Platformization and Informality: Pathways of Change, Alteration, and Transformation 3031114612, 9783031114618

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DYNAMICS OF VIRTUAL WORK

nd a n o i t a z i m r o Platf Informalitaynge, Alteration, and h Pathways of C n Transformatio

Edited by · Ursula Huws Aditi Surie

Dynamics of Virtual Work

Series Editors Ursula Huws Analytica Social and Economic Research London, UK Rosalind Gill Department of Sociology City, University of London London, UK

Technological change has transformed where people work, when, and how. Digitization of information has altered labour processes out of all recognition whilst telecommunications have enabled jobs to be relocated globally. ICTs have also enabled the creation of entirely new types of ‘digital’ or ‘virtual’ labour, both paid and unpaid, shifting the borderline between ‘play’ and ‘work,’ and creating new types of unpaid labour connected with the consumption and co-creation of goods and services. This affects private life as well as transforming the nature of work and people experience the impacts differently depending on their gender, their age, where they live and what work they do. Aspects of these changes have been studied separately by many different academic experts however up till now a cohesive overarching analytical framework has been lacking. Drawing on a major, high-profile COST Action (European Cooperation in Science and Technology) Dynamics of Virtual Work, this series will bring together leading international experts from a wide range of disciplines including political economy, labour sociology, economic geography, communications studies, technology, gender studies, social psychology, organization studies, industrial relations, and development studies to explore the transformation of work and labour in the Internet Age. The series will allow researchers to speak across disciplinary boundaries, national borders, theoretical and political vocabularies, and different languages to understand and make sense of contemporary transformations in work and social life more broadly. The book series will build on and extend this, offering a new, important, and intellectually exciting intervention into debates about work and labour, social theory, digital culture, gender, class, globalization, and economic, social, and political change.

Aditi Surie  •  Ursula Huws Editors

Platformization and Informality Pathways of Change, Alteration, and Transformation

Editors Aditi Surie Indian Institute for Human Settlements Bengaluru, India

Ursula Huws Analytica Social and Economic Research London, UK

ISSN 2947-9290     ISSN 2947-9304 (electronic) Dynamics of Virtual Work ISBN 978-3-031-11461-8    ISBN 978-3-031-11462-5 (eBook) https://doi.org/10.1007/978-3-031-11462-5 © The Editor(s) (if applicable) and The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Cover illustration: Matteo Colombo This Palgrave Macmillan imprint is published by the registered company Springer Nature Switzerland AG. The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Contents

1 Platformization  and Informality: Pathways of Change, Alteration, and Transformation  1 Aditi Surie and Ursula Huws 2 Platformizing  Informality, One Gig at a Time 13 Alessio Bertolini, Mark Graham, Mounika Neerukonda, Sanna Ojanperä, Balaji Parthasarathy, Janaki Srinivasan, Pradyumna Taduri, and Funda Ustek-Spilda 3 Work  on Online Labour Platforms: Does Formal Education Matter? 47 Uma Rani, Rishabh Kumar Dhir, and Nora Gobel 4 (In)Formality  and the Janus Face of the Platform: Production of the ‘Space of Taxi Driving’ between Everyday Realities and Rationalities of State and Market 89 Tobias Kuttler 5 Uberization:  The Periphery as the Future of Work?139 Ludmila Costhek Abílio

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6 Gojek  as Labour Infrastructure: Platformization of Work in Indonesia161 Onat Kiboroğlu 7 Feminist  Approaches to Location-Based Labour Platforms in India183 Pallavi Bansal and Payal Arora 8 Metaphors  of Work, from ‘Below’213 Ambika Tandon and Aayush Rathi 9 (Re)Conceptualizing  Gendered Structures of Informality for Domestic Workers in the Platform Economy237 Abigail Hunt and Emma Samman I ndex271

Notes on Contributors

Ludmila Costhek Abílio  is a researcher at the Centre of Labour Studies of the University of Campinas (CESIT-UNICAMP). She holds a PhD in Social Sciences from University of Campinas. She is a sociologist, specializing in labour studies, more specifically, contemporary forms of labour exploitation. For almost twenty years she has conducted empirical research on informal workers. She has investigated the work of cosmetic resellers—one million workers working for one single company—which led her to understand uberization beyond the plataformization of work. Over the past ten years she has followed transformations in the work carried out by motorcycle couriers in the city of São Paulo. Her main research focus has been the analysis of forms of labour control and management and current labour processes involving the uberization of work. She coordinates projects that investigate the relation between platform labour, informal work transformations, and workers’ health. Her first article on uberization, ‘Uberisation of Work: The Real Subsumption of Getting By?’ was published in 2017 on the Historical Materialism blog. She has published articles in Work Organisation, Labour & Globalisation, Journal of Labor and Society, New Media and Society, Global Labour Journal, Psicoperspectivas, and Historical Materialism, among others. Payal Arora  is a digital anthropologist and an award-winning author of several books, including The Next Billion Users. She is a professor at vii

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Erasmus University Rotterdam, and co-founder of FemLab.co, a feminist future of work initiative. Her expertise lies in digital inequality, global media cultures, and inclusive design. Several international media outlets have covered her work including the BBC, The Economist, Quartz, Tech Crunch, The Boston Globe, F.A.Z, The Nation, and CBC. Forbes named her the ‘next billion champion…the right kind of person to reform tech.’ She sits on several boards such as Columbia University Earth Institute and World Women Global Council in New York Pallavi Bansal  is working as an assistant professor at the Times School of Media, Bennett University. She is also pursuing a PhD from Erasmus University Rotterdam, The Netherlands, and is a junior researcher at Feminist Approaches to Labour Collectives (FemLab.Co). She holds an MSc in Media, Communication, and Development from the London School of Economics and Political Science and has previously worked with the United Nations in Geneva and The Times of India in Delhi. Now, she actively blogs for The Times of India and her research focuses on the feminist design of digital labour platforms. Alessio Bertolini  is a postdoctoral researcher at the Fairwork Foundation. Before joining the Fairwork Project, Alessio was part of the ‘Work on Demand: Contracting for Work in a Changing Economy’ project at the University of Glasgow. Within this broader project, Alessio had been investigating ideas and strategies used by different stakeholders and policy actors in the regulation of the platform economy from a comparative perspective. Alessio holds a PhD in Social Policy from the University of Edinburgh on the topic of comparative labour market regulations for non-standard workers in Italy and the UK. Both before and during his PhD, he was involved in several national and international research projects on the topic of labour market and welfare policies. Alessio has recently published a book titled Temporary Agency Workers in Italy and the UK: The Comparative Experience of Labour Market Disadvantage. For the Fairwork project, Alessio coordinates research in a number of countries in Europe and Latin America and is involved in a number of activities with a focus on the Global South.

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Rishabh Kumar Dhir  is research officer at the Research Department of the International Labour Organization and is working on issues related to the digital economy. He holds a PhD in Development Studies from The Graduate Institute of International and Development Studies (IHEID), Geneva. Nora Gobel  is junior research officer at the Research Department of the International Labour Organization and is working on issues related to the digital economy. She has a master’s degree in Economic Policy in Global Markets from Central European University. Mark Graham is the Director of the Fairwork Foundation. He is also Professor of Internet Geography at the Oxford Internet Institute, a faculty fellow at the Alan Turing Institute, a senior research fellow at Green Templeton College, a research affiliate in the University of Oxford’s School of Geography and the Environment, a research associate at the Centre for Information Technology and National Development in Africa at the University of Cape Town, and a visiting researcher at Wissenschaftszentrum Berlin für Sozialforschung and Technische Universität Berlin. Mark is an economic geographer with an interest in how digital technologies and digitally mediated practices affect economic and social inequalities. His research focuses on economic development, labour, power, participation, and representation. His recent books include The Gig Economy, Society and the Internet, and Digital Economies at Global Margins. Abigail Hunt  was a research fellow at Overseas Development Institute during the production of this publication and is now at the Trades Union Congress (TUC) in the UK. Her work focuses on labour and social care, public services and social protection, informal economies, and the future of work. Ursula Huws  Director of Analytica Social and Economic Research. She was Professor of Labour and Globalisation at the University of Hertfordshire from 2011 to 2021 and previously Professor of International Labour Studies at London Metropolitan University. The editor of the journal Work Organisation, Labour and Globalisation, she has published widely on changes in work and technology including the impacts on social reproduction and gender.

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Onat Kiboroğlu  holds a PhD from the National University of Singapore (2022); he researched on the genealogy and impact of Gojek as a form of urban infrastructure across Indonesia (Supervisor: Prof. Itty Abraham). Onat holds an MA in Southeast Asian Studies from National University of Singapore (2017) and a BA in Business Administration from Koç University in Istanbul (2015). Onat speaks fluent Turkish, English, Italian, and Indonesian. Tobias Kuttler studied geography and anthropology at Humboldt-­ University Berlin and Universidad Complutense Madrid, as well as urban and regional planning at Technical University Berlin and The University of North Carolina, Chapel Hill. He is a PhD candidate at University of Hamburg and member of the mobil.LAB Doctoral Research Group at Technical University of Munich. Previously, he was a research associate at the Department of Work, Technology and Participation at Technical University Berlin. His area of work is concerned with the relationship of urbanization, mobilities, technologies, and socio-spatial inequalities, with a particular interest in the intersection and application of spatial, mobilities, and postcolonial theory. He has been involved in several European research projects on urban-rural mobility and mobility disadvantages. He is writing his PhD dissertation on the transformation of the taxi industry under the impact of digital mobility platforms in Mumbai. His research is funded by a scholarship of the Hans-Böckler-Foundation on behalf of the Confederation of German Trade Unions. Besides the European and South Asian context, his regional research focus is on South Africa. Mounika Neerukonda  is research assistant at the Fairwork Foundation in India. She is also a student of the Integrated Masters of Technology (iMTech) programme at the International Institute of Information Technology Bangalore (IIIT-Bangalore). Her research interests include collective action in the gig economy, disability studies, and gender studies. She recently co-authored the papers ‘Creating an Accessible Technology Ecosystem for Learning Science and Math: A Case of Visually Impaired Children in Indian Schools’ (MCHV-­INAIS workshop, 2019), with Supriya Dey, Vidhya Y, Suprgya Bhushan, and Amit Prakash, and ‘Are Technologies (Gender)-­ Neutral?: Politics and Policies of Digital

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Technologies’ (ASCI Journal of Management, 2018), with Bidisha Chaudhari. For the Fairwork project, Mounika is gathering evidence about platform work conditions in India and analysing primary data. Sanna Ojanperä  is a research assistant on Fairwork’s ‘TVET and New Work in the Indian Platform Economy’ project. She is also a doctoral student at the Oxford Internet Institute, University of Oxford, and at The Alan Turing Institute, which funds her doctoral research and where she co-convenes the Data and Inequality Interest Group. Sanna is also a visiting researcher at the Centre for Advanced Spatial Analysis, University College London. Her doctoral research investigates the relationship between accessing work through online platforms and skill development. She is also interested in the applications of big data in international development. Her recent publications include ‘Digital Knowledge Economy Index: Mapping Content Production’; ‘Data Science, Artificial Intelligence, and the Future(s) of Work’; and ‘The Geography of Engagement in the Knowledge Economy: Regional Patterns of Content Creation.’ Before commencing her doctoral studies, Sanna worked as the quantitative research lead in an ERC-funded GeoNet project which studied how new economic practices and processes are taking root in sub-Saharan Africa as a result of changing connectivities. Before joining the Oxford Internet Institute (OII), she worked with the Digital Engagement team of the World Bank Governance Global Practice and the Inter-American Development Bank’s Strategic Planning and Development Effectiveness Unit. Sanna holds a master’s degree (2013) from American University’s School of International Service (Washington, DC). At School of International Service (SIS) she studied international development, governance, and quantitative research methods as an ASLA-Fulbright Fellow. Within the Fairwork project, Sanna is in charge of the quantitative analysis for the Technical and Vocational Education and Training (TVET) and New Work in the Indian Platform Economy project, which focuses on the intersection of digital labour platforms, skills development, and development cooperation. In this role she analyses survey data on platform workers in India and helps with the day-to-­day administration of the project. Balaji Parthasarathy  is the lead investigator for the Fairwork project in India. He is also a professor at the International Institute of Information

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Technology Bangalore (IIIT-Bangalore). His interests focus on the relationship between technological change, economic globalization, and social transformation. Within this broad focus, his work follows two threads. One thread examines the impacts of public policy and firm strategies on the organization of production in the information and communications technology (ICT) industry. The second thread deals with ICTs for Development, or ICTD. Here, his interests lie in understanding the deployment of ICTs in various domains of activity transforms social relationships, especially in underprivileged contexts. Uma Rani is senior economist at the Research Department of the International Labour Organization (ILO). Her research focuses on informal economy, minimum wages, and digital transformations in the world of work. Emma Samman  is a research associate with the Overseas Development Institute Think Tank and an independent consultant. Her research centres on the analysis of poverty and inequality, particularly gender inequality, the human development approach, the future of work, and the use of subjective measures of well-being to inform research and policy. Janaki Srinivasan  is a co-investigator for the Fairwork project in India. She is also an assistant professor at the International Institute of Information Technology Bangalore (IIIT-Bangalore). Her research examines the politics of information technology-based development. Her work has shown how gender, class, and caste shape Indian digital inclusion initiatives and focused on community computer centres, mobile phones, identity systems, and open information systems. She is exploring privacy, algorithmic control, and the role of intermediaries in digital transactions. For the Fairwork project, Janaki is involved in developing and implementing research and fieldwork strategies in India. Aditi Surie  is a Senior Researcher at the Indian Institute for Human Settlements where she does research and advisory work, and teaches. Her work examines platform capitalism from the Global South through transitions in livelihoods and economic structures for a variety of communities engaging with technology-mediated infrastructures.

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Pradyumna Taduri  is a researcher for the Fairwork India team. He holds an MSc from the International Institute of Information Technology Bangalore. He is interested in how ICTs shape work and labour. Pradyumna has recently co-developed a text-­based game about platform workers; he has also argued against crowdfunded welfare campaigns by for-­profit platform companies for their workforce. Previously, he has published an article on the effects of ICTs on classroom interactions. Pradyumna contributes to multiple aspects of the Fairwork project including operationalizing Fairwork principles for the Indian context and interviews with workers and platform companies. He facilitates the Fairwork scoring process with other members of the team, actively contributes to report writing, and assists with planning and coordination tasks. Aayush Rathi and Ambika Tandon  are senior researchers at the Centre for Internet and Society, where they lead the programme on labour and social justice. Their work has focused on the platformization of labour in India and designing policy frameworks to protect workers’ rights. Funda Ustek-Spilda  is a postdoctoral researcher and project manager at the Fairwork Foundation. She is interested in studying data invisibilities and visibilities, looking at the ethical, societal, political, and economic implications of being counted and not being counted in data. She has approached this theme from various angles within the fields of labour, gender, migration, and responsible technology design. Prior to joining the Fairwork Foundation, Funda held postdoctoral researcher positions at ‘ARITHMUS: How Data Make a People’ (ERC Research Project, 2014–2018) based at Goldsmiths, University of London; and ‘VIRT-EU: Values and Ethics in Innovation for Responsible Technology in Europe’ (EC FP7 Horizon 2020 Project) based at the London School of Economics. She holds a DPhil from the University of Oxford in Sociology (2015) and an MSc from the same university in Comparative Social Policy (2010). In her doctoral thesis, she studied survival strategies of women workers in Turkey as they navigated various jobs in the informal labour market. For the Fairwork project, Funda is involved in developing and implementing research and fieldwork strategies in India.

List of Figures

Fig. 2.1 Interview sample characteristics in India and the UK Fig. 3.1 Age distribution of workers by occupation. Source: ILO survey of workers 2017, 2019, 2020 Fig. 3.2 Education level of workers, by sector and regions. Source: ILO survey of workers 2017, 2019, 2020 Fig. 3.3 Field of study of workers with higher levels of education, freelance and microtask platforms by gender. Source: ILO survey of workers 2017, 2019, 2020 Fig. 3.4 Worker’s motivation to work on online platforms by type of platform, region, and gender. Source: ILO survey of workers 2017, 2019, 2020 Fig. 3.5 Importance of reputation to obtain work on freelance platforms by experience. Source: ILO survey of workers 2019, 2020 Fig. 3.6 Promoting and building profile on freelance platforms by experience. Source: ILO survey of workers 2019, 2020 Fig. 3.7 Subsidizing to obtain work on freelance platforms by experience. Source: ILO survey of workers 2019, 2020 Fig. 3.8 Training on platforms to obtain new skills or certification, by experience. Source: ILO survey of workers 2019, 2020 Fig. 3.9 Experience by hourly earnings (paid and unpaid) by type of platform for those for whom platform work is the main job, (in US$). Source: ILO survey of workers 2017, 2019, 2020

21 56 57 57 58 66 69 70 71 79

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Fig. 6.1 Spectrum of Labour Conditions in Indonesia Fig. 6.2 Map of fieldwork zones in Indonesia demarcated in circles. The green circle marks the Riau Islands as (1) (2018), the red circle Java as (2) (2018–2020), the yellow circle Bali as (3) (2019–2020) and the blue circle Sulawesi as (4) (2019). Map taken from ‘Indonesia Map Silhouette Isolated on Vector Image on Vectorstock,’ VectorStock, January 15, 2020, https://www.vectorstock.com/royalty-­free-­vector/indonesia-­ map-­silhouette-­isolated-­on-­white-­vector-­28784460

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List of Tables

Table 3.1 Distribution of workers across regions and platforms, 2017 to 2020 55 Table 3.2 Education level and types of tasks performed on microtask platforms59 Table 3.3 Field of study and types of tasks performed on freelance platforms61 Table 3.4 Average and median hourly earnings paid and total (paid and unpaid) by type of platform, region, and gender (in US$) 76 Table 3.5 Average and median hourly total earnings paid and total (paid and unpaid) by level of education and type of platforms, region, and gender (in US$) 78

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1 Platformization and Informality: Pathways of Change, Alteration, and Transformation Aditi Surie and Ursula Huws

Introduction Up till the 1970s, the notion that the Rest would follow in the footsteps of the West was intrinsic to the dominant development paradigm. Through industrialization and urbanization, the ‘underdeveloped world’ would replicate the experience of the advanced economies in the nineteenth century: growing employment in manufacturing, rising living standards, mass consumption…Now, it seems, it is the West that is following the Rest when it comes to the growing insecurity of work conditions. Part-time and short-­ contract work has been on the rise, along with that ambiguous category, self-employment. An extensive literature has now grown up around the issue of informal and precarious labour in the advanced economies. What

A. Surie (*) Indian Institute for Human Settlements, Bengaluru, India e-mail: [email protected] U. Huws Indian Institute for Human Settlements, Bengaluru, India Analytica Social and Economic Research, London, UK © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. Surie, U. Huws (eds.), Platformization and Informality, Dynamics of Virtual Work, https://doi.org/10.1007/978-3-031-11462-5_1

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relation does this bear to the condition of workers outside the OECD, where the vast mass of humanity is located? (Breman, 2013: 131)

In 2013, Uber hadn’t hit the global South. It had barely hit the streets of San Francisco where it was founded. Yet reading Breman eight years later encapsulates where we stand in the contemporary history of platformization and digitization. The global South has become a hotbed for investment and experimentation for technology companies. The ‘emerging markets’ of the global South are seen as good, viable opportunities for investors and entrepreneurs to find ways to bring the ‘next billion’ online (Arora, 2019). Developing economies have acted as significant economic zones of interest for the key players of the digital economy (Rossotto et al., 2018; Tsao, 2018). Digital platforms involved in communications, finance and banking, physical services, and labour, in particular, have focused on—and succeeded in—bringing the ‘next digital billion’ online (Arora, 2019). In the process, social media platforms like Facebook and information platforms like Google have increasingly become a de facto soft internet infrastructure in countries such as India, Nigeria, and Brazil (Bonnin, 2019; Chen & Qiu, 2019; Singh, 2019). For many first-time users in emerging markets, the internet is often synonymous with platform giants like Facebook (in relation to communications) and to forms of labour like those offered by platforms such as Cabify, Uber, or Didi Kuxing. It is telling that between 2009 and 2015 global internet bandwidth grew twentyfold, with mobile broadband supporting an estimated 3.8 million jobs in Africa (Kordunsky, 2017). Yet its arrival in emerging markets is experienced very differently from the libertarian, ‘sharing’ vision with which it was associated when it first emerged in California in the 1990s (Barbrook & Cameron, 2009). Apart from the infrastructural power of platforms, little has been done to conceptualize and articulate what their impact has been on the large swathes of peoples and economies bucketed in the global South. Breman’s, 2013 article, points to a premise that this book starts with. Developing economies have long been characterized by informality, unorganized economic activity, and poverty. These categories are almost co-constituted in how they have been used together to develop economic theory and policy advice, and shape the geopolitical balance of powers between the global North and South. For many, they epitomize the poor conditions of work

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that the global North is now experiencing under the current wave of platformization of work, employment, labour, and economic value creation. Preferring terms such as ‘precarization,’ many Northern academics have been slow to adopt or use the term ‘informality’ in the context of platformization, Indeed they have historically been slow to develop the conceptual frame of informality to understand employment in African and Asian ‘marginal economies.’ The term ‘informal economy’ can be viewed as arising from the slow recognition by Northern economists that nonwhite, non-industrial, unorganized, and localized services and production were, in fact, creating value and supporting livelihoods. Keith Hart’s gaze on the Ghanian economy and later the Kenyan economy was powerful enough to force Western economists to grasp the reality that Africans, far from being merely unemployed and unproductive were in fact producing value through their self-organized economic work (Hart, 2009). Breman’s ‘A Bogus Concept’ served as a reminder to the world of Northern academics and activists that the ‘rest of world’ has been living and working informally and precariously hitherto. It was written as a review and rejoinder to Guy Standing’s Precariat (2011) which presented precariousness as a new, destabilizing phenomenon, undermining the decent working conditions and fair wages that had been established across the world. It drew on his own extensive work at the International Labour Organization and a large body of research by scholars in the global North on the growth of zero-hour contracts, agency work, and other forms of short-term work, without recourse to stable occupational identities or careers, stable social protection or protective regulations relevant to them, against a backdrop of alarming rates of unemployment. Breman’s article reminded Northern academics and activists that the majority of the ‘rest of world’ was already living and working informally and precariously. In doing so, it acted as a precursor to the ways in which the ‘Uberization of work’ would be conceptualized in the years to come. Despite the fact that, for decades, feminist scholars (see for example Huws et  al., 1989) had been pointing out that the twentieth century ‘standard employment model’ developed in the global North during the period sometimes termed the ‘post-war Keynesian welfare state’ (Jessop, 1995) or ‘the Golden Age of Capitalism’ (Marglin & Schor, 1992) was never universal but only applied to relatively privileged white male workers in a few countries, it was still regarded as a  taken for granted

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employment norm by the majority of policy makers as well as academics. The rise of precarious employment in the global North thus came as a shock. That the majority of workers in a global Northern country may now be faced with exploitative working conditions, for which the employer cannot easily be held to account, is seen as an ‘unexpected outcome’ of the digital revolution. This was exemplified in the World Bank’s yearly World Development Report (2019) which flusteringly announced to world leaders and economists that the digital revolution, as seen through the meteoric rise of digital platforms in the world of work, could unravel the ways in which economic and political paradigms had hitherto been defined. The report failed to move on from this thesis, falling back on using the South as a vehicle to explore the grievances of the North. There has in general been a failure, on the part of scholars and politicians, to look beneath the superficial similarities between the forms of precarization newly arriving in the global North and those that are a long-established feature of labour markets in the global South. What does the experience of precarity mean for a worker brought up in the expectation of a secure, permanent job, with a defined occupational identity, guaranteed benefits and the prospect of a pension at the end of working life? And how does this compare with the experience of precarity of a worker from a household in which informal work is the norm, embedded in a community where most other workers are also informally employed, shaped by long-established cultural traditions and social practices? Can the disruption introduced by digital platforms really be expected to be the same in both contexts? Whatever the surface similarities, as both are subjected to the standardized algorithmic governance of global corporations, might the there not be profound differences, both in workers’ perceptions and in the actual outcomes of platformization? Breman, steeped in deep knowledge of economic and political informality in India based on years of research, begs the question what happens to the geopolitical balance of power if the West follows the Rest, rather than the Rest following the West’s pathways of industrialization and modernization. If digital platforms contribute to the rise of informal and precarious labour in advanced economies then what are developing countries even trying to achieve when the majority of their working populations are already informally and precariously employed? Conversely,

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what strategies might reverse these trends, given that they were never universal? To address such questions, this edited volume brings together accounts of what is happening in the global South in the context of platformization. In doing so, it attempts to rebalance some of the debates in the current literature by drawing attention to those aspects of the platform economy which tend to be neglected in discussions dominated by a few large platforms based in the global North. In particular, several of the chapters stress the continuities with past forms of informal employment as well as the disruptions brought by the incursion of global platforms. The gendered dimensions of informality are also highlighted in a number of contributions, two of which focus on domestic labour, a form of platform work that has received much less attention from scholars than the more visible kinds of work carried out, mainly by male workers, in public spaces. These contributions come from scholars who have had to contend with the blind spots in current theorizing around platforms because of the geographies they work in. Place matters. The acknowledgement of place delivers an honesty to empirical lifeworlds that are outside the normative structures of the global North. We offer these themes to our readers to provoke scholars into approaching the theorization of technology, platforms, and algorithmic systems from the global South. The first part of the collection is conceptual and thematic: descriptions of structural change along the pathways of platformization and informality. Here, we have two contributions that approach the platform and informality question from the macro level, to ask questions about how of globalized economies can be conceived within the paradigm of ‘development.’ The book begins with a chapter by Alessio Bertolini, Mark Graham, Mounika Neerukonda, Sanna Ojanperä, Balaji Parthasarathy, Janaki Srinivasan, Pradyumna Taduri, and Funda Ustek-Spilda on ‘Platformizing Inequality, One Gig at a Time’ which draws on an extensive body of qualitative research in India and the UK, conducted by the Oxford Internet Institute, in order to identify the commonalities between the global South and North in relation to platform work. They conclude that it is a mistake to view the relationship between the formal and informal sectors as a dichotomy. Rather, they argue, they are connected to each

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other and are in some ways mutually reinforcing. It is precisely those elements of platformization that appear to be ‘formalizing’ employment in the global South, such as the measurement of work and the standardization of communications, that are triggering ‘informal’ practices in response, as workers organize collectively and develop practices such as ‘multi-apping.’ They conclude by pointing to the increased social power of platforms vis-à-vis precarious workers in both contexts. This is followed by another chapter which adopts a global perspective that provides a frame for comparing platform work in the global South and North, this time focusing on spatially-independent platform work, drawing on surveys conducted by the International Labour Organization. Uma Rani, Rishabh Kumar Dhir, and Nora Gobel, in ‘Work on Online Labour Platforms: Does Formal Education Matter?’, examine the skills and education of the global platform workforce, pointing out that most of these workers are highly educated, in a labour market that is geographically skewed, with a high proportion based in developing economies while the clients for whom they work are predominantly in advanced economies. Based on detailed analyses of education, skills, recruitment, reputation (including investment in reputation-building), and earnings, they identify serious inequalities and skills mismatches. By examining the relationship between formal educational qualifications and actual labour market outcomes, they point to an emerging trend whereby, contrary to the intentions of policy makers, reliance on platform work is contributing to the informalization of work for highly educated workers in the global South. When the local labour market cannot supply the flexibility sought by workers, especially women workers with care responsibilities, they turn to platform work, thus reinforcing traditional gender stereotypes. Nevertheless, there are cases where some types of platform are providing some students and workers with opportunities to develop skills that formal educational systems fail to provide. They conclude by summarizing some of the policy challenges raised by such contradictions. The next set of contributions ground the ride hailing sectors within the geographies they operate in, countering the universal Uber model, and questioning how mobility platforms operate in non-Western contexts. Mumbai, Jakarta, Bali, and the Brazilian labour market form a context for mobility platform studies, and also anchor workers’ ontological

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experiences of being and unbeing whether those experiences are steeped in workers groups, pre-existing occupations embedded in  local economies or place-based neighbourhood-level organizing. The contribution by Tobias Kuttler uses a more close-up lens to examine platform work in a specific national context in the global South. His focus is on taxi driving in the city of Mumbai which he examines in the round. In ‘(In)formality and the Janus face of the Platform: Production of the “space of taxi driving’’,’ he places the development of ride-hailing platform in the larger historical context, in which taxi-driving has for decades constituted a variegated social space in which formality and informality have been reconstituted in shifting relationships between the state, firms, unorganized and organized workers and their unions. Drawing on interviews with drivers both of traditional metered taxis and platform-based ones, as well as the history of attempts to regulate it, he explores the changing conceptions of the city’s taxi sector. He points out that this has produced a contradictory picture in which on the one hand ‘modernization ambitions and the invocations of market rationality represent the platform firms’ and state agencies endeavours to make the space of taxi driving appear as an orderly, homogenous space’ while on the other ‘the state itself is engaging elements of informality to substantiate certain narratives or to avoid conflict.’ He concludes that these contradictory dynamics of formality and informality lead to profound asymmetries in the platform labour market, in which the platforms hold more and more information and power at the expense of workers and reflects on the implications of this, not just for platforms and workers but also for the state. Moving from South Asia to Latin America, Ludmila Costhek Abílio, in ‘Uberization: The Periphery as the Future of Work?’ also places platform workers in a broader historical context, in this case the informal labour market in Brazil, where cycle couriers, known as ‘bike boys’ already had a strong presence before the arrival of online delivery platforms. Using a biographical approach, she carefully traces the differences ‘uberization’ has made to their lives over time, using a Marxist conceptual frame in which this term is understood as a new type of management and control of work. In the context of the global South, this process brings new meanings and definitions to the concept of informality, which,

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instead of being seen as a marginal state, more or less synonymous with poverty, which can be overcome by the extension of labour laws and regulations as part of the economic development process, is reconstituted as a new norm: a form of subordination of a workforce ‘that is not hired or formally selected, which has no defined working hours or even formal prescriptions about the use of work instruments and modes of execution.’ She concludes that ‘elements typically associated with the periphery are becoming globally generalised in labour relations.’ We next turn to South-east Asia, the setting for Onat Kiboroglu’s chapter on ‘Gojek as Labour Infrastructure: Platformization of Work in Indonesia.’ Here, the focus is on ‘ojek drivers,’ who use motor scooters to transport passengers and goods through the dense traffic of Indonesia’s cities, another pre-existing form of work which has recently been disrupted by platforms, in this case Gojek. Drawing on a wealth of ethnographic evidence, the author demonstrates the ways in which the experience of platform work is shaped by the specific local context and the ways in which such work has historically been socially controlled. For example, the strong resistance of taxi drivers to Gojek in Bali, which can be read as a protest against becoming urbanized and modernized, is contrasted with a very different experience in Jakarta where Gojek has been welcomed by some as an emancipatory tool. This has led to costs and benefits that are regionally variable, making it easier for newly arrived migrants to enter urban labour markets on the one hand, while deskilling and disempowering traditional ojek drivers on the other. The final theme for this volume cuts across the theoretical and the sectoral, upending the investigation of the promises and perils of platform work by asking questions from the experiences of women. We begin with turning feminist lens to the design, power distribution, rulemaking, and structures of a digital labour platform. The domestic work sector has proven to be resistant to change, with platform firms having to contour their offerings to the ways in which this sector has been shaped by its history and legacy as women’s work. Two contributions, from South Africa and India, provoke us to question what a platform is when it mimics so much of these traditional working conditions and experiences of mistrust and failure to deliver on promises of secure or better work.

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The focus of the chapter by Pallavi Bansal and Payal Arora is on ‘Feminist Approaches to Location-based Labour Platforms in India.’ It takes a feminist lens to the literature on location-based digital labour platforms, highlighting their entrenched discrimination and exploitation while acknowledging that they also generate job opportunities for women. Pointing out that women suffer more than men from the precarity of informal labour markets, they argue that ‘a design overhaul is required for location-based platforms in India that should not amplify precarity; rather, it would enable them to commit to equity, diversity, empowerment, and social justice to improve lives.’ In what is essentially an optimistic vision of the liberatory potential of digital platforms, they conclude by summarizing the key principles that should inform a feminist design intervention ‘in the architecture and algorithmic management of platforms that centres the voices of marginalized women workers, and eventually benefits all digital workers.’ Ambika Tandon and Aayushi Rathi are the authors of the next chapter, ‘Metaphors of Work, from “Below”’ which, turning the standard discourses of the literature on the platform economy on their heads, explores the ways that platforms are imagined and understood by workers. They too pay special attention to the voices of women workers, so often unheard in this literature, which primarily expresses the ways in which platforms are conceived by a few large ‘disruptor’ companies, based in the global North. The chapter draws particularly on in-depth interviews with domestic workers in New Delhi and Bengaluru, for many of whom online platforms represent just one of several ways in which work can be found. Their findings contrast strongly with most preconceived accounts, revealing diverse responses depending on the particular context. Some workers, who have encountered platforms through the intermediation of human recruiters, express deep mistrust and resentment at their failure to deliver what was originally promised. Others, who interact with them through anonymous and mystified technological interfaces, experience them as invisible bosses who cannot be answered back to, pointing to a need to ‘pierce the technological veil.’ What is clear from their analysis is that the view ‘from below’ must play an essential role in the development of a full understanding of platformization in the global South.

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The final chapter in this volume, by Abigail Hunt and Emma Samman, also focuses on the topic of domestic work, hitherto so neglected in the literature on platform work. Entitled ‘(Re)conceptualising Gendered Structures of Informality for Domestic Workers in the Platform Economy,’ it places this in the broader context of how gendered structures of informality shape the emerging platform economy, in the process charting how domestic workers have been navigating these altering structures. Taking South Africa as its setting, the authors interrogate whether incremental improvements in working conditions, through traditional working relationships or new opportunities within the ‘platform economy,’ can achieve change in the broad structural conditions that restrict the availability and quality of work available to marginalized women or whether, instead, more radical changes are needed that upend existing power relationships and bring about transformative change. They conclude that, in the context of low- and middle-income countries, platforms offer mixed blessings. While offering work that is, broadly speaking, on a par with other casual or informal employment, it is also ‘indisputable that the platform model allows its clients to avoid the economic cost of guaranteeing employment for a set number of hours and providing the other benefits stipulated by South African labour law.’ The chapter ends with an assessment of policy solutions that might address the issues they have raised, in particular in the context of the Covid crisis—policy solutions that have a broader relevance not only in the global South but also globally. This volume brings together the voices of workers from a range of cities in the global South—Bengaluru, New Delhi, Mumbai, Jakarta, Bali, Cape Town and Sao Paolo. These cities have been caught in the twists of modernization and developmentalism preventing their characteristics from being seen and acknowledged in knowledge creation and within industry. This volume is an offering to allow academics and makers to see these cities for what they are and to acknowledge their workers for their agency and their place within the algorithmic systems of power. To the academic community, we offer the following and final provocations: do our definitions of what platforms are change when we read about domestic workers platforms in South Africa? Why don’t we use Jakarta’s experiences with scaled digital platforms as a way to rethink the infrastructural

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nature of platforms? What does the unique, and embedded nature of platform operations do to the ways in which we conceptualize algorithmic management? If the platform represents the possibility to dismantle economic hypotheses of progress and development, then what is the future of global power relations?

References Arora, P. (2019). The Next Billion Users. Harvard University Press. Barbrook, R., & Andy, C. (2009). The Californian Ideology. 6(1), 44–72. https://doi.org/10.1080/09505439609526455 Bonnin, J. (2019). How Digital Health Platform 1DOC3 Exploits the Global South. Diggit Magazine. https://www.diggitmagazine.com/column/1doc3-­ exploits-­global-­south Breman, J. (2013). A Bogus Concept? New Left Review, 84, 130–138. https:// newleftreview.org/issues/II84/articles/janbreman-­a-­bogus-­concept Chen, J.  Y., & Qiu, J.  L. (2019). Digital Utility: Datafication, Regulation, Labor, and DiDi’s Platformization of Urban Transport in China. Chinese Journal of Communication, May, 1–16. https://doi.org/10.1080/1754475 0.2019.1614964 Hart, K. (2009). On the Informal Economy: The Political History of an Ethnographic Concept. CEB Working Papers, 9(42), 1–22. Huws, U., Hurstfield, J., & Holtmaat, R. (1989). What Price flexibility? The Casualisation of Women’s Employment. Low Pay Unit, 56. Jessop, B. (1995). The regulation approach, governance and post-Fordism, economy and society. Blackwell Publishing. Kordunsky, A. (2017). In Africa, a Broadband Boom. Columbia Business School Ideas and Insights. https://www8.gsb.columbia.edu/articles/ideas-­work/ africa-­broadband-­boom Marglin, S.  A., & Schor, J.  B. (1992). The Golden Age of Capitalism: Reinterpreting the Postwar Experience. In Stephen A.  Marglin and Juliet B.  Schor (Eds.), The Golden Age of Capitalism: Reinterpreting the Postwar Experience, 1–45. https://econpapers.repec.org/RePEc:oxp:obooks:9780198 287414 Rossotto, C.  M., Lal Das, P., Ramos, E.  G., Miranda, E.  C., Badran, M.  F., Licetti, M. M., & Murciego, G. M. (2018). Digital Platforms: A Literature Review and Policy Implications for Development. Competition and Regulation

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in Network Industries, 19(1–2), 93–109. https://doi.org/10.1177/ 1783591718809485 Singh, R. (2019). Give Me a Database and I Will Raise the Nation-State. South Asia: Journal of South Asia Studies 42(3), 501–18. https://doi.org/10.108 0/00856401.2019.1602810 Standing, G. (2011). The Precariat: The New Dangerous Class. London: Bloomsbury. Tsao, A. (2018). Adventure Capital: Why Investors Have Growing Interest in New and Emerging Markets. Lavca. https://lavca.org/2018/04/02/adventure-­ capital-­why-­investors-­have-­growing-­interest-­in-­new-­andemerging-­markets/ World Bank Group. (2019). CHANGING NATURE OF WORK A World Bank Group Flagship Report WORLD DEVELOPMENT REPORT. http:// documents.worldbank.org/curated/en/816281518818814423/pdf/2019-­ WDRReport.pdf

2 Platformizing Informality, One Gig at a Time Alessio Bertolini, Mark Graham, Mounika Neerukonda, Sanna Ojanperä, Balaji Parthasarathy, Janaki Srinivasan, Pradyumna Taduri, and Funda Ustek-Spilda

Introduction The emergence of increasingly powerful and affordable digital technologies with applications across a range of domains to extract business value has generated much excitement about ‘digital disruption’ (see Hill, 2017). This phenomenon of disruption, in the language of the Regulation theorists,

Alessio Bertolini, Mark Graham, Mounika Neerukonda, Sanna Ojanperä, Balaji Parthasarathy, Janaki Srinivasan, Pradyumna Taduri, and Funda Ustek-Spilda contributed equally with all other contributors.

A. Bertolini (*) • M. Graham • S. Ojanperä • F. Ustek-Spilda Oxford Internet Institute, University of Oxford, Oxford, UK e-mail: [email protected]; [email protected] M. Neerukonda Indian Institute for Information Technology, Bengaluru, India International Institute of Information Technology, Bangalore (IIIT-Bangalore) Bengaluru, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. Surie, U. Huws (eds.), Platformization and Informality, Dynamics of Virtual Work, https://doi.org/10.1007/978-3-031-11462-5_2

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represents a change in the ‘regime of accumulation,’ based on Fordist mass production combined with mass consumption that has been prevalent since the mid-twentieth century, especially in the Global North (Jessop, 1990). While post-Fordism followed multiple trajectories, its leitmotif so far has been flexibility: flexibly-specialized production systems, flexible firms, flexible labour markets, and flexible accumulation (Peck, 2018).1 The latter two sources of flexibility are attributed by Regulationists to changes in the ‘modes of social regulation,’ or a set of co-evolving socio-political institutions, especially the Keynesian welfare state in its various forms (Jessop, 1990). Flexibility in labour has been a gradual process. Through growing de-­ institutionalization of the state-regulated employment relationship, and the individualization of the labour process to support the mobility, profitability, and productivity of capital (Castells, 2010) in the Global North, employment relationships have been restructured along the lines of more casualization and fewer protections. It is against this background that digital labour platforms have entered local labour markets as new intermediaries of informational capitalism, between an increasingly atomized labour force and capital (Srnicek, 2017). The work performed on these platforms has been called ‘gig work,’ a ‘non-standard form of employment,’ a term used to designate all employment that has a definite tenure, and is neither full-time, nor part of a subordinate and bilateral employment relationship (ILO, 2016). The proliferation of digital gig work may be construed as another sign of the growing ‘informalization’ of the workforce in the Global North that scholars such as Saskia Sassen (2001) have long suggested. In the Global South, where the majority of state-led development efforts have failed to generate sufficient economic opportunities, development agencies have pushed for structural adjustment and other market-oriented reforms since the 1970s (Gore, 2000). Although these policies also demanded flexible labour markets, unemployment and underemployment  So prevalent is the focus on flexibility that even in Scandinavian welfare states, which supposedly provided the highest rates of decommodification (Esping-Andersen, 1990), the idea of flexibility mixed with job security (or ‘flexicurity’) has come to dominate social policy debates (Heyes, 2011). 1

B. Parthasarathy • J. Srinivasan • P. Taduri International Institute of Information Technology, Bangalore (IIIT-Bangalore), Bengaluru, India

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remain high, especially among the young (World Bank, 2015). This has enabled platforms to project themselves as ‘a revolution in labour markets, suggesting that they can help lift people out of poverty’ (Graham et al., 2017: 2). So much so that the World Bank (2015: 1) views gig work as offering a win-win situation for both job-­givers and job-seekers by arguing that employers gain ‘broader access to specialized skills, more flexible and faster hiring processes, and 24-hour productivity’ while workers can ‘access and compete in global job markets, from anywhere at any time, as long as they have computer and Internet access.’ Randolph et al. (2019) go further to claim that platforms can serve as ‘on-ramps to formalization in the Global South.’ In this chapter, we argue against the popular idea that the proliferation of digital platform work is nudging labour markets in the Global North toward informalization, and those of the Global South in the other direction. For one thing, several international platforms operate in both the Global North and South using similar operational and management styles and structures. More importantly, employment relations and conditions are too complex, and constituted by too many diverse elements to be neatly slotted into ‘informal’ or ‘formal’ categories (which are themselves not clearly defined or binary). Moreover, contrary to earlier understandings, the distribution of informal and formal labour markets across the Global North and South is hardly as simple as was once claimed, a topic we return to in the next section. Instead, we argue that digital labour platforms restructure work relationships in both the Global North and South through a process which increasingly interweaves informality and formality together, one gig at a time. We build this argument by examining work on geographically-­ tethered platforms that provide food delivery, transport, domestic work, and personal grooming services. Leveraging case studies from a country in the Global North (the UK) and one in the Global South (India), we show how formality and informality are produced through the interaction of labour and capital in the gig economy in both geographies. We conclude that the single most important consequence of platformization is that it increases precarity and erodes employment and income security. But, before we go there, we trace the evolution of ideas around formal and informal economies that animate claims about the merits of gig economies, especially in the Global South.

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Informality and Formality: No Longer a Dichotomy The economic activities that took place in the informal economy (and the economy itself ) have been variously labelled shadow, black, grey, subterranean, parallel, submerged, underground, clandestine, and cash-in-hand (Henry, 1982). Following Hart’s (1973) introduction of the term ‘informal sector,’ scholars conceptualized the informal economic activities as the negation of formal economic activities, and the informal economy as what the formal economy was not: invisible, uncountable/unaccountable, temporary, unstable, and lacking protection against work-­ related risks.2 This framing of the informal economy as the opposite of the formal economy had a substantial colonial undertone to it and was also teleological. Thus, up until the 1960s, the modern capitalist economies of the Global North were seen as ‘formal economies,’ while the rest of the world had ‘informal economies.’ The aspiration and assumption was that the latter would develop into the former, if and when they acquired the necessary economic and infrastructural inputs and policies (Chen, 2012: 2). Mentions of the potential so-called positive qualities of informal economies focused only on petty trade and small-scale establishments,3 for instance emphasizing the wealth of opportunities to test entrepreneurial ideas and elasticity in terms of possible returns (e.g. there is no cap on how much a person can make, in contrast to standard jobs in the formal economy). In the early 1980s, the discussion took a different turn when sociologists (Gershuny & Pahl, 1979) pointed out that informal economies existed even within the British economy (which was otherwise considered formal) in the form of economic activities that people engaged in for alternative sources of income when they lost their jobs, became  In this chapter, we use ‘informal sector,’ ‘informal economy,’ and ‘informal employment’ interchangeably, even though we are aware there are important conceptual differences among them (Chen, 2012). This is not to overlook the differences between these concepts, but to provide an understanding of how they have an impact on the economies of both the Global North and South. 3  See how Keith Hart coined the term ‘informal economy’ in the 1970s to refer to the entrepreneurial activities of the urban poor in Ghana (Hart, 1973). 2

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long-­term unemployed, or as their purchasing power decreased due to a recession. Pahl (1984) also pointed out that conditions on the bottom rungs of the formal economy were akin to those in the informal economy, characterized as they were by low pay, high risk, temporality, and unpredictability. While Pahl and Gershuny mainly studied rural economic activities, Sassen (2001) pointed out that cities of the Global North were also changing through technological and organizational transformations in manufacturing and the growing contribution of services to their economies. Cities became hubs, providing producer services to the global economy by deploying emergent information technologies and relying on a highly educated and skilled labour force that was very well remunerated. The expansion of service provision by a well-paid labour force simultaneously generated demand for geographically tethered and relatively low-skilled consumption services, the provision of which was increasingly undertaken by disadvantaged populations (e.g. women, immigrants, and children), resembling the informal economic activities of the Global South (Castells & Portes, 1989; Portes & Sassen-Koob, 1987; Sassen, 2001). In the past decade and a half, technological advances, in the form of artificial intelligence and the proliferation of smartphones, have enabled digital platforms to play the role of intermediaries and reorganize the delivery of consumption items and services. Against this backdrop, it has become increasingly difficult to argue that the informal economy question is relevant for the Global South alone. Furthermore, informal economies have only continued to grow and emerge in unexpected forms and places, with changes to the global economy accelerating this process. As such, it has become equally difficult to argue that informal economies will turn into formal economies once the necessary steps have been taken. Also, by the late 1970s, a global economy had emerged that had begun to work as ‘a unit in real time on a planetary scale’ (Castells, 2010) tied together by global commodity chains (GCCs) (Gereffi, 1996) and global care chains (Barrientos, 2013; Hochschild, 2015; Yeates, 2004). This development not only challenged the distinction between the Global North and the Global South, but also the formal-informal dichotomy. For instance, the organization of production in the highly globalized garment sector, Mezzadri (2017) argues, is best understood as a multi-faceted social relationship cutting across a

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formal factory realm and an informal non-factory realm. So, informal employment characterized by atypical or non-standard work arrangements (including zero-hour contracts, temporary jobs, part-time, and otherwise fragmented job arrangements) in the Global North, and by precarious work arrangements which are not formally recognized, protected, or regulated in the Global South, have become the norm rather than the exception (Vanek et al., 2014). If academic literature has found it difficult to characterize formality and informality conceptually, their definition and measurement in practice and in the countries of our focus has been no less fraught. In India, the National Commission of Enterprises in the Unorganized Sector (NCEUS, 2009: 3) uses the terms ‘informal’ and ‘unorganized’ interchangeably and categorizes informal workers as ‘those working in the unorganized sector or households, excluding regular workers with social security benefits provided by the employers and the workers in the formal sector without any employment and social security benefits provided by the employers ‘terms.’ The employment structure in India remains largely informal. Even within the formal sector, which accounted for only 13.1% of employment in 2017–18, 40% of the workforce was informal (Murthy & Ramana, 2019: 3).4 In the UK, about 10% of economic activities are estimated to take place in the informal economy (Williams, 2014), a share significantly smaller than that of India’s. In the UK, informal paid work is defined as work that ‘involves the paid production and sale of goods or services which are unregistered by, or hidden from the state for tax, benefit and/or labour law purposes, but which are legal in all other aspects’ (Katungi et al., 2006). It is against this background of formal and informal economic activity that gig work has entered the Indian and UK economies. The following section summarizes its evolution, size and key characteristics in the two geographies.

 An important point to note at this juncture is that while estimates of the size of the informal economy exist, these are unlikely to capture the full volume of informal activities. Because the activity to be remunerated is by definition invisible in these instances (Williams, 2014) and individuals’ engagement in it is highly unpredictable and unaccountable, measuring the size of this economy is notoriously hard. 4

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 ig Work and the Gig Economy in the UK G and in India For the purposes of this chapter, our focus is on gig work that takes place through, via, on or mediated by digital labour platforms. Such work has changed existing work relationships, and created new ones in both the Indian and UK labour markets. Research on digital gig work shows that it tends to be contingent and casual, with workers often hired as independent contractors to complete specific tasks or ‘gigs’ (Aloisi, 2016; Prassl, 2018) and paid piece-rate on a per task basis (De Stefano, 2016; Woodcock & Graham, 2020). Gig work may be geographically-tethered or cloudwork. Geographically-tethered gig work requires the worker to be in a specific location to be able to perform the assigned task, such as delivering groceries or driving a passenger from location A to B. Cloudwork, by contrast, can be carried out anywhere and workers only need a reliable internet connection to perform their tasks. Cloudwork tasks range from online freelancing to microwork such as message transcription or labelling/identifying images. In this chapter, we focus solely on geographically-tethered gig work to understand how platforms are changing local labour markets. Initially welcomed as a new form of work that expanded job opportunities in a period of stagnant economic growth, and also promising flexibility and autonomy, the gig economy is now being critiqued for contributing to a shift towards temporary, piece-rate work arrangements that provide no employment protection or benefits for workers. Since the launch of Uber in London in 2012, the gig economy has grown rapidly in the UK, with the Government estimating that nearly 5% of the UK population was employed in some form of gig work in 2017 (Lepanjuuri et al., 2018). According to a recent, but pre-pandemic, OECD report, the size of the gig economy is estimated to range between 1 to 3% across the world, with the higher rate expected in more industrialized economies (Schwellnus et al., 2019). These numbers have increased substantially in the aftermath of the COVID-19 pandemic as more work shifted online and those who lost their jobs in other sectors started searching for work on the digital labour platforms (Spencer & Huws, 2021).

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In the Indian case too, the 2007 launch of the e-commerce site, Flipkart was similarly hailed as opening up work opportunities amidst high levels of unemployment. Flipkart was soon followed by other digital labour platforms operating in sectors including ride-hailing, courier services, food delivery, and domestic and personal care services (encompassing beauty, carpentry, electrical, and plumbing services). It is worth noting that the proliferation of platforms in India coincided with rising disposable incomes following the country’s growing integration into the global economy (at least in selected sectors) that had been taking place since the 1980s with Bangalore becoming a prominent hub for global software production (Parthasarathy, 2020). While it is challenging to measure the volume of workers or jobs in the Indian gig economy (Fairwork., 2020), some estimates put it at 15 million (Counterview, 2020). Fairwork India has estimated that more than 3 million workers were working on the 11 platforms they rated in 2020 alone (ibid). With the large numbers employed as gig workers in India, it has quickly become apparent that the emergence of such work has, in parallel, furthered a move towards work without employment protection or benefits in this case as well. In what follows, we illustrate how the co-existence and co-production of the informal and formal in the gig economy takes place and is sustained. We examine these processes along the two dimensions of visibility and predictability that are typically viewed as attributes of formal arrangements (Williams, 2010, 2012, 2014). Drawing on interviews carried out with gig workers in the UK and India, we lay out how visibility (and its opposite, invisibility) and predictability (and its opposite, unpredictability) take shape and, in turn, shape workers’ experiences in the gig economy.

Data and Methodology We conducted our interviews in the UK and India as part of the Fairwork Foundation’s research to evaluate the working conditions on geographically-­tethered labour platforms in the two countries. In the UK, we carried out semi-structured interviews with 55 workers on 11

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platforms,5 while in India the interviews covered 113 workers on 11 platforms. The UK interviews took place in various cities between October 2020 and March 2021, while the India interviews were carried out in Bangalore between November 2019 and March 2020. In the India interviews, workers’ ages ranged between 20 and 51, though more than half of the workers were in their 20s. The platforms whose workers we interviewed included some of the largest ones operating in both countries in terms of the size of their workforce, customer base, and investment. Figure 2.1 illustrates the distribution of these interviews across platforms and services. In both countries, our aim was to cover a range of worker occupations and under-represented groups among gig workers (e.g. women formed one such group in the majority of these occupations). In our India sample, work experience on platforms ranged from a week to five years, with most workers averaging around 21 months. Only 19 workers in the India sample worked on more than one platform (i.e. multi-apped) in the course of a work week. Indian interview sample

UK interview sample

113 interviews

Task Rabbit

Ola

Amazon

13

Swiggy

10

Food delivery

Uber

7

10

Ride-hailing

9

9

Food delivery 8

Uber Eats

7

Food delivery

8

Grofers

6 6

8

Courier

Delivery

20 11

Stuart

Zomato

Dunzo

Deliveroo

Domestic work

Ride-hailing

E-commerce delivery 12

Hyperlocal delivery

70 interviews

E-commerce delivery

Flipkart

E-commerce delivery

Bigbasket

E-commerce delivery

Urban Housejoy Company Grooming &

Grooming & domestic work domestic work 9 of the Indian interviewees were women. Two Zomato interviews were group-based thus leading to a larger sample.

Amazon Flex Food delivery

7 5

Uber

6

Ride-hailing

Helpling

5 6

Domestic work

5

Bolt

5

Ola

Pedal Me

Food delivery

Just Eat

Food delivery

Ride-hailing Ride-hailing

18 of the UK interviewees were women. Source: Authors.

Fig. 2.1  Interview sample characteristics in India and the UK

 Due to some workers multi-apping on several platforms at the same time, interviews with 55 workers yielded 70 interviews in the UK case. Figure 2.1 shows the breakdown. 5

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We recruited participants for interviews by advertising the study through communication channels and messaging apps frequented by gig workers, e.g. on workers’ unofficial WhatsApp groups, and used the strategy of snowballing to reach more participants. We also recruited them directly by hiring their services. In both countries, the aim was to capture as much variety as possible in terms of worker occupations as well as in terms of under-represented groups (e.g. women). Despite our best efforts, we had far fewer women interviewees in both the UK and India studies, which reflects the extent to which gig labour is gendered.6 Where ­possible, we recorded worker interviews and subsequently transcribed them. When participants did not want their interview to be recorded (most UK interviews and some of the Indian interviews), we took detailed field notes. We then analysed the transcripts and notes. The findings from the UK and Indian interviews were subsequently read against each other by all authors of this chapter, and iteratively interpreted to arrive at our arguments and conclusions.

 he Co-Production of Informality T and Formality in the Gig Economy The formal economy has traditionally been associated with being visible, its tasks countable, and its pay structures and working conditions stable and predictable. To the extent that the informal economy was conceived of as its opposite, it was characterized as invisible, u ­ ncountable/unaccountable, and unstable and unpredictable (Chen, 2012; Chen et al., 2004; Vanek et al., 2014). As we examine in the rest of this chapter, the gig workers we interviewed in the UK and in India told us how their work was visible in some ways and invisible in others, predictable in some ways and unpredictable and precarious in others. Our interest in this  Existing inequities in the ownership of smartphones and driver’s licenses (much lower among women), partially contribute to this imbalance (Siddiqui & Zhou, 2021). Recognizing this as a problem, some platforms have now started to institute initiatives to proactively hire more women. But, in parallel, their other measures, such as automatic early log-outs for women (for their so-­ called ‘safety’ reduces the earning potential and may have further disincentivizing effects for women (see further: Swiggy India) 6

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section is to demonstrate how informality (as characterized by invisibility and unpredictability) and formality (associated with visibility and predictability) are being co-produced and shaping each other in the organization and practice of gig work.

