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Post-pandemic future of work - How does digitization impact labour?
Exploring Economics, 2021
Post-pandemic future of work - How does digitization impact labour?
Authors: Neha Chauhan, Miguel Corredera, Krystian Lukasik, Filipa Reis
Review by: Dr Eleni Papagiannaki
This is an essay that was created as part of the writing workshop "The Economics of the New Reality – Looking at the World Under the Pandemic from Pluralist Lenses", published in July 2021.
The following article examines the challenges brought to the labour market by the use of digital technologies. First, the essay discusses issues of precarization of working conditions in the platform-based economy, rising income inequalities, the polarization of jobs and algorithmic biases. Next, it analyses possible solutions to the aforementioned problems such as universal basic income, platform cooperatives and legal regulations.
COVID-19 catalyzed digitization
With the World Health Organization’s declaration of COVID-19 as a pandemic in March of 2020, global lockdowns were put in place, accelerating the digitization of various sectors (Goldin, 2021). Despite its toll on global economies, the pandemic has catalysed digital transformation from retail and finance to education and healthcare. Simultaneously, it has also made essential goods and services out of reach for those without internet access (Mondato, 2020; Deloitte, 2020)
According to the International Labour Organization (ILO, 2020), with the start of the first lockdown, G20 economies set out to provide unprecedented amounts of emergency financial support in the form of job and income protection, in order to prevent the economy from collapsing. However, despite its global reach, the consequences of the pandemic have not been felt equally by all citizens. According to the World Economic Forum (2020), the jump in the digitization of day-to-day life brought on by COVID-19 lockdowns created an uncertain outlook of the labour market and the general economy, bringing serious challenges to worker’s well-being. It has intensified in-country and cross-country inequalities. According to the economist Nicholas Bloom (University of Stanford, 2020), the shift to working from home that is intended to keep the economy going could actually generate a global productivity slump and “threaten economic growth for many years”.
The existing vulnerabilities of the economic system in place have been exposed by the consequences of COVID-19 and the inequalities have deepened. Workers with limited means of protection (e.g. those in the informal market), women (who have been at the frontlines of providing essential services, do unpaid care work and registered higher unemployment rates than before the pandemic) and young professionals (who have struggled to enter the job market in 2020) have suffered the most negative impacts of the crisis (Mondato, 2020). According to the World Economic Forum (2020), the coronavirus pandemic has amplified inequalities in terms of nationalities, occupation, income, gender and race.
Additionally, the potential of machines and algorithms will increase and be more broadly used in the future, possibly matching the working hours performed by humans by 2025 (Goldin, 2021). The impact of digitization is thought to disrupt the prospects of workers in a variety of sectors - the Future of Jobs Survey suggests that by 2025 15% of a company’s workforce is at risk of disruption and 6% of workers will be fully displaced.
Impact of digitization on employment
2.1 Precarization of platform work
This rapid development of digital technologies over the last decades also brought to life new forms of work organization. One of the most notable examples of recent disruption in the labour market are online platforms such as Amazon Mechanical Turk, Deliveroo, TaskRabbit, or Uber. The platform business model shapes how an increasing number of industries operate: from writing and software development, transportation and tourism, to legal services and finance (Florisson and Mandl, 2018). Data shows that platform work has grown in recent years. According to the European Commission (2018), the use of online platform services as a client rose from 17% in 2016 to 23% in 2018 and according to the European Commission, 19% of Europeans consider working for an online platform in the future. This trend only gained significance in the era of COVID-19. This could be illustrated by the fact that during the pandemic (from 1st of January to 22nd of September 2020) the value of leading technology companies rose by an average of 41% with Amazon noting a 64% increase in stock prices (United Nations, 2020).
Fundamentally, a platform is a technological framework utilized for online matchmaking. That is, online platforms match the demand and supply of goods and services. In contrast, to traditional business models, “platforms don’t own the means of production, they create the means of connection” (Moazed and Johnson, 2016, p. 30). There are multiple terms used to express the nature of the labour market within the platform-based economy such as gig economy, work-on-demand, platform work, digital labour, crowd work and collaborative economy (Florisson and Mandl, 2018). Although each reflects a slightly different aspect of the process of “uberization” of the economy, they all share common characteristics.
