Gig Work and Platforms during the COVID-19 Pandemic in India

Shipra ( is a research scholar at the Centre for Informal Sector and Labour Studies, Jawaharlal Nehru University, Delhi. Minaketan Behera ( teaches at the Centre for Informal Sector and Labour Studies, Jawaharlal Nehru University, Delhi.
11 November 2020

The COVID-19 pandemic has impacted workers globally. This article attempts to highlight the impact that the pandemic-lockdown and its subsequent cautious relaxation in India had on geographically tethered on-demand gig workers.


The beginning of the year 2020 saw the spread of COVID-19 across the world. Taking a lesson from countries across the world, India went into a nationwide lockdown from 24 March 2020 and a subsequent phase-wise unlocking from 1 June 2020 (Pant and Shende 2020). During the lockdown, movement was allowed only after obtaining permission from the competent authority for providing or availing select essential services. The COVID-19 pandemic has been one of the world’s worst global crises and has disrupted the global economy at a massive scale, with billions of people forced to stay at home to contain the spread of infection. Among sectors that were immensely impacted by the pandemic were the platform economy and geographically tethered on-demand gig workers (TDGW), respectively. In India, a large section of its urban population is currently engaged with the platform economy in one way or the other. While the platform economy has been portrayed as being pandemic proof [1] or as one benefitting from chaos, it appears to be one of the worst hit (Ashok 2020; Shalev 2020; Ramakrishnan 2020).

Platform Economy and Gig Workers: Heterogeneous Entities

The platform economy is also called the digital economy or gig economy. The basic understanding of the business model of any platform economy is that the platform, using software applications (apps), acts as a digital mediator between service providers and the service requesters (Stewart and Stanford 2017; Prassl 2018). The idea is to make the process frictionless and provide speedy service through digital intermediation by matching a large pool of workers and customers (Prassl 2018). This fluid and temporary networked collaboration for production and sale of goods and services dissolves immediately after completion of the task and the individuals are set on seeking new assignments (Gandini 2016; Salamon 2019). 

Woodcock and Graham (2020) divide platform work into two types: geographically tethered work and cloud work. The former includes work that was present even before digital platforms came into being and required the presence of workers in particular geographical locations to perform the work. These are further divided into: (i) jobs where workers are visible, for example, food delivery or taxi workers; (ii) jobs that continue to be invisible, for example, home-cleaning services. The latter, cloud work, includes shorter digital tasks called micro-work and online freelancing. These are short-duration jobs that could be performed from anywhere geographically with an internet connection (Woodcock and Graham 2020). A similar division is suggested by Berg et al (2018), where they divide commercial digital labour into web-based platforms and location-based platforms. They divide the former category into freelance marketplaces, micro-tasking-crowd-work and content-based creative-crowd-work and the latter into accommodation, transportation, delivery, household services and local micro-tasking.

Platform Economy and Geographically Tethered Workers (GTW) in India

There is no proper government data available on gig workers in India. After a report indicating high unemployment of the country was leaked in January 2019, NITI Aayog asserted the claims to be false as the application-based ride-hailing cab companies had alone generated more than 2 million jobs (Salve and Paliath 2019; Nair 2020). It is estimated that the number of service providers involved with various platforms has increased from 8.5 million in 2016 to 11.7 million in 2017 and 15 million in 2018 (Tiwari et al 2019). 

2.1 Two-faced platform: During the pandemic, the platforms performed various activities. Uber and Ola provided commuting services to front-line healthcare workers (Mukhopadhyay and Mukhopadhyay 2020). Platforms like Zomato, Swiggy, Flipkart and Uber collaborated with various other platforms to provide essential services to the remotest places (Mukhopadhyay and Mukhopadhyay 2020; Buchholz 2020). Swiggy started various campaigns to feed the needy, and by 2 April,  had served 2,50,000 meals through initiatives like “Hope, Not Hunger” (Deccan Herald 2020). On the other hand, some of these platforms actively laid off their employees due to a  fall in revenue. In May 2020, Swiggy, Zomato, Ola, Uber and Airbnb fired 1,100, 520, 1,400, 600 and 1,900 employees, respectively (Kumar 2020; Bhargava 2020; Pant and Shende 2020). This ranged from 13%–25% of these platforms’ total workforce (Kumar 2020; Bhargava 2020; Pant and Shende 2020). In July 2020, Swiggy laid off 350 more employees (Abrar 2020). Though these workers were provided with two to three months’ salary and other benefits,[2] the job loss during this time hit them dearly. 

2.2 Platforms and Challenges faced by GTW during the COVID-19 Pandemic

2.2.1 Myth of Service Providers as Entrepreneurs: Attempt to fashion self-exploited workers   

The platform economy service providers or gig workers or “partners” function on a day-to-day basis, but are formally treated as entrepreneurs by the platform management. This treatment is an attempt to shift the risk of business to these “entrepreneurial-venture labour” and the same is normalised and glamourised by idealising entrepreneurial values in non-entrepreneurs. The individuals are presented as perceiving the work they perform as an investment that would bring returns other than wages in the form of a future payoff and the hope for a living wage and stable employment (Gina Neff 2012 cited in Salamon 2019; Gillian Ursell 2000 cited in Salamon 2019). This amounts to self-exploitation by performing “hope labour” (Kathleen Kuehn and Thomas F Corrigan 2013 cited in Salamon 2019). Thus, during the pandemic, the problems faced by the service providers were visualised as individual problems and the platform owner was not expected to provide them with any socio-economic support. This became acceptable and “normal” within India because almost 90% of India’s work is in an informal set-up with no clear policy. With government-imposed lockdown, the service providers had no scope of protection at any level. 

