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Subject: Unemployment

  • At 78%, Telangana district Nirmal on top in women’s share in informal workers

    Why in the News

    The Ministry of Statistics and Programme Implementation (MoSPI) has released the first district level estimates of India’s informal sector drawn from a large scale national survey. They come from the Annual Survey of Unincorporated Sector Enterprises (ASUSE) of 2025, which covers enterprises outside the corporate sector and outside agriculture. Women are 78% of all informal workers in Nirmal district of northern Telangana, the highest share recorded for any district. Female participation in informal work turns out to vary far more between districts than any national figure suggests. The districts where women dominate this workforce are also among the lowest paying, which is the tension the new granularity exposes.

    What does the Annual Survey of Unincorporated Sector Enterprises cover?

    1. The universe surveyed: It covers unincorporated establishments in manufacturing, trade and other services, which is the part of the economy usually described as the informal sector.
    2. What it leaves out: Agriculture is outside its scope, as are enterprises incorporated as companies.
    3. Coverage of this round: The report carries estimates for 757 districts.
    4. A caveat on district identity: MoSPI notes that the districts covered may not match the present administrative map, because boundaries, names and new districts have changed since.

    How wide is the spread between districts?

    1. The national benchmark: Across India women are 29% of informal workers.
    2. The bottom of the list: In Rudraprayag in Uttarakhand women are 6.7% of informal workers.
    3. A state boundary makes the difference: Nanded in Maharashtra, immediately across the border from the top ranked district, sits 288th with women at 31%.

    What regional pattern do the district numbers reveal?

    1. The top ten are regionally clustered: All ten districts with the highest female share lie in south India or the north east, in Telangana, Manipur, Meghalaya and Mizoram.
    2. Parity is rare: Women are at least half the informal workforce in only 25 districts, 22 of them in the south or the north east, with three in the east including Pakur in Jharkhand and Deogarh in Odisha.
    3. A third is a wider club: Women account for at least 33% of the informal workforce in 237 districts.

    Does a high female share come with better pay?

    1. The best payer among high share districts is modest: South West Khasi Hills in Meghalaya pays Rs 1.7 lakh per hired worker, about 35% above the national average of around Rs 1.3 lakh.
    2. The top paying district has few women: Dehradun pays Rs 4.6 lakh per hired worker, and women are 19% of its informal workforce.
    3. The pattern that follows: High female participation coincides with low earnings per worker rather than with better paid work.

    What do the ownership and concentration numbers add?

    1. Participation tracks ownership: Districts with the greatest female participation also carry the highest share of female owned proprietary establishments, and the leading district reaches almost 80% on that measure.
    2. Scale sits elsewhere: North 24 Parganas in West Bengal has the most informal workers, at 21.3 lakh, and the most establishments, at 16.6 lakh.
    3. Output is concentrated: The ten districts with the most establishments account for around 11% of total Gross Value Added (the value of output less the cost of inputs bought in, which is how a sector’s contribution is measured), and the top fifty for almost a third of it.
    4. The stated purpose of the release: MoSPI’s position is that the diversity of activity and local conditions makes granular statistics necessary for evidence based policymaking.

    Challenges to district level informal sector measurement

    1. Boundary churn breaks comparability: A district measured once cannot be tracked over time once it is split, merged or renamed before the next round. Eg. Telangana raised its district count from 10 to 33 in 2016.
      The Fix: Publish every round against a frozen reference map alongside the current one, so a district series survives reorganisation.
    2. Excluding agriculture removes most rural informal work: The survey frame leaves out the sector that still employs the largest number of informal workers. Eg. Agriculture remains the single largest employer in the Periodic Labour Force Survey’s distribution of workers.
      The Fix: Release the unincorporated estimates together with the labour force survey’s agricultural numbers as one district profile.
    3. A high female share can record distress rather than progress: Unpaid family labour and home based piece work enter the count as participation with no wage attached to it. Eg. Beedi rolling and garment stitching in home units are recorded as enterprise work paid at piece rates.
      The Fix: Report unpaid family helpers separately from hired workers for every district.
    4. Enterprise surveys miss the smallest and most mobile units: Vendors and units without fixed premises are hard to list, so they are undercounted at source. Eg. The survey and registration of street vendors required by the Street Vendors Act, 2014 remains incomplete in many towns.
      The Fix: Use municipal vending registers and welfare board rolls as a supplementary listing frame for mobile units.

    Conclusion

    The release turns a state level statistic into a district one, and that changes what an administrator can act on. The pattern it exposes is that where women work most in the informal economy, that work pays least, which is a question about the kind of enterprise available locally rather than about willingness to work. The milestone to watch is whether these estimates are repeated on the same frame, because a single snapshot cannot show whether participation and earnings are moving together or apart.

    Back2Basics: MoSPI and the National Sample Survey

    1. The ministry: MoSPI is the nodal body for India’s official statistical system and releases the national income and price statistics.
    2. The survey arm: The National Statistical Office conducts large sample surveys through the National Sample Survey, which began in 1950.
    3. The companion employment survey: The Periodic Labour Force Survey supplies employment and unemployment estimates, and it counts workers rather than enterprises.
    4. The advisory body: The National Statistical Commission, set up in 2005 on the Rangarajan Commission’s recommendation, advises on statistical priorities and standards.

