A worker in Kenya knows that if she lobbies for a higher wage, another worker in India or the Philippines can easily take her place. Online gig work is also inherently insecure. There is rarely any job security, and a worker who falls ill, becomes pregnant, or simply needs a break will lose their income.
Because most workers live in countries with little social security, this presents a real risk to the well-being of people who do not thrive in the online gig economy. Because online workers lack security, and because of the constant threat of competition , many are tempted to work extremely long shifts. Many hours are spent each day searching and bidding for gigs. Unsurprisingly to increase the amount of paid gigs, workers try and complete tasks at intense speeds, so they can move on to next gig or return to searching for more paid-work.
Working at high-speeds while sat at computer for long hours can have painful consequences while the reward is just a few dollars an hour. Therefore, an increasing number of workers around the world will soon find themselves doing it. As this global market for gig work expands, we need to think of ways to design a system that works not just for those who thrive in it the most successful workers , and not just for those who benefit most from it clients and platform owners.
Consumers, regulators, platforms, and workers each have distinct roles to play in creating this fairer world of work. First, consumers : you, as an internet user, have an important responsibility in this new world of work. In the last few decades, the fair-trade movement has encouraged millions of people to avoid coffee, diamonds, or running shoes that have been produced in unethical ways.
Second, regulators could do much more to help digital gig workers. Currently, a lot of this sort of work passes entirely underneath the radar of regulation. Taxes are rarely paid, and workers may not feel empowered enough to complain about the non-payment of wages.
Uber, for instance, has even designed bespoke technology to enjoin its drivers to evade state regulations. Virtual Workers and the Global Labour Market features international examples of emerging occupations and working conditions in new media, gaming, journalism, advertising and branding, software development and offshore services.
Buy eBook. These jobs might take anything from minutes e. The economist Guy Standing , meanwhile, predicts that by , platforms will mediate one third of all labour transactions. The scale and scope that some of these platforms can achieve is in part driven by the development of development of planetary-scale infrastructures of computation Bratton, To answer this question, we outline the scalar and spatial changes that have been occurring in labour markets, review their implications for the balance of power between labour and capital, and advance some possible responses to ensure that we do not get trapped in a global race to the bottom in which there are constant downwards pressures on wages and working conditions.
The argument that we make here is largely conceptual. We sought maximum diversity in our sample, and our respondents were characterised by a range of different attributes, such as number of hours worked on the platform, different types of work activities, and income earned.
Most workers in our sample had multiple accounts on various platforms such as Freelancer. We also recruited Upwork workers through social media Facebook and LinkedIn and snowballing.
The primary sampling goal was to ensure a diversity of worker experiences. As such, this paper presents selected cases that indicate the existence of activities, issues, and concerns, rather than a representative view.
Good Gig, Bad Gig: Autonomy and Algorithmic Control in the Global Gig Economy
Through the interviews we sought to understand the socio-economic background of workers, the nature and types of work done by these workers, career prospects, livelihood challenges, income, worker-worker and worker-client interactions, strategies to win bids, to stay competitive, to demand higher wages and negotiate working hours, and actions to avoid the various risks inherent to platform work.
All the worker names have been changed. To describe what is meant by a planetary labour market, it is first useful to describe what labour markets are. A nineteenth-century hiring fair, such as the one described by Thomas Hardy in Far From the madding crowd , is used by Fevre , in his book about the sociology of labour markets, as a way of illustrating an abstract definition of labour markets through five key distinct processes.
These are: informing employers employers learning about availability and skills of workers , informing workers workers learning about jobs , screening workers employers obtaining enough information about workers to decide if they could be hired for a job , screening employers workers learning about their employers , and offers to buy or sell labour the actual negotiations and pitches made by workers and employers. Labour markets, in other words, are a way of describing a convergence of workers and employers in specific places and times.
While scholars as far back as Karl Marx posited that this convergence in competitive labour markets is a fundamental characteristic of capitalist society, various planned economies in the late twentieth century likewise relied on the concept of a labour market to govern the management and distribution of the labour force Brown, However, while co-presence has traditionally been a necessary condition for most of these conditions, it has not been a sufficient one.
But, ultimately, those institutions and practices still require some level of space-time convergence between employers and workers. It is important here to distinguish between the way that labour markets have been conceived in orthodox classical economics, and their actual characteristics.
Orthodox conceptions put forward a perfectly competitive market that can provide both firms buyers and workers sellers with perfect information. In both cases, wages were relatively uniform and institutional forces only had a small influence. In practice, it is rare for labour markets to fit these sorts of perfect property, instead, labour markets function in imperfect and uneven ways.
Workers comprise different classes, genders, races, nationalities, and other groups that can get segmented into different functions in labour markets. These markets are further built on, and performed through imperfect information, irrational social behaviours, politics, institutional arrangements and practices, customs, and prejudices. Thus, we get segmented labour markets functioning at multiple scales and spaces to produce variegated outcomes for workers Craig, et al.