Making more Visible, or Rendering Invisible? Visibility is one of the key features used to distinguish formal from informal work. In this section, we examine two aspects of gig work which should, in principle, make work more visible (and by extension, more formal): first, intensive measurement and evaluation of work; and second, structured channels of support and grievance redressal for workers. But do these practices really take gig work (and workers) towards formalization? Our interviewees helped us paint a more nuanced picture of how visibility and invisibility operate in practice in the context of gig work. They also showed us why it is important to ask not just whether work is visible, but also who/what is being made visible and to whom.

Measurement and Valuation of Work Gig work is minutely measured. Thanks to digital technologies, including GPS trackers and complex algorithms, digital labour platforms can measure and track the work that happens on their platforms with more precision than ever before and in more detail than their non-digital counterparts. For instance, gig work can accurately gauge the demand and supply for a specific service at a given time (this is true across ride hailing, food delivery, domestic services, beauty and grooming services, or e-­commerce). Platforms rely on digital technologies that measure the precise time a task takes (to reach a destination, deliver a package, finish a housecleaning task); the exact distance a worker needs to travel to and from the jobs; and even the quality of work (for instance, by using before and after photographs that workers upload to the platforms to show the

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completion of their tasks).7 Thus, these new technologies of measurement make the hitherto uncounted metrics of a task (or even entire tasks or categories of work) measurable and countable. They also provide a new way of seeing and categorizing work, as work is broken down into multiple steps and tasks each of which can be quantified and valued.8 This shift may be seen as the visibilization of formerly uncounted/uncountable or invisible work. Nevertheless, it is important to ask what information is rendered visible to whom and what consequences this has for workers. Platforms have different reasons for measuring work and collecting data; and how and to what extent they do so. For one, they use this information to allocate work. In the informal economy context, for instance, the lack of data has often been cited as the reason for the invisibility of workers and the lax enforcement of labour laws (Chen et al., 1999; Erdoğdu & Toksöz, 2013; Gammage & Kabeer, 2016; de Soto, 1989). With gig workers, on the other hand, the availability of data about their work, hours, and wages carries the promise that this data will eventually make it possible for the government to detect violations of labour law and consequently, to improve work conditions; or at the very least, for workers to be able to resist unpaid work, as they will be able to demonstrate clearly what they worked on, when, and how much. However, in our fieldwork, we found out that, in both India and the UK, workers usually do not have access to the data collected about them, or they lack an understanding of these data. Describing how it works in the context of food delivery, Ben, 21, an Uber Eats rider in the UK (October, 2020) told us: Uber does not show the address of where you have to go (until you accept the job) […] You don’t know where you are going.

 During the pandemic, some platforms resorted to similar technologies to monitor whether workers were abiding by safety regulations (by asking them to upload selfies when wearing a mask), and if they were well (by submitting their body temperatures or their oxygen saturation tests) (Ustek-­ Spilda, Bertolini, et al., 2020a). 8  This is how it becomes possible to talk about house cleaning as the sum of cleaning x number of windows and y number of carpets, rather than as cleaning someone’s personal space. 7

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Ben essentially indicated that, unlike his offline counterparts, he could not know how long he would need to travel or how much he would make before accepting a job. Thus, even though this information existed, it was not available to Ben and, thus, was not useful for him. In India, under the Uber Plus rewards program for cab drivers, drivers can see their trip duration, but only if they have reached the platinum level, the third level of Uber Rewards, which drivers can achieve only after collecting a significant number of points through their rides and/or Uber Eats orders. In both countries, workers found it frustrating that even though the information was available to the platforms, it was not shared with them, which ultimately constrained their ability to make informed decisions on whether to accept a job or not. If some data are partially visible to workers, other kinds of data and measurements might not be visible to them at all. For instance, work allocation algorithms that match workers with customers are not available to workers. Before the emergence of platforms, work allocation in many sectors relied on individuals in key roles, such as dispatchers or fleet managers in cab services, making decisions and allocating work. To that extent, it depended on the discretion of individuals, and it was less standardized and, presumably, prone to significant biases. In contrast, digital labour platforms usually allocate work automatically using algorithms, often without a human or their discretion involved in the process.9 A variety of factors may influence how an algorithm allocates work, but workers are seldom told what these are. Based on our conversations with workers in both countries, as well as secondary research (Ferrari & Graham, 2021; Rosenblat, 2018; Wood et al., 2018) these factors may include distance to the job, worker ratings, or location, in addition to the volume of demand and supply. Platforms may decide on these factors based on their existing data and business goals; however, these logics are seldom made accessible to workers, flagging the broader issue of algorithmic opacity (Burrell, 2016). Atul, a 27-year-old driver who transitioned from driving an autorickshaw to a cab in Bangalore explained:  While this is not to say bias does not exist in this process or it is entirely avoidable, the role for an individual’s discretion is significantly reduced in this model compared to the previous forms of job allocation. 9

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I’ve been attached with Ola for three years and I still haven't figured out how much Ola gives me per kilometre.

Thus, while allocating work through seemingly standardized and objective algorithmic criteria may appear to be a step towards visibilization (and a move away from backdoor and discretionary management decisions), the lack of transparency in these algorithms makes it difficult for workers to understand why they are allocated or denied work, what work they are allocated and how much and why they get paid the amount they get paid. Moreover, workers state that their opacity makes it impossible for them to prove that algorithms might be working against their interests. For instance, the incentive structure offered by platforms often hinges on the number of jobs completed by workers within a certain time duration. While workers would like to maximize the incentives they receive, few understand why they receive a specific number of job in any period. To ensure that they are allocated more work, workers often resort to developing workarounds to avoid the platform limits or consulting their own contacts (using networks and channels that are not set up officially by the platforms) for tips. For example, Ola driver Kartick and his friends in Bangalore, India discussed how Ola generally stopped allocating rides to them as they neared their respective incentive targets for the day. Ola sets daily and weekly targets for its drivers, either in terms of completed rides or total amount of earnings, and any driver who completes this target on time receives a bonus (incentives). Kartick had on several occasions had the experience of stopping receiving rides on the app just when he only had a couple of rides left to complete his target. Then, when the clock struck midnight, he ended up not having achieved his daily incentive, which could sometimes make up 30% of his daily income. In a state of desperation, and to change this outcome, Kartick had at times tried to allocate himself rides using the Ola customer application on his phone or asked one of his friends to order a cab from Ola, in the hope that he would be the driver that was allocated the ride. On a good day, he got away with these tricks. But on a not-so-good day, Ola detected these orders, thanks to its location boundaries in the city (places within the city

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where Ola does not function) and the incentive was debited from Kartrick’s account. Much like Kartick’s story, in the UK too, David, 30, an Amazon driver (January, 2021) spoke of his reliance on informal networks and online groups to understand the platform’s work allocation mechanisms: Generally, they [Amazon] have a cut of 20-25 hours a week you are limited to, and you can't work more than six days in a row; they have not made anything official, it's just a rumour, but I noticed that when I have done 19 hours in a week I won't receive any more offers, and I have seen people saying the same thing on Facebook.

Hence, in both India and the UK, we found workers having to rely on informal strategies to work around the information asymmetries they experienced with platforms. Thus, even as platforms measure work and workers’ activities more intensively, the data and decision-making that result from these measurements are not equally visible to all. Therefore, we argue that it is important to always consider not merely that certain aspects are made visible through gig work but also scrutinize what is made visible to whom, and to what extent. This is because there are instances in which work becomes more visible to the platform but less visible to the workers, thus producing a complex combination of visibilization and invisibilization.

Support and Grievance Mechanisms at Work Another aspect of visibilization relates to the communication methods between workers and platforms, and the support and grievance mechanisms available to workers. Several platforms offer workers official, formalized and structured channels for communication and to raise complaints (often through the platform app itself ). These range from an SOS button in case of emergency, to raising tickets, or escalating issues in in-app chats, WhatsApp groups, talking to call centre executives, 24 × 7 helplines and visiting company offices. Compared to other jobs, these communication methods can be conceived as offering a formalized

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communication channel between workers and platforms, which workers can access whenever and wherever they need, mostly directly from their phone. For example, Parth, a food-delivery rider in Bangalore who had been working with Zomato part-time for a year, recalled: I was at LBS Nagar at 2 in the morning delivering an order. The locality wasn’t that safe, a little lower-class area. Some kids on a bike came and started harassing me for my phone and cash and other valuables. I pressed the SOS button for the police and they arrived within two minutes. I was very impressed.

Workers in the UK too shared examples where these communication channels had helped resolve their grievances. For instance, Jenny, 20, a cleaner in London, working for TaskRabbit (October, 2020), explained: .. a customer left me a bad review—and I told TaskRabbit what happened, and they reversed the review (there's a time window within which you can object).

These positive experiences, however, were not shared by all workers, and were rare overall. In fact, workers often emphasized that these communication channels existed in theory, rather than reality, as they were largely inaccessible to them. Several workers suggested that while knowing that these channels existed was better than not having them at all, they nevertheless had proven unsatisfactory to them for a variety of reasons. Ellen, 24, a rider for the courier company Stuart in the UK (October, 2020), mentioned that: With the help centre you get email replies within a day. There's also a Q&A. There's also a chat function but they connect you to someone in Bulgaria. It's quite difficult. I contact them three or four times a night. They are abysmal with that. They have filters to send a generic response. It's like speaking to a brick wall.

In India too, drivers and riders complained about the ineffectiveness of safety helplines, and how this contributed to their fear of driving and

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working at night. Kartick, the driver for Ola in Bangalore we mentioned earlier, told us that he slept in his car at night on empty streets. He was worried about doing so, because he frequently heard of other drivers being threatened and mugged in their cars at night. He added that he had given up on Ola’s helpline number because they never responded; adding that he depended on the local authorities for his safety and ‘left the rest to God.’ Once a complaint is registered, the follow-up might also be unsatisfactory, as illustrated by Someshwar, a 36- year-old appliance repairman who works on the Indian domestic services platform Housejoy: When a customer refused to pay me, I complained to Housejoy who sent legal notices to the customer. I still haven't been paid in four months.

Pedro, 33, a cleaner for Helpling in London (November, 2020), also noted: I have received bad feedback from some bad clients and one time I was successful with the company contesting it. I do this one time. But now I don’t bother. I work hard and I try to get clients for [the] company but they give no support. So now I don’t care. I am going to work for myself. They provide no help for me.

Thus, these communication channels, despite their visible existence, are not necessarily adequate in guaranteeing effective communication between the platforms and workers. Given the high level of dissatisfaction with these channels of communication, as well as with other aspects of their work, including pay and working conditions, gig workers resort both to the official, platform-­ mandated/created channels and to informal channels and forums which they set up themselves to protest the decisions made about them, or to present their demands to the platforms and the state. These protests inevitably have varying degrees of visibility. Until recently, organized trade unions dedicated to gig workers were few and far between; however, in the last three to four years, several trade unions and even a federation of

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unions have emerged for gig workers and existing unions have started to expand their services to gig workers in both the UK and India.10 Yet, much of the collective action in the gig economy is not through formal trade unions. We observed several cases where workers organized informally among themselves to help each other out at specific moments and against specific injustices. In both India and the UK, this often happened via messaging or social media platforms such as WhatsApp and Facebook. Workers in India also had established informal support networks among themselves through WhatsApp groups or conference calls with other workers who worked for the same platform since the formal processes for support and grievance proved ineffective and as they sought ways to protect themselves against on-the-job risks. As Bangalore-based Uber driver Shankar, put it, We are a group of six. At least three or four of us are always connected via call. We keep talking about traffic updates, rides, how much each has made…we are always there for each other. If anything happens to even one of us, all five of us will be there to help. We try to meet at least once a week when we have rides in nearby areas.

Workers also used these channels, networks and groups for purposes beyond helping each other out or sharing information. They leveraged these groups to protest and make themselves heard publicly. For instance, a rider on the food-delivery platform Zomato in India, Gurmeet, brought up his response to declining incentives: Yes, when incentives declined, management came, spoke and went, no change. All we could do was to log out. Since we don't have a registered union (sangha), we could not do more as they can complain to the police. All over Bangalore, strikes, all over Karnataka, strike.

 See for instance, the Independent Workers Union of Great Britain (IWGB), the Industrial Workers of the World (IWW), the United Voices of the World (UVW), and the App Drivers and Couriers Union (ADCU) in the UK, and the Indian Federation of App-based Transport Workers (IFAT) and All India Gig Workers Union (AIGWU) in India). 10

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Richard, 28, a Deliveroo rider in London (October, 2020), also shared how: When I first started working, there was an impromptu boycott of Nando's (a food chain) as they were taking too long to do it [prepare the order], not organised by union; a few guys impromptu were standing outside the restaurant saying ‘don't take orders.’ This has been quite a successful strategy sometimes. I've never been a part of the organizing—but I've rejected the orders. It definitely works.

Thus, in both India and the UK, workers used a variety of channels and their own strategies for making their attempts and struggles to improve their work conditions visible to the public eye. While platform-­mandated, official channels of redress and organization afforded workers the possibility to reach out to the management, their existence did not necessarily entail that platforms would offer adequate solutions. Moreover, the official mechanisms for sharing grievances within the platforms focused largely on making worker grievances visible to the management; they seldom offered any possibilities for workers to share these grievances with others working for the platform. Interestingly, the absence or ineffectiveness of such channels appears to have been crucial in giving rise to alternative channels of communication among workers and their (formal and informal) organization (both structured and less-structured variations of it) in the first place.

Predictability or Precarity? If visibility is one aspect of formality, predictability is another and the one we examine next. An oft-quoted benefit of the platform economy is that it brings employment and better incomes to people hitherto trapped in informal, unstable jobs. In this section, we look at incomes and working conditions in the platform economy, from the perspective of the workers themselves, focusing especially on the predictability of their income and its stability.

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In the Indian case, where many gig workers had previously worked in jobs where regular, frequent, and predictable payments were not commonplace, the fact that most platforms had a regular and automated payment process was important for the workers. Vimala, 36, a Swiggy delivery rider from Bangalore, (December, 2019) explained this difference as follows: Garments industry, they don't pay even after 1.5 months. We get orders from Chikpete, they tell us that the bills have not been processed, cheques are not processed, we have to listen, we can't force them to pay.

Even in the UK, where workers might have been more used to formal jobs and formalized payment arrangements, the payment cycle offered by platforms gave them more regular access to cash. Abdul, 41, an Uber and Bolt driver from London (October, 2020) also brought up the option to request payment at a time that worked for them: Uber pays you so quickly, and they give you more work. Bolt pays once a week on midnight on Sunday, with Uber you can request payment at any time, it's very good for liquidity.

Another aspect of pay that becomes seemingly more predictable and somewhat stable with gig work is the existence of standardized rates for specified tasks. Most platforms pay workers a fixed per-piece rate for each completed task and, additionally, [occasionally] offer workers incentives to make extra income. The criteria for earning these incentives are unilaterally defined by the platform but they usually require workers to complete a certain number of tasks within a fixed period, or at certain times during the day or the week. For most workers, their previous jobs did not have standard rates, and instead required some form of negotiation with their employers or customers about how much they would be paid for the work they completed. Thus, gig work formalizes certain aspects of the payment process and makes it more predictable for workers. When platforms pay workers regularly by depositing their earnings into their linked bank accounts, they simultaneously create a sense of stability and predictability for workers about when and how often they can expect to be paid.

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But even as payment cycles and individual task rates become more stable, we observed that, in both countries, a worker’s overall pay was aggregated over several such tasks and over a longer duration, which meant that workers’ incomes still remained largely unpredictable. Multiple factors feed into this unpredictability. For one, platforms define rates and incentives structures only for a specific—and usually very short—timeframe. Thus, it may last only for the course of a cricket season, as happens with the Indian Premier League matches in India. In the UK, many food delivery platforms offer higher rates during certain hours in the evenings or when weather conditions are poor and fewer workers are expected to be willing to work in those conditions. Crucially for workers in both India and the UK, these rates and incentive plans are not predictable, varying day to day, season to season, and year to year. So, even though per piece rates seem to be standard, how the per piece rates are established is far from being standard even for the same task. Furthermore, as Riyaz, a male food delivery worker in Bangalore on the Indian food delivery platform Zomato, told us: They change the structure whenever they want to. And they have so many conditions. If we don’t log in for a week, our basic daily incentive changes from INR 250 to INR 100, this is the structure for a fresher [a new joiner to the platform]. Recently, they decreased the overall incentive for 15 orders to INR 400 from INR 600.

Richard, 28, a Deliveroo rider in London (October, 2020), echoed Riyaz: If they make changes to their fee structure, and they can be really radical changes—and they notify you a couple days before! That's not okay, they should be more accountable to their riders.

Thus, platforms make unilateral (and from the workers’ perspective, arbitrary) changes to payment structures, which result in rapidly changing and unpredictable incomes for workers. Sometimes, these changes also reverse accrued or existing benefits, often without adequate notice. Roger, 25, a food delivery rider for Uber Eats based in London (October, 2020), complained:

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Pay depends on the months of the year but rates went down, with Uber at the beginning four pounds was the baseline now it's like one!

Akhilesh, a 35-year-old food delivery rider for Zomato had a similar experience: For all the jobs in the world, the salary increases with time. Only for us, the pay decreases with time. I think Zomato thinks that as time passes, we become younger, the roads become shorter, that is why they are decreasing the pay.

Together, these constantly evolving pay rates and incentive structures make it impossible for workers to predict their income in any meaningful way, or, in turn, to plan their lives around an estimated income. Therefore, even though platforms seemingly make payments more predictable and regular in the short-term through standardized rates and incentives, the long-term income expectations for workers remain unpredictable, given how platforms can unilaterally modify the rate and incentive structures which the workers rely on. Hence, despite the seemingly increased formalization in payments, the overall outcome is not that of increased formality in pay, but rather a mixture of predictable short-term payments with unpredictable long-term income. There is another aspect to the long-term unpredictability. While Surie and Koduganti (2016) draw on their study of Ola and Uber drivers in Bangalore to conclude that: ‘platform economy companies have given drivers a stable, mid-term period of time to accumulate wealth, which in turn has allowed them to stabilize and take short-term decisions by making large investments in their work, and to bear the risks of flexible working conditions in the short-term with more confidence,’ much has changed since 2016. Prominently, incentives that were offered to bring workers on board have declined. For instance, the incentives offered by Ola and Uber when they started their operations in Bangalore in 2011 and 2013 respectively (see, for example, Mohan, 2013) had fallen sharply by 2017 (Vignesh & Bansal, 2017). Similarly, incentive structures offered by Zomato and Swiggy were also significantly less favourable by 2019 (Menezes, 2019). Indeed, many worker strikes and attempts at

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organization among platform workers in various parts of India since that time have been related to the decline in incentives (Business Today India, 2019). Furthermore, gig work is often based on platforms’ promises that workers can choose to work whenever they want and plan their working time with freedom. While this can, in principle, offer workers the opportunity to decide exactly when to work, it does not often translate into more predictable working time in practice. In addition to the ever-­ changing rates and incentives, the choice of when to work is also constrained by how work is allocated and how much time workers spend in between jobs. Most gig workers, especially those engaged in ride-hailing and food delivery, spend a fair proportion of their workday unpaid as they wait to be allocated jobs and once allocated, fulfilling them. These waiting times vary widely and are often unpredictable. As Joe, 30, a Deliveroo rider based in Scotland (October, 2020), pointed out, They tell you whether it's not busy, moderate and busy times, but sometimes I have been sitting there for hours at a time. When it was busy, it wasn't busy to me!

Speaking to us in Bangalore in 2020, Kavya, one of the few female riders on Swiggy, who moved to the platform after the call centre she was working in closed, also mentioned the implications of long hours spent on the platform waiting for customers. I joined this platform so that I could make some money; I have a lot of debts from the previous referral business that ran into losses. I thought I could make at least 25,000 [rupees] a month here because that is what everyone told me, but I hardly get ten orders a day when I am logged in for almost ten hours. I cannot continue here like this.

Thus, we see how gig work promises workers the freedom to decide when they work, but, in reality, the way rates and incentives are structured makes this freedom rather unattainable and obscure, as workers’ incomes remain unpredictable and uncertain. As a consequence, gig work actually makes workers’ working time dependent on external, uncontrollable

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factors, rather than on individual choice. Indeed, the issue of unpredictable incomes was one of the primary concerns raised by worker collectives in India and the UK during the COVID-19 pandemic (Ustek-Spilda, Bertolini, et al., 2020a). A second consequence of unpredictable incomes is the rise of multi-­ apping. Together, the unpredictable—and often inadequate—income stream offered by gig work leads some workers to the practice of multi-­ apping: the practice of simultaneously staying logged in and ready for work on multiple platforms at the same time. We observed multi-apping both in India and in the UK. While holding multiple jobs simultaneously was not unique to the platform economy, multi-apping has specific characteristics and can be treated as an example of moving away from a structured work role and employment relationship. Our interviewees in the UK outlined some of these practices. Robert, 71, for example, simultaneously works as a driver for Uber, Ola, and Bolt (October, 2020). He told us how he kept: all three apps open on my phone, and take the first one that I like.

Similarly, Muhammad, 31, also a driver for Uber, Bolt, and Ola, who had to: Switch off the others when accept a job from an app, but otherwise open all the time [sic]. I use two phones. It is good if you work for multiple apps otherwise it's not good.

In India too, the president of an organized union for app-based workers concluded that: For drivers to earn their roz ki roti [daily income to survive], they need to have both apps [Ola and Uber] open simultaneously and work for both platforms. If not, it will be very hard for them to make ends meet in the city. Ajmal, 35, Worker representative in India (March,, 2021).

Thus, we find that, much like visibility and invisibility, predictability and unpredictability, too, coexist in the platform economy. Moreover, not

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only do predictable pay systems coexist with unpredictable income streams, they actually support them by triggering new practices such as multi-apping. Much like our examples of visibility, predictability as an indicator of formality, too, challenges the conception of formality and informality as binary categories. It shows, instead, how the two coexist and mutually constitute each other.

Conclusion Economic restructuring in recent decades in the Global North and the Global South has not followed identical trajectories. Despite this, the restructuring has led to a more tightly knit global economy, blurring the distinction between Global North and Global South and formal and informal economies. This blurring is also evident in the contemporary gig economy as technological advances have led to digital platforms inserting themselves as intermediaries between the supply of labour and the demand for various services. We have analytically shown in this chapter that the control platforms exercise over the work process weaves informality and formality together, one gig at a time. We looked at the cases of the UK and India to demonstrate how this interweaving of formality and informality happens in practice. Invisibility has long been understood as a hallmark of informality. This may refer to workers’ invisibility to regulators or be related to the undervaluation of certain kinds of work in the public eye (e.g. women’s work). But rather than conceptualizing invisibility as the opposite of visibility, we show that both visibility and invisibility are produced and manifested simultaneously in the gig economy. Efforts to make workers’ routines visible are evident in how platforms track workers. However, to the extent that this process is embedded in opaque algorithms, it is invisible to workers (and to consumers). Workers often have little idea of how control is exercised over their work. Thus, it is the processes by which work is controlled—rather than its classification as formal or informal—that determine what is made visible, to what extent and to whom. The control of work by platforms also illustrates why predictability, whether of incomes or of work hours, and precarity are also rendered

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ideal types. For platforms, predictability of pay is contingent on the number of hours workers put in. But for workers, while the pay-out period for their earnings might be predictable, the variation in payment structures and rates means that they often do not know how much they will be paid and whether they can rely on this income in the long term. So, while workers seemingly have the flexibility to schedule their work hours on a platform, the unpredictable pay structures and amounts mean they are often tethered to the platforms on a continual basis if they wish to rely on this work as their main source of income. We observed then that the platform economy is making people and processes more visible, both in the measurement of work, and communications with workers, while also restricting to whom they are visible. In many instances, the very act of rendering processes visible has triggered mechanisms and processes of invisibilization. An analysis of predictability and unpredictability (or precarity) yields similar conclusions. Together, these analyses of visibility and predictability (as elements of formality) help us argue that formality and informality in the platform economy coexist and even produce each other. This is a far cry from framing formality and informality in binary or teleological terms, with the platform economy pushing the Global South towards something called formalization. Hence, collective action (whether through established unions or less-structured worker groups) and other individual practices such as multi-apping have emerged out of the gig economy model and the unpredictability it has unleashed on gig workers. Far from formalizing work or making it predictable, the gig economy has actually triggered these relatively informal practices in response. Our analysis of the platform economy thus reinforces Roy's (2005) argument that, instead of viewing the formal/informal sectors dichotomously, they must be understood as ‘a series of transactions that connect different economies and spaces to one another.’ Castells goes on to characterize these connections by saying that the informational economy is about ‘connecting localities throughout the planet, according to criteria of valuation and devaluation enforced by social interests that are dominant in these networks’ (Castells, 2002). We hope that this chapter demonstrates the dominance of the social interests of platforms vis-a-vis gig workers.

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Appendix Interview Details Names are provided in alphabetical order The UK Abdul, 41—Abdul belongs to an ethnic minority and grew up in London. He has been working for Uber for over five years and about a year for Bolt. Ride-hailing is his main job though he also does some teaching jobs on the side. Ben, 21—Ben is an undergraduate student originally from London. He works for food delivery platforms as he finds it an easy way to earn some money during his studies and he finds it easy to work around his university commitments. David, 30—David is a full-time civil servant in the North of England. However, during the first covid lockdown he found himself with a lot of spare time after work and he decided to sign up to Amazon Flex to earn some extra money. Ellen, 24—Ellen is a young woman from the North of England. Due to her disability, she finds it difficult to have a standard job and therefore she has to rely on benefits and on gig work. Stuart allows her to be flexible on when to work. Jenny—Jenny grew up between the UK and Canada and she now lives in London. She is against working full-time and she prefers to work with flexibility and deciding her own working time. Aside from working for TaskRabbit, she also does some cloudwork. Joe, 30—Joe is originally from Scotland. After quitting university, he tried several different jobs, but he always went back to food delivery as he likes the freedom of it. He moves a lot throughout Scotland, and he likes the fact that he can pick up deliveries wherever he is. Muhammad, 31—Muhammad moved to London a few years ago from a war-torn country in the Middle East. Although he has a bachelor’s degree from back home, he finds it difficult to find a qualified job in the

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UK. He joined Uber a few years back to earn money. He later joined also Bolt and Ola to complement his income. Pedro, 33—Pedro is originally from Brazil. He moved to London to learn English and find some career opportunities. He joined Helpling to earn some money while he looks for other job opportunities. Richard, 28—Richard is a young man originally from London. After high school, he decided not to continue at university, though one day he hopes he can become a journalist. He works for Deliveroo as he hates the idea of a 9–5 job and he likes the flexibility. Robert, 71—Robert retired from his full-time job a few years ago but he found it quite boring, and he had too much free time. He therefore decided to join several ride-hailing platforms. This way he can earn extra money for travelling. Roger, 25—Roger is originally from London. He used to work in retail, but he did not like the environment. He decided to join Deliveroo as he thought it was an easy way of earning money. India Akhilesh, 40—Akhilesh was part of a group of Zomato riders we interviewed at Church Street in Bangalore. Atul, 27—Atul has four years of experience working on ride hailing platforms. He started out with an autorickshaw and transitioned to a cab two years ago. Before this, he worked for an ice cream distributor delivering to retail shops. Gurmeet, 25—Gurmeet joined Zomato as the flexible timings and having ‘no boss’ really appealed to him. He previously used to work as a salesman. He’s been delivering for Zomato for about a year. Kartick, 33—Kartick is a native of Bangalore, Karnataka. He is married and lives with his family in Electronic City. Prior to joining Ola, Kartick worked as a driver for an independent employer. He shifted to platform work as he wanted to be independent of employers. Kartick has been driving with Ola for the past 3 years. Kavya, 25—Kavya hails from the bordering state of Andhra Pradesh. She moved to Bangalore about 3 years ago. Kavya has been working with Swiggy, a food delivery platform, as a delivery executive for a month (as of February, 2020). Previously, Kavya used to work at a call centre in Bangalore for 1.5 years where she was paid a fixed salary of INR 12,000.

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After this call centre shut down, she was searching for work to pay off her debts when a friend referred her to Swiggy and told her that there is a possibility of earning between 25–30,000 a month. Parth, 35—Parth is from Bellary in Karnataka. He works part time for Zomato and delivers orders during the day. He then pulls a night shift at a local educational institution as an administrator. Parth has been delivering for Zomato for 1 year. Riyaz, 30—Riyaz is from Bangalore and delivers for Zomato in Bangalore’s Church Street neighbourhood, which houses several popular restaurants. Riyaz started delivering for Zomato 2 years ago when he lost his job as a mobile technician following demonetization. He was looking for a job with relatively low entry barriers and one that paid immediately: Zomato fulfilled both criteria and Riyaz signed up. Shankar, 26—Shankar is subcontracted by his friend, who owns the car he drives for Uber. They have an informal arrangement and Shankar has been working this way for Uber for the past 1.5 years. He previously used to work at a factory where he received a fixed monthly salary as well as insurance benefits. Someshwar, 36—Someshwar owns his own appliance service centre where he employs five workers. Over the years he has tried several digital platforms including Housejoy, Urban Company, JustDial, Sulekha, and Bro4U. Vimala, 36—Vimala chose to work for Swiggy after she heard from a friend that it pays on a weekly basis which suited her, as she had an urgent need of income to pay off loans.

References Aloisi, A. (2016). Commoditized workers. Case study research on labour law issues arising from a set of ‘on-demand/gig economy’ platforms. SSRN scholarly paper. ID 2637485. Social Science Research Network. https://doi.org/10.2139/ ssrn.2637485 Barrientos, S. W. (2013). “Labour chains”: Analysing the role of labour contractors in global production networks. The Journal of Development Studies, 49(8), 1058–1071.

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Burrell, J. (2016). How the machine “thinks”: Understanding opacity in machine learning algorithms. Big Data & Society, 3(1), 2053951715622512. Business Today India. (2019, September). ‘Zomato delivery boys go on strike in Mumbai, Bengaluru after food app cuts incentives’. Business Today 17. Castells, M. (2002). ‘Preface’. pp. ix–xi. In Livable cities? Urban struggles for livelihood and sustainability. University of California Press. Castells, M. (2010). End of millennium (Vol. 3). Wiley-Blackwell. Castells, M., & Portes, A. (1989). World underneath: The origins, dynamics, and effects of the informal economy. In The informal economy: Studies in advanced and less developed countries. The John Hopkins University Press. Chen, M. A. (2012). The informal economy: Definitions. Theories and Policies. Chen, M. A., Vanek, J., & Carr, M. (2004). Mainstreaming informal employment and gender in poverty reduction: A handbook for policy-makers and other stakeholders. Commonwealth Secretariat. Chen, M., Sebstad, J., & O’Connell, L. (1999). Counting the invisible workforce: The case of homebased workers. World Development, 27(3), 603–610. De Stefano, V. (2016). The rise of the “just-in time workforce”: On demand work, Crowdwork, and labor protection in the “gig economy”. Comparative Labor Law and Policy Journal, 37(3), 461–471. Erdoğdu, S., & Toksöz, G. (2013). The visible face of Women’s invisible labour: Domestic Workers in Turkey. International Labour Office. Esping-Andersen, G. (1990). The three worlds of welfare capitalism. Princeton University Press. Fairwork. (2020). Fairwork India ratings 2020: Labour standards in the platform economy. Oxford, United Kingdom. Ferrari, F., & Graham, M. (2021). Fissures in algorithmic power: Platforms, code, and contestation. Cultural Studies, 0(0), 1–19. Gammage, S., Kabeer, N., & van der Meulen Rodgers, Y. (2016). Voice and agency: Where are we now? Feminist Economics, 22(1), 1–29. Gereffi, G. (1996). Global commodity chains: New forms of coordination and control among nations and firms in international industries. Competition & Change, 1(4), 427–439. Gershuny, J. I., & Pahl, R. E. (1979). Work outside employment: Some preliminary speculations. Higher Education Quarterly, 34(1), 120–135. Gore, C. (2000). The rise and fall of the Washington consensus as a paradigm for developing countries. World Development, 28(5), 789–804. Graham, M., Hjorth, I., & Lehdonvirta, V. (2017). Digital labour and development: Impacts of global digital labour platforms and the gig economy on

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worker livelihoods. Transfer: European Review of Labour and Research, 23(2), 135–162. Hart, K. (1973). Informal income opportunities and urban employment in Ghana. The Journal of Modern African Studies, 11(01), 61–89. Henry, S. (1982). The working unemployed: Perspectives on the informal economy and unemployment. The Sociological Review, 30(3), 460–477. Heyes, J. (2011). Flexicurity, employment protection and the jobs crisis. Work, Employment and Society, 25(4), 642–657. Hill, J. B. (2017). Leading through digital disruption. Gartner inc.. Hochschild, A. R. (2015). Global care chains and emotional surplus value. In Justice, politics, and the family (pp. 249–261). Routledge. ILO. (2016). Non-standard employment around the world: Understanding challenges, shaping prospects. International Labour Office. Jessop, B. (1990). Regulation theories in retrospect and Prospect. Economy and Society, 19(2), 153–216. Katungi, D., Neale, E., & Barbour, A. (2006). People in low-paid informal work: ‘Need not greed’. Josepth Rowntree Foundation. Lepanjuuri, K., Wishart, R., & Cornick, P. (2018). Fairwork India ratings 2020: Labour standards in the platform economy. Department for Business, Energy & Industrial Strategy (BEIS). Menezes, N. (2019, September 17). ‘Zomato: Zomato delivery executives up in arms against incentive cuts’. The Economic Times/India Times. Online. Mezzadri, A. (2017). Sweatshop regimes in the Indian garment industry. Cambridge University Press. Mohan, R. (2013). ‘Uber enters India; Rolls out first in Bangalore’. YourStory. Com. Retrieved Oct 11, 2021, from https://yourstory.com/2013/08/uber-­ enters-­india-­rolls-­out-­first-­in-­bangalore/amp Murthy, S., & Ramana, V. (2019). ‘Measuring the informal economy–the Indian experience’. NCEUS. (2009). Skill formation and employment Assurance in the Unorganised Sector. National Commission for Enterprises in the Unorganised Sector. Pahl, R. (1984). ‘Divisions of labour’. Parthasarathy, B. (2020). Learning to “walk on two legs”?: Divergent trajectories and the future of India’s ICT services. In Deciphering India’s services sector growth (pp. 199–222). Routledge India. Peck, J. (2018). Pluralizing labor geography. In The new Oxford handbook of economic geography (pp. 465–484). Oxford University Press.

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Portes, A., & Sassen-Koob, S. (1987). Making it underground: Comparative material on the informal sector in Western market economies. American Journal of Sociology, 30–61. Prassl, J. (2018). Humans as a service: The promise and perils of work in the gig economy. Oxford University Press. Randolph, G., Galperin, H., & Khan, L. (2019). New opportunities in the platform economy: On-ramps to formalization in the global south. Just Job Network. Rosenblat, A. (2018). Uberland : How algorithms are rewriting the rules of work. University of California Press. Roy, A. (2005). Urbaninformality: Toward an epistemology of planning. Journal of the American Planning Association, 71(2), 147–158. Sassen, S. (2001). The Global City: New York, London, Tokyo. Princeton University Press. Schwellnus, C., Geva, A., Pak, M., & Veiel, R. (2019). Gig economy platforms: Boon or bane? https://doi.org/10.1787/fdb0570b-­en Siddiqui, Z., & Zhou, Y.. (2021). ‘How the platform economy sets women up to fail.’ Rest of World. Retrieved 11 October 2021. de Soto, H. (1989). The other path: The invisible revolution in the third world. Harper & Row. Spencer, N. H., & Huws, U. (2021). Platformisation and the pandemic: Changes in workers’ experiences of platform work in England and Wales, 2016–2021. In Seven ways platform workers are fighting back (pp. 9–24). Trades Union Congress. Srnicek, N. (2017). The challenges of platform capitalism: Understanding the logic of a new business model. Juncture, 23(4), 254–257. https://doi. org/10.1111/newe.12023 Surie, A., & Koduganti, J. (2016). The emerging nature of work in platform economy companies in Bengaluru, India: The case of Uber and Ola cab drivers. E-Journal of International and Comparative Labour Studies, 5(3). Ustek-Spilda Funda, Alessio Bertolini, Mounika Neerukonda, Pradyumna Taduri, Mark Graham, and Nancy Salem. (2020a). ‘COVID-19, the gig economy and the hunger for surveillance’. Retrieved Aug 9, 2021, from https:// www.adalovelaceinstitute.org/blog/covid-­1 9-­g ig-­e conomy-­h unger-­f or-­ surveillance/ Ustek-Spilda, F., Graham, M., Bertolini, A., Katta, S., Ferrari, F., Badger, A., Howson, K., Neerukonda, M., & Taduri, P. (2020b). Covid-19 policies leave gig workers out in the cold–bot Populi. Talking Digital Justice Online/Offline. Retrieved Aug 9, 2021, from https://botpopuli.net/covid-­19-­gigwork-­ precarity-­platforms

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Vanek, J., Chen, M., Heintz, J., & Hussmanns, R. (2014). Statistics on the informal economy: Definitions, regional estimates and challenges. Working paper (statistics), No: 2. WIEGO. Vignesh, J., & Bansal, V.. (2017, January 25). ‘Uber, Ola drivers hit hard by falling incentives–the economic times’. The Economic Times/India Times, Online. Williams, C. C. (2010). Evaluating the nature of undeclared work in South Eastern Europe. Employee Relations, 32(3), 212–226. Williams, C. C. (2012). Informal employment in the advanced economies : Implications for work and welfare. Routledge. Williams, C. C. (2014). The informal economy and poverty: Evidence and policy review. Universiy of Sheffield. Wood, A. J., Lehdonvirta, V., & Graham, M. (2018). Workers of the Internet Unite? Online freelancer organisation among remote gig economy Workers in six Asian and African Countries. New Technology, Work and Employment, 33(2), 95–112. Woodcock, J., & Graham, M. (2020). The gig economy: A critical introduction. In Cambridge, the UK. Polity Press. World Bank. (2015). The global opportunity in online outsourcing. World Bank. Yeates, N. (2004). Global care chains. International Feminist Journal of Politics, 6(3), 369–391.

3 Work on Online Labour Platforms: Does Formal Education Matter? Uma Rani, Rishabh Kumar Dhir, and Nora Gobel

Introduction Digital labour platforms have gained prominence in developing countries due to rising unemployment, and promoting platform work has become an easy strategy for governments to address the unemployment problem. This has resulted in massive investments in digital infrastructure and training of workers in a range of tasks through private training institutes by governments in developing countries. Some researchers consider digital labour platforms to be a silver bullet for development and addressing poverty in developing countries (Schriner & Oerther, 2014) as they are thought to provide gainful employment opportunities to low-skilled workers who are unemployed or in the informal economy. However, the available evidence shows that the majority of the workers performing tasks on online labour platforms are well educated with a graduate or post graduate degree (ILO, 2021; Berg et al., 2018). While the online labour U. Rani (*) • R. K. Dhir • N. Gobel International Labour Organization (ILO), Genève, Switzerland e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. Surie, U. Huws (eds.), Platformization and Informality, Dynamics of Virtual Work, https://doi.org/10.1007/978-3-031-11462-5_3

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platforms might not be creating opportunities for less educated low-­ skilled workers, there is a potential for the on-going digital transformations to increase informal and non-standard work among highly educated workers (ILO, 2021; OECD, 2020; World Bank, 2020). The online labour platforms (such as freelance, microtask, competitive programming) have created new ways of outsourcing work, enabling businesses to adjust their workforce, and have grown at a rapid pace over the past decade (ILO, 2021). The tasks performed on these platforms are quite diverse, from image and data annotation, transcription, labelling, and data processing on microtask platforms, to translation, legal services, and software development on freelance platforms and data analytics and artificial intelligence on competitive programming platforms. These platforms provide unprecedented possibilities for businesses to outsource work to a globally dispersed workforce that is available every day and round the clock, at a low cost. A large proportion of workers performing tasks on these platforms originate from developing countries, while the clients posting tasks are predominantly from advanced economies (ILO, 2021). A challenge in the labour market is matching jobs to workers with corresponding skills. A recent analysis shows that skills mismatch is quite widespread; globally only half of the workers have jobs corresponding to their educational levels, and these proportions are lower in developing countries (Stoevska, 2021). Digital labour platforms have the potential to improve this matching process to connect workers with clients or employers directly for work opportunities. However, the experience of many workers on digital labour platforms is marred by skill mismatches, especially on freelance and microtask platforms. Workers on online labour platforms need to have the relevant skills and knowledge to successfully perform tasks, and they do not require formal educational qualifications or certificates to offer their services on such platforms (Herrmann et al., 2019a). Workers from many developing countries, including women workers, who are increasingly performing tasks on these platforms, tend to be classified as ‘independent contractors’ or ‘self-employed’ by the platforms. As a result, they lack an employment relationship, and the associated labour rights and protections, in the same way as those in the informal economy.

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The labour markets in developing countries are characterized by a large proportion of workers in the informal economy. Attaining higher education in developing countries is quite expensive and households often invest their scarce resources, or depend on subsidized higher education by the State. In the development trajectory, higher education leads not only to high-quality formal employment with income and work protection for workers but also to improving the content of their work (Berg et  al., 2018). It allows workers to use their skills in the offline formal jobs in occupations for which they were trained and gain social and economic mobility. The rise of online labour platforms raises concerns regarding the nature of the work opportunities created, as it might push well-educated and highly skilled workers in developing countries to pursue work under precarious or informal work arrangements. In this context, understanding the role of formal education for shaping labour market outcomes in terms of both access to work and earnings on online labour platforms emerges as a critical area of research. This chapter focuses on understanding the relationship between formal educational qualifications and the nature of work opportunities being generated and the working conditions of the workers. The findings show that workers participating on these platforms have attained high educational qualifications but experience a skills mismatch and increasing precarization through platform work, as their work relationships increasingly resembles those of workers in the informal economy. The chapter further argues that factors other than educational qualifications play a critical role in accessing platform work and that geographical discrimination is prevalent. The chapter is organized as follows. The next section reviews the literature on the factors that help workers to access work on online labour platforms. Section 3 presents the data and provides some basic characteristics of platform workers on online labour platforms. Section 4 provides some insights into the skills mismatch on online labour platforms. Section 5 draws on ILO surveys and interviews to explore the different factors that enable workers to access work on online labour platforms and the challenges faced by workers from developing countries in accessing work. Given all these challenges, Sect. 6 explores the returns to education and experience on freelance and microtask platforms. The final s­ection

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concludes by raising questions with regard to whether the government initiatives to promote such platforms for creating employment opportunities is the most appropriate strategy.

 ccessing Work on Online Labour Platforms: A Do Educational Qualifications Matter? Platforms are introducing algorithmic management practices, which is a distinct mode of management and a fundamental departure from traditional human resource management. The algorithmic management practices introduced by platforms for allocating and evaluating work on online labour platforms represent a paradigm shift in how clients (demand) and workers (supply) are matched. Online labour platforms use automated matching processes that are based on reputation and ratings, which can have implications for the future of work. This has particular implications with regard to the relationship between educational qualifications and labour market outcomes for workers. The traditional signalling mechanisms of educational qualifications and certificates to address the risk of adverse selection, which are commonly utilized for recruitment in offline formal jobs, are being replaced by the platform’s review and rating system (Herrmann et al., 2019a, 2019b) and to build trust (Huws et al., 2018; Gandini et al., 2016) on freelance platforms.

Reputation as New Signalling While reputation acts as a key informational signal for clients, it is based on a number of metrics including ratings, client reviews, project history, and worker profile, among others. These indicators are translated into a reputational proxy, which then allows platforms to algorithmically recommend workers to clients and reduces the search costs for the clients to fill job openings for technical tasks (Horton, 2017; Gandini et al., 2016). On platforms that provide fixed-price contracts, where the outputs are clearly defined and do not require continuous monitoring, workers with good reputational histories and higher ratings are more likely to win the

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bids compared to those with low ratings (Lin et al., 2016). The reliance on such a reputational system for signalling can result in the creation of an entry barrier for new workers and could lead to a ‘superstar effect.’ For instance, on one freelance platform, the top one per cent of workers were more frequently hired to work on larger projects, and received 27% of the total wages paid on the platform (Gomez-Herrera et al., 2017). Reputation and ratings also play a key role on microtask platforms where the tasks are low-end, repetitive, and of a short-term nature. The entire process of allocating and evaluating tasks and rewarding the workers is algorithmically managed, with no human interaction with the worker. On such platforms, reputation and ratings are key to accessing work. New workers, in particular, face difficulties in accessing work as they do not have an established profile and a reputation on the platform (Kuek et al., 2015). In order to access work on such platforms, workers often perform unpaid tasks to earn qualifications and demonstrate skills and expertise, which helps them to access work (Rani & Furrer, 2019; Hara et al., 2018). New entrants further face competition from experienced workers, who are able to use tools and scripts which notify them about the new tasks and allow them to access these tasks rapidly (Hanrahan et al., 2019; Berg et al., 2018). On microtask platforms, completing tasks accurately is critical for workers to maintain a good reputation, and workers also rely on peer learning and online forums to fill gaps in their skill development (Margaryan et al., 2020; Gadiraju et al., 2015). Such reputation systems on platforms have faced criticism that they may lead to possible biases and lack proper information feedback. For instance, a study examining the reputation system of a large online labour platform found that the proportion of workers who received five stars (the highest rating) increased from 32 to 85% over a period of six years (Filippas et  al., 2017). The proportion of workers with a high rating increased because raters (clients) lowered their standards, providing good feedback despite being unsatisfied, to avoid serious consequences for the workers or due to a fear of retaliation. While the system does not allow workers to give bad feedback to the client, the fear of retaliation is present as workers can still complain, bad-mouth the rater, withhold future cooperation, or take other forms of action. The study also found that when the opinions of raters (clients) are not publicly shared, they tend to be far

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more open in their reviews about poor performance. The usefulness of such a reputation system can be questioned, given the prevalence of such a bias and given that top-censoring over time results in feedback scores becoming less useful about the quality of work of workers. Furthermore, Herrmann et al. (2019b) point out that the educational systems are generally governed and supervised by State accreditation systems. However such regulatory mechanisms are not present with regard to platforms. Platforms exclusively design their review systems and control the algorithms determining the evaluation of workers, thereby wielding the power to influence the employability of workers.

Trainings and Skill Certifications on Platforms While there is no explicit educational requirement on most platforms, they do advertise the different skill needs that are required to perform the tasks. These skill needs are continuously changing, thus requiring workers constantly to re-educate and retrain themselves (Kokkodis, 2019). Platforms also provide regular information on highly desirable skills through their press releases and advanced skill recommendation systems. This can motivate freelancers to pursue the acquisition of new skills and either specialize in a certain area or diversify their skillsets, to enable them to have access to different types of tasks or projects. However, instead of relying purely on formal education for developing these new skills, such workers also tend to rely on informal ways of learning, such as self-study, learning by doing or participating in online courses (Herrmann et al., 2019a). Many platforms provide voluntary skill certification schemes that help newcomers to enter the market, and these also allow workers on the platforms to reskill themselves (Kässi & Lehdonvirta, 2019). Some of the skill certification schemes are computer-based skills assessment tests, which help clients to assess the ability of workers to perform certain tasks. Kässi and Lehdonvirta (2019) found that while the first few skill certificates tended to bring in returns of around 15%, these returns declined as workers accumulated more skill certificates. In some sense, workers who have accumulated a significant work history and have a good reputation on the platform do not benefit from such skill certificates.

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Signalling in Developing Countries The interplay between signalling and information asymmetry on online labour platforms has been found to have particular implications for workers in developing countries. Agrawal et al. (2016) have argued that for clients, most of whom tend to be from developed countries, information impediments, such as limited knowledge of the former employers or educational institutions of workers from developing countries, pose challenges when hiring workers who may be geographically, socially, and culturally distant. Similarly, studies have found that there is a home market bias, and that clients hold stereotyped views regarding the quality of workers from developing countries (Galperin & Greppi, 2017; Gomez-­ Herrera et  al., 2017). There is also occupational segregation between workers from developing and developed countries (Huang et al., 2019; Beerepoot & Lambregts, 2015). Rani and Furrer (2019), in their study of microtask platforms, found that workers from developed countries performed the best-paid tasks, such as content creation and writing, while the workers from developing countries performed low-end, low-paid tasks such as content access or data collection. Gender is another factor that tends to interact with signalling, information asymmetries and biases on platforms, thereby shaping labour market outcomes for women and men differently (Herrmann et  al., 2019a; Chan & Wang, 2018; Adams-Prassl & Berg, 2017). Research has shown that workers on such platforms need to develop individual traits, tactics and know-how for manoeuvring within and outside the platform that go well beyond formal educational qualifications and skills so as to improve their labour market outcomes (Han et  al., 2020; Sutherland et al., 2020; Jarrahi & Sutherland, 2019). This paper situates the role of educational qualifications, particularly for workers from developing countries on online labour platforms, in a context where the literature on signalling and information asymmetry is evolving and a range of factors besides education is being identified as playing a role in determining labour market outcomes for workers. By engaging with such factors, the paper unravels the precarious nature of platform work as well as the challenges workers face in accessing work on online labour platforms.

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Research Methods and Descriptive Statistics The data for this chapter is drawn from two large ILO surveys conducted on microtask, freelance and competitive programming platforms. The survey of workers was conducted on five major microtask platforms between February and May, 2017, and those on two freelance platforms and five competitive programming platforms between October, 2019 and January, 2020 (see Table 3.1). All the platforms where the workers were surveyed operated globally. In addition to these global surveys, the chapter also draws on in-depth interviews conducted with freelancers in developing countries using LinkedIn and social media platforms between April and September, 2019. The methodological details are available in ILO (2021). The sample in the global survey comprised 2861 platform workers, of whom 1629 were from advanced economies. For this chapter we confine our analysis to the sample of workers from developing countries, which comprised 1231 workers (43% of the total sample) who were spread across 57 countries in Asia (663), Africa (108), Arab States (11), the transition countries of Central and Eastern Europe (hereafter termed ‘Eastern Europe’) (262) and Latin America (187) (see Table 3.1). The respondents were distributed across the different types of platforms: microtask (868); freelance (306) and competitive programming platforms (57). The sample included a higher proportion of workers from Asia compared to other regions. This was partly because we had targeted workers in India on Amazon Mechanical Turk (AMT) for making comparisons with workers in the United States. Women comprised about 37% of the workers in the global sample, while in developing countries they comprised one-fourth of the sample. The average age of the platform workers globally was about 31 years, and it was higher in developing countries (35  years). Across regions, the workers from Africa were on average younger than the workers from other regions. Workers engaged on competitive programming platforms were quite young (21 years) compared to microtask (30 years) and freelance platforms (32 years) (see Fig. 3.1).

Note: Figures in parenthesis are proportion of female workers Source: ILO survey of workers 2017, 2019, 2020

Developed countries Total 449 (41%)

49 (31%) 11 (55%) 153 (29%) 50 (40%) 43 (49%) 306 (34%) 143 (54%)

Number of respondents (% of women)

Africa Arab states Asia Eastern Europe Latin America Total Total

Freelancer, Upwork

Main platforms covered

Developing countries

Freelance & contest-based

Global surveys

58 (19%) 0 455 (20%) 212 (26%) 143 (17%) 868 (21%) 1481 (46%) 2350 (37%)

62 (2%)

AMT, Clickworker, CrowdFlower, Microworkers, Prolific

Microtask

CodeChef, Codeforces, HackerRank, Iceberg, Topcoder 1 (0%) 0 55 (2%) 0 1 (0%) 57 (2%) 5 (0%)

Competitive programming

Table 3.1  Distribution of workers across regions and platforms, 2017 to 2020

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Fig. 3.1  Age distribution of workers by occupation. Source: ILO survey of workers 2017, 2019, 2020

 ducational and Skill Profile of Workers E on Online Labour Platforms The workers on online labour platforms were generally well-educated, with over 60% having attained a university degree. Workers from Asia surpassed all other regions educationally, with about 80% having a university degree. On freelance platforms a higher proportion of workers (85%) had attained a university degree compared to those on microtask (71%) and competitive programming (39%) platforms (see Fig.  3.2). The lower proportion of workers with university degrees on competitive programming platforms could be due to a sample bias as the survey captured mostly young students using platforms for honing their skills, about 70% of whom were currently pursuing their university degree. The proportion of workers pursuing their education was lower on freelance (25%) and microtask (21%) platforms. Across all regions, a higher proportion of women were well-educated compared to men on both microtask (76 and 70% respectively) and ­freelance platforms (87 and 84% respectively). The gender differences were quite stark in Latin America, where more than 80% of the women had attained a university degree compared to only 60% of men. A high proportion of well-educated women performing tasks on freelance and microtask platforms could possibly indicate that the local labour market

57

20

40

60

80

Secondary and below Higher secondary Bachelor’s degree Postgraduate degree and above

Total

Female

Male

Latin America

Eastern Europe

Asia

Africa

Microtask

Freelance

Competitive programming

0

Percentage of respondents

100

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Fig. 3.2  Education level of workers, by sector and regions. Source: ILO survey of workers 2017, 2019, 2020

Fig. 3.3  Field of study of workers with higher levels of education, freelance and microtask platforms by gender. Source: ILO survey of workers 2017, 2019, 2020

is not able to provide better-paid opportunities. It could also be due to women’s household and care responsibilities that prevent them from accessing work outside their homes. The platform workers were not only well educated but a substantial proportion of them had specialized qualification in STEM (science, technology, engineering, and medicine) education on microtask (56%) and freelance platforms (43%) (see Fig. 3.3). The remaining workers on these platforms were specialized in economics, finance, and accounting and in arts and other social sciences. While men were more likely to have attained STEM education, a higher proportion of women had attained their education in other social sciences.

58  Percentage of respondents 60 80 100 20 40

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Total

Male

Female

Latin America

Asia

Eastern Europe

Africa

Microtask

Freelance

Competitive programming

0

Could not find other employment Pay is better than other available jobs To complement pay To improve skills/network/career perspectives Because of work flexibility As a form of leisure/l enjoy it Other

Fig. 3.4  Worker’s motivation to work on online platforms by type of platform, region, and gender. Source: ILO survey of workers 2017, 2019, 2020

Despite high levels of education, what motivates workers to participate and perform tasks on these online labour platforms? About 30% of the workers in developing countries were unemployed before they started work on online labour platforms. The main motivation to perform tasks varied depending upon the type of platform: the attraction of work flexibility (freelance and microtask), to complement incomes (microtask), and to hone and improve their skills (competitive programming) (see Fig. 3.4). Women’s motivation to perform work on these online labour platforms was related to the flexibility that it provided as it allowed them to take care of household and care responsibilities and to complement their household incomes. These motivating factors underline some of the existing issues in the local labour market and society. Workers are often unable to earn a decent income from job opportunities available in the local labour market, and as a result they complement their incomes by working on platforms. Gender roles and expectations, even among highly educated women, can limit their access to formal job opportunities, while platforms provide the flexibility which allows them to engage in the labour market. Finally, technical education in universities, such as in software development and data analytics, has not kept pace with market needs, and competitive programming platforms fill this learning gap by providing students with the necessary expertise to hone their skills.