    Matching Previous Year Question

    “[2023, GS3, 15 marks] Most of the unemployment in India is structural in nature. Examine the methodology adopted to compute unemployment in the country and suggest improvements.”

  • [7th September 2026] The Hindu OpED: India’s unemployment data dilemma

    [7th September 2026] The Hindu OpED: India’s unemployment data dilemma

    Question (2023, GS3): “Most of the unemployment in India is structural in nature. Examine the methodology adopted to compute unemployment in the country and suggest improvements.
    Linkage: This is the most direct match. The transition to high-frequency monthly indicators based on CWS directly challenges how India computes its unemployment. CWS captures employment status over a short seven-day reference period (which is why seasonal peaks like the kharif sowing season show a temporary drop to 5.1%), but it fails to address the underlying structural nature of informal underemployment.

    Mentor Comment

    India has converted its official unemployment estimate from a quarterly and yearly release into a monthly indicator, measured on the Current Weekly Status approach. The latest Periodic Labour Force Survey (PLFS) reports the unemployment rate for those aged 15 years and above at a four-month low of 5.1 per cent in July. The review period coincided with the peak of the kharif season, when demand for agricultural labour rises for land preparation and transplanting. The tension is that a higher frequency reading is being asked to measure a labour market where roughly 90 per cent of the workforce is informal and tens of millions of workers circulate seasonally. A rate can be published every month without becoming a measure of the quality of work behind it.

    What is the Periodic Labour Force Survey, and what changed?

    1. What it is: The Periodic Labour Force Survey is the household survey through which India produces its official employment and unemployment estimates.
    2. The reference period: Under the Current Weekly Status (CWS) approach, a person’s activity status is determined on the basis of the preceding seven days.
    3. What the change is: The survey has moved from quarterly and yearly unemployment data to a monthly indicator, raising the frequency of the headline rate without altering the sample’s household basis.

    What does the July reading actually show?

    1. The headline: The unemployment rate for those aged 15 and above marked a four-month low.
    2. The rural share of the move: The overall decline was owing to rural areas, where unemployment fell to 4.5 per cent from 5 per cent.
    3. A supply side signal: The month recorded an increase in the labour force participation rate, meaning a larger share of the working age population entered the labour market.

    Why is a seasonal reading not a structural improvement?

    1. The month is the agricultural peak: July hiring rises for land preparation, transplanting and allied activities, so the decline reflects the calendar rather than a turn in the market.
    2. The affected sectors are the seasonal ones: Construction, agriculture, small trade, logistics and local services all fluctuate seasonally, and the fall concentrates there rather than in formal sector jobs.
    3. A falling rate can mark distress: A decline in unemployment can indicate distress-driven entry into low-productivity jobs rather than genuine employment creation.
    4. The correct status of the number: A monthly unemployment figure functions at best as a leading indicator, not as a comprehensive measure of labour market health.

    Why does informality defeat a high-frequency headline rate?

    1. The scale of the informal market: Various reports place around 90 per cent of the population in informal work, where wage payments are negotiated informally rather than contracted.
    2. The workers the frame misses: Independent labour studies estimate 30 to 35 million seasonal labourers moving across India annually, forming the backbone of urban construction and infrastructure.
    3. Underemployment does not register: Disguised employment and underemployment are widespread, and neither shows up in a status that records whether a person worked.
    4. The granularity is missing: Data is sketchy on wage growth, hours worked, job quality, occupational shifts and sector-wise employment trends, so the rate carries no information about the nature of the job.

    What do mature labour markets do differently?

    1. The common benchmark: Most advanced nations count unemployment through a Labour Force Survey built on the definition of the International Labour Organization (ILO), which fixes what counts as employment, unemployment and labour force participation.
    2. The depth behind the number: The United States, Japan, the European Union and the United Kingdom hold decades of household survey data carrying full-time versus part-time status, hourly wages, job duration, labour mobility and unemployment spells.
    3. The administrative spine: Those markets run payroll surveys, unemployment insurance records, formal contracts and extensive administrative databases alongside the survey, so the headline rate is corroborated rather than standalone.

    Can administrative data close the gap?

    1. The sources already exist: Employees’ Provident Fund Organisation and Employees’ State Insurance Corporation payroll data, Goods and Services Tax based enterprise information, income tax records, corporate payroll data, gig economy employment data and rural wage indicators are all being built up.
    2. They do not yet speak to each other: These sources remain fragmented, so none can be used to cross-check the survey’s monthly movement.
    3. The gap they would close: A large informal employment market is difficult to track through a household survey alone, which is precisely the market these registers touch at the formal edge.

    Challenges to the revamped Periodic Labour Force Survey

    1. A short reference period counts any work as employment: A person engaged for as little as an hour on a single day in the reference week is recorded as employed, so a full-time job and a day of casual work carry the same weight. Eg. Unpaid work in a family enterprise is counted as employment.
      The Fix: Publish hours worked and earnings distributions alongside the headline rate, so the composition of employment is visible.
    2. The household frame loses the circulating worker: A survey records a person at their usual residence, so a worker moving between a home district and a distant worksite can be missed at both ends. Eg. Urban construction runs on labour that its home district still records as resident.
      The Fix: Link the survey frame to social security registration numbers, so a worker traced at the destination is not lost at the origin.
    3. Unemployment is the wrong headline where there is no income support: Without unemployment insurance a worker cannot afford to remain unemployed, so joblessness appears as low-paid self-employment rather than in the rate. Eg. A person selling goods on the street with no earnings floor is counted as employed.
      The Fix: Publish an underemployment and working poverty series with each monthly release.
    4. Monthly sampling limits disaggregation: A monthly sample supports a national and rural-urban split, not a State, district or occupational reading. Eg. The release carries no monthly breakdown by sector or by occupational shift.
      The Fix: Pool three consecutive monthly rounds into a rolling State level estimate published alongside the headline.

