Rewiring Labour Market Architecture: India Must Fix Skill Matching at the Firm Level
Productivity gains depend as much on expanding skills as on aligning firm-level demand and institutional incentives to ensure effective utilisation
A background note can be accessed here: OECD: Skills Use in Workplace
Dr. Bornali Bhandari: Professor, National Council of Applied Economic Research (NCAER)
SDG 8: Decent Work and Economic Growth | SDG 9: Industry, Innovation and Infrastructure
Ministry of Skill Development and Entrepreneurship | Ministry of Labour and Employment
In India’s labour market, where formal credentialing coexists with large informal employment, what institutional or employer-side practices perpetuate skill misuse, and how should policy address the gap between skill supply and productive utilisation?
At the micro level, a skill match exists when a worker’s competencies align with job requirements; mismatches arise when workers are under-skilled, over-skilled, or unable to fully deploy their capabilities. In India, these forms coexist with skills shortages (i.e. an insufficient number of workers) and skills gaps (i.e. workers do not have requisite competencies), reflecting imperfect adjustment between labour supply and employer demand.
Institutional practices contribute to this misalignment. Public sector recruitment often attracts overqualified applicants for limited posts, leading to underutilisation. In MSME-dominated sectors, firms frequently require multi-skilled workers capable of performing tasks beyond narrowly defined roles, yet formal job descriptions and wage structures do not always reflect this demand.
The coexistence of formal credentials with informal employment, such as platform work, further complicates matching. Conversely, informally acquired but uncertified skills may be deployed within formal enterprises without formal recognition .
Recognition of Prior Learning (RPL) seeks to bridge this divide. Policy must therefore strengthen demand-side clarity by identifying firm-level job roles, associated competencies, and wage structures, while encouraging MSMEs to incentivise reskilling. Conducting an Occupational-Wage-Employment Survey (OWES) would anchor these efforts in systematic labour market evidence.
What policy levers are most critical for ensuring that skill development translates into meaningful workplace application in India’s services and manufacturing sectors?
The distinction between acquiring skills and applying them productively at work is central to labour market efficiency. Ensuring their translation into workplace outcomes requires structured engagement between training institutions and firms, particularly in services and manufacturing. Industrial Training Institutes (ITIS) can collaborate with industry to simulate real production environments through shared classrooms and laboratories, while industry professionals serve as guest faculty and teachers undertake industry deputations. Apprenticeships, internships, and placement cells further institutionalise pathways from training to employment.
The government’s ongoing ITI upgradation efforts must be aligned with sectoral needs identified at the firm level. Beyond technical training, counselling and mentoring – from school through college – are essential to guide students toward realistic career trajectories aligned with their abilities and prevailing labour market demand.
Within firms, workplace mentoring, structured on-the-job training, and incentives for employee certification strengthen skill application. Local partnerships between enterprises and training providers can facilitate reskilling and upskilling ecosystems, ensuring that skill supply continuously adjusts to evolving production requirements rather than remaining detached from them.
Effective policy relies on real-time insight into skills utilisation and mismatches. What data ecosystems and governance mechanisms should India prioritise to measure skill use at scale, and how can these be embedded in adaptive policy processes rather than static reporting?
Effective policy on skill utilisation depends on granular, real-time labour market intelligence rather than episodic reporting. The Ministry of Skill Development and Entrepreneurship (MSDE) and the National Council of Applied Economic Research (NCAER) skills gap framework launched in 2025 outlines a seven-step approach: macro analysis of sectoral employment shares and clusters; medium-term growth simulations using input-output models; an Occupational-Wage-Employment Survey of non-agricultural enterprises; vacancy and skills surveys; big data analytics in non-agriculture; district-level analysis of agriculture and allied sectors; and structured stakeholder consultations.
To execute this, a core methodological shift is required: instead of asking firms only about generic “skills,” policymakers must map actual tasks performed within each job role, ideally aligned to detailed National Classification of Occupations (NCO) codes. Working backwards from task profiles to required competencies enables more precise matching. Such data should feed into a dynamic Labour Market Information System capable of informing training curricula, wage benchmarks, and regional planning. International systems such as O*NET provide a reference point. India’s National Career Service dashboard offers a foundation but requires further modernisation to support adaptive policy design.
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