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Skills Mismatch on Online Labour Platforms The impressive educational profile of platform workers in developing countries raises questions about their participation in platform work, making it important to better understand the tasks they perform and the content of their work. As mentioned earlier, workers on microtask platforms perform short-term, repetitive, and low-end tasks related to training artificial intelligence and machine learning processes (such as categorization, transcription, collecting and verifying data, and moderating content) and promoting products and services (such as accessing content, content creation, and doing market surveys). Performing these tasks does not require high levels of education or specific skills. While tasks such as transcription and content creation might require some specific writing skills, when they are fragmented into smaller and simpler tasks, they do not require any specific skills, and often lead to deskilling of specialized work (Cheng et al., 2015; Kittur et al., 2013). More than 80% of the microtask workers, irrespective of their field of specialization, performed tasks related to the promotion of products and services, which included promotion of YouTube channels or websites, or writing fake reviews for tourist destinations, restaurants, hotels, or products, which were also ethically questionable (see Table 3.2). It is not surprising that workers were performing these tasks as a number of Table 3.2  Education level and types of tasks performed on microtask platforms Type of task performed (%)

Study field Economics, finance, and accounting Engineering IT and computers Medicine and natural sciences Other social sciences Total

Number of respondents

Artificial Promotion of intelligence / products and machine learning services

164

82

85

138 129 74 103 608

75 81 74 84 79

86 87 89 73 84

Source: ILO survey of workers, 2017

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governments in developing countries are promoting platforms as a way to creating work opportunities and training workers to equip them with basic skills, instead of utilizing their specialized skills for setting up public and private industries, which could help in the productive transformation of the economy and generating formal jobs. Such skills mismatches on microtask platforms have also been observed among workers in the advanced economies (European Parliament, 2017; Bertschek et  al., 2015). Rani and Furrer (2019) have further observed that as workers gain more experience the variety of skills they performed becomes larger instead of narrowing or becoming more specific, ­demonstrating the insecurity many workers face in accessing tasks on the platforms (reflecting a perception that being too specialized might limit their opportunities to obtain further work). The workers in the survey also reported a perception of skills mismatch, irrespective of their level of experience and education, and felt that they possessed the skills to perform more demanding tasks. There were varying degrees of skills mismatches on freelance platforms, as workers performed a range of activities from design, translation, legal, and accounting services to computer programming and data analytics, reporting that they often could not find tasks that corresponded to their skills. Many workers with qualifications in IT and computing and medicine performed tasks not necessarily related to their field of study (see Table 3.3). For instance, around half the workers with a degree in the field of IT and computers performed technology-related tasks, and 40% of them offered professional services, while the remaining workers did tasks that were often unrelated to their skills. Similarly, about half the workers with qualifications in medicine and natural sciences performed business services or sales and marketing tasks, unrelated to their education. Such skills mismatches have also been observed in Russian freelance platforms, wherein about one-third of the workers have been found to be engaged in tasks that are unrelated to their field of study (Shevchuk & Strebkov, 2021). Workers who had graduated with economics, finance, and accounting qualifications had a comparatively better skills match, as they performed tasks related to business or professional services (65% and 56% respectively).

65

29 34 47 57 50

28 50 30

87

257

Business services

62

Number of respondents

Source: ILO survey of workers 2019, 2020

Economics, finance, and accounting Engineering IT and computers Medicine and natural sciences Other social sciences Total

Study field

20 27

23

14 24 47

37

Data analytics

9

32 54 27

11

Technology-­ related

Type of task performed (%)

33

31

50 46 27

21

26

23

18 24 50

24

Sales & Creative marketing

Table 3.3  Field of study and types of tasks performed on freelance platforms

63

76

68 40 73

56

Professional services

19

23

29 10 13

19

Other

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The in-depth interviews with workers in some of the developing countries also revealed that the tasks they performed on platforms often did not correspond to their educational qualifications. A worker who had a Bachelor’s degree in procurement was performing legal transcription and hospital data entry. Similarly, another worker who had studied anthropology was performing transcription tasks for clients from Australia, Europe, and the United States for US$ 0.50 per minute. Their main motivation to work on platforms despite the skills mismatch was to earn a living due to lack of job opportunities in the local labour market. The absence of a vibrant local labour market to absorb the educated youth is also resulting in their engagement in new kinds of tasks, such as outsourced academic or content writing, which raises moral and ethical concerns. These tasks are increasingly outsourced by students based in the United States, the United Kingdom and other advanced economies, and are picked up by workers in the English-speaking developing countries. These tasks relate to preparing assignments and theses at the master’s and Ph.D. level for anonymized students across various disciplines (from social sciences to biological sciences) and has been labelled as ‘contract cheating.’1 One of the respondents mentioned that he had prepared a number of master’s theses and term papers, and was involved in preparing two Ph.D theses. He was expecting to receive about US$ 600 for writing nearly 68 pages of a Ph.D thesis. These workers often prepare assignments in disciplines where they have little knowledge. To enhance their knowledge and expertise in a specific field, and to be able to deliver the assignment within the specified time, workers mentioned that they utilize online courses, websites and YouTube videos. Workers are motivated to perform academic writing due to the incomes they can earn from it, but the nature of the tasks being outsourced through platforms raises major questions about its relevance for the local economy and society and the future career prospects of the workers. It also underlines the ­precarious nature of online platform work for workers in some developing countries. In contrast to microtask and freelance platforms, skill mismatches were not observed on competitive programming platforms as most of the  For more details, see: https://www.cbsnews.com/video/cbs-news-investigation-finds-kenyans-­ paid-by-american-students-to-cheat/?intcid=CNM-­00-10abd1h#x 1

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respondents (79%) reported that they had adequate skills to participate in contests or competitions on such platforms. These platforms provide workers with an opportunity to participate and compete against each other in contests or competitions to complete projects relating to design, coding, software development, debugging, etc. that are posted either by companies or the platform. About 78% of the respondents regularly participated in such competitions, saying that it helped them to improve their problem-solving skills and gain expertise in coding and programming. To demonstrate their expertise, workers on competitive programming platforms often add their ratings and rankings to their resume when they apply for formal jobs, which is not the case with other types of platforms. For instance, a number of IT companies such as Google, Yahoo, Microsoft, Paypal, etc. hire workers from competitive programming platforms such as Topcoder, because they can provide evidence that they have problem-solving skills and can complete tasks in a short time with little support (Choudhury, 2018). This section has demonstrated that online labour platforms attract mostly young, well-educated workers in developing countries who would be qualified to engage in formal offline jobs and not low-skilled workers from the informal economy. However, the fact that these workers are engaged in platform work indicates that there are challenges to creating employment opportunities in the local labour markets which are not being adequately addressed by the governments in these countries. While online labour platforms have the potential to provide income-generating opportunities, it is important to address the issue of skills mismatch and underutilization of the skills of the workers, apart from other challenges related to working conditions.

 ecruitment and Accessing Work on Online R Labour Platforms While there seems to be a weakening of the relations between formal education and accessing work on online labour platforms, it is important to understand the extent to which this is due to the algorithmic management practices adopted by the platforms for allocating work (ILO, 2021).

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This section explores how workers are recruited and how they access work on online labour platforms, and looks at the implications of platform design, especially for workers in developing countries.

Recruitment on Online Labour Platforms As is often the case in any labour market, there are certain prerequisites for recruitment on platforms. On online labour platforms, for workers to be able to open accounts, they need to have access to capital assets such as a computer or laptop, a reliable electricity supply and internet connectivity. Workers also require financial resources to bear the costs related to subscription or membership plans, fees for accessing tasks, internet, and maintenance costs. This way of organizing work allows platforms to shift their responsibility for investing in capital assets and their operational costs to the workers. Workers on freelance platforms may also be required to install some hardware and software programs to ensure continuous monitoring of work and work processes, which is an additional cost. The diverse talent pool of workers that is built on online labour platforms, so as to attract clients to outsource work, is not necessarily based on educational levels and experience gained in a particular field (Rani & Furrer, 2019), as is often the case in the offline labour market for formal jobs. The recruitment process on platforms varies depending upon the type of platform. Some freelance platforms verify the skills of the workers by conducting rigorous screening processes that can last from one to three weeks (such as Toptal), or there is a team of designers who review the applications from potential workers (such as 99designs) before an account can be opened. In competitive programming and microtask platforms, workers can open an account and there is no rigorous process for vetting specific skills (ILO, 2021). Often, on most of the platforms, there is no prerequisite to mention the applicant’s educational qualifications (Herrmann et al., 2019a), though worker profiles often provide information about their skills and expertise. Platforms have the power to refuse registration to a ‘user’ either at their own discretion, or due to certain stipulations laid down in their terms of service agreements (ILO, 2021). This discretionary power of the

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platforms has an impact especially on workers from certain developing countries who often face barriers in accessing work on platforms despite their high levels of skills and expertise, thereby exacerbating their precarious situation (Rani & Furrer, 2021; Graham et al., 2017; Beerepoot & Lambregts, 2015).

 ccessing Work on Online Labour Platforms: A Empirical Evidence What do the survey results show about the main criteria for accessing work on online labour platforms? This section explores some of the hurdles that workers reported facing in order to obtain work and improve their access to work in the ILO survey. Reputation  As mentioned earlier, the reputations of workers on platforms are decisive for accessing work, and are intended to mitigate the client’s risk that arises from information asymmetry on platforms. The metrics used to build reputation include ratings, reviews and feedbacks and the number of completed jobs. Most workers reported that high ratings (84%) and reviews and feedback from previous clients (80%) were very important for workers to access work on freelance platforms while half of them emphasized the importance of a high number of completed jobs (see Fig. 3.5). In this regard, it is important to note that there were no differences across educational levels. On some freelance platforms, such as 99designs, a Designer Curation team reviewed user profiles and assigned them levels (Entry, Mid, Top) based on factors such as conceptual thought, technical skills and client satisfaction. Depending on their level, workers had access to more clients, better paying contests, lower platform fees, and enhanced visibility. An analysis of workers on freelance platforms shows that reputation or high ratings and the number of completed tasks or projects become even more important for obtaining work as workers gain experience. However, the importance of client reviews and feedback declines as workers spend more years on these platforms (see Fig. 3.5). This pattern coincides with the findings of other studies that have highlighted that there is a risk that

Percentage of respondents

66  100 90 80 70 60 50 40 30 20 10 0

U. Rani et al.

Reputation on platform (high ratings) < 6 months

Reviews/feedback from past clients

6 months - 1 year

1 - 2 years

High number of completed jobs 3 - 4 years

5+ years

Fig. 3.5  Importance of reputation to obtain work on freelance platforms by experience. Source: ILO survey of workers 2019, 2020

new entrants might not be prioritized due to their low ratings (Galpaya & Senanayake, 2018) since the matching of clients and workers is done algorithmically on platforms based on the metrics (ILO, 2021). This system of reputation also has some critical shortcomings because platforms may allow some workers to flout the rules. For instance, on some freelance platforms, experienced workers with high ratings reported that they were allowed to remove or hide a certain number of low ratings. This is, however, not possible for new entrants, who might have to pay for arbitration if they do not agree with their ratings. Upwork allows “Top Rated” freelancers to remove/hide one review/rating from their profile. I simply requested to remove a particular low rating. —Male respondent on freelance platform Upwork (Pakistan)

High ratings or rankings are also important in microtask and competitive programming platforms to obtain work or to participate in specialized competitions. On microtask platforms, the ratings of the workers are influenced by client reviews and the platform’s algorithms, which can accept and reject tasks based on a predefined coding system. The rejection of tasks is quite common, with almost 95% of the workers in the sample

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reporting having had their work rejected. This has an impact on the workers’ ratings and thus their access to work in the future. Workers on these platforms have to consistently submit high quality results and require a high approval rate in order to maintain their access to work. For instance, on AMT if the workers’ ratings are below the threshold of 95%, then they might not be able to access further tasks (Berg et al., 2018). Lower ratings can also lead to deactivation of the workers’ accounts, and often there is no communication between the platform and worker about the reasons for the rejection of work or deactivation of accounts (ILO, 2021). Despite the concerns discussed earlier, such as the lack of information about ratings due to biased evaluation as well as the lack of transparency in the system, reputation on online labour platforms continues to be vital for accessing work, even for highly educated workers. Accessing Work through Payments  Some freelance platforms feature in their design the provision of an option for workers to purchase a ‘subscription or membership plan’ so that they can improve their access to work and have an advantage over others. For instance, on Freelancer, when workers open an account they have the option of buying such plans that are priced between US$0.99 and US$69.95 per month. Workers are motivated to subscribe to the paid services, as the algorithms used for matching clients and workers are set in such a way that workers who have subscription plans are more likely to be preferred, and have higher chances of getting projects and tasks compared to others. This process improves the intermediation services and attracts clients to the platforms, as it is efficient, but it transfers the costs of intermediation to the workers. As workers depend on platforms for their income, they have little choice but to incur these costs, while, at the same time, this system potentially also presents an obstacle for workers from developing countries who might not have the means to pay for it (ILO, 2021). There is the option to upgrade and become a premium member, which provides more benefits to freelancers. —Female respondent on freelance platform Upwork (Albania)

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Another service often provided by freelance platforms is the option to buy priced services for a fee, which improves the visibility of workers on the platforms when they upload their projects and proposals. This allows workers with financial resources, rather than those with skills and qualifications, to have their projects or proposals reviewed faster or to ensure that their profiles appear at the top of the list when potential clients search for workers. To ensure better intermediation, platforms such as Upwork or PeoplePerHour sell ‘connects’ or ‘proposal credits’ which allow workers to bid for projects and a substantial proportion of respondents (73%) reported paying for submitting proposals on Upwork. The practice of requiring a subscription plan to access work, or paying fees to bid for projects, illustrates a context wherein skills and educational qualifications, as well as reputational mechanisms, do not necessarily define access to work so much as the financial capacity of the workers. Such a situation can pose an entry barrier for workers from developing countries and those from low-income households, as they might not be able to make such investments due to lack of adequate financial capacity. This can result in such workers facing de facto discrimination with regard to their participation on these platforms. Therefore, simply opening an account on platforms need not necessarily mean that workers from developing countries have access to work in the labour markets of advanced economies due to their comparative cost advantage, because in order to gain entry they also require financial capability along with reputation, experience and skills. Promoting their Work  To improve their chances of getting more work, about 75% of the workers surveyed reported that they spend a substantial amount of time updating their profiles and project portfolios on freelance platforms, as this is an important indicator in the algorithmic matching process. Workers on freelance platforms also use other mechanisms, such as building a social media presence (26%), actively requesting past customers to complete feedback or training (37%) and developing a separate platform to promote their work (9%) (see Fig.  3.6). All of these tasks involve a lot of time and workers on average spend one-third of their working time, or 9 hours in a typical week, doing such tasks. Workers are

Percentage of respondents

3  Work on Online Labour Platforms: Does Formal Education…  100 90 80 70 60 50 40 30 20 10 0

Upload portfolio of previous work < 6 months

Actively request past customers to complete feedback/rating 6 months - 1 year

Build a social media presence

1 - 2 years

3 - 4 years

69

Develop a separate (off platform) website promoting work 5+ years

Fig. 3.6  Promoting and building profile on freelance platforms by experience. Source: ILO survey of workers 2019, 2020

unable to build these costs into their bids for projects, as is often done by self-employed or independent contractors in the offline labour market, due to competitive pressures, and they end up subsidizing their work.  Subsidizing their Work While building up reputation and profile is important to access work on platforms, there is also an intense competition on freelance platforms as workers compete with each other for tasks and projects globally. In an attempt to have regular access to work, workers adopt a number of strategies such as accepting work for low pay or performing tasks for free (79%); underbidding for projects (66%); and accepting work that they would otherwise decline (37%). Workers adopt these strategies irrespective of the number of years they have spent on platforms (see Fig. 3.7) and their educational levels. Despite having experience, workers find it difficult to access work and many of them accept tasks which they would have otherwise declined. Workers on microtask platforms perform tasks for free, as they build up their profiles to demonstrate that they have the skills to perform the task (ILO, 2021). These strategies help workers to increase their exposure, improve their ratings and build their profiles. The need to resort to such strategies by highly educated workers in order to gain access to work on a regular basis underlines how the platforms’ design perpetuates insecurity and precarity for these workers.

70  Percentage of respondents

U. Rani et al. 100 90 80 70 60 50 40 30 20 10 0

Accepted work for low pay or for free < 6 months

Underbidding projects

6 months - 1 year

1 - 2 years

Accepted work that you would otherwise decline 3 - 4 years

5+ years

Fig. 3.7  Subsidizing to obtain work on freelance platforms by experience. Source: ILO survey of workers 2019, 2020

Training and Re-skilling  Platforms also incentivize workers to build their profiles and enhance their skills by providing them with online training tools and courses, and skill certification schemes (ILO, 2021). About 20% of respondents on freelance platforms reported that they completed classes or training to obtain such certifications. In this regard, as mentioned earlier, evidence shows that such certifications have a positive impact on earnings, especially among new entrants compared to experienced workers, where work history and ratings tend to have a greater impact (Kässi & Lehdonvirta, 2019). About 47% of the respondents on freelance platforms reported that they had completed classes or training courses to obtain new skills, and one fifth of the workers reported having obtained certifications on platforms. Such trainings and certifications seemed to be important to all workers, irrespective of their levels of experience (see Fig. 3.8). Workers also try to diversify or attain specialized skills to be able to access work, a practice that was reported by about 35% of the respondents on freelance platforms. This new way of training and skilling however, unlike formal educational systems, is not governed by state regulations, which also raises some fundamental questions with regard to their governance mechanisms, the transferability of skills outside of the platform, and the disproportionate influence of platforms in shaping human capital.

Percentage of respondents

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71

60 50 40 30 20 10 0

Completed classes or trainings to obtain new skills < 6 months

Completed classes or trainings to Specialized skills or unique services obtain certifications

6 months - 1 year

1 - 2 years

3 - 4 years

5+ years

Fig. 3.8  Training on platforms to obtain new skills or certification, by experience. Source: ILO survey of workers 2019, 2020

Skill development in competitive programming platforms is quite particular, providing an opportunity for workers to learn new skills or upgrade their current skill sets, establish networks and improve their employability. About 78% of respondents on competitive programming platforms reported that they competed regularly (more than once a month) in programming contests. The contests also helped these workers to solve interesting challenges that companies are confronting in addition to helping them to develop their programming competencies and adapt to the changing needs of the market (Brito & Gonçalves, 2019).

 hallenges for Workers from Developing Countries C and Strategies for Accessing Work: Geographical Discrimination While reputation is important, there are also other factors related to platform design that can influence workers’ access to work. In the ILO surveys, workers expressed that they faced barriers related to their geographical location and nationality in accessing work, as many p ­ latforms blocked workers from certain developing countries from opening an account. Workers who managed to open an account on the platform faced difficulties in accessing better-paid tasks, because these were often limited to

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workers from advanced economies (Rani & Furrer, 2021; Galperin & Greppi, 2017; Beerepoot & Lambregts, 2015). Many of the workers accessing work on online labour platforms in developing countries had migrated from small towns and villages to the cities for education and employment opportunities, with the hope of finding a formal job and bringing about socio-economic mobility. However, despite being highly educated, they were unable to find employment opportunities that were commensurate with their educational qualifications and had initially joined business process outsourcing (BPO) companies for transcription tasks and later started working on freelance platforms. Their motivation to work on platforms was largely because of the need to earn income, which was critical to supporting their families back home in towns and villages. The lack of opportunities in the local labour market and the barriers they faced on platforms led many of the platform workers to devise a number of innovative means to access work which skirted legal boundaries, thereby raising ethical and moral concerns. The dependence of highly educated workers on such mechanisms to access work is representative of the prevailing precarity. Four of these mechanisms are described below: Use of Virtual Private Network (VPNs) and Remote Desktop Computers (RDCs)  Workers used VPNs and RDCs2 to open accounts on platforms as well as access well-paid tasks. This made it possible for them to mask their real location and claim to be based in a favourable location, such as a city in the United States. Some workers were also able to use VPNs to clear qualifications tests for accessing work which they would otherwise have failed, when taking them from their real location, to get around the fact that some platforms are designed to exclude workers from certain geographical locations. When workers from developing countries are able to mask their location and present as being based in an  Virtual Private Network (VPN) is an application that allows the user to change the IP address of their device (such as a laptop or computer) and mask their actual geographical location, and select another geographical location which is more suitable. Remote Desktop Computer (RDC) is an application that allows the user to connect their computer to a ‘host’ computer from a remote location, and the user can use the applications on the host computer from anywhere. 2

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advanced economy, the VPN allows them to circumvent the exclusionary design of such platforms. The utilization of such mechanisms has also created a market for VPN services. For instance, one worker paid US$ 12 per month for a subscription to a VPN service, although its use also entailed a big risk, as the worker’s account could be blocked instantly by the platform if the VPN failed. As explained by one respondent: I bought a VPN, which is set to a US IP address. It’s all about using a VPN, and also you can’t log into an account without the VPN, and if the VPN fails, the account is closed instantly.

The use of VPN allowed workers to enhance their incomes as they were able to access better quality and higher paying tasks. One of the respondents was able to earn about US$ 450 per month through platform work, by using Connecticut, United States as a location instead of their home country. While the respondent underlined that the quality of work was important to attract clients, the respondent also emphasized: I think the clients believe that I am in the US because otherwise I don’t think they would give me work.

Purchasing Fake Identification Documents  For opening an account on platforms, workers need to provide documents such as passports, licenses, etc., for verification purposes. As workers mask their real location and identity and use the identity of others, this has led to a demand for such identity documents and created a market for fake identification documents. Such documents cost between US$ 6 and US$ 30, and the price varies depending on the ID of the preferred country. As expressed by one of the respondents: Depending on what document you want, US documents are the most expensive and others like UK, Canada, Australia are less expensive.

Purchasing Platform Accounts  To be able to access work, workers also resorted to purchasing platform accounts from other workers. A parallel market has been created where such accounts are bought and sold through

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informal channels, such as Facebook groups. The price of these accounts can vary depending on the reputation and rating of the account being sold. For example, one respondent explained that accounts can be priced between about US$ 18 and US$ 910, with the more expensive accounts having a high rating and reputation and thus giving them access to well-­ paid tasks. While workers invest financial resources in acquiring such accounts (sometimes multiple accounts), they nevertheless face the risk of sudden deactivation by the platform if they are found out, and have to bear the entire loss in the process. As emphasized by one respondent: I have faced deactivation with almost five platform accounts. First one I bought for US$ 60, the second one for US$ 90, and the third was US$ 100.

Working with Clients Off-platform  Another strategy used by workers to access work is to establish contacts with clients off-platform or to find clients through other channels, such as Facebook and LinkedIn. For instance, one respondent explained that as platforms do not allow for sharing of contact information with the client, the respondent included their email address within the content produced for the client. The client would then get in touch with them directly through the email and provide them with tasks off the platform. Another respondent highlighted that specific Facebook groups exist particularly for workers from certain countries or regions where tasks are posted and clients can be reached directly, which gives them an opportunity to access work.

 arnings of the Workers on Online Platforms E and their Precarious Conditions The earlier sections have shown the skills mismatches that workers from developing countries face on online labour platforms and the challenges they face in accessing work, despite being highly educated. Factors such as ratings and reputation, subscriptions plans and fees, and the geographical location of the workers are quite important for gaining access to better-paid tasks on the platform, compared with formal education. In such a context, how do these highly educated workers fare in terms of

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their earnings on online labour platforms? Work through online labour platforms was the main source of income for 44% of the workers in developing countries in the ILO surveys. A higher proportion of workers on freelance platforms (63%) were dependent on it as their main source of income compared to workers on microtask platforms (40%) and competitive programming platforms (2%). Workers from Latin America (61%) were more highly dependent compared to Africa (47%), Asia, and Eastern Europe (40% respectively). At the same time, about 27% of the online workers reported that their household income was not enough to cover their monthly basic necessary expenses, despite being highly educated and that they resorted to multiple means to access work on a regular basis. Platform work was the only paid job for about 59% of the respondents. The remaining 41% of the workers had a main job where they were engaged as casual workers (20%), freelancers (25%), or as self-­ employed (11%), and salaried employee (43%) and supplemented their income with platform work. This section analyses the earnings for workers on online labour platforms. It also examines whether workers with higher education and experience are remunerated with better earnings, as is often observed in the offline formal labour market.

Returns to Education and Experience The level of educational attainment and experience are important pre-­ requisites for accessing work and for negotiating salaries to obtain employment in the offline formal labour market. This continues to be considered as an important signal of the quality and capability of the worker (Herrmann et al., 2019a). Online labour platforms ­fundamentally challenge this notion and, instead, substitute reputations as a key indicator to prevent adverse selection while outsourcing tasks or projects to workers (Herrmann et al., 2019b). The ILO surveys of freelance and microtask platforms capture the hourly earnings of workers based on the number of hours worked and income earned in a typical week. The total number of hours worked includes both paid and unpaid work. Unpaid work is considered in the analysis, as it constitutes an important aspect for workers to access work

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Table 3.4  Average and median hourly earnings paid and total (paid and unpaid) by type of platform, region, and gender (in US$)

Freelance

Microtask

Total

Total By region Africa Asia Eastern Europe Latin America By gender Male Female Total By region Africa Asia Eastern Europe Latin America By gender Male Female

Paid

Total (paid and unpaid)

Observa-­ Mean Median tions

Observa-­ Mean Median tions

8.3

5.1

205

5.5

3.8

206

8.8 7.8 9.1 8.0

5.7 4.0 5.8 5.8

31 95 47 27

5.7 4.9 6.2 5.9

3.9 3.1 4.7 4.1

29 97 47 28

8.6 7.8 2.8

5.2 4.9 1.6

127 77 704

5.1 6.1 2.1

3.5 3.8 1.2

127 78 708

1.7 3.0 3.3 2.1

1.0 1.7 2.1 1.2

44 369 172 119

1.3 2.2 2.2 1.7

0.6 1.2 1.3 1.0

44 367 175 122

2.8 2.9

1.5 1.7

558 146

2.1 2.0

1.2 1.2

562 146

4.1

2.0

909

2.8

1.4

914

Source: ILO survey of workers 2017, 2019, 2020

and they spend almost one-third (9  hours per week) of their working time performing such unpaid tasks. The surveys also captured incomes earned from other jobs in a typical week, which is not considered for analysis here; the hourly earnings quoted here relate only to platform work (see Table 3.4). Workers on average earned US$ 4.1 per hour for paid work, a sum that declined to US$ 2.8 when unpaid work was included. The earnings of workers on freelance platforms were three times more (US$8.3) than for those performing tasks on microtask platforms (US$ 2.8) due to the different nature of the tasks performed. There was some variation in earnings across regions, with workers in Eastern Europe (US$ 9.1) and African regions (US$ 8.8) having comparatively higher hourly earnings

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than those in Latin America (US$ 8.0) and Asia (US$ 7.8) on freelance platforms. The gender pay gap on online labour platforms was quite mixed. Women workers earned less in an hour (US$ 7.8) than male workers (US$ 8.6) when only paid work was considered, and these findings corroborate with other studies which have found higher hourly earnings among men compared to women doing similar work (Aleksynska et al., 2021; Herrmann et  al., 2019a, 2019b; Adams-Prassl & Berg, 2017). These differences in earnings could be due to gender-based occupational segregation on platforms (Chan & Wang, 2018; Beerepoot & Lambregts, 2015). However, when unpaid work is considered, the situation reverses, and women workers have higher mean (US$ 6.1) and median earnings (US$ 3.8) compared to male workers for total hourly earnings. This could indicate that women spend less time compared to men in searching for work or building up their profiles, or doing unpaid or free tasks to build reputations. The average earnings of workers on microtask platforms were comparatively lower, and workers in Eastern Europe (US$ 3.3) and Asia (US$ 3.0) had higher hourly earnings compared to those in Latin America (US$ 2.1) and Africa (US$ 1.7). This could be due to a larger proportion of workers in Eastern Europe and Asia working on AMT and CrowdFlower, where the remuneration for tasks is slightly better than other platforms, such as Microworkers, where most of the workers from African countries were engaged (Rani & Furrer, 2019). There was no gender pay gap in average hourly earnings here, either for paid work or for total earnings. Though tasks on microtask platforms are low-end, repetitive, and short-term compared to specialized tasks on freelance platforms, we found that a large proportion of workers who had attained a postgraduate degree also performed these tasks. There was very little difference between the hourly average and median earnings of workers who had attained a higher secondary education and those who had attained a postgraduate degree on microtask platforms (see Table  3.5). Further, women with a postgraduate degree tended to earn less than those with higher secondary education. This shows that the workers’ skills remain underutilized, and they are not adequately compensated in relation to their educational levels.

Total Male Female Total Male Female Africa Asia Eastern Europe Latin America

2.2

4.2 3.9 – 1.9 1.8 2.6 2.3 2.1 2.3 1.9

6.4 5.5 7.7 2.3 2.5 1.7 – 3.1 4.8 2.3 3.5

5.1 5.2 5.0 2.0 2.0 1.9 3.3 2.8 2.3 2.9 2.8

1.2

2.9 2.7 – 0.9 0.9 1.2 0.8 0.8 1.4 0.9

Higher secondary and below

Higher secondary Bachelor’s Postgraduate and below degree degree and above

Source: ILO survey of workers 2017, 2019, 2020

Total

Total

Microtask

Freelance

Median

Mean

1.6

3.3 3.4 3.3 1.2 1.2 1.2 1.3 1.7 1.4 1.3

1.9

4.7 3.9 5.5 1.2 1.3 1.0 – 1.6 2.8 1.1

Postgraduate Bachelor’s degree and degree above

Table 3.5  Average and median hourly total earnings paid and total (paid and unpaid) by level of education and type of platforms, region, and gender (in US$)

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There is some relation between earnings and education levels for all workers and female workers on freelance platforms, while the differences in earnings are very small for male workers across the different levels of education. Women with a postgraduate degree have higher average and median earnings than men. This may reflect clients resorting to gender cues of trustworthiness, pliability, and cooperativeness on platforms when considering female workers (Chan & Wang, 2018). The low returns to higher education might imply that there are other factors, such as ratings and reputation and work history, among others, which might influence earnings. Across regions, there is no correlation between education levels and earnings in Latin America, while there is some correlation in Asia and Eastern Europe. In the offline formal labour market, earnings are also often influenced by the worker’s years of experience, apart from educational levels. However, the relationship between hourly average earnings and workers’ experience is skewed towards the left for both freelance and microtask platforms (see Fig. 3.9), indicating that workers’ experience does not necessarily translate into higher earnings. There is very little difference in earnings between the new entrants and those with more than five years experience on both freelance and microtask platforms. The increase in

Fig. 3.9  Experience by hourly earnings (paid and unpaid) by type of platform for those for whom platform work is the main job, (in US$). Source: ILO survey of workers 2017, 2019, 2020

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earnings with more experience is marginal and on freelance platforms is less than a dollar per year initially, and after four years of experience, there is no increase in earnings (see Fig. 3.9). A statistical analysis conducted also shows there is no relationship between hourly earnings and education and years of experience for workers on Upwork. The only factor that correlated with hourly earnings was having four to five customers per week, and this was especially so for men. On microtask platforms, the differences in average hourly earnings between new entrants and those with more than five years of experience is less than a dollar. Despite having work experience on microtask platforms, workers end up performing low-end and low-paying tasks such as promotion of products and services and algorithmic training and machine learning activities (Rani & Furrer, 2019). The statistical analysis shows that there is some correlation between hourly earnings and education for male workers on microtask platforms. The only significant results were that workers who primarily performed tasks on Microworkers and CrowdFlower platforms received lower earnings compared to those working for the AMT platform. Workers from Eastern Europe had significantly higher earnings compared to those from Africa. Contrary to the existing literature on signalling on platforms (Agrawal et al., 2016; Gandini et al., 2016), the findings also indicate that there is no guarantee for workers, especially from developing countries, either on microtask or freelance platforms, that if they were to gain more experience and build up their ratings and reputation they would then be able to access work more easily and gain higher earnings. Although there is no clear correlation between earnings and education levels, the findings should be interpreted cautiously in the context of the platform economy, as workers need to have skills and knowledge, gained through either formal education or other informal means, in order to be able to access and perform the tasks and receive a positive evaluation. The fact that other factors besides education play an important role in determining earnings and access to work on platforms raises serious concerns. Apart from low earnings on freelance and microtask platforms, the platform business model classifies workers as ‘self-employed’ or ‘independent contractors,’ which results in a precarious situation for workers because they lack an employment relationship, and thus are not entitled

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to any social security benefits that are made available to employees with a contract. This practice allows platforms to devolve their responsibility towards providing any form of social protection to the workers and saves labour costs (ILO, 2021). In developing countries, where the informal economy is large and access to social protection is limited, the platform business model exacerbates such a situation even for the highly educated and pushes them into the informal economy. The ILO surveys show that a majority of the workers on freelance and competitive programming platforms lacked access to various forms of social security benefits, while on microtask platforms, where a higher proportion of workers had access to such benefits, this was largely due to their other job or dependency on a spouse or other family member to access them.

Conclusion Platform work is increasingly being promoted in developing countries as a means to address the challenges of unemployment and creatie income generation opportunities. Online labour platforms, in particular, have emerged to attract workers in developing countries who tend to be highly educated rather than low-skilled informal economy workers. While governments in many developing countries have been focusing on transitioning from the informal to the formal economy, at the same time the reliance on platform work for creating work opportunities, especially for the highly educated, raises important concerns about the precarious nature and informalization of work. In many developing countries, attaining a high level of education has been considered a route to accessing formal employment opportunities, which offer the associated labour protections and benefits. By focusing on the relationship between formal educational qualifications and labour market outcomes for workers on online labour platforms, this chapter has drawn attention to an emerging trend whereby the reliance on platform work in its current modalities leads to informalization of work for highly educated workers and also poses serious challenges for accessing work. This chapter has shown that in developing countries highly educated workers are performing tasks on online labour platforms that do not

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correspond to their educational qualifications. They often also face barriers to accessing work due to the platform design, where reputations or subscription plans or additional fees can play an important role, and geographical exclusion is common. Some platform workers are hence forced to develop innovative ways to access work that often skirt legal boundaries. Even after putting in such efforts and attaining high educational qualifications, incomes earned through platform work are low, and access to social protection is extremely limited. In short, the high levels of formal educational qualifications have little relevance for the labour market outcomes on online labour platforms for such workers. Despite high levels of education, workers continue to perform these tasks  to earn an income or complement their insufficient earnings given the lack of alternative employment opportunities in the local labour market. Developing countries have been heavily investing in education over the past decade to equip their workforce with diverse skills sets to ensure social and economic mobility. However, the outcome of the digital transformation process through labour platforms is a matter of concern, as human capital is underutilized, due to widespread skills mismatches, low incomes and lack of employment and social protection benefits, especially among highly educated workers. From a development perspective, an important question would be how technology can be deployed to utilize the human capital of the educated workforce to bring about a productive transformation which contributes towards economic and societal development. The findings from the chapter also point to some fundamental shortcomings in the labour markets of many developing countries. First, the local labour market is unable to provide decent work opportunities with well-paying jobs to workers, including those who already have an existing job. This results in workers being dependent on platforms either to earn their living or to complement their incomes, despite their high levels of education. Second, traditional gender roles and expectations continue to persist in developing countries even among highly educated women, and, as the local labour markets are unable to provide the flexibility they seek, many turn towards platform work so as to manage their work and care responsibilities. Third, there is a need to revisit educational policies because formal educational systems are falling short of addressing the

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needs of a transforming labour market. The chapter has demonstrated how competitive programming platforms are providing young students and workers with an opportunity to hone their skills and expertise that is lacking in the formal system. Given this context, from a development perspective it becomes important to see how digitalization can help highly educated workers to become catalysts for productive transformation and contribute to the advancement of the economy and societies, otherwise it risks creating sweatshops of digital labour. This would require identifying sectors that have growth potential and investing in developing the relevant skills and knowledge. It also requires that job creation policies integrate labour market, industrial and technology policies so as to ensure that workers’ skills are utilized in an efficient way.

References Adams-Prassl, A., & Berg, J. (2017). ‘When home affects pay: An analysis of the gender pay gap among Crowdworkers’. SSRN scholarly paper ID 3048711. Social Science Research Network. https://papers.ssrn.com/abstract=3048711 Agrawal, A., Lacetera, N., & Lyons, E. (2016). Does standardized information in online markets disproportionately benefit job applicants from less developed countries? Journal of International Economics, 103(November), 1–12. https://doi.org/10.1016/j.jinteco.2016.08.003 Aleksynska, M., Shevchuk, A., & Strebkov, D. (2021). Online platform work in Russia and Ukraine: Gender differences in earnings and work satisfaction. In T.  Karabchuk, K.  Kumo, K.  Gatskova, & E.  Skoglund (Eds.), Gendering post-­soviet space: Demography, labor market and values in empirical research (pp. 277–299). Springer. Beerepoot, N., & Lambregts, B. (2015). Competition in online job marketplaces: Towards a global labour market for outsourcing services? Global Networks, 15(2), 236–255. Berg, J., Furrer, M., Harmon, E., Rani, U., & Silberman, M.S. (2018). Digital labour platforms and the future of work. International Labour Organization. Bertschek, I., Ohnemus, J., & Viete, S. (2015). Befragung zum sozioökonomischen Hintergrund und zu den Motiven von Crowdworkern. ZEW. https://www.econstor.eu/handle/10419/130515.

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4 (In)Formality and the Janus Face of the Platform: Production of the ‘Space of Taxi Driving’ between Everyday Realities and Rationalities of State and Market Tobias Kuttler

Introduction Digital mobility platforms1 emerged at the beginning of the 2010s. By now, despite legal hurdles and local opposition, they have established themselves as an integral part of urban mobility landscapes globally. In this process, digital mobility platforms have contested established forms of intermediate public transportation (IPT) in the urban realm, such as (shared) taxis and rickshaws.  Also called cab aggregators, transit network companies, ride hailing or ridesharing companies

1

T. Kuttler (*) mobil.LAB Doctoral Research Group, Technical University of Munich, Munich, Germany Department of Socioeconomics, Faculty of Business, Economics and Social Sciences, Universität Hamburg, Hamburg, Germany e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. Surie, U. Huws (eds.), Platformization and Informality, Dynamics of Virtual Work, https://doi.org/10.1007/978-3-031-11462-5_4

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The entry of mobility platforms into South Asia’s urban service economy has directed renewed scholarly interest towards theorizing and exploring empirically the nature of informality and the formal/informal dichotomy. The lines between formality and informality have rarely been distinct (see e.g. AlSayyad & Roy, 2003). However, the rapid proliferation of digital platforms in fields such as transportation have further complicated the conceptualization as well as experiences of informality. Taxi services have been described as informal, both in academic literature and policy debates, since they are often not commissioned, endorsed, or licensed by the state (Cervero & Golub, 2007; Kumar et al., 2016). Firstly, taxis and other forms of paratransit in the Global South have been associated with poverty and extra-legality, low reliability and quality as well as insecurity for passengers and portrayed as an unruly sector (Cervero & Golub, 2007; Rizzo, 2011). Secondly, taxi driving is considered informal labour, since it is a form of self-employment that is dominant in the urban service sector in South Asia, characterized by minimal social welfare provisions and low security.2 Dual conceptualizations of formality and informality carry an inherent understanding that informal transport is inferior to formalized services, a viewpoint that has been criticized as ‘reductionist, orthodox and economistic […], detach [ing] mobility practices from their respective social-­ historical context’ (Rekhviashvili & Sgibnev, 2020: 4). Furthermore, informality is often understood as a transitory phenomenon that eventually diminishes or disappears in the process of economic modernization. Critical scholarship has termed such perspectives ideological and overly confident in the kinds of economic mechanisms proposed in mainstream development paradigms (Bhowmik, 2012; Sood, 2012). When investigating changes in taxi driving, the analytical limits of the transport policy and labour perspectives on informality become apparent. These perspectives cannot fully grasp the politics of the embedding of the platform economy in the highly fragmented and unequal cities of the Global South, nor can they fathom the discursive space in which the  Due to decreasing employment in the formal manufacturing sector and a continuing agricultural crisis in rural areas, South Asia’s urban labour is strongly informalized. In India, almost 90% of the urban workforce is part of the informal sector (Sood, 2012; Surie & Koduganti, 2016). 2

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governance of the transport sector takes place or the everyday experiences of operators and passengers. Therefore, in this contribution, the taxi sector will be regarded as a socially-produced economic space (Lefebvre, 1991). This space is produced by different forms of taxi driving—most importantly the metered taxis—that are long established in many cities in both Global North and South, and the ride-hailing services that emerged in the beginning of the last decade. For the case of the taxi sector in Mumbai, I am interested in how ideas of formality and informality shape conceptualizations of the taxi sector, and how such conceptualizations interact and collide with the experiences and practices of everyday taxi driving. Mumbai is a city in constant change, reflecting how the city’s and its region’s spatial and mobility patterns changed in the last decades. Once home to a sizeable manufacturing sector, a decline in industrial production caused an employment shift to the service sector.3 Today Mumbai is recognized as one of the most important banking and financial centres in Asia. Large infrastructure projects and a construction boom have changed the built environment within Mumbai City. But particularly the wider Mumbai Metropolitan Region (MMR) has been profoundly transformed. Efforts to decentralize housing and economic activities spurred the development of satellite townships. More recently, the periphery of the metropolitan region has been converting into a landscape of consumption according to the urbanites’ demands for leisure and entertainment. Hence, in the last decades, municipalities in the urban agglomeration of Mumbai, such as Navi Mumbai and Kalyan-Dombivali, have experienced higher population growth than the core city (Kennedy & Zérah, 2008; Bhagat & Jones, 2014). Although employment opportunities have also moved outwards to some degree, cities in the outer MMR still economically depend on the core city (Phadke, 2014). This has resulted in longer commutes from peripheral homes to workplaces in the core city, but also increased intra-peripheral travel. Transportation infrastructure development is only slowly catching up with the rising need for longer  Although a shift in employment to the service sector occurred, manufacturing activity has not completely disappeared in Mumbai, but has become informal and invisible within in the city limits or has dispersed away from the city centre to locations in the Mumbai region (Harris, 2012; Finkelstein, 2015). 3

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and tangential commutes, hence such trips are increasingly undertaken in private cars by the growing middle classes (Shirgaokar, 2014). Platform-­ mediated mobility services are appealing to these middle classes in the wider MMR, but also in the core city, where the current public transportation system is operating beyond capacity and completion of the metrorail system will take time.

 owards a Spatio-Temporal Perspective T on Informality Formalization and informalization are always processes of contestation and negotiation over reputation, legitimation, and sovereignty between the state and unorganized, self-employed workers. Before ride-hailing services emerged in India, the IPT sector in South Asia had been fragmented and off-limits to the engagement of large corporations. Hence, informality as a contested sphere was negotiated between the state and the worker with: self-employed workers following different strategies and tactics gaining recognition and access with both formal and informal elements. This has been studied most thoroughly for the case of street vending (e.g. Schindler, 2014; Anjaria, 2011; Bhowmik, 2012).4 Just as the boundaries between formality and informality are constantly renegotiated in street vending, similar processes can be observed in transportation.5 On the other hand, the state itself acts as an informal agent, as it deploys a condition of calculated deregulations and operates according to many different rationalities and logics. Ananya Roy argues that the state is not a homogenous entity, but ‘is a deeply informalized entity, one that

 Street vendors, for example, may apply for formal licenses and become part of unions to secure access to vending space and other resources, and simultaneously pay fees to local gangs or hafta to placate the authorities. 5  E.g. Turner and Hạnh highlight how motorbike taxi drivers in Hanoi, when interacting with the police ‘leverage their personal identity as elderly, poor or war veterans to pay the lowest fines possible,’ concluding that drivers are ‘quick to draw on such resources to maintain their rhythms while reducing frictions’ (Turner & Hạnh, 2019: 58). 4

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actively utilizes informality as an instrument of both accumulation and authority’ (Roy, 2009: 81). When platform operations commenced in South Asian cities, firms entered this highly contentious space and presented themselves as a pathway towards the formalization of urban transport services and its labour, in addition to highlighting their contribution to solving urban and environmental problems and responding to customer demands for comfort and safety (NITI Aayog, 2018; Chin et al., 2018). Platform drivers themselves draw on narratives of modernity, customer orientation, and empowerment that they associate with platform labour (Pollio, 2019). The notion of self-entrepreneurship and empowerment is ingrained in the designation of the ‘driver partner’—as the platform firms call their drivers (Mishra & Bathini, 2020)—and is actively reinforced through the platforms’ algorithmic techniques (Munn, 2017). Due to the increasingly tense employment situation, the promise of job creation and stable incomes for a growing urban population offered by the platforms was received positively by India’s political and economic elites, which have exhibited widespread faith in IT-based and urban-centric growth strategies since the 1990s (see e. g. Kennedy, 2013). Despite their formal appeal, globally operating platform companies have also challenged state institutions in different parts of the world by engaging in informal and extra-legal tactics (Pollio, 2019, 2021; Sopranzetti, 2021). In India, ride hailing services circumvent transportation legislation by making use of a taxi permit type that was not originally not intended for inner city taxis (Sharma, 2019). The appearance of platform firms has expanded contestation over formality to a triangular relationship between the state, the firms, and the unorganized, self-employed workers (and their unions). In order to analyse and understand the politics of digital mobility platforms from the perspective of (in)formality in this contribution, I place the notion of contestation at the centre of a spatial and temporal conceptualization of informality that reflects, on one side the contentious ways by which labour is (in)formalized by the state, and, on the other, the modes of negotiating access to urban space and resources. Such a spatial, temporal, and procedural perspective, I argue, is essential when trying to understand taxi driving at the contested interstices of

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formality and informality. First, such a perspective allows recognition of constantly changing conditions of labour in the course of India’s ongoing urban transformation. Work and employment relations in a city are produced in the entanglement of global, regional and local flows and are interwoven with urban spatial and temporal rhythms. Many of the informal work opportunities in cities like Mumbai, Bengaluru, and Delhi rely on formal flows of global capital. This interrelation is most evident in the case of globally connected economic enclaves and office complexes that create a round-the-clock demand for a wide range of supporting services, including mobility services, that is most flexibly and adaptively met by the informal sector (Parthasarathy, 2017). A large migrant labour force is attracted by these opportunities even though these migratory processes may have been driven originally by specific local and regional conditions at the place of origin, such as agrarian crisis and social marginalization. Local spatial and temporal rhythms ultimately shape individual working arrangements, such as setting up vending stalls or providing services at a particular spot at a specific time of the day, when the volume of passers­by is high or when the police are not patrolling. Second, such a perspective connects livelihood activities to further elements of daily life including not just sociality but also political and religious participation. With a growing presence of marginalized populations in the formal workforce in liberalized India and rising self-esteem and awareness, class struggles and caste identities are more often mediated and displayed in public spaces (Parthasarathy, 2017). In Mumbai, the occupation of taxi or rickshaw driving often coalesces with expressions of political and religious affiliation. Especially in Mumbai’s taxi trade, community identity formation has been significant historically, as in the case of the hereditary taxi community of the Chillia (Bedi, 2018). But also elsewhere, drivers have assumed an important role in political mobilization, as in the case of motorcycle taxi drivers in Bangkok (Sopranzetti, 2014, 2021). To enable a spatial and temporal view of the urban platform economy, it is suggested here to investigate taxi driving from the perspective of the production of space (Lefebvre, 1991). Approaching taxi driving as a

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social space makes it possible to view labour in its historical and spatial dimensions. Both dimensions play an important role in the transformation of Mumbai’s taxi sector and for our understanding of urban platform work. Lefebvre’s spatial theory facilitates an investigation of the power relations and contestations that drive the platformization of urban transport. Besides unveiling the political economy of platforms, this approach also reveals the spaces that reach beyond economic rationalities, such as the everyday lived experiences of drivers and the spaces of non-­conformity in taxi driving. This investigation is grounded in the everyday experiences and biographies of taxi drivers and their driving practices and contrasted with the perspectives of those who are detached from the everyday practice of driving, such as decision makers, planners, and other experts. With Henri Lefebvre, I argue that taxi driving is a distinct social space that is produced at the intersection of different ways of conceptualizing and experiencing taxi driving. Drivers’ conceptualizations of taxi driving—what Lefebvre calls spatial representations—are primarily constituted by their everyday spatial practices of generating a livelihood through driving on the one hand and, on the other hand, the emotions, symbols, and identities that are attached to taxi driving—what is called the lived space. Experts’ conceptualizations of taxi driving are, however, guided by different rationalities, such as the need to plan, to govern, or to spur economic growth. These different levels of immediacy of contact with and abstraction from the urban reality create a dynamic and contested social space of taxi driving that is constantly changing. The aim of this contribution is then to trace how ideas and notions of formality and informality are reiterated and employed in the transformation of taxi driving towards platformization. Rather than understanding platformization as formalization of hitherto informal labour and services, I argue that different notions of formality and informality have played significant roles in the evolution of Mumbai’s taxi sector since at least the 1990s. Furthermore, elements of informality are deployed by state and corporate actors in their aspirations and proceedings, as well as by drivers and other collaborators in their everyday driving.

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Methodology The fieldwork this paper is based on is part of an ongoing PhD project and was conducted between January 2019 and February 2020—shortly before the Covid-19 pandemic altered everyday life significantly in Mumbai. The fieldwork consisted of observations, unstructured conversations, semi-structured and in-depth interviews with drivers, vehicle owners, fleet managers, and passengers. Additionally, a semi-structured interview guide was used for interviews with representatives of authorities and the platform companies as well as planning and transport experts, urbanists, and a consumer organization. Access to drivers, as well as owners of vehicles and fleets, was gained by first, starting with personal contacts of friends and acquaintances and then undertaking snowball sampling to get in touch with further persons engaged in the taxi sector. Secondly, I scouted sites with a potential presence of both metered and platform-based taxis—such as taxi stands and gas stations—and introduced myself to drivers at these sites directly. Thirdly, in what could be called a ‘passenger-seat ethnography,’ drivers were accompanied in their daily routines in ride-alongs by booking cabs via the app or via street hailing respectively (see i. a. Kusenbach, 2003; Dawson, 2017). During most interviews, a Hindi- and Marathi-speaking field assistant accompanied me. Using these different approaches, I encountered driving networks consisting of family members and friends who owned and operated single cars or small self-organized fleets and also gained access to larger driving networks, including owners and managers of fleets who again introduced me to other colleagues.6 Fieldwork was mainly conducted in the M (East) ward of the eastern suburbs of Mumbai, where vehicle driving is one of the major sources of income for low-income households (Bhide, 2015). Taxi drivers residing or having their ‘base’—such as a taxi stand—in the eastern suburbs obviously do not confine their operations only to this part of the city. A  According to some of these fleet owners, by the time of completing the research in early 2020, there were only a handful of large fleet owners (with more than 50 vehicles) in Mumbai providing cars and drivers to one of the large competitors operating in the city. 6

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passenger request or the platform algorithm could potentially send a taxi driver to any location within the region (and beyond). Owing to this relational and networked construction of drivers’ everyday reality, it felt imperative to gradually expand fieldwork activities to the island city of Mumbai, the western suburbs and locations in the wider Mumbai Metropolitan Region (MMR) in order to understand and contextualize the specific socio-spatial conditions of taxi driving in different parts of the city and region. Visits to the outer areas of the metropolitan region helped me to understand the dynamics of the growing MMR and the characteristics of the mobility system in areas that are sparsely served by rail or bus transportation (yet), and the different conditions and requirements for taxi driving outside of Mumbai City. Furthermore, as part of the urban mobile ethnography, different modes of transportation were used to criss-cross the region, such as trains, buses, platform-based taxis, metered and shared taxis and rickshaws, but I also traversed them on foot and on a bicycle. Thereby I experienced how ‘embodied mobile practices animate lived experience’ while being among other people in movement (Vannini & Scott, 2020: 62; see also Spinney, 2015).

 axi Driving in Mumbai and the Emergence T of Mobility Platforms In Mumbai, to date, three different types of taxis are operating: the original black-and-yellow metered taxis (kaali peelis), radio taxis and platform-­based taxis. Prior to the emergence of digital platforms, new regulations and taxi permit types had already spurred diversification of the taxi sector in recent decades. Radio taxis emerged as a new type of taxi in the late 2000s, operating under newly-created regulatory provisions and types of licenses. Owned by private firms, centrally managed in call centres and equipped with GPS and air-conditioning, they paved the way for the platform-mediated cabs that started operations in Mumbai in 2013.

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Mumbai’s Metered Taxis The kaali peeli taxi is the successor of the horse-drawn Victoria taxi and was the quintessential form of IPT in Mumbai in the second half of the twentieth century. Even after the auto rickshaw emerged as a means of transportation, the metered taxi maintained its importance for last-mile mobility, since auto rickshaws are not allowed to operate in central Mumbai. In contrast to the IPT’s informal image, the metered taxi sector in Mumbai is heavily regulated. The modalities of the taxi permit system are determined by federal and state regulations which limit the number of permits issued to drivers and restrict the circle of permit applicants.7 Several informants perceived Mumbai as the most heavily regulated city in India when it comes to traffic and taxi operation. Such comprehensive regulation of the metered taxi sector protects the conventional metered taxi business, but also impedes its adaptation to a changing market for taxi services. Nevertheless, several regulations have initiated important changes, including the requirement to operate cars with Compressed Natural Gas (CNG) propulsion, the replacement of manual meters by digital ones, the gradual replacement of old vehicles with new models including air-conditioned (AC) cabs, and the issuing of new taxi and rickshaw licenses from 2016 on. Mumbai’s metered taxi trade has long been dominated by hereditary taxi communities such as the Chillia Muslims from Gujarat, and Sikhs from Punjab, but also Muslims from Pratapgarh in Uttar Pradesh. Social webs of ancillary services that are indispensable for daily taxi operations have developed within these communities over decades, such as maintenance, car washing, and spare part procurement. These networks have  Taxi permits for metered taxis are issued under the Motor Vehicles Act 1988. These taxi permits are limited in their number, demand the use of CNG as fuel and require the installation of digital taximeters. For taxis in Maharashtra, further regulations limit the circle of potential drivers, as they need proof of domicile in Maharashtra and a working knowledge of Marathi in order to obtain a public service vehicle (PSV) badge that is required to operate a taxi. The fare system of metered taxis is fixed and the proper installation of meters is regularly checked by the authorities. Metered taxi stands need approval by road transport authorities and the number of taxis allowed to ply is limited. Finally, the speed of taxis and other commercial vehicles is automatically controlled by on-board units. Mumbai Traffic Police is known for strict imposition of traffic rules. 7

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created stability and reliability for drivers and other collaborators and sectors, as well as a certain familiarity for passengers. Furthermore, the communities have successfully synchronized these infrastructures with the changing urban realities of Mumbai, allowing them to continue their operations despite a profound transformation of the taxi sector (Bedi, 2018, 2020). Such social infrastructures have been particularly vital for Mumbai’s Muslim communities, who have been facing exclusion from public life and marginalization in the formal job market to date (Hansen, 2001; Contractor, 2012; Shaban, 2018). The extent of adherence to the kaali peeli was particularly revealing when the emerging radio taxi companies initially had difficulties in attracting kaali peeli drivers to operate radio taxis (Bedi, 2016a). When the app-based cabs where introduced in Mumbai, some of the taxi driving communities, particularly migrant communities from Pratapgarh and other regions in northern India, openly embraced the platforms. Others, such as the Punjabi Sikhs and the Chillia, rejected them. Many older kaali peeli drivers from the established taxi communities have secured relatively stable financial and social positions, or are supported financially by their children; hence, older taxi drivers, e.g. from the Sikh community, have been found to continue kaali peeli driving because they value the sociality among peers and feel loyal to the kaali peeli sector (Kuttler, 2022). Mumbai’s taxi sector is also well known for the collectivization and organization among its drivers. Powerful taxi unions negotiating on behalf of the taxi drivers, most notably the Mumbai Taximen’s Union formed in 1962, played a substantial role in making taxi driving a comparatively secure and respected occupation in pre-liberalization Bombay (Bedi, 2018, 2020). However, many kaali peeli drivers expressed their shrinking trust in the established taxi unions of Mumbai, suggesting that the power and influence of the unions is diminishing (see also Bedi, 2018).

