INTRODUCTION
Digital labour platforms have dramatically altered the nature of work across both developing and developed economies. Gig economy platforms, spanning ride-hailing, food delivery, domestic help coordination, and freelance microwork, have fundamentally transformed employment structures and challenged conventional regulatory frameworks [1]. Unlike traditional employment relationships, characterised by stable wages, social security contributions, and collective bargaining rights, platform-mediated work operates through algorithmic management systems that classify workers as "independent contractors" or "partners," circumventing established labour protections [2].
The platform economy represents a distinct phase in the evolution of global capitalism, one that Srnicek [3] characterises as "platform capitalism": a model in which digital intermediaries extract value from network effects while externalising the risks of employment onto workers themselves. This transformation carries particular significance for emerging economies, where informal labour markets have historically constituted the primary source of livelihood for large segments of the population. The intersection of platform technologies with pre-existing informal labour structures creates novel forms of precarity that demand rigorous scholarly attention and innovative policy responses.
In India, the gig workforce grew by 55% between FY2021 and FY2025, reaching 1.2 crore (12 million) workers, with non-agricultural gig work projected to constitute 6.7 per cent of the workforce by 2029-30 and contribute INR 2.35 lakh crore to GDP [4]. This expansion has been accompanied by persistent structural deficits: the Economic Survey 2025-26, presented in Parliament on 29 January 2026, explicitly called for governance reforms in the sector in view of continuing income volatility and financial exclusion [4]. In Brazil, 38.6 per cent of jobs remained informal as of Q4 2024, with platform work constituting a rapidly growing subset of this informal economy [5]. Within this subset, historically marginalised groups are disproportionately represented: Black and Pardo workers together account for 59 per cent of Brazil's app-based workforce, despite comprising a smaller share of the labour force overall [6]. The implication, in both countries, is that the platform economy's most precarious segments are also its most racially and socially stratified.
This comparative study is timely in light of recent regulatory and labour-movement developments in both countries. In India, app-based delivery and ride-hailing workers organised two coordinated nationwide actions in early 2026: a strike on New Year's Eve protesting the newly issued draft Social Security rules [7], and an "All India Breakdown" on 7 February demanding government-notified minimum fares under the Motor Vehicle Aggregator Guidelines, 2025 [8]. Together, these actions signal a widening gap between policy rhetoric and conditions on the ground. The Economic Survey's call for reform and the rules issued under the Code on Social Security, 2020, which introduced a minimum engagement threshold for social security eligibility, mark India's gradual, if contested, movement toward codification [9]. In Brazil, the withdrawal of Complementary Bill (PLP) 12/2024 from the legislative agenda and the continued pendency of Bill 536/2024 illustrate an unresolved tension between worker protection and platform autonomy [10]. The legislative gridlock is evident in the courts. The Supreme Federal Court is hearing the case for the employment status of app-based drivers under general-repercussion theme 1.291 (RE 1.446.336, rapporteur Justice Edson Fachin), a trial on which public hearings have already been held, and whose final judgment will bind all similar claims in Brazil [11]. Until the ruling, lower labour courts continue issuing divergent decisions, causing continued legal uncertainty for workers and platforms. Brazil's regulatory and judicial non-commitment stands at the other extreme from India's piecemeal, yet incomplete, central codification.
Research Questions
This study addresses three interconnected research questions: (1) What changes has the expansion of the gig economy brought to labour market structures in India and Brazil, and to what extent? (2) How do intersecting axes of identity, caste, race, gender, and class mediate access to platform work and social protection within each country? (3) What policy lessons emerge from comparing the institutional trajectories of the two countries?
In answering these questions, this paper contributes to platform labour scholarship in the Global South and informs evidence-based policy debate at a time when both regulatory spheres remain in flux. The comparative framework isolates institutional and regulatory variation while holding structural features, large informal sectors, constitutional social-rights commitments, and intersectional labour hierarchies relatively constant. To address the policy gaps identified through this comparison, the paper develops an Intersectional Social Floor (ISF), a framework for extending social protection to platform workers along the intersecting axes of caste, race, gender, and class.
LITERATURE REVIEW
The literature on gig work and labour informality in the Global South has expanded considerably, yet comparative studies between India and Brazil remain rare. This review draws on three bodies of scholarship that converge at the intersection of the political economy of informality, social protection, and platform labour.
