Foresight 7. Gig economy

Background

In the traditional economy businesses provide capital and employees provide labour. This is being disrupted by technology, specifically the multitude of digital matching and sharing platforms [1]. Platform websites (e.g., TaskRabbit, Amazon Mechanical Turk) enable work to be broken down into components facilitating tasks (“gigs”) to be allocated as needed. Many industries and traditional employment models are being disrupted by the gig economy with outsourcing of services a key feature in the delivery industry with carriers acting as subcontractors, drivers for Uber disrupting the taxi industry, and programmers in IT, with code developers picking up work as sub-contractors. A diverse array of organisations already provide goods and services using digital platforms to match consumers and service providers in the sharing economy (also known as the collaborative economy, access economy or peer economy). These sharing economies have been described as an emergent means of exchanging goods, services and experiences, a practice that marks the 21st century [2]. Advocates seek disruptive opportunities as a way to address major societal challenges, build new social relations, foster economic vitality and enhance resource efficiency. Sceptics point to the precarious nature of sharing and highlight the uncertainty around winners and losers within sharing economies. At the heart of the sharing economy and its cousins (Fig. 1) [3], gig work has been characterised as the model of hiring labour on demand (Productivity Commission report on Digital Disruption) [4].

Figure 1. The sharing economy and the three defining characteristics types of platforms: consumer-to-consumer interactions C2C, temporary ACCESS and physical GOODS. Source: Frenken, K, J. Schor, 2017.

Work in the gig economy is often depicted as flexible by businesses and those who run the platforms that offer work, or as exploitative by labour activists and commentators [5]. Scientists are also taking advantage of this digital disruption and increasing connectivity through the use of citizen scientists who collect data for program such as Climate Watch, Redmap and Reef Life Survey. Several types of “resource labs” already specialize in types of science (e.g., fish aging, genomics, cell culture, proteomics), with researchers ordering analysis and experiments [6]. Other aspects of the scientific endeavour could also be outsourced at a relatively cheap and easy cost. There is an oversupply of PhD graduates that represents a floating pool of potential talent that could be utilised in the dispersed gig economy.

Scenario

  • The declining investment in science (e.g. Privatisation of Science) necessitates some level of sharing/outsourcing of scientific work. A number of things that scientists do start to be outsourced, such as data entry, data cleaning, analytical programs, lab experiments and field surveys.
  • Increased participation in overseas ventures leads to more innovation in sharing R&D options with local partners and/or gig workers representing a temporary work force.
  • Science “gigs” are posted to a job-board, and a pool of potential suppliers bid or accept the offer for that particular piece of work. Successful bidders are directed to a cloud-based site to download the requested job and make delivery within a fixed period of time.
  • Rating of the individuals involved in the work package occurs, in analogously fashion to AirBnB hosts and visitors, Expedia travel writers, and eBay suppliers and customers. This leads to greater confidence in the system and develops into a rating of the levels of skill for each potential gig worker, allowing better matching of each job package.
  • Projects begin to be completed on faster timelines, project costs are lower, as overheads for workers, insurance, leave loadings, and all the associated costs of permanent employment are avoided by science employers.
  • Net long-term results of sharing and gigging results in diminished infrastructure and number of employees at science organisations such as CSIRO, which reorientate to organise funding, the submission of work packages and monitoring the returns – i.e. a science gig R&D corporation.

Indicators: How would we know this was “starting to happen”?

  1. Increase in use of casual staff on projects (50% of project staff)
  2. Individual research businesses offering ‘gig’ services, such as editing, data entry, collection, and design used on more than 25% of science projects.
  3. Payment demanded by citizens for involvement in citizen science projects (25% of projects)
  4. Business offering the matching of workers to jobs advertised by agency scientists (>5 businesses)
  5. Project leader is the only permanent staff member on a project (25% of projects in a portfolio)

Scoring of indicators

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