in government and academia to explore how factors such as ethnicity, disability, education, country of birth and household circumstances affect individual's wage levels and pay progression. However, the value of the dataset would be significantly enhanced if the link was extended to the most recent census.In the proposed project, we will work with ONS to link person records from the 2021 Census for England and Wales to the longitudinal ASHE, using a new and improved linkage protocol (ONS' Reference Data Management Framework, RDMF). The RDMF will also be used to relink ASHE to the 2011 Census for England and Wales, generating a longitudinal ASHE-Census dataset spanning at least a decade. The linkages between ASHE and Census will be quality assessed and documented. The project will focus on generating research-ready data, supported by a program of training and user support.The resulting longitudinal ASHE-Census dataset will represent a significant improvement in the UK's data infrastructure for labour market research, providing more reliable earnings data, larger samples, and better employer information, than is currently available from existing household surveys. This dataset will support the aims of the program by using Census 2021 to provide fresh insights into the experiences of employees, enabling policymakers to make better-informed decisions.
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