The Political and Economic Informality
The International Labour Organisation's Decent Work framework [12] and the conceptualisation of informality as a structural feature of developing economies, advanced by scholars such as Chen [13] and Meagher [14], provide the baseline for understanding why platform work in India and Brazil cannot be adequately analysed through frameworks developed for advanced economies. Chen [13] argues that informality is not a transitional deviation from formal employment norms but a permanent structural feature of developing economies, embedded in historical patterns of production, social relations, and state capacity. This challenges the assumption that platform work serves as a stepping stone toward formalisation.
Meagher [14] extends this analysis by demonstrating that informal labour markets are not isolated from the formal economy but are structurally integrated with it. Platform labour represents a new modality of this integration: digital intermediaries connect informal workers to formal market demand while preserving the informal status of the employment relationship itself. Workers thus generate value within formal market structures while remaining excluded from the social protections associated with formal employment, a tension with direct implications for the social protection gaps examined later in this paper.
Caste, Race, and Labour Market Segmentation
In India, the relationship between informality and caste is well documented. Harriss-White [15] shows that Dalit and Adivasi workers are systematically concentrated in the most precarious segments of informal labour, including sanitation, construction, and domestic service, with caste operating as a labour-market segmentation mechanism that channels lower-caste workers into low-wage, hazardous occupations lacking social security.
According to Fairwork India Ratings 2024, eleven platforms were evaluated, and only six of ten scored, with Ola, Uber, and the logistics platform Porter scoring a zero on all five principles [16]. The Fairwork scores reflect the policies and practices of the platforms, not the demographic makeup of their workforces. Therefore, on their own, they cannot demonstrate caste-differentiated treatment. Nevertheless, as per survey-based studies, India’s gig workers, ride-hailing, delivery and logistics work have been over-represented by Scheduled caste, Scheduled tribe and Other backward classes workers [34]. If verified, evidence of that kind would be consistent with the concentration of the lowest governance scores in exactly these sectors, producing, though not establishing, the reproduction of caste-based labour-market segmentation in the platform economy. This hypothesis is examined further in the Results section.
In Brazil, racial labour-market segmentation has been documented by Cardoso [17], whose analysis of national household survey data reveals persistent disparities in employment quality, wages, and social-protection coverage between white and Black/Brown workers. The most recent national data confirm this pattern: as of Q4 2024, the informality rate stood at 41.9% for Black workers and 43.5% for Brown workers, compared with 32.6% for white workers and an overall national average of 38.6% [5]. These disparities reflect the enduring legacy of slavery, racial discrimination, and unequal access to education and social networks, parallels to the caste-based segmentation described above that this paper's comparative framework is designed to draw out.
Platform Capitalism and Algorithmic Management
Srnicek [3] identifies platforms as a new type of firm that monopolises data and infrastructure while relying on externalised labour, a business model that creates structural incentives to minimise labour costs and avoid regulatory compliance. Graham and Anwar [18] extend this analysis through the lens of "planetary labour markets," arguing that digital platforms facilitate the extraction of value from workers across the Global South while maintaining minimal accountability for working conditions. The UNDP's 2025 regional background paper on gig employment in Latin America and the Caribbean [19] documents how platform work in the region is characterised by income volatility, an absence of social protection, and limited upward mobility, findings that closely mirror the Indian evidence discussed above.
The Fairwork Brazil Ratings 2025, titled "Indebtedness and Precariousness," evaluated ten major platforms and found that eight scored zero out of ten overall, with only InDrive and Superprof achieving one point each (Table 1) [20]. This finding is particularly consequential given the workforce's demographic profile, as it affects the 77% of app-based workers in Brazil who are under 40 years old, and Black and Pardo workers constitute the majority of this already-precarious group [6]. Taken together with the caste-stratified Fairwork India results discussed above, the evidence suggests that platform governance failures in both countries fall disproportionately on workers already marginalized along intersectional lines.
Social Protection and Transformative Framework
The social protection literature offers essential tools for assessing policy responses to platform labour. Devereux and Sabates-Wheeler [21] develop the concept of "transformative social protection," extending beyond income transfers to address the structural causes of vulnerability and exclusion. This framework is particularly relevant to platform work, where precarity stems not only from low incomes but from the absence of collective bargaining rights, algorithmic opacity, and discriminatory task allocation.
The International Social Security Association's comparative review of platform worker protections [22] identifies common gaps across national regulatory frameworks: the failure to recognise platform workers as employees, inadequate coverage under social insurance schemes, and the absence of mechanisms for collective representation. The OECD's 2025 report on social protection in Latin America [23] identifies Brazil's high non-wage labour costs as a structural driver of informality, while noting that digital contribution mechanisms hold potential for widening social protection floors, a possibility this paper revisits in its policy framework.
