Authored by Ms. Sejal Jain, Associate at Prakant Law Offices and Ms. Subhashmin Moharana, student at National Law University Odisha
Unemployment has long been a persistent challenge in India, but the recent wave of layoffs seems to have added fuel to fire. In recent years, Indian IT giants such as TCS, Infosys and Wipro have collectively reduced their workforce by approximately 2,000 at mid-senior positions, citing skill gaps and AI efficiencies. Even MNCs such as Amazon and Microsoft laid of thousands of employees in India, further exacerbating the cyclical unemployment triggered by economic fluctuations. Rapid technological disruption and large-scale labour displacements have exposed the inadequacy of the current social security framework.
The Atal Beemit Vvakti Kalyan Yojana, ABVKY, presently functions as a limited safety net, covering only a narrow segment of India’s workforce. While it promises a 50% wage replacement, this benefit is available only for a period of three months and is restricted to Employees’ State Insurance (ESI)-eligible workers. As a result, a significant portion of displaced white-collar and middle-income employees remains excluded from meaningful income support.
The trend points to an escalating issue, the replacement of mid-level jobs by AI-driven “black-box” algorithms, and it is expected that with further automation, this process and the resultant labour displacement will continue to increase. Even in developed countries, the trend is similar: companies profit more from laying off people and paying hefty severance packages than continuing to pay salaries.
To address this gap, a special Cyclical Unemployment Benefit Policy is being proposed, which will focus on the short-term loss of jobs due to mass layoffs. Based on the international research on reskilling and the Indian NITI Aayog roadmap of creating AI-related jobs, it focuses on reskilling the displaced employees to incorporate AI into their work. One of the goals of this policy would be to serve the interests of the impacted middle-income groups, retraining them through AI training and providing access to web-based materials to adjust to the AI-driven work environments.
Under this policy, beneficiaries would be required to demonstrate that they lost their job in the previous month through layoff, termination, or resignation because of skills mismatch; demonstrate active job-seeking; complete the prescribed courses in AI reskilling; and show proof of taxes paid in the past six months (or lesser, if service tenure is lesser) while in service. The policy framework would strengthen employer obligations by mandating severance pay equivalent to at least two weeks of salary for every year of service, along with six months of outplacement and reskilling support. In addition to this, the beneficiaries would also receive access to free, government-funded online AI courses. It is pertinent to note that these benefits would be terminated with re-employment. Further, if a beneficiary fails to secure employment after six months despite demonstrable efforts, they would receive a subsistence allowance of INR 1,500 per month for an additional two months, after which support would cease.
Currently, India lacks an institutional mechanism dedicated to reskill the employees to integrate AI into their work. The country needs an established independent National Unemployment and Reskilling Authority (NURA) under the control of the Ministry of Labour with a board consisting of a labour ministry representative, employer federation representative, trade union representative, and independent AI/education experts. Even developed countries have similar branches of ministry, like Skills Future Singapore, Germany’s Federal Employment Agency, and the Workforce Innovation and Opportunity Act (WIOA).
Under such a mechanism, employers planning to lay off would have to first inform this authority about the planned layoff at least 4 months in advance, allowing the authority to plan for the reskilling program for the affected employees. The authority would also be responsible for tracking and publishing information on layoffs and coordinating reskilling initiatives in collaboration with platforms such as SWAYAM and Coursera. These partnerships would allow large-scale, cost-effective online training without the need for new infrastructure.
The main challenge in India remains its large low-income population, which barely pays tax. The only way out of such low-income households is through sustained employment. Despite India’s attempts to tackle unemployment in the country, its policies remain targeted towards a relatively small and financially contributing segment: tax-paying, middle-income employees affected by mass layoffs, who constitute approximately one to two per cent of the workforce during peak periods. Under the proposed framework, employers would bear the initial burden through severance and outplacement support, with fiscal responsibility shifting to the state only after six months, by which time most beneficiaries are expected to have reskilled and re-entered the workforce. Government-funded online AI courses further minimise costs, while advance layoff notifications enable authorities to plan effectively and avoid sudden surges in benefit claims. Even the final safety net of INR 1,500 per month for two months is narrowly tailored to provide temporary relief rather than long-term dependence. By prioritising reskilling, the policy also enhances long-term productivity, positioning itself not merely as a welfare measure but as an investment in a future-ready economy.
However, while such targeted intervention may yield favourable outcomes for the identified beneficiary group, it risks presenting a misleading picture of success in the broader context of unemployment in India. The perceived effectiveness of the scheme would stem more from its limited scope than from its capacity to address systemic unemployment. In practice, it fails to engage with the vast segment of non-tax-paying workers who remain outside the formal economy and lack both income security and the means to contribute to taxation. These individuals are often the most vulnerable to economic shocks and technological displacement, yet they are structurally excluded from contributory or tax-linked welfare mechanisms. As a result, any positive outcomes achieved by the scheme would reflect selective coverage rather than comprehensive resolution.
Accordingly, while India’s attempts to mitigate cyclical unemployment through reskilling-led and employer-supported interventions are laudable, they represent only an incremental step toward a much larger objective. Addressing the realities of a dual labour market where formal, tax-paying employment coexists with widespread informality requires a more expansive and inclusive social security vision. Until unemployment protection and skill development frameworks are meaningfully extended to informal and economically marginalised workers, India’s response to cyclical unemployment will remain incomplete. The path forward, therefore, lies not merely in refining targeted schemes but in reimagining labour welfare in a manner that reconciles technological progress with social and economic equity.