Measuring inequality-driven skills gaps in the UK labour market
Lead Research Organisation:
The Alan Turing Institute
Department Name: Research
Abstract
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).
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).