The ILO's 2024 report on the gig economy [24] notes that India's Code on Social Security recognised platform workers on paper but did not establish enforceable standards for wages, working hours, or algorithmic accountability, a gap only partially addressed by the rules issued in 2026. A clear gap remains in the literature: comparative work that holds intersectional social structure constant across countries while varying institutional context. This paper addresses that gap directly.
RESEARCH AND DESIGN METHODS
The present study, conceptualised as a most-similar-systems comparison, undertakes a qualitative comparative policy analysis of platform-labour regulation and social protection in India and Brazil. The structural conditions in the two countries, in particular, their large informal sectors, constitutional commitments to social rights, deep ascriptive stratification of the labour market (caste in India, race in Brazil), and rapidly expanding platform economies, make the comparison analytically productive. Their paths for the variable of interest diverge sharply. India has moved towards gradual statutory codification at the federal level, culminating with the Social Security (Central) Rules, 2026, while in Brazil, the question of platform work remained in limbo between a stalled legislature and a pending binding ruling of the Supreme Federal Court. By controlling structural conditions and varying institutional responses, we can effectively analyse the impact of regulatory design on platform-driven exclusion.
Evidence Base
The study utilises three publicly available sources of evidence. Firstly, the published national statistics are used as reported by their producing agencies: first, for India, aggregate estimates from the Periodic Labour Force Survey (PLFS) 2022–23 [25] and the Economic Survey 2025–26 [4]; second, for Brazil, informality rates disaggregated by race from IBGE’s PNAD Contínua (Q4 2024 release [5]) and the demographic profile of platform workers from published analyses of Brazilian survey and administrative data. We emphasise that none of the survey microdata was retrieved for this study. Because the PLFS does not have a platform-affiliation identifier, the indirect official estimates of India’s platform workforce are drawn from NITI Aayog (2022) and the Economic Survey 2025–26, and their derivation is an explicit limitation, not hidden.
Secondly, the Fairwork India Ratings 2024 (for 11 platforms) and the Fairwork Brazil Ratings 2025 (for 10 platforms) provide a standardised, externally benchmarked account of platform governance. To give Fairwork scores to platforms, each of the five principles of Fairwork- fair pay, fair conditions, fair contracts, fair management, and fair representation is worth two points each [26]. A couple of properties of these data constrain our inferences in two important ways: the scores reflect platform policies and practices, not the demographic profile or outcomes of each platform’s workforce; and they are researcher-assessed using evidentiary thresholds, not worker-reported.
Thirdly, a structured content analysis was conducted on the legal and policy corpus of each country. For India the Code on Social Security, 2020 [9]; the draft rules pre-published in December 2025; the Social Security (Central) Rules, 2026 [27]; NITI Aayog (2022) [28]; the Economic Survey 2025-26 [4]; and the Rajasthan (2023) [29] and Karnataka (2025) state legislation [29,35]. As for Brazil, the CLT was amended in 2017; the Complementary Bill (PLP) 12/2024, Bill 536/2024 [10], the Supreme Federal Court’s general repercussion Theme 1,291, and the OECD assessment (2025) [23].
Four criteria were used to code each document. The first concerned how the platform–worker category was drawn. The second issue was what kinds of social-protection commitments were made, which involved enforceable obligations rather than mere recognition. The third issue was the distribution of regulatory powers among government tiers. The fourth was compliance and enforcement mechanisms. The two authors assigned codes independently and discussed them when differences arose.
Case Comparability
Table 1 presents a summary of the dimensions that are comparable between India and Brazil, and those that are not, and outlines the analytical implications of each for the argument.
