Understanding the UK’s productivity problems: new technological solutions or a case for the renewal of old institutions?

Research output: Contribution to journalReview articlepeer-review

Authors

External organisations

  • TUC

Abstract

Purpose: The purpose of this paper is to examine the nature, causes and consequences of the UK’s productivity problems and whether these may be addressed through the new technologies of artificial intelligence (AI). Design/methodology/approach: This paper reviews the literature on productivity to explain how it relates to earnings within different theoretical frameworks, advocating a “power over rents” framework as most realistic. It explains the UK’s twin productivity problems and reviews their potential causes, critically assessing the capacity for new technologies of AI to address them. It highlights the enduring importance of distribution and the design of work to improving the UK’s productivity. Findings: The authors find that the UK’s productivity problems will not be solved by AI technologies due to technical and socio-technical challenges which will require the significant re-design of work. The authors highlight the importance of aggregate demand, which has been inhibited by the shifting distribution of income towards capital and rising inequality of earnings. These issues suggest an important role for trade unions and a renewal of the institutions of employment regulation and collective bargaining. While reversing recent trends raises considerable challenges, the authors observe renewed interest in trade unions from previously hostile thinktanks and international institutions including the IMF and OECD. Originality/value: This paper advocates adopting a “power over rents” theoretical framework to understanding productivity and the distribution of gains. This provides a clear rationale for the role of trade unions, employment regulation and collective bargaining in improving distributional outcomes, raising firm-level productivity and achieving real productivity growth at an aggregate level.

Details

Original languageEnglish
Pages (from-to)296-312
Number of pages17
JournalEmployee Relations
Volume41
Issue number2
Publication statusPublished - 11 Feb 2019

Keywords

  • Artificial intelligence, Collective bargaining, Inequality, Productivity