First and foremost, the work on the platform is mediated on a multi-sided market. That is, there are multiple different actors involved in the platform work. Usually, a client – the one who requests the task (e.g. advertiser on Facebook or passenger on Uber); a worker – an individual or crowd that performs the task (e.g. Uber driver or Facebook’s user); and a platform – the infrastructure that manages and coordinates interactions between other parties. That specific technological architecture brings novel challenges to the labour market. Mostly because, unlike the traditional market, technological infrastructure that allows transactions to occur is also an actor that has its own economic interests. Some scholars (Srnicek, 2017) more explicitly argue that “platforms […] are designed as a mechanism to extract and use […] data: by providing the infrastructure and intermediation between different groups, platforms place themselves in a position in which they can monitor and extract all the interactions between these groups. This positioning is the source of their economic and political power”. In consequence, platform business models generate a profound asymmetry of information on the market since both the worker and the client have access only to the information selected by the platform (Marciano, 2020).
Despite effectively functioning as employers or labour brokers, platforms purposely position themselves as mere intermediaries (Florisson and Mandl, 2018). Thanks to that, platforms can blur traditional employment relations and outsource salaried labour to self-employed workers (Valenduc Vendramin, 2016). Platforms’ practice of unilateral enforcement of the contractual terms and digital contracting leave the workers without the choice over their employment status and push them towards working as freelancers or independent contractors rather than employees. On the one hand, this lowers the costs and liabilities for the platform and, on the other hand, it leaves the workers in a disadvantageous position on the labour market. This trend of precarization of work captivates growing numbers of workers, not without severe consequences for their rights and social protection (Hauben et al., 2020). The unclear status of platform workers results in multiple negative effects.
Firstly, minimum wage regulations customarily do not apply to self-employed contractors, therefore, income generated on the platforms is low and unstable. Platform workers are paid by task (approx. a few cents per task) which amounts to no more than 4 dollars per hour (Prassl, 2018). Moreover, platform workers have to cover the costs of materials, tools, equipment rental and insurance related to their particular task (Hauben et al., 2020). The wage is also influenced by dynamic pricing mechanisms utilized by the platforms which can unilaterally dictate the price for the task without any consultation with the worker. This is an additional dimension of online platforms’ unprecedented control over the workers and clients achieved through subtle algorithmic methods of behavioural nudges (Schor et al., 2020). As a result, platform workers receive not only a considerably smaller salary than workers doing analogous jobs outside of the platform-based economy but also their work is subjected to extreme unpredictability and instability.
Secondly, the working conditions within the digital platform business model also increase the risk of precariousness. The on-demand character of the platform work comes with the high work intensity and with the pressure resulting from constant competition between the workers which is embedded in the platform architecture (Hauben et al., 2020). Nonetheless, this type of employment produces a worry about not having enough work – 58% of platform workers examined by Berg et al. (2018) reported insufficient task availability. Moreover, for every hour of paid work, a worker has to spend 20 minutes of unpaid activities linked to searching for a task, doing qualification tests or writing reviews (Berg et al., 2018). Finally, the platform model does not provide almost any training or career development paths (Hauben et al., 2020). Hence, platform work entraps workers into a cycle of precarious work, therefore, it impedes transition to secure careers and reduces social mobility (Florisson and Mandl, 2018).
Thirdly, collective bargaining is incompatible with platform architecture. Platform tasks are organized and evaluated exclusively through digital interaction with the platform (Hauben et al., 2020). Moreover, workers execute their tasks in isolation and are deprived of opportunities to communicate with each other and platforms actively undermine the efforts to organize independent worker representation (Prassl, 2018). In addition, in some jurisdictions because of workers' self-employed status, there is no legal possibility to join the trade union, hence, workers are excluded from the right to collective bargaining (Florisson and Mandl, 2018). This factor combined with the fact that the status of independent contractors is barely linked to any institutional security (especially ensured by labour unions) results in online platform workers being completely deprived of institutional power (Vandaele, 2018).
2.2 Polarization of jobs - mid-skill level jobs will be lost?
Recent labour force reports from the World Economic Forum (2020) and ILO (2020; 2021) show evidence of a contrast between massive job losses in hard-hit sectors (such as accommodation and food services, arts and culture, retail, and construction) and the positive job growth evident in a number of highly skilled services sectors (such as information and communication, and financial and insurance activities) - even is this variation differs from country to country.