Table 1. Comparative Analysis between India and Brazil
| Dimension | India | Brazil | Analytical Implication |
|---|---|---|---|
| Labour-market informality | ~90% of employment is informal [24] | 38.6% (Q4 2024); 41.9% Black, 43.5% Pardo, 32.6% white workers [5] | Platform work enters an already informal labour market in India; in Brazil, it re-informalises segments of a partially formalised labour market. |
| Estimated platform workforce | 7.7 million (2020–21); projected 12 million (FY2025) and 23.5 million by 2029–30 [28] | ~1.5–2.1 million app-based workers [5] | Scale asymmetry; both growing faster than regulation |
| Legal classification of platform workers | Statutory sui generis category ("gig worker"/"platform worker") under the Code on Social Security, 2020, recognised but not employees [9]. | Unresolved; pending binding STF ruling (Theme 1,291, RE 1.446.336); lower labour courts divided | India chose a legislative definition; Brazil awaits a judicial definition |
| Social-security architecture for platform workers | Aggregator-financed welfare: 1–2% of turnover, capped at 5% of payments to workers; e-Shram registration; 90/120-day eligibility thresholds [36] | General contributory regime (INSS); workers may contribute as individual taxpayers or MEI (Individual Microentrepreneur); no platform-specific statute in force | India's scheme is platform-funded but threshold-gated; Brazil's is universal in form but contribution-dependent in practice |
| Regulatory authority | Concurrent: central labour codes + state Acts [29, 35] | Predominantly federal (CLT); municipal regulation marginal | Federalism enables Indian sub-national innovation and fragmentation; Brazilian federalism centralises the question |
| Collective representation | Active app-worker unions (e.g., IFAT, TGPWU) without statutory collective-bargaining standing for gig workers | Union structure tied to recognised employment categories; app-worker mobilisation (e.g., 2020 "Breque dos Apps") outside formal structures | Neither country grants platform workers bargaining standing, the shared gap that the ISF's fourth component addresses |
| Anti-discrimination framework | Constitutional caste protections and reservations, which do not extend to platform engagement (non-employment, private) | Racial Equality Statute (Law 12.288/2010); pay-transparency Law 14.611/2023 limited to formal employment | Existing equality law in both countries presupposes employment relationship platforms disclaim |
| Enforcement capacity | Portal-based compliance (Shram Suvidha/e-Shram) [37]; thin inspectorate | Active labour prosecution service (MPT) and labour judiciary, but with divided jurisprudence | India's model risks under-enforcement; Brazil's risks inconsistency |
Source: Compiled by Authors [5], [9], [24], [28], [29], [35], [36], [37].
Limitations
The claims made in this paper are limited by five limitations. (1) The analysis relies solely on published aggregates and other secondary sources, which do not allow for the estimation of statistical models or causal inference. (2) Platform workers' estimates for India are indirect estimates since no national survey currently identifies platform workers directly. (3) The terms caste and race historically and institutionally refer to distinct categories; the comparison here treats them as analogous forms of labour-market stratification, not as equivalent. (4) The empirical data show that Fairwork ratings are reflective of evidence platform governance and not the workforce composition. Therefore, claims that link low-scoring platforms to marginalised groups are framed as hypotheses consistent with such data but not demonstrated by it. (5) Limited gender-disaggregated evidence characterises platform work in both countries, and consequently, the gender analysis. (6) Evidence regarding engagement patterns against the eligibility thresholds for 2026 is based only on disclosure by a single platform; no representative data on the day’s engagement per worker per financial year exists for India’s platform workforce yet.
RESULT AND DISCUSSION
An initial analysis identifies strong similarities between India and Brazil, alongside important contextual differences, based on the most recent data from both countries.
The Indian Case
India's platform economy illustrates a paradox common to large emerging-market democracies: explosive growth in absolute participation, accompanied by a regulatory architecture that recognises gig work in principle while leaving its most casualised and caste-marginalised participants outside the practical reach of social protection.
Rapid Expansion Amid Structural Constraints
As noted in the Introduction, India's gig workforce grew by 55% between FY2021 and FY2025. This growth has been driven by the rapid proliferation of smartphone users (exceeding 800 million) and the expansion of digital payment infrastructure, with monthly UPI transactions reaching 15 billion [4]. The quick-commerce sector, exemplified by platforms such as Blinkit and Zepto, is projected to add two million gig jobs by 2026, extending platform work beyond metropolitan centres into smaller cities [4].
This quantitative expansion, however, masks persistent qualitative deficiencies. The Economic Survey 2025-26 acknowledges that gig workers continue to face income volatility and financial exclusion, and that social-protection coverage has not kept pace with the workforce's growth [4]. The survey's call for governance reform implicitly recognises that existing regulatory frameworks remain inadequate to the structural challenges posed by platform-mediated employment, a recognition that the February 2026 driver strikes brought into sharp public view [8].
Platform work in India is also heavily gendered as only 28% of the gig workforce is female, concentrated on home-based platforms such as beauty services and domestic-work coordination [25]. This distribution reflects broader patterns of labour-market segmentation, in which women's participation in mobile and public-facing work remains constrained by safety concerns, domestic responsibilities, and social norms.
Caste and Platform Work in India
Reading the Fairwork governance scores alongside the available compositional evidence suggests significant inequalities within India's platform economy. As noted above, platforms that disproportionately employ lower-caste workers, particularly in ride-hailing and logistics, scored zero on fair conditions, fair management, and fair representation (Table 2) [16]. This pattern suggests that caste-based labour-market segmentation operates within the platform economy much as it does in traditional informal labour markets.