In these segmented or split markets, workers have little opportunity to cross into other groups and are thus constrained to a limited set of outcomes: with factors like gender or race influencing segmentation with, for instance, women earning lower wages than men Bonacich, ; Reich, et al. The takeaway point here is that labour markets function in complex, imperfect, exclusionary ways.
When speaking about a physical meeting place, like a hiring fair, the very concept serves as a multi-scalar abstraction. This is not to say that workers are not enrolled into global-scale associations and production networks.
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Indeed, workers in many economic sectors have been for centuries Hunt, If they do not know when and where, workers cannot find jobs and employers cannot hire workers. Much of this discussion assumes a located place of work — a farm, factory, or office that a worker needs to be physically present in, in order to perform their duties.
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But, as the nature of work changes, so too must our conceptions of the boundedness of labour markets. Here it is useful to draw on the concept borrowed from geography of a relational understanding of space Massey, Rather than only thinking of space as a canvas, it is rather something that can also emerge from social relations Hudson, This vision of space as relational and emergent, rather than pre-existing, is useful because it offers a productive way of understanding the impact of digital technologies on labour markets.
If we apply these sorts of understandings to the contexts of work, the boundedness of earlier visions of labour markets evaporate.
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As the places of work move beyond single locations, this offers us a pathway for thinking through the impacts of globalisation on workers. Like Jones , we build our understanding of a planetary labour market on a relational understanding of space. Specifically, we draw from Doreen Massey [ 14 ] who argued that:.
While digital work platforms have enabled the potential coming together of employers and workers on a planetary scale, the labour market for digital work that is developing is characterised by both asymmetrical scalar relationships and uneven spatial ones: with workers and employers having very different possibilities to read and participate in the labour market.
Rhythms of Creativity and Power in Freelance Creative Work
It facilitates a confluence that can transcend the spatial boundaries that constrained the convergence of employers and workers, but remained shaped and characterised by multi-scalar and asymmetrical technological, political, social, cultural, and institutional factors. This strategy is neither intended to imply that these are the only concerns, or that there are not benefits such as flexibility to workers at the individual scale. In the case of online outsourcing, employers i. Employers list requirements needed from their workers on online labour platforms, and workers from around the world then bid on those jobs — allowing employers to collect certain information they need about any potential worker.
The fact that online outsourcing platforms tend to have a massive oversupply of workers on them Graham, Lehdonvirta, et al. When we compare the ways in which employers learn about workers to the ways that workers learn about jobs, the scalar differences in how workers and employers can read the planetary labour market become apparent.
However, it is also obvious that there are distinct patterns to the supply of online labour power with large concentrations of workers in a few countries. Employers can join these platforms to either find workers in specific places for instance when language skills are needed or to put a job out to competition from workers that can be located anywhere. This huge number of people who sign up to look for jobs ends up creating a huge oversupply of labour. In Table 1 , we present data collected from Upwork on a single day in October , to estimate at the potential oversupply of labour on the platform.
Even with such a low threshold of what constitutes work, we see a massive oversupply in the sample of countries in Table 1. Globally, less than seven percent of people who register for jobs are ever able to secure one [ 15 ]. While the geography of online labour is far from equally spread around the world, the relative ubiquity of digital connectivity, and the affordances that digital labour platforms provide, mean that employers can now find new workers on the other side of the world in minutes, as long as workers have relevant ICT tools and internet connectivity.
However, for workers, the combination of the global market and the oversupply of labour power or at least the perception of the oversupply of labour power is experienced as something that significantly depresses the wages they are able to command see also Graham, Hjorth, et al. Other people are bidding too low and it was people from the Philippines and India.
I was angry because they bid too little and Yet they are happy. Yeah, I was quite pissed off there; I was like no way are they doing this! Workers on online outsourcing platforms naturally have a geographically expanded pool of jobs to bid for, compared with the jobs available in their local labour markets. Most platforms allow workers to bid for jobs from anywhere. However, this differs from a simple state of co-presence for two reasons. First, while workers can learn about task vacancies on platforms, clients often reveal relatively little about themselves. Second, these platforms tend to facilitate vertical communication rather than horizontal communication between workers , thus limiting the associational power of workers.
On the first point, the ability for workers to learn basic information about the jobs, but relatively little about their bosses is particularly pronounced for workers doing short-term and fixed-price jobs such as document conversion, transcription, and writing jobs. Some longer-term jobs such as Web-chat support, digital marketing, and virtual assistants should in theory allow workers to learn more about clients and their businesses over the course of time and therefore build a relationship of trust with them.
Yet, even with these longer-term jobs, many workers struggle to get know their clients. A Kenyan data entry worker, Eidi, noted that despite working on a content generation project for over a year, she only knows her line manager who sits in Uganda and has no idea who the main client is, or the owner of the project. Here it important to remember that the affordances of online outsourcing platforms are designed for workers and clients to connect with one another, rather than for workers to connect with each other. Historically, the inability for workers to have any effective virtual co-presence has severely limited associational power Wood, Lehdonvirta, et al.