During the first 6 months of 2020, while workers with university degrees saw a jump of 3% in the number of jobs available, workers with middle-level qualifications saw a decrease of 5% and low-skilled workers also saw a decrease of 9%. Furthermore, within each occupational group, education levels played a role in the disproportionate effect of the pandemic. Workers with low education levels will tend to have lower incomes and are less available to work from home due to the nature of their jobs when compared with workers with tertiary education (Darvas, 2020).
COVID-19 has forced organisations to adapt to the new normal and digitalise their operations quickly making digitization a “must-have” (Mondato, 2020). The World Economic Forum (2020; Goldin, 2021) and ILO (2020; 2021) report that skill gaps will continue to increase in the next 5 years as in-demand skills will continue to change to adapt to new realities. The top skill groups employers will be looking for include “critical thinking, analysis, problem-solving, active learning, resilience, stress tolerance and flexibility”. What this means is that 40% of employees will require reskilling and when compared to 65% of businesses in 2018, in 2020, 94% of businesses expect employees to pick up new skills on the job. According to the ILO (2020; 2021), there is a risk of an uneven recovery from the pandemic, resulting in greater inequality, since job losses have disproportionately affected low-paid and low‑skilled jobs.
2.3 Income inequality. Will the wage gap increase?
The ILO (2020; 2021) estimates that global labour income has declined about 4.4% of global GDP in 2020 (not accounting for income support policies). Despite the efforts of governments across the world to provide job retention schemes, millions of workers still lost their jobs and saw their incomes collapse. A survey (Coy, 2021) conducted in 37 countries concluded that 3 out of 4 households saw their income decline, where 82% of low-income households were the most affected. In response, many countries came up with improved accessibility to cash transfers such as unemployment minimum-income benefits or universal transfers, to keep people from going into poverty. The labour income losses were very similar across the different country income groups - however, the disparities are larger when looking within countries and across geographical regions. The largest loss was experienced by workers in the Americas (10.3%) while the smallest loss was felt in Asia and the Pacific (6.6%). According to the ILO (2020; 2021), the theoretical share of jobs that can be done remotely is approximately 38% of jobs in high-income countries, 25% in upper-middle-income economies, 17% in lower-middle-income economies and 13% in low-income economies (World Economic Forum, 2020; International Labour Organization 2020; 2021). Despite income support measures have mitigated some of the impacts of the pandemic, evidence from country data shows that these impacts were uneven across the workforce, with losses being bigger for young workers, women, the self-employed, and low- and medium-skilled workers (Deloitte, 2020).
The individuals most affected by COVID-19 are those who were already in a most vulnerable situation before the pandemic and as a result of the recession, an estimated 88 to 115 million people could fall back into extreme poverty in 2020. Additionally, 60 million people in the world’s 74 poorest countries will be in extreme poverty by the end of 2021 as rich countries have the financial ability to bail out firms and provide social safety cash transfers. At the same time, the pandemic increased the income of the ultra-rich. Just in the United States of America, the wealth of the top 5 billionaires increased 26% and the combined income of all US billionaires increased more than the total wealth of all African countries. The biggest winners of the pandemic have been, unsurprisingly, those in the technology sector - conferencing platforms, social media groups and digital retail vendors.
2.4 Algorithmic inequalities
In addition, the unprecedented use of algorithms can deepen pre-existing inequalities in society. Today, artificial neural networks are composed of many interconnected units that are each capable of computing one thing; but instead of computing sequential instructions based on top-down instructions given by formal logic, they use a huge number of parallel processes, controlled from the bottom-up based on probabilistic inference. This form of logic implies they function based on probabilities subject to the data they have access to or were trained with. They are the basis for what is called "deep learning" (Cole, 2019).
Relatively little attention is paid to how data are collected, processed and organized prior to the algorithm. A major driver of bias in AI is training data, most machine-learning tasks are trained on large, annotated data sets. Deep neural networks for image classification, for instance, are often trained on “ImageNet”, a set of more than 14 million labelled images. In natural-language processing, standard algorithms are trained on “Corpora” consisting of billions of words. Researchers typically construct such data sets by searching websites, such as Google Images and Google News, using specific query terms, or by aggregating easy-to-access information from sources such as Wikipedia. Such methods can unintentionally produce data that encode gender, ethnic and cultural biases (Zou & Schiebinger, 2018).
