The Platform Economy and Labour Welfare in Indian Cities: the Case of Ola and Uber Drivers


In recent years, with the emergence, availability, and widespread usage of the internet in Indian cities, platform economy companies such as Ola, UrbanClap, and Swiggy have been on the rise. While digital platforms are particularly lucrative for both company owners and customers, the nature of employment offered by such companies, which rearranges existing boundaries between informal and formal labour, has wide-ranging implications on its workers’ social and economic well-being. Thus, drawing on the existing literature on taxi drivers’ experience in the platform companies of Ola Cabs and Uber Technologies Inc. in Indian metropolitan cities, this paper seeks to examine these implications, with particular emphasis on the role of technology—surveillance and rating systems—and the nature of power asymmetry between both employers and drivers and customers and drivers. The effects that institutional mechanisms, such as state legislations and labour unions in these spaces, have on drivers’ welfare are also explored. Finally, using a textual approach, the paper attempts to infer whether this flexibility in work offered by aforementioned companies has improved labour welfare, or if it is merely a modern attempt to ‘precaritise’ work.


The recent advent of technology, data, and internet usage in Indian metropolitan cities has had enduring effects on urban neoliberal society. The twenty-first century emergence of digital platforms, which serve as ‘digital matchmakers’ by linking labour demand and supply with consumer demand, must also be located within this milieu. Service-based companies such as Swiggy and UrbanClap, that fall under this ambit of the ‘gig economy’, not only provide a wide range of services to the middle-class urban consumer, but also opportunities for business and employment to millions. Most importantly, the nature of work offered by these aggregators blurs binary distinctions between formal and informal work in India and subsequently risks neglecting labour welfare in the name of offering flexibility and autonomy. Employees of two platforms that experience the above-stated nature of work are the ride-hailing service companies of Ola Cabs and Uber Technologies.

Employing a textual approach, this paper thus seeks to evaluate the implications of Ola and Uber taxi-drivers’ work on their social and economic well-being based on a perusal of existing literature in the field, particularly interviews of drivers in the aforementioned companies. The paper is organised into five distinct sections. The first section briefly traces the context in which the two companies emerged and the reasons for their rapid proliferation in urban India. The factors that account for high demand in the labour market for Uber and Ola drivers are also outlined. The second section examines their nature of work at length and the legitimacy of the benefits extended by the two platform companies to their drivers. The role played by technology, specifically surveillance and rating systems, in constituting unequal power and information relationships in these spaces is analysed in the third section. This is followed by an appraisal of the role of state legislations and labour unions in addressing their grievances. The paper then concludes with an answer as to whether the perceived flexibility and freedom in employment offered improves labour well-being or merely ‘precaritises’ work.

Ola and Uber’s Emergence and the Uberisation of Work

Ola Cabs, headquartered in Bengaluru, was established in late 2010. The ride-hailing company is the largest mobility platform in the country and extends its services to 2.5 crore customers across 102 cities. Uber, an American-based company and Ola’s main business rival, was launched 3 years after the latter and claims that India is its second largest market after the United States. Together, the two companies reportedly have 9.5 lakh drivers across India and have captured 95% of the market for platform taxis (Surie and Koduganti 2016, 5; Surie 2018). Thus, the rationale behind choosing Ola and Uber for this analysis is that they are a microcosm of the larger phenomenon of Indian platform economy services in terms of their spatial reach, sizeable numerical share of workers and consumers, and their near duopolisation of the taxi-hailing service.

The large consumer pool for on-demand transport services in metropolitan cities is bolstered by the penetration of smartphones, availability of internet and data connectivity, and the favourable demographic of a young population (Surie and Koduganti 2016, 4). It is in this context that the arrival of these companies, that match potential riders and drivers through algorithms in a mobile application, must be understood.