Emergence of Digital Mobility Platforms At first sight, platform-mediated cab driving has little in common with kaali peeli operations. Passengers are not hailed on the street, but assigned to drivers via platform algorithms. Hence, app-based taxis do not have

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dedicated public spaces where they park and wait for passengers. When the mobility platforms commenced operations in Mumbai, the bonuses for completing a certain number of trips in a time period (called ‘incentives’) formed the main part of drivers’ income and attracted many new drivers to the platforms. The number of app-based taxis increased quickly in Mumbai from roughly 10,000 app-based cabs in 2014 to 75,000 in 2019. Meanwhile, the number of metered and radio taxis in the city decreased to 45,000 in 2019 (Rao, 2019; Korde, 2017).8 Although operations differ substantially between the two types of taxis, fieldwork also revealed considerable continuities between conventional and platform-mediated taxi driving (Kuttler, 2020). Drivers of conventional and platform-attached taxis often share similar biographies. Most app-based taxi drivers hail from other parts of India, and made Mumbai their (temporary) home to earn a living. It is often a friend, family, or community member who serves as the entry point into the driving business by providing a temporary place to sleep, social connections, financial support and access to a vehicle. In conversations with kaali peeli drivers I came to understand that these essential initiating steps of becoming a platform driver today are remarkably similar to those of becoming a kaali peeli driver in the in the 1970s. The working conditions and demands of app-based taxi drivers and vehicles received widespread attention in the autumn of 2018, when a 10-day strike of Ola and Uber drivers forced many commuters to rearrange their daily travel. Since incentives decreased further and working conditions worsened, app-based taxi drivers—unlike many of their colleagues driving metered taxis—have not been able to achieve stable and secure financial and living conditions. The Covid-19 pandemic has further increased the vulnerability of platform drivers and their relationships to the platform firms (Korreck, 2020). In the following paragraphs, I argue, along the lines of Lefebvre’s theory of the production of space, that with the emergence of digital mobility platforms, new social spaces are being produced in a triad of differing imaginations and conceptualizations of the driving profession, digitally-­ mediated driving practices and new forms of lived spaces.  In addition, more than 200.000 licensed auto rickshaws operate in the city.

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Representations of Taxi Driving This chapter aims at tracing how the taxi sector of Mumbai is conceptualized as a distinct space. While the lived space and the spatial practices also need to be distinguished, as will be discussed later, I will first highlight different processes of how spatial representations of taxi driving are created. In contrast to the unvarnished and ‘unfiltered’ immediacy of the lived and perceived spaces, spatial representations are abstractions of a spatial reality: Often a generalized or a reduced and channelled version of reality that is produced to serve a particular purpose, for example for representation in maps or plans (Lefebvre, 1991: 38, 106–107). The process of abstraction is infused with power in several ways: in the form of the expert knowledge that decides which elements to represent and which to omit; and in the power that the representation can assume, since a map or a plan can be mistaken for representing the totality of a spatial reality (e.g. in a road navigation system). In the ‘space of taxi driving,’ this concept has important implications: Taxi driving—as it is treated in legal regulations, governance frameworks and the day-to day bureaucracy of road authorities, as well as discussed in political debates or in court proceedings—is different from the spatial practice of taxi driving as conducted and experienced by taxi drivers. Consequently, how the space of taxi driving is conceived differs between those who are alienated from the everyday experiences of taxi driving, and those who are the operators themselves or immediately contribute to taxi operations (car owners, mechanics, cleaners). Hence, contestations over taxi regulation involving different state authorities, platform firms, unions, and the operators reflect different conceptualizations of the space of taxi driving. As will be discussed in the remainder of the article, the boundaries between formality and informality are negotiated in these conceptualizations of the space of taxi driving. When identifying contested boundaries between formality and informality, three different conceptualizations of the taxi sector that are most relevant that will be explored: first, driver entrepreneurship on mobility platforms as an economic growth engine and ‘ridesharing’ as a problem solver; second, the outdated meter taxi and the need for modernization;

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and third, market rationality in the taxi sector and the notion of ‘fairness.’ Subsequently, the resulting limbo of mobility platform regulation will be charted, with a focus on the state of Maharashtra.

 ntrepreneurship, the ‘Driver Partner’ E and the Mobility Platform as an Urban Solution Platform firms like Uber and Ola have positioned themselves as drivers of technological and managerial innovations, and suggest that by deploying their digital architectures they create novel ways of tackling a range of pressing challenges that states and societies have been seemingly incapable of solving. At the core of these narratives is the creation of income opportunities. Platform firms consider their drivers to be entrepreneurs. They are self-employed, hence the firms bear no formal obligations towards their ‘driver partners.’ Instead of social security, platform firms highlight and advertise the flexibility and adaptability of their driving model as a major benefit to drivers (‘Earn anytime, anywhere’).9 The entrepreneurship model is largely welcomed both by state authorities and media outlets and the associated rhetoric is continuously reiterated by them, ‘based on shared neoliberal assumptions that the online cab aggregator’s model of entrepreneurship is good for the Indian economy and will contribute to the growth story of a developing nation striving to become a global economic powerhouse’ (Mishra & Bathini, 2020: 406). Interestingly, members of the business community and Indian media have ‘put out messages mistakenly lauding platform economy companies for “formalizing” the Indian urban workforce’ (Surie & Koduganti, 2016: 30), while in fact they are actually organizing informal self-employment in the transport sector. Emphasizing platform work as a solution to job shortage is tied to positioning digital mobility platforms as solutions to urban problems. Platform firms conceptualize mobility platforms as ‘ridesharing,’ suggesting that they open up the potential to meet growing mobility demand while at the same time solving urban and environmental  https://www.uber.com/in/en/drive/

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problems. Uber, for example, argues that when usage of existing vehicles is maximized by limiting or erasing regulatory barriers, traffic congestion, and environmental impacts are reduced and customer demand is adequately met (Chin et al., 2018). The federal government has been subscribing to these arguments referring to evidence from Western countries, citing reports from large globally operating consultancies (MoRTH, 2016; NITI Aayog, 2018). It is evident here that the appeal of modernity is ascribed to recommendations of consultancies and Western ‘best practices,’ a phenomenon that can also be observed in related debates on smart urbanism (Datta, 2019) and urban sustainability (Hackenbroch & Woiwode, 2016).10

Taxi Modernization Since mobility platforms emerged in Indian cities, contestations between state agencies, platform firms, unions, and drivers have revolved mostly around formalization of transport services. These contestations over platform regulation in Maharashtra expose another conceptualization of the taxi sector: the imagination and aspiration that contemporary taxi services should reflect, or act as figurehead of a modern city that conforms to ‘world-class’ standards (Bedi, 2016a). Imaginations of an orderly and disciplined city, coupled with modern urban aesthetics, have driven attempts to formalize and regulate taxi services in Mumbai. Additionally, due to a growing invocation of safety in public spaces, especially of women, duly emphasizing passenger safety has become an important identifier for contemporary taxi services. The restructuring of Mumbai’s taxi sector is tied to ambitions that seek to establish a superior quality of life for urban elites by creating a clean and orderly urban environment and a disciplined, nuisance-free public life (Ghertner, 2015). Kaali peeli taxis became a target of discipline by  Interestingly, consultancies also celebrate romanticized narratives of informality when it comes to highlighting the potential for the creativity of entrepreneurship, as McKinsey did in its Vision Mumbai report: ‘The taxi driver: You don’t need GPS with shim around. He knows every nook and cranny of the city’ (Bombay First and McKinsey, 2003; as cited in Ong, 2011: 85; and McFarlane, 2012: 2803). 10

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formalization when transport authorities and city planners sought to ‘modernize’ Mumbai’s taxis from 2006 on, supported by a broad coalition of business elites, middle class citizens, and the transport industry. Such aspirations were a consequence of an increasing antipathy of middle classes and urban elites since the 1990s towards kaali peeli taxis and their operators. Kaali peeli drivers’ behaviour and appearance11 did not conform any longer with Mumbai’s middle class’ and elites’ expectations for conduct and courtesy. Furthermore, environmentally aware urban citizens increasingly condemned the air pollution produced by taxi vehicles. Due to the state’s alleged apathy towards such ‘nuisance’ and the inability to regulate it, demands to discipline the drivers and the whole sector were frequently voiced (Bedi, 2016a). These interventions resulted in the IPT sector becoming heavily regulated even before the arrival of digital mobility platforms in Mumbai, with several aspects of the taxi trade, such as the permit system and vehicle regulation, being formalized and the status of taxi operators as independent, self-employed workers challenged and exacerbated (Bedi, 2016b, 2020). Urban modernization ambitions are furthermore connected to aesthetic norms that aim at transforming the city’s built environment into an iconic landscape with ultramodern skyscrapers, airports and retail centres (Watson, 2014). For Mumbai, as for many cities in South Asia, urban remodelling has followed the example of major Western cities, but also cities such as Dubai, Shanghai, and Singapore that have profoundly changed their image and appearance in recent decades and claim to occupy top positions in the league of world-class cities (Ong, 2011). To the leaders of Mumbai’s modernization coalition, the city’s aging fleet of Padmini cabs did not conform with these aesthetics; neither did drivers’ reluctance to wear uniform and insufficient upkeep of the vehicles that many passengers frequently complained about meet such expectations. Hence, an aesthetic rejuvenation of taxis along Singapore’s model of taxi liberalization was envisioned, privileging taxi fleets with new air-­ conditioned and more comfortable vehicles (Bedi, 2016a).

 Common criticisms include a lack of courtesy, ride refusal, and refusal to ply by the meter, among others. 11

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Digital mobility platforms seem to respond perfectly to such urban imaginaries, an impression platform firms actively nurture by cultivating an image and branding themselves as innovative and customer-centric (Anwar, 2018). To foster this image, platform firms train and admonish drivers to greet, dress, and behave properly towards passengers. Drivers are expected to understand English and to keep the car in a well-­ maintained condition (Balachandran & Dutta, 2014; Rohatgi, 2015). As observed during numerous rides with platform-attached cabs, many drivers take great care to make the passengers comfortable. This appeal contributed to the enthusiastic welcome of digital mobility platforms by middle class citizens. The consumer organization Mumbai Grahak Panchayat commented on the experiences with Ola and Uber: ‘The passengers—hitherto harassed and exploited by the taxi/auto drivers—had a smile on their face as they got a comfortable journey ride at a most affordable price’ (Deshpande, 2017a, 2017b). Similar narratives were reiterated by state institutions in their documents.12 The regulation of mobility platforms received further impetus due to safety concerns of passengers. In the wake of the sexual assault of a woman by a driver of an app-based cab in Delhi in 2014, authorities responded to passengers’ safety concerns. They required platform firms—that had preferred to position themselves as ‘technology companies’ and not taxi operators, and had hitherto rejected responsibility for driver behaviour (Subramaniam, 2014)—to take passenger safety more seriously (Pani, 2017). Incidents of sexual assault and rising demands for ensuring women’s safety has triggered criminalization and stigmatization of low-class migrant drivers. Suspicion towards them created a sense of insecurity among migrants (Parikh, 2018; Govinda, 2020). Particularly in Maharashtra, such stigmatization has been perpetuated by the Shiv Sena’s  The state-commissioned committee in charge of revising the fare structure of taxis and rickshaws in the state, reproduces the narratives about unruly metered taxi drivers and asserts an overall improvement of commuter experience due to the entry of app-based taxis. Among kaali peeli drivers, it observes a ‘growing awareness among them about the need for consumer-orientation, better upkeep of their vehicles, and playing by the rules’ (Khatua et al., 2017:ii). The state itself—while putting restrictions on mobility platforms—has emphasized the need for ‘retaining the advantages of efficient demand/supply matching, dynamic price discovery and better commuter experience and up-gradation/modernization of taxi services’ in the notification for the City Taxi Rules, 2017 (Home Department, Government of Maharasthra, 2017). 12

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history of anti-migrant rhetoric (Tare, 2008), which continues to be voiced to the present day (HT Correspondent, 2021). As Sneha Annavarapu has recently argued, the consequent sense of mutual suspicion and distrust between women passengers and young male cab drivers can potentially reproduce and deepen social divides and inequalities (Annavarapu, 2021).

 arket Rationality and Notions of ‘Fairness’ M in the Taxi Sector With the emergence of digital mobility platforms in India, narratives of market rationality and fairness in competition have been invoked from various directions, however to make different claims. Publications by the Government of India have affirmed its trust in market rationales and justified its decision to opt for a liberalization of taxi permits (MoRTH, 2016; NITI Aayog, 2018). The Government of Maharashtra, however, argued early on that a ‘level playing field’ in the taxi sector can only be achieved by regulation and formalization (Deshpande, 2017a, 2017b). Established taxi and rickshaws drivers and unions also frequently lament the unequal competition in the field (Tiwari, 2015; see also Khatua et al., 2017). Finally, platform firms also invoke the narrative of fairness in their communication, as was the case when Uber and platform drivers filed a petition in Bombay High Court, citing unfair treatment of drivers on mobility platforms as the grounds for their petition. The above examples exhibit the various roles regulation can assume in an economic sector that is contested along the lines of formality and informality. Digital mobility platforms and many government authorities have been demanding deregulation of the taxi market to achieve fair competition. However, frequent attempts to regulate or put restrictions on platforms, petitions, and public interventions by the platform companies, and subsequent court proceedings confirm observations made elsewhere that free market supporters in fact seek reregulation and practice selective market interventionism (Peck, 2010). The need for selective regulatory intervention reveals a contradictory portrayal of Mumbai’s taxi sector: the modern taxi is envisaged as a standardized, formalized

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customer-­oriented service, and depicted as an economic activity that functions according to free market forces. Despite their modern appeal, taxis are operating in a heavily informalized field whose workers have long been neglected and disregarded by the state, not being treated on a par with workers in formal employment relationships. Furthermore, for a liberalized economy such as that of India, fair competition is considered a cornerstone of economic success and growth. Consequently, with the emergence of mobility platforms, maintaining the imaginary ideal of market fairness requires constant market intervention in this informalized environment. While producing many contradictions, moral economy claims of fairness are in line with the practices of the neoliberal state and economy: state interventions that promote and enhance fair competition are seen as ‘moral’ (Vila-Henninger, 2019: 241). However, the neoliberal economy promotes ‘an ethic of self-reliance,’ hence state and the market are ‘placing responsibility for fairness and competition on the individual’ to foster market order. Since in the contemporary platform economy every collaborator is considered as an entrepreneur who is potentially constantly productive and connected (Rossi & Wang, 2020), success or failure is ultimately seen as an outcome of individuals’ capacities as entrepreneurs. Another site of state intervention related to a different invocation of fairness has also been taking effect on the taxi sector in Mumbai. In the late 1960s and 1970s, the Shiv Sena political party became popular and powerful thanks to a promise and aggressive campaign to solve the employment crisis. Arguing that the Marathi-speaking population was historically disadvantaged, it demanded that the majority of jobs should be reserved for ‘local,’ Marathi speaking people (Heuzé-Brigant, 1999). This campaign was accompanied by an anti-migrant rhetoric that initially targeted South Indians, but later also migrants from Uttar Pradesh, Bihar, and other North Indian regions particularly. In 1991 a state rule was introduced (but has been strictly enforced only since 2006) that allowed only persons who could provide proof of residency in the state for at least 15 years to drive taxis and rickshaws. Since a large part of Mumbai’s taxi and rickshaw drivers originate from Uttar Pradesh, Bihar, and other north Indian states, this was seen as an intervention to reduce

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the dominance of migrants in the IPT sector (V. A. Singh, 2012). Following another round of aggressive anti-migrant campaigning, due to rivalry between the Shiv Sena and another nativist party, a rule was introduced by the state government that aspiring taxi (and rickshaw) drivers would need to prove ‘working knowledge’ of the Marathi language (Press Trust of India, 2010).

Regulatory Limbo Because of the recent history of taxi regulation in Maharashtra, regulatory bodies found themselves in a contradictory situation when new regulations for platform mobilities were to be drafted: On the one hand, digital platforms fitted perfectly into their ambition for modernization of the taxi sector. Such modernization, many commentators argued, was best achieved by enforcing minimal regulation and leaving further configuration of the sector up to market forces. On the other hand, authorities had to respond to the fact that mobility platforms—by using the Tourist permit system to operate taxis—challenged the regulatory regime that the authorities had imposed in a tedious and contested process on Mumbai’s metered taxi sector, and questioned the principle of ‘fairness’ in competition. Since the established taxi unions had previously given in to most of the authorities’ modernization demands, it was no surprise that opposition from the metered taxi sector against mobility platforms was particularly fierce. Demands for regulation further grew with increasing passenger safety concerns. Since criminalization of young males and xenophobic rhetoric are often interlinked in Mumbai, foregrounding the need for strict police verification of drivers concomitantly aims at holding migrants at bay. Responding to these complex and conflicting requirements, in Maharashtra the government took an ambiguous approach to platform regulation. In its communications, the government has voiced a critical stance towards mobility platforms since 2015. Together with Karnataka, West Bengal, and the National Capital Territory (NCT) of Delhi, Maharashtra was among the first states to draft specific regulations for mobility platforms. In 2017, the government of Maharashtra announced

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implementation of the Maharashtra City Taxi Rules (which had been under preparation since 2015) requiring app-based taxi firms to apply for licenses as well as drivers for individual permits according to the new rules.13 Most states, including Maharashtra, have also included regulations for driver conduct and passenger safety in their policies (CSTEP, 2020). Despite these attempts, implementation and enforcement of these new regulations in Maharashtra have been continuously delayed. Opposition from the mobility platform firms towards new regulations has been particularly fierce where regulations require drivers to obtain permits under regular or newly-created taxi permit types, since the barriers to obtain such permits are higher than for Tourist permits. From the perspective of the platform firms, the new regulations are regressive and hamper business (Rai, 2016; Poovanna, 2020). Consequently, the introduction of new regulations has been a continuous source of contention between platform firms and state governments to date, most prominently in the NCT, Karnataka, and Maharashtra, and the success of regulation attempts has varied greatly from state to state. In Maharashtra, the large platform companies ignored the new regulations from the start and no applications were made under the new scheme (Korde, 2017). Instead, Uber moved to the Bombay High Court, filing a petition there against the new Rules (Press Trust of India, 2017c).14 In August 2017, the Bombay High Court in an interim judgement ruled that the City Taxi Rules discriminate between metered taxis and app-based taxis, to the benefit of metered  Similar to other states—including Karnataka, Haryana, Gujarat, and the NCT of Delhi—the new regulations foresee that drivers may not continue operating on Tourist permits, but must obtain a contract carriage permit under section 74 of the MV Act 1988, or, as in the case of Maharashtra, they shall convert their Tourist permits into an ‘App Based City Taxi Permit,’ a permit type specifically created for the purpose of the new policy (CSTEP, 2020). Other states have abstained from imposing such strict permit requirements; e.g. in Kolkata—the second city in India where historically a significant number of metered taxis has been operating—platform-attached cabs are allowed to continue to operate on Tourist permits (Government of West Bengal, Transport Department, 2015). 14  Thereby Uber supported an earlier petition by six drivers against the ‘arbitrary restrictions’ of the Rules, arguing that the permits under the new rules would cost them 10 times more than what it would cost drivers of black and yellow taxis (Press Trust of India, 2017b). Three hundred drivers also approached the BJP state leadership including the then chief minister asking them to oppose the new laws, citing low CNG supply in the state as one of the reasons (Ahmed, 2017). 13

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taxis, and asked the state government to ‘rationalize’ the rules (Press Trust of India, 2017a).15 The legal uncertainty was additionally fuelled by proceedings on a ‘side stage’ of state regulation and by federal interventions. First, a continued tussle over anti-migrant provisions in taxi regulation created uncertainty for migrant drivers. Since the City Taxi Rules foresaw that drivers would obtain authorization to operate public service vehicles, the 15-year domicile and the Marathi language rules also applied to drivers of app-based taxis. For several years the question remained unresolved, with the state government and different benches of the Bombay High Court variously challenging or confirming the domicile and language rules.16 Interventions from the federal level also added to the regulatory patchwork. After a case of sexual assault in an app-based cab, in late 2015 the federal government issued recommendations for regulating mobility platforms, suggesting restrictions on mobility platform operations (Chakraborty & Poovanna, 2015). A turnaround took place when a ministerial committee issued further recommendations in December 2016, aiming at the liberalization of taxi permits (MoRTH, 2016; CSTEP, 2020). In the most far-reaching intervention from the federal level to date, the Supreme Court ruled in mid-2017 that a commercial driving license is not needed to operate light commercial vehicles, including taxis. After the respective government order was issued in April 2018, platform firms started to hire drivers who only possessed non-­commercial driving licenses (Press Trust of India, 2018).

 In its ruling over Uber’s petition, the court compared Maharashtra to western cities to position the state as a showcase for liberal regulation: ‘In London and America, there are regular cabs and Uber cabs plying on the same fares. Maharashtra should also do something like that and be a trend-­ setter for other states in India’ (Press Trust of India, 2017a). 16  When new rickshaw permits were issued in 2016, aspirant drivers were required to undergo a verbal Marathi test. This provision was challenged by several petitions in court, followed by a trial of strength between different benches of the High Court, judging over the Marathi rule four times in 2016/17, variously confirming the Marathi rule by terming it ‘reasonable’ or rejecting it as ‘completely illegal’ (Sequeira, 2016, 2017). Again, in 2019 the Bombay High Court ruled in favour of the Marathi language rule (Benwal, 2019), a decision that was also confirmed by the recommendations of the Khatua Report. Additionally, the state government upheld the 15-year domicile rule for the proposed, but not yet enforced, City Taxi Rules in early 2020, against the recommendation of the Khatua Committee (Sen & Jain, 2020b). 15

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Regulation attempts at the state level were given added traction again when, in March 2020, the Maharashtra government decided to limit the scope for surge pricing of platform-attached cabs (Sen & Jain, 2020a). However, also in early 2020, it decided not to enforce the City Taxi Rules until further notice.17 In a parallel but related process, the state government revised the fare structure of metered cabs; the new fare regulations came into force in March 2021 (Press Trust of India, 2021).18 Putting an end to such contestations at the state level, in November 2020 the Federal Government took the long-awaited step of harmonizing regulation of platform-based taxi services across all states. As part of the Motor Vehicles (Amendment) Act, 2019, the Government of India issued the Motor Vehicle Aggregator Guidelines, introducing inter alia limits to surge pricing and commission charges, and requiring platform firms to obtain licenses from the states (Mukul, 2020).

Interim Conclusion: The Orderly Space of Taxi Driving Vs. State Informality Retracing the disputed pathway towards regulation of mobility platforms, two interlinked processes stand out that produce the conceived space of taxi driving. On the one hand, modernization ambitions and the invocation of market rationality represent the platform firms’ and state agencies’ endeavours to make the space of taxi driving appear as an orderly, homogenous space. This is characteristic of the abstract space, as Lefebvre writes: ‘This space […] destroys the historical conditions that  After the High Court ruling of 2017, the state government declared that it would not take coercive steps against the platforms and drivers for the moment, and the final court’s decision was postponed. The issue of new Tourist permits to Ola and Uber drivers was temporarily stalled in 2019 (Sen, 2019). In 2020, the Government of Maharashtra stated again that it would not take any steps until the court took a final decision in the case of the petitions filed by Uber and several drivers in 2017 (Swati Deshpande, 2020). 18  A parallel process concerning the revision of fare structures for both metered and app-based cabs was initiated in 2016, when the state government formed the Khatua Committee with a brief to revise the fare structure of metered and app-based taxis. The Committee submitted the report in 2017; however, it was only after the new state government came into power that a decision on the recommendations given by the committee was taken in March 2020 (Sen & Jain, 2020a; Press Trust of India, 2021). 17

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gave rise to it, its own (internal) differences, and any such differences that show signs of developing, in order to impose an abstract homogeneity’ (Lefebvre, 1991: 370). The appearance of homogeneity ‘is instrumental for both capital and the modern state […]. Abstract space permits continuous, rational economic calculation in the spheres of production and exchange, as well as comprehensive, encompassing control in the realm of statecraft’ as Brenner and Elden (2009: 358) observe. On the other hand however, since government and judiciary institutions pursue a variety of parallel and sometimes contradictory objectives, the state itself is engaging elements of informality to substantiate certain narratives or to avoid conflict, producing a heterogeneous and ambiguous space. As Ananya Roy notes, the state ‘valorizes and regularizes certain forms of informality and marginalizes, even criminalizes, others. It is through this logic of informality that the state polices an arbitrary and fickle line between legality and illegality, creating a territorialized flexibility and demonstrating its political potency’ (Roy, 2018: 2243). This logic becomes apparent, to give one example, in the Maharashtra state’s 2020 order to cap surge pricing of mobility platforms, while continuing to tolerate the usage of the Tourist permit, and refraining from enforcing the City Taxi Rules. Due to unresolved legal questions and contradictory government orders, but also frequent petitions by the platform firms to challenge the regulations, drivers have been left uncertain about the status quo of regulations and face the negative consequences of the calculated regulatory void (Sharma, 2019). The attempt to leave responsibility to follow ‘proper’ formalities entirely with drivers, instead of ultimately tightening regulations for platform firms, is best illustrated by the way that the transport authorities conducted a ‘crackdown’ on drivers without proper licenses and permits in late 2019 (Mahale, 2019),19 at a time when uncertainty about the exact provisions for drivers peaked and the new Maharashtra state regulations were still awaiting a final decision in court. Similarly, drivers in Delhi and Bengaluru faced repercussions due to  It is important to note here, however, that the responsible authorities delivered a different interpretation of their intervention. Reportedly they wanted to put pressure on platform firms, but did not have the jurisdiction to take action against them. An official was cited as saying: ‘We don’t want to target drivers as the issue is much larger. However, we feel these drivers should create some pressure on the companies’ (Mahale, 2019). 19

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regulatory uncertainties and legal tussles between the state and the platform firms.20 State informality does not mean that the calls for ‘fairness’ and concerns for those who lose out in the transformation of the taxi sector are not genuine.21 The perceptions of those close to the everyday reality of taxi driving—especially drivers and, to a more limited degree, the taxi unions—continuously permeate the boundaries of the space that is conceptualized by decision makers and platform firms. The implementations of many recommendations of the Khatua Committee demonstrate such permeation of the abstract space: They not only exhibit an appreciation of the concerns of metered taxi drivers and their livelihoods, but also a recognition of the realities of taxi driving in Mumbai on the ground and the legitimacy of the metered taxi sector. Such entanglements of abstract and perceived spaces create contestations and friction that several authors have observed at the meeting point of different rationalities in the neoliberal city and economy (Cresswell, 2010; Tsing, 2011). Speaking with Lefebvre, fragmentary regulatory processes reveal that the power of ‘experts’ and the knowledge involved in creating an abstracted version of the reality are never complete. State recognition of taxi drivers’ concerns also reflects that on the everyday, small scale level of interactions between state institutions and workers in the unorganized sector there is always the possibility of a ‘helpful informality of the state’ that opens up spaces of opportunity for workers (Roy, 2018: 2244).

 This was the case when mobility platforms were banned temporarily in 2014 in Delhi. In another case, drivers without commercial driving licenses were fined by the police in Bengaluru in 2018, although the federal government had previously legalized operations of commercial vehicles with standard driving licenses (Prasad, 2018). Again in Karnataka, drivers suffered a couple of anxious days when the state government withdrew the license of a large platform firm for six months, but lifted the ban a week later (Menezes & Peermohamed, 2019). 21  Indeed, that the concerns were taken seriously is clearly perceivable in the report of the Khatua Committee, and this was also the impression of the author while interviewing Mr. Khatua personally, as well as other representatives of the responsible state authorities. 20

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The Spatial Practice of Taxi Driving ‘The spatial practice of a society secretes that society’s space; it propounds and presupposes it, in a dialectical interaction; it produces it slowly and surely as it masters and appropriates it’ (Lefebvre, 1991: 38). The material reality of a city and how it is produced and reproduced in social processes can be perceived by the senses of the human body (Schmid, 2005: 210–11). Hence the material dimension of spatial production is also called the ‘perceived space.’ This also includes the relation of material objects to one another, called by Lefebvre the ‘urban reality, the routes and networks which link up the places set aside for work, “private life” and leisure’ (Lefebvre, 1991: 38). A person’s daily routine creates a certain form of cohesion between these objects, even if they are far apart from each other. As Lefebvre argues, ‘[a] spatial practice must have a certain cohesiveness, but this does not imply that it is coherent in the sense of intellectually worked out or logically conceived’ (Lefebvre, 1991: 38). Lefebvre calls this mental component of spatial practice the ‘spatial competence and performance’ of actors (Lefebvre, 1991: 38). For the spatial practice of taxi driving, spatio-temporal competence is crucial in order to create cohesiveness, for example when organizing the working day in an urban environment, origins, destinations and routes need to be coordinated with places of living, socializing and other elements and requirements of life and synchronized with the temporal elements that shape a work day, such as rush hours and prayer times. When driving for mobility platforms, however, algorithms take over many of the everyday tasks and decisions of drivers, including how to navigate the streets. The perspectives of the state authorities and platform firms on the taxi sector sometimes differ substantially from drivers’ own perceptions of their occupation. However, I interviewed drivers who reiterated and substantiated the appeal of modernity in their everyday driving as advertised by the platform firms. These drivers highlighted the superior appearance and service characteristics of app-based mobility services. In conversations, they emphasized that their services cater to Mumbai’s business and technology-oriented elites. One driver stated: ‘I drive for Uber, because customers are corporate customers, they are busy with themselves, I just drop them. Because it is an international company, no? Corporate people

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travel all over the world. So they have only one app, Uber. They use it in America and England, and they use it in India also.’ Some of the interviewed drivers also valued the flexibility of working for mobility platforms. When targets for incentives were easy to achieve in the early years of platform operations, drivers were able to benefit from flexible and adaptable working hours and hence could accumulate considerable wealth. However, drivers reported that incentives had decreased between 2017 and 2020, and commissions increased to 30% of the ride fare (see also Sharma, 2019). These developments prompted drivers to drive longer hours, but eventually made it impossible to meet the bonus targets. Hence, most interviewed drivers dismissed the narratives of liberty and agency, and instead their perceptions resulted from their everyday driving practices and experiences of the deteriorating work conditions. They felt that they were controlled by the platform algorithms, having little leeway when trying to organize their working day according to their needs and preferences. One platform driver stated: ‘I have to work like hell, I cannot miss a single day. Mumbai is a costly city to live in, so what to do, you have to accumulate. It’s not like you are your own boss. To be your own boss, you have to work even harder.’ Talking to me in English, he described his experiences with platform work: We drivers are just running and running, like donkeys. But if you go running behind him, he will make you run even more. He will never tell you to stop. He will go on using you.

During my fieldwork, I frequently met drivers who operated on 14–17 hour shifts.22 Such drivers mentioned negative health, mental repercussions and sleep deprivation as consequences, conditions that were often plain to see. Additionally, the surveillance of drivers has been increasing steadily over the years, particularly with the implementation of fleet schemes (Kuttler, 2022). Fleets operate on minimum business guarantee schemes,  This was the situation until November 2020, when the Central Government of India set a new rule that drivers of app-based taxis could not operate on continuous shifts for more than 12 hours (Nandi, 2020). Many drivers kindly showed me their driving statistics in the app. 22

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whereby fleet managers, vehicle owners and drivers receive a fixed remuneration on completion of a certain number of rides. Within the fleets, the performance of drivers is constantly monitored and drivers who miss their targets are reprimanded. Also, additionally to algorithmic control, fleet managers, and vehicle owners can track the location and status of a driver in real time, and drivers who operate outside the platform or for a different firm face exclusion from the fleet. According to a fleet manager, stricter control measures, including human intervention, represent the platform firms’ reaction to increasing refusal of rides and other forms of driver ‘misbehaviour.’ Such digital surveillance coupled with human intervention constitutes an increasingly comprehensive regime of driver control. Platform drivers have to follow and respect many rules that the platform firms impose regarding the treatment of customers, ride cancellations, platform loyalty and the repayment of loans, contributing to the platforms’ public image as transparent and formal. However, the working conditions of the drivers are characterized by uncertainty and opacity of procedures, and constant fear of failing to comply with these rules and the threat of accounts being blocked as a consequence. The mechanism by which the algorithm assigns passengers to drivers is not transparent to drivers; furthermore, destinations of passengers are not revealed to the drivers until the passenger boards the car and the ride is started. Drivers frequently complain about unjustified fees that are imposed—e.g. for having to cancel rides of passengers who did not show up—or not being able to receive a lucrative ride with the surge pricing mechanism. Another factor is the rating system, which is supposed to ‘enable the service quality and build trust in the system through discipline and control mechanisms’ (Verma et al., 2020: 190). However, it has been found that the algorithms of the rating system disadvantage the drivers, which generates fear of exclusion and dependency (Verma et al., 2020). The fact that most of the communication between the firms and the drivers is in English adds to the confusion and frustration of drivers. Distinguishing metered and app-based taxis as two different sectors does not match the driving realities of drivers. In the course of the fieldwork it was observed that many platform drivers switch frequently between different driving jobs, from metered taxi or rickshaw driving to platform driving (and back), some engaging in platform and rickshaw

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driving at different times of the day. Furthermore, platform drivers employ all kinds of strategies to secure additional incomes, or incomes at all, for example by catering to regular customers off-platform, or acting as a street-hailed taxi. One driver who was desperate to earn a sufficient income established a particular work routine: Few taxis want to operate at Bhendi Bazaar, because it is very crowded there, but there is high demand. That’s why in the morning time, I wait there and take passengers without the app.

Such decreasing loyalties to specific forms of taxi driving make it difficult to distinguish clearly between metered and app-based taxi sectors. Also, notions of longevity and personal growth that are implied in the entrepreneurship concept are deconstructed by the immediacy of drivers’ need to generate a livelihood. With platform firms constantly changing their operational rules and models as well as remuneration schemes, drivers stated that they would drive on platforms only as long as it was viable for them.23 Drivers who had formerly been employed in formal but low-­ skilled and low-paid jobs were also found to return to the formal sector when incentives decreased and work routines became increasingly exhausting. Such flexible pragmatism was, however, different from the fatalistic pragmatism found among newly-arriving migrants from impoverished backgrounds who had often pooled family and friends’ resources to acquire a car and desperately needed to generate sufficient income to repay loans and debts. Many interviewed drivers from such backgrounds expressed dissatisfaction about their working conditions. However, despite shrinking payments and increasing work strain, they kept on with app-based taxi driving due to limited alternatives, and due to the responsibility for their families at home and the associated pressure. Given that the physical and mental strain that drivers endure is gradually increasing, and many interview partners were very vocal in criticizing their working conditions, it is significant that most platform drivers interviewed were aware neither of governments’ attempts to regulate the  For owners of cars, leaving the occupation usually requires selling the car that is often not paid off yet, and dealing with remaining loan obligations. The roles of the burgeoning second-hand car market as well as the loan system for sustaining the volatile and contested workings of mobility platforms are beyond the scope of this chapter. 23

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platforms nor of the subsequent contentions and negotiations between the state, unions, and platform firms. One reason for this unawareness is probably that the platform firms did not inform the workers about the new Maharashtra government regulations (since they ignored these regulations). With the exception of the Maharashtra Rajya Rashtriya Kamgar Sangh, a union that supported drivers and vehicle owners in a 10-day strike in the autumn of 2018, no intermediary institution or union dedicated to app-based drivers existed in Mumbai until 2019.24 Both metered and app-based drivers frequently reported that they had little trust in the government and unions helping their cause, alleging that state and union actors were involved in the platform business themselves. Until surge pricing and driving hours were capped in 2020, most aspects of driving on platforms were governed and formalized by the firms themselves and not by state regulations. Accordingly, drivers’ demands are usually addressed directly to the companies and not the state. This is different from the established meter taxi sector, where drivers employ a variety of tactics along the lines of formality and legality, but ultimately collective agitation of taxi drivers and unions is always directed towards the state. This is also visible in the process of fare negotiations: Drivers make official claims and representations to the state for a formal fare hike via their unions. When demands are not met by the state or when a fare revision is considered overdue, due to rising fuel and living costs, drivers translate their demands into practice: They bargain about the fare with passengers, refuse rides, or tamper with meters. One could argue that such tactics are, as Anjaria suggests, ‘the gradual usurpation of the legal by the ethical’ to achieve drivers’ claims, ‘worked out not in a proceduralist realm of rights but in a “material” […] realm grounded in everyday practice’ (Anjaria, 2011: 65). He concludes that ‘the particular nature of these practices signals moments of negotiation of what illegality might mean in practice’ (Anjaria, 2011: ibid). Tactics of platform-based drivers that aim at creating higher autonomy in taking decisions and scheduling the workday, such as asking passengers about their  In September 2019, the Indian Federation of App-based Transport Workers was formed in Mumbai. 24

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destinations on the phone and cancelling ‘unprofitable’ rides, can also be interpreted in such a way. Contrasting metered and app-based taxi driving practices, it can be observed that the driving occupation has changed fundamentally. Many long-time kaali peeli drivers described the prestige of the taxi occupation and the good reputation that kaali peeli drivers enjoyed when they started driving in the 1970s and 80s. ‘Passengers used to treat me with respect; they addressed me as Saheb,’25 a driver who has been driving a kaali peeli in central Mumbai for 40 years told me wistfully. Many of older kaali peeli drivers interviewed expressed a strong connection and loyalty to the driving occupation and their vehicles, since they were the sources of socio-economic uplift for their families, and were also associated with better future prospects for their children. Strong and effective driver organization coupled with a distrust towards state institutions has also strengthened their adherence to the metered taxi sector. Consequently, many of the drivers interviewed had returned to driving kaali peelis after trying out platform-mediated driving. Such enthusiasm was completely absent among the app-based taxi drivers who were interviewed. Even those initially attracted by the progressive appeal of the platforms were completely disenchanted. Platform drivers perceived their occupation solely as a way to earn a livelihood, without attaching further sentiments to it. Furthermore, many drivers perceived the occupation as temporary. On the one hand, educated members of the middle class have been found to engage in driving as a part-time occupation in addition to their regular jobs. On the other, many migrant drivers also understood taxi driving and their absence from home as a temporary phase, and tried to earn as much as possible from platform driving to start their own enterprises at their home locations. Impediments to the organization and collectivization of platform drivers, as well as individualization of their everyday work routines, have contributed further to the eroding appreciation of taxi driving as a dedicated occupation. Platform firms, on their side, have implemented measures such as reward programs to strengthen drivers’ loyalty to their platforms. However, they have not adopted measures to support drivers’ adherence 25

 ‘Sir’ or ‘Master’ in Hindi and Urdu

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to the driving occupation. Business analysts and platform representatives see high attrition rates of drivers as a sign of a consolidating and maturing market and not a problem of worker exploitation. Furthermore, high fluctuation among drivers is understood to benefit the platform firms, since new drivers enter platform-based driving with fewer expectations than the previous cohort of aspiring drivers (Shankar, 2017; BI India Bureau, 2017). Platform firms’ global ambition is to eventually operate autonomous vehicles in order to become profitable, an ambition that is facing opposition in India due to the large workforce that is employed in the sector (Shetty, 2020; Press Trust of India, 2019). One may argue that due to this opposition platform firms are indifferent towards drivers, imagining them rather as machines or robots than human beings.

The Lived Space of Taxi Driving The lived space, also called representational space, is the sphere of emotions, symbolism, and imagination. Lefebvre notes: ‘Representational space is alive: it speaks. It has an affective kernel or centre: Ego, bed, bedroom, dwelling, house; or: square, church, graveyard. It embraces the loci of passion, of action and of lived situations.’ (Lefebvre, 1991: 42). Imagination seeks to appropriate and change the physical reality, thus the lived space ‘overlays physical space, making symbolic use of its objects’ (Lefebvre, 1991: 39). The lived space is also the dominated space—dominated by spatial abstractions that interfere with lived realities. The notions of dominated and lived space can well be applied to both metered and app-based taxi driving. State regulations, algorithms, and platform rules are permeating the space of taxi driving as an economic practice. Surveillance measures control the whereabouts and the offline-­ online status of app-based drivers. However, the surveillance of interactions inside and in the surrounding of the car is limited, enabling moments of sociality among drivers and between drivers and passengers; but they also give rise to mutual insecurities among drivers and passengers as described above, and different types of misconduct from both passenger and driver side. Hence, the car interior, in particular, exposes the contradictory nature of the lived space that entails both realms of

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alienation and liberation, and embodies the ambiguity of experience and suffering (Bertuzzo, 2012; Kipfer et al., 2008). As argued in the introduction, the practice of taxi driving for a livelihood cannot be separated from daily life activities such as sociality, but also political and religious participation. Unlike their colleagues operating platform-attached vehicles, for kaali peeli drivers in Mumbai the taxi stands are at the core of their everyday lived space. Taxi drivers mingle with rickshaw drivers, street vendors, residents, and even retired drivers who continue to enjoy the company of their former colleagues. In interviews, drivers repeatedly emphasized that the immediacy of social interaction is an advantage they value and do not want to miss, and that this immediacy could not be possible in the same way in platform-based operations. Interaction at taxi stands is characterized by ‘sociality of proximity,’ as Harrison (2008) argues, a non-intentional sociality that nevertheless can carry the possibility of intentional relations and acts. Although not intended to generate immediate gains or benefits, nevertheless ‘through […] repeated everyday interactions drivers preserve and create channels through which they attempt to enable their physical, social, and economic mobility,’ as Sopranzetti (2013: 79) observed among bike taxi drivers in Bangkok. Hence, the taxi stand is not only an important site for sharing information among colleagues, but also for political and community organization and group identity formation. The lived spaces of taxi stands are often represented in their material configurations, expressing both their importance for social gathering and the symbolic function of taxi stands. Many taxi stands across Mumbai city and the metropolitan region are equipped with benches and covered waiting areas for passengers and drivers. Signs of political affiliation and shared identity are also commonly found. While everyday conversation seems casual and sporadic, relationships among drivers and between drivers and the locality they serve are characterized by commitment. Often, the composition of drivers at taxi stands has seen little change over the years, hence the drivers practice a level of familiarity and mutual trust that is unique to a particular taxi stand. Drivers who operate from a taxi stand are often organized in an association. It was observed that the rules of queuing and passenger assignment differ between taxi stands. The members

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collectively decide about such rules in order to create fairness among drivers, enabling each driver to generate a minimum daily income. Such social qualities and the shared history of a taxi stand play a significant role for the drivers and members of taxi associations. Taxi associations assume active roles in the neighbourhood, and commonly take part in religious festivities in the area, thereby cutting across community boundaries. The president of an association operating at a taxi stand in eastern Mumbai drew attention to the cross-community character of such engagement: ‘We engage in Ganpati Festival also, we help everyone in their festivals, there is no restriction based on religion here in this place.’ While the taxi stand is the base for the operations of metered taxis, app-based taxis do not have regular spots where they take breaks and socialize. In 2019 and 2020, when I conducted my fieldwork, I observed that new passengers were already assigned to drivers before the current ride was completed. Hence, drivers needed to go offline to take a break. Once or twice a day, drivers had to refill CNG at one of the sparsely-­ distributed gas stations, forcing drivers to wait in line for 30 or 45 min regularly. Because of the need to move forward in line continuously, these breaks did not allow them to interact with other drivers or take lunch or bathroom breaks. Instead, drivers have shifted their social activities and coordination with fellow drivers to telecommunications and messenger groups. Such groups also serve as sources of information, for example about traffic conditions, and act as parallel platforms for the procurement of driving jobs that arise via insider networks outside the realm of the formal platforms.26 In the dense inner-city areas of Mumbai, especially around train stations and on main roads, parking and halting is often a contentious issue for platform drivers. When taking a break on the roadside, many drivers reported that they were reprimanded by traffic police and alleged that they were specifically targeted due to their affiliation with platforms. Drivers who live in outer areas of the Mumbai Metropolitan Region and  In one such messenger group a driver showed to me, guests of a hotel in a popular weekend destination in the vicinity of Mumbai—where platform operations had been banned in 2017— were connected to platform drivers from Mumbai by a hotel employee for full day-round trips. 26

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sometimes spend the night in their cars in Mumbai also reported similar incidents. Such contestations can have further influence on drivers’ schedules. For example, on the roadside in the area around one gas station in the Bandra-Kurla-Complex, car cleaners are only tolerated by the traffic police if they operate at night or in the early morning hours. Hence, drivers who want to get their cars cleaned have to park their vehicles late at night. Although regular spots for platform drivers to interact are not available, unlike experiences with mobility platforms elsewhere in the world (Wells et al., 2020), driving on mobility platforms is not a socially isolated practice in Mumbai. While platforms provide little scope for drivers to deviate from algorithmically-managed driving, it is in the lived spaces that drivers practice non-conformity and opposition to platform domination. While the taxi stand is the place for sociality, identity formation and political opposition in metered taxi driving, the moving vehicle is assuming importance as a lived space in platform-based taxi driving. Equipped with one or several phones, drivers practically establish permanent communication lines with their driver colleagues or friends and families at home. While handling passengers and manoeuvring through dense traffic, often drivers simultaneously run video chats (or entertainment programs) on one phone, and process their bookings in the application on the other phone. Such simultaneity is not only an expression of the entanglement of different mobilities and processes of spatial production, but also an embodiment of the ‘mobilization’ of place-making and sociality. These converging mobilities whereby far-away people and places are regularly integrated into everyday driving create a sense of cohesion and familiarity for drivers. Such ‘dwelling-in-motion’ (Sheller & Urry, 2006) or ‘mobile homeliness’ (Jirón Martínez, 2012) needs more attention when trying to understand the invisible, ‘informal’ spaces and practices of the platform economy. Since it is characteristic of the lived spaces that they are uncoded, non-verbal, and difficult to describe without simultaneously creating representations of space (Lefebvre, 1991: 39), place-making in taxi driving can assume very subtle and nuanced forms that remain hidden to the researcher’s gaze. However, interactions between drivers and passengers inside the car remain an ongoing source of tension. Reacting to women’s accusations

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towards drivers who have violated their personal space, including sexual assault and other physical harm, state authorities have imposed stricter safety measures on platform companies, such as the installation of panic buttons. Eventually, platform companies had to deviate from their earlier standpoint that they cannot be held accountable for misbehaviour of drivers, and have meanwhile strengthened their customer complaint mechanisms, established training programs for drivers and imposed codes of driver conduct (Saha & Dixit, 2020). One may speculate about whether platform companies will bank on incidents of ‘driver misbehaviour’ in order to introduce further driver surveillance mechanisms such as cameras and artificial intelligence, similar to the plans of Amazon for delivery vehicles in the US (McFarland, 2021).

Conclusion In the preceding paragraphs, I have traced different conceptualizations of the space of taxi driving along the lines of formality and informality, drawing on Lefebvre’s triad of perceived space, spatial representations, and representational space. I have argued that the state and the platform firms envisage the taxi sector as a homogenous space characterized by ‘fair competition’ and with the potential to act as a motor of economic growth and job creation. However, despite promoting the free market and deregulation, federal and state governments have actually intervened selectively into the market of mobility platforms in order to pursue further objectives, such as the Maharashtra state’s nativist agenda and the need to address the safety concerns of urban middle classes. More recently, the concerns and demands of both metered and app-based taxi drivers have been represented more strongly in the governance of the taxi sector. Hence, due to different objectives and rationalities, state and federal regulations and jurisdictions have often been contradictory, leading to uncertainty among the drivers. Regulation of mobility platforms in India cannot be perceived as a fixed status quo, but as an ongoing process of constant contestation and negotiation. I then contrasted conceptualizations of the taxi sector by the state and platform firms with the spatial practice of taxi driving and the lived

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reality of drivers. I observed that the appeal and sense of agency that was attached to the entrepreneurial ‘driver partner’ has almost completely vanished among drivers. Instead, shrinking remuneration, long works hours, ‘robotic driving,’ an all-embracing regime of surveillance and opaque algorithmic proceedings came to dominate everyday driving practice in the period before the pandemic. The everyday reality of platform-attached driving contradicts images of mobility platforms as fostering driver empowerment and entrepreneurship-­based economic growth. Concluding from the above observations, I argued that such a mismatch is not surprising, since platform firms engage narratives of entrepreneurialism emphatically in their public communication, in order to deploy a much more powerful spatial abstraction more silently: that of the digital space. These two abstractions reveal the double-sided face of the platforms: in contrast to the individualistic, empowering appeal of entrepreneurship that mobility platforms nourish, in the digital space drivers and their activities are reduced to pieces of data and information. The driver’s movement, the streets, buildings and customers become computer-readable and part of a virtual image, ‘designed, coded and executed as a technical scientific “representation”’ (Newlands, 2020: 9). These data points are inserted into platform algorithms for efficient and profitable operations. Platform firms work to deploy both abstractions of the ‘space of driving.’ On the one hand, they continuously generate a predictable, transparent and ‘formal’ front appearance, tailored to the needs of their target groups. This appearance actively engages and aligns with the federal government’s projects and narratives, such as those relating to job creation (Mishra & Bathini, 2020) or for collaboration in prestigious and innovative projects such as the Nagpur e-vehicle trial (Shah, 2018). Meanwhile platform firms deploy the abstraction of digital space, an assemblage of algorithmic and human interventions that is highly unpredictable and volatile to drivers, putting them under constant stress and pressure. But such platform rationality, with its totalizing representations of the drivers, movements, and the urban reality, are susceptible to failures and frequent errors (Newlands, 2020; Leszczynski, 2020). Indeed, in Mumbai, platform drivers’ portrayals of platform operations reveal how the cartographic representation in the driver’s app and the accuracy of the GPS do

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not match the urban reality drivers encounter, frequently leading to disputes with customers and the platforms’ helplines. Technological glitches have also been exploited by drivers to make additional gains, such as in the case of the ‘Ola fraud’ (Thaver, 2020). Mobility platforms constantly update their systems, algorithms, and driver communication strategies to eliminate glitches, increase driver control and ‘optimize’ performance of drivers (Munn, 2017; Shapiro, 2018; Newlands, 2020). Changes of algorithms come without prior announcement to the drivers, and the functioning of algorithms is not revealed, since the mechanisms are understood as trade secrets (Shapiro, 2018). In addition to constantly changing algorithms, the platforms have also adjusted their operation models to respond to ‘driver misbehaviour,’ such as cancelling rides or conducting rides off-platform. In the case of the fleet model in Mumbai, platform firms have complemented algorithmic surveillance with human intervention and place-based measures, as outlined above (Kuttler, 2022). This example shows that platform firms, instead of further reducing the complexity of their digital abstractions of the reality, actively seek to integrate aspects of the perceived and lived space of drivers into platform mechanisms to make their processes more robust. Arguably, such a shift to complex abstractions of urban space illustrate platform firms’ attempts to avoid blindness towards the urban ‘messiness’ that platform work is located in. Such blindness can be the result of excessive reliance on automated and algorithmic control—what has been called ‘algocracy’ by several authors (Danaher, 2016; Evans & Kitchin, 2018). A fundamental part of this elaborated assemblage of algorithmic and human intervention is an information deficit among the drivers about the different elements of their work—for example about the customer’s destination—while fleet managers and dispatchers always have real-time information about the driver’s status. Such asymmetries have been identified as key features of the power relations between the firms and their drivers (Rosenblat & Stark, 2016). Shapiro argues that these asymmetries are calculative: They are a mechanism to establish platform firms’ market positions by creating a specific ‘platform rationality,’ which works against the often-invoked ‘market rationality’ (Shapiro, 2020). In the current platform regime, exchange on digital mobility platforms is predominantly structured by the rules and norms that the platform firms created

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themselves for the purpose of generating profit. Shapiro (2020), referring to Callon and Muniesa, observes that extreme asymmetries emerge when single dominant market actors try to control ‘the design and negotiation of architectures that organise market encounters’ (Callon & Muniesa, 2005: 1243). This kind of informal platform regime is notably different to the deployment of informality by the state, which is characterized by different, often non-aligning rationalities. Acknowledgments  The author would like to thank his research participants and field assistants in Mumbai. He is also grateful to the volume’s editors, Aditi Surie, and Ursula Huws, for their valuable comments and feedback on earlier drafts, which helped to improve the chapter.