The concentration of Dalit and Adivasi workers in platform sectors with the poorest working conditions reflects what Harriss-White [15] describes as the "social ordering" of labour markets, in which caste identity determines access to dignified, protected employment. Platform algorithms do not explicitly incorporate caste as a variable, yet the concentration of lower-caste workers on the lowest-scoring platforms reproduces caste-based hierarchies through mechanisms such as network effects, customer ratings, and task-allocation algorithms, a dynamic examined further below in relation to algorithmic management.
State-level legislative initiatives in Rajasthan (2023) and Karnataka (2025) represent early responses to these challenges [29, 35]. Rajasthan became the first Indian state to enact legislation specifically addressing platform worker rights, including provisions for social security and grievance redressal; Karnataka followed suit with similar legislation in 2025. These state-level initiatives, however, remain unevenly implemented and lack the comprehensive coverage needed to address the structural drivers of platform-mediated exclusion.
Gradual Codification with Persistent Gaps
India's regulatory trajectory is characterised by gradual, if incomplete, codification at the central level. The Code on Social Security, 2020, represented a landmark recognition of gig and platform workers in national labour legislation [9]. The Social Security (Central) Rules, 2026, gazetted on 8 May 2026, give this recognition operational effect, requiring a minimum engagement of 90 days with a single aggregator, or 120 cumulative days across multiple aggregators, within a financial year for eligibility, with a worker's daily engagement counted on any day income is earned, regardless of amount [27]. As Figure 1 illustrates, this threshold sits well above reported patterns of platform engagement: the average Zomato delivery partner worked 38 days in 2025, with only 2.3 per cent of partners active for more than 250 days [30]. This threshold has been criticised for structurally excluding the most casualised workers, including inter-state migrants, seasonal workers, and women in part-time platform work, with one analysis estimating that the majority of India's gig workforce would fail to clear either threshold under current engagement patterns [27].
The Union Budget 2025 proposed identity cards and access to healthcare (Ayushman Bharat) for gig workers, a positive step toward enumeration and basic health coverage [4]. Contributory social-insurance coverage, however, remains weak, and the proposed mechanisms do not address the underlying power imbalance between platforms and workers: registration and enumeration, while necessary, fall short of establishing enforceable minimum standards for wages, working hours, and algorithmic transparency.
The state-level initiatives in Rajasthan and Karnataka demonstrate the potential for sub-national policy innovation [29,35], but also illustrate the challenges of regulatory fragmentation in a federal system. Platform workers operating across state boundaries may fall through regulatory gaps, and the absence of national minimum standards creates competitive pressure that drives standards downward.
![Figure 1. Reported average annual engagement of Zomato delivery partners (38 days, 2025) compared with the minimum-engagement eligibility thresholds under the Social Security (Central) Rules, 2026: 90 days with a single aggregator, or 120 days cumulatively across multiple aggregators, per financial year. Source: compiled by the authors from [27] and [30].](/site-assets/articles/dc39ad36-3cc9-428f-be50-9a77c9b88a45/figures/figure_e26a7b7e1a0ea079.png)
The Brazilian Case
Brazil's platform economy presents a contrasting trajectory: a labour market already structured by deep racial hierarchies, onto which platform work has been layered without disrupting, and arguably reinforcing, those hierarchies, while legislative and judicial responses remain caught in unresolved institutional conflict.
Racialised Informality in the Digital Economy
As noted in the Literature Review, platform work in Brazil is distinctly racialised: Black and Pardo workers together constitute 59 per cent of the app-based workforce [6], and national informality rates run roughly nine to eleven percentage points higher for Black and Brown workers than for white workers [5]. This pattern is consistent with the interpretation that platform work has layered onto, rather than disrupted, Brazil's racial labour hierarchy. Rather than offering a pathway to formalisation and social inclusion, the digital economy has concentrated the youngest and most racially marginalised workers in its most precarious task categories.
Race and Gender in Brazil's Platform Economy
As shown in Table 1, no Brazilian platform received even a basic score for fair conditions or fair representation, with eight of the ten major platforms evaluated scoring zero overall [20]. This is a universal failure with particular consequences for Black and Pardo workers, who constitute the majority of the platform workforce and already experience the highest rates of informality in the broader labour market.