Yet, how is this high demand for ride services met with adequate supply of drivers? What is it that draws the driver to work for these companies? Based on ethnographic studies conducted on Uber and Ola drivers in various cities, there are both ‘push’ and ‘pull’ factors that account for the aforesaid questions. Tractor drivers who migrate from rural areas transition from agricultural work to taxi services due to unstable and low incomes, droughts, and low agricultural output; they put up their agricultural land and other immovable assets as collateral in order to loan their cars to drive (Surie 2017, 14). A crucial factor to consider for those hailing from religious minority groups or so-called lower castes is the social discrimination and stigma they experience in their villages. They migrate to cities with the expectation of not only a degree of anonymity from their minority identities but also of achieving upward socio-economic mobility. Muslim Uber driver interviewees from New Delhi, for instance, noted that they ‘grabbed’ the opportunity to work in companies that do not discriminate (Prabhat, Nanavati and Rangaswamy 2019). To not be asked about their religion or caste, much less for it to be documented during on-boarding, provided them with a sense of consolation (Kashyap and Bhatia 2018, 177).

As for ‘pull’ factors, both migrants and native city-dwellers are drawn to these companies by virtue of the ostensible employment advantages offered by them and the upward socio-economic mobility that follows. For one, the systematisation of work in the form of timely and regular receipt of incomes (either daily or weekly) and salaries directly being transferred into their bank accounts are seemingly favourable terms of work. Besides enabling them to save a portion of their income in the short-term, it caters to their immediate cash requirements and high costs of living in cities. More significantly, payments are made to drivers not based on the number of hours they work but on the number of trips they make, coupled with incentive packages based on the magnitude of trips made or business generated in a day. That they have the flexibility to structure their own work hours and subsequently their days off, appeals to many potential drivers and confers them with a sense of autonomy. Workers often choose this work because there is no “boss” (Schor, et al. 2020, 838). An Uber driver interviewee from Bengaluru, for instance, expressed a sense of freedom at his income being directly correspondent with his hard work, in contrast with his former job in a private taxi company (Surie and Koduganti 2016, 19-21).

Apropos of these purported social and economic benefits, several scholars have hailed platform companies as revolutionary for the future of work. Economists like Arun Sundararajan (2016) lauded their advent as the “end of employment”. So much cultural capital has Uber amassed that the neologism ‘Uberisation of work’ is used to describe the new flexibility of work that is moving away from a standard employment relationship of full-time protected employment (Surie 2017, 13).

On the other end of the spectrum, however, are sociologists who view these freedoms and individualisation as illusory. They argue that platform companies merely exacerbate the perils and precarities of informal work and use algorithmic controls to manipulate workers within a modern capitalist structure (Surie and Koduganti 2016, 2; Schor, et al. 2020, 835-836). Platforms are but an extension of Fordist organisations that devolve risks onto labourers and dispossess them of rightful social protection (Vallas and Schor 2020, 8). The stance advocated by sociologists continues to be legitimised by certain drivers’ dissatisfaction with their work. For the first time in 2017, resentment culminated in a series of strikes by Ola and Uber drivers across cities like Bengaluru, Mumbai, and New Delhi. Plummeting incomes and incentives, unwarranted penalties, and lack of social security measures were the ostensible causes (Kumar 2017).

What then are the structural arrangements that actuate drivers’ protestations against their working conditions? How is labour injustice perpetuated? These issues will be addressed in the subsequent sections.

Aggregators and Drivers: Hierarchical Power Dynamics

Of prime importance to understanding the power asymmetry between platform-owners (aggregators) and drivers is how the latter group is labelled by the formerUber calls them ‘driver-partners’ and Ola classifies them ‘micro-entrepreneurs’ (Sharma 2019). This, in essence, implies that drivers are not conventional full-time employees in the traditional sense of the word, but independent contractors instead; correspondingly, Uber and Ola are not employers. The capital-labour relationship is between the driver and the digital platform, the latter of which must serve only as an intermediary (Gandini 2018, 1040). Thus the previously described rhetoric of flexibility, autonomy, and sense of ownership that initially lures drivers into the job is a corollary of being part-time contractors. Needless to say, becoming self-employed entrepreneurs is aspirational for many drivers, especially migrants. However, they are ‘partners’ and ‘entrepreneurs’ in name only and this has sizeable implications on their social and economic well-being.

