Moving Beyond Bare Minimum: Will India Inc’s Gig Economy Confront Gender And Social Discrimination?

Sushmita Som


This post is part of our Symposium on Law and Political Economy in India After Covid.

Currently, nearly 3 million of India’s workforce is employed in short-term, non-permanent platform-based work, commonly referred to as the gig economy. India’s gig economy has been booming over the last decade, with platform-based jobs generating employment for its largely youth workforce at a much higher rate than the organized sector.  These platforms facilitate the sale of goods and services by independent contractors, consultants and temporary workers in segments such as food and beverages, information technology, content creation, and creative fields like design. In the gig system already characterized by uncertainty about job security, the devastating impact of COVID on the economy has additionally raised the question – in times of demand scarcity, whom does the labour market privilege? In this context, it is imperative to take a look at how the gender and social prejudices already present in the labour market reinforce themselves in rampant discrimination in the gig economy to create barriers for individuals, and how we may conceptualize a new framework to create a more equitable marketplace going forward.

The Pursuit of Gig Tradeoffs – Social Benefits for Flexibility

The rapid growth of the gig economy is significantly owed to a seemingly mutually beneficial relationship between gig workers and platforms, which are companies that use or host their services. From the worker’s perspective, ‘gigs’ offer them the flexibility to decide their jobs, working schedules and hours, and at times even set rates for their labour depending on the nature of the work. Crucially, the gig economy offers a promise of equality in India’s competitive labour market – to graduates who may find it easier to get gigs than conventional jobs without work experience or a network, opportunities to work from home, and to women constrained at home for whom gigs are promising opportunities to monetize their skills at a time and place of their choice. From the company’s perspective, they can focus on their core competencies by outsourcing work to freelancers, and cutting down on operational and employee costs, including social benefits. Therefore neither party is beholden to the obligations and expectations of a traditional employer-employee relationship. Tech platforms like Uber, Zomato, Swiggy, UrbanClap pose as neutral digital intermediaries that connect a task-seeker with a task-provider via their algorithm, which is presumably non-discriminatory. Nevertheless, as players who want to create demand for their services and pursue profits, they are subject to market pressures such as the public’s preferences, biases and prejudices which percolate into their own decisions.

In India, gig work is arguably the culmination in a long history of labour-based discriminatory practices rooted in gender, race and caste. Traditionally, their identities have positioned them as outsiders to the formal labour market, forcing those devoid of class or caste power to undertake undesirable gigs – such as scavenging or garment manufacturing in sweatshops. These gigs provide minimal to no security or traditional benefits that can be expected by other job seekers. Even in the skilled gigs or tech-driven platforms, without any collective power such as unions formed by traditional workers, or dedicated human resources management software or personnel available for traditional employees of these companies, gig workers have little resistance to the biases which cause barriers to their work conditions. It is important to note that when workers are legally categorized as independent contractors instead of employees, they are made to exist in a vacuum not covered by labour protections, which leaves them open to discrimination on the basis of their sex, gender, religion and caste without recourse.

Algorithm Enabled Discrimination  – License To Misbehave

The mechanisms or algorithms used in these seemingly neutral platforms may in fact, create conditions that provide opportunities to discriminate based on the gig worker’s gender and social data. According to a 2017 study on bias in the online freelance market places TaskRabbit and Fiverr, the design of the platforms exacerbated social biases and workers’ gender and race significantly impacted their ability to do business, even if the tasks were virtual in nature. There are mainly two input avenues through which the data collected on these sites may lead to discrimination. First, platforms may require gig-workers to self-report demographic information for verification, and create profiles displaying their names (indicating gender and religion), and headshots to engender consumer trust. On platforms like UrbanClap where the consumer has the option to choose the gig worker, these profiles may allow perceived stereotypes to determine the choice. Even on cab and food delivery aggregators where tasks are assigned by the algorithm based on availability and proximity of the gig worker, consumers may decide to cancel rides or reject deliveries out of prejudice. Notably, in 2019, a Zomato customer attempted to cancel his order and seek a refund or a “non-Muslim rider” after seeing on the app that his food delivery had been assigned to a Muslim rider. Interestingly, the Jabalpur Police issued a bond to the customer under s. 107 & 116 with a promise not to commit “a breach of peace”. But it doesn’t solve the issue of discrimination. While Zomato didn’t acquiesce to the refund in this case, practically, the gig worker would have found it impossible to legally object against the discrimination. One reason is that consumers often reserve the right to cancel tasks or orders in a short window after assignment of the job without incurring a penalty, especially on ride-hailing and food-delivery services. Further, pursuing claims under under anti-discrimination labour law also raises the question of who would be liable. Is the liability on the principal (the aggregator) who is engaging the independent contractor (the delivery rider) under a contract for services, or is the contract between the service user and the independent contractor?