Algorithms have been tried by different corporations in order to optimize their hiring processes. Amazon has stopped utilizing its hiring AI system but other major companies already use or look forward to implementing it.
Critics of such systems have long contended the idea that while assessments do have some predictive validity (intrinsic bias of the algorithm), they often disadvantage minorities despite the fact that minority candidates have similar job performance to their white counterparts, therefore, indicating the technology should be further examined prior widespread use (Raghavan, 2020).
Another important point is how directors label their employees. The way how institutions define good workers and bad workers can also affect the way in which artificial intelligence or algorithms treat employees. Some workers might be favoured even though other workers out-perform them in “non-categorized” domains (Zuiderveen, 2018).
There are also gender-related problems. In 2015, researchers at Carnegie Mellon used a tool called AdFisher to track online ads. When the scientists stimulated men and women browsing online employment sites, Google’s advertising system showed a listing for high-income jobs to men at nearly six times the rate it displayed the same ad to women. In another study, researchers from the University of Washington found that a Google Images search for “C.E.O.” produced just 11 per cent of women, even though 27 per cent of chief executives in the U.S. were women by the time (Garcia, 2016).
3.1 In response to platforms - regulations, cooperatives and unions
In response to the global trend of decreasing the quality of work caused by the growing prevalence of digital platforms both OECD and ILO suggested various mitigation policies (ILO, 2019; OECD, 2018). OECD suggests the implementation of a clear definition of a platform worker in order to reduce their vague employment status. What’s more, OECD proposes extending labour rights to non-standard workers including the self-employed. Also, the need to address the monopolistic position of the platform companies and their ability to fix prices is underlined. ILO takes a more bold approach and calls for establishing a Universal Labour Guarantee which would ensure minimum worker’s rights regardless of their work arrangement. According to ILO, this measure should be implemented together with international governance infrastructure for online platforms which would facilitate payments of social security across borders. Additionally, to allow platform workers an easier transition to traditional labour markets ILO calls for universal entitlement for lifelong learning that would enable people to gain new skills, re-skill and upskill.
Those are all necessary steps to mitigate deviations on the labour market within a platform-based economy. However, to truly reshape the digital labour market, workers need to create organizations that don’t have exploitation at the core of their operation. A good example of an organization that is on the one side perfectly adapted to the digital ecosystem and on the other is not socially harmful are platform cooperatives (Burnicka and Zygmuntowski, 2019). In contrast to venture capital-financed digital labour platforms, platform cooperatives are by definition democratic institutions in which workers-members own and control the platform they participate in (Vandaele, 2018). This alternative bottom-up approach addresses the online platform problems of wage inequalities, digital surveillance and lack of subjectivities simply because they have solidarity embedded in the very technological and organizational design.
3.2 Labour Unions and Universal Basic Income
While a technological revolution seems inevitable, there are growing concerns about millions of jobs becoming redundant and getting replaced by machines. Most believe that low-income and manual jobs are more at risk of getting displaced by machines and AIs, and therefore, the world might witness an exacerbation of existing inequalities and social injustices. In such a situation, the pressure on governments’ social welfare programs in the future is likely to increase. In this context, Labour Unions need to be prepared to address these concerns and develop a plan which enables technological change to benefit all the workers.
An idea that is often discussed to address the rising concerns about the availability and security of jobs is the policy of Universal Basic Income (UBI) or unconditional basic income. As the name suggests, this program involves the transfer of fixed income by the government to its citizens, with minimal or no requirements for receiving this money, on a regular basis. While such a scheme has generated a lot of interest recently, there is a lot of scepticism surrounding the viability and effectiveness of such a program (Ravaillon, 2018; Kearney and Mogstad, 2019).
A guaranteed basic income could potentially provide much-needed income security and help reduce deep inequalities prevalent across the world (Spies-Butcher et al., 2020). The economic turbulence brought on by the coronavirus pandemic has forced many countries to implement UBI-like policies that earlier would have been politically unacceptable. In March 2020, as the Covid-19 virus spread across the globe, more than 500 academics and politicians signed an open letter to the governments to urge them to enact emergency UBI. As millions lost their jobs and livelihoods, this seemed like the only reasonable step, especially in societies where citizens were mostly employed in the informal sector with no access to any social safety net.
