The Gig labour market has emerged as a disruptive and transformative force in the Indian labour scenario, which rewrites employment patterns through digital platforms like Uber, Swiggy, E-Kart that link independent workers with customers and businesses that offers short-term, flexible and freelance employment. These kinds of employment arrangements have not only created new income opportunities but also put labour rights and legal frameworks under pressure. Advancements in digital communication and technologies, preference for flexible jobs fuels rapid expansion of gig labour around the world, which is projected to grow with a market of $1.847 trillion by 2032.  This exponential growth in the gig economy disrupts the dynamic Indian labour market, and it is important to understand its influence on the employment patterns. This paper explores the complexity of the Indian gig labour market in the current scenario.

Research Methodology

This study takes a mixed-methods approach to really dig into how India’s gig labour market has changed and where it’s heading. Combining both qualitative and quantitative data, pulling in theory to explore not just how people work, but what platforms are actually like, what could come next, and how recent policy changes like the Labour Codes are shaking things up. The methods here build on some solid earlier research. Sharma and Sharma (2025) ran semi-structured interviews with 23 gig workers in Delhi. Then they coded the interviews and found seven big themes, things like economic stability, platform policies, government rules, job security, flexibility, gig economy pressures, and the fact that work options aren’t always steady. Dhanya (2025) mixed descriptive and quantitative methods, using secondary data from the Periodic Labour Force Survey (PLFS 2019-2022), but filtered for people aged 18-45 living in cities, with at least secondary education, incomes below the 75th percentile, and access to a mobile or bank account. Dhanya estimated gig worker numbers 2.83 million in 2019, dropping to 1.83 million in 2021, and projected out to 2047 using exponential smoothing (base case: 61.6 million; best case: 90.8 million), assuming nothing wild happens, and added in GNI per capita data from the World Bank and MoSPI. Oyer (2020) took a different route, pulling together existing research and secondary data like CPS 2016-2017 for work hours, Uber data for flexibility, but didn’t do new interviews. Campion (2019) reviewed the literature and used BLS survey data to estimate gig worker numbers and offer practical recommendations.

For this paper, we are leaning on Sharma’s interviews for the main qualitative insights, and then using numbers for demographic breakdowns, like 91.3 percentage of workers are men, 82.61 percentage have at least finished secondary school. We also runs a econometric OLS regression with time-series data from 2018 to 2025 to see how gig work affects unemployment. The dependent variable is the unemployment rate; independents are number of gig workers (in millions), GDP growth, and internet penetration. The results: R²=0.426, the gig worker coefficient is -0.416 (so more gig workers, less unemployment), but it’s not statistically significant, and the sample size is small (n=8). Theoretically, we are drawing on labour process theory (algorithmic deskilling), platform capitalism (how platforms extract data), dual labour market theory (secondary markets), and precarity theory (job insecurity). Data also comes from NITI Aayog and ILO. All together, this approach gives a well-rounded look at what’s going on in the gig market how it works, how policy shapes it, and what that means for workers and the economy.

Literature Review

The gig economy is shaking up how people work all over the world. Short-term, flexible jobs you find through apps and platforms aren’t just a side note anymore; they’re changing the rules of traditional employment. This review pulls together what top researchers have found, both globally and in India. It leans on mainly four papers Oyer (2020), Campion (2019), Sharma and Sharma (2025), and Dhanya (2025), and ties in theories like labour process theory, platform capitalism, dual labour market theory, and precarity theory. The focus: how gig work patterns are shifting, what’s happening on the ground with platforms, how new policies (like India’s Labour Codes from November 2025) are landing, and what it all means for workers and economies. There are still big gaps, especially when it comes to measuring gig work, understanding how different workers experience it, and figuring out how to regulate it.