While Fordism enhanced workplace bargaining power based on the ability of workers to threaten to stop the entire production chains by uniting workers at the point of production i. The point is if by fixing labour in a place often gives it power, it can also be undermined by multiple spatio-temporal fixes created by capital since the crises of the s oil price crisis, —74 stock market crash, the fall of the Bretton Woods system Harvey, , Put differently, relocation of production gave new entrant labour forces a sense of class identity and bargaining power at the workplace, but the ease with which production can be relocated meant an undermining of marketplace bargaining power and threats of job losses.
The mobility of capital through the reorganisation of production techniques fragmentation and relocation of production has tended to weaken the associational power of workers due to the incorporation of a mass of unemployed and unorganised workers who are hard to unionise. Workers lack a sense of collective identity as a working class and a weak state regulatory framework delegitimize trade unions, making it incredibly hard for such organisations to deliver benefits to workers Silver, These trends have continued with the emergence of digital work that can be performed by a global pool of unorganised workers separated by large physical distances, and workers lacking common linguistic and cultural characteristics.
The inability for platform workers to have any effective virtual co-presence severely limits their associational power see also Wood, Lehdonvirta, et al. This largely relates to the nature of digital work, the technical structure of platforms, the transaction of digital work through the Internet, and a global pool of workers who are fragmented and commoditised. The demand by clients for work to be completed before a set deadline forces workers to confine themselves to their workplaces usually their rooms , working long hours with high work intensity to avoid losing wages.
Mukondi, in Kenya, was doing Internet research for a U. She was working close to 80 hours a week and as a result, did not have enough time to meet other online workers in the locality or socialise with them. We asked all workers in our sample if they know anyone in their locality and if they meet with them regularly.
While some workers knew other digital workers in their cities, they usually find it hard to socialise with them. Instead, workers tend to utilise whatever time they have to find new work, instead of trying to establish connections with workers either through the Internet or locally. Since there is intense competition between workers on a global scale on these outsourcing platforms, it is understandable that workers will want to prioritise continuously looking for work instead of developing capacities for collective organisation. The extreme physical separation of digital workers also makes any collective organisation or physical co-presence unlikely.
We found a few local networks of platform workers in Ghana, Nigeria, and Kenya, where multiple small groups of workers two—three workers in a group have developed close working collaboration. Workers also develop networks through social media, although the utility of such networks to transform worker power is debatable. Figure 1 indicates that a range of underlying economic, social, and political factors end up bringing into being particular geographies of work.
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While work can in theory be done from anywhere, myriad factors end up influencing concentrated economic geographies of jobs. Irrespective of its actual geographies, digital work is sufficiently mobile for workers and clients to feel that the marketplace they are operating in is truly global. The result is that workers can lose a sense of any collective organisation and feel replaceable, while clients exploit this lack of associational power of workers to exert their demands on workers also see point 5, below.
Ben, a virtual assistant in Kenya, explains the feeling of being replaceable. The way platform work is designed and transacted over the Internet reveals stark asymmetries in the ways in which employers and workers obtain information about each other, and what they do with that information. While the bidding process enables labour power to be bought as a commodity in the market, a real sense of this commodification comes from the nature of the digital work and types of job contracts offered on digital platforms. Since these tasks can finish quickly, they have to repeat the same bidding process in order to secure new jobs though some experienced and top-rated workers may get repeat clients who offer work to them directly.
There are relatively few jobs advertised that allow some form of trust or working relationship to develop between workers and the employers — usually hourly contract jobs such as digital marketing, social media management, and virtual assistant. But even here there is a problem. Some platforms give clients the power to pay workers only if they are satisfied with their work, and as a result, some workers do not get paid even after they submit their work to clients.
This level of fragmentation of work and commoditisation of labour power means the employer-employee relations become contingent Barker and Christensen, ; Barley and Kunda, , and employers are under no obligation to help workers build long-term careers on platforms.
The potential for workers to gain experience and build up knowledge and skills for future career development is also constrained, meaning less scope for workers to upgrade to high-skilled and high-income jobs on platforms. A data entry worker with no formal education and training is highly unlikely to go on to software development tasks or graphic design. Since only workers bid for jobs posted by employers, they signal or give information to employers on platforms about their quality which employers use to screen workers and make an informed decision who to award the contract.
Employers, with all the information about bidding workers at their disposal, are able to choose the workers they want to work with, which might be the worker with the lowest hourly rate, or the top-rated worker irrespective of their rate. In other words, clients have the ability to access all the information they need before awarding the job contract to workers, who usually know little about their clients see point 4, below.
The technical infrastructure of the platforms generates and amplifies an information asymmetry between buyers and sellers of labour — in order to favour the buyers see Graham, Hjorth, et al. One of the most significant tools that employers use to learn about workers is the rating system Wood, Graham, et al. As one worker, Mukasa, in Uganda, told us, no client is willing to work with new freelancers with no ratings or feedback, making it difficult for newcomers to land a job easily on platforms.