Even before the pandemic, the world was witnessing a growing interest in the concept of UBI. When Andrew Yang ran for the Democratic nomination in the USA in 2020, he proposed a program called the “Freedom Dividend”, which would entail a payment of $1,000 to every American adult. He argued that this would help compensate for the jobs and livelihoods that would be lost due to automation and AIs. A similar argument has also been put forward by Tesla and SpaceX CEO Elon Musk in favour of a UBI. Facebook founder Mark Zuckerberg has also voiced his support for a standard base salary for everyone which would help in meeting the basic needs and act as a “cushion to try new ideas”.
Marx, in his magnum opus Das Capital, examines the idea of “free” wage labour in a capitalist system. According to him, a worker is “free in the double sense, that as a free man he can dispose of his labour-power as his own commodity, and that on the other hand, he has no other commodity for sale”. In contradiction with pre-capitalist forms of Modes of Production, in capitalism, a worker is “free” to choose his/her/their employer. However, this freedom is constrained by the economic coercion of this system. In the presence of a huge reserve army of labour, subsistence-level wages in the economy, and lack of control over any means of production, workers effectively have no choice but to accept the terms of the contract laid down by the employer. In this context, a scheme like UBI can assist in changing power relations in the job market and improve the bargaining power of the workers (Standing, 2017; Susskind, 2020). A UBI gives workers the ability to refuse an unreasonable employment contract and therefore, labour contracts become voluntary in a true sense.
Given the fact that UBI is widely regarded as a “socialist scheme”, it is quite surprising to see that UBI is being endorsed by Silicon Valley’s tech-billionaires. The fiscal burden of implementing a policy like UBI is often recognized as the main obstacle for governments anywhere to embrace this welfare program in its full force. The fund would have to be generated either by raising taxes or by considerably cutting down on government social spending on services such as health and education, which are especially important in developing countries. It is important to note that despite making a global profit of £5 billion in 2018, Facebook received a tax credit of £11 million. Similarly, research shows that the super-rich are able to find loopholes and rarely pay their fair share of taxes (Saez & Zucman, 2020). In this context, the intentions of these tech-billionaires seem ingenuine. Their version of UBI is starkly different from what is proposed by the left-leaning politicians and policy-makers. In their version, the high cost of the proposed scheme has to be met by reducing or shutting down the existing welfare programs. While an assured basic income can bring significant difference to a person’s life, rolling back the various development programs would have serious implications on countries in the Global South which continue to fare poorly in their human development indicators.
In the light of the pandemic, the conversations about UBI have again taken centre stage. Many countries have adopted programs similar to UBI in order to deal with the growing joblessness and uncertainty about the future. As discussed, there are different versions of UBI and different ideas regarding how the program would be funded, the amount which would be handed out, and various other details. It is important for different stakeholders to evaluate this scheme vis-à-vis other welfare programs. Different countries have different requirements depending on the stage of development they are at, and therefore, cookie-cutter solutions regarding the policies to adopt in order to deal with the automation of work would not be ideal. Well-meaning and profound interventions are required to reduce the growing income and wealth disparities and to make sure that automation remains socially sustainable.
With automation and AIs soon becoming the reality of the workplace, labour unions would have novel challenges to deal with in the future and they need to come up with plans to blunt the impact. The most common response would be to push for reskilling and retraining of the workers to make them better suited for the ‘jobs of tomorrow’. In order for this to work, it is important for the Unions to demand for advance notice of technology implementation. This would help the workers be better prepared for the changes in the workplace. In order to ensure that workers remain employable even with the changing scenario, unions should also bargain for government investment in education, skill development, etc. With growing monitoring and tracking, labour unions should also demand legislation in order to protect workers’ autonomy and dignity and to also prevent abusive supervision practices.
3.3 In response to algorithmic biases
Some initiatives have been already established in order to deal with algorithmic biases. For example, Mortiz Hardt and Solon Barocas, of Google Research and Microsoft Research, respectively, created FAT ML (Fairness, Accountability, and Transparency in Machine Learning) an interdisciplinary workshop that includes the analysis of algorithmic bias in bail decisions. However, despite the efforts of FAT ML and others, few people are equipped to hold a rigorous discussion about how to ethically mine data. And, considering the scope of the problem, tech companies aren’t seriously addressing the issue either. It seems it may take a shock from outside the tech industry to exert force on these issues.
