The Union Budget 2025 introduced several measures to formally recognize gig and platform workers, providing them with social protection schemes. However, the Periodic Labour Force Survey (PLFS) 2025 has not been updated to capture the unique nature of gig work, leaving significant gaps in official labour statistics.
The NITI Aayog's 2022 report on India’s gig and platform economy projects that the gig workforce will grow to 23.5 million by 2029-30. Despite such projections and legal recognition under the Code on Social Security, 2020, the PLFS still categorizes gig workers under ambiguous classifications like self-employed, own-account workers, or casual labour. This statistical invisibility makes it difficult to fully understand the employment conditions of gig workers and hampers effective policy planning and welfare delivery.
Gig and platform-based work is expanding rapidly, with jobs in food delivery, ride-hailing, digital freelancing, and home services becoming mainstream.
NITI Aayog's 2022 report projects that India's gig workforce will grow to 23.5 million by 2029-30.
In response, the 2025 Union Budget extended key social protection measures to gig workers, marking a significant step toward acknowledging their role in the economy.
Code on Social Security, 2020: This code recognizes gig workers as those involved in income-generating work outside traditional employer-employee relationships, such as platform-based work (using digital platforms to access clients or services).
Challenges:
Legal definition of gig workers remains too broad. It fails to sufficiently distinguish gig workers from other categories like self-employed or casual laborers.
Social Security Fund: A fund for gig and unorganised workers is mandated, but its effective functioning relies heavily on precise labour data.
Inclusion Without Visibility:
The PLFS includes gig workers within broad categories like self-employed or own-account workers. However, it does not specifically identify gig workers.
This means key aspects of gig work — such as multiple app-based employment, income volatility, and lack of formal contracts — remain unaccounted for.
Misclassification and Misrepresentation:
PLFS treats gig workers (e.g., food delivery drivers) the same as traditional self-employed individuals like shopkeepers, even though their work conditions, income sources, and risk exposures differ significantly.
Gig work often entails:
No formal contracts.
Dependency on digital platforms.
Flexible but precarious task-based jobs.
These aspects are not adequately captured in the survey data.
Gig work stands apart from traditional self-employment in several critical ways. Here's a comparison between traditional self-employment and gig work, highlighting the distinct nature of gig work:
|
Traditional Self-Employment |
Gig Work |
|---|---|
|
Independent Control |
App-mediated and Algorithmically Controlled |
|
- The individual typically has full control over their work, deciding when, where, and how to operate. |
- Gig workers are often subject to algorithms and app-based platforms (e.g., Uber, Zomato), dictating when and how work is assigned. |
|
Stable or Cyclical Income |
Highly Volatile Income |
|
- Traditional self-employment often has a predictable income stream, even if it varies based on the season or industry. |
- Gig workers face significant income instability, often varying day-to-day or hour-to-hour based on platform demand, client availability, or task completion. |
|
One Business/Employer |
Multi-platform, Task-based |
|
- Self-employed individuals typically work with a single business or employer, or may own a business serving multiple customers. |
- Gig workers typically engage in multiple app-based platforms (e.g., driving for Uber, delivering food for Swiggy, freelancing on Upwork) simultaneously, taking on task-based work instead of long-term contracts. |
|
Ownership Over Work |
Zero Ownership, High Precarity |
|
- Traditional self-employed individuals often own their business or have full control over the outcome of their work. |
- Gig workers generally have zero ownership over the platform, the clients they serve, or even the terms of engagement, resulting in high precarity and little job security. |
|
Recognised Legally |
Largely Invisible Statistically |
|
- Self-employed individuals are recognized in legal frameworks, tax systems, and business registrations. |
- Gig work is often statistically invisible, with workers classified under broad categories like “self-employed” or “casual labor,” making it difficult for policymakers to accurately assess this growing workforce. |
Initiatives like e-Shram registration, digital ID issuance, and inclusion in schemes like Ayushman Bharat PM-JAY aim to bring gig workers into the formal welfare framework.
Problem: These initiatives are difficult to track and monitor without accurate data on gig workers, undermining their ability to deliver effective social protection.
The 2025 PLFS revision introduced some changes, such as:
A larger sample size.
Monthly estimates.
Expanded rural coverage.
However, it still does not distinctly identify gig workers or address the core issue of statistical invisibility.
To adapt to the changing workforce, India's labour statistics framework needs to evolve. Key suggestions include:
Updating PLFS Codes: Incorporate a distinct category for gig and platform workers.
Survey Modifications: Introduce special survey modules or time-use surveys to better capture task-based work.
Leverage Digital Data: Use digital trace data from platforms (e.g., Uber, Urban Company) to supplement and improve official statistics.
Welfare Monitoring: Ensure that beneficiary data from welfare schemes is fed back into national employment databases to monitor delivery and outcomes.
Having inclusive and accurate labour data is essential not only for representation but also for ensuring that welfare policies are effective, equitable, and reach the right people. It will enable the government to make informed policy decisions, monitor the gig economy, and track welfare outcomes.
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Every aspirant is unique and the mentoring is customised according to the strengths and weaknesses of the aspirant.
In every Lecture. Director Sir will provide conceptual understanding with around 800 Mindmaps.
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