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Sen, S, & Jain, B. (2020b). Maharashtra government gives Ola, Uber immunity in ‘complaints against one-two drivers’. Accessed October 04, 2021, from https://timesofindia.indiatimes.com/city/mumbai/maharashtragovernment-­g ives-­o la-­u ber-­i mmunity-­i n-­c omplaints-­a gainst-­o ne-­t wo-­ drivers/articleshow/74570245.cms?utm_source=contentofinterest&utm_ medium=text&utm_campaign=cppst Sequeira, R. (2016). High Court: It’s Not Unreasonable to Ask Automen to Know Marathi. Accessed October 04, 2021, from https://timesofindia.indiatimes. com/city/mumbai/hc-­i ts-­n ot-­u nreasonable-­t o-­a sk-­a utomen-­t o-­k now-­ marathi/rticleshow/55485692.cms?utm_source=contentofinterest&utm_ medium=text&utm_campaign=cppst Sequeira, R. (2017). State's Marathi norm for auto permits totally illegal: Bombay High Court. Accessed October 04, 2021 from https://timesofindia.indiatimes.com/city/mumbai/states-­m arathi-­n orm-­f or-­a uto-­p ermits-­t otally-­ illegal-­bombay-­high-­court/articleshow/57421115.cms Shaban, A. (2018). Lives of Muslims in India: Politics, exclusion and violence. London: Taylor & Francis. Shah, A. (2018). Ola's sputtering India electric vehicle trial a red flag for Modi plan. Accessed July 18, 2021 from https://www.reuters.com/article/ us-­india-­autos-­electric-­insight-­idUSKCN1GL0CL Shankar, S. (2017). Undeterred by high attrition rate, Ola and Uber banking on drivers in their 20s. Accessed October 06, 2021, from https://economictimes. indiatimes.com/small-­biz/startups/undettered-­by-­high-­attrition-­rate-­ola-­ and-­u ber-­b anking-­o n-­d rivers-­i n-­t heir-­2 0s/articleshow/58935423. cms?from=mdr Shapiro, A. (2018). Between autonomy and control: Strategies of arbitrage in the “on-demand” economy. New Media & Society, 20(8), 2954–2971. https:// doi.org/10.1177/1461444817738236 Shapiro, A. (2020). Dynamic exploits: Calculative asymmetries in the on-­ demand economy. New Technology, Work and Employment, 35(2), 162–177. https://doi.org/10.1111/ntwe.12160 Sharma, C. (2019). Aggregators, driver-partners and the state. Economic & Political Weekly, 54(46), 55–61. Sheller, M., & Urry, J. (2006). The new mobilities paradigm. Environment and Planning A: Economy and Space, 38(2), 207–226. https://doi. org/10.1068/a37268

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Shetty, S. (2020). Uber’s self-driving cars are a key to its path to profitability. Accessed October 06, 2021, from https://www.cnbc.com/2020/01/28/ ubers-­self-­driving-­cars-­are-­a-­key-­to-­its-­path-­to-­profitability.html Shirgaokar, M. (2014). Employment centers and travel behavior: Exploring the work commute of mumbai’s rapidly motorizing middle class. Journal of Transport Geography, 41, 249–258. https://doi.org/10.1016/j.jtrangeo. 2014.10.003 Singh, V. A. (2012). Rickshaw owner with cancer takes on 15-year domicile rule for drivers. Accessed October 04, 2021 from https://mumbaimirror.indiatimes. com/mumbai/other/rickshaw-­o wner-­w ith-­c ancer-­t akes-­o n-­1 5-­y ear-­ domicile-­rule-­for-­drivers/articleshow/17863140.cms Sood, A. (2012). A future for informal services? The cycle rickshaw sector as case study. Economic and Political Weekly, 95–102. Sopranzetti, C. (2013). The owners of the map motorcycle taxi drivers, mobility, and politics in Bangkok. Cambridge: Harvard University. Sopranzetti, C. (2014). Owners of the map: Mobility and mobilization among motorcycle taxi drivers in Bangkok. City & Society, 26(1), 120–143. https:// doi.org/10.1111/ciso.12030 Sopranzetti, C. (2021). Shifting informalities: Motorcycle taxis, ride-hailing apps, and urban mobility in Bangkok. Geoforum. https://doi.org/10.1016/j. geoforum.2021.04.007 Spinney, J. (2015). Close encounters? Mobile methods, (post)phenomenology and affect. Cultural Geographies, 22(2), 231–246. https://doi.org/10.1177/ 1474474014558988 Subramaniam, K. (2014). Uber rape case: Liability under a cloud. Accessed October 04, 2021, from https://www.thehindu.com/news/national/uber-­ rape-­case-­liability-­under-­a-­cloud/article6674287.ece Surie, A., & Koduganti, J. (2016). The emerging nature of work in platform economy companies in Bengaluru, India: The case of Uber and Ola cab drivers. E-Journal of International and Comparative Labour Studies, 5(3). Tare, K. (2008). Migrants are defaming city: Uddhav. Accessed October 04, 2021 from https://www.dnaindia.com/mumbai/report-­migrants-­are-­defaming-­city-­ uddhav-­1143275 Thaver, M. (2020). Explained: How taxi drivers in Mumbai manipulated the ola app to dupe customers. Accessed on March 31, 2021 from https://indianexpress.com/article/explained/how-­cab-­drivers-­in-­mumbai-­manipulated-­the-­ ola-­app-­to-­cheat-­customers-­7064946/

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Tiwari, K. K. (2015). Why swabhiman taxi union is opposing mobile app-based private cab services. Accessed July 10, 2021, from https://www.businesstoday. in/latest/corporate/story/mobile-­app-­based-­private-­taxi-­aggregators-­why-­ they-­are-­being-­opposed-­54318-­2015-­07-­10 Tsing, A. L. (2011). Friction: An ethnography of global connection. Princeton: Princeton University Press. Turner, S., & Hạnh, N. T. (2019). Contesting socialist state visions for modern mobilities: Informal motorbike taxi drivers’ struggles and strategies on Hanoi’s streets, Vietnam. International Development Planning Review, 41(1), 43–61. https://doi.org/10.3828/idpr.2018.10 Vannini, P., & Scott, N. (2020). Mobile ethnographies of the City. In O. B. Jensen, C. Lassen, V. Kaufmann, M. Freudendal-Pedersen, & I. S. G. Lange (Eds.), Handbook of Urban Mobilities (pp. 59–67). London: Routledge. Verma, R. K., Vigneswara Ilavarasan, P., & Kar, A. K. (2020). Inequalities in ride-hailing platforms. In A. Athique & V. Parthasarathi (Eds.), Platform capitalism in India (pp. 177–198). New Delhi: Springer. Vila-Henninger, L. (2019). The moral economy of neoliberalism: How voters use neoliberal ideology to (de) legitimate undocumented worker access to labor markets. Sociological Inquiry, 89(2), 239–262. https://doi.org/10.1111/ soin.12260 Watson, V. (2014). African urban fantasies: Dreams or nightmares? Environment and Urbanization, 26(1), 215–231. https://doi.org/10.1177/ 0956247813513705 Wells, K. J., Attoh, K., & Cullen, D. (2020). “Just-in-place” labor: Driver organizing in the uber workplace. Environment and Planning A: Economy and Space, 0308518X20949266. https://doi.org/10.1177/0308518X20949266

5 Uberization: The Periphery as the Future of Work? Ludmila Costhek Abílio

Introduction This article addresses the uberization of work, its definition, and how it can be understood as a new type of control, management, and organization of work. This trend is presented as being global, currently crossing the world of work as a whole, although with some particularities according to the social contexts where it develops. In general terms, uberization includes three core elements that will be presented below and are interrelated: processes of informalization of elements that govern labour processes; the transformation of workers into just-in-time workers; and the centralization of labour control through oligopolies. This article argues mainly that uberization refers to a broad process, which involves other processes and labour relations that go beyond digital platforms. Therefore, we argue that digital platforms give visibility to processes that have been articulated globally for decades in the world of work. Therefore,

L. C. Abílio (*) Universidade de São Paulo (USP), São Paulo, Brazil e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. Surie, U. Huws (eds.), Platformization and Informality, Dynamics of Virtual Work, https://doi.org/10.1007/978-3-031-11462-5_5

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platforms not only promote uberization, but also have a central role in materializing—and making more recognizable—the current processes of transformation of the forms of subordination and control of work, but uberization is not restricted to them, nor does it start with them. Thus, uberization is currently present and will be present in the short term in a wide range of occupations, in addition to the online platforms most discussed in the current wave of work platformization. This includes new work regulations, new modes of subjectification, and new forms of oligopolies. These elements converge towards an increasingly rationalized use of the labour force, as well as to the unaccountability of the state and capital for the survival and livelihood of workers, resulting in a transfer of risks and costs to them, which makes them fully responsible for managing their own livelihood. In a nutshell, the condition of just-in-time workers refers to the reduction of workers to pure labour force, which should be available and used according to the determinations of the companies, whereby the management of workers’ survival as human beings is entirely transferred to workers themselves. We may thus be experiencing a new stage in the conflict between capital and labour, one which is successful in reducing and using—in a rationalized, organized, and centralized way—workers as pure labour force. As will be discussed, informalization processes are one of the core elements of uberization (Abílio, 2020a, 2020b). They involve both the conditions of multitudes of workers—who are turned into informal workers—and the loss of fixed, regulated, and stable forms of many determinations of the labour process. The definition of informalization processes used in this chapter addresses the lack of distinction between what is and what is not working time, the nature of the labour costs and who bears them, and what is and what is not considered a workplace, in addition to unclear social determinations of what is and what is not work. The platformization of different types of work catalyzes this loss of stable and recognizable forms of work more evidently, while further deepening informalization processes, especially with regard to the rules that define compensation, work distribution, and the definition of working time. Therefore, we see multitudes of workers subsumed to obscure and ever-­ changing determinations related to the value of their working hours, the forms of work distribution, and the working time that is socially necessary to ensure their social reproduction.

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The challenges posed by uberization become more complex when analyzed from the perspective of the countries of the South. The Brazilian reality, which is the focus of this article, has historically been marked by abyssal inequalities, high and persistent rates of informal work, precarious work, and low pay. Workers have long lived as just-in-time workers, their participation in contractual standard relations is unstable and temporary. They combine different activities—many of which are not even recognized as work—to guarantee their livelihoods. Informality is the norm and its expansion is not, in fact, something new. What appears to be new is the way in which peripheral ways of life are centrally appropriated, administered and managed by new means under the umbrella of uberization. Looking at these peripheral ways of life, this article discusses the transformations in the work of delivery couriers, who are currently facing a profound process of uberization. Understanding uberization as a new type of work management and control, it is argued: first, that uberization is a global trend, which involves processes of informalization of work that permeate the world of work globally and the transformation of workers into on-demand workers; second, that peripheral ways of life are being subsumed and managed in new ways, which are catalyzed by the algorithmic management of work that goes hand in hand with processes of oligopolization of economic activities; and third, that elements typically associated with the periphery are becoming globally generalized in labour relations, resulting in definitions such as the ‘gig economy’ and even the ‘platformization’ of work.

 efinition of Uberization: Informalization D Processes, Subordinated Self-Management, and Just-in-Time Workers Today we face from different perspectives the conundrum of how multitudes of informal workers can be centrally controlled by some companies. What kind of informality is it that guarantees the rationalized and efficient use of a workforce that is not hired or formally selected, which

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has no defined working hours, or even formal prescriptions about the use of work instruments and modes of execution? Informal work then gains new meanings and definitions. As an exception, a synonym for poverty, margin, or element to be overcome by the universalization of labour laws and regulations, today informal work undoubtedly presents itself as the norm and even a probable horizon of labour relations. Thus, the organization of work in formal relations seems to come closer and closer to structuring elements of informal work. Subordinated work through digital platforms has been gaining visibility worldwide. The hypothesis presented here is that labour studies scholars turn their attention to online platforms because they give concreteness and visibility to the informalization processes (Abílio, 2020a, 2020b) that today permeate and pressure labour relations. These informalization processes are not easily defined; the loss of stable and fixable forms refers to several aspects. These all relate to what David Harvey defined decades ago as ‘organization in dispersion,’ which he argued was one of the core elements of flexible accumulation (Harvey, 1989). Informalization processes include new ways of organizing working hours, which are far from punch clocks and harder to be recognized and mapped. They also include the growing difficulty in determining labour costs and who bears them. They further entail a contemporary confusion as to what is, and is not, working time, as well as what is, or is not, paid and unpaid work. Finally, a consideration of informalization leads us to discussions and uncertainties about the role and participation of labour exploitation in contemporary forms of capitalist accumulation. Amid these indistinctions and obscurities, we can, however, recognize a successful means of transferring risks and costs to workers, as well as a core element of current modes of labour exploitation and domination— the transfer of part of the work management to workers themselves, defined here as subordinated self-management (Abílio, 2019). Subordinated self-management is deeply related to the flexibilization of work and to Toyotism. Toyotism can be understood in a broad sense as more than simply an industrial production system (Antunes, 2018). Here, it expresses a new trend in controlling work, which has been marked from the 1970s onwards. This perspective takes us to some extent to the way in which Gramsci defines Fordism (Gramsci, 1971), or David

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Harvey’s concept of flexible accumulation (Harvey, 1990). In other words, the ways in which captive work is organized and controlled synthesizes ways of life, values, forms of regulation, notions of justice and dignity and the cultural dimensions that underlie the accumulation and exploitation of work at a given time. Toyotism is used here as a synthesis of different elements and processes, just as we should understand uberization today. Toyotism involves the new productive arrangements made possible by information technologies. It includes the liberalization of financial and investment flows, which in these decades have brought enormous mobility to capital and new forms of work organization at a global level. Toyotism is therefore also related to the outsourcing of work. It has to do with the possibility of an enormous global dispersion of work, where the recognizable forms of who works for whom and how become less clear. So we see corporations that outsource all their production and distribution, articulating work globally in a manner which is analogous to slavery; sweatshops; creative and highly skilled workers working from home instead of in offices, Toyotist factories, retail, and distribution corporations; among many other elements that are difficult to fully map and that today make up global production chains. Just-in-time production, a central element of Toyotism, appears to be unfolding into just-in-time work in the context of uberization. New forms of control and organization of production, distribution, and commercialization enable production to be guided by demand, which eliminates risks and costs of production and commercialization. These arrangements have provided technical-political means that involve the deployment of the labour force according to demand. The broad definition of Toyotism involves mainly a shift in disciplinary models of work. From the Taylorist figure of Charles Chaplin, whose body is watched and controlled at all times like a rebellious body-mind that can escape at any moment, we move to workers who are entirely subordinated and controlled, but who dedicate their commitment, knowledge, and participation to increasing productivity (Paulani, 2001). These are disciplinary models that incorporate, in a controlled manner, the active participation of workers in production processes, who then manage their own working time—in a subordinated manner; who bear

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the instruments of their own work, managing them; who actively contribute to the increasing of the productivity of their own work. The most visible side of this exercise of subordinated self-management—which has expanded during the pandemic—is working from home. Stable and regulated definitions of workplace, labour costs, and working time are eliminated. Workers work for goals, deadlines, products. Workers bear the costs, some of which are impossible to account for or specify precisely. Workers become managers of their own working hours and work execution. However, they remain subordinate. Therefore, the definition of subordinated self-management expresses the internalization of control by workers, not as their autonomy or subversion, but as an exercise of their own subordination. This element today lies at the heart of contemporary forms of domination and labour exploitation. The notion of subordinated self-management demands a critical confrontation with the forging of a neoliberal subjectivity (Laval & Dardor, 2016). This is less about focusing on the ‘entrepreneurship of the self ’; more on examining contemporary forms of managing and controlling work. It includes, therefore, an attempt to escape, to some extent, from discursive production—which involves public policies, labour regulation and theoretical productions—around workers who now supposedly see themselves and act as ‘entrepreneurs of their selves,’ driven by a competitive rationality in different areas of their lives. The notions of self-­ employment and microentrepreneur today obscure some elements that structure the world of work and may slide into complicated approaches based on the relation between the production of academic knowledge and the experience of workers. These approaches can attribute to workers a false consciousness of their own condition, dangerously shifting the understanding of self-management to that of self-deception. This leads us to a second challenge, which starts in the periphery and moves to the centre. Subordinated self-management structures the world of work in the periphery, and is present in a flexible labour market, which consists of strongly polarized inequalities and the non-generalization of standard employment. In this sense, when nothing or very little is guaranteed, self-management should be considered, not as a characteristic of backwardness related to underdevelopment or even as a marginal element of capitalist development, but as a core element of social

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reproduction and capitalist accumulation in the periphery. However, today we see centralized, rationalized, and monopolized ways of subordinated appropriation of this life management. The responsibility for social reproduction is now transferred to workers themselves, who are then represented as entrepreneurs of their own lives. The subordination and use of workers’ labour are maintained, but state and capital withdraw when it comes to guaranteeing their social reproduction. This reconfiguration has to be understood alongside the processes of centralization of capital and financialization, which go hand in hand with the abyssal inequalities that socially organize us today. The development of the just-in-time worker represents the dismantling of the historically conquered limits on the use of labour power, which, as we well know, have gone hand in hand with the constitution of protections and labour rights. The duration of the working day, its intensity, and the ergonomics of the workplace represent, at the same time, the results of past disputes about the efficient and rationalized use of labour power and also about the minimum parameters that set definitions of dignity and humanity. These conflicting determinations refer to ever-­ changing social definitions of leisure, social rights, ageing, family, housing, food, among many other elements. The consolidation of the just-in-time worker means that they are likely to be socially recognized as pure labour power, becoming entirely and solely responsible for managing their own survival. If we look at app delivery workers, we see what is at stake: being available, but not having any guarantees about how much their working day is worth; bearing themselves the unproductive or idle time of the working day which now are transformed into unpaid working time; being available for a 12-hour working day, six or seven days a week—and this does not even mean a compensation corresponding to a minimum wage (Aliança Bike, 2019). This just-in-time work also involves the informalization of the means of work management. In Brazil, we see hundreds of thousands of workers—such as app delivery workers—being subordinated to a few companies. They must be available for work, but have no guarantees regarding work distribution, pay, and compensation. They bear the risks and costs, set daily goals that guarantee their livelihood; however, they depend entirely on the determination of companies on the distribution of work,

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which is based on obscure and changing rules and does not have stable or fixed forms (Abílio, 2017a, 2020a). The volatility and fierce omnipresence of these rules lie at the heart of the life of uberized workers, who are thus obliged to develop daily strategies based on determinations that cannot be unveiled or fixed (Rosenblat, 2018; Rosenblat & Stark, 2016); the only clarity is that nothing is guaranteed. From this perspective, workers’ everyday strategies, knowledge, and skills—rendered invisible repeatedly—make up an unstable and insecure way of living that involves a permanent engagement of the self in order to guarantee their own social reproduction. Today, just-in-time workers lie at the heart of contemporary forms of work management. When we look at digital platforms and algorithmic management, we see that these very strategies, knowledge, and skills have been turned into data (Couldry & Mejias, 2019; Zuboff, 2019) and incorporated as rationalized—and automatable—elements of management. Algorithmic management (Rosenblat, 2018; Rosenblat & Stark, 2016; Abílio, 2020b) materializes the possibility of controlling, in a dispersed and centralized way, hundreds of thousands of workers as just-in-time workers, who are mapped and managed individually and collectively. Criteria that are humanly defined—but hardly recognizable and locatable—are creating the rules of the game through technical means which today offer the possibility of combining a huge range of variables, a combination entirely aimed at the most rational and least costly use of labour power. Inserted into a relationship that we can consider despotic—in this case, a type of algorithmic despotism (Abílio, 2020a, 2020b)—subordinated self-managers, such as app delivery workers, have to plan their daily strategies, even though they are not aware of the ranking and evaluation criteria, reasons for getting suspended or disconnected or variations in the pay of their working hours, with bonuses that are never really guaranteed. The companies, on the other hand, present themselves as technology companies, supposedly responsible for providing a merely technical and neutral means for best matching supply and demand. In fact, they do not just subordinate workers in a despotic manner but also other companies, as food delivery app companies have been doing with restaurants and other businesses (Madureira, 2020).

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This despotic relationship is nevertheless informal; it is not easy to categorize, because the rules by which it operates are obscure and ever-­ changing. It is indeed precisely this lack of form that maintains it as a despotic relationship. The rules of the game do not have stable or fixed forms but resemble a ‘white block’ [bloqueio branco] (see Baptistella, 2021): it is not through contracts or agreements of any kind but from their own daily experiences that delivery workers come to understand that refusing a delivery will be penalized by the company by a kind of informal suspension and that they will not receive new delivery requests afterwards, for a period (that is also unclear) that will directly affect their pay and the number of their working hours. Informalization also operates in the apparent contradiction that despotism is exercised in an intimate relationship with what appears to be workers’ autonomy—commonly presented as ‘entrepreneurship of their own lives,’ encapsulated in Uber’s slogan: ‘be your own boss.’ In the companies’ marketing, the dream of worker autonomy seems to have been finally achieved: work wherever you want, whenever you want, however you want. In reality, the less pre-established/agreed the definitions of working hours, pay, and work intensity are, the more powerful they are for enabling the use of workers as pure labour power, on demand. Far from being an exercise of freedom, this apparent autonomy is achieved based on the worker’s own decisions, which, however, are subject to rules which they do not have any power to determine individually. This is subordinated self-management, which is entirely aimed at guaranteeing their own social reproduction. Taking uberization as a ‘new form of work management, control, and organisation’ (Abílio, 2017a) involves the understanding that informality was never an exception, but has now been established as the norm and a horizon for labour relations; this means thinking about how the employment category is being reconfigured, and recognizing that uberization is not associated with formal employment, but permeates and modifies it. In a broader sense, this means that the subsumption of labour in the contemporary world involves new types of centralized control that go hand in hand with the dispersion and loss of stable forms of work, elements that increase the transfer of risks and costs to workers, while simultaneously using them as an available, disposable, and usable source of

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labour power according to the determinations of companies. The regulatory and minimally protective mediations of traditional work are eliminated. A study of drivers and app delivery workers therefore exposes the core elements that shape the future outlook for work.

 he Delivery Courier: From Outsourced T to Platform Worker In Brazil, delivery workers who ride motorcycles are called ‘motoboys.’ Those who deliver by bicycle, on the other hand, are called ‘bike boys,’ so this term is used to refer to them in this chapter. Delivery workers were already common and have had a strong presence in large Brazilian cities, such as São Paulo, for decades, long before platform companies tapped into this sector. Studying the life trajectories of delivery workers therefore allows us to closely follow the changes in this profession, which also illustrate broader changes in the world of work. Signing up to work instead of being hired, the forms of selection that are made informal by a dispersed competition with no fixed rules and the development of GPS and automated instruments that replace and control workers’ knowledge of the city are some elements of these changes. Looking at the trajectories of delivery workers also allows us to better understand how platform labour arrived in a historically flexible world of work, composed of formal and informal occupations, side hustles, and a combination of different jobs. Nevertheless, it also enables us to see how platform labour makes an appropriation of workers’ quest to establish themselves as self-employed workers, trying to escape the humiliations, injustices, and oppressions that are also present in formal employment. Mario1 is 49 years old, and today he turns to his friends to be able to at least pay his electricity bill. When he was 20, he tried to follow in his father’s footsteps. His goal was to become a metalworker, which he managed to do for a few years. In retrospect, he sees that the routine he had in his various jobs led him to try all his life to be a self-employed worker.  The names of all respondents were changed to protect their identities.

1

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He says that his work experience has had three main elements: being a delivery worker, an informal seller of goods, and a metalworker. Mário is a black man who lives in the periphery of the city of Campinas, has graduated from high school, and has attended some technical courses. Like most Brazilian workers, he has a wide range of knowledge and skills acquired in the many professions and occupations that have shaped his trajectory, comprising formal jobs, micro-enterprises, informal work, activities that are barely recognized as work and side hustles. He keeps records, mementos, and files in a memory box from the jobs and occupations he has had. In his youth, he worked in supermarkets as a cart attendant, and took technical courses to become a welder and a forklift operator. For five years, he worked as a metalworker. After resigning from that, he moved through various jobs and side hustles. He was a sales promoter, did paint jobs and home repairs, was an attendant in a cell phone store, was a stock person in a supermarket, and then a welder again. He also worked as a telephone technician. In the early 2000s, he became a delivery worker. Far from having a linear professional trajectory, between 2000 and 2010, he moved from this occupation to welding jobs and other activities. His trajectory can be defined as a kind of escape from employment. The daily humiliations and injustices sometimes culminated in his revolt and resignation or in dismissals by his employers. More than ten years before our interview, he established himself as a self-employed delivery worker, an increasingly unstable and unsustainable condition. In the last decade, he has combined his work with that of a merchandiser; in general, he resells replicas of famous brands. His delivery work feeds back into his sales. He sets strategies so that his circulation as a delivery worker can be integrated with his purchasing and selling of products.2 In the 1990s, the large scale process of outsourcing deliveries promoted the expansion and reconfiguration of this profession. In the early 1980s, before the outsourcing of this type of work, the motorcycles were not owned by the workers; instead, they were provided by the companies that hired these workers directly. As narrated by Afrânio, who was interviewed  This idea of feeding back and the combination of different activities as a means of earning a living is developed in depth in the analysis of cosmetics resellers (Abílio, 2011). 2

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in 2014—when he was 51 years old and had been in this profession for 32 years—workers took the bus to the company, spent the day making deliveries, and returned home at night. At that time, their work involved a different logistical pattern, which today seems very distant and which used to define the intensity and forms of distribution of work. The distribution of deliveries throughout the city relied on calls made from public telephones. The expansion of delivery companies expanded job offers, establishing a well-structured organization that was, at the same time, socially invisible. These workers gained visibility in the pandemic as food delivery workers, however, the work of delivery couriers has been essential for decades for the circulation of documents, personal items, and, more recently, for e-commerce distribution. These workers also do notary services, and transport fragile items such as flowers and blood samples. Brazilian delivery workers in London, for example, even transport organs for transplants.3 They can carry out a range of different activities under the delivery courier label, such as being a motorcyclist who works for insurance companies or transporting and selling car batteries, in addition to providing assistance to drivers when their cars break down. With the development of outsourcing in the 1990s, the number of jobs offers expanded significantly, and new forms of compensation were introduced, many involving the transfer of costs to workers, not to mention the risks. Paid by delivery or by workday, these workers went through a process of formalization and professionalization of their occupation, especially in the early 2000s. The popularization of access to cell phones in the first decade of the 2000s reconfigured the distribution and pace of this work. In 2009, President Luís Inácio Lula da Silva approved a bill that recognized the profession of delivery workers. The formal employment contract involved two categories, called by workers ‘contract work’ or ‘sporadic work’—also sometimes called by them ‘exploradic.’ In the former category, workers were hired by third-party companies and outsourced on a fixed basis to another company. They had a fixed monthly salary and pre-established working hours. Under this condition, delivery workers had lower compensation, but safer working conditions, as their  This fact emerged during interviews I conducted in the United Kingdom in 2018.

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compensation was not based on the number of deliveries. In the ‘exploradic’ work, on the other hand, workers received a salary corresponding to the minimum wage established for the category plus delivery fees, based on the distance travelled. When Mário became a delivery worker at the beginning of the millennium, it was already legal to transfer the costs of the motorcycle to the delivery person. Deaths and accidents of motorcycle riders—not only of delivery workers, but also of low-skilled and low-income workers—had also become commonplace and accepted, with these workers increasingly bearing the risks and costs of commuting to work and dying on the streets of the city (Biavati & Martins, 2009).4 Formal employment brought some protection and guarantees regarding the health and safety of workers against the high risk of accidents. The earnings of sporadic delivery workers, however, were calculated based on the distances travelled for each delivery and in general at least 40% of the delivery price was retained by the company. As previously mentioned, throughout his career as a delivery worker, Mário managed to carry out the project many of these workers aspired to before uberization: to break with the exploitation of outsourced delivery companies and build his own portfolio of clients, for whom he worked as a self-employed person. For eight years, he managed to make a living out of that. In 2015, in the midst of the economic crisis and the successful establishment of the oligopolization process of a few companies that operate through digital platforms, Mário saw his possibilities vanish in the face of the brutal reduction of his compensation, and the bankruptcy of third-party companies and many of his clients. With an unlicensed motorcycle, an expired driver’s license, and no money for even his electricity bill, the only option that he still has, at least for the time being, is

 Who dies in traffic accidents involving motorcycles in the city of São Paulo? This was the question asked in the report entitled ‘Mortos e feridos sobre duas rodas: Estudo sobre a acidentalidade e o motociclista em São Paulo’ (Dead and injured on two wheels: A study on accidents and motorcyclists in the city of São Paulo). It was concluded that these people were ‘gas station attendants, waiters, valet drivers, drivers, doormen, security guards and watchmen, helpers, mechanics, electricians, and builders. In 2008, more than half of the deaths from 9 p.m. to 6:59 a.m. involved motorcyclists working for services, store and market attendants, and workers in good production, maintenance and repair.’ (Biavati & Martins, 2009: 13) 4

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to find a way to become regularized as a delivery worker with an uberized application. A few years after the consolidation of platform-based delivery companies, the profession of delivery worker is being profoundly reconfigured. The amount earned in an hour of work has been significantly reduced (Abílio, 2020a), and decreased even more during the pandemic (Abílio et  al., 2020). The number of accidents involving delivery workers has increased (Agora Jornal, 2020; G1, 2020; Resk & Carvalho, 2020). In the state of São Paulo, from March to April 2020, 56 motorcycle riders died in traffic. This represented an increase of almost 50% in the number of deaths compared to the previous year (Agora Jornal, 2020). According to the government of São Paulo, between April and May 2020, 39 delivery workers died in traffic, almost twice as many as in the same period in previous years (G1, 2020). In 2019, the number of deaths of cyclists in traffic in the city of São Paulo grew by 63%, totaling 36 deaths; the main hypothesis relates the increase in deaths with the growing number of bike boys in the city (G1.2020). With uberization, minimum health and safety protections are eliminated—as part of the informalization process, workers do not even have a defined and stable support infrastructure. Before, workers waited their turn for delivery on an old sofa at a third-party company; today, even access to water and restrooms becomes a challenge in the management of their everyday informal strategies and negotiations. As outsourced workers, delivery riders were aware of the rules that defined the distribution of work, as well as the amount received for each delivery. Exploitation was evident, as were everyday injustices. But today these are intensified. We must not lose sight of the fact that one of the main strategies for app companies to oligopolize this sector was the offer of higher compensation per delivery, which made many workers give up their jobs to dedicate themselves exclusively to app deliveries. However, the app delivery companies that managed to oligopolize the delivery sector achieved the feat of informalizing delivery workers and then significantly lowering the value of their work. In 2014, in a survey that included 45 respondents in the city of São Paulo, 11% of them had an income under two minimum wages. That is, 89% made two minimum wages or more, with 40% earning more than

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three minimum wages and 9% above five minimum wages (Abílio, 2017b). Uberization leads to a lowering of the value of labour power and to the maintenance or increase of long working hours. In a collective survey carried out in 2020 within the scope of the Labour Reform Studies and Monitoring Network with 270 delivery workers across the country, it was found that before the pandemic 47.7% earned less than two minimum wages. In the pandemic this situation worsened. While the app companies saw the number of deliveries grow exponentially, the drop in workers’ compensation intensified—with 73.3% now earning less than two minimum wages, and 34.4% earning less than one minimum wage (Abílio et al., 2020). In addition, platform companies centrally promote an extremely precarious occupation, which in the past existed in a dispersed and poorly organized way—bike boys. Bicycles have been associated with sustainable development, but today they have become the great symbol of uberization in Brazil. There has been a shift from the idea of clean energy to the brutality of the use of physical energy in urban traffic for subsistence. The image of a young black man, riding his bicycle with a bag on his back, holding the handlebar with one hand while using the other hand to look at his cell phone, in the middle of heavy traffic on roads that do not even have bike lanes, now exemplifies this contemporary form of exploitation. The vast majority of bike boys who deliver for applications in São Paulo are black and under 27 years old. On average, these workers earn BRL 936 per month (less than the minimum wage), mostly working between nine and twelve hours a day, from Monday to Sunday (Aliança Bike, 2019). A comparative analysis between uberized workers and ‘classic’ outsourced workers shows that the bike boy profession becomes blacker and younger when it becomes uberized, that is, informalization processes translate into the increased participation of young black people (Abílio, 2020a). In just a few years, the bike boy profession has expanded, with multitudes of workers distributed through the streets of the city in an explicit example of precariousness. Today they seem to form the base of the uberization pyramid. Carlos is 19 years old. He says he has ‘done it all’: he has resold cosmetics with his mother, sold ‘coxinha’ (a popular food in Brazil) in front of soccer stadiums, worked in civil construction. He is a white resident of

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the periphery of São Paulo. With a high school diploma, many resumes sent out, and no answers received, he repaired his father’s old bicycle, had his registration accepted, and went to work for an app delivery company. He works an average of 12 hours a day, from six to seven days a week. In general, he dedicates as much time as needed to make BRL 100 per day during the week and BRL 150 on weekends. On the day of the interview, in 2020, we talked at length, because he had been unfairly suspended by the company—customers’ complaints translate into a brief suspension of the delivery worker, who does not even have well-established means to defend and regularize himself. On another day, he cancelled his second interview; he was in the hospital because his brother, who is also a bike boy, had been hit by a car while working. He recovered well, escaping the statistics that show an accelerated increase in the death rate of cyclists in the city of São Paulo, which is most likely associated with the expansion of the bike boys working for delivery apps (Resk & Carvalho, 2020). He and his brother hang out in the city’s squares while they wait for the next delivery. Each young black man sleeping in the square with his head on a delivery bag5 is a mapped element, a point that integrates the cartography of tens of thousands of points distributed throughout the city, points which must be linked in the most efficient way—in the time-space relationship—according to the demand for their labour. Algorithmic management today monitors/manages each individual and the flow of the crowd. The workers’ daily arrangements—their daily survival strategies—are also incorporated as data, as elements of management, and generate new commands, which, on the worker’s side, lead to new strategies. In this permanently unequal feedback, companies control all the rules of work. Algorithms are not neutral. The algorithmic distribution of work is not a random roulette that spins without any hands (Gillespie, 2014). Algorithmic management is the possibility of translating ways of life, social relations, trajectories, and inequalities into manageable data that will produce and reproduce inequalities and labour exploitation mechanisms. It provides the possibility of assigning rides to slums for black drivers and rides   Prized photo by Tiago Queroz—https://www.obrasdarte.com/wp-content/uploads/2020/01/ Tiago_Queiroz.jpg 5

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to the centre of São Paulo for white drivers.6 It provides the possibility of offering bonuses to breadwinner delivery workers when night falls and they are going home.7 It provides the possibility to engage workers by making more rides available today and almost none tomorrow. It provides control based on the total lack of guarantees. Workers engage in work, having this activity as their main source of income. But while they are in this relationship, all the possibility of carrying out their work is in the hands of the company. Evaluation, scoring, and bonus mechanisms are created and materialize this despotic control that defines who can work, when, for what price. Companies present themselves as mediators of supply and demand, but in fact they are the ‘invisible hands’ of the market. Surge prices, increases in the value of labour when it rains, decreases in value when there are many workers available, how workers are distributed in space and time, the definition of the size of the contingent of workers and continual reductions in the value of the working hour. All these elements are managed and controlled by the companies. Carlos wants to go to a college of Physical Education; he has also tried to become a professional soccer player. His plan for the near future is to get a formal job with fixed hours, which would enable him to work during the day and attend college in the evening. Working as a bike boy has not allowed him time to study. In addition to the long working day, he spends several hours a day commuting by public transport from the periphery to the centre of São Paulo, where he leaves his bicycle in a bike rack. He keeps looking for a job. The question is whether being a bike boy has become not only a degraded entry point, but also a means of staying in the labour market. The delivery worker’s career is being constituted under these terms—going from a bike boy to a delivery worker becomes achievable, as he says, ‘it would be the next level.’ It should be noted that when delivery platform companies entered the market, those who joined them initially earned much more than they would have had they been working for third-party companies. In a field research project carried out in 2014, I found delivery workers who had  A perception reported by a black Uber driver, when compared to his white co-worker.  As reported by an interviewed delivery courier.

6 7

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left their jobs to start working exclusively for platform companies. They told me they made an income equivalent today to seven minimum wages. That year, working for an app also represented the possibility for delivery workers to break with the forms of exploitation practiced by third-party companies and to be able to manage and define the distribution and duration of their working day, while also earning more, as the app company retained a smaller portion of the amount paid for the service provided. However, after five years, the working conditions for uberized workers have changed profoundly. Platform companies aim to monopolize the economic sectors in which they operate. When it comes to delivery workers, most small third-party delivery companies cannot compete with platform companies; even the large ones only manage to maintain their business precariously. Furthermore, competition has begun to arise among platform companies. The strategy to gain market share is to lower the value of the work, ensuring more attractive prices for customers. In a context of economic crisis, characterized by increased unemployment and a growth in informal work, the supply of delivery workers rises. The scale of the new army of delivery workers changes their professional profiles, as well as their relationship with work. We then see a change in this professional work that now—regardless of professional experience, costs, knowledge, and skills required for this activity, as well as the time dedicated to work— presents itself as amateur work (Dujarier, 2009; Abílio, 2011, 2017a). This is another core aspect of the informalization of work. The comparison between a taxi driver and an Uber driver, for example, makes this amateurization process clear. While the former is considered a professional, the latter is recognized as an amateur worker, a kind of permanently temporary worker (Abílio, 2011). This development also involves new forms of work certification, which bypass the State: these are also informalized and guaranteed by the vigilant activity of the multitude of consumers.8

 For an in-depth discussion about amateur work, see Abílio, 2011, 2017a, 2019.

8

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Conclusion: Gig Economy or Uberization? In central countries, uberization through digital platforms receives a name that, from the perspective of the South, should be rejected from the get-go. The ‘gig economy,’ often translated into Portuguese as ‘economia dos bicos’ (the economy of side hustles), becomes a circumscribed part of Northern economies, gaining visibility by its rapid growth, despite its small share (Huws et al., 2018). If we look at the periphery, what is at stake is not the promotion of an ‘economy of side hustles,’ but a new type of appropriation—monopolized and productive—of peripheral ways of life. Companies that are born with oligopolistic horizons, which manage to enter and dominate their sectors of activity, combining financialization with new technologies, make it difficult to recognize and map the relationships between labour exploitation and valorization. App companies present themselves as mediators of supply and demand, legalizing themselves as technology companies. In fact, through technical-political means, they have found ways to productively manage ways of life that are typically peripheral. In the periphery, the life trajectories that comprise the labour market are actually not well-known. Indeed no data are collected that even come close to describing the core elements that make up the world of work in Brazil. Static and generally dualist categories do not capture the unstable and permanent transitions between formal and informal work, family businesses, side hustles, and jobs that are not even recognized as work. Far from having a well-defined professional life, workers survive by permanently taking advantage of opportunities, when nothing is guaranteed. High turnover in formal employment, temporary contracts, job networks accessed via churches, training courses, work and income generation programs, illicit activities, side hustles, family businesses which are quickly frustrated, daily arrangements that guarantee their survival and are not even considered work are some of the elements that allow people to live. Life strategies make up not only the daily life of a large part of the Brazilian population, but also the reality of the Brazilian world of work and, more than that, the specific forms of exploitation, accumulation, and social reproduction in the periphery. In the 1970s, Francisco de Oliveira (2003) cast his keen eye on informality, showing that what seemed to be margins, delays, or residues of development were in fact at

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the core of the transfer of risks and costs of capitalist social reproduction to workers. The ‘organisational talent of thousands of pseudo-small business owners’ (Oliveira, 2003: 68) can be understood as this subordinated self-­ management that informally composes the infrastructure, services, distribution, and circulation of goods, in an intimate relationship with urbanization, industrialization, and the very formation of the working class. The hypothesis assumed here, that guided our definition of uberization is that central countries are facing the productive spread of elements that historically make up workers’social reproduction in the periphery. This spread is materialized in the so called ‘gig economy’. Uberization is a definition that is under construction, aiming to express that this diffusion is not circumscribed to platform labour, but can be understood as a major trend that will crisscross labour markets. This is an important perspective that must be addressed in future research. Acknowledgements  The analysis in this chapter presents the results of the following studies I have carried out over the past ten years: (1) A postdoctoral research (FEA-USP) entitled Is the ‘new middle class’ going to heaven? funded by the São Paulo Research Foundation (FAPESP), between 2012 and 2015; (2) Postdoctoral research (CESIT/UNICAMP) entitled ‘From ‘new middle class’ to ‘the new poor,’ funded by Coordination for the Improvement of Higher Education Personnel (CAPES) between 2017 and 2021; (3) Postdoctoral research finished  in 2022, based at CESIT/UNICAMP, entitled Uberization: Health and working conditions of just-in-time workers, funded by the Ministry of Labor and Employment (MPT- 15th região), (4) Principal Investigator of the National qualitative research on informal work, funded by the Perseu Abramo Foundation, between 2018 and 2019. This chapter was translated by Carmen Reis.

References Abílio, L. (2011). Labour make up: A case study of 800,000 cosmetics resellers. Work Organisation, Labour & Globalisation, 5, 96–110. Abílio, L. (2017a, February). The uberisation of work: The real subsumption of ‘getting by. Historical materialism blog. Accessed Mar 9, 2020, from www. historicalmaterialism.org/blog/uberisation-­work-­real-­subsumption-­getting

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Abílio, L. (2017b, April). The very modern and precarious ‘independent worker’: Contemporary forms of labour exploitation, control and organisation. Paper presented at the International Labour Process Conference. University of Sheffield. Accessed Oct 18, 2021, from https://www.ilpc.org.uk/ Portals/7/2017/Documents/PaperUpload/ILPC2017paper-­A rticle%20 LC%20Abilio%20ILPC_20170328_021159.pdf Abílio, L. (2019). Uberização: do empreendedorismo para o autogerenciamento subordinado. Revista Psicoperspectivas: Individuo y sociedad., 18(03), 1–11. Abílio, L. (2020a). Uberização e juventude periférica. Desigualdades, autogerenciamento e novas formas de controle do trabalho. Novos Estudos. CEBRAP, 39, 579–597. https://doi.org/10.25091/s01013300202000030008 Abílio, L. (2020b). Digital platforms and uberization: Towards the globalization of an administrated south?/Plataformas digitiasi e Uberização: globalização de um Sul administrado? Contracampo, 39(1), 12–26. https://doi. org/10.22409/contracampo.v39i1. Accessed Oct 18, 2021, from English version https://periodicos.uff.br/contracampo/article/view/38579/html_en Abílio, L., de Almeida, P. F., Amorim, H., Cardoso, A. C. M., da Fonseca, V. P., Kalil, R. B., & Machado, S. (2020). Condições de trabalho de entregadores via plataforma digital durante a COVID-19. Revista Jurídica Trabalho E Desenvolvimento Humano, 3. https://doi.org/10.33239/rjtdh.v.74 Agora Jornal. (2020, May). Morte de motociclistas cresce durante a quarentena em São Paulo. Agora Jornal. Accessed Oct 18, 2021, from https://agora.folha. uol.com.br/sao-­p aulo/2020/05/morte-­d e-­m otociclistas-­c resce-­d urante-­ periodo-­de-­quarentena-­em-­sp.shtml Aliança bike. (2019). Pesquisa de perfil de entregadores ciclistas de aplicativo. Aliança Bike. Antunes, R. (2018). O privilégio da servidão. Boitempo. Baptistella, C. (2021). Pra quem tem fome. In vigilância e controle algorítmicos no processo de trabalha de um aplicativo de entrega em Curitiba’, Master dissertation, Department of Tecnology and Society studies. Universidade Federal do Paraná. Biavati, E., & Martins, H. (2009). Mortos e feridos sobre duas rodas: Estudo sobre a acidentalidade e o motociclista em São Paulo. CET. Couldry, N., & Mejias, U. (2019). The costs of connection. How data is colonizing human life and appropriating it for capitalism. Stanford University Press. Dujarier, M. (2009). Le travail du consommateur. La Découverte. G1. (2020, May 23). Acidentes de trânsito em SP caem 40% na quarentena, mas mortes de ciclistas e motoqueiros crescem. G1. Accessed Oct 18, 2021,

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from https://g1.globo.com/sp/sao-­paulo/noticia/2020/05/23/acidentes-­de-­ transito-­em-­sp-­caem-­40percent-­na-­quarentena-­mas-­mortes-­de-­ciclistas-­e-­ motoqueiros-­crescem.ghtml Gillespie, T. (2014). The relevance of algorithms. In T. Gillespie, P. Boczwoski, & K.  Foot (Eds.), Media technologies: Essays on communication, materiality and society. MIT Press. Harvey, D. (1989). The condition of postmodernity: An enquiry into the origins of cultural change. Blackwell. Gramsci, A. (1971). Americanism and Fordism. In Q. Hoare & G. Smith (Eds.), Selection from the prision notebooks of Antonio Gramsci (pp.  279–313). Lawrence & Wishart. Harvey, D. (1990). The condition of postmodernity: An enquiry into the origins of cultural change. Blackwell. Huws, U., et  al. (2018). Work in the European gig economy. Foundation for European Progressive Studies (FEPS). Laval, C., & Dardor, P. (2016). A nova razão do mundo. Boitempo. Madureira, D. (2020, February 08). Como apps de entrega estão levando pequenos restaurantes à falência. BBC News. Oliveira, F. (2003). Crítica à razão dualista/O ornitorrinco. Boitempo. Paulani, L. (2001). O papel da força viva de trabalho no processo capitalista de produção–uma análise dos dilemas contemporâneos. Estudos Econômicos, São Paulo, 31(4), 695–721. Resk, F., & Carvalho, M. (2020, January 22). Mortes de ciclistas sobem 64% em SP. Estadão conteúdo. Rosenblat, A. (2018). Uberland: How algorithms are rewriting the rules of work. University of California Press. Rosenblat, A., & Stark, L. (2016). Algorithmic labor and information asymmetries: A case study of Uber’s drivers. International Journal of Communication, 10, 3.758–3.784. Zuboff, S. (2019). The age of surveillance capitalism: The fight for a human future at the new frontier of power. Public Affairs.

6 Gojek as Labour Infrastructure: Platformization of Work in Indonesia Onat Kiboroğlu

Introduction Gojek is the largest digital platform company in Indonesia with its drivers constituting almost 2% of the working population (2 million out of approximately 100 million, with up to 300,000 Gojek drivers in Jakarta alone1). Digital platforms such as Gojek connect people and execute transactions within an abstract realm, in the process also reformatting the everyday uses of tangible commodities such as motorcycles, smartphones, and  For context, over 99% of companies in Indonesia are small and medium-sized enterprises, and only around 3% of the working population of Indonesia is employed by large companies. Gojek drivers are recorded as, loosely insured ‘partners,’ hence do not fall exactly under the ‘informal’ definition widely used in labour studies. ‘’Gojek: Delivery Workers Struggle in Indonesia.’ libcom.org, June 28, 2019. https://libcom.org/blog/gojek-delivery-workers-struggle-indonesia-28062019 1

O. Kiboroğlu (*) Southeast Asian Studies Department, National University of Singapore, Singapore, Singapore e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. Surie, U. Huws (eds.), Platformization and Informality, Dynamics of Virtual Work, https://doi.org/10.1007/978-3-031-11462-5_6

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helmets; they allow the maximum utilization of the potential economic value of otherwise idle objects through novel forms of labour. By delivering food, packages and passengers according to GPS commands based on granular data drawn from silicon chips embedded in their helmets, jackets and smartphones,  members of the Indonesian urban populace now exchange their sweat and toil for algorithmically set commissions on Gojek’s centralized digital platform. This is a fundamental shift in the intrinsic value of labour and everyday tools. The presence of Gojek enables the monetization of the routine usage of these objects (Parker et al., 2016). In parallel with this fundamental shift arising in the nexus of ‘the tangible and the intangible’ Gojek’s emergence also sheds light on the complex nature of employment in Indonesia, allowing discussions to further scrutinize what forms of work can be deemed as ‘formal’ or ‘informal.’ Gojek’s ubiquitous digital platform is a centralizing, formalizing, and disciplining force within a relatively decentralized and enduringly precarious labour market. Equally important, the rather profound commercial intervention of Gojek into the Indonesian street economy enables various degrees of freedom (Sen, 1999), legitimacy (Lee, 2018) and emancipation for its drivers (Ford & Honan, 2017). Best conceived across a spectrum rather than in absolute terms, this chapter aims to illustrate how the terms ‘informal’ and ‘formal’ are not necessarily opposite versions of a single phenomenon but complementary forms of realities lived and imagined within the larger universe of popular economies. Gojek’s intervention into the labour market of Indonesia bridges the two ends of the spectrum, creating a hybrid alternative. This chapter positions digital platform-based work in Indonesia as a novel and practical solution which does not necessarily provide all the advantages of having a formal labour contract, but as one which does alleviate key pain points many workers within popular economies face in their everyday experience. Ergo, the diagram below classifies the key criteria in which the ‘Gojek effect’ (Ford & Honan, 2017) is experienced by the Indonesian labour force and how the platform’s presence reorganizes long-established features of popular economies. The spectrum presented in Fig. 6.1 lays out a relativist illustration of various degrees of labour conditions in contemporary Indonesia. Popular economies (Simone, 2014) refer to a large and vivid universe of activity

6  Gojek as Labour Infrastructure: Platformization of Work… 

Informal transactions

Formal transactions

Formal transactions

No job security and no income certainty

No job security and some income certainty

Some job security and high income certainty

No union power

Some union power through informal mobilization

Some union power through formal mobilization

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Spectrum of Labour Conditions in Indonesia

Independent ojek driver

Gojek driver

Office worker under contract

Construction worker

Manufacturing worker with union affiliation

Street food peddler

Police officer/Bureaucrat

Fig. 6.1  Spectrum of Labour Conditions in Indonesia

among those on the left of the spectrum. The mesh of informal and formal transactions, ad hoc solutions to practical problems, sparse income certainty and little to no effective opportunity for mobilization against precarious working conditions all together come to define the overall labour conditions for most workers in Indonesia. Only those who lie at the right of the spectrum have a palpable level of job security and some level of collective representation against clearly defined employers and relevant government agencies. The dominant narrative in digital platform labour literature is that platform workers are completely disenfranchised because they are classified as contractors while facing increasing algorithmic control of their work, leaving them with the worst possible outcome (Dubal, 2017; Rosenblat, 2018; Franco & Ferraz, 2019; Choudary, 2020). Contemporary labour scholars have pointed out many valid reasons for concern with regard to labour conditions within digital platforms, most notably the perpetuation of low-paying insecure jobs by a new generation of corporations. Yet a closer investigation of platforms’ everyday presence on the ground and the overall benefits and costs of having such digital infrastructure in an urban context warrants a more complex evaluation (Glöss et al., 2016; Ford & Honan, 2019).

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A framework built upon the aforementioned range of conditions allows a more nuanced approach,2 taking account of the reality that Gojek’s intervention into the labour market of Indonesia bridges the two ends of the spectrum, creating a hybrid alternative. This chapter positions digital platform-based work in Indonesia as a novel and practical solution which does not necessarily provide all the advantages of having a formal labour contract, but does alleviate key pain points many workers within popular economies face in their everyday experience.

Methodology This paper presents two main research components. The first surveys an array of literatures, drawn from platform studies, labour studies, urban studies, and Southeast Asian studies, aiming to draw together theoretical perspectives and second-hand observations which can illuminate the salience of Gojek’s presence in Indonesia. The second component is a years-long thread of fieldwork, beginning in the Riau Islands in 2018, touching many spots across Java, moving onto a brief stint in Sulawesi during the summer of 2019 and ultimately wrapping up during the early-­ COVID era of 2020 in Bali. When brought together, the theoretical and practical methodologies resemble the platform itself, an assemblage that can only be accessed by simultaneously interacting with it virtually and physically. Just as the platform cannot be thought of without its inherent software and hardware components, this chapter aims to achieve an inherent street-­level practicality, while also addressing other forms of contemporary discourse.  ‘Taking the lived realities, mobilities, connectivities and people’s feelings of belonging as a vantage point for defining ‘areas’ may lead to the approval of a ‘constructed geography’… may also lead us to disapprove of the rather stiff boundaries that are drawn by structuring area studies programmes into Japanese Studies, Chinese Studies, Korean Studies, Southeast Asian Studies, South Asian Studies, Central Asian Studies, and the like. Oftentimes, and increasingly so in the wake of globalization, ‘there is more Delhi in Oman than in India’, a phrase referring to the strong presence of Indian communities in Oman. Transregional, transnational, and translocal (‘transversal’ in one word) connectivities are most visible and relevant for people’s lives; they reflect geographies that are not defined by borders between territorial or maritime spaces, but by the feeling of belonging regardless of ‘where in the world’ one is physically located.’ (Derichs, (2020) 2

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The methodology used in this paper for the study of the platform does not have the ambition of achieving a precise collection of purely empirical data. Instead, the research sought to lay out the platform in terms of its often contradictory qualities, producing ethnographic accounts that are inevitable based on what Clifford (2010) calls ‘partial truths.’ De-emphasizing solemnity from ethnographies, Clifford contends that ethnographies are almost fictional, yet highly telling, regardless of their accounts: …ethnographies are salient archives and economies of truth. Power and history flow through them, in ways their authors, who are often mere observers, cannot and rather shall not, fully control. The first-hand accounts in this account, are thus best read as qualitative data that sit at the nexus of several academic disciplines, illuminating the ways in which various vocabularies, assumptions and demographic imaginaries do not necessarily translate into hard realities on the ground. The ultimate purpose of such methodology is to obtain information that illustrates the way urban Indonesians see, construct, navigate and interpret situations in their everyday life. (Martinez, 2018)

The map in Fig. 6.2. illustrates at a macro scale my journey through Indonesia collecting photographs, written accounts, oral interviews, official and unofficial documents, text messages and, ultimately, significant observations that shaped the core themes of this chapter. Beginning in the Riau Islands of Batam and Bintan, moving on to Java, Sulawesi, and Bali, I talked with dozens of Gojek drivers, recruiters, and government officials, in order to put together a comprehensive study of how the platform is brought to existence by the urban realities of Indonesia. As the paper progresses, this set of qualitative data is presented to the reader as threads of practical truths, but also as avenues for further curiosity into the inner workings of the archipelago’s rich societal dynamics.