Gender intersects with race to compound these disadvantages. Brazil's Law No. 14.611/2023 mandates gender pay-transparency reporting in formal employment relationships [31], but this requirement has not been extended to the platform sector. In the absence of pay transparency, gender-based wage discrimination in algorithmically managed work may operate without scrutiny or accountability. The intersection of race and gender in platform work produces what Crenshaw [32] terms "intersectional marginalisation", a form of disadvantage that is qualitatively distinct from, and not merely additive to, its component parts.
Legislative Impasse and Platform Power
Brazil's regulatory trajectory has been marked by legislative volatility and the persistent influence of platform-industry interests. Bill 12/2024, which had proposed a 12-hour workday limit for platform workers alongside new social-security contribution requirements, was withdrawn following criticism that it failed to preserve worker autonomy [10]. The subsequent Bill 536/2024 remains under review and has not resolved the underlying tension between worker protection and platform autonomy.
Judicial deferral, rather than resolution, has compounded this legislative volatility. The Supreme Federal Court recognized the question of classifying the platform driver as Theme 1.291 (RE 1.446.33612, rapporteur Justice Edson Fachin), and has held public hearings with workers, platforms and researchers. Its judgment will be a binding precedent for the thousands of lawsuits awaiting trial. The Court has clarified, separately, that the analysis is distinct from that of the contracting of “pejotização” (Theme 1.389), whose nationwide suspension of the case does not reach app drivers and couriers [38]. Pending the Theme 1.291 ruling, lower labour courts continue to reach divergent outcomes; some are recognising employment relationships, while others uphold the platform’s classification. Workers who are complementary to one another by nature are available under the general contemplation of the code of conduct use. All provisions in the code of conduct that are used shall be treated as complementary provisions.
The Fairwork Brazil 2025 report notes that the fate of Bill 12/2024 and the uncertainty surrounding Bill 536/2024 reflect a structural mismatch between platform business models and the CLT-based labour framework [20], whose foundational assumptions, rooted in the 1943 Consolidation of Labour Laws and its 2017 amendments, presuppose an employment relationship characterised by subordination and continuity. Platform work, organised around algorithmic management and task-based engagement, directly challenges these assumptions.
In both countries, Fairwork's five principles are referenced aspirationally in policy discourse but remain structurally unfulfilled, particularly with respect to fair representation and fair conditions (Table 2). This gap between policy rhetoric and regulatory reality reflects the structural power of platform corporations, which have resisted binding obligations while extracting substantial value from worker labour.
Table 2. Fairwork ratings of selected digital labour platforms, India (2024) and Brazil (2025).
| Country | Platform | Sector | Fair Pay | Fair Conditions | Fair Contracts | Fair Management | Fair Representation | Total /10 |
|---|---|---|---|---|---|---|---|---|
| India | Big basket | Grocery delivery | 1 | 2 | 2 | 1 | 0 | 6 |
| India | Swiggy | Food delivery | 0 | 2 | 2 | 2 | 0 | 6 |
| India | Urban Company | Domestic/personal services | 1 | 2 | 1 | 2 | 0 | 6 |
| India | Zomato | Food delivery | 0 | 2 | 2 | 2 | 0 | 6 |
| India | BluSmart | Ride-hailing | 0 | 1 | 2 | 2 | 0 | 5 |
| India | Zepto | Grocery delivery | 0 | 2 | 2 | 0 | 0 | 4 |
| India | Amazon Flex | Logistics/delivery | 0 | 1 | 0 | 1 | 0 | 2 |
| India | Flipkart | Logistics/e-commerce | 0 | 0 | 0 | 1 | 0 | 1 |
| India | Ola | Ride-hailing | 0 | 0 | 0 | 0 | 0 | 0 |
| India | Porter | Logistics | 0 | 0 | 0 | 0 | 0 | 0 |
| India | Uber | Ride-hailing | 0 | 0 | 0 | 0 | 0 | 0 |
| Brazil | InDrive | Ride-hailing | 1 | 0 | 0 | 0 | 0 | 1 |
| Brazil | Superprof | Tutoring/services | 1 | 0 | 0 | 0 | 0 | 1 |
| Brazil | 99 | Ride-hailing | 0 | 0 | 0 | 0 | 0 | 0 |
| Brazil | Americanas–Ame Flash | Delivery | 0 | 0 | 0 | 0 | 0 | 0 |
| Brazil | iFood | Food delivery | 0 | 0 | 0 | 0 | 0 | 0 |
| Brazil | Lalamove | Logistics | 0 | 0 | 0 | 0 | 0 | 0 |
| Brazil | Loggi | Logistics | 0 | 0 | 0 | 0 | 0 | 0 |
| Brazil | Parafuzo | Domestic services | 0 | 0 | 0 | 0 | 0 | 0 |
| Brazil | Rappi | Delivery | 0 | 0 | 0 | 0 | 0 | 0 |
| Brazil | Uber | Ride-hailing | 0 | 0 | 0 | 0 | 0 | 0 |
Note: The scores indicate verifiable evidence of what the platform did at the time of assessment; a zero indicates insufficient evidence. The scores capture platform governance rather than the demographic composition or outcomes of the platform workforce.