    Conclusion

    A statistical system has been made faster without being made deeper, and the two are not substitutes. The unresolved question is whether the survey will be judged on how often it reports or on whether it captures the working lives of a largely informal workforce. Frequency answers a demand from markets and commentary; job quality answers the policy question of whether participation is converting into stable, higher-productivity work. Until the administrative registers are integrated into a single frame, the monthly rate will keep being read as a verdict it cannot deliver.

    Back2Basics: International Labour Organization

    1. Formation: Established in 1919 under the Treaty of Versailles, and it became the first specialised agency of the United Nations in 1946.
    2. Headquarters: Geneva, Switzerland.
    3. Structure: It is the only tripartite United Nations agency, bringing together governments, employers and workers of member States with equal standing in its decision making.
    4. Why it matters here: Its conferences of labour statisticians set the international statistical definitions of employment, unemployment and the labour force that national surveys are benchmarked against.
  • Next employment challenge is better jobs

    Next employment challenge is better jobs

    Why in the News

    India’s employment has grown from 47.15 crore in 2014-15 to 64.33 crore in 2023-24, according to RBI’s KLEMS database, shifting the debate from job quantity to job quality and employability.

    Core issue: Aggregate employment data does not reveal formalisation, real wages, social security, or career stability. NITI Aayog’s skilling blueprint therefore emphasises industry-linked, demand-driven and outcome-oriented skilling for future employment.

    What has the employment base actually delivered?

    1. The foundations were widened through five channels: Infrastructure development, formalisation, financial inclusion, skilling and encouragement to entrepreneurship together expanded the base of paid work.
    2. The addition is 17.18 crore workers over nine years: The provisional KLEMS series records that increase through 2023-24, averaging about 1.9 crore workers a year.
    3. The 2 crore aspiration needs a definition: It cannot mean 2 crore salaried government posts created every year.
    4. An aggregate count cannot settle the debate: A number of workers added says nothing about whether the work is formal, better paid or capable of progression.

    Why do the labour data series not answer the same question?

    1. The monthly bulletin measures a seven day window: The Periodic Labour Force Survey (PLFS), the official household survey of employment, publishes a monthly bulletin whose Current Weekly Status classifies activity over the preceding seven days.
    2. The annual survey measures the year: Usual status captures the durable yearly pattern of a person’s activity.
    3. The two series answer different questions: Monthly and weekly status figures track short term movement, and annual usual status figures assess structural progress.
    4. Mixing them distorts the reading: The two are not interchangeable, so a monthly movement cannot stand as evidence of structural gain.
    5. A national employment dashboard is the proposed instrument: It would report formalisation, real wage growth, social security, hours worked, sectoral productivity and movement from low income work into stable careers.

    What does the 9 crore figure actually describe?

    1. 9 crore young Indians sit outside all three activities: They were neither in education, employment nor training, excluding those actively seeking jobs.
    2. Most of that group is in domestic duties: About 88 per cent were engaged in unpaid household work.
    3. The group is not the same as the unemployed: Describing all 9 crore as unemployed is inaccurate, since a person in domestic duties is not seeking paid work.
    4. Five constraints keep young women out of paid work: Unpaid care, safety, mobility, social norms and limited local opportunities restrict the choices available to them.

    Is educated unemployment the same as graduate unemployment?

    1. Educated unemployment among first time entrants is real: Graduates leaving education face a genuine gap between qualification and placement.
    2. The two claims are not equivalent: Graduates forming a large share of unemployed youth does not mean most graduates are unemployed.
    3. Training volume is already large: More than 1.64 crore candidates have been trained or oriented under the Pradhan Mantri Kaushal Vikas Yojana.
    4. Apprenticeship has scaled since 2016: Over 56.08 lakh apprentices have been engaged in that period.

    What does the start-up record show about job creation beyond the state?

    1. Start-ups report more than 23 lakh direct jobs: Recognised start-ups had reported that figure by April 2026.
    2. The unicorn count moved from four to over 120: India had four firms valued above one billion dollars in 2014 and now has over 120, with a combined valuation exceeding 350 billion dollars.
    3. Half the ventures come from outside the metros: Around half of recognised start-ups emerge from Tier II and Tier III cities.
    4. Nearly half carry a woman in a leadership role: Over 45 per cent of recognised start-ups had at least one woman director or partner by December 2025.

    Where does public employment fit in the next decade?

    1. Public hiring must be transparent and timely: Sanctioned vacancies should be filled through transparent processes, with examination integrity and timely results treated as non-negotiable.
    2. The state cannot be the sole employer: A country adding millions of workers each year cannot place them all in government posts.
    3. Enterprise scale-up is the next step: Helping viable micro-enterprises grow, formalise and hire is the route to the volume public hiring cannot supply.

    Why are women the decisive measure of the next transformation?