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1

2

3

4

Fig. 6.2  Map of fieldwork zones in Indonesia demarcated in circles. The green circle marks the Riau Islands as (1) (2018), the red circle Java as (2) (2018–2020), the yellow circle Bali as (3) (2019–2020) and the blue circle Sulawesi as (4) (2019). Map taken from ‘Indonesia Map Silhouette Isolated on Vector Image on Vectorstock,’ VectorStock, January 15, 2020, https://www.vectorstock.com/royalty-­ free-­vector/indonesia-­map-­silhouette-­isolated-­on-­white-­vector-­28784460

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 ommodification of Labour within C Popular Economies Labour studies scholars have long assumed that work can be roughly divided into ‘skilled’ and ‘unskilled’ forms, implying that some forms of labour require no level of skill. This is an assumption that is commonplace across the board in every genre of the wide spanning literature.3 This paper contends that there is no such thing as ‘unskilled’ labour. Every form of labour requires a particular set of unique skills, though some skills are valued less due to their high supply in their particular contexts. As a relevant example, being able to ride a motorcycle through urban traffic may be seen as ‘unskilled’ labour in Jakarta or Denpasar, yet it is considered a rare ‘skill’ in Istanbul, where motorcycle taxi drivers make up to five times the average salary.4 As opposed to specialized services that require a personal ‘edge,’ skills in the labour market are commodified as a result of the way they are valued by corporations. The smaller the range in service quality consumers expect from the workers who perform any particular job, the closer a service is to being considered as having become a commodity. Gojek, in its most crude definition, is an intermediary digital platform that serves as a two-sided market place that is an automated taxi dispatcher for the masses. The skill it takes to drive a passenger from point A to point B is assumed to be not particularly unique by employers. Driving a motorcycle can be observed to be something that practically everyone in Indonesia knows how to do and, as a result, the cost per mile driven does not boast a high premium and does not vary significantly in price. This also discourages ‘haggling’ because, in the case of a commodified platform service, the process of customer conversion (the sales term for getting someone to take your taxi) is automated. Whatever taxi shows up in front of a passenger waiting for a ride, the chances are that that passenger will get in. There is therefore no  Contemporary scholars such as Simone (2004), Kusno (2013), Malasan (2017), and Sopranzetti (2018) frequently and illustratively imply the underrated value of ‘skills’ that are required to perform various forms of seemingly mundane crafts (street food preparation, improvised urban transportation, construction work etcetera), but do not explicitly assert such a position. 4  Per an interview with the CEO of Scotty, a motorcycle taxi company in Turkey during 2018. 3

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need for the taxi driver to wait at a port or subway exit to grab customers. No ‘sales pitch’ is needed as no further convincing and negotiation are required. Given that the user will not be able to immediately discern that some other taxi driver would be able to do a meaningfully better job driving him or herself across town, the transaction is in a sense a ‘trust-minimized’ version of the independent ojek5 practice. Users of Gojek trust the overall brand and negotiate with the platform itself, rather than directly with drivers whom, before the arrival of the platform, they would have known personally and selected to run errands for them6 and shuttle their items around town. This process of centralized intermediation removes the drivers’ agency in determining prices and exact routes, rendering their work a fungible product and hence a commodity. As Gojek has expanded across Indonesia, many forms of urban relationships have become commodified and impersonal, as every ride is now with someone new—a fleeting experience both for the driver and the passenger, despite its physically close and dependent nature.7  Indonesian motorcycle taxi driver.  ‘Nguyen, in her late 50s with a ready smile, has been driving a xe ôm moto taxi in Hanoi for 17 years; today, she rarely has a passenger whom she doesn’t already know. ‘Most of my customers, like me, live in this alleyway,’ she says, sitting on a low blue stool at a tea stand in Hanoi’s Old Quarter. ‘Some work in banks, and they don’t have time to pick their kids up, so I do it. Others are elderly or blind, and they need someone to take them around. If anyone needs to carry their food from the market, they’ll call me, and I’ll go and collect it. ‘There are around 10,000 xe ôm drivers still working in Hanoi. Most are male, in their 50s, and have little formal education. Few regularly use smartphones…The almost familial relationships that xe ôm drivers have built within their communities are practically impossible to replicate through a one-size-fits-all technology platform, demonstrating the difficulties that venture-backed unicorns face as they try to build regional businesses.’—Lampard, A. (2021, January 12). The motorbike taxis versus the unicorns. Retrieved January 13, 2021, from https://restofworld.org/2021/fighting-off-the-unicorns/ 7  ‘Many consumers in Vietnam prize the personal relationships they have built with their xe ôm drivers and are unlikely to sacrifice those for anonymous reviews on smartphone screens and impersonal conversations with customer service centres. ‘When Grab and Gojek expanded from nothing to everything in the matter of a few years, those relationships became commodified, they became impersonal. Every Grab ride was with someone new,’ says Onat Kibaroğlu, a Ph.D. candidate at the National University of Singapore who researches the ride-hailing industry. ‘The rudimentary business model is the same, but there is no intermediary. Now you have a third party that matches you up with an algorithm and gives you a cheaper price. The business model from afar seems the same, but it’s actually completely different.’ Nguyen, the xe ôm driver, is not worried for her business. ‘No, not at all,’ she says, as she waits to take her next passenger to the bus station. ‘All of my clients know me. They’ve got my number, and anywhere they want to go, they just call me.’—Lampard, A. (2021, January 12). The motorbike taxis versus the unicorns. Retrieved January 13, 2021, from https://restofworld.org/2021/fighting-off-the-unicorns/ 5 6

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Along with the basic benefits of safety, certainty, and camaraderie it arguably presents to its ecosystem of users and drivers, Gojek does not operate in a techno-utopian fantasy. The digital platform rather finds a way to thrive in a practically messy and intellectually conflicted form of compressed sense of temporality. Smartphone technology in general directs us towards a passive trust in widely delegated and obfuscated algorithms (called ‘apps’ in shorthand), reforming our daily routines and our thresholds of patience and expectations of comfort (Berry & Dieter, 2015). The omni-presence of Gojek in urban settings creates ambitious tendencies of automated and accelerated modes of action. On-demand services compress everyday experiences of movement and perceptions of ‘speed’—catalysing a growing expectation of human effort (labour), in its most mundane sense, to be ‘on-demand.’ Food should be ready at the door in 20 minutes, traffic must routinely be beaten in the quickest fashion possible, a package should arrive at its destination across the city within the next hour. Any deviations from these novel temporal standards within urban spaces are collectively perceived as a nuisance and a disintegration of urban infrastructure, similar to the experience of water pipe leakages or electricity outages. Contemporary urban perceptions of temporality are thus increasingly squeezed into bits (in both literal and technical meanings of the term), due to the widespread prevalence of ‘on-demand’ technologies.8 Keeping up with increasing speed and momentous action is not the exception, but the routine, the collective norm—raising the bar most significantly for workers. Just as a leaking pipe is replaced by another identical one, a Gojek driver may immediately be replaced with another one in any case of nuisance and mishap. As in every other fundamental industrial shift, the working class is expected to keep up with a new temporal standard and be constantly racing against time (Castells, 1996), this time not within factory walls but across a vast urban setting. The pervasive presence of Gojek has significant implications for economic value creation in urban spaces. As such platforms become part of  ‘…the meaning of on-demand is not necessarily ‘now’, but ‘when I want it’.’ Read This Before You Build Uber for X.  Retrieved December 16, 2020, from https://blog.ycombinator.com/ read-this-before-you-build-uber-for-x/ 8

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everyday usage, certain personal assets become ‘idle’ and time that is not dedicated to work (spare time) now becomes an ‘opportunity cost’ (Rosenblat, 2018). Michel Foucault (1984) noted that idleness was regarded as a threat to stability (read: profitability) within modern society,9 potentially a form of passive resistance against being disciplined into a particular schedule.10 Gojek offers ‘bonuses’ for completing a set of rides or deliveries (typically five) and most drivers depend on these bonuses to make enough money to cover their daily gas, maintenance, data and, in some cases, rental expenses. Many drivers I met throughout my fieldwork showed me their version of the Gojek application, where the user interface is meticulously designed to keep you online, gamifiying your daily job schedule by incentivizing you to go for just one more ride to complete the next set for the tranche of bonuses. The bonuses tend to be convincing amounts, often worth two or three times a typical ride (up to 100 k rupiah). Alex Rosenblat (2018), in her ethnographic study of Uber drivers in the USA, describes this system as hanging the carrot in front of a rabbit to keep it running.11 In such a context, many workers who, especially in the Global North or Singapore, switch to driving for a platform such as Uber, Lyft of Grab in order to ‘not have a boss’ find themselves working for one anyway, only this time it is a rather dynamic, faceless, and subtly operating algorithm.

 ‘… a complex unity a new sensibility to poverty and to the duties of assistance, new forms of reaction to the economic problems of unemployment and idleness, a new ethic of work, and also the dream of a city where moral obligation was joined to civil law, within the authoritarian forms of constraint. Obscurely, these themes are present during the construction of the cities of confinement and their organization.’—Foucault, Michel. ‘Madness and Civilisation,’ Selected Interviews and Other Writings, 1972–1977. 10  One of my interviewee pengojek in Bali mentioned that when he began riding for Gojek‘…it was first to fill in empty time.’ He noted that that he typically works 12 hours a day, 6 days a week on average. Given he is expected to be at the weekly ceremonies at his village, so he turns off the application once a week in order to ‘…become offline.’ 11  ‘Surge pricing, which multiplies prices for passengers and earnings for drivers during periods of high demand, is another form of algorithmic management that encourages drivers to relocate to certain areas at certain times. The drivers get in-app notifications, heat maps, and emails with real-­ time and predictive information about spikes in demand. A driver who wants to go home and is trying to log out might be prompted with an automatic message: ‘Your next rider is going to be awesome! Stay online to meet him.’—Rosenblat, Alex. Uberland. University of California Press, 2018. 9

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Uber, as documented by Rosenblat (2018), tracks a variety of personalized statistics such as ride acceptance and cancellation rates, hours spent logged in to the application and number of trips completed, using the accelerometer in drivers’ phones along with GPS and a gyroscope which record drivers’ performance in granular detail. Using such data, the platforms display selected statistics to individual drivers as motivation tools, such as ‘You’re in the top 10% of partners!’. Such measures of management resemble a Fordist risk-versus-reward system with classic Taylorist efficiency principles. Given that such a relationship between labour and capital occurs across a vast geography of cities, it can be observed that, in a post-Fordist context, the labour force is typically not confined to factory and workshop spaces, but is diffused into the broader society, leaking out and over conventional boundaries (Raunig, 2013) to circulate within the traffic jams of metropolitan cities.12 Rearranging everyday frictions round the clock, Gojek catalyses a revamped and digitalized Taylorist industry. Gojek’s presence within the digital skin of urban Indonesia showcases the ultimate form of scientific management of labour, where workers’ efficiency—both in relation to their specific spatial position within a city and their speed of service delivery, measured in seconds—are optimized through digital algorithms that are run by artificial intelligence. Such an extreme level of labour productivity fits into the analogy of capitalism as ‘…a battery that continues to accumulate energy without a pause—the energy of labour’ (Boutang, 2012). From such a standpoint, the widely used, yet often narrowly understood, concept of ‘on-demand platforms,’ must be assessed in the light of the key concepts of ‘temporality’ and ‘power.’ This discussion thus aims to demonstrate the non-neutral nature of operational features in ride-hailing algorithms and their managerial power informed by core Taylorist principles. Similar to how the introduction of horse-trams in Jakarta disciplined people to think in ‘clock-time’ (Cote, 2014), an analysis of the ride-hailing software portrays how the mainstream prevalence  ‘…workers during the nineteenth Century used to shoot clocks in the city squares denouncing the time mechanism of their exploitation. Now, in their rebellion, precarious workers need to shoot the calendars which mark the separation and non-continuity of time and their alienation… like the migrant, the precarious worker is constantly looking for a place to rest.’—Negri, Antonio, Counter-­ melody, 2013. 12

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of ride-hailing platforms alters contemporary notions of ‘time,’ ‘risk’ and ‘reward’ with regard to everyday urban living and labour. The centralized operations of Gojek streamline the company’s expansion into highly disparate contexts throughout Southeast Asia, but it does result in mishaps for their own drivers on a day-to-day basis due to the occasional abuse of the system by their vast user base. A common issue with having a faceless boss is the phenomenon of ‘order fiktif ’ (fake order). The ongoing issue was quoted frequently as a significant nuisance during my interviews, as many users of Gojek—either just for ‘fun’ or sometimes through entering a mistaken address—order food through to the app, only to leave the driver with it, stranded. The driver drops by the given restaurant, pays from his own pocket to pick up the food, arrives at the designated address and finds no receiver. As many users still use the apps ‘cash-based,’ the driver is compensated only weeks later, after tedious deliberations with local ‘community leaders,’ who have to send the complaints up to the nearest ‘management office’ (which could simply be a Gojek employee with a phone in more off-the-grid areas) so the drivers can get refunded. For workers who depend on $5 to $10 of work per day to sustain their livelihood, this common ordeal is notably distressing.

Emancipatory Technology Through creating a rather simple (in industry terms, user-friendly and ‘human-sounding’13) interface that does not require much technical knowledge to access work opportunities and transportation alternatives, Gojek has eased, legitimized, modernized, and rationalized existing habits of informal transportation and transactions within popular economies—transactions and crafts which were previously deemed ‘illicit’ and ‘lower-class.’ As urbanist literature increasingly suggests that cities ought to be evaluated for the extent to which they respect ‘the right to the city’ (Harvey, 2008; Bunnell & Goh, 2020), this section aims to detail how  An expression in user-experience parlance, where an application employs everyday language instead of a ‘machine-like’ vocabulary within its front-end user interface. Many of Gojek’s publications across platforms such as Medium and Linkedin use this particular terminology in order to communicate its user experience. 13

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the presence of Gojek lowers the barriers to entry into the labour market for domestic migrants. Built as a digital translation of the social dynamics of urban Indonesia, the core business model of allowing anyone with a driving license to take up a job can be read as a form of emancipatory technology. Popular economies of urban Indonesia are built upon a vast supply of domestic migrants, moving typically in a circular or seasonal urban-rural migration pattern. It has always been much easier to become a pengojek (ojek driver) than finding a formal job in a new setting for a migrant, but Gojek further brings down the entry barriers and hence offers enhanced agency, as the mobile platform technology lessens the importance of local geographical and traffic-related knowledge (due to the widespread prevalence of GPS systems on mobile phones) and social capital (the ability to rapidly sign up for a job). Through the deployment and training of smartphones, mass recruitment events and ease of access to relevant social connections over social media, becoming a pengojek is hence rendered much easier than before, creating a positive network effect where the supply of more drivers incentivizes more users as the waiting time and prices for fares are dramatically reduced. Indeed, many Gojek drivers act as recruiters themselves, as I have observed in my brief fieldwork in Batam. The signing up process of becoming a pengojek is streamlined through ‘recruiters’ who prevail in multiple WhatsApp or Telegram chat groups, avoiding the need to enter certain ‘offline’ social circles, which tend to have more stiff boundaries, to be able to obtain provisional (although in practice often permanent) job opportunities. Beyond geographical knowledge and social capital, for many Indonesians, regardless of whether their upbringing was urban or rural, the learning process of driving a motorbike starts almost concurrently with starting to walk. As one pengojek put it in an interview with the author, Indonesians tend to ‘…wear the bikes like [they] wear shoes (…sepeda seperti sepatu).’ It is no exaggeration, given how agile yet seemingly comfortable (albeit with serious physical pains induced through tough and persistent labour) many Indonesians are in using motorbikes in one of the world’s most (if not the most) chaotic traffic flows. This

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mainstream knowledge of motorbike driving14 is also a key advantage both for the migrants, in terms of minimizing their potential job training, and also the companies which ‘partner’ with them, maximizing the relevant and available pool of labour. In one notable case, a child born on February 2019 in Lampung province, was named after GoPay, the e-payment platform by ride hailing unicorn Gojek.15 In contrast to Gojek’s widespread popularity in Java, Sulawesi, and Sumatra, however, it has provoked negative reactions in Bali, where it has become a flash-point of resistance and violence. Unlike in metropolitan Indonesia, where they are perceived as providing certain degrees of freedom, which are beneficial, the ride-hailing platforms are seen there as disrupting a societal system that has been long embedded within Balinese culture. Regions in Bali are subdivided into a total of approximately 1000 ‘banjar,’ a local form of rural commune, typically around a square mile in size and with approximately 500 residents (80–100 families make up a banjar). Each banjar cooperates in regulating various aspects of everyday life at regular community meetings—deciding on local issues such as the use of agricultural land, punishment of local crimes, and the ‘fair ordering’ of picking up rides for local taxi drivers. Banjars have their own ‘pecalang’16 who direct the traffic during ceremonies. One can notice the boundaries of a banjar by keeping an eye out for the signs on roads and their central building ‘bale banjar.’17 Banjars handle the daily governance of their small populations by issuing identification documents, developing basic infrastructure such as roads, allocating labour for village cleaning and resolving everyday petty crime. Typically, in every banjar there is a head, a secretary, and an accountant. People contribute a certain amount of funding (informal tax) and the banjar is expected to take care of its  A demographic feature, that for example is not true for Istanbul, another gridlocked city, yet with much fewer motorbike solutions for passenger mobility—hypothetically due to not enough knowledge, trust, and interest in using motorbikes for daily commutes. 15  ‘Parents name newborn son ‘Gopay’ after Gojek’s e-payment service, get Go-Pay credit as gift’, Coconuts Jakarta, 21 March 2019. https://coconuts.co/jakarta/news/parents-name-newborn-songopay-­Gojeks-e-payment-service-get-go-pay-credit-gift/amp/ 16  Colloquial for guard, vigilante, and makeshift traffic police 17  Administration 14

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own area, more so than the national government. Within such a socioeconomic context, significant occurrences of conflict between driveres for on-demand platforms (typically immigrants from Sumatra, Sulawesi, or East Java) and local Balinese taxi drivers have broken out since the platforms were launched in Bali in 2015.18 For immigrant workers, taking up the job of riding an ojek has been made much easier with the advent of the GPS-based ride-hailing platforms, because the virtual maps embedded into such mobile applications allow non-locals to leapfrog into having a thorough geographical knowledge of a foreign land. GPS technology resembled science fiction in the early 2000s with a few references to it in popular spy movies (Stone, 2017),19 yet it rapidly became an everyday and almost mundane technology within less than a decade. Through a digital translation of its physical infrastructure on a screen, a city simulates itself within an embedded electronic culture and economy, creating its own virtual double through a complex architecture of intricate bits of information networks (Davis, 1959): ‘Grafting technologized knowledge onto what “locals” are supposed to know intimately, that is, the city, enables outsiders and new migrants to no longer learn the city in the old way. The ojek was once a superior resource of local geography. Ask and you shall receive a complex and knowledgeable answer of turn left-left-right-left-right. How is the man of the street to compete with Google Maps’ ability to make the city legible?’ (Lee, 2018). This phenomenon evokes how Lefebvre (2003) presented a seminal vision for ‘the city,’ in which its users (migrants and ‘locals’ alike) manage, negotiate, and mould urban space for themselves, beyond the control of the state, traditions, or industrial powers. The everyday assemblage of GPS and ride-hailing technologies, therefore, assists otherwise ‘alien’ immigrants to carve out a living for themselves, however perpetually precarious.  Jacobs, Harrison. “Why should we make foreigners rich?’: Taxi drivers are taking on Uber and Grab in Bali, and some are turning to violence’. 23 June 2018. https://www.businessinsider.sg/ uber-grab-bali-attacks-taxi-drivers-2018-6/?r=US&IR=T 19  Indeed the founding story of Uber in the mid 2000s is traced back to one of the founders’ astonishment at a James Bond scene where a car is moving through the map in a GPS system. Ten years on, the company has realized this ‘fictional’ scene en masse. 18

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This renders Gojek an emancipatory form of technology, providing an inclusive tool for newcomers to a ‘city,’ who would otherwise have fewer means of penetrating the stiff social barriers of entry into a conservative culture like that of Bali. In reaction to such a dilution of their exclusivity within the Balinese labour market, banjar affiliated local taxi drivers have erected signs demarcating certain zones as no-go20 and aggressively enforcing these territorial lines on multiple occasions,21 struggling to keep out the drivers for the online platforms, who offer much more competitive fares and access a wider online client base for ride orders. My main interlocutor in Bali, Gede, was a local independent driver living near the Sanur area of Bali,22 who also drove for Gojek, with a strong command of English and considerable knowledge of the history of the island. One of the first things I learned from him was that the first metered taxis appeared in the early 1990s with BlueBird, who entered the Balinese market via a collaboration with travel agencies, at a time when most tourists who came to Bali did so using travel agencies. In his own words, ‘…BlueBird in the early 1990s had the same issues Gojek has today.’ Gede also mentioned how many Gojek drivers in Bali are from the neighbouring islands of Java, Sulawesi, and Makassar and ‘they will work anywhere… at Friday prayer time it is very difficult to get a Gojek ride, so obviously immigrant Muslims drive for Gojek, we can tell from this phenomenon.’ In their own fieldwork in Denpasar, Makassar, and Jakarta, Ford and Honan (2017) have noticed that Gojek has observably benefited from its nationalist and ‘indigenous’ positioning. Setting itself apart as a ‘local’ interlocutor with relevant actors in contexts of backlash and aggression  photos taken by the author during my fieldwork in Bali between late 2019 and early 2020.  ‘On the fiercely tribal island, an influx of tourist dollars has steadily expanded the local economy even as a sense of territorialism reigns. Traditional taxi drivers speak of Bali’s ‘local wisdom’, which dictates that they alone should attend to the needs of tourists staying in their banjar, or local community.’—Jacobs, Justin. Southeast Asian Globe. May 7 2018. ‘Traditional Bali taxi drivers’ aggressive war on ride-sharing apps’—http://sea-globe.com/war-on-ride-sharing-apps-bali/ 22  Gede is part of the ‘Bali Innova Community,’ which is a community of Toyota Innova owner taxi drivers, who contribute around 10 k rupiah per month to form an informal safety net for each other in cases of accidents. Toyota offers them discounts on repairs and tyre changing. There have been many groups like this for a long time, but they have proliferated since Uber entered the market; groups like ‘UBD’ (United Bali Drivers) formed to collaborate and mitigate the problems Uber cause and also have the collective leverage to ask for bargains and perks from the local distributors of Japanese automotive companies who lease/sell them the taxis. 20 21

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(in contrast to Singaporean Grab and American Uber23), Gojek has been less of a target in the execution of governmental regulations and ‘street brawls’24 between online platform and taxi drivers on the ground. It can be speculated that the resistance by the rural banjar taxi drivers against Gojek drivers is an existential one. It is about whether or not a form of technology should ‘be’ somewhere, rather than ‘in which ways’ or ‘at what cost of labour.’ All protests are not created equal—the seemingly similar ‘anti-Uber,’ ‘anti-Grab,’ and ‘anti-Gojek’ protests across Southeast Asia tend to be concerned less often with what limitations the ride-­hailing technology should operate and more often at what cost to labour rights and conditons. In Medan,25 the protests were about lay-offs by the company due to the alleged fraud by a large group of drivers. In Jakarta, both Grab and Gojek drivers, often in collaboration, protested against their pseudo-employers for lowering their bonus rates, hence reducing their daily income.26 In Bali, however, the protests are fundamentally about ‘platformization’ itself. They represent a resistance by the banjar taxi drivers to being ‘digitized’ and therefore becoming ‘disciplined by an algorithm,’ Balinese banjar-based taxi drivers are thus not simply protesting against ‘Gojek,’ they are indeed protesting against becoming urbanized and commodified.

 Notable though during my fieldwork in Bali I noticed that there is a latent nostalgia for having Uber around. Drivers across Indonesia (also in Singapore, in line with the findings in my previous fieldwork during early 2018) tend to speak fondly of Uber and none are happy it left. They univocally claim Uber gave wider insurance packages and better support to their drivers. 24  According to Gede, ‘… the biggest fights happened in Ubud and Canggu provinces as those areas they are serious about their drop only policy… once they do drop-only, that also discourages the ‘drop’ option too, drivers do not want to return empty.’ 25  ‘Drivers of online transportation services Gojek were seen carrying out a demonstration carrying banners in front of Gojek office at North Sumatra, Indonesia. During demonstration drivers online transportation services require a system established by the company that is considered detrimental to drivers and unilateral termination of partners by Gojek company.’—‘Drivers Of Online Transportation Services Protest In Indonesia’. November 22, 2018. Barcroft Media via Getty Images.—https://www.gettyimages.com/detail/news-photo/drivers-of-online-transportation-services-Gojek-were-seen-news-photo/1064338008 26  ‘The demand is still the same, rationalizing the tariff and asking the House of Representatives (DPR) to revise Law No. 22/2009,’—‘100,000 Online Ojek Drivers to Hold Protest on Monday.’ 11 April 2018, Tempo.Co.—https://en.tempo.co/read/917493/100000-online-ojek-drivers-tohold-protest-on-monday 23

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In light of this hypothesis, the linkages between ‘platformization,’ ‘digitization,’ and ‘urbanization’ instigate the concept of ‘biopower’ as a useful theoretical tool. Michel Foucault (1980) defines biopower as a mechanism for rendering an individual ‘modern,’ ‘malleable,’ and ‘orderly.’ Biopower operates on an individual person as a form of ‘disciplining the body,’ enabling the individual to fit into an emerging context of industrialization and urbanization. In contemporary urban contexts, keeping up with increasing speed and momentous action is not the exception but the norm, setting the bar for ‘on-demand’ workers to be consistently ‘racing against time’ (Castells, 1996). As anthropology focuses particularly on those moments when technology intersects with personal experience (Waterson, 2006), employing an ethnographic methodology to narrate such a case study offers novel insights into social, economic, and political processes. Balinese taxi drivers’ resistance to Gojek therefore, can be read as a stand against becoming ‘modernized’ and ‘industrialized’ by digital platforms.27 Such a phenomenon presents a unique and compelling opportunity for an ethnographic investigation to inform urban theory. The expansion of Gojek into Bali, and the potent resistance that the platform has experienced is an archetypal case study that illustrates the expansion of urban circulations across Southeast Asia and an existential resistance towards them.

Conclusion This paper brings together the genres of contemporary labour studies, urbanism studies and Southeast Asian studies in order to illustrate the ways in which a technological archetype has been moulded by, and has in turn impacted, the daily circulations of millions of Indonesians. One of the premises of this paper is that the scholarly literature on emerging  It is important to note that this sentiment is not necessarily shared by all Balinese drivers I have encountered during my fieldwork. In one of my interviews, I asked a pengojek how often he works, and he replied ‘everyday, except for ceremony days,’ echoing many other Balinese drivers I talked to. When I mentioned taking time off for holidays, he claimed ‘I am already on vacation with you—this is like vacation for me, not work.’ I could sense that he saw his driving as an opportunity to improve himself, especially his English language—a method he refers to as ‘auto-didact,’ so the job felt quite fulfilling to him, at least more than one might initially suspect. 27

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digital platforms has so far failed to fully capture the ways in which such platforms have been shaped by urban conditions (Davidson et al., 2018). By dissecting how a platform like Gojek is essentially a practical solution that is moulded by urban realities, this chapter has aimed to address the concepts of money, labour, and the city within the intellectually fascinating context of urban Indonesia. Looking at the differing experiences in different regional contexts across the Indonesian archipelago, the chapter has illustrated the complex and variable benefits and drawbacks the platform has caused in the lived realities of the working class. Gojek has legitimized, modernized, and thus urbanized, the habits of informal transportation and transactions within popular economies—transactions and crafts which were previously deemed ‘illicit’ and ‘lower-class.’ As the hundreds of millions of Gojek users invest their trust in the overall brand and negotiate with the platform itself, rather than directly with drivers, as in the past, the drivers’ ability to determine prices and exact routes is removed, rendering their work a fungible product and hence a commodity. Relationships become commodified and impersonal—in short, urbanized, as every ride is now with someone new and strange, representing a fleeting experience both for the driver and the passenger, despite its physically close and dependent nature. By opening up to strangers a ‘right to the city’ (Harvey, 2008), Gojek has also lowered the barriers to entry into the urban labour market for domestic migrants from rural areas. Echoing Lefebvre’s (2003) seminal vision of ‘the city,’ in which its residents manage, negotiate, and mould urban space for themselves, the everyday usage of GPS and ride-­ hailing technologies allows immigrants to carve out a living for themselves, one that is, however, perpetually precarious.

References Berry, D. M., & Dieter, M. (2015). Postdigital aesthetics: art, computation and design. Palgrave Macmillan. Boutang, Y. M. (2012). Cognitive capitalism. Polity Press. Bunnell, T., & Goh, D.. (2020, December). Where is the future for cities? 18.

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Cote, J. (2014). Cars, conduits, and kampongs: The modernization of the Indonesian city, 1920–1960. Brill. Castells, M. (1996). The rise of the network society. Wiley-Blackwell. Choudary, S. (2020). The State of the Platform Revolution 2021. Platformation Labs Production. Clifford, J., Fortun, K., & Marcus, G. E. (2010). Writing culture: The poetics and politics of ethnography: A School of American Research advanced seminar. University of California Press. Davidson, Nestor M., Michèle Finck, & John Infranca. (2018). The Cambridge Handbook on the Law of the Sharing Economy. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3247816 Davis, F. (1959). The cabdriver and his fare: Facets of a fleeting relationship. American Journal of Sociology, 65, 158–165. Derichs, D., Malmi, T., & Schnieder, C. (2020). The effect of dialogue and relative performance reports on professionals’ productivity. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3313500 Dubal, V. (2017). The Drive to Precarity: A Political History of Work, Regulation, & Labor Advocacy in San Francisco’s Taxi & Uber Economies. Berkeley Journal of Employment and Labor Law, 38(1). UC Hastings Research Paper No. 236, 2017. Ford, M. (2011). Workers and intellectuals: NGOs, trade unions and the Indonesian labour movement. ASAA Southeast Asia Publications Series, NUS Press. Ford, M., & Honan, V. (2017, December). The Go-Jek Effect. Digital Indonesia, 31, 275–288. Ford, M., & Honan, V. (2019). The limits of mutual aid: Emerging forms of collectivity among app-based transport Workers in Indonesia. Journal of Industrial Relations, 61(4), 528–548. Foucault, M. (1984). History of Sexuality’ Part Five (Vol. 1). Éditions Gallimard. Foucault, M., & Gordon, C. (1980). Power/Knowledge: Selected interviews and other writings 1972–1977. Pantheon Books. Franco, D.  S., & Ferraz, D.  L. D.  S. (2019). Uberização Do Trabalho e Acumulação Capitalista. Cadernos EBAPE.BR, 17, 844–856. Fuchs, C. (2014). Digital labour and Karl Marx. Routledge. Glöss, M., Mcgregor, M., & Brown, B. (2016). Designing for labour. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. Graham, M., & Anwar, M. A. (2018). Digital Labour. In J. Ash, R. Kitchin, & A. Leszczynski (Eds.), Digital geographies. Sage.

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7 Feminist Approaches to Location-Based Labour Platforms in India Pallavi Bansal and Payal Arora

Introduction As the third-born daughter of a poverty-stricken couple in the Indian city of Delhi, Rinky would consider herself fortunate on days she wouldn’t have to sleep on an empty stomach. While the parents tried to send their three daughters and one son to the school initially, Rinky and her sisters were compelled to drop out prematurely due to lack of funds. Soon after, the sisters were occupied with the household chores; however, Rinky started looking for work at a young age. Despite the odds, she wanted to make a difference in her life and provide financial support to her family. She took up menial jobs in the beginning, such as cleaning houses, cooking, and washing dishes. However, she earned a meagre amount of Rs 5000 (about 70 USD) every month and often felt disrespected and constrained by her job. It was then that someone in the neighbourhood advised her to learn driving and become a driver-partner with the

P. Bansal (*) • P. Arora Erasmus University, Rotterdam, Netherlands e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. Surie, U. Huws (eds.), Platformization and Informality, Dynamics of Virtual Work, https://doi.org/10.1007/978-3-031-11462-5_7

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app-­based taxi services operating in the city. She thought this move would dramatically change her life, as she witnessed some of her male friends turning into micro-entrepreneurs and taking home a lucrative income of Rs 40,000 (about 540 USD) every month. After fighting a battle at home with her family and neighbours who opposed the idea of her becoming a cab driver, she spent three months learning to drive at her own expense. Having achieved this, she then arranged her identification documents, applied for a driving license, procured a car from a fleet owner, and then partnered with a digital ride-­ hailing platform. She was then given some basic training about the app functionality and how to conduct herself professionally. Soon she had the steering wheel in her hands, and it felt like an achievement to her. Besides enabling flexible work hours, the job provided her with a sense of independence. However, she was always worried about how the rides were assigned to her, driving on unknown routes, and boarding male passengers. Gradually, she also realized that she was earning less than her male friend, since she was not able to drive for seventeen or eighteen hours a day due to lack of physical strength. It was then that her male friend revealed that he was spending a major portion of his income on repaying the car loan he had taken out, and that is why he worked longer hours. ‘Rinky’ and her ‘male friend’ are composite characters but represent real stories of many such women and men in India for whom the platform-­based economy has seemingly opened a plethora of employment opportunities. Across the world, a large number of people find work and perform jobs through apps, platforms, and websites (Heeks, 2019; Graham et al., 2020). The annual worth of the platform economy has been estimated at approximately 50 billion USD. However, Heeks (2019) admits that these figures are more of a rough estimate, considering the rapidly changing nature of this economy. As elsewhere, the platform economy has grown exponentially in India over the last decade, with an estimated 3.03 million platform workers and employees employed across 11 platforms such as Ola, Uber, Swiggy, Zomato, Urban Company, and others (Fairwork, 2020). These platforms have had a positive impact in Global South economies, opening up new opportunities in relation to employment, inclusion, earnings, career advancement, flexibility, and the reduction of commuting and environmental costs. Some researchers

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also consider that location-based platforms have the potential to facilitate women from lower-income backgrounds to find work. Some of these platforms often provide workers with their own smartphones or other communication devices and impart the necessary skill training, for example upskilling women with limited digital literacy and providing them with access to digital technologies, besides providing other necessary infrastructure (Aneja & Shridhar, 2019; Kasliwal, 2020). Women workers in India are no longer confined to stereotypical jobs such as salon and care work; they are venturing into hitherto male domains such as cab driving and delivery services. According to a report by Mishra and Ajmal (2019), there are about one million workers in the delivery sector and the number of women workers grew to 67,900  in 2019 from 40,000 in 2018. The Babajob platform recorded an increase of 153% in women’s applications for driver jobs in the 2016 fiscal year (PTI, 2016). According to the Road Transport Yearbook for 2015–16 (the latest such report available), 17.73% of the 15 million licensed female drivers drive professionally (PTI, 2020). Though there are no distinct figures available for how many women are registered with Ola and Uber as drivers, ride-hailing app Ola confirmed a rise of 40% every quarter in the number of female drivers registered with them (Sridharan, 2018). Moreover, cab aggregation service Uber announced a tie-up with a Singapore-based company to train 50,000 women taxi drivers in India by 2020 (PTI, 2015). While the premise of digital platforms generating jobs for women is promising, one cannot ignore the accompanying precarity, persistent gender pay gap, and reinforcement of the structural and gender inequalities that persists in practice (van Doorn, 2017). For instance, the assignment of work on these platforms is often gendered. The resultant ‘digital labour’ is managed and controlled by efficiency-oriented AI-enabled algorithms, which has often resulted in the neglect of the needs of disadvantaged groups, amplifying their precarity, and increasing the mental stress associated with the incentive-based gamification model (Schmidt, 2017). Moreover, there is already growing evidence of AI-based discrimination—attributed largely to the lack of diversity in datasets as well as in the IT teams that shape the logic of these systems (see Dastin, 2018; Israni, 2017; Marr, 2019). Hence, there is a possibility that societal biases,

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including gender norms, can be transferred into the machines as technology reflects the values of its creators. In the earlier contextual example, Rinky could not comprehend the algorithmic-driven ride allocation or drive for seventeen or eighteen hours in a day to capitalize on the incentives offered for working long hours. In fact, had she wanted to take out a loan for a vehicle in her own name, it would have been difficult for her due to the gender gap in financial inclusion in India. There is a dearth of academic research contextualizing gender with respect to digital labour platforms in the Global South. Here, India makes a complicated, and critical research site where issues of gender intersect with class, caste, and religion in one of the most religiously and ethnically diverse nations of the world. Hence, in this chapter, we map the contemporary and global socio-cultural and political understandings around low-income women workers specifically in the Indian context and intersect these with global scholarship on the platform economy, and, specifically, the location-based services sector in India. We investigate the gender-based concerns, opportunities, and recommendations from the growing literature on algorithmic bias that shapes digitally mediated work in the Global South. We argue that it is necessary to go beyond the sensitization of institutions and people to gendered issues and persistent precarity within the location-based platform economy in India. Building on these global and interdisciplinary and empirical-based learnings, we propose a feminist approach to design and development of this sector for an inclusive future of work. The chapter begins with a discussion of what constitutes a feminist approach to informality, which marks the nature of the platform economy. This follows with a review of the literature on the work opportunities created by the platform economy. With a special focus on location-based platform labour, platform design is discussed in relation to multiple dimensions. First, we focus on platform architecture, encompassing the design choices made to attract and retain an ecosystem of producers and consumers—platform workers and consumers of work— covering the platform business model, the creation of network effects, the position of platform workers, and the power dynamics between platform owners and workers. Second, we look at algorithmic management, involving the governance, tracking, and evaluation of platform workers. We

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focus particularly on the platformization of women’s labour and the associated vulnerability, discrimination, and informality. Finally, we argue for a feminist design intervention in platform architecture and algorithmic management. While the focus of this chapter is India, several examples from other countries in the Global South have been drawn on that resonate closely with the Indian case. Where necessary, comparisons have also been made with gig platform practices in the Global North. The purpose of this transnational and cross-sectoral comparative approach is to emphasize the cross-cutting nature of precarity, informality, and exploitation as platforms become key mediators in the future of work in the Global South. We consider that there is a need for cooperative and solidarity-based approaches that decentre capitalistic values, replacing them with care-based values if we are to reimagine and reconstitute fair and inclusive work conditions.

 elationship between Feminist Approaches R and Informality Presently, the informal economy consists of more than 50% of the global work force and more than 90% of micro and small enterprises worldwide (ILO, n.d.). In a typical emerging economy, the informal economy contributes 35% of gross domestic product and employs 70% of the workforce (Loayza, 2016). Looking closely at informal employment in the Global South, it stands at around 70% in Sub-Saharan Africa, 60% in South Asia, and more than 50% in Latin America (World Bank, 2019). Further, Vanek et al. (2014) argue that informal employment is a greater source of employment for women as compared to men in these regions. While these statistics clearly indicate the large size of the informal economy, they also highlight informal employment as the way of living and earning for the majority of the people, especially women, in low-income countries. Looking closely at India, nearly 90% of the population (around 450 million people) work in the informal sector (unincorporated small or unregistered enterprises) with no minimum wages or social security (Sharma, 2020). Here as well, women are heavily represented in the

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informal economy where they work without formal protection and there is a high risk of exploitation (Verick & Chaudhary, 2014). In this scenario, the digital labour platforms are entering existing informal and heterogeneous labour markets that are mostly unorganized and fragmented in India. These markets function with little or no standardization of labour conditions or rights (Aneja & Shridhar, 2019). In certain cases, the platform services disrupt the regulated service providers, whereas, in other cases, they fill the gaps in the previously flawed work culture of the locally—or less-organized market. Some scholars argue that the platform economy, popularly referred to as the ‘gig’ economy, is further informalizing existing informal labour practices (Chaudhary, 2020) as the platforms offer limited or no workplace security. Platform workers are often misclassified as independent contractors, rather than being labelled as the actual employees of the firm, and this prohibits them from accessing employee benefits. The expansion of the platform economy allows for flexible sources of income outside the conventional employment, but at the same time, fosters a new class of precarious workers, termed the ‘cybertariat’ (Huws, 2015). These factors become particularly important for women, who are structurally and socially more vulnerable to external shocks than men, and hence often face double discrimination. In fact, the automated decision-­making entrenched in the platform economy may reproduce and perhaps amplify the gendered discriminatory practices that have long pervaded these contexts, as evident in the growing literature on algorithmic bias and oppression (Lee et  al., 2015; Rosenblat & Stark, 2016; Hanrahan et  al., 2017; Page et  al., 2017; Muller, 2020). Thus, digital labour platforms may also reproduce the precarious conditions women face in the informal economy. Chant and Pedwell (2008) suggest examining how the gendered social processes and institutions shape (and constrain) the availability of relevant infrastructure and decent work avenues for the marginalized population. For instance, poor women workers in an informal economy face a series of issues such as maternity and other women-related health risks, gendered violence, unsafe working conditions, wage gaps, and inequalities in the context of women’s unpaid reproductive work. Thus, it is necessary to conduct a thorough analysis of ‘the complex relationships

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between informality, gendered relations of power and poverty’ (Chant & Pedwell, 2008: 1) in order to understand women’s unequal position. In this context, a feminist approach to platform work seeks to identify the structural inequalities in the division of labour, dissect the power relations, and uncover the gendered attitudes and representations on location-­ based labour platforms. It also needs to address multiple forms of social identities intersecting with each other and the related exploitation: A key feature of feminist analyses is an intersectional approach that recognises combinations of different social identities, such as race and migration status that, along with gender and class, differentiate experiences, because they are affected differently by the interactions of multiple ‘pillars of exploitation’ including patriarchy, racism and capitalism (Delaney & Macdonald, 2018: 6).

In our opening example, Rinky faced multiple forms of exploitation and oppression, including her low-income background, confronting the patriarchal mindset at home, her fear relating to safety on the road due to her gender, and specific features of platform capitalism such as a lack of transparency in ride allocation and wages. Komarraju et al. (2021) in their empirical study of on-demand platforms, note how new forms of professionalism facilitated by digital platforms offer a pathway to reduce culturally embedded forms of discrimination, described as ‘servitude’ in the case of beauty work in India. They also argue that, despite the promise and claims of professionalism, platform beauty work in India has introduced new forms of informality by creating an ‘additional dimension of customer control to work which is already controlled by algorithms and category managers’ (Komarraju et al., 2021: 15). It is important to acknowledge that these new forms of informality accompanied by algorithmic governance are likely to increase in the future, with the growth of digital labour platforms. Moreover, we need to consider the workers’ choices, whether they aspire for professionalism, and want the flexibility associated with platform work. Flexibility traditionally has been preferred by women more than men due to the disproportionate shouldering of care work by females. Therefore, ‘we need to go beyond the formality-informality

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binary to address desired values for flexibility, professionalism, choice, dignity, and decency of work’ (Arora & Chowdhury, 2021: 3) if we are to build feminist systems and practices. For this, Arora and Chowdhury (2021) propose fostering accountable collectives that are reflexive and function transparently even when they are self-organized. All these arguments direct us toward centring the voices of marginalized women workers in the platform economy and unravelling the complex layers of entrenched digital informality and formality. This can offer insights into how pathways to formality in algorithms, pay, work conditions, social security, healthcare, maternal, paternal, and childcare benefits, alongside desired flexibility, choice, incentives, and freedom, might be created. Thus, such measures can improve the overall quality of digital jobs for all kinds of platform workers.

 rganizing Labour Online: Opportunities O of Platform Work Platform-based business models facilitating digital interactions and transactions between people have become an important part of the global economy since early to mid-2000. Driven by technological innovations, platforms have disrupted the labour market in India, where they act as ‘digital matchmakers’ linking labour demand and supply, both for web-­based and for location-based digital labour (Schmidt, 2017).1 These digital platforms allow people to work remotely and have created an online freelance market (e.g. on platforms such as Flexing It and Amazon Mechanical Turk) offering greater flexibility and autonomy for workers. Within this domain, location-based services such as transport (e.g., Uber, Ola), delivery (e.g., Zomato, Swiggy) and household services (e.g., Urban Company, Housejoy) have created opportunities  Schmidt (2017) has classified web-based digital labour platforms (or cloud work performed remotely via the internet) into freelance marketplaces, micro tasking crowd work and contest-based creative crowd work; and location-based digital labour platforms (or gig work bound to a specific location) into accommodation, transportation, and delivery services, and household and personal services. 1

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for micro-entrepreneurship and self-employment, especially for the lower strata of society, as they tend to have low entry barriers. In relation to location-based platforms, Choudary (2018) points out that such platforms allow workers to better utilize their surplus assets, such as a car or bike. Simultaneously, they augment workers’ capabilities with the help of technology, for instance GPS enables people without the knowledge of the city’s road network to find their way to a destination. A participatory-design based workshop conducted by Dillahunt and Malone (2015) concluded that platforms can even empower marginalized communities, particularly those with low education and income levels, by mitigating barriers to employment through imparting skills and training, providing links to finance capital and building trust in relation to monetary transactions and personal data. Though gig work is not something new with respect to low-income countries, this scenario suggests that there will be further growth in self-­ employment and contingent work in the coming years. The platform economy, by linking individuals and households directly to consumers or businesses, helps to expand options for income generation. It has even created a set of new opportunities in gig work such as ‘shared-ride drivers, homestay hosts, e-commerce logistics, e-commerce sellers, and small-­ scale e-commerce producers’ (Ng’weno & Porteous, 2018: 5) as well as expanded opportunities for traditionally informally organized industries by integrating artisans and domestic workers. This, in turn, helps to increase consumer choices and, eventually enables greater demand for the type of work being exchanged on the platform. However, the rise of the platform economy has created new work opportunities for two kinds of people: those seeking temporary, flexible work enabling additional income; and those who are economically dependent on the platforms as their primary source of income. The first category is generally seen in Global North countries such as the United States, where 90% of platform workers rely on labour platforms for supplementary income (Galperin & Randolph, 2019). The second category is usually witnessed in the Global South, where workers seek job opportunities through online platforms, partly due to the lack of opportunities available in the formal sector or elsewhere (Aneja & Shridhar, 2019). This highlights two challenges for the latter: first, the growing dependency of

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such people on the platform economy; and second, the increasing number of people in the informal sector. Galperin and Randolph (2019) point out that the large informal workforce across the Global South comprises a significant portion of platform workers, which is rising every year. Both global and homegrown platforms in the Global South are responsible for this growth. For instance, American ride-hailing company Uber operates in 63 countries as of December 2018 and is providing work to 3.9 million drivers globally (Uber Newsroom, 2018). Likewise, Ola, an Indian ride-hailing service has empowered about 1.5 million drivers in India to become ‘entrepreneurs’ (Ola, 2020) and has diversified into countries like UK, Australia, and New Zealand. An Indonesian-based transportation and service provider, Gojek, is facilitating work for over 2 million drivers across Indonesia, Vietnam, Singapore, and Thailand (Gojek, 2020). The success story of Gojek is credited to its usage of motorcycle taxis in congested places like Jakarta, which is a relatively faster method of transportation in comparison to Uber cars. The Gojek platform gives financial assistance by providing loans and smartphones to drivers and assists with the paperwork to register as a legal driver. They succeeded in overturning a ban on the app2 within 12 hours because of the considerable political capital they had accrued due to the growing dominance of the platform (Koskinen et al., 2019). This led researchers to contemplate whether ‘platforms originating in the Global South are better positioned to consider the contextual and institutional factors in their targeted locations’ (ibid, p. 326). This raises questions about how the concerns and sensibilities of the Global North are different from the Global South, and to what extent the ‘Western’ norms and practices are normative across contexts. Amplifying this situation are the unique cultural norms and laws of the different regions, even in the Global South, that often govern the availability of opportunities to work for a digital platform, especially for women workers. However, there is paucity of academic literature that discusses women platform workers and the associated gender issues in the Global South  Gojek and ride-hailing platforms were perceived as disrupting the traditional modes of transportation 2

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context. The next sections will discuss how platforms are designed and the challenges raised by this before focusing more specifically on the platformization of women’s labour.

 latform Design and Challenges: Architecture P and Algorithmic Management Architecture The dominant narrative surrounding digital labour platforms holds that they empower workers to become entrepreneurs. In this context, platforms view themselves as ‘intermediaries’ whose role is to provide the relevant infrastructure and market access to labour (Choudary, 2018). As a result, workers are not classified as ‘employees’ of the firm who assigns the tasks. They are treated as ‘independent contractors’ and referred to as ‘partners’ by certain platforms; yet this designation comes with few partner privileges. This arrangement helps to enforce the ‘immunity’ of both the buyer of the service and the platform company and protects them from any obligations pertaining to formal employment responsibilities (van Doorn, 2017). Many of the rights and benefits provided for in the employment or labour laws of different countries are only applicable to people who are deemed to be ‘employees.’ Being treated as independent contractors limits the protections available to workers (such as minimum wages, working hours and gratuity pay), social security (Employees Provident Fund, Employees State Insurance), and collective bargaining rights. (Fairwork, 2020: 11)

The primary business goal of any platform is to create a well-­functioning and efficient market that can produce economies of scale and ultimately increase their market share and profit margin. For this purpose, they initially attract customers through competitive pricing and workers with temporarily favourable policies. However, once these platforms are established and network effects are achieved, they modify their policies, including terms and conditions, to generate a more profitable and efficiency-­oriented model (Choudary, 2018). A high level of unilateral

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power is afforded to the platform owners as they reserve the right to change the contract at any time, including termination of their user account, which makes workers insecure and also prevents them from appealing against what are often automated decisions (Aloisi, 2015; van Doorn, 2017). Moreover, platforms have a tendency to pass on the risks and costs of liability to the workers, after including certain clauses in the agreements that workers are often unable to comprehend (Choudary, 2018). For instance, Uber denied any responsibility for an accident that was caused by the negligence of one of its drivers in San Francisco while he was searching for customers using Uber’s app as ‘the company did not consider a driver logged into Uber’s phone application and searching for fares to be ‘providing services’ on its behalf ’ (Pfeffer-Gillett, 2016: 235). This example demonstrates how platform companies can employ the minutiae of legal agreements to avoid responsibility. Further, the agreements are sprawling in terms of length (typically around 55,000 words), ‘where the software licencing contracts have effectively evolved into work contracts’ (Schmidt, 2017: 11). The platform companies keep recruiting new workers, partly in anticipation of previous workers deserting the platforms due to intolerable terms of service, which ultimately leads to a ‘surplus population’ of underemployed gig workers. Referring to this process as ‘superfluity,’ van Doorn (2017) discusses how platform companies valorize the tension between the indispensability and expendability of workers, which enables them to keep hiring rates high and labour prices low. These scenarios lead to the exploitation of workers and create unfavourable work conditions, with workers operating from a position of weakness while the power and control lies in the hands of platform providers. There is constant job insecurity among workers who consider themselves to be easily replaceable and tend to overwork, due to low pay rates and the way that job bidding processes operate (Graham et al., 2018). Gig workers also face irregularity in terms of income because their payment is generally task-based rather than time-based, and platforms do not have control over the number of tasks the workers shall receive in a day or month. Working in isolation further prevents platform workers from developing adequate forms of representation or collective bargaining regarding these issues.

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The International Labour Organization (ILO) has framed a structural dimension of decent work through a ten-indicator measurement framework. These indicators are: first, employment opportunities; second, adequate earnings and productive work; third, decent working time; fourth, combining work, family, and personal life; fifth, work that should be abolished with reference to child and forced labour; sixth, the stability and security of work; seventh, equal opportunity and treatment in employment; eighth, a safe work environment; ninth, social security; and tenth, social dialogue and employers’ and workers’ representation (ILO, 2013). Measured by these criteria, platform workers suffer a ‘decent work deficit’ as they are paid below the minimum wage and are seldom covered by any sort of social protection. Heeks (2017) analyses how this ‘decent work’ framework of the ILO is not even suitable for emergent platform work in relation to several categories—‘worker communication, employment status, data protection/privacy, nature of communication, clarification of rules, handling of complaints and disputes, clarity on clients and tasks, and two-sided rating systems’ (Heeks, 2017: 33). Similarly, other areas need to be considered in the digital gig economy standards, such as enabling stability while providing flexibility; paying for unrecognized labour time that workers spend searching for work, understanding platform procedures, or upskilling themselves; and ensuring a physically and psychologically safe work environment. In this scenario, the legal landscape must evolve worldwide through government intervention to formalize the existing informal platform economy. However, van Doorn (2017) argues that even if platform workers get reclassified as employees, the extensive control exercised by platforms would continue and could even be intensified. He describes how Uber monitors its drivers extensively to produce performance metrics and controls them algorithmically (in ways that are discussed in the next section). Furthermore, the platforms control how much of the information they collect is accessible to the workers, leading to structural inequality in the gig economy (Heeks, 2017). In fact, inequality is generally seen as a pervasive feature of the platform economy, whether it is gender-based or race-based. Atanasoski and Vora (2015) note that platform workers, ‘continue to perform historically

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undervalued racialised and gendered work’ (Atanasoski & Vora, 2015: 5). In this context, van Doorn (2017) points out that service work is frequently carried out by migrant workers, people belonging to lower income groups or people of colour in the USA, thereby reinforcing class domination in the workforce. However, these scenarios are often ignored when policies for the governance of the platform economy are framed. To make platforms more inclusive, there is a need to conduct in-depth research to understand the gendered and racialized experiences, especially with respect to countries in the Global South such as India, where the issues of class, gender, religious, and caste domination are complicated and intersecting.

Algorithmic Management These dynamics continue to evolve in the digital matchmaking economy, especially in ride-hailing and on-demand service platforms such as Uber, Ola, Swiggy, Zomato, and Urban Company, where algorithms now do the work previously carried out by human resource managers, middle managers, accountants, and customer service representatives (Schmidt, 2017). For instance, service platforms like Urban Company leverage Artificial Intelligence (AI) to serve over 2 million customers by using automated decision-making for capturing customer demand, scheduling a service, and assigning a service professional (Your Story, 2019). The same article describes how Urban Company’s AI tools investigate the historical data of professionals to better profile them, study the work patterns of providers to understand if they have failed to deliver services on certain days, track the highly rated professionals according to their user ratings and then match the demand and supply in a particular location. On such platforms, algorithmic management does not only mean allocating appropriate service providers to users, but also regulating almost all aspects of the job. Möhlmann and Zalmanson (2017) define a variety of characteristics of algorithmic management: the ability to track workers’ behaviour; constant performance evaluation of workers with rewards and penalties; automated decision-making without human intervention; workers’ interaction with a ‘system’ rather than humans, where

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opportunities for feedback and negotiation are minimal; and low transparency, because the big data analytics are rarely accessible—even to the workers in the organization. Raval (2019) also notes that these technologies are mostly deployed and refined through their use on informal and semi-formal workers due to lack of transparency and regulation of this sector. While the automated machines perform administrative and repetitive tasks for the white-collar workers, allowing them to focus on cognitive and productive tasks, blue-collar workers are subjected to algorithmic management and control, leading to ‘double marginalization’ of the vulnerable population. These factors sometimes exacerbate the precarious situation of workers because they seldom understand how the match-making algorithms work and are often exploited by these app-based platforms as a result of information asymmetry (Choudary, 2018). Choudary describes how the functional design of platforms where workers do not have access to the firm’s data processing and intelligence foster greater power of platforms over workers and how some of these platform-centric algorithms push worker behaviour into line with the platform requirements. For instance, the labour platforms even control and continually modify the variable prices, forcing workers to align themselves with and follow the algorithm’s requirements. Moreover, the algorithms are designed to create persistent pressure on the drivers by using psychological tricks, as Scheiber (2017) explains in the context of Uber: To keep drivers on the road, the company has exploited some people’s tendency to set earnings goals—alerting them that they are ever so close to hitting a precious target when they try to log off. It has even concocted an algorithm similar to a Netflix feature that automatically loads the next program, which many experts believe encourages binge-watching. In Uber’s case, this means sending drivers their next fare opportunity before their current ride is even over (Scheiber, 2017: paragraph 8).