Source: Compiled by Authors from [16], [20].
Algorithmic Management and Discrimination
A critical dimension of platform-mediated exclusion concerns the role of algorithmic management systems in reproducing, and potentially amplifying, existing inequalities. Platform algorithms determine task allocation, pricing, and worker ratings through processes that lack transparency or accountability [2]. While these algorithms do not explicitly incorporate caste or race as variables, they may reproduce discriminatory outcomes through proxy variables such as geographic location, language, or customer ratings, the mechanism noted above in relation to India's caste-stratified Fairwork scores.
In India, lower-caste workers concentrated on specific platforms may experience algorithmic discrimination through customer-rating systems that reflect caste-based prejudice. In Brazil, the concentration of Black and Pardo workers in the lowest-paying platform tasks may reflect algorithmic reinforcement of racial stereotypes about competence and reliability. The absence of algorithmic transparency in both countries prevents rigorous assessment of these dynamics, but the consistent concentration of marginalised workers in the most precarious platform segments is consistent with discriminatory mechanisms operating through proxy variables.
The ILO's 2025 recommendations on social protection floors identify algorithmic accountability as an emerging priority, calling for anti-discrimination features in task-allocation systems and for worker access to algorithmic auditing [33]. Neither India nor Brazil has yet implemented binding requirements for algorithmic transparency, leaving workers vulnerable to discriminatory automation without recourse, a gap the framework proposed below is designed to address directly.
The current evidence cannot rule out alternative explanations such as selection into platform sectors shaped by asset ownership, geography, and education, while deciding between discriminatory and compositional mechanisms requires the algorithmic transparency that neither jurisdiction currently demands.
TOWARD AN INTERSECTIONAL SOCIAL FLOOR
This paper proposes an Intersectional Social Floor (ISF), introduced in the Introduction, as a comparative policy framework for extending protections to gig workers in large emerging economies. The model integrates three analytical traditions: structural approaches to labour-market analysis, transformative social protection theory [21], and intersectional feminist scholarship [32].
The structural approach recognises that platform work is embedded within broader patterns of informality, inequality, and state capacity, and that interventions addressing platform work in isolation from these contexts are unlikely to achieve sustainable improvements in worker welfare. The transformative social protection framework extends beyond income transfers to address the power relations and institutional arrangements that produce vulnerability. Intersectional analysis ensures that policy design accounts for the compounding effects of caste, race, gender, and class on labour-market outcomes.
Core Components of the Intersectional Social Floor
The ISF model comprises five interconnected components:
1. Automatic Registration and National Identity Infrastructure. Platform workers should be automatically registered in national social-protection systems upon commencing platform work, with integration between platform databases and government social-protection infrastructure ensuring enrolment becomes the default rather than requiring worker initiative. India's proposed identity cards for gig workers represent a preliminary step in this direction [4], though automatic registration would go further.
2. Portable Social-Insurance Accounts. Workers should be able to open portable accounts that accumulate contributions across multiple platforms and employment arrangements, addressing the fragmentation of platform work in which workers may engage with several platforms simultaneously or sequentially. The OECD identifies digital contribution mechanisms as having significant potential to expand social-protection floors in Latin America [23], a potential that portable account structures could help realise.
3. Algorithmic Anti-Discrimination and Transparency. Algorithmic task-allocation systems should incorporate anti-discrimination features and be subject to regular auditing, including disclosure of the variables used in algorithmic decision-making, testing for discriminatory outcomes across caste, race, and gender categories, and meaningful channels for workers to contest algorithmic decisions. The ILO's 2025 recommendations provide a template for such requirements, though binding implementation remains absent in both countries [33].
4. Recognition of Worker Collectives. Collectives of platform workers should be recognised as social partners with legal standing for collective bargaining, addressing the fundamental power imbalance between individual workers and platform corporations. As shown in Table 1, no Brazilian platform scored well on fair representation, and Indian ride-hailing platforms serving lower-caste workers scored zero on this principle, underscoring the urgency of this component [16], [20].