Worldwide, the gig economy isn’t exactly a revolution. It’s more like a steady evolution. Oyer (2020) points out that as tech makes it easier to connect people with work, independent contracting and gig jobs are growing, but they haven’t replaced the old idea of “employment.” Using U.S. data, he shows gig workers come in every age, income, and education level just like regular employees. One big difference: gig workers have more flexible hours, so their weekly work patterns are all over the place (a lot fewer 40-hour weeks). Flexibility is a win for many, especially in low-skill sectors where apps like Uber give people a backup job when times are tough. But there’s a flip side: less security, unpredictable income, and tricky policy problems like taxes and figuring out who counts as an “employee.” Oyer warns against piling on regulations he argues that fierce competition already keeps platform power in check, and that things like portable benefits help workers without locking them in. Still, there’s a lot we don’t know. Surveys miss a ton of gig work (especially side gigs), so it’s hard to get solid numbers.

Campion (2019) backs this up but shifts the focus to practical advice. He estimates there are about 56.7 million gig workers in the U.S. (maybe an over-count, depending on which survey you trust) and says gigs are a big part of career growth and how companies staff up. Gig work appeals to people who want autonomy, but there’s a risk of burnout, especially for “slashies” juggling multiple jobs. Companies need to rethink HR policies, like hiring practices that help close gender gaps. While Campion zeroes in on the U.S., his takeaways fit globally, especially when it comes to managing a workforce that doesn’t have traditional ties to employers.

Turning to India, Sharma and Sharma (2025) dig into gig work in Delhi through interviews with 23 gig workers. They mapped out seven big themes: economic stability (a huge concern, with pay bouncing around thanks to algorithm-driven pricing), platform policies (lots of complaints about unclear rules and sudden deactivations), government policy (calls for more support), job security (arbitrary firings worry people), flexibility (appreciated, but can lead to overwork), the gig economy’s influence (it’s pushing more people to think of themselves as entrepreneurs), and inconsistent job options (work isn’t always there when you want it). Demographically, the group was mostly male, mostly with secondary education, and had between one and five years of gig experience. Most worked in ride-hailing or food delivery. For a lot of these workers, gigs are a safety net in a country where youth unemployment is high, but the trade-off is constant precarity. By 2030, India could see the gig workforce grow to 23.5 million, adding a projected 90 million non-farm jobs.

Dhanya (2025) adds a broader view for the future, using national survey data to estimate that gig work dipped from 2.83 million to 1.83 million between 2019 and 2022, but forecasts a big jump by 2047 up to 90.8 million in the best-case scenario, making up almost 15 percentage of India’s non-agricultural workforce. Problems like algorithm bias and job insecurity aren’t going away, but there’s real opportunity. India already leads the world in global freelancing supply at 27 percentage. Dhanya suggests policies like national registries and portable benefits, matching new Labour Code rules that require platforms to pay into welfare funds for gig workers. By August 2025, over 337,000 registered gig workers had access to health insurance and pensions.

On the theory side, these studies draw from a range of academic ideas. Labour process theory (think Braverman, 1974) helps explain why Indian gig workers face so much surveillance apps track their every move, and the work itself gets broken down into parts so that anyone can do it, which often leads to lower productivity for older workers (as Oyer notes for Uber drivers). Platform capitalism, dual labour market theory, and precarity theory all add layers to understanding why gig work looks the way it does, and why it’s so hard to regulate or measure.

Theoretical Framework

Labour Process Theory (LPT), pioneered by Harry Braverman in Labour and Monopoly Capital (1974). LPT examines how capitalism transforms labour processes to extract surplus value, emphasising managerial control, deskilling, and the degradation of work. In the gig economy, platforms act as modern capitalists, using technology to commodify labour while masking exploitation under the guise of flexibility and entrepreneurship. Drawing on recent studies, including empirical insights from Indian e-commerce warehouses and qualitative analyses of platform workers, this note explores how LPT illuminates the Indian gig market’s realities.