    1. Participation rose by 18 percentage points in six years: Female labour force participation in usual status rose from 23.3 per cent in 2017-18 to 41.7 per cent in 2023-24.
    2. Women already hold the financial access base: Women hold 56 per cent of Pradhan Mantri Jan Dhan Yojana accounts and receive about two-thirds of Micro Units Development and Refinance Agency (MUDRA) loans.
    3. Self-help group membership crosses 10 crore: More than 10 crore women are members of self-help groups.
    4. Basic services cut unpaid work time: Tap water, clean cooking fuel and sanitation reduce drudgery, and housing ownership strengthens household assets.
    5. The next set of supports is different in kind: Affordable childcare, safe transport, working women’s hostels, flexible formal work, digital access and quality jobs closer to home are what convert participation into stable employment.

    Challenges to raising job quality in India

    1. Informality caps wage and social security gains: Over 90 per cent of India’s workforce is informal, so an added job does not automatically carry provident fund cover, a written contract or paid leave. Eg. Food delivery and ride hailing platform workers are engaged as partners rather than employees, which keeps them outside provident fund and gratuity cover.
      The Fix: Make registration of workers on the e-Shram database a condition of enterprise credit and subsidy eligibility, so formal status follows the finance.
    2. Services led growth absorbs few workers: Services drive output growth but employ under 30 per cent of the workforce, so the fastest growing sector is the weakest job creator. Eg. India’s information technology and business services exports are among the largest in the world, and the sector employs a small fraction of the non-farm workforce.
      The Fix: Tie manufacturing incentives to verified employment created rather than to output or investment alone.
    3. Skill supply is not matched to demand: About half of Indian graduates are assessed as employable, so training volume does not convert into placement. Eg. The India Skills Report has repeatedly placed graduate employability near the 50 per cent mark.
      The Fix: Make industry co-certification and verified placement outcomes the release condition for skilling programme funds.
    4. Weak manufacturing limits absorption of semi-skilled labour: Manufacturing contributes about 16 to 18 per cent of India’s Gross Domestic Product (GDP) against roughly 26 per cent in China. Eg. Textiles, leather and food processing remain fragmented across units too small to enter export supply chains.
      The Fix: Direct production incentives towards labour intensive sectors rather than towards capital intensive electronics assembly alone.
    5. Rural distress is measured too late to act on: High frequency labour surveys have historically been confined to urban areas, so rural conditions are captured only once a year. Eg. The quarterly PLFS bulletin covered urban areas alone for years after its launch.
      The Fix: Extend quarterly survey coverage to rural areas and integrate provident fund and National Career Service records into a single release.

    Conclusion

    The employment question India argues about is no longer the employment question it measures. Scale has been settled by the last decade. Quality has not, and no official series reports it as one trackable outcome. The unresolved tension is that a government judged on a headline count has little incentive to build the measure that would show whether the count is worth having.

    Unemployment in India

    1. Definition: The International Labour Organization (ILO) treats a person as unemployed when they are of working age, meaning 15 years and above, without work, currently available for work and actively seeking it in a reference period.
    2. Nodal measurement body: The National Sample Survey Office under the Ministry of Statistics and Programme Implementation is the principal body estimating unemployment in India.
    3. Recognised types: Frictional, structural, cyclical, seasonal and disguised unemployment are the standard categories, with disguised unemployment concentrated in agriculture where marginal productivity is near zero.
    4. Administrative sources supplement surveys: Employees’ Provident Fund Organisation, Employees’ State Insurance Corporation and National Pension System payrolls are used to estimate formal job creation.

    Government Initiatives for Employment Generation

    1. Mahatma Gandhi National Rural Employment Guarantee Act, 2005: Guarantees 100 days of wage employment in a financial year to a rural household whose adult members volunteer for unskilled manual work.
    2. e-Shram portal: A national database that issues unorganised workers a Universal Account Number and gives them single point access to welfare schemes.
    3. PM SVANidhi: Provides collateral free working capital loans to street vendors to restart and expand their businesses.
    4. PM Vishwakarma: Offers collateral free credit, skilling and toolkits across 18 traditional artisan and craft trades.
    5. Pradhan Mantri Viksit Bharat Rozgar Yojana: An employment linked incentive approved in July 2025 with a Rs 99,446 crore outlay, targeting 3.5 crore jobs over two years.
    6. PM Internship Scheme: Launched in 2024 to place 1 crore young people in internships with large companies over five years.
    7. National Career Service portal: Matches job seekers with employers, adding 17.23 lakh employers and 1.38 crore new job seekers in 2024.

    [2023, GS3, 15 marks] Most of the unemployment in India is structural in nature. Examine the methodology adopted to compute unemployment in the country and suggest improvements.

  • Rural India needs jobs, not wage guarantees

    Rural India needs jobs, not wage guarantees

    Why in the News

    An opinion piece argues that a new rural wage-guarantee scheme has recorded low uptake among the rural workforce, and contends this shows rural India needs durable, income-generating employment rather than a guaranteed-wage safety net. The scheme pays a guaranteed wage for a fixed number of days, which the piece contrasts with sectors such as food processing, renewable energy and small and medium enterprises (SMEs), which it argues could generate sustained employment rather than a temporary income floor. The tension is between a safety-net approach to rural distress and a growth-oriented approach that builds durable non-farm jobs.

    Why has the wage-guarantee scheme seen low uptake?