In 2018 the EU presented the General Data Protection Regulation (GDPR). The GDPR includes a “right to explanation,” stating every user has the right to consult why an algorithmic decision was made about him or her. This law has signified a meaningful departure from previous regulations, nevertheless, it still faces problems with respect to defining under which circumstances the “right to explication” (and other similar tools) can actually be executed by a citizen. A general critique with respect to how public institutions or citizens address corporations’ algorithms is based on the idea of private property. Under the label of “proprietary software,” the algorithms are considered trade secrets, therefore they cannot be examined by the public and affected parties in such a simple manner. Such an act is subject to be considered as a violation of due process.
The COVID-19 pandemic forced governments across the world to take unprecedented social and economic measures to safeguard the health and income of individuals. Mandatory lockdowns changed the outlook of the labour market, catalysing digitization. The increased job polarization that resulted from the pandemic is reinforcing the wage gap within and across countries, deepening pre-existing inequalities particularly those affecting workers with limited means of protection, women, people of colour and young professionals. Moreover, the rise of online platforms worsened the working conditions of those who did not lose jobs.
Considering the current momentum on IoT technologies, reliance on algorithms and artificial intelligence will continue spreading among companies; nonetheless the potential biases these technologies might affect the totality of users. It is important that all of the agents that participate in the digital economy are attentive and commute under the same regulatory frameworks. Governments should participate in the debate and define frameworks and limits for the use of such data and methods, as well as for the possible effects their extensive use might have over the working sector, workers and the overall economy.
With growing concerns over the socioeconomic effects and the consequent pressure on social welfare programs of the increasing labour polarization and digitisation of jobs, UBI is taking centre stage as a potential way to provide income security and reduce inequalities. However, there are various versions of UBI schemes and funding mechanisms all of which need to be considered vis-à-vis other welfare programs - one size does not fit all.
In light of the changes catalysed by the pandemic, labour unions face new challenges which must be addressed to safeguard the fair treatment of workers particularly by pushing for the reskilling and of workers, bargaining for more investment in education and skill development, demanding for advance notice of technology implementation and demanding legislation that protects workers’ autonomy and dignity.
In addition, new forms of workers’ organization have to emerge to combat the precariousness of working conditions within the platform-based economy. Understandably, regulatory measures are a necessary first step in the process of creating the work ecosystem that allows employees to establish institutional representation. However, instead of constantly chasing technological change with regulations, the idea of reshaping the digital landscape through the development of platform cooperatives seems to bring more promises to the labour market.
Finally, we want to emphasize that despite the significant contributions of algorithms and artificial intelligence to the way we live nowadays, it is important to always cautiously observe the implementation and further development of these technologies. We invite governments, companies and individuals to be conscientious on the way in which they each interact with these technologies.
Berg, J., Furrer, M., Harmon, E., Rani, U. and Silberman, M.S. (2018). “Digital labour platforms and the future of work - Towards decent work in the online world”. International Labour Organization. https://www.ilo.org/wcmsp5/groups/public/---dgreports/---dcomm/---publ/documents/publication/wcms_645337.pdf
Burnicka, A., J. Zygmuntowski. (2019). „Platformowy kooperatyzm jako silnik solidarnego rozwoju”. Fundacja Instrat. https://instrat.pl/wp-content/uploads/2019/11/CoopTech-Platformowy-kooperatyzm-jako-silnik-solidarnego-rozwoju.pdf
Clifford, C. (2019). Why everyone is talking about free cash handouts—an explainer on universal basic income. CNBC. https://www.cnbc.com/2019/06/27/free-cash-handouts-what-is-universal-basic-income-or-ubi.html
Coy, P. (2021). “The Legacy of the Lost Year Will Be Devastating Inequality Covid amplified every structural bias that exists.” . https://www.bloomberg.com/news/articles/2021-03-10/covid-pandemic-made-racial-income-inequality-much-worse.
Darvas, Z. (2020). “COVID-19 Has Widened the Income Gap in Europe." Bruegel. https://www.bruegel.org/2020/12/covid-19-has-widened-the-income-gap-in-europe/.
