Global Online Labour Markets: Theoretical Perspectives and Initial Findings

Thursday, 14 July 2016: 16:15
Location: Hörsaal III (Neues Institutsgebäude (NIG))
Oral Presentation
Vili LEHDONVIRTA, Oxford Internet Institute, University of Oxford, United Kingdom
We present theoretical perspectives and early results from iLabour, an ERC Starting Grant funded project on the social construction of global online labour markets on the Internet. The iLabour project is premised on the idea that ICT adoption is changing the social, technical, and institutional arrangements through which work and earnings are allocated in society. Previously these arrangements were shaped and enforced through processes of legislation, collective bargaining, and local negotiation. Today, as part of the ongoing digitization of almost every aspect of society, these arrangements are increasingly being shaped and constrained by private software systems that mediate between workers and employers.

The project studies transnational online labour markets (OLMs), also referred to as online work platforms, online staffing platforms, and crowdsourcing marketplaces, forming part of the so-called “platform economy” or “gig economy”. For example, a platform called Upwork has 4.5 million registered workers (contractors) and over a million registered employers (clients) in 180 countries, and facilitated over half a billion USD worth of transactions last year. The project addresses questions about the size and growth of these markets, the kinds of rules that they institutionalize, the politics and processes through which these rules are shaped, and the ways in which traditional labour market organizations as well as novel worker initiatives succeed or fail in influencing their rules. We will also ask what kind of an economy OLMs contribute towards – a global race to the bottom, a playful economy of moonlighters, or a network economy of individual entrepreneurs? We tackle these questions through an ambitious programme of conventional social research and innovative data science methods, including transaction data obtained directly from Upwork.