    1. Wage ceiling below market rates: Where the scheme’s guaranteed wage sits below prevailing local market wages for casual labour, workers have limited incentive to enrol, since informal market work pays more for the same effort.
    2. Seasonal mismatch: A fixed-day guarantee does not align well with the seasonal peaks in rural labour demand during sowing and harvest, when private demand for labour already absorbs much of the available workforce.

    What alternative does the piece propose?

    1. Food processing: Expanding food processing capacity near production zones can absorb rural labour in agro-processing roles that persist beyond a single season.
    2. Renewable energy: Rural solar and biomass energy projects can generate sustained local employment in installation, operation and maintenance roles.
    3. Small and medium enterprises: Supporting rural SMEs with credit and market access can create employment that grows with demand, rather than being capped at a fixed number of guaranteed days.

    Unemployment in India

    1. The International Labour Organization (ILO) defines an unemployed person as someone of working age, without work, currently available to work and actively seeking work in a reference period.
    2. India’s unemployment carries several distinct types: frictional, structural (a mismatch between workers’ skills and market demand), cyclical, seasonal, disguised (as in agriculture, where more people are employed than the work requires), and chronic.
    3. Over 90 percent of India’s workforce remains informal, which limits meaningful, secure job creation regardless of headline employment growth.
    4. Manufacturing contributes only about 16 to 18 percent of GDP, well below China’s roughly 26 percent, constraining the sector’s capacity to absorb surplus labour.

    Government Initiatives for Employment Generation

    1. Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA), 2005: Guarantees 100 days of rural wage employment a year to any adult member of a rural household, and is the specific scheme this op-ed’s wage-guarantee critique concerns.
    2. PM Vishwakarma: Provides collateral-free loans, skilling and toolkits to artisans across 18 traditional trades.
    3. PM Vishwakarma Rozgar Yojana / Employment Linked Incentive (ELI) scheme: Approved with an outlay of about 99,446 crore rupees, targeting 3.5 crore jobs over two years.
    4. e-Shram Portal: A national database that issues unorganised workers a Universal Account Number and links them to social security schemes.
    5. DAY-NRLM: Mobilises the rural poor into Self-Help Groups to build self-sustained livelihoods.

    Challenges in Unemployment

    1. Survey design undercounts informal and rural work: Household surveys do not fully capture home-based, gig or platform work within the roughly 90 percent informal workforce, and rural labour force surveys have historically run at a lower frequency than urban ones. Eg. Rural Periodic Labour Force Survey (PLFS) data was measured only annually for years, while urban data was collected quarterly, understating rural distress in real time. Fix. Move rural PLFS to the same quarterly frequency as urban surveys and explicitly incorporate underemployment into the headline definition.
    2. Capital-intensive growth limits absorption: Investment has flowed disproportionately toward information technology and infrastructure rather than labour-intensive sectors capable of absorbing low and semi-skilled workers. Eg. Services now drive the largest share of GDP growth while employing under 30 percent of the workforce, the jobless growth pattern this op-ed’s wage-guarantee critique responds to. Fix. Direct incentive schemes toward labour-intensive sectors such as textiles, leather, food processing and electronics assembly rather than capital-intensive ones alone.

    Conclusion

    The piece argues that a wage-guarantee scheme with low enrolment is evidence that rural India’s underlying problem is a shortage of durable jobs, not a shortage of a temporary income floor, and that policy should shift resources toward sectors capable of generating sustained rural employment.

    Back2Basics: Periodic Labour Force Survey (PLFS)

    1. The PLFS is India’s principal household survey for estimating employment and unemployment, conducted by the National Sample Survey Office (NSSO) under the Ministry of Statistics and Programme Implementation (MoSPI).
    2. It reports unemployment on three measures: Usual Status (activity over the preceding year), Current Weekly Status, and Current Daily Status, the last of which best captures underemployment.
    3. It has historically surveyed urban areas quarterly but rural areas only annually, a frequency gap that limits its ability to track rural distress as it develops.

    “[2023, GS3, 15 marks] Most of the unemployment in India is structural in nature. Examine the methodology adopted to compute unemployment in the country and suggest improvements.”

  • NITI Aayog: Degrees like BA, B.Sc, B.Com have ‘weak job linkages’, need reforms

    NITI Aayog: Degrees like BA, B.Sc, B.Com have ‘weak job linkages’, need reforms

    Why in the News

    NITI Aayog has flagged that unemployment among graduates remains far higher than the national average, and that over-reliance on generic degrees such as BA, B.Sc and B.Com is contributing to the problem. The finding comes amid a renewed push to redesign India’s skilling architecture toward specialised, job-linked programmes.

    What does NITI Aayog’s assessment find?

    1. Most graduates work outside their field of study: Over 90% of India’s graduates are employed in roles not aligned with their qualifications.
    2. The disconnect is curriculum level: NITI Aayog states that curriculum in most institutions remains outdated and misaligned with evolving industry needs, producing degrees and diplomas with weak job linkages.
    3. The proposed direction is sector specific: The think tank makes the case for moving toward specialised, job-linked programmes in high-growth sectors such as green industries and electric vehicles, with greater emphasis on apprenticeships.

    Conclusion

    NITI Aayog’s assessment reframes graduate unemployment as a curriculum design problem rather than only a labour demand problem, and its recommendation is a shift from generic degrees toward sector-specific, apprenticeship-linked training in high-growth industries.

