British Journal of Industrial Relations最新文献

The English National Health Service (NHS) is one of the largest employers in the world. It is currently suffering from high employee turnover and rising numbers of job vacancies. This article uses five waves of NHS Staff Survey data (2018–2022) to try to understand the relationship between line manager quality and staff intention to quit. It estimates pooled cross-sections with data on close to 400,000 individuals and approximately 130 NHS Trusts. The analysis adjusts for a wide variety of confounding variables, including hospital trust fixed effects. We also check for omitted variables and potential endogeneity. Our econometric estimates point to the important influence that line manager quality has on employees’ intentions to quit or stay. This study's novel results suggest that an increase in line manager quality by one unit (on a scale from 1 to 5) is associated with a substantial decrease in NHS employee quit intentions of 17 percentage points.

Productivity is a crucial goal for firms, yet training investments to develop employee skills and enhance productivity face scrutiny. Despite considerable research into training inputs and performance outcomes, several gaps remain. We investigate the differential effects of managerial and staff training on labour productivity, drawing on human capital theory to explain the value of training investment for workforce development. The analysis focuses on 19,289 firm-year observations from the UK Employer Skills Survey and Investment in Training Survey paired with the Business Structure Database in five waves over a 9-year period, accounting for potential selection bias. Examining several measures of training for different occupational categories and formal-informal modes of training, we find that productivity rose with greater training investment for both managers and staff. Among staff occupational categories, training for professionals and associate professionals yielded particular benefits for the firm. The interaction of staff and managerial training generated further gains, illustrating the value of complementary skill development for different employee levels, especially prioritizing intensity of training expenditure over broad coverage.

This paper examines the impact of artificial intelligence (AI)-enabled technologies on employee pay in the United Kingdom. We use matched nationally representative data from the Employers’ Digital Practices at Work Survey and an original survey of 6000 UK workers and apply machine learning techniques to uncover relationships between AI technology and employee pay across qualification and occupation skill groups. We find that lower skilled workers were the primary beneficiaries of AI, but this effect was contingent on the extent of worker interaction with AI. Further analysis shows that employee involvement in pay determination facilitates a more equitable distribution of AI-related pay benefits by enabling a significant uplift in pay among lower qualified workers. Overall, while the implications of AI for pay outcomes are broadly positive, the study highlights the need to strengthen workplace voice mechanisms to ensure a more equitable distribution of benefits from the growing use of AI.

David Marsden enriched and extended the field of employment relations with his interdisciplinary and comparative practice. This introduction to the special issue honouring his work examines the nature of David's contribution and analyses his influence on employment relations and adjacent fields. The article highlights David's original engagement with the social science questions of his day, and his comparative craft, which entailed sensitivity to difference and a commitment to grounded, institutionally embedded analysis. Previewing the articles that make up this special issue, this introduction shows how David's work provides signposts to a better world of work.

The Annual Survey of Hours and Earnings (ASHE) is based on an annual 1% sample of employee jobs and provides many of the UK's official earnings statistics. These statistics are produced using official weights designed to make the achieved sample in each year representative of the population of employee jobs in Britain by gender, age, occupation and region. However, we show that jobs in small, young, private-sector organisations remain significantly under-represented after applying these weights. To address this issue, we develop new weights and demonstrate their importance through policy-relevant examples. Our new estimates suggest that the bite of the National Living Wage is greater than previously reported, and the gender pay gap is wider. We conclude that a new official review of the methodology for ASHE is merited to improve the accuracy and reliability of data informing earnings analysis and research in the United Kingdom.