Lee et al. (2015) reveal the impact of algorithms and data-driven management on human workers in the context of their study of international ride-sharing apps Uber and Lyft and highlight how some of these algorithms are not human-centred. They discovered that sometimes drivers,

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especially women, rejected male passengers without profile photographs at night due to safety concerns. However, the assignment algorithms treated them as defaulters and lowered these drivers’ acceptance rates. They also criticized the use of the reputation systems that provide numeric metrics to rate the service quality of drivers, because many drivers felt that the passengers didn’t rate them appropriately in relation to their performance and services. A. Mishra (2019) argues that workers are forced to work for long hours (around 16–18 hours in a day) with few breaks by the food delivery industry because the algorithms favour such ‘high zone’ people. The incentives are based on the hierarchical order of the hours put in by the delivery workers, progressively increasing on each order and the benefits are maximized in bad weather conditions that are dangerous for two-wheeler delivery partners. Another important dimension is whether delivery partners are charged when the order is delayed and based on which data. Another often neglected factor is the importance of considering the cultural/religious implications while designing these platforms in certain countries. For example, in one case a customer based in Hyderabad, India refused to accept an order of food from a Muslim delivery person. While people on various social media platforms slammed the customer, they also suggested that platforms should not disclose the names of the delivery because this could reveal their religion (The Indian Express, 2019). This case could also have been avoided if the algorithm had been trained to identify such customers as ‘potential threats’ since the man had requested a Hindu delivery person in the special instruction column of the app. Additionally, there are instances, and a growing awareness, of ways in which Artificial Intelligence can be biased, sexist, and racist. For example, a risk assessment algorithm used by judges in Wisconsin, US was criticized for predicting that black defendants would have a higher risk of recidivism (Israni, 2017). Facial recognition software integrated into mobile phones is ineffective for identifying people of colour and women, because the algorithms are only well-trained for recognizing white males (Marr, 2019). Amazon realized in 2015 that its scoring system based on AI was favouring male candidates over females for software developers and other technical jobs. This happened because Amazon’s algorithms

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had been trained by observing patterns in profiles of candidates submitted over a 10-year period, most of whom were men (Dastin, 2018). Crawford (2016) argues, ‘Like all technologies before it, artificial intelligence will reflect the values of its creators’ (Crawford, 2016: paragraph 14) and that this explains how societal values that are biased against women can be transferred into machine learning algorithms, which are mostly developed by men (Leavy, 2018). This raises concerns about the prevalence of gender biases, especially in home service platforms, where traditionally male-dominated jobs, such as for plumbers, electricians, and carpet-layers, may not be assigned to women workers because the algorithms could be trained on data sets that exclude or underrepresent women in this profession. It suggests a need for further analysis and investigation into whether the workers’ evaluation and rating parameters are gendered as well. Match-making algorithms, it turns out, can be deeply gendered, exploitative, and allow for more informality to creep in, due to their lack of transparency and regulation. Few workers understand the ‘logic’ that governs their work and must often alter their behaviour, even if it is detrimental to their own well-being to bring themselves into line with the platform’s requirements. Hence, a feminist design intervention, in both the platforms’ architecture and algorithmic management, which centres the marginal voices could help both the platforms and the policymakers to flag up embedded biases and work towards non-exploitative solutions. These marginal voices could be represented best by women platform workers, as explained in the next section.

 latformization of women’s Labour: P Vulnerability, Discrimination, and Informality Initially, it was expected that the platform economy would be more favourable to women due to its embedded flexibility and the digital and skills training imparted by the location-based platforms which were foreseen as facilitating the digital inclusion of women. This is especially relevant to women workers in low-and middle-income countries, who are

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20% less likely to own a smartphone than men, with 300 million fewer women than men using the internet on a smartphone (GSMA, 2020). However, flexibility, though a ‘conditioned choice’ for women to participate in the platform economy, is rarely carried out in practice. An empirical study carried out by Hunt et al. (2019) with respect to womens’ engagement in the gig economy in Kenya and South Africa revealed two significant concerns related to flexibility. Firstly, workers generally live far away from more affluent clients’ neighbourhoods, and poor transport links hinder their flexibility in terms of the location of gigs. Secondly, flexibility in relation to the time does not generally work in favour of the worker since it is employer- or client-driven. Workers are compelled to work on weekends and at odd hours to increase their acceptance rates and improve their ratings, which makes it particularly difficult for women to accept many gigs for safety reasons. The platform economy has also faced criticism in relation to gender pay gap. A study conducted by Cook et  al. (2020) documented a 7% hourly earnings gender gap amongst a million drivers on Uber in the United States. They attributed this mainly to three factors: experience on the platform; constraints over where to work (driven largely by where workers live, rather than safety concerns); and preference for driving speed. A similar study carried out by TeamLease Services in India noted an 8–10% salary difference per month between male and female delivery executives (Kar, 2019). Here, safety remained a key concern, followed by lack of two-wheeler riding skills. Further, the assignment of work on these platforms is often gendered (Chaudhary, 2020). Women are compelled to take up highly stigmatized, underpaid, and trivialized work (beauty services, massage services, etc.) and formalized care work (childcare, eldercare) professionally (Ticona & Mateescu, 2018; Chaudhary, 2020). The combination of paid care work with the unpaid care work that women are expected to shoulder at home tends to put an additional burden on them. Kasliwal (2020) also criticizes the gender discrimination facilitated by the on-demand platforms by ‘providing ways to filter service professionals based on gender’ (Kasliwal, 2020: 10). Moreover, these platforms mirror the traditional

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gendered roles prevalent in the broader society and uphold discriminatory gendered norms. As Hunt and Machingura (2016) note, women are identified as domestic workers on some platforms both in terms of the female dominated image representation and the language that uses the feminine form. In the Indian context, Chaudhary and Verick (2014) stress the need to take a holistic approach to improving the working conditions for female workers by imparting relevant skills through education and training programs, providing healthcare, maternity and child care benefits, ensuring secure and accessible transport, and creating ample job opportunities. They argue for a need to develop gender responsive policies that can minimize the ‘gender-constraints’ that most women face, such as lack of wage parity, discrimination at work and risks of exploitation, particularly in the informal economy. It can be concluded that examining the gendered precarity that is emerging in the informal platform economy and donning a feminist lens could point the way to improving overall working conditions in a way that could benefit all workers.

Feminist Design ‘All that we do, almost all the time, is design, for design is basic to all human activity. The planning and patterning of any act towards a desired, foreseeable end constitutes the design process’ (Papanek, 1972: 23). In this context, our argument for redesign foresees an inclusive future of work by considering a bottom-up approach and channelling the voices of marginalized workers. Drawing on our intersection of gender focus, layered with the complexities of location-based digital labour work in the Global South, we advocate for a ‘Feminism by Design’ framework for redesigning the architecture and algorithms of platforms. Though multiple authors have contextualized feminist design, our research problem resonates most closely with the Design Justice Intersectional Feminist Framework (Costanza-Chock, 2018) for platform architectural design and Feminist HCI (Bardzell, 2010) for algorithmic design.

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Costanza-Chock (2018) explores the ideas of Black feminist intersectionality3 and the matrix of domination4 to define Design Justice, which was conceived for everyday design practices and can be applied to any field, as follows: Design justice is a field of theory and practice that is concerned with how the design of objects and systems influences the distribution of risks, harms, and benefits among various groups of people. Design justice focuses on the ways that design reproduces, is reproduced by, and/or challenges the matrix of domination (white supremacy, heteropatriarchy, capitalism, and settler colonialism). Design justice is also a growing social movement that aims to ensure a more equitable distribution of design’s benefits and burdens; fair and meaningful participation in design decisions; and recognition of community-­based design traditions, knowledge, and practices. (Costanza-­ Chock, 2018: 533)

For instance, a platform’s architectural design will be just when it equally takes the responsibility of the risk and cost of liability that it generally passes on to the workers. On location-based platforms, the workers do not get compensated for the cost of their time incurred while looking for work; however, the platforms benefit whenever there is a high availability of digital labour (Choudary, 2018). Similarly, one of the ways to break the matrix of domination is to adopt gender-neutral language and representation. As mentioned earlier, some platforms categorize domestic work as women-oriented by using female dominated imagery. Platforms can also encourage a community-building culture rather than being individualism-­oriented by giving social incentives to those drivers who pick up rides when an assigned driver is unable to reach the destination instead of penalizing him or her.

 Kimberlé Crenshaw introduced the concept of ‘intersectionality’ to feminist theory by discussing multiple forms of exclusion faced by Black women due to their gender, race, and class. These concepts  are seen as interlocking systems that are often experienced by an individual together (Crenshaw, 1989). 4  Patricia Hill Collins expands the ideas of Crenshaw and links gender, race, and class to interlocking systems of oppression. This concept helps us to reimagine how power, oppression, resistance, privilege, penalties, benefits, and harms are distributed in a society (Collins, 2002). 3

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Based on the ideals of democratic inclusion and social justice, Design Justice rethinks the design process by centring the voices of marginalized people who are directly impacted by the outcome of the process and challenges us to work towards non-exploitative solutions based on the lived experiences of common people while making design an accountable, accessible, and collaborative process. Employing these principles would mean involving digital workers in the design process so that their lived experiences can be understood, making the entire system transparent, including drafting legal contracts in simple and local language, affording power to the workers to collectivize, increasing access by providing financial literacy, and ensuring health, maternity, and social security benefits without passing the cost of this fund allocation to the workers, among other things. The second part of the Feminist Design will focus on the platform algorithms, which form the central element of a technology-dependent platform design. HCI (Human-Computer Interaction), in its general sense, is the interface where the interaction between humans and computers takes place. Feminist HCI scholar, Bardzell (2010) makes a case for integrating feminist values such as agency, fulfilment, identity, equity, empowerment, and social justice into the HCI design. She suggests using feminist standpoint theory that advocates for the use of women’s viewpoints as an alternative point of departure in social science research and introduces a new domain of user research, called the ‘marginal’ user. This theory holds that women’s worldview and knowledge is different from men’s due to patriarchal societies limiting their access to certain resources, and this limitation should be channelled as a resource rather than being gender blind. The qualities proposed by her for HCI are pluralism, ecology, participation, advocacy, embodiment, and self-disclosure. These Feminist HCI principles will form the basis of our argument for redesigning platform algorithms. Pluralism and Ecology. According to Bardzell, it is important to investigate and nurture the marginal while resisting a universal or totalizing viewpoint. She stresses the need to consider the cultural, social, regional, and national differences when developing technology. The quality of ecology further urges designers to consider the broadest contexts of design artifacts while retaining an awareness of the widest range of stakeholders. This means that AI developers cannot afford to leave out any stakeholder

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in the design process and should also consider whether their algorithms might reproduce any social bias. Participation and Advocacy. Bardzell suggests ongoing dialogue between designers and users to explore an understanding of work practices that can inform design. This also means that if the platform companies and AI developers are oblivious to the needs and concerns of labour, they may end up designing technology that could unintentionally sabotage users. Furthermore, an advocacy position should be taken up carefully. In the earlier mentioned study conducted by Cook et al. (2020), ‘driving fast’ was considered as a performance evaluator but ‘safety’ was not, an outcome of the way that designers run the risk of imposing their own ‘male-­ oriented’ values on users. Embodiment. Bardzell writes about the quality of embodiment in respect to meaningful interactions with the technology and acknowledging the whole humanity  of individuals to create products that do not discriminate based on gender, religion, race, age, physical ability, or other human features. This concept should also be applied in relation to how users rate workers and whether they discriminate based on appearances or other factors. Hence, there is a strong need to include a qualitative rating system along with the quantitative ones and juxtapose it against worker ratings of customers in cases of sexual harassment, rudeness, violence, and other such negative behaviours. Self-disclosure. The quality of self-disclosure recommended by Bardzell calls for users to be aware of how they are being computed by the system. The design should make visible the ways in which it affects people as subjects. For instance, platform companies could display the variables and the corresponding algorithmic weighting per task assigned on the smartphone screen of the workers. So, if a platform driver has not been allocated a certain ride due to his or her past behaviour, then the technology should be transparent in revealing that information to him or her. Uncovering the weighting applied in various decision-making algorithms will enable platform workers to reform their behaviour and give them a chance to communicate back to the companies in case of discrepancies or other issues. Feminist design calls for a more inclusive and participatory platform design that involves interacting with and understanding the various

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stakeholders, bringing the margins to the centre to recalibrate the power dynamics—to address the data deficit on the location-based digital labour platforms in the Global South and, specifically, the voices, aspirations, and emancipatory potentials as well as concerns of women workers within these domains.

Concluding Thoughts Women are more severely discriminated against in the informal labour market as compared to the formal labour market, with precarity being reinforced and reproduced on informal digital labour platforms. This chapter has attempted to bring together the literature on the location-­ based digital labour platforms in the Global South, focusing specifically on India, using a feminist lens. This lens helped us to identify the entrenched discrimination, exploitation, and informality in the platform economy while acknowledging the premise of the location-based platforms that they can generate job opportunities. We argue that a design overhaul is required for location-based platforms in India that should not amplify precarity; rather, it should enable them to commit to equity, diversity, empowerment, and social justice to improve lives. Hence, we suggest a feminist design intervention in the architecture and algorithmic management of platforms that centres the voices of marginalized women workers and eventually benefits all digital workers. Platforms need to ensure that the architecture and algorithms are designed to enable workers and are not just optimized for creating network effects and maximizing customer satisfaction. On the contrary, they need to see the extension of worker’s satisfaction as a form of customer gratification given that these two realms reinforce one another. Programmers could create a more empowering pathway for women workers by sensitizing themselves to the needs of women like Rinky, who are at an intersection of exploitation and oppression. With the entrenched gender norms burdening women with familial duties and limiting their access to education and skills training, such an intervention in platform design could promise genuine change. While cultural change often takes a long course, by placing women at the centre, platforms can accelerate this shift.

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Muller, Z. (2020). Algorithmic harms to workers in the platform economy: The case of Uber. Columbia Journal of Law and Social Problems, 53(2), 167–210. Ng’weno, A., & Porteous, D. (2018). Let ‘s be real: The informal sector and the gig economy are the future, and the present, of work in Africa. Center for Global Development, (Figure 1), pp. 1–8. Ola. (2020). Ola cabs, about Ola. Available at: https://www.olacabs.com/ about.html Page, X., Marabelli, M., & Tarafdar, M. (2017). Perceived role relationships in human-algorithm interactions: The context of Uber drivers. ICIS 2017: Transforming society with digital innovation. Papanek, V. (1972). Desgin for the real world: Human ecology and social change. Bantam Books. Pfeffer-Gillett, A. (2016). When “disruption” collides with accountability: Holding ridesharing companies liable for acts of their drivers. California Law Review, 104(1), 233–268. https://doi.org/10.15779/Z380854 PTI. (2015, March 13). Uber will train 50,000 women taxi drivers in India by 2020. Hindustan Times. Accessed Jun 18, 2020, from https://tech.hindustantimes.com/tech/news/uber-­will-­train-­50-­000-­women-­taxi-­drivers-­in-­india-­ by-­2020-­story-­UVZRiR6lUzAf09RduOBIdI.html PTI. (2016, June 6). More women taking up odd jobs; opt to be cab drivers, guards. India Today. Accessed Jun 21, 2020, from https://www.indiatoday.in/ pti-­f eed/story/more-­w omen-­t aking-­u p-­o dd-­j obs-­o pt-­t o-­b e-­c ab-­d rivers-­ guards-­620252-­2016-­06-­06 PTI. (2020, January 5). Women chauffeurs drive change, one ride at a time. PTI News. Accessed Jun 21, 2020, from http://www.ptinews.com/news/ 11120683_Women-­chauffeurs-­drive-­change%2D%2Done-­ride-­at-­a-­time Raval, N. (2019). Automating informality: On AI and labour in the global south, global Information Society Watch 2019: Artificial intelligence: Human rights, social justice and development. Available at: https://giswatch.org/node/6202 Rosenblat, A., & Stark, L. (2016). Algorithmic labor and information asymmetries: A case study of Uber’s drivers. International Journal of Communication, 10, 3758–3784. https://doi.org/10.2139/ssrn.2686227 Scheiber, N. (2017, April 2). How Uber uses psychological tricks to push drivers’ buttons. The New York Times. Accessed May 23, 2020, from https://www. nytimes.com/interactive/2017/04/02/technology/uber-­drivers-­psychological-­ tricks.html Schmidt, F. A. (2017, January). Digital labour Markets in the Platform Economy: Mapping the political challenges of crowd work and gig work. Freidrich Ebert Stiftung, pp. 1–32. Available at: https://library.fes.de/pdf-­files/wiso/13164.pdf

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Sharma, Y. S. (2020, January 19). National database of workers in informal sector in the works. The economic times. Accessed Feb 12, 2021, from https:// economictimes.indiatimes.com/news/economy/indicators/national-­database-­of-­ workers-­in-­informal-­sector-­in-­the-­works/articleshow/73394732.cms Sridharan, A. (2018, May 18). When women get behind the wheel … to get ahead. The hindu business line. Accessed Jan 18, 2021, from https://www. thehindubusinessline.com/news/variety/when-­w omen-­g et-­b ehind-­t he-­ wheel-­to-­get-­ahead/article23928754.ece The Indian Express. (2019, October 23). ‘Hyderabad man refuses to accept food from Muslim delivery person, people slam him online’, The Indian Express. Accessed May 23, 2020, from https://indianexpress.com/article/trending/ trending-­i n-­i ndia/hyderabad-­m an-­r efuses-­t o-­a ccept-­f ood-­f rom-­ muslim-­delivery-­boy-­incident-­irks-­people-­online-­6087584/ Ticona, J., & Mateescu, A. (2018). Trusted strangers: Carework platforms’ cultural entrepreneurship in the on-demand economy. New Media and Society, 20(11), 4384–4404. https://doi.org/10.1177/1461444818773727 Uber Newsroom. (2018). Uber Newsroom, Uber. Accessed Mar 18, 2020, from https://www.uber.com/en-­IN/newsroom/company-­info/ Vanek, J. et al. (2014). Statistics on the informal economy: Definitions, regional estimates & challenges. Available at: http://www.wiego.org/sites/default/files/ publications/files/Vanek-­Statistics-­WIEGO-­WP2.pdf Verick, S., & Chaudhary, R. (2014). Women’s labour force participation in India: Why is it so low? Available at: https://www.ilo.org/wcmsp5/groups/ public/%2D%2D-­asia/%2D%2D-­ro-­bangkok/%2D%2D-­sro-­new_delhi/ documents/genericdocument/wcms_342357.pdf World Bank. (2019). World development report 2019: The changing nature of work. https://doi.org/10.1596/978-­1-­4648-­1328-­3. Your Story. (2019). UrbanClap leverages AI to serve 2 million customers, Your Story. Accessed Mar 17, 2021, from https://yourstory.com/2019/01/ urbanclap-­leverages-­ai-­serve-­2-­million-­customers

8 Metaphors of Work, from ‘Below’ Ambika Tandon and Aayush Rathi

Obviously, each new appearance among men stands in need of a new word, whether a new word is coined to cover the experience or an old word is used and given an entirely new meaning. (Arendt, 1963, pp. 25–26).

Introduction Various disciplines have produced literature on digital platforms— broadly categorized as technological interfaces enabling the exchange of goods and services—with little consensus on what platforms are and how they impact economic and labour systems. Features that are commonly associated with platforms include their role in increasing efficiency in supply chains, their deployment of cutting-edge technology, and their ability to ‘disrupt’ existing modes of provision of services and goods

A. Tandon • A. Rathi (*) Center for Internet and Society, Bengaluru, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. Surie, U. Huws (eds.), Platformization and Informality, Dynamics of Virtual Work, https://doi.org/10.1007/978-3-031-11462-5_8

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(Jarrahi & Sutherland, 2019). The use of metaphors and carefully curated taxonomy has been crucial in cementing this idea of the digital platform as a technological layer objectively matching supply and demand (Gillespie, 2017). Described variously as algorithms or aggregators, how platforms are imagined remains largely limited to on-demand companies, or the ‘Uber-­ for-­X’ model, at the expense of other models of intermediation. A large share of the literature also focuses on a few dominant platforms headquartered in the Global North, such as Uber and Amazon Mechanical Turk (ibid.). Within the literature on on-demand platforms, a key area of research has been their impact on labour relations and conditions of work, particularly in sectors such as logistics and transportation. However, far less is known about workers’ perceptions of what platforms are—across platforms types—and how workers experience them. This is particularly relevant in the Global South given the entrenched social and labour market inequities that platformed labour grapples with. Gender, one of the key axes of inequality that mediate platform labour, also remains understudied due to the absence of women workers in ‘masculinized’ sectors such as logistics and transportation, particularly in contexts with strong gender-­based occupational segregation (Rathi & Tandon, 2021). Social inequalities have become even more stark during the Covid-19 pandemic, with several categories of informal sector and gig workers facing a shock to their income without any safety nets or support. As companies continue to grow their profits and force workers to absorb the risks of income insecurity and exposure to disease, it becomes critical to understand and challenge power asymmetries between workers and platforms. Platform imaginaries are key to sustaining these unequal relations of power, including during the pandemic, when platforms aimed to market themselves as providers of essential services and infrastructure. This chapter will seek to document and understand how workers experience different types of digital platforms, and how workers’ imaginaries of platforms differ from popular and academic conceptions. Through the chapter, we focus on the abstractions and materiality of platforms as perceived by workers across sectors, including domestic and care work and transportation. We also shed light on the asymmetries of power and

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information that shape workers’ experiences of platforms. The structure of the chapter is as follows: we discuss the divergent ways in which the existing literature has imagined digital platforms, followed by how platform companies market and position themselves. After a brief discussion of methodology, we then bring forth a few conceptualizations that demonstrate how workers experience and navigate their power-laden relationship with platforms. To conclude, we bring these articulations together and point to some future research directions they provoke. We end with a note on the methodology informing this chapter.

 ow Have Academics Made Sense H of Platforms? Vallas and Schor (2020) argue that platforms are a ‘distinctive form of economic activity’ identifiable by their ‘unique characteristics which reside in the selection, control, and evaluation of the labour that service providers perform.’ In a review of literature on the gig economy, they find that academics use various metrics to develop typologies of platforms— on the basis of the skill levels of service providers, the nature of the work, whether work is performed offline or online, or the kind of product being produced (ibid.). Jarrahi and Sutherland (2019), in another review of the literature, find that various conceptions of the sharing economy characterize platforms as efficient and scalable technologies which create trust and value for businesses and customers across markets. Oh and Moon (2016) further find that some authors focus on the generation of autonomy and flexibility for workers and customers through digital platforms. Flexibility, in particular, has been touted as one of the primary benefits of work in the platform economy, with expected potential for providing increased economic opportunities to migrant and women workers (Chaudary, 2020). Within the discipline of information systems and management, Asadullah et al. (2018) focus on the technical design of platforms—defining them as a technological building block for complementary services to be erected upon. Others highlight the commercial business model of

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platforms as multi-sided networks or markets that enable transactions across businesses and consumers. Scholars in this field characterize platforms as having features such as innovativeness, which derives from both cutting-edge technology and new business models, and scalability, including the potential to grow across markets and national borders (ibid.). Management literature also discusses the efficiency of platform models in creating value in and ‘disrupting’ industries with latent potential such as hospitality and transportation (Mishra & Tripathi, 2020). A smaller set of literature within economics and informatics also documents the manner in which workers experience platforms. Scholars such as Krishna (2020) and Woodcock (2020) employ ethnographic and interview-based methods to exhaustively outline the points at which workers interact with platforms, including their experiences of registration and assignment of tasks. These ethnographies offer a glimpse into workers’ imagination of platforms, which is also useful in determining modes of collective action. However, most literature in this field has focused on the transportation and logistics sectors, limiting the discussion to these more masculinized and dominant modes of organization of platforms. This chapter will juxtapose the experiences of workers across sectors, with the aim of nuancing discussions around the platform economy. In particular, we also focus on the experience of women workers from marginalized communities as they imagine and experience different modes of platformization.

How Do Platforms Make Sense of themselves? In the early 2000s, commentators began unpacking the discursive usage of the metaphor of ‘platforms’ by social media companies and online content aggregators to describe their operations (Gillespie, 2010). The usage of ‘platforms’ has been argued to be intentional, embedding the solutionist ideas upon which the Silicon Valley model of social change has prospered (Morozov, 2013). For every social nail, there is a technological hammer. Indeed, what unifies several of the social media companies is the regularity with which they have resisted being classified as

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‘media companies’ (see, e.g., Helft, 2008). Instead, they prefer to be thought of as ‘technology companies.’ More recently, the metaphor has been adopted by firms across a range of businesses, and not necessarily that of information brokerage. In the literature analysing these developments, these entities, and their foray into mainstream urbanity and capitalism, have been referred to as the ‘platform economy,’ the ‘platform society,’ and even ‘platform capitalism.’ Uber is a classic example. Uber’s (2021) Terms of Service, under Clause 2, describe Uber as a ‘technology platform that enables users of Uber’s mobile applications or websites to arrange and schedule transportation and/or logistics services with independent third party providers of such services.’ Urban Company, a major personal services platform in India uses the exact same language in describing itself as only a technology platform (Urban Company, n.d.). In other words, platform companies claim to be solely in the business of matching transacting people, alluding to the structure of the marketplace. However, tensions have begun to emerge between platforms’ ironclad terms of service and their public positioning. In an interview, Abhiraj Bhal, the co-founder of Urban Company, was quoted as explicitly disavowing the connotation of neutrality via the technology platform terminology, When we started out, we started out as an open marketplace. And so did pretty much everybody else. Thankfully, we realised that the open marketplace approach doesn't work. […] Our platform is not just a technology platform to give them access to customers, but also a formalization and transformation platform for them. (Neha Dewan, 2021)

The narrative of transformation is pervasive and reinforces the techno-­ optimist notion that platforms are our best bet for solving some of the world’s big problems. As scholars have previously noted, the cultural roots of engineering itself lie in the assumption of a silver bullet technological fix to societal problems (Johnston, 2017). Having a solution, however, is not enough. What matters is to be able to save the world and make money while doing so. Borrowing from Tom Slee’s (2016) description of Silicon Valley, digital platforms see themselves, and also present

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themselves, as being about more than just making money; they are also in it to build a better future. The transformational nature of platforms also relies on a celebration of the narratives of disruption as progress, and of entrepreneurial risk-taking as the future of work. Through the rest of this chapter, we highlight tensions in the varying understandings of platforms that various actors, and platform workers in particular, share, as identified by us through interpretive ethnographic work.

Methodology This chapter draws on a mix of desk research and primary data collection collected over two years of research at the Centre for Internet and Society (CIS). Data on the domestic work sector was collected between June and November, 2019. We conducted 65 in-depth semi-structured interviews with multiple stakeholder groups, primarily in New Delhi and Bengaluru. We also organized two focus groups with domestic workers in New Delhi. We picked Delhi and Bengaluru as the field sites because both cities are key nodes in the placement networks of domestic workers in the country, and a high concentration of digital platforms in this sector are operational in these cities. We interviewed workers who had found work or were seeking work through platforms, representatives of platform companies, unions, and government stakeholders. We also interviewed a small number of workers who had sought work with traditional placement agencies to compare their experiences with platform workers. Our respondents included both men and women workers, as well as Savarna, Dalit, and Bahujan workers, largely from low-income families. Workers across both field sites were interviewed in spaces familiar to them. Most often, this would be their homes. The interviews were in several languages, including Hindi, Kannada, and Tamil. Interviews in New Delhi were undertaken by the authors, while interviews with workers in Bengaluru were undertaken by grassroots activists in Bengaluru, affiliated with the Domestic Workers Rights Union (DWRU). They were first-time researchers, and were given basic training

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in conducting interviews. They used existing networks with members to identify domestic workers on platforms, and brought rich experience and knowledge about workers’ conditions and challenges of collectivization to the project. The research team at CIS also conducted empirical research through the pandemic to document the shifts it triggered, through in-depth telephonic interviews with workers and union members from the transportation and delivery sectors. We also draw on these data to reflect on the implications of our findings in the context of the pandemic. As data collection in the domestic work sector was undertaken before the onset of the Covid-19 pandemic, the findings reflect realities before the economic and humanitarian crisis the pandemic triggered. In many cases, the pandemic accelerated the trends we identify, including widening gaps in structural inequality between workers and platforms.

Notes from below Platforms as a New Intermediary Class Many of the service industries in which digital platforms have intervened in the Global South have a historical (and ongoing) presence of an assortment of intermediaries and middlemen. Some examples are taxi companies, domestic workers’ placement agencies (including non-profits and churches) and broad-based manpower agencies for temporary low-­ wage work. While these intermediaries have historically served important roles in ensuring the functioning of labour markets and co-existing with public employment systems, they have also been associated with exploitative operational practices. Key here is the power that these intermediaries have enjoyed vis-a-vis informal workers (typically with marginalized socio-economic identities). They have been known to manufacture workers’ dependence on them for their sustenance, particularly migrant workers who may not have kinship or social networks in cities to fall back on. Domestic workers we interviewed spoke of exploitative agencies who

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brought them to cities with promises of work, and placed them in live-in domestic work with poor conditions of work and low wages. Workers also told us stories of wage theft experienced by them or others in their network while working with placement agencies. One of the value propositions of platform companies has been the elimination of such informal, exploitative intermediaries (see Ola, 2013). Abhirah Bhal, the founder of Urban Company, among India’s most popular domestic and care work services providers, put it in these words: In the traditional directories, it was the same set of people that were registered on multiple platforms as there was a fee to be paid for listing. They had the resources to do so. In turn they were aggregators that had a list of handymen with them to whom they would then pass on the job for a commission. Many times, such aggregators would pay the professional just a nominal amount, keeping the larger fee for themselves. We have disrupted that model by cutting out the middlemen and enabling these people with the skill set to register directly with us. (Agarwal, 2016)

These value propositions of digital platforms have been internalized in policy thinking in India as well. Celebrating the role of digital labour platforms in the Indian economy, the Economic Survey 2020–21 says, 10.22 […] Digital platforms have emerged as enablers for employment creation with the power to easily discover job seekers and job providers in the absence of middlemen (Ministry of Finance, 2021).

Our findings from workers’ interviews challenge these claims. We found that digital platforms perform many similar functions to the intermediaries they have sought to replace. These include training workers, connecting them with customers/employers, and charging fees for these work opportunities. In fact, we found that in the domestic work sector platforms have inserted themselves as yet another layer of intermediaries between workers and customers, working with, rather than replacing, existing intermediaries. In the transportation sector, as well, platform companies have been found to be increasingly working with exploitative

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intermediaries, carving out legal, managerial, and operational distance between drivers and the platform company (Kozlowska, 2019). We found that several domestic work platform companies were recruiting workers through staffing companies and analog placement agencies. Such workers continue to be attached primarily to analog agencies, and their terms of work are determined by these agencies. The economic gains of platformization in these structures are shared between platform companies and placement agencies. Even as placement agencies’ work of lead generation is undertaken by platform companies, for domestic workers, the conditions of work remain precarious, and wages low. Such is the distance between platforms and workers that many workers (registered on platforms) who we reached out to for interviews were unaware of the existence of those digital platforms. For workers actively seeking recruitment by digital platform companies, we found a variety of channels through which workers’ exposure to digital platforms was initiated. These included advertisements in popular media, word-of-mouth networks, and physical registration camps organized by platforms in workers’ urban residential areas (Rathi & Tandon, 2021). Platforms across sectors use these registration strategies, particularly in the early stages of penetration, as information about them is not dispersed across workers’ communities. Mamta, a domestic worker in New Delhi, told us about how she was registered on a marketplace platform. One day, a madam approached lots of people in the neighbourhood for different kinds of blue-collar work. They told us that they are a service for domestic help and security guards. They didn’t give any other information. She said they will get work for everyone—that one day we will get a call offering work. She asked everyone for photocopies of their Aadhaar card. Some people refused because they didn’t trust that woman, but I agreed. She had spoken to me nicely, and had asked me about my problems in finding work as a domestic worker (Rathi & Tandon, 2021).

Of these routes, our observation was that those leading to the greatest uptake of platform work were word-of-mouth networks and registration camps. In fact, none of the workers we spoke to suggested that they had

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started platform work because of an advertising campaign. The embodied presence of known platform workers, or of platform representatives is crucial here, not only in encouraging uptake but also in shaping workers’ understanding of digital platforms. Mamta, like many others who had registered on marketplace platforms, did not have any information about the name or nature of the platform she was giving information to, or its functioning. These workers did not imagine that they were dealing with a technological or data-driven entity at all. Their relationship was with human recruiters with whom they had interacted in-person or by phone, who had gathered their information and promised to provide work in exchange. When asked about how the company goes about providing work, or the basic functions of the platforms, workers had no response other than describing the person that they had been in touch with. They certainly had no idea of platforms’ asymmetric aggregation strategies—that the supply of workers on platforms far outstrips the demand from customers to reduce the possibility of client dissatisfaction resulting from unavailability or delays in the availability of workers (Evans, 2003). Months after being registered, Mamta and all other workers in the locality continued to wait for work. Only one of the 200 workers who had been registered received a call, and the employer’s home in that case was too far for the worker to travel there every day. This bred deep distrust among workers for the recruiters, who took their information with promises of jobs that never materialized. We found that women workers were more anxious about falling prey to identity theft or other forms of misuse of their documents, and in some cases were berated by their families for trusting strangers with these. For them, fleeting interactions with platform representatives were yet another instance of ‘outsiders’ making promises of a better life, but instead exploiting them for their data with no benefits accruing back to them or their communities. We (the researchers) often encountered the same suspicious gaze being cast upon our information-seeking motivations. In their early days of entering the market in India, Uber and Ola, now dominant players in the on-demand ride-hailing sector, followed similar templates to approach workers for manual onboarding—sympathizing with the poor state of work in the sector, and promising better work

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through a digital platform. Jagan, a cab driver in Telangana, said that Uber approached them when they first started operating in the city. They would come and note our names, phone numbers and car numbers on the pretext of understanding our issues, and seek our guidance on how to start a taxi service.

Jagan narrated that, subsequently, the platform representatives asked all drivers to come to an upscale hotel to introduce them to the platform, and show them a glimpse of the potential earning opportunity if they switched to the platform. They made promises of monthly earnings upwards of INR 100,000 to Jagan and other drivers. Companies also had, and continue to have, a referral policy to onboard workers. In the excitement of the early days, several workers told their families in the villages to send more men to work on platforms because of the earning opportunity. Workers were drawn in with the promise of working as ‘partners and not drivers,’ because of the social value attached to the term ‘partners.’ As these companies gained a dominant position in the market and eroded workers’ earnings through exponentially increasing commissions, workers later felt trapped and guilty for having lured others into this work, as platforms progressively hiked up commissions and reduced incentives. Across ride-hailing and domestic work, workers critiqued the terminology of partnership used by platforms as a means to onboard more workers without empowering them to set their own terms of work or allowing any space for negotiation.

It’s Not You, it’s the Algorithm Invoking the neutrality manufactured through the platform imagery, representatives of platforms have often deferred to the algorithm apparatus as a decision-making entity independent of the organization. Eric Alexander, Uber Head of Business (Asia), responding to concerns around Uber’s algorithmically determined pricing strategies, asserted that

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There is no human intervention at all (in surge pricing). It works on an algorithm based on the data available. The idea is to make a cab available for the customer. It depends on the demand and supply in a particular area (Bureau, 2018).

Platforms have regularly furthered a disembodied narrative of the algorithmic apparatus. In these narratives, platforms are conceptualized as oracular manifestations of ideologies of neutrality, and their operations are unknown even to the platform companies. In the quote above, the black box metaphor often used to describe the un-explainability of algorithmic decision-making, is invoked as both a sword and a shield. Cleverly, the technicality of the platform allows it to operate in its own economic rationalization of human ordering. Workers, on the other hand, frequently invoke the socio-technicality of algorithms. In stark contrast to the alleged distance between platforms companies and their algorithms, workers locate algorithms as giving life to rules set by platforms. The black box metaphor is invoked not in ways that fetishize the technology, but as the material object through which managerial power is directly executed. Put another way, workers do not experience algorithms as static objects. Rather, they are shaped as a consequence of the assemblage of actors involved in platform economies. For example, workers complained about companies introducing additional tasks to be performed without prior intimation as well as reducing payment per task. We found that responses to such developments could be shaped by income levels, caste, and gender. For instance, in one case, a large on-demand company branched out into bathroom deep cleaning and tasked existing workers in the cleaning segment with taking this on as well. The introduction of bathroom cleaning was a cause for alarm among some workers as they considered that this demeaning work should be performed only by workers of a lower caste status. Rahul, a cleaner with an on-demand company in Delhi, said When we had signed up for the deep cleaning category, we were only expected to clean the living rooms, bedrooms etc. Now, they have started also including kitchen cleaning and bathroom cleaning within their deep cleaning category. We did not sign up for this, we are not domestic workers. But we will do this anyway as we have no choice.

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The disembodied framing of algorithms also allows platform companies to manufacture distance from the labour outcomes of platform work. Challenging these notions to hold platform companies accountable for algorithmic outcomes, a large body of work has showcased the reliance of platform companies on proprietary data collection and analysis techniques to manage workers by setting the terms and conditions of work (Lee et al., 2015; Rosenblat & Stark, 2016). Called algorithmic management, these strategies of worker control apply the logics of appification to corporate workforce management, such as human resource, surveillance, and payroll functions. In practical terms, key aspects of the work arrangement take place at the technological interface of the mobile phone application—this includes reducing workers to their virtual ‘accounts’ through which work is allotted, payments and incentives are recorded, and performance is tracked and evaluated. The scale and expansion of the digital economy is, thus, predicated on various outsourcing strategies of key corporate functions (through technological tools) and through the subcontracting of work itself. These, along with the outsourcing of capital asset ownership and recurring expenses to workers, all contribute to the alleged slickness and efficiency of digital labour platforms. In line with earlier work, we also found that platforms also use intensive surveillance tools to track workers’ performance and smooth the experience for customers, including location tracking, recording calls with customers, facial recognition to verify workers’ identitities on mobile apps, and ‘before’ and ‘after’ pictures in the case of domestic work. Workers we spoke to are acutely cognizant of being surveilled through these technological systems, and even reminded of the platforms’ panoptic gaze by platform companies’ representatives. Rights-based literature has also documented the manner in which these tools are operationalized to bring further inequality in work outcomes and ultimately, violate workers’ rights. Companies optimize incentive-setting algorithms to coerce workers into working longer hours, taking routes that bring higher profits for the company (Rosenblat & Stark, 2015). In a similar vein, there have been public allegations of companies manipulating algorithms to control workers’ behaviour and prevent protests and other collective action. Taking the example of Uber, instances have been recorded of drivers being pushed into positions of dependency through manipulation of

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incentives (Athreya, 2020). For example, allegations of manipulation have been levelled against food delivery companies who punish workers who participate in protests by manipulating the algorithm to reduce the number of opportunities offered to them (Kauntia, 2020). Workers are forced to go about their work without critical information and with feedback mechanisms that penalize them for seeking information. For instance, in the ride-hailing and food delivery sectors, workers are not provided with information about the location of drop-offs, which would allow them to plan their journey across the city. At the same time, incentive structures are tied to accepting as many orders or rides as possible, and spending more unpaid time logged in looking for tasks. Food delivery workers are not given an indication of the amount of time they are allotted in which to deliver an order, and are penalized after delivery for a delayed order. Across the world, Uber drivers’ contracts contain penalties for calling customers to confirm the destination of the drop-off, or from exchanging any contact whatsoever except for the return of lost property (Uber Help, n.d.; The Supreme Court of the United Kingdom). We found similar prohibitions across sectors, even if not contractually stipulated. Domestic workers we interviewed were discontented with the repeated discouragements from calling customers to confirm the task particulars and location. Workers reported being forced to spend more unpaid time waiting for customers’ responses, travelling across the city for work that remained unfulfilled, and then being additionally penalized for delays in service delivery. The lack of agency in determining work also creates a contradiction for workers who enter with the expectation of flexibility. One interviewee, Prabhu, a driver in Bengaluru, while talking of his experience as a ‘partner’ on the Urban Company platform, said ‘They say you are the boss, but they control everything using the app.’ He complained that drivers were forced to pay fines if customers cancel, or in case of delays due to movement restrictions as a result of the Covid-19 pandemic. Workers deal with the inherent ‘unknowability’ of platforms through building networks of kinship and trust, although these may also be shaped by markers of gender and caste (Lalvani, 2019). Contrary to what platform companies would like one to believe, their algorithms are objects of, and shaped by culture (Seaver, 2017). Algorithmic operations in

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platform economies are inherently unstable, and are wholly enacted by the variety of actors and situated in the social positions of these actors. At times, these actors treat algorithmic pronouncements as oracular. At other times, these actors attempt to game algorithmic processes in order to achieve individualized, beneficial outcomes. Engineers tweak code, altering the rules of engagement significantly (Cristopher, 2021). What is clear, then, is that the supposition of an inner truth of the algorithm is misleading. Workers’ experiences and algorithmic processes are relational, and the technical pedestals algorithms are perched on need to shift.

Platforms as ‘Essential’ Service Providers The outbreak of covid-19 has coincided with the most intensive period, yet, of gig worker organizing in India (Rakheja, 2020; Kauntia, 2020; Shroff, 2021). The Indian state’s prominent initial response to the outbreak of covid-19 in March 2020 was the imposition of a sudden and overarching national lockdown. Even as imposing lockdowns had already become a part of public health responses in northern geographies, the specific demographic, labour market, and public welfare realities in India meant that the public health crisis rapidly escalated into a humanitarian one. Another part of the government’s covid-19 response kit was a wholesale rollout of public and private data-intensive technological ‘solutions.’ Digital labour platforms, too, sought to occupy the pre-existing and emergent institutional voids during this period. Through the various phases of the lockdowns, operational forbearance was offered, through executive state power, to various activities considered ‘essential.’ This forbearance was extended to digital platforms engaged in the delivery of essential provisions such as food. Digital labour platforms, not primarily in the business of delivery, adopted strategies to be regarded as essential. For example, platforms providing transportation services positioned themselves as providing healthcare services. Platforms offering home-­ based care services, too, advocated for their essential roles (Press Trust of India, 2020).

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For a workforce that had largely remained invisible to the state and was severely impacted by the pandemic, this was a moment of policy reckoning. The significance of being regarded as workers providing services was not lost on digital workers. Very quickly they found themselves mired in a double bind, We have been deemed essential workers by the government and on-demand services remain up and running. However, with few restaurants remaining open and few orders coming in, they [the workers] are forced to work long hours for little money, and in risky conditions as roads remain deserted because of the lockdown (Mawii, 2020). — A gig workers’ collective representative (at a roundtable organized by the authors)

Further interrogating the motivations behind platforms’ advocacy efforts and their sudden recognition in government imagination, gig worker collectives at the same roundtable also asked: At what personal cost to our health and safety must we continue to serve the interests of platforms and their customers? (Mawii, 2020)

As the pandemic unravelled, digital platforms enjoyed regulatory forbearance and increasingly began to play an infrastructural role in the pandemic response. Many platforms took on government partnerships and public infrastructural roles to kick-start or continue operations (Mukhopadhyay & Mukhopadhyay, 2020; Surie, 2020). As urban spending and consumption patterns rapidly changed with the turn towards the digital as covid-safe, many platforms consolidated their market positions commanding more inflated financial valuations. At the same time, socio-­ economic support for workers from either platform firms or the government continued to be absent, or piecemeal at best, leaving them with no option but to put their bodies in epidemiological jeopardy or risk hunger and impoverishment. It is ironic that food delivery and hunger management were mediated by a workforce without any food security.

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These developments rendered Mbembé and Meintjes (2003), prescient. The essay begins with the assertion that ‘the ultimate expression of sovereignty resides, to a large degree, in the power and the capacity to dictate who may live and who must die.’ None of this is to say that the authoritarian reactionary measures adopted by states globally, and in India in particular, should not have been pursued. While still authoritarian measures, they were intended for the preservation of life at large. Instead, what they are a reflection of is the way that the dwindling public infrastructure in the neoliberal state is ceding ground to private governance (Lee, 2021). Mbembe’s imagination of who gets to live and who dies did not adequately capture this outsourcing of necropolitical power to private actors. As Tyner (2019) argues, peoples’ positions in capitalist frameworks condition their exposure to death, rendering death productive as a tool for capitalist accumulation. Perhaps it is telling, then, that the two years following the pandemic have witnessed the most intense period of worker organizing in India in the platform economy, albeit still nascent. The necropolitics of sustaining urban India has indeed become a latent rallying point for platform workers, in the aftermath of the pandemic. A quick glance at social media accounts of workers’ collectives and workers also showcases the severity of injury and risk to life that inheres in the performance of gig work, not just in a pandemic but even otherwise (Dhawan, 2021; Pradhan & Beniwal, 2021). As we write this, platforms are competing with each other to provide the shortest delivery time guarantees, further commodifying and endangering the bodies of an already highly precarized and atomized workforce (Press Trust of India, 2021).

Concluding Remarks We have argued that, depending on their vantage point in the platform economy, various other stakeholders experience platforms very differently from how platforms narrate their value. Some conceptualizations, notably by platform companies themselves, place primacy on the technical interface, rather than the sociopolitical implications, of the digital economy. At the same time, platforms also invoke the realities of labour

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informality and inefficient supply chains to argue that their intervention will produce better outcomes for all participants in the ecosystem, including customers and workers. Both conceptions are critical to the political project of platforms—ensuring their classification and regulatory treatment as technology companies and seeking regulatory forbearance with the promise of better and more efficient economic systems. These conceptualizations are also intimately interlinked. The imagination of platforms as technology companies feeds into their role as disruptors of inefficient supply chains. In particular, we have juxtaposed workers’ narratives of gig work against platforms’ self-proclaimed terminologies of identification. Disproving claims of disruption, some workers experience platforms as one among multiple layers of intermediaries that provide them with work, with no tangible impact on their work arrangements or conditions. Another conception, common among workers who have registered on marketplace platforms, is that their interaction has only been with human recruiters and agencies who have sought them out with promises of higher wages and, ultimately, better lives. On-demand platforms sold the imagination of partnership to workers, attaching a higher social status to their work and promising higher degrees of autonomy and flexibility that accompany partnerships. These promises were eventually belied by companies, breeding deep distrust and resentment among workers. On the contrary, workers on on-demand platforms interact with platforms primarily as technological interfaces, with an invisible boss dictating and controlling all aspects of their work. This is radically different from the experience of platforms for workers in the marketplace segment—implying that all platforms cannot be seen to be equal. The disembodiment of on-demand platforms works in the favour of companies as they fetishize algorithms and obfuscates their role in shaping workers’ lives. This unknowability is debilitating for workers’ ability to understand, let alone change, most aspects of their work, from planning day-to-day travel routes to accessing the meagre amount of support offered by platforms during the covid-19 pandemic. Platforms positioned themselves as providers of essential services among a slew of data-driven technological solutions to crises triggered by the pandemic. In this role, they came to

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algorithmically determine whose lives were placed at risk in pursuit of crisis-driven capitalist accumulation. These articulations of platforms result in some interesting provocations for the shape being taken by the public understanding of and public policy for algorithmic systems. Future research must be directed towards devising policy frameworks that account for the divergences in workers’ experiences of platforms as a result of the widely varying business models they adopt. These models could, for instance, create experiences that are completely lacking in contact with the digital systems, or govern workers’ lives through disembodied algorithms. These nuances highlight the limitations of approaches that call for algorithmic transparency as a substitute for protecting workers’ rights. It is clear that more is needed. Piercing the technological veil in the way that workers do is imperative to build processes of accountability that work. For that, we need to understand what platform managers, engineers, and key personnel were planning for when the systems were conceived, the outcomes that were generated and their responses to unexpected outcomes. Acknowledgements  This chapter forms part of the ‘Labour Futures’ research project, hosted at the Centre for Internet and Society, India, and supported by the Internet Society Foundation. The names of the authors have been listed in reverse alphabetical order.

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Johnston, S. F. (2017). Technological parables and iconic illustrations: American technocracy and the rhetoric of the technological fix. History and Technology, 33(2), 196–219. https://doi.org/10.1080/07341512.2017.1336851 Kauntia, N. (2020, August 27). How Swiggy threatened to ‘suspend’ protesting Delhi workers after second pay cut in seven months. The Caravan. https://caravanmagazine.in/news/swiggy-­pay-­cut-­delhi-­worker-­delivery-­protests-­aigwu Kozlowska, H. (2019). A web of ‘partner’ companies keeps Uber out of trouble in Poland. Heinrich Böll Stiftung. https://us.boell.org/en/2019/10/17/ web-­partner-­companies-­keeps-­uber-­out-­trouble-­poland Krishna, S. (2020). Spatiotemporal (in)justice in digital platforms: An analysis of food-delivery platforms in South India. In R. K. Bandi, R. C. R, S. Klein, S. Madon, & E. Monteiro (Eds.), The future of digital work: The challenge of inequality (pp.  132–147). Springer International Publishing. https://doi. org/10.1007/978-­3-­030-­64697-­4_11 Lalvani, S. (2019, July 4). Workers’ fictive kinship relations in Mumbai app-­ based food delivery|platypus [blog]. Castac. https://web.archive.org/ web/20191019031017/http://blog.castac.org/2019/07/workers-­fictive-­kinship-­ relations-­in-­mumbai-­app-­based-­food-­delivery/ Lee, C. J. (2021, January 4). The necropolitics of COVID-19. Africa is a country. https://africasacountry.com/2020/04/the-­necropolitics-­of-­covid-­19 Lee, M.  K., Kusbit, D., Metsky, E., & Dabbish, L. (2015). Working with machines: The impact of algorithmic and data-driven management on human workers. Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, 1603–1612. https://doi.org/10.1145/2702123.2702548. Mawii, Z. (2020). Covid-19: And relief measures for gig workers in India. Tandem Research. Retrieved October 7, 2021, from http://tandemresearch.org/blog/ covid19-­and-­relief-­measures-­for-­gig-­workers-­in-­india Mbembé, J.-A., & Meintjes, L. (2003). Necropolitics. Public Culture, 15(1), 11–40. Project MUSE muse.jhu.edu/article/39984. Ministry of Finance. (2021). Economic survey of India 2020-21–volume 2. Government of India. https://www.indiabudget.gov.in/economicsurvey/ Mishra, S., & Tripathi, A. R. (2020). Literature review on business prototypes for digital platform. Journal of Innovation and Entrepreneurship, 9(1), 23. https://doi.org/10.1186/s13731-­020-­00126-­4 Morozov, E. (2013). To save everything, click here: Technology, solutionism and the urge to fix problems that Don’t exist. Public Affairs. Mukhopadhyay, B., & Mukhopadhyay, B.K. (2020). COVID-19 and the gig economy: ‘For a few dollars more’. Tripura Times. https://www.researchgate.net/

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publication/340756767_COVID-­19_and_the_Gig_Economy_%27For_a_ Few_Dollars_More%27 Oh, S., & Moon, J. Y. (2016). Calling for a shared understanding of the ‘sharing economy.’ 1–5. https://doi.org/10.1145/2971603.2971638. Ola. (2013, October 28). Olacabs–Creating entrepreneurs out of drivers. https:// www.youtube.com/watch?v=y2Hj9Dtgm6I Press Trust of India. (2020, May 1). Urban Company seeks relaxation for essential services amid coronavirus crisis. YourStory.Com. https://yourstory. com/2020/05/coronavirus-­lockdown-­urban-­company-­seeks-­ecommerce-­ relaxation-­norms/amp Press Trust of India. (2021, August 18). Grofers offers ten-minute online grocery delivery in 10 cities. The Economic Times. https://economictimes.indiatimes.com/tech/startups/grofers-­offers-­ten-­minute-­online-­grocery-­delivery-­ in-­10-­cities/articleshow/85419556.cms Rakheja, H. (2020, December 29). 2020 In Review: From Zomato and Swiggy to Ola and Uber–A year of protests by India’s gig workers. Inc42 Media. https:// inc42.com/infocus/year-­end-­review-­2020/from-­swiggy-­to-­ola-­a-­year-­of-­protests-­ by-­indias-­gig-­workers/ Rathi, A., & Tandon, A. (2021). Platforms, power and politics: Perspectives from domestic and care work in India. Centre for Internet and Society. https://cis-­ india.org/raw/platforms-­power-­and-­politics-­perspectives-­from-­domestic-­ and-­care-­work-­in-­india Rosenblat, A., & Stark, L. (2015). Uber’s drivers: Information asymmetries and control in dynamic work. SSRN Electronic Journal. https://doi.org/10.2139/ ssrn.2686227 Rosenblat, A., & Stark, L. (2016). Algorithmic labor and information asymmetries: A case study of Uber's drivers. International Journal of Communication, 10(27). Retrieved from: https://ijoc.org/index.php/ijoc/article/view/4892/ 1739 Seaver, N. (2017). Algorithms as culture: Some tactics for the ethnography of algorithmic systems. Big Data & Society, 4(2), 2053951717738104. https:// doi.org/10.1177/2053951717738104 Shroff, K. (2021, December 29). Inside the winter of discontent for India’S gig workers. The wire. https://thewire.in/labour/india-­gig-­workers-­protests-­pil. Slee, T. (2016). What’s yours is mine: Against the sharing economy. OR Books. Surie, A. (2020, August 17). Pandemic exposure: ‘Platform’ infrastructure in public use in India. COMPAS. https://www.compas.ox.ac.uk/2020/pandemic-­ exposure-­platform-­infrastructure-­in-­public-­use/

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Tyner, J. (2019). Dead labor: Towards a political economy of premature death. University of Minnesota Press. https://doi.org/10.5749/j.ctvckq9nc Uber Help. (n.d.). Contacting drivers or co-riders after a trip. Uber. Retrieved January 19, 2022, from https://help.uber.com/riders/article/contacting-­ drivers-­or-­co-­riders-­after-­a-­trip Uber India Systems Private Limited. (2021). Terms and conditions. https://www. uber.com/legal/en/document/?name=general-­terms-­of-­use&country=india &lang=en. Urban Company. (n.d.). Terms and Conditions. https://www.urbancompany.com/terms Vallas, S., & Schor, J. B. (2020). What do platforms do? Understanding the gig economy. Annual Review of Sociology, 46(1), 273–294. https://doi.org/ 10.1146/annurev-­soc-­121919-­054857 Woodcock, J. (2020). The algorithmic panopticon at Deliveroo: Measurement, precarity, and the illusion of control. Ephemera: Theory and Politics in Organization, 20(3). http://www.ephemerajournal.org/contribution/algorithmic-­ panopticon-­deliveroo-­measurement-­precarity-­and-­illusion-­control-­0

9 (Re)Conceptualizing Gendered Structures of Informality for Domestic Workers in the Platform Economy Abigail Hunt and Emma Samman

Introduction The emergence of digital technology, and the growth of ‘platform’ and gig economies have blurred boundaries, creating irregular employment relationships. This has contributed to a weakening of the power of the labour movement. Informality remains a major challenge. For the time being at least, it is here to stay.1

Platform economy growth is notable in female-dominated sectors traditionally characterized by informality, including care, and domestic work. A long history of theoretical work has sought to understand the gendered structures underpinning persistent—and in some cases growing—informality, and empirical investigation has extensively documented women’s  Spooner et al. (2021)

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A. Hunt (*) • E. Samman ODI, London, UK e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. Surie, U. Huws (eds.), Platformization and Informality, Dynamics of Virtual Work, https://doi.org/10.1007/978-3-031-11462-5_9

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position in some of the most vulnerable segments of the informal economy, where they experience a higher poverty risk, lack of protections, and challenges organizing for better conditions. Less attention to date has been paid to how these gendered structures of informality shape the emerging platform economy, and how domestic workers navigate these altering structures. This chapter aims to address this gap. First, we briefly review theoretical frameworks for understanding gendered dimensions of informality as well as dominant policy approaches—e.g. those aimed at ‘incremental formalization’—and situate the platform economy within these debates. Drawing on quantitative (worker survey, platform records) and qualitative (worker and key informant interviews) data we collected on platform domestic workers in South Africa, we then examine their lived realities and working conditions vis-à-vis ‘traditional’ domestic work. This allows us to assess whether and how the emergent ‘platformization’ of domestic work signals a shift in the structural conditions of the ‘traditional’ sector in this context. South Africa offers a particularly fruitful setting for such research: domestic worker mobilization resulted in the adoption of one of the most advanced regulatory frameworks for the sector globally, though a large cohort of workers remain in the informal economy, for reasons including widespread regulatory non-compliance. Through this analysis we interrogate whether incremental improvements in working conditions, through traditional working relationships or new opportunities within the ‘platform economy,’ can effect change in the broad structural conditions that restrict the availability and quality of work available to many marginalized women in South Africa or whether, instead, more radical changes are needed that upend existing power relationships and bring about transformative change. We conclude by considering whether it is possible to conceive of structural change within the platform economy that would permit a meaningful departure from the poor working conditions frequently associated with de facto informality. We offer alternative forward-looking scenarios for what this might look like, while outlining broader tensions with processes of formalization in economies characterized by widespread informality.