Labour process theory (LPT) provides a critical lens for understanding capitalist intensification through control, deskilling, degradation, and resistance; central to LPT is the conversion of labour power into surplus value via managerial dominance, manifested in platforms’ algorithmic control that monitors, rates, and incentivise workers under an illusion of autonomy, as seen in delivery apps like Swiggy or Zomato where GPS tracking enforces metrics such as 80percentage acceptance rates and under-30-minute deliveries, with customer ratings acting as panopticon tools leading to penalties or deactivation, further evidenced by a 2025 Bangalore warehouse study involving 74 interviews and six weeks of observation revealing Warehouse Management Systems (WMS) logging productivity at 120 items per hour, error rates, and idle time, triggering interventions and blending digital surveillance with human oversight to resolve labour indeterminacy, exacerbated by subcontracting affecting over 70 percentage of gig workers on one-month contracts that fragment accountability; Braverman’s deskilling thesis applies as platforms fragment tasks, reducing delivery riders’ navigation skills to app-directed execution and warehouse roles to isolated steps like scanning and stowing without planning input, aligning with low education levels (secondary or less) and high turnover (two-thirds under six months), perpetuating interchangeability and hindering mobility in a nation with 23 percentage youth unemployment; degradation emerges as the outcome, with precarious conditions including income instability (₹15,000–25,000 monthly, fluctuating), absent benefits for 85percentage pre-2025 reforms, and physical/mental strain from relentless pacing (one pick every 30 seconds), alongside emotional labour in customer interactions under rating pressure, disproportionately affecting marginalised groups like women (10-15percentage of workforce facing safety risks) and migrants, though the 2025 Social Security Code mandates 1-2percentage platform contributions for welfare, yet enforcement lags; LPT also recognises resistance amid consent, where Indian gig workers accept incentives like bonuses but counter through shirking, app tampering, or strikes (e.g., 2025 Swiggy protests in Delhi over pay cuts), supported by unions like the Indian Federation of App-based Transport Workers (IFAT), though subcontracting limits organisation, as observed in warehouse evasion tactics despite deactivation fears; policy implications underscore strengthening the 2025 Labour Codes for algorithmic transparency, minimum wages, and union rights, especially with AI integration in 60 percentage of platforms by 2025 intensifying deskilling, while research should expand ethnographic studies to rural gigs and gender dynamics; ultimately, through LPT, India’s gig economy reveals a site of exploitation where platforms extract value via commodified labour, degrading work despite apparent flexibility, and with threefold growth projected in five years, balancing innovation with protections is essential to transform precarious gigs into sustainable opportunities.

Platform capitalism, theorised by Nick Srnicek (2017), frames this as a capitalist phase where digital platforms act as dominant intermediaries extracting surplus via data monetisation, network effects, and monopolistic control, operating as “lean” entities outsourcing labour and assets to minimise costs while scaling through user data, with lean platforms like Uber most relevant to gigs, thriving on network effects where value grows with users and commodifying data as “new oil,” often externalising risks to workers through 20-30 percentage commissions and embodying “necrocapitalism” by profiting from vulnerability, intersecting in India with 90 percentage informal workforce to amplify inequalities by digitalising precarious work; applying this to India reveals platforms as data empires collecting geolocation, preferences, and metrics (e.g., Zomato’s GPS logging every 10 seconds) for optimisation and third-party sales without worker shares, per a 2025 ILO report, aligning with Srnicek’s model of minimal ownership focusing on harvesting; monopolistic control via network effects sees Ola/Uber holding 80percentage of ride-hailing and Swiggy/Zomato 70percentage of delivery, enabling commission hikes from 15 percentage in 2020 to 25-30percentage in 2025, subsidising growth for lock-in then extracting rents, manifesting “platformed precarity” with dependency and deactivation risks, amplified by 70percentage subcontracting fragmenting accountability, though 2025 Labour Codes counter via 1-2percentage turnover contributions to welfare funds amid enforcement challenges from monopoly lobbying; exploitation externalises risks, treating workers as contractors enduring 12-14 hour days, hazards like accidents, and mental strain from ratings, with 75percentage income instability from demand fluctuations and self-borne costs, gender/social disparities (women 10-15 percentage due to safety, migrants facing biases), and a 2025 delivery study showing wages akin to low-skilled trades but with higher precarity via gamification, critiqued as Srnicek’s “vampiric” lean platforms draining value sans human capital investment; resistance includes strikes (e.g., 2025 Swiggy protests in Delhi over cuts) and unions like IFAT demanding fair algorithms and profit shares, with Labour Codes enabling bargaining and state bills like Jharkhand’s imposing fees, challenging monopolies; policy implications necessitate data privacy (e.g., Digital Personal Data Protection Act, 2023), transparency, and wages, with AI in 60 percentage of platforms by 2025 warranting longitudinal research on rural expansion and gender to curb excesses; ultimately, platform capitalism embodies a double-edged sword of dynamism amid exploitation, and with 62 million workers by 2047, balancing profits with rights via deeper reforms like the 2025 Codes is vital to humanise this digital frontier.