    “[2015, GS3, 12 marks] The nature of economic growth in India in recent times is often described as a jobless growth. Do you agree with this view? Give arguments in favour of your answer.”

  • India’s youth crisis is about the absence of jobs, not just examination reform

    India’s youth crisis is about the absence of jobs, not just examination reform

    Question (2023, GS3): Most of the unemployment in India is structural in nature. Examine the methodology adopted to compute unemployment in the country and suggest improvements.
    Linkage: The editorial contends that youth agitations and demand for cheaper coaching address only the symptoms of the crisis, whereas the foundational issue is structural unemployment—the deep-seated absence of final job opportunities for qualified youths at the end of their preparation.

    Mentor comment

    The Hindu’s editorial argues that India’s youth unemployment problem is a jobs crisis, not merely an examination reform problem. The youth agitation that forced the resignation of the then Union Education Minister produced a government commitment to examination reform, including free online coaching for competitive examinations using India’s Digital Public Infrastructure. The editorial contends that cheaper coaching addresses only the preparation stage of the crisis, while the deeper problem is the absence of jobs at the end of that preparation.

    What does the data show about the scale of the crisis?

    1. Coaching costs have risen, not fallen: Private coaching now costs 16% of what an average Indian family spends on a child’s education, up from 12.5% in 2018. Nearly a quarter of that spending occurs during the higher secondary years, when students prepare for competitive examinations.
    2. Seat scarcity dwarfs coaching costs: Over 22 lakh candidates appeared for this year’s medical entrance examination for about 1.4 lakh undergraduate seats, with fewer than 10,000 of those seats at the top 50 colleges. The Joint Entrance Examination for engineering colleges shows a similar pattern.
    3. Undergraduate enrolment has fallen for the first time: For the first time since the All India Survey on Higher Education began in 2011, undergraduate enrolment fell by 93,322 in 2023-24, sharpest among young men.
    4. The fall is regionally concentrated: Uttar Pradesh recorded the steepest decline, with undergraduate enrolment down 1.53 lakh even as diploma enrolment rose 1.38 lakh, suggesting students are substituting away from degrees that do not lead to jobs.
    5. Formal, secure jobs remain rare among graduates: Periodic Labour Force Survey unit level data shows that of every 100 graduates aged 15 to 29 in 2025, only 26 held regular salaried employment, and only four held a salaried job with both a contract and social security.

    Why has growth not translated into jobs?

    1. Manufacturing has not absorbed graduates: Manufacturing, the sector best placed to absorb India’s college graduates, remains at around a sixth of gross value added, well short of the quarter of the economy the government has long promised.
    2. Private investment has retreated: Corporate investment fell from 17.3% of GDP in 2007-08 to 10.3% in 2024-25, unmoved by the cut in the corporate tax rate from 30% to 22% in 2019.
    3. Regulatory enforcement has turned selective: The editorial states that a regulatory and enforcement zeal that selectively targets enterprises has disproportionately affected medium sized companies, the segment best placed to generate jobs.

    Conclusion

    The youth employment crisis has two distinct ends: preparation for jobs, and the jobs themselves. Free coaching addresses only the first. The editorial’s position is that public investment in industrial capacity, export-disciplined industrial support, and a less selective regulatory posture toward medium sized enterprises would do more for youth employment than examination reform alone, citing Vietnam as a comparator that has used this route.

  • The fact is youth unemployment has a household cost

    Why in the News

    Periodic Labour Force Survey (PLFS) 2025 data records youth unemployment at 14.8 percent and a Not in Employment, Education or Training (NEET) rate of 40.1 percent among the tertiary-educated, and the argument advanced from this data is that graduate joblessness is a household-level economic cost, not only an individual setback. A young person’s inability to find work does not only reduce that person’s own income, it removes an income the household had budgeted around, often after the household had itself financed the degree that produced no job.

    What is the household cost, distinct from the individual one?

    1. Sunk cost of financing the degree: Households that borrow or spend savings to fund a graduate’s education absorb that cost with no return if the graduate cannot find matching work, a loss the individual unemployment rate does not price in.
    2. Deferred contribution to household income: A household budgets around the expectation that an educated young adult will begin contributing income at a certain age; unemployment past that age forces the household to keep supporting a wage-earner it had expected to become a net contributor.
    3. Compounding effect on savings for other dependants: Money a household would have redirected toward a younger sibling’s education, a parent’s healthcare, or retirement savings instead continues to support an unemployed graduate.
    4. Psychological and bargaining costs within the household: Prolonged dependence on parents past the expected age of self-sufficiency affects a young adult’s standing and decision-making power within the household, a dimension PLFS-style employment data cannot itself measure but that the 40.1 percent NEET rate among the tertiary-educated makes newly visible.

    How does the tertiary-educated NEET rate compare with the general NEET pattern?

    1. Tertiary-educated NEET rate far exceeds the general rate: At 40.1 percent, the NEET rate among India’s tertiary-educated youth is markedly higher than the NEET rate among youth without a degree, inverting the usual expectation that more education reduces the risk of disengagement from work.
    2. Concentration in urban, aspirational households: The households most likely to have financed a tertiary degree, and to therefore carry the sunk cost described above, are disproportionately urban and lower-middle income, the segment for whom a graduate’s income was budgeted as a route out of that bracket.

