White Paper

This critical and scoping review essay analyses digital labour markets where labour-intensive services are traded by matching requesters (employers and/or consumers) and providers (workers). It focuses on digital labour markets which allow the remote delivery of electronically transmittable services (i.e. Amazon Mechanical Turk, Upwork, Freelancers, etc.) and those where the matching and administration processes are digital, but the delivery of the services is physical and requires direct interaction. The former broad type is called Online Labour Markets (OLMs) and is potentially global. The latter broad type is termed Mobile Labour Markets (MLMs) and is by definition localised. The essay defines and conceptualises these markets proposing a typology which proves to be empirically valid and heuristically useful. It describes their functioning and the socio-demographic profiles of the participants, reviews their economic and social effects, discusses the possible policy implications, and concludes with a research agenda to support European level policy making. It alternates the discussion of ‘hard’ findings from experimental and quasi-experimental studies with analysis of ‘softer’ issues such as rhetorical discourses and media ‘hyped’ accounts. This triangulation is inspired by, and a tribute to, the enduring legacy of the work of Albert O. Hirschman and his view that ideas and rhetoric can become endogenous engines of social change, reforms, and policies. This essay tries to disentangle the rhetoric with available empirical evidence in order to enable a more rational debate at least in the discussion of policies, if not in the public arena.

To understand what the future holds for intelligent automation, we surveyed 537 North American and European organizations, ranging from banks and insurers (property/casualty/life), to healthcare payers (see methodology, page 22). Our findings reveal significant new trends. The first centers around the ability of intelligent automation to improve materially upon what people can do, as well as unlock meaning from data using process analytics. In some cases, organizations are automating work completely through digitization by re-imagining and instrumenting a process from its inception to harness the power of emerging digital technologies, such as social, mobile, analytics and cloud (or the SMAC StackTM). Our research also shows that through these technologies, humans are attaining new levels of process efficiency, such as improved operational cost, speed, accuracy and throughput volume. By using increasingly more astute technologies, smart businesses are doing a much better job of tackling complex process opportunities. In short, they are fast becoming force-multipliers to people who are still essential to process work in banking, healthcare, life sciences and insurance.

The depth and extent of the global financial crisis suggest the economic system is likely to undergo some major changes. This paper looks at one aspect of how 'tomorrow's capitalism' could differ from the neo-liberal model of the past: the balance of the United Kingdom's economy. In particular, it focuses on employment and asks how government can support the transition to a stronger and more balanced labour force. While the immediate fiscal and monetary measures introduced by the Government and Bank of England to combat the recession are important, our focus is on longer term patterns of growth and government action.

It is a pleasure to introduce Technology at Work v2.0: The Future Is Not What It Used To Be. This report is the third in a long-term series of Citi GPS reports coproduced by Citi and the Oxford Martin School at the University of Oxford in order to explore some of the most pressing global challenges of the 21st century. The report further explores the concepts introduced in our February 2015 report, Technology at Work: The Future of Innovation and Employment, which marked the introduction of the Oxford Martin Programme on Technology and Employment, a long-term programme of research at the University of Oxford supported by Citi that will focus on the implications of a rapidly changing technological landscape for economies and societies. In this new report, Oxford Martin School academics; Dr. Carl Benedikt Frey, Associate Professor Michael Osborne and Dr. Craig Holmes expand their theories on the changing nature of innovation and work and the associated implications for the future of employment and society more widely. Based on their methodology that predicted 47 percent of US jobs were at risk from automation, the authors now look at the probabilities of jobs at risk across the world as well as the disparities of job risk between cities.

This paper is about long-term navigation in environments whose appearance changes over time - suddenly or gradually. We describe, implement and validate an approach which allows us to incrementally learn a model whose complexity varies naturally in accordance with variation of scene appearance. It allows us to leverage the state of the art in pose estimation to build over many runs, a world model of sufficient richness to allow simple localisation despite a large variation in conditions. As our robot repeatedly traverses its workspace, it accumulates distinct visual experiences that in concert, implicitly represent the scene variation - each experience captures a visual mode. When operating in a previously visited area, we continually try to localise in these previous experiences while simultaneously running an independent vision-based pose estimation system. Failure to localise in a sufficient number of prior experiences indicates an insufficient model of the workspace and instigates the laying down of the live image sequence as a new distinct experience. In this way, over time we can capture the typical time varying appearance of an environment and the number of experiences required tends to a constant. Although we focus on vision as a primary sensor throughout, the ideas we present here are equally applicable to other sensor modalities. We demonstrate our approach working on a road vehicle operating over a three-month period at different times of day, in different weather and lighting conditions. In all, we process over 136,000 frames captured from 37 km of driving.

As the automation of physical and knowledge work advances, many jobs will be redefined rather than eliminated—at least in the short term.

As automation technologies such as machine learning and robotics play an increasingly great role in everyday life, their potential effect on the workplace has, unsurprisingly, become a major focus of research and public concern. The discussion tends toward a Manichean guessing game: which jobs will or won’t be replaced by machines? In fact, as our research has begun to show, the story is more nuanced. While automation will eliminate very few occupations entirely in the next decade, it will affect portions of almost all jobs to a greater or lesser degree, depending on the type of work they entail. Automation, now going beyond routine manufacturing activities, has the potential, as least with regard to its technical feasibility, to transform sectors such as healthcare and finance, which involve a substantial share of knowledge work.

The potential of artificial intelligence and advanced robotics to perform tasks once reserved for humans is no longer reserved for spectacular demonstrations by the likes of IBM’s Watson, Rethink Robotics’ Baxter, DeepMind, or Google’s driverless car. Just head to an airport: automated check-in kiosks now dominate many airlines’ ticketing areas. Pilots actively steer aircraft for just three to seven minutes of many flights, with autopilot guiding the rest of the journey. Passport-control processes at some airports can place more emphasis on scanning document barcodes than on observing incoming passengers. Skills needs anticipation becomes the practice that harmonises the labour market via insights into educational policies: in the mid- and long-term horizon, the labour market obtains professionals with competencies relevant for market needs, thus closing skills gaps, because educational institutions had enough time and information to adjust. The problem of identifying future skills needs is becoming more and more acute in the current dynamics of the global economy. To smooth the transition caused by the pace of economic globalisation and environmental degradation, and strengthen their position in the new digital world, governments need to envisage the long-term development of critical sectors of the national economy, or shifting to new ones. Of particular interest are the technology-driven industries, as the focal points concentrating research and development, foreign direct investment, talent and cutting-edge technology. Technology can also partially substitute for labour and thus influence the structure of demand: skill-intensive jobs increase in number, while jobs with routine tasks can be replaced by technology. Middle Skill jobs also face a skills set change caused by changing technologies. Skills are required for R&D and innovation, but also for adopting and adapting technologies (business skills, management skills) and for operating and maintaining technologies.

Will the revolution in digital and information technologies make us obsolete? Will jobs be lost and never replaced? Will wages drop to intolerable levels? History and economic theory and evidence suggest that in the long term, such fears are misplaced. However, in the short and medium term, dislocation can be severe for certain types of work, places, and populations. In the transition period, policies are needed to facilitate labor market flexibility and mobility, introduce and strengthen safety nets and social protection, and improve education and training.