Precarity theory, popularised by Guy Standing in ‘The Precariat: The New Dangerous Class’ (2011), frames this as a neo-liberal outcome creating a “precariat” class marked by insecure employment devoid of seven labour securities labour market (adequate jobs), employment (protection), job (skill retention), work (safety), skill reproduction (training), income (stability), and representation (unions) extended by Judith Butler (2004) as ontological vulnerability amplified by structures, and in gig contexts as “digital precarity” via algorithmic control, intersecting in India’s 90percentage informal workforce to heighten risks for migrants, women, and low-skilled workers, warning of social instability without universal services; applying this, Indian gig workers exemplify income/employment insecurity with 30-50 percentage earnings volatility leading to debt (e.g., 2025 urban delivery study averaging ₹18,000–₹22,000/month, over 40 percentage facing deactivations), work/skill deficits through high accident rates (one in three riders annually), chronic stress from 12-14 hour days, and deskilling in repetitive tasks limiting mobility amid 23percentage youth unemployment, representation/social gaps with fragmented unions (though IFAT gains post-2025 Codes) and 85percentage lacking benefits until reforms mandate aggregator-funded welfare, and broader implications of a “dangerous class” fuelling strikes (e.g., 2025 Swiggy protests) and inequality, yet some agency in flexibility; policy implications advocate stronger Labour Code enforcement, algorithmic transparency, and basic income pilots, while research should prioritise longitudinal well-being studies and AI’s precarity-deepening role; ultimately, through precarity theory, India’s gig market unveils a vulnerability landscape where flexibility conceals systemic insecurity, and with 62 million workers by 2047, inclusive policies are vital to avert fragmentation and foster equitable growth.

Dual labour market theory (DLMT), developed by Peter Doeringer and Michael Piore in the 1970s, posits segmented markets into primary (stable, high-wage jobs with benefits, training, ladders, unions in formalised sectors like manufacturing/IT, fostering security and mobility via internal markets) and secondary tiers (low-wage, unstable roles with turnover, minimal benefits, little enhancement, typically casual/part-time/temporary, occupied by marginalised migrants/women/low-skilled, driven by structural factors like discrimination/education barriers/ employer cost-minimisation, limiting mobility and creating traps), intersecting in India with 90 percentage informality to amplify formal-informal dualism; applying DLMT, India’s gig economy embodies the secondary segment while traditional represents primary, with patterns/wage disparities/mobility barriers reinforced digitally by platforms; gig roles align with secondary traits like instability/low wages/absent protections, earning 62 percentage less than offline counterparts with fluctuations from demand/algorithms/ fuel, high turnover (70percentage under six months from burnout/inconsistency), scarce benefits (85 percentage pre-2025 Codes lacking insurance/pensions, uneven enforcement now), demographics of 90.2 percentage secondary education or less dominated by marginalised migrants/youth entering due to primary barriers (e.g., degrees/networks), platforms evading responsibilities via “independent contractor” status externalising risks like maintenance/accidents, creating precarious periphery contrasting primary IT/ manufacturing with Rs. 50,000+/month salaries/EPF/career progression; contrasts show primary insulated from volatility via contracts/regulations while gigs absorb shocks (e.g., 2025 monsoons), perpetuating inequality as secondary subsidises primary (e.g., e-commerce via gig logistics), women (10-15percentage) confined by safety; mobility barriers include educational mismatches/algorithmic biases/discrimina tion, trapping workers with long hours (12-14 daily) limiting upskilling/low savings, exploitation via gamification encouraging overwork/illusion of flexibility extracting surplus in informal-dominated economy where platforms gate-keep, yielding social costs like mental health/debt/ intergenerational poverty; DLMT urges bridging via policies like 2025 Codes mandating 1-2percentage contributions for welfare/”formalising” secondary, skill programs (e.g., NSDC)/minimum wages enhancing mobility, resistance through unions like IFAT/strikes (e.g., 2025 pay protests) challenging segmentation; through DLMT, India’s gig market reveals deepened dualism entrenching secondary precarity amid primary stability, and with 23.5 million by 2030, inclusive policies are vital for equitable growth, future research exploring AI’s segmentation role ensuring evolution beyond peripheral traps.