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 endered Dimensions of Informality, Domestic G Work, and the Rise of the Platform Economy Conceptualizing the Informal Economy The term ‘informality’ masks considerable diversity that researchers have long sought to describe and quantify. There are numerous—and, at times, inconsistent—conceptual understandings and definitions.2 The prevailing view is that the broad term can refer either to the nature of an enterprise or an employment relationship. Informal enterprises are unincorporated or unregistered (and together comprise the informal sector), whereas informal employment relationships are not subject in law or in practice to ‘national labour legislation, income taxation, social protection or entitlement to certain employment benefits (advance notice of dismissal, severance pay, paid annual or sick leave, etc.).’3 Workers active in the informal sector and those otherwise in an informal employment relationship together make up the informal economy. Chen (2012: 4) identifies four dominant schools of thought regarding the nature and composition of the informal economy, which understand it as follows: dualism, as separate from the formal economy and an employer of last resort; structuralism, as a source of low-cost labour and inputs for formal enterprises, bolstering their competitiveness; legalism, as a response to the excessive regulation of the formal economy; and voluntarism, as a collection of people who have decided to work informally to evade the costs of formal regulation.4 She writes: ‘[g]iven the heterogeneity of the informal economy, there is merit to each of these perspectives as each school reflects one or another “slice of the (informal) pie… But the informal economy as a whole is more heterogeneous and complex than the sum of these perspectives would suggest”.’5 Recognizing the  Charmes, 2016; Kanbur, 2014; Bonnet et al., 2019.  Hussmans (2002: 7), following the 2002 ICLS definition and upheld in the 2015 ILO Recommendation no. 204. OECD/ILO (2019) provides a set of operational criteria to assess employment relationships, among which the key criterion is employer contributions to a social security scheme such as a pension (p. 175, Table A.A.2). 4  See Chen, 2012: 4–5. 5  Chen (2012: 5). 2 3

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heterogeneity of individuals, employment relationships, and sectors within the informal economy allows for a plurality of motivations and experiences and, in turn, more nuanced, targeted and effective policy responses. Strategies to uphold the rights of informal workers and to bring about meaningful improvements to their working conditions are often implicitly or deliberately rooted in a theory of change. The most widely held view is that policy should aim to provide workers with the benefits associated with a standard economic relationship, even in countries where the majority of the population is self-employed in the informal economy: ‘The recognition of the existence of an employment relationship is … critical. It is through this relationship that workers and employers gain access to the regulations meant to protect their respective rights.’6 However, formalization—which requires a series of policy and societal changes, upheld by law—should be distinguished from formalism—‘a set of legal rules which are meant to be applied independently of social interests or public policy.’7 The distinction is important because it underlines the fact that regulation alone is not enough to uphold the rights of marginalized informal workers; rather, it is critical to consider how economic, social and political institutions shape and are shaped by prevailing laws. The International Labour Organization (ILO)’s advocacy of ‘incremental formalization’ supports the progressive extension of labour and social protections to workers in the informal economy.8 Per Chen (2011: 180), ‘formalization should be seen as a gradual ongoing process involving incremental steps and different dimensions leading towards varying degrees and types of formality. [It] will not proceed quickly or automatically for all those who choose to formalize … [and it] will not be feasible or desirable for all informal enterprises or all informal wage workers.’9  ILO 2016a: 2. See also Ryder (2014): ‘By making formalization of informal work an ILO priority, we have made an important choice.’ 7   Cornell Law School Legal Information Institute (https://www.law.cornell.edu/wex/legal_ formalism) 8  Recommendation 204 advocates the progressive extension ‘in law and practice, to all workers in the informal economy, social security, maternity protection, decent working conditions and a minimum wage that takes into account the needs of workers and considers relevant factors, including but not limited to the cost of living and the general level of wages in their country’ (ILO, 2015). 9  https://www.wiego.org/rethinking-formalization 6

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Incremental steps within this framework introduce the benefits of formality to informal workers, then progressively enact sanctions for non-­ compliance. Ideally, formalization debates should distinguish different types of self-employment and employment in the informal economy, given that each has its own needs and constraints.10 Other critiques reject the idea of formalization as an incremental process. For Heintz (2020: 87), the emphasis on a trajectory of formalization even in countries where most workers are self-employed in the informal economy may be misguided: ‘The formalization debate is flawed because it considers the path to formality to be an incremental one, based on individual enterprises or jobs and the idea that informality can be thought of as a simple continuum.’ His analysis of the dynamics of informal employment suggests instead that a deeper, structural transformation of the economy is a necessary driver of shifts toward better quality work.

Informality and the Domestic Work Sector The domestic work sector is characterized by a high degree of informality globally. It remains largely ‘off the books,’11 performed outside of labour regulations and social protections, either because of a lack of appropriate or comprehensive regulation, because of non-compliance with existing regulation or because regulations are unenforced. More fundamentally, the failure of regulation to ensure decent work in the sector can be linked to gendered, racialized, and classed perceptions of domestic work and workers. Paid domestic work is often perceived as an extension of women’s unpaid work within the household, reinforcing beliefs that it is ‘undeserving’ of decent remuneration and working conditions. Poor conditions can be further linked to other inequalities that intersect with gender, including class, migration status, and race and ethnicity. One implication is that paid domestic work typically benefits ‘better off’ women while reinforcing the power differential between them and the domestic 10 11

 Chen (2006).  Farvaque, 2013 cited in ILO (2016a: 25).

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workers whose labour they benefit from.12 Blackett (2020: 110) traces the roots of this power differential to global histories of slavery and colonialism,13 writing that ‘[t]hose global histories help to explain the remarkable consistency in persisting understandings of domestic workers’ status and subordinated social location across a range of contexts in the Global North and the Global South. They intersect with racial hierarchies and patriarchal norms to render domestic workers, and their work, invisible…’. The location of domestic work intensifies this invisibility. It is largely conducted by isolated workers in the private sphere, but is also ‘characterized by considerable labour migration across borders.’ The resulting atomization of workers constrains their possibilities to organize to demand better conditions from employers or governments. Attempts to improve conditions in the domestic work sector achieved considerable momentum with the 2011 adoption of ILO Convention no. 189 on Domestic Work (and Recommendation no. 201), ratified so far in over 50 countries. The intent was to compel domestic governments to recognize domestic work as ‘real work’ and to insist on domestic workers’ entitlements to the rights and protections typically associated with a formal employment relationship, including contracts, adequate wages, social protection, health, and safety in the workplace, and the ability to organize and engage in social dialogue.14 Absent explicit recognition, even where appropriate labour laws exist, ‘these may, de facto, totally or partially exclude domestic workers from labour rights and social security.’15 Moreover, Blackett (2020) suggests that the Convention has a broader vision in establishing ‘an alternative and transnational … regulatory framework that ensures their meaningful incorporation into the corpus of a labour law that fosters equality.’ Debates continue on the extent to which formalization through regulation alone marks a step change where the refusal to recognize or comply with labour codes is widespread. As Blackett (2020) posits,  Chen (2011) provides a typology of the types of employment statuses and arrangements that characterize domestic work. 13  Blackett (2020). 14  Ibid. (Blackett, 2020). 15  ILO (2013: 17). Even where social insurance schemes formally cover domestic workers, it may contain provisions that exclude some categories of domestic workers (see ILO, 2016a: 20, Box 3). 12

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When we use the label of informality, are we getting closer to naming and redressing historical forms of societal marginalization, exclusion, inequality and invisibility? … Because they are subject to a contract of employment, the legalistic, private law reasoning goes, they work in the formal economy…. But [i]n formalizing the informal in domestic work, it is important not simply to adopt the asymmetrical law of the household workplace that perpetuates domestic workers’ substantive inequality and structural invisibility. In other words, becoming visible through formalization mechanisms does not simply mean becoming a subject of state-made law.

Therefore, the more comprehensive policy-focused frameworks aimed at the formalization of domestic work are multi-pronged, while being underpinned by the importance of the recognition of the employment relationship regardless of the employment model (e.g. workers’ direct employment by households, multiple employers, and/or arrangements brokered through an agency). The ILO outlines four core aspects of formalization, serving as both indicators and focus areas for concrete action.16 These involve, first, the scope and implementation of labour regulation frameworks. Legal recognition of domestic workers as workers with rights is critical yet, even where regulation is in place, it is not always responsive to the realities of domestic work arrangements. For example, challenges to workers’ enjoyments of the full benefits of formalization may occur when non-standard employment arrangements prevail (e.g. part-time engagement or work with multiple employers), meaning domestic workers are wholly or partially excluded from labour rights and social security. The second core element relates to social protection, given that formalization is inherently linked to workers’ access to social protection, notably employment-linked social insurance. Yet the design of insurance schemes may directly or indirectly exclude some categories of domestic workers, for example due to entitlement-related stipulations around length of employment, hours of work or minimum hours of work, as well as contribution levels and benefit portability. Non-compliance by employers and workers can be a key factor limiting access, even where domestic

16

 ILO, 2016a; ILO, 2017.

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workers may be covered within the legal or policy framework for social protection. Wider consideration of the labour market institutions that enable or constrain formality forms the third core area of the ILO’s framework. This includes, for example, private and public agencies that recruit and/ or place domestic workers and which can be associated with challenges to identifying an employment relationship as a result of lack of clarity over which party (agency or household) carries the employer obligation, or where workers are conceptualized by the agency and/or household as independent, own-account (self-employed) workers, despite the relationship being characterized by dependence and hierarchy. Conversely, key institutions promoting the recognition and improved quality of working conditions of domestic workers include domestic workers’ unions and other supportive organizations. The fourth aspect addresses the social dimensions of domestic work. Reflecting the concerns of Blackett (2020), this area concerns the norms, values, traditions, attitudes, and perceptions that influence the way domestic workers are seen, treated, and recognized in practice. Central to this are the perceptions of domestic workers—and domestic work— notably among employers, who often think of the worker as an extension of the family, because of the highly personalized and close proximity nature of their interactions, which contributes to workers’ labour not being seen as ‘real work.’ In this chapter we draw on these four core pillars to explore the extent of formality in the traditional and platform sectors. We also consider what the platform economy appears to offer workers, although it operates largely outside the formal sphere, and the potential consequences of this new operating model.

L inking Informality, the Platform Economy and Domestic Work Platform-mediated labour is a form of non-standard employment that usually (though not inherently) lies outside a fully formal labour

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relationship.17 Elsewhere, we have argued that gig work is qualitatively different from other forms of informal work in that a lack of stability and protections are ‘designed in’ to the operating model, i.e., based on the exploitation of gaps in legislation, rather than a lack of enforced regulation or absent policy attention to recognizing and improving the conditions of informal workers.18 As in the informal economy more widely, the determination of an employment relationship lies at the heart of many struggles around working conditions in the platform economy. Workers are invariably classified as self-employed independent contractors by platforms, yet they often remain economically dependent on the platform intermediary (as are employees). This contradiction lies at the crux of numerous legal struggles that have taken place globally in recent years, with courts being called upon to judge whether different types of platform work are best characterized as self-employment (implying that workers do not qualify for labour protections or employer social insurance contributions) or constitute a form of disguised employment (such that their employers may be operating in breach of domestic labour law). In some instances, governments can be said to have contributed to informalization, for example by exempting platforms from statutory employer responsibilities or not enforcing tax compliance to support company growth and their professed mission to create jobs.19

 Both informality and non-standard employment refer to ‘various types and degrees of labour market segmentation’ and are often described in terms of ‘discontinuities in labour markets’ (Carré, 2020), however the terms are not synonymous. According to the ILO (2016b), non-standard employment arrangements deviate from the conventional understanding of ‘work that is full time, indefinite, as well as part of a subordinate relationship between an employee and an employer.’ In high-income countries, shifts toward non-standard forms of wage employment ‘sever workers from access to employment-related social protection’ thereby increasing the incidence of informal work (Carré, 2020: 53). In low- and middle-income countries, the implications for levels of informality are less clear-cut as the counterfactual is often an informal work arrangement, however, according to Carré (2020: 52), ‘non-standard working arrangements most often entail reduced or no employment-­related social protection and sometimes reduced coverage under some labour standards, depending on the country.’ 18  Samman et al., 2021; Hunt and Samman (2020). 19  See Hunt and Machingura (2016) on the Kenyan government’s prioritization of increasing economic opportunities over recouping taxes, and van Doorn and Vijay (2021) on the Dutch government and the Helpling domestic work platform. 17

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The platform economy has significantly increased its presence in the domestic work sector in high- and middle-income countries alike, a trend that has attracted scholarly attention. Some recent analysis alludes to the ‘incremental formalization’ debate, highlighting ways in which the potential to gradually improve workers’ conditions has been subsumed under platforms’ self-serving agendas. To illustrate, Ticona and Mateescu (2018) highlight that while care work platforms in the USA seek to formalize employment through technologies that increase workers’ visibility, notably to formal institutions such as the Internal Revenue Service (IRS), this visibility largely serves the interests of platform companies and clients while failing to provide meaningful formalization of employment relationships and exacerbating existing inequalities experienced by workers. Similarly, studies of domestic cleaning platforms in Amsterdam, Berlin, and New York have found that by ‘selectively formalizing’ some aspects (e.g. payment, communication, hiring, and evaluation) and intensifying other aspects normally associated with informal domestic work (e.g. lack of protections and bargaining power, income insecurity, and vulnerability to discrimination), companies advance their own business interests and benefit clients, but at the expense of workers (Van Doorn & Vijay, 2021; Van Doorn, 2020). Yet to date less attention has been paid to the growth of on-demand domestic work in low- and middle-income countries, where large existing informal economies mean that increased use of platform labour for social reproduction work may lead to a shift towards capitalist relations of ‘service labour’ and increased capital accumulation and concentration,20 thereby signalling a significant structural shift—albeit away from workers’ interests. In the remainder of this chapter, we consider how the platform economy for domestic work in South Africa articulates with a large traditional sector that is highly regulated in law, albeit less in practice, and the implications for workers.

 Huws, 2019.

20

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 he Rise of the Platform Economy in South T Africa: Shifting Structures of Informality in the Domestic Work Sector? As elsewhere, domestic workers in South Africa are ‘exploited, undervalued—and essential.’21 South Africa provides a striking example of how the social dimensions of domestic work play out in practice, with persisting and deeply entrenched patterns of intersecting inequality and discrimination. The domestic work sector—which employed 13.5% of the country’s women workers as of 202022—is overwhelmingly comprised of poor black African women, a historically marginalized population. Migrant labour, from rural areas within the country or adjoining countries, forms a significant share of this workforce. Critically, ‘the low social status and undervalued nature of domestic work has roots in the historical use of specific racial and cultural groups as servants and slaves,’ exacerbated by the racialized nature of relations between black domestic workers and their white ‘madams,’23 a pattern that has lingered despite Apartheid ending.24 With growing black affluence, class has featured more prominently in domestic worker-employer relations. Nonetheless, ‘[t]he result of the complex interplay between gender, race, and class is, in many cases, a perception amongst employers that the domestic worker is a lesser creature,’ one outcome of which is the persistence of paternalistic relationships between domestic workers and their employers.25 Rogan and Alfers (2019) highlight the gendered inequalities that segment South Africa’s informal economy (which accounts for just under one-third of the workforce) and the marginal status of domestic workers

 See du Toit (2013).  STATS SA (2020). 23  The seminal study of this theme is Cock (1980). 24  See Archer (2011), du Plessis (2011), Jansen (2019). 25  Jansen, 2019: 191. 21 22

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therein.26 They show that workers in private households, most of whom are domestic workers and 79% of whom are female, occupy the bottom tier of the informal economy with the highest poverty risk. At the other extreme, employers, 87% of whom are male, occupy the highest tier with the highest earnings and lowest poverty risk. While most domestic work arrangements are traditional, a small but rapidly growing number of workers are registering with domestic work-­ focused platforms, which typically offer a digital, smartphone-operated app which enables clients to access the profiles of a range of workers whose availability and profile match their preferences for domestic service provision, as well as worker recruitment and payment. Digital platforms invariably evolve and change regularly, owing to company efforts to optimize market engagement and their app technology, at times in response to workers’ concerns. In this chapter we contrast literature on the ‘traditional’ domestic sector with data we collected from domestic workers using platforms in South Africa.27 The mixed-methods study involved a nine-round longitudinal survey with up to about 650 workers (depending on the round) on a domestic work platform in South Africa conducted between August 2018 and February 2019 as well as the analysis of platform data on its workforce. We complemented this research with qualitative interviews with workers who were active or previously active on gig platforms (16 direct interviews and one focus group discussion comprised of 10  Their study draws heavily on the informal economy model proposed by global research and policy network Women in Informal Employment: Globalizing and Organizing (WIEGO) (see Chen 2012). The WIEGO model reveals a high degree of segmentation by employment status and enterprise type, with employment status, in turn, segmented by gender and other social characteristics (e.g. caste, class, race), all of which influence poverty outcomes. Earnings are highest among informal employers and regular informal wage workers at the top (mainly male), followed by own-­ account workers and, finally, casual wage workers (of all genders). Industrial outworkers or homeworkers and contributing family workers (mostly female) have the lowest earnings and correspondingly, the highest risk of poverty. The model has received empirical support from Chen et al. (2005) for Costa Rica, Egypt, El Salvador, Ghana, India, and South Africa; and from Gindling and Newhouse (2014) using standardized data across 98 countries. 27  The research is part of a broader two-year research project exploring gender and the platform economy in Kenya and South Africa (Hunt et al., 2019). The project sought to understand the experiences of gig workers (male and female) working on a household services platform in Kenya alongside those of domestic gig workers (virtually all female) in South Africa. Given the focus of this chapter on domestic work, it draws on the South Africa component of this work. 26

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­ articipants), and three key informant interviews (one academic team, p one domestic worker union representative, and one platform representative).28 Our sample of platform workers reflects the demographics of the broader ‘traditional’ sector—98% of our survey respondents were female and 97% were black African. Indeed, many on-demand workers we interviewed had previously engaged in domestic work through traditional means or continued to do so alongside platform-mediated gigs.

Labour Regulation, Social Protection, and Institutions While domestic work conditions have historically been extremely poor and characterized by informality and exploitation, strong labour institutions have been central to formalizing the ‘traditional’ sector. Notably, unions such as the South African Domestic Service and Allied Workers Union (SADSAWU) and allies have led sustained campaigns for decent wages and adequate workers’ protection, resulting in the establishment of a relatively robust regulatory framework specific to domestic work. Increasingly committed government-based labour institutions have been key to the development and oversight of this framework. One of the most notable regulatory achievements for domestic workers working in traditional arrangements in South Africa is Sectoral Determination 7 (2002) which mandated a minimum wage and basic working conditions, such as formal employment contracts and the compulsory registration of workers, with the Department of Labour—a change that enables them to benefit from the Unemployment Insurance

 Identifying gig workers is a key challenge facing researchers of gig platforms. By securing the collaboration of a domestic work platform that was genuinely open to independent research on its workforce, we were able to gain access to its workforce for our survey and qualitative interviews, and the platform’s data on the supply of and demand for gig work, as well as in-depth interviews with platform representatives (see Hunt et al., 2019 for more details). In the qualitative work, we included gig workers working for this same platform and others. We found that this approach offered the best trade-off between access and independence, but it was not without challenges. A key issue that emerged is that survey data suggested far more positive experiences and perceptions of gig work than those that arose in our interviews with workers. We hope to revisit this issue in future work. 28

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Fund (UIF), one of the key social insurance mechanisms in the country.29 These stipulations laid the foundation for further reforms. While the minimum wage for domestic workers was initially set at 75% of the national minimum in 2018, it has since increased to a higher rate and is due to converge with the national average in 2022.30 The entitlement of domestic workers to the national minimum wage—notwithstanding critiques of the level of the national minimum wage being too low to meet the current cost of living—is an important symbol of their march towards pay parity with other workers. Furthermore, following a Constitutional Court decision in March 2021, the Department of Employment and Labour began the process of amending the Compensation for Occupational Injuries and Diseases Act of 1993 (COIDA) to extend workers’ compensation to domestic workers, again explicitly placing them on an equal legal footing with other employees.31 In addition, there is some evidence of formalization stemming from an interplay between regulation, labour institutions, and the social dimensions of domestic work. For example, South Africa’s Commission for Conciliation, Mediation and Arbitration is a mechanism that domestic workers often use, usually when a working relationship is terminated. Blackett (2020) finds that its work has led to a ‘subtle, slow shift in perception of the domestic worker-employer relationship as a relationship of servitude to a decent work relationship, and one that was taking place in the minds of both workers and employers … the dispute resolution mechanism is fast, close, and cultivates the sense that domestic workers belong there. Its very existence says, this is a recognised employment relationship, subject to the “rule of law” in the workplace.’ These important advances in legal protection notwithstanding, important gaps remain which mean that in practice many workers remain in situations of informality. Critically, several categories of domestic worker  The UIF covers five categories of benefits: namely unemployment, maternity, illness, and adoption and survivor benefits in the event of the contributor’s death. The contribution rate is 2% of the employee’s salary (1% by the employer and 1% by the employee) paid monthly through the payroll tax collected by the South African Revenue Services or paid directly to the UIF by those in informal or irregular employment (https://www.social-protection.org/gimi/gess/ShowWiki.action?wiki. wikiId=855) 30  Business Insider SA (2021). 31  Republic of South Africa (2021), SERI (2020). 29

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remain excluded from social protection. For example, one recent estimate suggests that approximately one-third of the domestic workers who work the requisite 24 hours or more per month remain unregistered with the Unemployment Insurance Fund (UIF), one of the key social insurance mechanisms in the country.32 These include foreign individuals working on contracts, as well as individuals employed for less than 24  hours monthly by a single employer—a key barrier given that many domestic workers work part-time and/or for multiple employers.33 This eligibility barrier is a long-standing challenge to the formalization of domestic work—and improvements in the conditions and economic security of domestic workers. There are no precise figures on foreign national domestic workers in South Africa,34 not least because they are more likely to work in less formalized segments of the sector. Nonetheless, the available evidence suggests that their number is sizeable,35 and increasing, As Fish (2013: 215) puts it: ‘the rapid increase in transnational migration is changing the composition of the domestic work force and complicating the application of established legal rights.’ Furthermore, ongoing xenophobic attitudes towards migrant workers in the country suggest that political opportunities for the reform of social protection to extend coverage to migrant workers may be limited.36 Employer non-compliance and domestic workers’ limited awareness of their rights (and consequent lack of impetus to demand compliance from employers) further impede implementation.37 In some cases, compliance may only be partial, for example when an employer provides a contract but does not register with/pay contributions to the UIF or the Compensation Fund. Even when more aware and entitled, many  Liao, 2019.  Department of Labour (2001), du Toit (2013) 34  According to Vanyoro (2021: 8): ‘The number of undocumented migrants in the country is not known and remains highly contested. There are no reliable estimates of how many of these migrants are women, as gender-disaggregated migration data are largely unavailable’ 35  Vanyoro (2021) cites a 2013 ILO study showing that Johannesburg, 35.6% of employed black women who originated from outside Gauteng province worked in private households compared with 9% of employed black women born in the province, though she stresses that most labour migration in the country is internal. 36  See Mukumbang et al. (2020); Human Rights Watch (2020). 37  Gobind et al. (2013). 32 33

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domestic workers fear losing their jobs and therefore do not question employers if they take actions which contravene the law, while others feel unable to navigate the claimant application process.38 Other persistent protection gaps include medical care, pensions, employment injury benefit, and family and maternity benefit.39 For example, on paper at least, a range of maternity protections now cover domestic workers—including some four months of paid maternity leave (at up to 60% of the contributor’s prior income) for those enrolled in the UIF (which mandates monthly employer and worker contributions).40 Yet while these provisions represent major progress in securing domestic workers’ maternity protection coverage, access to benefits remains low in practice given that many workers do not meet the criteria for UIF enrolment or meet the criteria but are not enrolled. In the platform economy, however, many of these indicators of informality can be clearly identified as inherent to the platform design and operating procedures. Although independent contractors are formally included in the Sectoral Determination, in practice, the predominant working pattern of platform workers, which involves providing work to multiple clients for relatively short booking lengths, means that very few would meet the requisite 24  hours with a single employer to become registered with the UIF. Indeed—as is common across the platform economy in other sectors and geographies—the platform company’s insistence on being merely a technology provider that links ‘independent contractor’ workers with clients means that, for the moment at least, any worker engaged in more than 24  hours per month via the platform is conceptualized—by the platform at least—as being engaged by a range of clients, thereby simultaneously relinquishing any possibility of themselves being obliged to fulfil the employer contributions mandate. Perhaps unsurprisingly, platform workers’ coverage by public social protection is low: the platform’s polling of its workforce in 2019 suggested that just 5% of on-demand domestic workers reported being  Magwaza, 2011; Liao, 2019  ILO, 2016c. For further discussion of limitations in accessing maternity benefits see Hunt et al. (2019). 40  See South Africa 1996, Dupper 2002, South Africa (2002), all cited in Hunt et  al., 2019, Department of Labour (2001). 38 39

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registered for UIF, while 32% did not know.41 Moreover, 27% of our survey respondents lived in households receiving a South African Social Security Agency (SASSA) grant (while 12% were unsure), compared with 70% of households nationally; given the economic insecurity of these workers, it follows that they are critically in need of the rights and protections that employee status would confer. An important point is that—in common with the traditional sector—our research suggests a significant share of migrant workers active on platforms, meaning that most of this group remain outside the remit of statutory protections. For platform workers, a widespread lack of coverage by social protection meant that routine life events such as childbirth risked further exacerbating acute economic insecurity. For example, they described how a lack of access to maternity leave income left them in extreme financial precarity—especially pertinent given that a majority were single mothers: I was pregnant when I joined the platform [but] it’s no work no pay there. I wasn’t active for three months. (Simlindile, Johannesburg) I asked worker support if they assist us during maternity leave and they said they don’t. That was the toughest time of my life. (Charlene, Cape Town)

Critically, platform workers’ engagement in collective action was fledgling. Formal platform worker organization is nascent in South Africa, with few signs of effective institutions or successful organizing in the on-­ demand domestic work sector. None of our survey respondents reported membership in any formal group that would advocate for their rights: one-third (32%) professed not to know how to join such a group and one-quarter (26%) felt that such organizations were for workers in the ‘formal economy.’ While South Africa’s main domestic workers’ union, SADSAWU, reported some complaints from platform workers it had not yet had the capacity to focus on them, and also noted that workers would need to be members of the union to receive structured assistance.42 Yet many workers were in informal communication with one 41 42

 Key informant interview, platform representative.  Key informant interview, SADSAWU representative.

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another—three-quarters of survey respondents (74%) reported interacting with others on a regular basis, most commonly through social media (notably, WhatsApp). While a helpful support to these workers in lieu of other options, these private collective groups remain highly informal and no formal organizing or bargaining agreement has yet emerged. In October 2021, the domestic work platform took the important step of committing to recognizing any trade union or other worker organization that approaches the platform, albeit with the stipulation that ‘they [the organization] can prove … that they have sufficient representation’ amongst registered workers’—and with little clarity provided around what constitutes ‘sufficient representation.’43 The extent to which this commitment will lead to workers’ engagement in bargaining with the company and influence over working conditions remains to be seen.

Social Dimensions As in the ‘traditional sector,’ violence against on-demand domestic workers is a concern, with particular risks stemming from providing services to a range of different and unknown clients in their homes.44 Indeed, several workers reported instances of rude, aggressive, or abusive treatment while working behind closed doors. Some evidence suggests that investing in skills development, certification and other forms of domestic work ‘professionalization’ are important for its increased societal and economic valuation.45 Indeed, the platform we collaborated with in South Africa had sought to challenge client perceptions of domestic work as a low-value commodity by presenting it as professional service meriting ‘above market’ rates.46 But although the company had started out with a higher price for clients, intending to pay workers higher rates, clients did not make bookings until hourly rates were lowered; ‘razor thin margins and no willingness to pay’ among  SweepSouth (2021)  Hunt & Machingura, 2016, op. cit. 45  ILO (2017), op. cit. 46  Key informant interview, platform representative. 43 44

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clients made raising earnings an extremely challenging proposition for a start-up company.47 Moreover, several interviewees spoke of a continued lack of respect from clients for the service they were providing and poor treatment at the hands of clients, suggesting that on-demand models have not yet been associated with domestic work—or workers—being increasingly valued by clients. The location of gigs was also a challenge—the persistent legacy of racial and economic segregation in South Africa means many workers live in township or other low-income areas that are geographically far from their more affluent clients’ neighbourhoods, distinguished by poor transport links and extremely high levels of crime and insecurity. This was a particular concern for workers travelling between gigs, especially in the early morning hours, who related several instances of armed and aggressive robbery. Last month when I was on my way to my booking, the robber wanted to take my handbag that had my cell phone inside. I could not afford to give away my handbag to the robber, so the robber stabbed me with a knife. (Akumzi, Cape Town)

Our research suggests that domestic workers’ experiences of abuse and violence as a result of travelling to clients’ homes was not entirely new; security risks are intrinsic to their experiences of navigating urban spaces in South Africa. Indeed, a lack of bargaining power with platform clients also contributed to domestic workers’ security risks, as workers reported having limited ability to negotiate later gig start times which would enable them to leave their homes later and in daylight hours. However, what is new is the additional risk posed by platform modalities which can see workers sent to several gigs in one day, thereby increasing risks as they travel between them, with the risk being intensified given that workers’ low incomes mean they are forced to travel on public transport, with major transport hubs and this type of transport itself regarded as locations where robbery and theft are common.

47

 Ibid.

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The implication of this discussion is that the formal/informal categorization is not a binary nor is the approach of ‘incremental formalization’ necessarily a linear, one-way trajectory towards formalization—and by implication a continuous journey towards improved working conditions. While the legal advances previously discussed have formally established domestic workers as entitled to many labour and social protections similar to those of other workers, these advances so far appear to cover only a subset of the traditional domestic workforce who meet the eligibility requirements and whose employers have complied with the regulations. A critical question is how policy can support the extension of these protections to all domestic workers, and how to balance the need for labour rights and social protection with the very real need for employment. The platform economy throws into relief this trade-off, emphasizing economic opportunities, although these are often outside the remit of the hard-won regulations the Sectoral Determination has made possible—through not meeting coverage criteria and because of platforms’ overt efforts to disavow their position within a formal employment relationship.

 eyond the Formal/Informal Divide: What Do B Platforms Offer? Determining the extent of formalization of the domestic work sector requires investigating the extent to which core elements of formalization are in evidence and the share of the domestic worker population that they cover. However, where informality is widespread, this comparison of traditional work arrangements with platform work raises critical questions—namely what difference a regulatory code governing domestic work makes to the sizeable share of domestic workers who are working in traditional arrangements informally, what opportunities the platform economy makes available and whether platform-based work offers any improvements to their working conditions. It’s easier working through the platform because in this day and age, jobs are scarce. You cannot go to someone’s gate and knock for a job, otherwise they will shoot you. (Jane, Johannesburg)

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Platform workers take on individual ‘gigs’ without any guarantee of further employment, an operating model that entails clear risks. The model allows platforms and clients to contract workers only when they need them, meaning the platform can respond to fluctuating demand at minimal cost. Moreover, the ability of clients to book (and cancel) cleaners on an ad hoc basis—designed into the platform model—introduces additional uncertainty for workers. Utilization rates for ‘full-time’ workers (those available for work 5 or more days weekly) averaged around 60% over a one-year period,48 while the irregularity in receiving bookings meant that some platform workers experienced significant changes in their incomes from week to week, as demonstrated in average variation from mean earnings of close to 50% weekly. Depending on the booking length, the platform was charging its clients a variable rate depending on the number of hours booked—with the maximum hourly rate nearly four times as high as the government-­ mandated minimum wage for domestic workers. As of December 2018, average weekly platform earnings were around 45–50% higher than the minimum wage for domestic workers working at least 27 hours weekly, which equalled ZAR 616 or PPP $100. These earnings fall short of a living wage,49 and do not account for higher overhead costs of platform work—e.g. mobile phone credit and transport costs between gigs. Nevertheless, over half of survey respondents (56%) reported being satisfied or very satisfied with their pay, with a significant share reporting that their hourly earnings were higher than they would be in other types of work: 37% reported that working under the platform was more lucrative than other jobs on an hourly basis and 40% indicated this was ‘sometimes’ the case. These findings suggest that despite the inadequacy of platform earnings, they are still better than other options—notably traditional domestic work arrangements.

 We computed utilization rates, discounting voluntary ‘days off’ between November 2017 and December 2018. 49  Between June 2018 and mid-September 2019, weekly earnings for workers with five or more days availability per week, excluding voluntary ‘days off,’ averaged ZAR 900 (PPP $145). Estimates of household income needed for a household of four to exceed the poverty line are ZAR 5276 (Finn, 2015) and ZAR 4125 per month (Budlender et al., 2015). 48

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Moreover, we have found that in low- and middle-income country contexts, platform economy offerings are likely to be on a par with other casual and informal jobs which constitute the bulk of employment opportunities.50 In addition to providing economic opportunities at a higher hourly rate (volatility notwithstanding), platform-mediated work may even offer some advantages over other casual and informal jobs in that, while the platforms we examined have not sought any linkages with the public social protection system, there have been limited attempts to develop some private forms of provision which in many cases were the first type of employment-based insurance workers had ever knowingly been entitled to access. The platform involved in our research in South Africa, for example, had instituted various measures aiming to improve working conditions, including making available accidental death and disability cover to workers via a private insurance company (meaning that workers had some compensation in the event of work-related injury before they had any legal recourse to COIDA). At the same time, it is indisputable that the platform model allows its clients to avoid the economic cost of guaranteeing employment for a set number of hours and providing the other benefits stipulated by South African labour law. Therefore, it could be argued that an important competitive advantage of platform models derives from their de facto support to purchasers of domestic workers’ labour to not comply with labour or social security regulations.51

Conclusions We find that while platforms offer some positive features that workers value (and which have improved their working conditions), at their current scale, the ability of platform companies to alleviate the broad structural economic conditions that restrict the availability and quality of work available to marginalized and disadvantaged women in South Africa is likely to be limited. Moreover, their very existence may serve to  Hunt & Samman, 2020.  For more discussion on the limitations of the private insurance model, see Hunt & Samman, 2020.

50 51

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undermine the regulatory framework that has been the product of years of campaigning by and on behalf of domestic workers, movements that can be said to transcend labour issues, being grounded in ideals of social justice that aim to overcome inequalities linked to gender, race, and migratory status.52 There is a strong case that the profit-making model of platform companies in South Africa currently depends on the historic inability of domestic workers to establish a de facto employment relationship (and the better conditions that accompany it), as well as poor compliance with existing regulation governing traditional domestic work.53 If these barriers faced by workers are tackled in a meaningful way, then platform models may struggle to exist. Platform companies, in turn, argue that innovation is needed to provide employment (notably where unemployment is high, as in South Africa); that they provide their own forms of support to workers where this is viable (e.g. private insurance); and that their operating model bolsters work quality in other ways in settings where poor quality work is endemic. Indeed, they have even overtly acknowledged their failure to ensure that workers enjoy the full benefits afforded by the legal framework, referencing the reality of widespread informality and poor working conditions in the traditional sector, noting that their offer occupies a middle-ground of comparatively better work. Yet, if regulators insisted upon an employee relationship between themselves and those providing labour through their platform (and attendant obligations in terms of worker taxation and employee contributions), platforms contend, then their profit-making ability (and the economic opportunities they facilitate) would be severely hampered. The broader trade-off in this scenario is not new—being one between regulation and the role of the state in

 See Blackett, 2020.  This may reflect employer unwillingness to register their domestic workers and thereby contribute to the fund, but there is also ample anecdotal documentation of administrative failings in the registration system—e.g. problems with the online registration process, a lack of online support in registering workers, overwhelmed call centres, employer accounts of not receiving a worker’s registration number after making a submission, and a lack of government communication with or training for employers on compliance (see Liao, 2019; Crouth, 2020). 52 53

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setting standards versus the need of workers for jobs—including demand for jobs that do not fulfil all the criteria associated with decent work. Going forward, consideration of how to ensure workers’ access to full labour and social protections will be critical as the platform economy grows—not least as many platform workers are simultaneously active in the ‘traditional’ sector, meaning that measures to recognize multiple employment relationships within social protection systems will become ever more important. There are several avenues that can be pursued here. The first is juridical. In South Africa (as elsewhere), platform workers in the transport/ride-hailing sector have challenged their independent contractor status, most notably through class action suits between drivers and Uber SA.  An earlier court case initially granted drivers employee rather than independent contractor status, and the associated protections, but this decision was reversed on appeal. Most recently, in March 2021, inspired by successful counterpart litigation in London, lawyers stated their intention to launch another class action suit in South Africa on behalf of Uber drivers wishing to be recognized as employees.54 An innovative and seemingly successful example stemming from juridical proceedings took place in Denmark in 2018, where 3F, the country’s largest trade union, and digital platform Hilfr concluded a collective agreement for domestic gig workers (formally permitted for employees but not self-employed persons). The agreement opened the possibility for platform workers to benefit from labour and social protections by enabling them to choose whether they prefer the status of an employee (the default, after 100  hours of service provision) or a freelancer. Employees are entitled to a minimum wage, and rights including partial payment in the event of late cancellations, pension contributions paid by Hilfr, paid holidays and sick leave, rest breaks, and the need to follow a process for worker dismissal.55 Munkholm et al. (2018: 118) argue that such agreements are congruent with the Danish model of collective bargaining and with competition law ‘as long as the circumstances of each contract of service are characteristic of employment and as long as the service providers are not genuinely self-employed.’  Staffing Industry Analysts (2021).  Munkholm & Schjoler, 2018.

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A second avenue is to ‘incrementally formalize’ the platform economy. This potentially has the advantage of extending some benefits to workers independently of their employment relationship, however any improvements are likely to be ad hoc and gradual at best. There are several examples of steps that platform companies and other actors have taken to extend social protections to platform workers. In South Africa, during our two-year study, the platform we worked with put in place numerous measures seeking to increase workers’ incomes and reduce their volatility, increase protections and benefits, and promote information sharing among workers, training, and skills development.56 In the USA, the National Domestic Workers’ Alliance has developed Alia, a digital portable benefits programme through which multiple clients can contribute to each cleaner’s account, which cleaners then use to purchase benefits including disability, accident, critical illness, and/or life insurance and paid days off.57 And in Indonesia, the largest gig platform—Gojek— became a global pioneer in 2018 by developing the SWADAYA programme, a partnership between the platform, BPJS Ketenagakerjaan (BPJS Employment)—part of the country’s public social security system—and Bank Mandiri to make it easier for platform drivers to register and pay BPJS Employment Social Security contributions, making accident and life insurance available.58 However, the extent to which such innovations will lead to broader, sustainable transformative change is not yet clear, not least because there is a high degree of uncertainty surrounding those measures that are subject to the whims of platform companies who can change their operating terms overnight should they wish, heightening the risk of sudden rollback of advances, for example if directed by their business needs. Furthermore, the proactive role of the South African government in supporting improved conditions in the platform economy within this approach is far from guaranteed, given the lack of comprehensive enforcement of the existing regulatory framework in the traditional sector (and associated widespread informality).

 See Hunt et al., 2019: 73, Box 10.  https://www.myalia.org/ 58  Hunt et al., 2019: 70. 56 57

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A third—and more radical—avenue to support platform workers involves challenging the primacy of the standard employment relationship as a prerequisite for access to labour and social protections. For example, Fudge (2020) argues that the formalization debate is unhelpful to informal workers because of its focus on standard employment relationships and that instead, ‘[p]olicy-making needs to look for functional equivalents to the institutional role that the employer played in the standard employment relationship.’ She proposes a focus on work as an activity, not a relationship: ‘work relations are no longer seen solely in terms of the subordinated employee, and its alter ego, the independent contractor… It is also critical to move away from a map of personal work relations that depicts them as a series of concentric circles with the contract of employment at its core.’ This shift away from contractual relationships opens up consideration of a range of instruments for dealing with social and economic risks, and not simply employment risk. Maternity benefits, for example, become subsumed under social security, not labour law. The discussion of the social and economic structures underpinning formality and informality highlight broader issues relating to the securing of meaningful improvements in conditions of widespread informality and economic insecurity. Does the way in which formalization has proceeded further embed existing inequalities within the domestic work sector, including by reinforcing the systemic exclusion of those most in need of labour and social protections, or rather has it enabled a paradigm shift in perceptions of domestic work that will motivate the extension of decent working conditions? We have argued that the platform economy throws this tension into relief, by bypassing the official regulatory framework while offering piecemeal and ad hoc protections that, despite their insufficiency, offer some concrete improvements in the view of platform workers who might otherwise be working in traditional—and highly informal—arrangements. However, at present, the operating model— particularly in highly informal settings—does not seem to offer (and indeed, may preclude) possibilities for broader structural transformation. Tucker and Anantharaman (2020) articulate the dangers: ‘Challenging entrenched structures of elite power is so difficult that some caution incremental change and propose only Band-Aid solutions to ameliorate

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some of the most acute forms of suffering caused by racial capitalism.’59 Future research will be needed in particular country contexts to explore the types of regulation that will offer forms of support to platform workers that advance the decent work agenda as well as showing what broader structural changes might look like. We believe this will become more relevant than ever in the years to come. The Covid-19 pandemic has had a markedly detrimental impact on the domestic work sector, owing to falls in demand associated with public health concerns and economic contraction. In South Africa, the impact on black African women domestic workers in the informal economy was particularly acute. For example, a survey that the SweepSouth domestic workforce platform conducted of its workforce between June and August 2020 found that their living costs had increased considerably (by one-third) alongside falls in earnings and increased underemployment. One consequence was that 75% of respondents reported having reduced their food budget, highlighting their extreme precarity.60 Similarly, the survey of 602 domestic workers carried out by domestic workers alliance Izwi in early 2020 found that ‘most domestic workers would not receive wages, resulting in more hunger and evictions’ because only about 20% had been registered for the UIF when the pandemic struck’.61 While the longer-term impact of the pandemic on the platform economy is uncertain, if economic insecurity among client households persists, the platform economy model—with the minimal commitments it requires clients to make—may well become more attractive, entrenching largely informal working conditions, and intensifying the need for a concerted policy response. Acknowledgements  We extend thanks to Fairuz Mullagee, Social Law Project, for comments on an early draft of this chapter, as well as Sofia Trevino and Adriana Paz, International Domestic Workers Federation, for their overarching reflections on informality and the domestic work sector which helped refine our  Tucker, J. L. and Anantharaman, M. (2020: 296).  SweepSouth, 2020. 61  See Crouth, 2020. 59 60

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thinking for this chapter. We are also grateful to the co-authors of the original research on which this chapter is based: Sherry Tapfuma, Grace Mwaura, and Rhoda Omenya, as well as Kay Kim, Sara Stevano, and Aida Roumer.

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Index

A

Academic writing, 62 Accidents, 151 Africa, 2 Agency work, 3 Algorithmic despotism, 146 Algorithmic governance, 4 Algorithmic management, 9, 146 practices, 50 Algorithmic systems, 5 Algorithms, 23 Alia, 261 Amateurization, 156 Amazon Mechanical Turk (AMT), 54, 55, 190 Amsterdam, 246 Appification, 225

Arab States, 54 Artificial intelligence, 17, 48 Attrition rates, 120 Autonomy, 19 B

Babajob, 185 Bahujan, 218 Bali, 6, 164 Bangalore, 94 Bangkok, 94 Banjar, 174 Bank Mandiri, 261 Batam, 173 Bathroom deep cleaning, 224 Beauty services, 200

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. Surie, U. Huws (eds.), Platformization and Informality, Dynamics of Virtual Work, https://doi.org/10.1007/978-3-031-11462-5

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272 Index

Beauty work, 189 ‘Before’ and ‘after’ pictures, 225 Bengaluru, 9, 218 Berlin, 246 Big data analytics, 197 Bihar, 107 Bike boys, 148 Bike lanes, 153 Biographies, 95 Black African women, 247 BlueBird, 176 Bolt, 32 Bonuses, 170 Boycott, 31 BPJS Ketenagakerjaan, 261 Brand, 168 Brazil, 2 Brazilian, 141 Brazilian labour, 6 Break, 122 Business process outsourcing (BPO), 72 C

Cabify, 2 Campinas, 149 Cape Town, 10 Capital assets, 64 Capitalist accumulation, 142 Care-based values, 187 Care responsibilities, 57 Care work, 185 Carpet-layers, 199 Caste, 94, 224 Certification, 254 Childbirth, 253 Childcare, 200 Chillia, 94 City Taxi Rules, 110

Class struggles, 94 Cleaning houses, 183 Clickworker, 55 Cloudwork, 19 CodeChef, 55 Codeforces, 55 Collective agreement, 260 Collective bargaining rights, 193 Collectivization, 99 Commission for Conciliation, Mediation and Arbitration, 250 Commodified, 167 Community meetings, 174 Compensation for Occupational Injuries and Diseases Act (COIDA), 250 Competitions, 63 Competitive programming platforms, 48 Complaints, 27 Content creation, 59 Contract work, 150 Control, 37, 116 Courier services, 20 COVID-19 pandemic, 19 CrowdFlower, 55 Customer complaint, 124 Cycle couriers, 7 D

Daily life, 94 Dalit, 218 Data, 24 annotation, 48 collection, 225 processing, 48 Deactivation, 74 Death, 229

 Index 

Debugging, 63 Decent work, 195 Delhi, 94, 108, 183 Delivery couriers, 141 Denmark, 260 Denpasar, 167, 176 Deregulation, 106 Design Justice, 202 Deskilling, 59 Destinations, 114 Developing economies, 2 Didi Kuxing, 2 Digital disruption, 13 Digital economy, 2 Digital literacy, 185 Digital platforms, 2, 4 Digitization, 2 Dimensions, 5 Disciplining force, 162 Discrimination, 9 Domestic cleaning platforms, 246 Domestic labour, 5 Domestic workers, 9, 10, 191 placement agencies, 219 unions, 244 Domestic Workers Rights Union (DWRU), 218 Driver partner, 93 Drivers, 96 Dubai, 104

Entrepreneurship, 101 Entrepreneurship of the self, 144 Essential workers, 228 Ethnicity, 241 Expenses, 225 F

Facebook, 2 Facial recognition, 198 Fake order, 172 Fake reviews, 59 Feminist, 8, 183–205 design, 9, 187 Fieldwork, 164 Fleet managers, 96 Flexibility, 6 Flexible accumulation, 142 Flexing It, 190 Flipkart, 20 Focus group, 248 Food delivery, 20, 35 Fordism, 142 Fordist mass production, 13 Formalization, 23, 150, 240 Formalizing, 6 Formalism, 240 Freelance platforms, 48 Freelancer, 67 Freelancer, Upwork, 55 Freelancing, 19

E

Earnings, 6, 75 Eastern Europe, 54 Education, 6 Eldercare, 200 Electricians, 199 Employee benefits, 188 Entrepreneurs, 102

G

Gamification, 185 Gendered dimensions, 5 Gendered violence, 188 Gender inequalities, 185 Gender-neutral language, 202 Gender norms, 186

273

274 Index

Gender pay gap, 77 Gender roles, 58 Generate job opportunities, 9 Geographical discrimination, 49 Geographically-tethered platforms, 15 Gershuny, Jonathan I., 17 Ghanian economy, 3 Gigs, 19 economy, 141 work, 14 worker organizing, 227 Global care chains, 17 Global commodity chains (GCCs), 17 Global North, 2 Global South, 2 Gojek, 8, 161 Google, 63 GoPay, 174 Governance, 196 Government stakeholders, 218 GPS, 23, 97 Grab, 177 Graduate, 47 Gratuity pay, 193 Grievance, 27 Gujarat, 98 H

HackerRank, 55 Hart, Keith, 3 Health, 115 and safety, 151 Healthcare, 201 Helmets, 162 Highly educated workers, 6, 48 Hilfr, 260

Hindi, 218 Home service platforms, 199 Hourly earnings, 75 Housejoy, 29, 190 Hyderabad, 198 I

Iceberg, 55 Identity theft, 222 ILO Convention no. 189 on Domestic Work, 242 Immigrant, 176 Incentive, 33 Income taxation, 239 Incremental formalization, 238, 240 Independence, 184 Independent contractors, 19 India, 2 Individualization, 14 Indonesia, 8 Industrialization, 4 Informal economy, 3 Informal enterprises, 239 Informal networks, 27 Informal transport, 90 Informalization, 6, 139 Informalization processes, 140 Informality, 2, 3 Institutions, 240 Insurance, 261 Intermediaries, 14, 219 International Labour Organization, 3 Internet connection, 19 Interviews, 20 IPT sector, 92 Istanbul, 167 Izwi, 263

 Index  J

Jakarta, 6, 167 Java, 164 Just-in-time workers, 139, 140, 145 K

Kaali peelis, 97 Kalyan-Dombivali, 91 Kannada, 218 Karnataka, 30, 108 Kenya, 200 Kenyan economy, 3 L

Labour law, 10 Labour regulation frameworks, 243 Labour relations, 139 Labour rights, 145 Latin America, 7, 54 Legal services, 48 Licenses, 98 Life trajectories, 148 LinkedIn, 54 Lived experiences, 95 Lived space, 95 Live-in domestic work, 220 Loans, 116 Local labour market, 62 Lockdowns, 227 London, 150 Low pay, 17 Low-skilled workers, 48 Lyft, 197 M

Machine learning processes, 59 Maharashtra, 102

275

Maharashtra City Taxi Rules, 109 Maharashtra Rajya Rashtriya Kamgar Sangh, 118 Makassar, 176 Manpower agencies, 219 Marginal economies, 3 Marketplace, 217 Massage services, 200 Mass recruitment events, 173 Maternity, 188 protections, 252 Measurement, 23 of work, 6 Medan, 177 Mental stress, 185 Merchandiser, 149 Metered taxis, 91 Methodology, 165 Metrics, 50 Microentrepreneur, 144 Microsoft, 63 Microtask platforms, 48 Microwork, 19 Microworkers, 55, 80 Middlemen, 219 Migrants, 8 communities, 99 labour force, 94 Minimum wages, 152, 156, 193 Mixed-methods study, 248 Modernization, 4 Modernize, 104 Modernity, 93 Monetization, 162 Motivation tools, 171 Motoboys, 148 Motorcycles, 161 Motorcycle taxi, 94 Motor Vehicles (Amendment) Act, 111

276 Index

Multi-apping, 6, 36 Mumbai, 6 Mumbai Grahak Panchayat, 105 Mumbai Taximen’s Union, 99 N

Nando, 31 National Domestic Workers’ Alliance, 261 Navi Mumbai, 91 Neighbourhood-level organizing, 7 New Delhi, 9, 218 New York, 246 Nigeria, 2 99 designs, 64 Notice of dismissal, 239 O

Occupational segregation, 53, 214 Ojek, 168 Ojek drivers, 8 Ola, 26 On-demand, 169 Online courses, 52 Online forums, 51 Online training tools, 70 Organization in dispersion, 142 Outsourcing, 143, 149 Overwork, 194 P

Pahl, Raymond Edward, 17 Paid days off, 261 Pandemic, 19 Participatory-design, 191 Partnership, 223

Part-time, 18 Passengers, 96 Patriarchal norms, 242 Paypal, 63 Payroll, 225 Pecalang, 174 Pengojek, 173 People of colour, 196 PeoplePerHour, 68 Performance metrics, 195 Permits, 98 Personal care, 20 Photographs, 165 Piece-rate, 19 Placement agencies, 221 Planners, 95 Platform architecture, 186 Platform beauty work, 189 Platform workers, 7 Platformization, 2, 140 of work, 141 Plumbers, 199 Poor performance, 52 Post graduate degree, 47 Poverty, 2 Poverty risk, 248 Pratapgarh, 99 Precarious employment, 4 Precariousness, 3 Precarization, 3 Precarity, 4 Private training institutes, 47 Production chains, 143 Professionalization, 150 Profiles, 68 Programming contests, 71 Prolific, 55 Protests, 226 Punjab, 98

 Index  Q

Qualitative rating system, 204 R

Race, 241 Racial hierarchies, 242 Racist, 198 Radio taxis, 97 Rankings, 63 Ratings, 25 Recruiters, 9, 173 Recruitment, 6, 64 Registration camps, 221 Regulatory code, 256 Rejected, 67 Rejection of tasks, 66 Remote Desktop Computers (RDCs), 72, 73 Reputation, 6, 50 Restaurant, 172 Review, 28 Riau Islands, 164 Rickshaws, 89 Ride hailing sector, 6 Ridesharing, 101 Routes, 114 Russian freelance platforms, 60 S

Salon, 185 São Paulo, 148 Sassen, Saskia, 17 Savarna, 218 Scalability, 216 Scotland, 35 Sectoral Determination 7, 249 Self-employed, 48

277

Self-entrepreneurship, 93 Sexist, 198 Sexual assault, 105 Shanghai, 104 Shared taxis, 89 Shifts, 115 Shiv Sena, 107 Sick leave, 239 Side hustles, 148 Signalling, 53 Singapore, 104 Skills, 6 certification schemes, 52, 70 mismatch, 48 Smartphones, 17, 161 Social interaction, 121 Social justice, 9, 202 Social media platforms, 2 Social protection, 3, 243 Social reproduction, 144–145 work, 246 Social security, 193 benefits, 81 Social space, 95 Software, 20 development, 48 licencing contracts, 194 South Africa, 8, 200, 238 South African Domestic Service and Allied Workers Union (SADSAWU), 249, 253 South African Social Security Agency (SASSA), 253 South Asia, 7 Spatial practices, 95 Spatially-independent platform work, 6 Spatial representations, 95 Speed, 169

278 Index

Sporadic work, 150 Staffing companies, 221 Standard employment model, 3 Standard employment, 144 Standardization of communications, 6 State agencies, 7 Stigmatized, 200 Street vending, 92 Strikes, 30 Structural adjustment, 14 Structural change, 238 Subjectification, 140 Subordinated self-management, 142 Subordination, 140 Sub-Saharan Africa, 187 Subsumption of labour, 147 Sulawesi, 164, 174 Sumatra, 174 Surge pricing, 111, 116 Surveillance, 115, 225 Surveys, 49 SWADAYA, 261 SweepSouth, 263 Swiggy, 32, 34, 184 T

Tamil, 218 Targets, 26, 115 TaskRabbit, 28 Tasks, 19 Taxi driving, 7, 90 Taxi stands, 96, 121 Taylorist, 171 TeamLease Services, 200 Telangana, 223

Telegram, 173 Temporary jobs, 18 Text messages, 165 3F, 260 Topcoder, 55, 63 Toptal, 64 Tourist permit, 112 Toyotism, 142 Trade unions, 29 Traffic congestion, 103 Training programs, 201 Transcription, 48 Translation, 48 Transnational migration, 251 Transparent, 204 Trip duration, 25 U

Uber, 2 Uber Eats, 33 Uberization, 139 of work, 3 UK, 5, 15 Unemployed, 58 Unemployment, 47 Unemployment Insurance Fund (UIF), 249–251 Unions, 7 United States (USA), 246 University degree, 56 Unpaid reproductive work, 188 Unpaid tasks, 51 Urban Company, 184, 190 Urbanization, 178 Urban-rural migration, 173 Uttar Pradesh, 98, 107

 Index  V

Vehicle owners, 96 Violence, 254 Virtual Private Network (VPNs), 72, 73 W

Wages, 24 Waiting times, 35 Websites, 59 West Bengal, 108 WhatsApp groups, 22 White block, 147 Women, 103 workers, 6 Word-of-mouth networks, 221 Workarounds, 26

Work contracts, 194 Worker groups, 38 Worker representative, 36 Working conditions, 31 Working from home, 144 Working hours, 8, 193 Working time, 140 Work intensity, 147 World Bank, 15 Y

Yahoo, 63 YouTube, 59 Z

Zero-hour contracts, 3 Zomato, 28, 184

279