In a bid to address persistent gaps in India’s labour market, NITI Aayog has proposed linking government funding for skilling programmes to verified employment outcomes. These outcomes include job placements, employment retention and wage growth.

In its recent report, Reimagining Skilling for Viksit Bharat@2047, the think tank cited Skill Impact Bonds as an example of a “pay-for-results” model. Under this framework, private institutions finance training programmes upfront and the government reimburses them when they meet predefined employment benchmarks.

The proposed shift, if implemented, would introduce greater competition among training providers, as funding would follow outcomes rather than the number of students skilled. This could also give learners greater incentive to choose programmes that offer better employment prospects, as per Shantanu Rooj, founder and CEO of learning and employability solutions provider TeamLease Edtech.

“There is no performance management or outcome accountability for skill providers today, which means that they do not have a hope of rising and a fear of falling. This policy will ensure competition between service providers for the largest share of money from the learners, and that will only happen when quality actually improves,” he said.

However, moving to an outcome-based model could create its own implementation challenges, particularly around tracking once training is completed. Verifying whether a candidate remains employed, how their wages change over time and whether those outcomes can be directly attributed to a particular training programme would require robust data and monitoring systems.

The Finer Print

Alongside outcome-linked funding, NITI Aayog has also proposed Digital Skills Vouchers hosted on Digital Lifelong Learning Accounts, or Skill Passports. The vouchers would be redeemable only at government-accredited training institutions, allowing learners to effectively choose where they want to spend their skilling allocation.

The vouchers could be issued through programmable digital payment rails such as e-RUPI and Central Bank Digital Currency (CBDC) wallets, enabling the government to track their use and direct funding towards specific beneficiaries or sectors. Depending on the learner’s profile, the funding could either fully subsidise a course or operate via a co-pay model.

This would shift the flow of funding from a largely provider-led model towards one in which learners have greater purchasing power. NITI Aayog believes the vouchers could also be used to target priority groups, including women and people from tribal regions, while steering workers towards sectors such as semiconductors, green energy and advanced manufacturing.

For training providers, however, this could mean significant changes to existing business models.

“Most training providers’ courses have largely not changed over the last few years because they have been able to fill the seats regardless,” Rooj said.

If funding starts following learners and employment outcomes, providers would have to compete for both. This could force them to work more closely with employers and tailor their courses to the skills actually demanded by local industries.

Rooj expects this to create a tighter relationship between training providers and employers, with courses becoming more responsive to sectoral, geographical and cyclical shifts in labour demand.

“I think we will see a tighter relationship between training service providers and employers, and they will have to become more nimble. Courses will not remain constant; they will keep on changing depending on the sectoral, geographical, cyclical needs of the industry, and this will benefit the learner for sure,” he added.

The Bigger Picture

The proposed reforms come against the backdrop of India’s expanding working-age population and the growing need to improve the quality of employment. According to NITI Aayog, India has a median age of 28 years and a labour force of 65.4 Cr individuals.

Yet, affordability remains a major barrier to accessing higher-quality skilling programmes. More than 80% of workers pursue programmes that are either free or cost less than ₹10,000, which the think tank says reflects financial constraints rather than learner preference.

These constraints can be particularly acute for primary earners who cannot afford to give up wages for extended periods to acquire new skills, as well as women who may lack control over household finances and therefore have limited ability to choose the courses they want.

Against this backdrop, the government allocated ₹34,000 Cr towards skilling in FY26. NITI Aayog’s proposal therefore shifts the focus from simply increasing spending to ensuring that existing funding produces measurable results.

At its core, the proposed model seeks to make government spending more accountable: training providers would have to demonstrate outcomes, while learners would get greater control over where public funding is spent.

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