Data Analysis

This section presents a comprehensive analysis of data to elucidate the dynamics of India’s gig labour market, incorporating qualitative themes, quantitative demographics, econometric modeling, and comparative global insights. The analysis draws on  Oyer (2020) for global pros/cons and work profiles, Campion (2019) for practice-oriented overviews, Sharma and Sharma (2025) for qualitative interviews and thematic coding, and Dhanya (2025) for forecasts and Vision 2047 projections. Data encompass gig workforce estimates (15 million in December 2025, up from 7.7 million in 2020-21, contributing 1.25 percentage to GDP), sector distributions (ride-hailing and food delivery at 30.44percentage each), and policy shifts from the November 21, 2025 Labour Codes implementation, which have registered over 3.37 lakh gig workers for portable benefits by August 2025. Quantitative elements include frequency distributions from a non-probability sample of 23 Delhi gig workers (Sharma & Sharma, 2025) and an OLS regression on employment impacts (2018-2025 time-series data). Qualitative data derive from NVivo 12.0 thematic coding, yielding seven dimensions with occurrence frequencies: economic stability (70), online platform policies (39), government policies (29), job security (26), work flexibility (25), gig economy influence (23), and inconsistent work options (18). Limitations include urban bias in the sample and small n=8 for econometrics, potentially affecting generalisability.

Quantitative Analysis: Demographics and Projections

Demographic data from Sharma and Sharma (2025) reveal a skewed profile typical of blue-collar urban gigs: 91.30 percentage male (21 out of 23), reflecting safety and mobility barriers for women (only 8.70 percentage female), consistent with national trends where women comprise 10-15percentage of gig workers. Education levels indicate low barriers: 34.78 percentage (8) 10th pass, 47.83percentage (11) 12th pass, and 17.39 percentage (4) graduates, underscoring gigs as an entry point for undereducated youth amid 23percentage unemployment. Experience shows mid-level engagement: 73.91 percentage (17) with 1-5 years, 17.39 percentage (4) over 5 years, and 8.70percentage (2) under 1 year, suggesting retention through flexibility but high turnover risks. Industries are balanced: ride-hailing and food delivery at 30.44 percentage (7 each), home services at 21.73 percentage (5), and digital freelancing at 17.39 percentage (4), aligning with national dominance of transport/delivery (60-70percentage).

Projections from Dhanya (2025), using exponential smoothing on filtered PLFS data (2019-2022: 2.83 million declining to 1.83 million, with assumptions of no shocks), estimate base growth to 61.6 million by 2047 (14.89 percentage non-agricultural labour) and optimistic to 90.8 million, supported by GNI per capita trends from MoSPI. This contrasts Oyer (2020)’s global caution that gigs grow steadily without displacing traditional employment, as evidenced by no substantial decline in India’s formal sector despite gig surges. Econometric analysis via OLS regression (dependent: unemployment rate percentage; independents: gig workers in millions, GDP growth percentage, internet penetration percentage; n=8, 2018-2025) yields R²=0.426 (adjusted -0.004), F-statistic 0.991 (p=0.482), with gig coefficient -0.416 (p=0.202), suggesting a 1 million gig increase may reduce unemployment by 0.42 percentage but insignificantly, echoing Oyer (2020)’s view of gigs as an alternative safety net during downturns without macro-level disruption. GDP growth (-0.055, p=0.759) and penetration (0.050, p=0.723) are also insignificant, indicating data limitations like approximations from NITI Aayog.