5. Minimum Standards for Fair Work. Enforceable minimum standards for earnings, working hours, and occupational safety should apply to all platform workers, regardless of engagement duration or platform type, addressing both the exclusionary effects of India's eligibility threshold for social security benefits [27] and the absence of minimum standards in Brazil's current framework. The Fairwork Foundation's five principles provide a ready-made standard for defining these minimum requirements [16], [20].
Implementation Pathways
Implementing the ISF requires coordinated action across governance levels. At the national level, legislative frameworks must establish definitions of platform work, platform obligations, and worker rights; India's Code on Social Security and Brazil's ongoing legislative and judicial processes provide the institutional context for such action. At the sub-national level, state and municipal governments can pioneer approaches that inform national policy, as Rajasthan's platform-worker legislation already demonstrates [29]; in Brazil, municipal regulations in São Paulo and other major cities could establish local precedents.
At the international level, the ILO's 2025 recommendations on social protection floors provide a global standard toward which national policy should aspire [33]. Regional cooperation between India and Brazil, potentially through the IBSA Dialogue Forum or the G20, could facilitate knowledge exchange on platform-labour regulation.
Feasibility and Platform Industry Concerns
The ISF model is designed to be feasible within existing institutional constraints. Automatic registration and portable accounts can leverage existing digital infrastructure, India's Aadhaar system and Brazil's CPF and PIS/PASEP systems, while algorithmic auditing requirements can be implemented through existing competition and consumer-protection authorities. Recognition of worker collectives builds on constitutional guarantees of freedom of association in both countries.
Nothing in these recommendations should fundamentally disturb the operations of large platform companies. The model does not mandate employment reclassification, though this remains a legitimate policy option, nor does it impose prohibitive compliance costs; rather, it establishes a floor of minimum protections that platforms should provide as a condition of operating in these large and growing markets. The economic contribution projected for platform work, INR 2.35 lakh crore to India's GDP by 2029-30 [4], demonstrates that the sector can bear the costs of decent work standards.
CONCLUSION
The gig economy in India and Brazil has expanded sharply in absolute terms, as the most recent national data from both countries confirms. This expansion, however, has not constituted a meaningful pathway to social inclusion for historically marginalised workers. In India, social-protection coverage has not kept pace with workforce growth, a gap the Economic Survey 2025-26 itself acknowledges and which the February 2026 driver strikes brought into public view [4], [8]. In Brazil, platform work retains the highly racialised character documented throughout this paper, with Black and Pardo workers constituting the majority of the app-based workforce while experiencing the highest rates of informality [5], [6]. In both countries, the digital economy has amplified rather than disrupted pre-existing structures of labour-market disadvantage.
The comparison between India and Brazil is analytically productive precisely because it isolates institutional and regulatory variation while holding structural features, large informal sectors, constitutional social-rights commitments, and intersectional labour hierarchies relatively constant. The two countries nonetheless exhibit markedly different regulatory trajectories. India shows gradual but credible codification at the Centre, evidenced by successive budget commitments and the Social Security (Central) Rules, 2026, alongside pioneering state-level legislation in Rajasthan and Karnataka. Brazil, by contrast, remains caught in a combined legislative and judicial impasse: proposals have been advanced and withdrawn under platform-industry pressure, While the Supreme Federal Court is still taking a binding decision regarding driver classification (Theme 1,291), the underlying legal question remains unanswered and lower-court jurisprudence is divided [11].
The Fairwork Foundation's comparative ratings provide an evidence base demonstrating that neither country has enacted enforceable minimum standards on earnings, algorithmic transparency, or collective representation, conditions this paper has argued are necessary for the social inclusion of platform workers [16], [20]. The Intersectional Social Floor model presented here offers a structured response to these gaps, combining automatic registration, portable social insurance, algorithmic accountability, collective representation, and minimum fair-work standards.
This study's limitations should be acknowledged. The cross-sectional nature of the survey data precludes causal inference, and the proxy variables used to identify platform workers in both surveys remain imperfect. Future research should extend this comparative frame to additional emerging economies such as Indonesia and Nigeria, incorporate longitudinal panel data as it becomes available, and examine the gendered dimensions of India's 2026 regulatory reforms in greater depth.
The regulatory flux evident in both countries presents both risk and opportunity. As platform work continues to expand, the window for establishing protective frameworks narrows. grounded in the comparative evidence assembled here, responsive to structural constraints, and attentive to the intersectional dimensions of labour-market disadvantage, developments that make it more urgent, not less.