    Conclusion

    Youth unemployment at 14.8 percent and a 40.1 percent NEET rate among the tertiary-educated do not describe an individual labour market outcome alone. They describe a household that financed an investment in education and is not yet receiving the income return it planned around, a cost that persists in household budgets even where it does not appear in an individual’s own unemployment statistic.

    Youth unemployment in India

    1. About: Youth unemployment measures joblessness among the working-age population, typically 15 to 29 years, whose job search outcomes diverge sharply from the adult labour force.
    2. Rationale for tracking it separately: Youth unemployment behaves differently from the aggregate rate because young workers are more likely to be first-time job seekers with no accumulated informal-sector fallback, so a downturn hits them earliest and hardest.
    3. Recognised typology: Unemployment among India’s youth spans frictional joblessness during the transition from education to work, structural joblessness from a skills mismatch, and disguised underemployment in low-productivity family enterprises and agriculture.
    4. Jobless growth in services: Services drive the largest share of GDP growth but employ under 30 percent of the workforce, limiting the sector’s capacity to absorb new entrants.
    5. Skill deficit at graduation: Only about half of India’s graduates are assessed as readily employable, per employability surveys, pointing to a curriculum gap rather than a shortage of degree holders.
    6. Weak manufacturing absorption: Manufacturing contributes only 16 to 18 percent of GDP, well below the roughly 26 percent contribution in China, limiting the formal, labour-intensive job creation India’s youth bulge needs.
    7. Informality as the default outcome: Over 90 percent of India’s workforce remains informal, so even youth who do find work often find it without security, benefits, or a written contract.
    8. Female youth workforce deficit: Caregiving duties, domestic responsibilities, and mobility constraints keep young women out of paid employment at a much higher rate than young men.

    Challenges in addressing youth unemployment

    1. Survey methodology undercounts informal and gig work: PLFS-style surveys do not fully capture home-based, gig, or platform work within India’s overwhelmingly informal workforce. Eg. Platform-based delivery and ride-hailing work is not consistently classified in the survey’s job categories. Fix. Update survey instruments to explicitly capture gig, platform, and digital work categories, aligned with International Labour Organization and System of National Accounts definitions.
    2. Low-frequency rural data delays policy response: Rural employment data has historically been measured only annually, compared with quarterly urban estimates, masking rural distress in real time. Eg. A poor monsoon’s effect on rural non-farm employment often does not show up in national data until the following year’s release. Fix. Extend the quarterly PLFS survey design to rural areas at the same frequency as urban areas.
    3. Capital-intensive investment bias: Investment continues to flow toward capital-intensive sectors such as information technology and infrastructure rather than the labour-intensive sectors that absorb semi-skilled youth. Eg. Automation in manufacturing has reduced the labour intensity of new capacity even as output has grown. Fix. Direct production-linked incentives toward labour-intensive sectors such as textiles, leather, and food processing, alongside the existing electronics-focused schemes.
    4. Demographic dividend at risk of becoming a demographic trap: A youth bulge that cannot find work stops being an economic asset and starts becoming a fiscal and social liability as the cohort ages without having built savings or skills. Eg. State of Working India 2026 estimates 9.2 crore youth in the NEET category nationally. Fix. Expand the government’s employment-linked incentive schemes and apprenticeship mandates specifically targeted at the 21 to 29 age cohort.
    5. Weak coordination across employment data systems: Employees’ Provident Fund Organisation payroll data, the National Career Service portal, and PLFS survey data are not integrated, making it hard to track whether a given policy intervention is actually creating net new jobs. Eg. The Employment Linked Incentive scheme announced in 2025 tracks payroll additions but not whether they represent new jobs or reclassified existing ones. Fix. Build a single integrated employment data dashboard drawing on EPFO, NCS and PLFS data for real-time tracking.

    Back2Basics: NEET (Not in Employment, Education or Training)

    1. An internationally used labour-market indicator that counts young people who are neither working, studying, nor undergoing any training, distinct from the unemployment rate, which only counts those actively seeking work.
    2. Captures discouraged job seekers and those who have withdrawn from the labour force entirely, a population the standard unemployment rate does not measure.
    3. The State of Working India 2026 report estimates roughly 9.2 crore Indian youth in this category.

    Matching Previous Year Question

    “[2023, GS3, 15 marks] Most of the unemployment in India is structural in nature. Examine the methodology adopted to compute unemployment in the country and suggest improvements.”

  • The other ‘NEET’ that India needs to address

    Why in the News

    Fresh Periodic Labour Force Survey (PLFS) data on the Usual Employment and Unemployment Rate shows nearly 40 percent of Indian graduates aged 25 are unemployed, alongside an estimated 9.2 crore Indian youth falling into the Not in Employment, Education or Training (NEET) category. The State of Working India 2026 report situates this alongside India’s demographic dividend, the working-age population bulge the country has counted on as a growth advantage. A youth cohort large enough to drive growth is instead showing a graduate unemployment rate high enough to raise doubts about whether that dividend is being converted into productive work.

    What does the NEET measure capture that the unemployment rate does not?