Firstly, taxi fares are decided solely by the aggregator with the driver having no say in the matter. Moreover, drivers are not allowed to ask for or accept tips, and are to strictly follow the route designated by the aggregator (Ola Cabs n.d.). Autonomy for drivers is thus merely a misconception in this regard. Secondly, cars can be owned and managed by drivers, neatly fitting into the aggregators’ tall claims of transforming drivers into an asset-owning class of businessmen. Reality paints a grimmer picture: ownership of cars often leads to debt-traps and only adds to the precariousness in work. In order to finance their cars, drivers either tend to borrow loans from banks or lease cars from the aggregators themselves. Interviews of Ola drivers in Bengaluru suggest that in the case of the latter, incomes were lowered because Ola charged higher loan instalment rates than the market rate and that a certain commission had to be paid to the aggregator daily. Adding to this the high operational costs for the car, such as fuel and servicing which had to be borne by the drivers, they thus find themselves trapped in a vicious cycle: as the operational costs and time spent driving increase, the costs incurred rise. This in turn means that drivers invariably find themselves spending 12 to 16 hours a day for 7 days a week on the road (Aneja and Shridhar 2019, 34). In the case of Mumbai, Uber and Ola retracted offers of receiving collateral-free loans with an easy documentation process in 2017. Incentives that were initially provided were also retracted (Sharma 2018).

In sum, indirect controls imposed by aggregators on the number of work hours and incentives have forced drivers to work increased hours and take few to no days off in order to finance their loans lest their assets be seized and their jobs be lost. In this way, debt accumulation forcefully retains drivers in the platform. Thirdly, given that they are treated as independent contractors, incomes are erratic in nature and compound the precariousness of work. Drivers are neither guaranteed any fixed income nor a minimum number of customers per day by the aggregator. Incomes are wholly dependent on the number of rides made. While this could provide them economic autonomy, exorbitant loan interest rates and the need for cash to survive in metropolitan cities with high costs of living compel them to work unreasonably long hours (Aneja and Shridhar 2019).

In other words, the appropriation of agency and monopolisation of power by aggregator companies have impelled Indian drivers to view their jobs not as temporary, freelancing ones but as full-time livelihoods, contrary to the very meaning of the word ‘gig’ which alludes to the former description (Prabhat, Nanavati and Rangaswamy 2019). Worse still, undertaking full-time work is not coupled with being recognised as full-time employees. To this extent, they are bereft of the benefits enjoyed by employees in the formal sector—social security benefits such as maternity benefits, disability cover, employment injury and insurance benefits, retirement safety nets etc.; labour rights including minimum wage requirements; and job security measures (Surie 2020). It is evident that conditions of work echo labour conditions experienced in the informal sector. At the same time, however, drivers cannot enjoy the meagre benefits of informal labour either. Long-term social relationships between employers and employees in the informal sector provide social capital and informal social protection to workersa personal loan for a worker’s ailing family member or employment for a kinship member, for instance (Aneja and Shridhar 2019). Such informal perks and social dependence are markedly absent in gig economy spaces.

It can, therefore, be inferred that the aggregator gets the best of the worlds of both informal employment and formal employment. Ola and Uber are not obligated to provide social security benefits to drivers and at the same time treat them as conventional employees, imposing controls and extracting maximal labour. The driver, on the other hand, bears the brunt of both worlds—he/she neither enjoys the security of full-time labour nor the autonomy and flexibility of gig work. Prosperity to aggregators accrues at the cost of dispossessing drivers of the same.

The Role of Technology: Surveillance and Rating Systems

Hierarchical power and information dynamics between aggregators and drivers are further entrenched by technology. Unlike conventional employment spaces where there is a human-to-human relationship between the employer and employee, the relationship between platform companies and drivers is primarily dictated by automated algorithms in mobile applications (Aneja and Shridhar 2019). Aggregators are granted unwarranted access to drivers’ personal information including where their cars are situated, the driving routes they follow, the number of rides each of them makes and the time taken for each, and passenger-acceptance rate. Consider this: drivers are continuously monitored by the global positioning system (GPS) navigation tool from the moment they log into their devices. Their locations are tracked not only during the course of work i.e. when they are driving a passenger, but also during breaks and while driving without a passenger. At the end of the work day, too, their last location is noted. Therefore, in utilising such precise surveillance systems, taxi aggregators exercise overt control over drivers’ work outcomesimposition of penalties upon refusal of too many rides or taking longer breaks, terminating upon cancellation of too many rides, and so on. An Ola driver interviewee from New Delhi complained that because he is compelled to follow the route dictated by Ola, he is not allowed to take shorter, more familiar routes. Consequently, he reaches the destination late and has to pay the price himself (Aneja and Shridhar 2019, 37).

