Measuring inequality-driven skills gaps in the UK labour market

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部的结构性不平等问题。要做到这一点，我们必须了解结构性不平等如何影响劳动力市场中工人所拥有的技能。当招聘过程受到歧视的影响时，当雇主在选择申请人时考虑性别和种族等特征时，劳动力市场就会出现结构性不平等。这种歧视将产生长期影响，因为受歧视的个人不仅被剥夺了提高收入的机会，而且也被剥夺了发展新技能的机会。这种技能发展的缺乏将影响他们未来获得的工作类型，并将在他们的职业生涯中产生累积影响。虽然有大量关于结构性不平等如何影响收入（例如性别工资差距）的研究，但它们影响工人技能的方式尚未得到很好的理解。这给政策制定者带来了困难，因为如果不考虑这些不平等如何影响技能发展，促进工人技能发展的计划可能会无效或产生意想不到的后果。该项目将深入了解招聘歧视如何影响英国工人所拥有的技能。其次，它将考虑雇佣歧视对工人技能的影响如何影响个人（例如，在收入方面）和他们所在的行业（例如，在生产力损失方面）的结果。这些见解将来自使用一个模型进行的模拟实验，该模型描述了个人如何在英国劳动力市场中在不同的工作之间流动。通过模拟存在雇佣歧视效应的情景，并将产出（例如工人技能组合、工人收入）与不存在雇佣歧视效应时产生的产出进行比较，将衡量这些效应的影响。这项工作有三个主要目标：1)从英国就业数据构建劳动力流动网络（LFN），描述个人如何在英国劳动力市场的工作之间流动；2)开发一个模型，模拟工人在工作之间的流动，并准确地再现1)中产生的LFN； 3)使用该模型模拟招聘歧视影响存在/不存在的情景。确定这些影响如何影响工人的技能和结果，如工人收入和行业生产力。英国LFN将使用家庭和收入年度调查与2011年人口普查之间的联系数据来构建。这个项目将是第一个大规模量化招聘偏见对工人技能的影响的项目。由于评估招聘偏见影响的传统方法的局限性，以及数据可用性问题，这在以前是不可能的。这项研究的结果将指导有关如何解决招聘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影响旨在培养工人技能的政策干预措施的构建，以改善工人和经济的成果。所开发的模型还将为政府和第三部门提供一个工具。当前，政府正面临着转变劳动力市场（例如促进“绿色”就业）的巨大压力，向政策制定者提供这些见解和工具，使他们能够制定数据驱动的政策，这一点尤为重要。

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).