Measuring inequality-driven skills gaps in the UK labour market

衡量英国劳动力市场中不平等驱动的技能差距

• 批准号: ES/Z502443/1
• 负责人: Kathyrn Fair
• 金额: $ 17.22万
• 依托单位: The Alan Turing Institute
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=ES%2FZ502443%2F1
• 关键词: Measuring; inequality; driven; skills; gaps

To unlock the full potential of the UK economy, structural inequalities within the UK labour market must be addressed. To do this, we must understand how structural inequalities shape the skills that workers within the labour market possess. When hiring processes are impacted by discrimination, where employers factor in characteristics such as gender and ethnicity when choosing between applicants, structural inequalities will be present in the labour market. This discrimination will have long-term effects, as the individuals discriminated against are not only denied an opportunity to advance their earnings, but also an opportunity to develop new skills. This lack of skills development will impact the types of jobs they obtain in the future, and will have a cumulative impact over the course of their careers. While there is a large amount of research regarding how structural inequalities impact earnings (e.g. gender wage gaps), the way they influence worker skills is not well understood. This creates difficulties for policymakers, as programmes promoting worker skills development may be ineffective or have unintended consequences if they do not account for how these inequalities shape skills development. This project will provide insights into how hiring discrimination influences the skills that UK workers possess. Secondarily, it will consider how the impact of hiring discrimination on the skillsets of workers effects outcomes for individuals (e.g. in terms of earnings) and for the industries in which they work (e.g. in terms of lost productivity). These insights will result from simulation experiments performed using a model describing how individuals move between jobs within the UK labour market. By simulating scenarios where hiring discrimination effects are present, and comparing outputs (e.g. worker skillsets, worker earnings) to those produced when hiring discrimination effects are absent, the impact of these effects will be measured. The work has three main objectives: 1) the construction from UK employment data of a labour flow network (LFN) that describes how individuals move between jobs in the UK labour market, 2) the development of a model that simulates the movement of workers between jobs and accurately reproduces the LFN generated in 1), and 3) the use of this model to simulate scenarios where hiring discrimination effects are present/absent, to determine how these effects influence worker skillsets and outcomes like worker earnings and industry productivity. The UK LFN will be constructed using data from the linkage between the Annual Survey of Households and Earnings and the 2011 Census. This project will be the first to quantify the impact of hiring biases on the skillset of workers at a large scale. This has not previously been possible, due to the limitations of conventional methods for assessing the impacts of hiring biases, as well as data availability issues. The results of this research will guide discussions on how to address the impacts of hiring biases on workers and on the economy as a whole. Insights gained from this project will shape the construction of policy interventions aimed at developing worker skills, in service of improving outcomes for workers and the economy. The model developed will also provide a tool for governments and the third sector. Delivering policymakers these insights and tools, so they can institute data-driven policy, is especially important in current times, when governments are facing strong pressures to transform labour markets (e.g. to promote "green" jobs).

为了释放英国经济的全部潜力，必须解决英国劳动力市场内的结构性不平等。为此，我们必须了解结构不平等如何影响劳动力市场中工人所拥有的技能。当雇用过程受到歧视的影响时，雇主在申请人之间选择诸如性别和种族之类的特征时，劳动力市场将出现结构性不平等。这种歧视将产生长期影响，因为受歧视的个人不仅否认有机会提高收入，而且还否认了发展新技能的机会。缺乏技能发展将影响他们将来获得的工作类型，并将在其职业生涯中产生累积影响。尽管有大量研究关于结构性不平等的影响如何影响收入（例如性别工资差距），但他们影响工人技能的方式尚未得到充分了解。这给政策制定者带来了困难，因为促进工人技能发展的计划可能是无效的，或者如果他们不考虑这些不平等如何影响技能发展，则会产生意外的后果。该项目将提供有关雇用歧视如何影响英国工人拥有的技能的见解。其次，它将考虑雇用歧视对工人技能的影响如何影响个人的成果（例如，收入而言）和他们工作的行业（例如，就生产力损失而言）。这些见解将是由使用模型进行的模拟实验产生的，该模型描述了个人如何在英国劳动力市场中的工作之间移动。通过模拟存在雇用歧视效应的方案，并将不存在雇用歧视效应的产出（例如工人技能，工人收入）进行比较（例如工人技能，工人的收入），将测量这些影响的影响。这项工作具有三个主要目标：1）劳动流网络的就业数据（LFN）的构建，描述了个人在英国劳动力市场的工作之间如何移动，2）模拟工人在工作之间的运动的发展，并准确地重现了1）和3）在1）中产生的LFN，以及3）效果的效果，以确定范围的效果，以确定这些效果/遇到的效果，这些效果会影响这些效果，这些效果会影响这些效果，这些效果是这些效果/效果，这些效果是这些效果/效应的效果，工人收入和行业生产力。英国LFN将使用来自家庭年度调查与收入与2011年人口普查之间的联系的数据来构建。该项目将是第一个量化雇用偏见对工人技能大规模技能的影响的项目。由于评估雇用偏见的影响以及数据可用性问题的常规方法的局限性，以前没有可能。这项研究的结果将指导有关如何解决雇用偏见对工人和整个经济的影响的讨论。从该项目中获得的见解将塑造旨在发展工人技能的政策干预措施，以改善工人和经济的成果。开发的模型还将为政府和第三部门提供工具。在当前政府面临巨大的压力以改变劳动力市场的压力（例如，促进“绿色”的工作）时，提供这些见解和工具，以制定这些见解和工具，以制定数据驱动的政策。