Secondly, in the interests of transparency and efficiency, the search function on these platforms rank workers according to their assessments, display ratings and feedback from previous customers, which impacts workers’ employment prospects. Here, the input data in terms of feedback taken may be influenced by perceived gender and social stereotypes, and the result would be that the mechanisms of the aggregators in fact reinforce the biases and prejudices of the traditional labour market, and result in hiring inequalities. Research has indicated that positive public evaluations reduces gender discrimination against women.[1] The study on TaskRabbit and Fiverr found that the opaque review process enabled potential consumers to act on their pre-existing biases, particularly where user feedback influenced placement of search rankings. Predictably, women and African-American workers were ranked lower, affecting both the volume of work and rates they could commission.

Consequently, gig workers who bear the brunt of these biases that are enabled by or seep into the algorithms may respond to the discrimination by accepting precarious jobs and lower remuneration. For women, the gig economy has often replicated the gender pay gap in the traditional labour market, and research indicates that globally the gap may range from 7-37% across sectors. Studies and experiments have routinely found that women gig workers face discrimination in both hiring and remuneration, as the algorithms enable potential customers to act on notions of women’s capabilities for a job, their productivity, and ability to negotiate rates in a contract.

Gig Work After COVID-19: Race to the Bottom?

The central problem plaguing the gig economy in India has arguably been one of exploitation – the availability of abundant labour has kept prices ultra-competitive. For example, most start-ups rely on freelancers or interns from the gig economy, whose payments are significantly lesser than regular employees, but ‘flexible’ working hours are far longer than 9-5. In companies like Zomato, delivery riders are incentivized to work long hours and have their earnings slashed to as low Rs. 30 per order.  In most delivery based companies, the riders are pushed to complete tasks while racing against the clock, which adversely increases the risks undertaken by them for these gigs – both personal in terms of safety and financial as motor expenses for damage are borne by the riders. Even the onus of providing them any extra benefit is shifted on to the user who is nudged to tip the rider for prompt service. Therefore, even in regular times, platforms offset their costs onto the market. This will only be aggravated in the aftermath of COVID-19, and push predatory and discriminatory practices in the unregulated marketplace.

The global pandemic and ensuing lockdowns have seen an unprecedented slowdown in the economy, with mass unemployment across all sectors as work came to a grinding halt during the lockdowns. Even as work resumes in staggered phases in the unorganized sector and workers resume jobs in construction and factories, social distancing norms will have a greater disruption on location-based gig work than gig that are virtual or can be worked from home. For instance, personal services in India such as domestic work, beauty and wellness services are performed largely by women, and cannot be adapted to virtual work.

The economic impact of the pandemic has been gendered, and is disproportionately borne by women. For instance, the lockdowns and social distancing norms have intensified the workload for both chores and childcare in households, the bulk of which is shouldered by women as unpaid care work. Virtual schooling also means that while children may have physically attended school and a creche, they are now at home and need to be taken care of. In nuclear families, it would mean that women whose gigs require them to work outside of the home – like women Ubers drivers – would be constrained from actively working due to parenting stress. Women also bear the brunt of barriers aggravated by domestic violence. As reports of violence against women rise globally and complaints of domestic violence reach a 10 year high in the COVID 19 lockdowns, an increasing number of women find themselves confined in abusive environments at home which impact their mental, physical and sexual wellbeing along with their ability to participate in the economy.

This may lead to a large number of women being pushed out of the labour market, losing fragile gains made over the last decade for their economic independence. Further, the demand for services remain low as spending patterns remain conservative due to both actual and potential layoffs across the unorganized and organized sector. The result is that in this scarcity of resources and opportunities, the gig economy will be saturated with labour supply, and hiring discrimination may well reflect on these platforms.

Whose Problem is it Anyway?