Deloitte. (2020). “The Acceleration of Digitization as a Result of COVID-19." https://www2.deloitte.com/global/en/blog/responsible-business-blog/2020/acceleration-of-digitization-as-result-of-covid-19.html.
European Commission. (2018). “Flash Eurobarometer 467: The use of the collaborative economy”. http://data.europa.eu/euodp/en/data/dataset/S2184_467_ENG.
Florisson, R. and I. Mandl. (2018). “Platform work: Types and implications for work and employment–Literature review”, Eurofound
Garcia, M. (2016). Racist in the machine: The disturbing implications of algorithmic bias. World Policy Journal, 33(4), 111-117.
Goldin, I.. (2021).”COVID-19 Is Increasing Multiple Kinds of Inequality. Here's What We Can Do about It." World Economic Forum. https://www.weforum.org/agenda/2020/10/covid-19-is-increasing-multiple-kinds-of-inequality-here-s-what-we-can-do-about-it/.
Hauben (ed.), H., Lenaerts, K. and Waeyaert, W. (2020). “The platform economy and precarious work”. Employment and Social Affairs, Policy Department for Economic, Scientific and Quality of Life Policies, European Parliament. https://www.europarl.europa.eu/RegData/etudes/STUD/2020/652734/IPOL_STU(2020)652734_EN.pdf
International Labour Organisation (ILO). (2019). “Policy responses to new forms of work: international governance of digital labour platforms”. https://www.ilo.org/wcmsp5/groups/public/---dgreports/---cabinet/documents/publication/wcms_713378.pdf
International Labour Organization. (2021,). ILO Monitor: COVID-19 and the World of Work. Seventh Edition. Updated Estimates and Analysis. Report. https://www.ilo.org/wcmsp5/groups/public/@dgreports/@dcomm/documents/briefingnote/wcms_767028.pdf.
International Labour Organization.(2020).The Impact of the COVID-19 Pandemic on Jobs and Incomes in G20 Economies. Report. https://www.ilo.org/wcmsp5/groups/public/---dgreports/---cabinet/documents/publication/wcms_756331.pdf.
Kearney, M. S., & Mogstad, M. (2019). Universal basic income (UBI) as a policy response to current challenges. Aspen Institute. https://www.economicstrategygroup.org/publication/universal-basic-income-ubi-as-a-policy-response-to-current-challenges/#:~:text=Universal%20Basic%20Income%20(UBI)%20as%20a%20Policy%20Response%20to%20Current%20Challenges,-November%2021%202019&text=SUMMARY%3A,the%20negative%20impact%20of%20automation.
Lui, A., & Lamb, G. W. (2018). Artificial intelligence and augmented intelligence collaboration: regaining trust and confidence in the financial sector. Information & Communications Technology Law, 27(3), 267-283.
Marciano, A., Nicita, A. & Ramello, G.B. (2020). Big data and big techs: understanding the value of information in platform capitalism. European Journal of Law and Economics. 50, 345–358. https://doi.org/10.1007/s10657-020-09675-1
Moazed, A. and N. Johnson. (2016). Modern Monopolies: What It Takes to Dominate the 21st Century Economy. Macmillan..
Mondato. (2020). Pandemic-Fueled Digitization: Who Remains Excluded?. Mondato Insight. November 18, 2020. https://blog.mondato.com/pandemic-fueled-digitization-many-remain-excluded/.
Moody-Stuart, M. (2019). Universal basic income is best response to automation. Financial Times. https://www.ft.com/content/5f931514-e46e-11e9-b112-9624ec9edc59
Organisation for Economic Co-operation and Development (OECD). (2018). The Future of Social Protection: What works for non-standard workers?. https://www.oecd.org/social/Future-of-social-protection.pdf.
Prassl, J. (2018). Collective voice in the platform economy: challenges, opportunities, solutions. ETUC. https://www.etuc.org/sites/default/files/publication/file/2018-09/Prassl%20report%20maquette.pdf.
Raghavan, M., Barocas, S., Kleinberg, J., & Levy, K. (2020). Mitigating bias in algorithmic hiring: Evaluating claims and practices. In Proceedings of the 2020 conference on fairness, accountability, and transparency (pp. 469-481).