Rosenblat and Stark (2016) aptly observe that platform companies skew the asymmetry of information in their own favour by imposing ‘soft-control’, by perpetually watching over drivers’ actions. In one sense, Ola and Uber drivers are no different from their factory-worker counterparts who are under constant supervision and surveillance, the only difference being that the former group is deprived of health and social security benefits (Calvão and Thara 2019, 235). The pervasive role played by disciplinary technology and data in the hands of the aggregator has three implications on working conditions and worker well-being: one, given that drivers’ locations are tracked throughout the day, they are no longer part-time gig workers but full-time employees (as opposed to how they are labelled); two, constant monitoring has aggravated their work intensity by increasing hours of work and decreasing hours of repose; three, intrusive surveillance has struck a blow to the transparency and autonomy they are promised as ‘driver-partners’ and ‘micro-entrepreneurs’.

The relationship between customers and drivers is also predominantly governed by technology. Upon completion of the ride, both passengers and drivers are expected to evaluate and provide feedback on the service experience through a rating system of one to five stars. Interviews with drivers indicate that some of them are not aware that the ratings are given by passengers, still less understand why their ratings plummet or surge (Ahmed, et al. 2016, 5070). Many times they are rated poorly due to circumstances not in their control, pointing to a failure in the system to serve as an accurate reflection of their work—in cities like Bengaluru, Mumbai, and New Delhi with poor roads and severe traffic congestion, cancelling rides because of inaccessible locations, prolonged waiting time, or failure of the passenger to arrive on time is inevitable.

One could argue that the mechanism is reciprocatory in that drivers get to rate passengers as well. The key difference, however, lies in the fact that passengers’ livelihoods are not contingent on sustaining a high rating, unlike drivers, whose number of gigs drops once ratings fall, either because the app stops sending them rides or because customers generally prefer riding with highly-rated drivers (Aneja and Shridhar 2019, 36-37). An asymmetric power-relationship is created whereby drivers are forced to adopt a passenger-pleasing behaviour, lest they be rated badly, while passengers are in a position of power given that a low rating has little consequence for them (Slee 2017). Working in constant apprehension of poor ratings, oft-times given arbitrarily, exacerbates their uncertainties and constrains access to work opportunities.

Surveillance and rating systems in platform work spaces, which are purportedly designed to discipline workers but allow for undue control over their work lives, echo the renowned philosopher Michel Foucault’s observations about surveillance, power and knowledge, and discipline. Methods employed by platform companies seek to “induce a state of conscious and permanent visibility that assures the automatic functioning of power” (Foucault 1975, 201). The nature of employment in these spaces manifests as a decentralised panopticon wherein every actor involved is both an object and instrument of surveillance and control (Ghincea n.d.). For Foucault (1975, 27), “there is no power relation without the correlative constitution of a field of knowledge.” In this context, drivers, customers, and aggregators are acquainted with the knowledge of surveillance and rating systems, drivers ceaselessly being watched, and rewards and punishments in the form of incentives and penalties, respectively. It is this, coupled with the decentralised nature of the platform system, that underpins and sustains unequal power dynamics in these spaces.