Thematic coding from Sharma and Sharma (2025) underscores precarity: economic stability dominates (70 occurrences), with workers facing 30-50 percentage income volatility from demand fluctuations, exacerbated by external costs, aligning with Oyer (2020)’s risk transfer from employers to individuals. Online platform policies (39) critique algorithmic opacity, e.g., deactivations mirroring Oyer’s monopsony concerns, though competition in India (e.g., Ola/Uber duopoly) mitigates this per recent ILO reports. Government policies (29) highlight calls for regulation, addressed by 2025 Labour Codes mandating 1-2 percentage aggregator contributions for welfare, enabling portable benefits for 3.37 lakh gig workers. Job security (26) reveals arbitrary terminations, while work flexibility (25) is valued heterogeneously (Oyer, 2020), yet leads to overwork (12-14 hours daily). Gig economy influence (23) reshapes norms toward entrepreneurship, and inconsistent options (18) reflect erratic assignments, perpetuating underutilisation.

Table 1. Socio-Demographic and Occupational Distribution of Urban Gig Workers

(Data Source: Sharma & Sharma, 2020)

Integrating Campion (2019)’s recommendations, these themes suggest practical adaptations like inclusive hiring to counter gender gaps. Oyer’s reinforces: flexibility accords surplus, but volatility imposes risks, as in India’s subcontracting (70 percentage of gigs).

Globally, Oyer (2020)’s CPS data shows independent workers’ varied hours, paralleling India’s urban blue-collar tilt. Theoretical links: platform capitalism’s data extraction (Srnicek, 2017) amplifies exploitation, labour process theory’s deskilling evident in algorithmic task fragmentation, dual market theory’s secondary segmentation in low-mobility gigs, and precarity theory’s risk-shifting amid 90percentage informality. The Labour Codes mitigate this via formalisation, but gaps (e.g., rural enforcement) persist. Overall, the data indicate gigs as a pro-cyclical safety net boosting non-farm jobs (90 million by 2030) but entrenching inequality, with policy reforms pivotal for equitable evolution.

India’s four new Labour Codes the Code on Wages, 2019; the Occupational Safety, Health and Working Conditions Code, 2020; the Industrial Relations Code, 2020; and the Code on Social Security, 2020 enacted between 2019 and 2020 but delayed due to rule-making and state alignment, consolidate 29 outdated central laws into a streamlined framework balancing worker protections with business facilitation, representing a major reform since Independence by shifting from rigid job safeguards to formalisation, flexibility, and inclusivity; the central government notified rules in 2021-2023, with full nationwide implementation effective December 15, 2025, following the Ministry of Labour and Employment’s November 21, 2025 announcement, described as a “structural reset” through digital compliance, reduced paperwork (from 1,500+ to ~500 filings), and single registration, with over 30 states/union territories aligning for uniformity; key provisions include the Wages Code’s national floor wage (₹178/day as of 2025, inflation-adjusted), gender pay equality ban, digital payments, and bonuses up to ₹21,000/month; the OSHWC Code’s night shifts for women with consent/safety, 8-hour daily limit (extendable to 12 with overtime), free health check-ups for over-45s, and 5-year single licenses; the IR Code’s lay-off threshold rise to 300 workers (no approval below), fixed-term benefits, 10percentage union recognition, and essential services strike bans; and the Social Security Code’s extension of EPF/ESI/gratuity to unorganised sectors, National Social Security Board, Aadhaar-linked portable benefits, and 1-2 percentage aggregator contributions (capped at 5percentage worker payments) for gigs; impacts enhance “Ease of Doing Business” via FDI attraction, potential 4-day work-week (48-hour cap), gender equity, and gig formalisation (12-15 million workers in 2025) yet concerns over platform costs like Zomato/Swiggy, emphasising that overall the codes foster a “win-win” for growth and equity contingent on robust enforcement.

