Introduction
Policy makers need to respond appropriately to changing, flexible, 'non-standard' work arrangements to ensure labour market policy effectively balances flexibility and security both for workers and employers. However, despite its growing importance, relatively little is known about workers in the gig economy. This project aims to use machine learning techniques to help discover more about the constraints and preferences of gig workers, in order to develop and disseminate a set of policy recommendations.
This project received funding from the Turing-HSBC-ONS Economic Data Science Awards 2018.
Explaining the science
The growth of flexible work arrangements in the labour markets of many developed economies has prompted a surge of academic and policy interest. Progress has been limited by the fact that conventional sources of data are poorly designed to detect both the presence of these types of workers and the intricacies of their working lives.
This project will apply 'unsupervised' machine learning methods to three newly-commissioned survey datasets. These cover the economy as a whole as well as providing further detail on two specific areas: the social care sector and online platforms. To better understand heterogeneity among gig-economy workers, a clustering algorithm will be applied to survey data, extracting a small number of worker 'types' representing workers following similar work practices.
Project aims
The goal of this project is to fill the gap in the literature about the gig economy by applying machine learning techniques to three new datasets. The gig economy is thought to cover a wide array of workers who may differ in ways that are important for policy. By grouping workers with machine learning techniques, the aim is to build a greater understanding of this variation.
Additionally, through careful survey design, analysis will be conducted on the extent to which workers value various key features of employment contracts, making it possible to infer the constraints and preferences of gig workers.
Based on these analyses a set of policy recommendations will be developed and disseminated through communications and media networks to policymakers, international organisations, businesses and the broader public. The ultimate goal being to ensure that UK economic policy is made in light of the best research possible.
Applications
There is strong demand for high quality evidence of the kind this project will produce, from labour economists working on the changing nature of work in advanced democracies, to policy makers who are considering the appropriate reform of employment, tax, and social security regulation to reflect recent labour market change.
This research will be directly relevant for any UK government department involved in the design and evaluation of labour market policy, in particular the Department for Work and Pensions (DWP), but also the Department for Business, Energy & Industrial Strategy (BEIS) and HM Treasury (HMT). As the growth of the gig economy is an international phenomenon, the research will also be relevant to international organisations such as the International Labour Organization (ILO) and the Organisation for Economic Cooperation and Development (OECD).