Institutional Protections: Collective Bargaining and the Role of the State

Given the unique organisational structure of companies like Ola and Uber, mobilising drivers to negotiate their terms of work is especially complex. For one, drivers are unaware of what terms, conditions, and claims their work entails, much less be able to negotiate the same. Some of them were reportedly unable to comprehend their terms of work owing to language barriers and unfamiliarity with digital infrastructure. Labour contract agreements traditionally involve signing tangible papers; here, the physical signing process is replaced by the click of a button, leaving drivers oblivious of the fact that they are providing consent to a formal contract (Aneja and Shridhar 2019, 35). Spatial constraints further impede their capacity for collectivisation. Taxi-drivers have a dispersed presence in the city. Providing taxi services does not necessitate drivers to gather in a single, physical workplace. While this may provide them with a sense of autonomy, the individualisation of work inhibits not only intra-worker interaction to share their grievances, but also the development of a distinct labour identity that is vital to organising demands, forming unions and bargaining collectively. Additionally, there is a lack of human intermediation between taxi aggregators and drivers; following from this, channels of communication between the two in order to file complaints or express grievances are ambiguous and elaborate (Sharma 2019). Moreover, that the profession is highly demanding, with drivers working for more than 12 hours a day to make ends meet, means that any time spent negotiating terms with aggregators or the State is an opportunity to earn income lost (Aneja and Shridhar 2019, 35).

Owing to these limitations, there is, as of now, no overarching collective of gig workers across various sectors in India. Sectional unions such as Ola-TaxiForSure-Uber Drivers and Owners’ Association (OTU) and Indian Federation Of App Based Transport Workers (IFAT) have focused more on altering terms with aggregator companies and less on the legal framework that fails to recognise them as employees (Nair 2019).

Subsequently, solid legislative mechanisms to protect gig workers’ rights and social security are equally inadequate. Despite the sizeable number of labour laws in India that intend to regulate the same, it was not until 2019 when the Code on Social Security was passed that platform workers were recognised as a distinct class of labour. Under the Code, platform workers are eligible for the aforementioned social security benefits, not labour rights. Central and State governments can implement welfare schemes covering their concerns, which too is contingent on how effectively labour unions can gather support and influence State authorities. Furthermore, this is not to say that protections are guaranteed. Platform companies are recommended, either solely or in collaboration with the State, to contribute to welfare schemes but are not mandated to ensure social protections (Surie 2020). This, in essence, betrays the failure of legislations to keep pace with the changing nature of work in urban India.

The central question thus persists: has the glorified Uberisation of work which manifests in platform economy spaces really lived up to the merits it promised to its workers?


In examining a single case of Ola and Uber taxi-drivers, this paper appraises the various features and power structures of the platform economy and what they mean for the worker and his/her socio-economic welfare. The relationship between aggregators and drivers, and drivers and customers which are dictated by power and information hierarchies indeed ‘precaritise’ work, which is to say workers become increasingly anxious given the uncertain nature of their work. Initially lured by the prospects of owning a car of their own, structuring their work hours and days off, and earning timely incomes, drivers invariably find themselves caught in debt traps, working unreasonably long hours, and earning sub-par and erratic incomes. Moreover, it is evident that aggregators circumvent the legal system by labelling drivers independent contractors thereby depriving the latter of social security and other welfare benefits. Drivers, in contrast, view their work as permanent, full-time and their primary source of income. In one sense, platform employment is a mere extension of the grave informality that shackles the rural worker.

This is challenged by the two-fold role played by technology in exacerbating precarity. One, owing to incessant monitoring, drivers are constantly in fear of erring and being penalised, more often unfairly than not, as driver interviews show. This quashes the rhetoric of freedom, flexibility, and transparency that surrounds perceptions of working in the platform economy. Secondly, technological devices replace human beings as work intermediaries in these spaces. Relations between different stakeholders are governed by automated systems. Work does not necessitate convening in a physical workspace and interaction between workers is muted. As a consequence, the capacity to unionise and negotiate terms with either aggregators or the State is minimised. Here, the exercise of power becomes equally invisible and undecipherable (Aneja and Shridhar 2019, 9). In precis, what would, in the organisational structure of any conventional employment be the role and responsibility of the State, employer, or labour unions, is devolved to the worker.

The present paper is not in the least an exhaustive account of either the socio-economic perils platform workers are faced with or the structures that underpin them. Issues that are specific to each city such as their degree of cosmopolitanism, literacy rates, language barriers, and the extent to which migrants are accepted, equally affect drivers’ socio-economic well-being. The influence of the identities of workers, particularly those of gender, caste, and religion raise interesting questions of their own and actuate more complex debates on the issue. Discourse around the issue must therefore not be confined to legal-institutional mechanisms and policy measures that could alleviate the platform worker’s perils, but must also focus on the structural factors that generate such inequalities in the first place.


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