In such situations where gender discrimination demands more unpaid care work of women in their households, it is difficult to ascertain who is liable for the creating windows for discrimination – the platforms whose structures enable discrimination, or the families which expect women gig workers to perform all the additional care work. Although it is a social discrimination extending into the labour market, it would be useful to imagine how liability for discrimination can be enforced within that market. It also remains to be determined – who should be intervening in the gig economy to tackle existing discrimination and promote an inclusive workspace? Should it be companies, users or the government who can improve conditions for gig workers? Should platforms make it more difficult for discrimination to occur by making cancellations and price-cutting difficult? And should they affirmatively push for women gig workers to be patronized by users? In my opinion, this task of overhauling the existing framework is a more interesting and useful than the limited question of determining liability for discrimination in the absence of specific legislation. However one answers it, will reflect in gig policies in the labour market.

The government’s obligation to promote labour rights may appear easier to ascertain when the labour market as a whole is in turmoil, but its intervention in the present economic crisis has been to dilute existing labour protections to boost economic growth at the cost of workers’ rights and welfare. Since labour is a subject in the Concurrent List, both union and state governments are capable of changing existing laws. With burgeoning pressure from sectors to exempt them from existing labour laws to extend work to 12 hours/day, suspend inspection of worksites and minimum wage requirements, many state governments have emerged as deregulators to promulgate ordinances that are prima facie in derogation of Tripartite Consultation (International Labour Standards) Convention, 1976 ratified by India.

The COVID-19 lockdowns also resulted in a migrant crisis, where around 14 crore location-based workers belonging to the lowest economic strata had to return home due to the inability to earn living wages. While the Central and State governments are offering relief packages to alleviate their distress, it remains to be seen whether demographics factor in the work allocated to the migrants under employment schemes.

The government’s larger response to pitfalls of the gig economy largely centres around providing social security to gig workers. In India, the Ministry of Labour is expected to roll out a scheme providing social security including pension, provident fund, and health insurance for its millions of workers in the unorganized sector, including the gig economy. In a similar approach, the State of California recently passed an Assembly Bill that extends an employee classification status to gig workers performing work that is the same as the business of the company, and thereby afford them labour protections such as minimum wage, health benefits, and unemployment benefits. Under the new AB5 ruling, the onus has shifted on employers to prove that the worker is not an employee. Ostensibly, the motivation was to regulate ridesharing aggregators like Uber and Lyft, which are reluctant to shoulder social obligations similar to that of a traditional employer. However, they are not intended to function as regulatory oversight on discrimination.

On the other hand, gig-enabling platforms, even though subject to market pressures and social biases, exercise a significant amount of power over workers they engage. By way of affirmative algorithmic action, or by way of policies, platforms maintain the ability to reduce opportunities for customers to act on their bias in hiring and remuneration. For example, in response to criticisms on racial bias against African American hosts and guests on its platform, Airbnb released a 30 page report outlining new nondiscrimination policies, which included measures to de-emphasize user photographs on its platform and introduce an instant booking and payment option on listings to limit hosts from screening by race.

The idea of algorithmic affirmation action is appealing, but poses its own set of challenges especially when the gig economy is premised on shifting risks onto individuals. Chiefly, it raises the concern whether discrimination be tackled without changing the essential quid-pro-quo model of the gig economy. It would be interesting to consider what protections can platforms offer against discrimination to its gig workers without having to mould itself into a traditional employer. In my opinion, platforms need to raise their minimum basic standard to neutralize the pre-existing selection and reviewing biases encoded into their algorithms for a fairer platform, even if it cannot neutralize the discriminatory motive or intent of its users.

Determining liability or enforceable obligations on companies in the absence of specific legislations is ambiguous terrain, but as seen above with Airbnb, the shifting public discourse on discrimination in the gig economy has resulted in companies consciously reviewing their policies to tackle biases on their platforms in response to public demands for accountability. In the economic disruption caused by COVID-19, these platforms have the opportunity to contribute to a structural transformation of the workplace, but it will only be possible after a data-driven understanding of gendered and differential social impact of the lockdowns on gig workers. The need of the hour for them is not sole focus on investor returns, but simultaneously  reviewing their algorithms, engaging with workers on barriers they encounter on these platforms, and the choices they are able to exercise within these structures.

[1] Bohren, J. A., A. Imas, and M. Rosenberg, The dynamics of discrimination: Theory and evidence, 109(10), The American Economic Review, 3395-3436 (2019)

Sushmita is a 2020 graduate of the National Law School of India University, Bengaluru and will be joining Khaitan & Co. in Mumbai. Her research interests include financial regulation, fintech and corporate governance.

Picture Credits: The Information Age

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