Theoretical Implications: The Indian gig economy challenges traditional labour theories, highlighting shifts in power dynamics and worker agency. Theoretically, it aligns with platform capitalism (Srnicek, 2017), where digital intermediaries extract value through data and algorithms, commodifying labour in low-wage contexts like India. This leads to “platformed precarity,” amplifying vulnerabilities for 15 million workers amid informality (90 percentage are informal). Precarity theory (Standing, 2011) is evident, as gig work erodes securities, creating a “precariat” class with volatile incomes and no benefits pre-2025 reforms. Dual labour market theory reveals segmentation: gigs form a secondary tier of unstable jobs, contrasting primary formal employment. Labour process theory critiques algorithmic control, deskilling workers in repetitive tasks. The 2025 codes theoretically bridge this by formalising protections, but may reinforce capitalism if enforcement lags. Overall, it prompts rethinking human capital theory, where flexibility boosts productivity but risks inequality.

Managerial Implications: Managerially, the gig economy demands adaptive HRM practices in India. Platforms must navigate talent acquisition in a “gray zone,” where workers are contractors yet require motivation. The codes impose 1-2 percentage welfare contributions, raising costs but improving retention through benefits like portable health coverage. Managers should invest in algorithmic transparency and training to combat deskilling, aligning with Oyer (2020)’s emphasis on flexibility’s value. For workforce management, gigs enable agile scaling but challenge traditional models; Campion (2019) recommends hybrid strategies for engagement. In India, this means addressing gender gaps (10-15percentage women) via safety policies. Post-codes, managerial focus shifts to compliance, potentially fostering innovation in talent pools

Practical Implications: Practically, the gig market offers economic empowerment but heightens precarity. Workers gain flexibility amid 23 percentage youth unemployment, yet face income volatility (Rs.15,000-Rs. 25,000/month). The codes provide practical relief: over 3.37 lakh registered for portable benefits by August 2025, reducing health risks. For platforms, compliance eases FDI but may pass costs to users/workers. Policy-wise, it drives formalisation, supporting Vision 2047’s 62 million gigs. Practically, this means better dispute resolution and minimum wages, but rural gaps persist. Sharma & Sharma (2025) highlight worker awareness needs for full utilisation.

Conclusion

This study has comprehensively examined the evolution of India’s gig labour market through a mixed-methods approach, including qualitative thematic analysis from 23 Delhi gig workers (Sharma & Sharma, 2025) revealing dominant concerns like economic stability (70 NVivo occurrences) and platform policies (39), quantitative demographics showing male dominance (91.3percentage) and secondary education prevalence (82.61percentage), and an OLS econometric regression (2018-2025 data) yielding an insignificant gig coefficient of -0.416 on unemployment (R²=0.426), the research underscores gigs as a pro-cyclical safety net amid 23percentage youth unemployment, yet one perpetuating precarity through algorithmic control, income volatility (30-50percentage), and inconsistent options. Theoretical frameworks labour process theory highlighting deskilling and surveillance (e.g., GPS tracking enforcing 80percentage acceptance rates), platform capitalism critiquing data extraction and monopolies (e.g., Ola/Uber’s 80percentage ride-hailing share), dual labour market theory positioning gigs as a secondary segment with limited mobility, and precarity theory framing the “precariat” class with eroded securities illuminate systemic exploitation, amplified by India’s 90percentage informal workforce and subcontracting (70percentage of gigs). The November 2025 Labour Codes mark a pivotal intervention, extending portable benefits like health insurance and pensions via 1-2percentage aggregator contributions, registering over 3.37 lakh gig workers by August 2025, and enabling women’s night shifts, though without reclassification, potentially passing costs to workers and facing enforcement gaps in rural areas. Global insights from Oyer (2020) reinforce that gigs enhance flexibility without displacing traditional employment, with heterogeneous worker profiles (e.g., varied hours, gender pay gaps) and calls for efficiency-maximising regulations like portable benefits, while Campion (2019) advocates adaptive HRM. Theoretical implications challenge traditional models by extending precarity to digital contexts; managerial implications urge platforms to invest in transparency and training for retention; practical implications highlight empowerment through reforms but necessitate algorithmic audits and skill programs for inclusivity. Limitations include urban sample bias, small econometric n=8, and pre-Code data reliance, suggesting future longitudinal studies on AI impacts and rural gigs. Ultimately, India’s gig economy embodies a double-edged sword of dynamism and vulnerability; balanced policies under the Labour Codes are essential to harness its potential for equitable growth and transforming precarious traps into sustainable opportunities.