Gigging or rigging: The Hidden Costs of Experimentation in the Gig Economy

Professor Anand Nandkumar and Professor Sripad Devalkar

India’s gig economy offers flexibility but also faces challenges. Platforms struggle with profitability as discounts and heavy investments erode margins, straining gig workers in the process. Policies should focus on supporting workers directly rather than solely targeting platforms which may be less profitable than assumed.

India employed about 7.7 million workers in the platform-based gig economy in 2019-20, which amounts to nearly 1.3 percent of the total workers in India.

Significantly, employment in the gig economy is expected to expand to about 23.5 million workers by 2029-30 and account for about 4.1% of the total livelihood in India.¹

The gig model of work characterised by short-term contracts and freelance work in India has its roots in the early 2000s, ironically in what is now a relatively less prevalent form of gig work, the white-collared gig economy. The advent of the “narrow-band” internet resulted in the proliferation of digital platforms and marked the beginning of online freelancing. Platforms like Freelancer and Upwork, where individuals could offer their skills and services globally, initially fueled the gig economy. However, the gig economy, per se, was still nascent, with limited labour participation.

The early 2010s marked a significant turning point for the Indian gig economy with the entry of ridehailing platforms. Since these platforms offered flexible work opportunities to their participants, who could sign up as independent contractors and earn income based on the number of rides completed, their entry marked the beginning of the blue-collared gig economy. At about the same time, food delivery platforms began to gain a footprint while relying on a network of delivery partners who worked on a gig basis. Around the mid-2010s, the Indian gig economy began diversifying and expanding into niches beyond ride-hailing and food delivery to new sectors like household services and online tutoring. The proliferation of broadband internet connectivity and smartphones played a crucial role in their growth, smoothening individuals’ access to gig work. The COVID-19 pandemic further accelerated its adoption on the customer’s side, as traditional businesses and income sources were disrupted. Today, Indians increasingly rely on gig platforms for a variety of services ranging from daily commutes to food and household services.

Yet, the profitability of gig economy platforms in India varies widely by sector, business model, and scale of operations. While some platforms have achieved profi tability, others still face challenges in attaining sustained profitability for several reasons. Customer acquisition costs continue to be high, with platforms spending heavily on marketing and discounts to attract users, with these costs often being well above the customer’s lifetime value. With minimal differentiation in service offerings, platforms frequently resorted to heavy discounting to incentivise customer adoption, which led to the erosion of margins with limited stickiness to the platform.

Moreover, fixed costs related to onboarding participants on the demand and supply sides of the gig economy and executing operations that tend to be local presents the gig economy platforms with a slew of additional challenges. With thin margins, every area of operation has to have sufficient scale to be viable enough for the local fixed costs to be recovered, and inaccurate predictions about the addressable market size in a precinct can often lead gig economy businesses to bleed losses. Further, these businesses must operate at a minimum scale in almost every precinct to achieve sustained profitability. Demand prediction algorithms that predict yearly and monthly demand could be better and often result in over or underestimation of demand, the latter especially leading to increases in locational fixed costs from excess recruitment of delivery agents in a precinct. Further, several gig economy businesses also need demand predictions at more granular time intervals, such as at an hourly level, to meet customer expectations, which adds to the challenges. For these reasons, despite their initial success, gig platforms continue experimenting with business models to discover the precinct to operate and the “optimal” scale to operate in each precinct.

Although these experiments might, at first glance, appear to be costless, they seem to have imposed costs on some of their participants, especially the service delivery agents. When platforms wanted to gain a foothold in India, several offered generous incentives to the agents to woo them to the platform. Tempted by these incentives, many, including women, students, and workers from the informal sector, rushed in. Eventually, when imperfections in the algorithms began to surface, and the competition intensified, the delivery agents faced the brunt. Discount wars left the platforms with little elbow room to pay incentive fees to the agents. This has ultimately resulted in high-income variability for delivery agents, putting several of them under severe pressure to meet their ends. Also, the requirement of the platforms for delivery agents to enter the gig workforce with assets such as smartphones, bikes, or cars has only exacerbated their stress and insecurity. Given the friction that may prevent gig workers from transitioning back into the informal sector, delivery agents often have to settle for long work hours to offset the loss in incentive payments despite the poor terms of work. Some labour experts and unions argue that the plight of delivery workers is no better, if not a shade worse, than those who do freelance work in the informal sector.

To remedy the working conditions of gig workers, there has been a call for policy measures that mandate gig platforms to offer superior terms of work, including the provision of social security, health insurance, and training to enable gig workers to transition to better careers than just be short term contractors. The key assumption underlying these recommendations is that the platforms currently exercise significant monopsony power over delivery agents and that the platforms simply need to give up some of their profits to improve the terms of work of the gig workers. Our conversations with the gig platforms suggest that this may not necessarily be true. We reckon the hassles encountered by gig workers are an artefact of the initial business models guided by faulty assumptions of market size and competition and the inability to forecast demand accurately. Therefore, costly mandates directed at platforms will significantly threaten their profitability and even force some of them to go out of business. An alternative may perhaps be provisions that directly help delivery agents, such as providing them health insurance and social security and skilling them for careers beyond the gig economy. In addition, infrastructure such as superior toilets and road and traffic regulations might also go a long way in improving the work conditions of gig workers.

Undoubtedly, the platform-based blue-collared gig economy can add significant value to customers and contribute significantly to the economy. But it needs to be done “right”. Policy actions need to ensure that all participants in the gig economy gain from its presence. While venture capitalists, investors, and platforms must reap adequate returns, participation in the gig economy should also ensure that the transition from the informal sector is better for the gig workers.