Ravallion, M. (2019). Guaranteed employment or guaranteed income? World Development, 115, 209–221. https://doi.org/10.1016/j.worlddev.2018.11.013
Sadowski, J. (2017). Why Silicon Valley is embracing universal basic income. The Guardian. https://www.theguardian.com/technology/2016/jun/22/silicon-valley-universal-basic-income-y-combinator
Saez, E., & Zucman, G. (2020). The Triumph of Injustice: How the Rich Dodge Taxes and How to Make Them Pay (1st ed.). W. W. Norton & Company.
Sandvig, C., Hamilton, K., Karahalios, K., & Langbort, C. (2016). Automation, algorithms, and politics| when the algorithm itself is a racist: Diagnosing ethical harm in the basic components of software. International Journal of Communication, 10, 19.
Schor, J.B., Attwood-Charles, W., Cansoy, M. et al. (2020). Dependence and precarity in the platform economy. Theory and Society 49, 833–861. https://doi.org/10.1007/s11186-020-09408-y
Sodha, S. (2018). Mark Zuckerberg’s got some cheek, advocating a universal basic income. The Guardian. https://www.theguardian.com/commentisfree/2017/jul/10/mark-zuckerberg-universal-basic-income-facebook-tax
Spain’s embattled government proposes a new anti-poverty scheme. (2020, June 4). The Economist. https://www.economist.com/europe/2020/06/04/spains-embattled-government-proposes-a-new-anti-poverty-scheme
Spies-Butcher, B., Phillips, B., & Henderson, T. (2020). Between universalism and targeting: Exploring policy pathways for an Australian Basic Income. The Economic and Labour Relations Review, 31(4), 502–523. https://doi.org/10.1177/1035304620964272
Srnicek, N.. (2017). The challenges of platform capitalism: understanding the logic of a new business model. The Progressive Policy Think Tank. https://www.ippr.org/juncture-item/the-challenges-of-platform-capitalism
Standing, G. (2017). Basic Income - And How We Can Make It Happen. Pelican.
Susskind, D. (2020). A World Without Work: Technology, Automation, and How We Should Respond. Metropolitan Books.
United Nations. (2020). The Impact of the COVID-19 Pandemic on Trade and Development: Transitioning to a New Normal. United Nations Publications. https://unctad.org/system/files/official-document/osg2020d1_en.pdf
University of Stanford. (2020). The Productivity Pitfalls of Working from Home in the Age of COVID-19. Stanford News. 2021. https://news.stanford.edu/2020/03/30/productivity-pitfalls-working-home-age-covid-19/.
Valenduc, G. and Vendramin, P. (2016). “Work in the digital economy: sorting the old from the new”. European Trade Union Institute.
Vandaele K. (2018). Will trade unions survive in the platform economy?. European Trade Union Institute. https://www.etui.org/sites/default/files/Working%20Paper%202018.05%20Vandaele%20Trade%20unions%20Platform%20economy%20Web.pdf
Wadhwa, V. (2018). Why universal basic income can’t solve the challenges of automation. Duke Corporate Education. https://www.dukece.com/insights/why-universal-basic-income-cant-solve-challenges-automation/
Washington, A. L. (2018). How to argue with an algorithm: Lessons from the COMPAS-ProPublica debate. Colo. Tech. LJ, 17, 131.
The Independent (2020). Why over 500 academics have called for universal basic income. https://www.independent.co.uk/voices/letters/coronavirus-universal-basic-income-ubi-poverty-economy-business-migrants-a9408846.html
World Economic Forurm. (2020). The Future of Jobs Report. Report. http://www3.weforum.org/docs/WEF_Future_of_Jobs_2020.pdf.
Yeomans, E. (2018) Tech giants build army of lobbyists to fight off regulations, The Sunday Times, 23 October. Available online at: https://ww•w.thetimes.co.uk/edition/new•s/tech-giants-build-army-c)f-lobbyists-to-fight-oft-tougher-regulations-2qq39vOvi [Accessed 22 March 2021].
Zou, J., & Schiebinger, L. (2018). AI can be sexist and racist—it’s time to make it fair.
Zuiderveen Borgesius, F. (2018). Discrimination, artificial intelligence, and algorithmic decision-making.
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