Some theories suggest that ethnic minority students who anticipate discrimination in the labour market may invest more in easily observable human capital, such as education, to signal their productivity to employers. Empirical research has been hampered, however, by a lack of direct information on anticipated labour market treatment. We link ethnic minority student expectations of facing discrimination in the labour market to subsequent performance in high-stakes certificated national exams in England. Our findings suggest that anticipating labour market discrimination is associated with better exam performance, consistent with the view that students are seeking to counteract potential future penalties.

Using a new dataset combining the British Household Panel Survey (BHPS) and Understanding Society (UKHLS), this paper examines the current state of intergenerational income mobility in the UK. This extends previous evidence in several directions, with a focus on younger cohorts of individuals born between 1973 and 1992. I find evidence of considerable intergenerational persistence in the transmission of resources at the household level with an intergenerational elasticity of 0.26 and a rank coefficient of 0.30. This picture of mobility remains at the individual level and under a range of robustness tests that address traditional methodological concerns. While mobility is relatively low at the national level, I find meaningful differences in income mobility rates across the country. More generally, regions with lower income in the North of England display substantially lower levels of both relative and absolute income mobility than regions in the South.

The UK, along with other industrial countries, has recently undergone rapid technological change associated with robotics and artificial intelligence, jointly known as automation technologies. This is precipitating significant transformations in terms of how work is organised, what tasks are performed and what skills are essential for success. Quantifying the scale and nature of these changes to skills requirements is important as we aim to better understand the impacts of automation technologies on the UK labour market. Without the right mix of skills, economies cannot respond and adapt, nor take advantage of new advances in technology that will help to improve productivity. This is why research into skills is a central part of the Pissarides Review into the Future of Work and Wellbeing. In this report, we use a comprehensive dataset of online job postings from Adzuna, which provides information on the type of positions advertised and the skills that they require. We focus on the evolution of skill requirements from 2016 to 2022. The period, selected based on the availability of data, offers a valuable perspective on the recent shifts happening in the UK labour market. These changes are reflected in the rapid emergence of new skills and the disappearance of other, old skills. Notably, most skills becoming obsolete and new ones emerging to take their place, are related to IT. For example, while in 2016 it was common to see job postings requiring basic computer literacy, like web navigation, in 2022 we find an increased demand for new IT skills such as cybersecurity, the ability to work with AI and cloud solutions.

Many studies emphasise the potential for widespread job displacement from exposure to AI. Fewer studies examine the actual impact on job creation as well as displacement, skills demand, and the quality of jobs. Since AI may have multiple positive and negative consequences, it is important to know what drives outcomes, and what factors moderate its impact. Drawing upon theories of technology adoption, we present an empirical study of factors influencing decision maker perceptions of AI, which we hypothesize mediate organization and environmental factors and adoption. We theorize two moderators for the impact of AI on net job creation, skills demand, and job quality. First, Regional Innovation Readiness reflects the availability of enabling resources in the local environment, in the form of an educated workforce and the connectivity infrastructure. Second, High Involvement HRM is an investment orientation which includes employees in the process of adoption. We test our hypotheses using primary data collected from 1012 organizations across all sectors of the UK economy. We find both Regional Innovation Readiness and High Involvement HRM play a significant role in influencing positive and negative outcomes from AI adoption. We discuss the significant implications for policymakers as well as managers.