Most of the service providers faced major dilemmas during the lockdown. As the service providers came from different socio-economic classes, their approach to the situation was different. The workers with some source of income in villages preferred going back as surviving in cities would have been costlier. The workers who had bought vehicles for their work had to stay back in cities and earn enough to service the loans (Lalvani and Seetharaman 2020). Uber and Ola drivers who have to pay EMIs for their cars have complained about not getting enough work to even justify the cost of sanitisers and petrol they are spending on (Economic Times 2020). 

2.2.2 Platform’s Autonomy Over Monetary Decisions 

While the autonomy at work discussed by gig workers seems important for the worker, it is, in reality, “autonomy over minute decisions” (Aaron Shapiro 2017 cited in Wood et al 2019). The amount to be charged from the customers is announced by the management and not negotiated with the workers (Woodcock and Graham 2020; Nair 2020). When the platforms resumed the work post lockdown, the service providers attached with them had neither enough work to make decent wages nor were they allowed to set the price they would be paid per task. In most cases, the platforms decided to lower their prices and the service providers had no say over the decision. In a study by Lalvani and Seetharaman (2020), the number of workers getting 16–20 orders a day went down from 30.9% of the surveyed workers before lockdown to 7.2% post lockdown. During the lockdown, despite working for more than 10 hours a day, workers did not get incentives as they did previously. The service providers have claimed that they have not received any minimum guarantee or pay packages from the platforms (Lalvani and Seetharaman 2020). The service provider is thus at the mercy of the platform for the allotment of tasks, setting up the price and getting a minimum guarantee. 

2.2.3 Platform and Workers Safety and Security

During the COVID-19 pandemic, whenever the platform talked about their services being safe, they meant it in the context of users and not in relation to service providers. Every measure, from temperature checks to installation of Aarogya Setu app to wearing safety gears, is determined and enforced by the platform on the service providers (Nair 2020). The same is not expected of the customer. Except for ride-hailing platforms where one infected customer can infect the following customers, the customers are not even expected to take any safety measures when contacting the delivery personnel. Therefore, the platform is not an unbiased mediator between the service requester and service provider. The relationship, in fact, is skewed in favour of customers and platform owners.

There have been cases where the platforms have forced service providers to deliver in containment zones as well just to make their customers satisfied (Ashok 2020). Platforms like Ola promised their drivers and their spouses Rs 30,000 as insurance, which they can claim if they get infected by COVID-19 (Pant and Shende 2020). Due to previous bad experiences, the service providers have no faith in these insurance promises (Lalvani and Seetharaman 2020). 

Many platforms have started providing messages and training to service providers through in-app notifications and other forms (Lalvani and Seetharaman 2020). Many delivery platforms have also started the practice of contactless delivery, which involves leaving the package at the door, taking a picture of it, ringing the bell and waiting for the customer to pick it from the distance (Ashok 2020). 

The platform companies very conveniently call delivery partners “heroes” or “Corona warriors” on the one hand, and refuse to take adequate measures to safeguard these heroes on the other (Lalvani and Seetharaman 2020). Platform service providers are compelled to make a tough choice between economic survival and physical survival. 

2.2.4 Dispensable “Partners”  

Workers involved with digital platforms are not hired with the aim of nurturing long-term bonds between workers and clients. They are presented as interchangeable personnel (faceless service providers) that provide services for the clients (Woodcock and Graham 2020). The work in the platform economy is such that workers are not expected to socialise with or form any relationship with the customer, but instead, the platform is aimed at reducing the relationship between the two parties into a ranking and reputation system (Gandini 2016).  During the COVID-19 pandemic, on the one hand, many service providers left or were made to leave the platforms, while on the other, there were many new entrants to the platforms. As and when platform mediators either increase their cut or decrease the service providers’/partners’ pay, the media highlights how workers go on strike. Most such protesters are either taken off the app or warned that their protest would be seen as misconduct. Even before the COVID-19 crisis, a few platforms’ service providers were feeling the heat of economic downfall. The ones who agreed to the cut were allowed to continue, while those who raised their concerns after a few warnings were taken off the platform. 

2.2.5 Platform Economy and Precariatisation: Taylor’s algorithm at work 

No matter what work one does, there is a rhythm or a pace with which an individual performs their respective tasks. This changes from person to person, and from one situation to another. When Frederick Taylor and Frank B Gilbreth came up with time-motion studies, they brought upon themselves resentment from workers (Braverman 1974). They developed an algorithm in the form of a manual, which stated how to perform each industrial operation efficiently, thereby taking away decision-making power from workers and breaking their natural rhythm (Braverman 1974). It increased efficiency but led to more intensive exploitation of workers. The platform is actively involved in standardisation of services and shaping up of the labour process (Veen et al 2019). The difference from Taylor’s time to now is that the issue has moved beyond exploitation to the irrelevance of workers, leading to precariatisation. 

Conclusion and Way Forward 

GTWs across the world are facing serious challenges in present times in terms of falling income, safety, security and dispensability. It is for the first time in history that the conditions of workers are the same globally in real time. Indian gig workers need to push for legislation to protect themselves. Policymakers need to take this critical situation seriously, properly studying efforts being made by policy advocacy groups around the world.

Shipra ( is a research scholar at the Centre for Informal Sector and Labour Studies, Jawaharlal Nehru University, Delhi. Minaketan Behera ( teaches at the Centre for Informal Sector and Labour Studies, Jawaharlal Nehru University, Delhi.
11 November 2020