    1. NEET counts withdrawal, not just joblessness: The unemployment rate only counts people actively seeking work; NEET (Not in Employment, Education or Training) also captures young people who have stopped searching or never entered education or the labour force, a group the standard unemployment rate misses entirely.
    2. 9.2 crore youth estimated in the NEET category: The State of Working India 2026 report’s estimate of 9.2 crore places the scale of youth disengagement well above what headline unemployment figures alone would suggest.
    3. Graduate unemployment concentrated among the young: Nearly 40 percent of 25-year-old graduates are unemployed, a rate far higher than unemployment among the working-age population as a whole, showing that a degree has not translated into a job for this cohort at the pace the labour market absorbs less-educated job seekers.
    4. Gender skew within the NEET population: Young women make up a disproportionate share of the NEET category, reflecting caregiving responsibilities and mobility constraints that keep them out of both education and paid work even when jobs exist locally.

    Why does graduate unemployment run higher than overall unemployment?

    1. Skill mismatch between degrees and job requirements: Employers report that a large share of graduates are not employable in the roles the formal sector is creating, because curricula have not kept pace with industry requirements.
    2. Weak absorption capacity in manufacturing: Manufacturing’s share of GDP has stayed well below the level needed to absorb a growing pool of educated job seekers into formal, better-paid work, pushing graduates toward informal or underemployed roles instead.
    3. Aspirational mismatch with available jobs: A graduate degree raises the reservation wage and the kind of work a job seeker will accept, so graduates wait longer for a suitable formal-sector opening rather than take the informal work a non-graduate would accept immediately.
    4. Delayed labour market entry compounds the count: Prolonged job searches by graduates keep them in the unemployed count for longer than less-educated job seekers, who exit into informal work faster even at lower wages.

    Conclusion

    The NEET count of 9.2 crore and the near-40 percent graduate unemployment rate among 25-year-olds point to a mismatch between what India’s education system produces and what its labour market currently absorbs. Closing that gap over the remaining years of India’s demographic dividend, rather than after it starts to narrow, is the reform window the data points to.

    Back2Basics: Periodic Labour Force Survey

    1. Conducted by the National Sample Survey Office (NSSO) under the Ministry of Statistics and Programme Implementation, the principal source of employment and unemployment data in India.
    2. Uses the Usual Status approach, based on a person’s activity over the preceding 365 days, alongside the Current Weekly Status approach for more recent snapshots.
    3. Was redesigned to provide quarterly urban estimates in addition to the earlier annual survey, though rural high-frequency coverage remains thinner.
    4. Feeds the official Unemployment Rate and Worker Population Ratio figures cited in Parliament and used for policy design.

    Matching Previous Year Question

    “[2023, GS3, 15 marks] Most of the unemployment in India is structural in nature. Examine the methodology adopted to compute unemployment in the country and suggest improvements.”

  • What young want, and why creating good jobs is no longer optional

    Why in the News

    Almost 70 per cent of urban job seekers surveyed in Delhi said they were looking for a job that would place them on their ideal career path from the start, instead of settling for any job. The survey covered over 3,000 randomly sampled men and women, 24 years of age on average, living in middle-class residential areas of the capital, and was conducted in the summer of 2023. Their stated career goal was predominantly salaried or formal-sector employment. The Periodic Labour Force Survey (PLFS) for the same year records an urban labour market that cannot supply that goal, with less than 50 per cent of the urban workforce in salaried jobs. A follow-up experiment then exposed a random subset of the same job seekers to real-world job openings and salaries, and re-surveyed them a year later. Correcting their information lowered their expectations and left their aspirations untouched, so the contest is over who adjusts, the young or the labour market.

    What is the Periodic Labour Force Survey (PLFS)?

    1. Purpose: The PLFS is the official household survey that estimates how many people are working, seeking work or outside the labour force, and in what kind of work they are engaged.
    2. Nodal body: The National Sample Survey Office under the Ministry of Statistics and Programme Implementation conducts it and is the principal source of employment estimates in India.
    3. Activity status measures: Usual Status classifies a person by activity over the preceding 365 days, while Current Weekly Status treats a person as unemployed if they did not work even one hour in the reference week.

    What do young urban job seekers actually want from work?

    1. A career path, not a job: Almost 70 per cent said they wanted an opening that put them on their ideal career path from the start rather than any available job, and more men said this than women.
    2. Formal salaried work is the goal: The stated career goal was predominantly salaried or formal-sector employment rather than casual or own-account work.
    3. Women lean harder towards salaried jobs: More women job seekers aspired to salaried positions than men did.
    4. Only 14 per cent of women prefer self-employment: Just 14 per cent of the women interviewed said they would rather work for themselves.
    5. A third of men want to run enterprises: More than a third of the men wanted to start their own businesses.
    6. Public sector preference is a myth: A comparable share of these men and women were looking for private-sector salaried jobs, which cuts against the dominant narrative of a strong preference for government jobs.

    How far does the urban labour market fall short of those preferences?

    1. Salaried work is a minority outcome: Less than 50 per cent of India’s urban workforce holds a salaried job.
    2. It is scarcer still for the young: Merely one in every three employed 24-year-olds holds a salaried job, a lower share than for the workforce as a whole.
    3. Government jobs are a tenth of the market: No more than 10 per cent of the urban workforce is in the public sector or government jobs.
    4. The formal private sector is barely larger: Only about 15 per cent of the urban workforce is in the formal private sector.
    5. Self-employment is the largest single category: Of those working, 40 per cent are self-employed.
    6. Most self-employment is subsistence, not enterprise: An overwhelming majority of these businesses hire no worker at all and report an annual turnover of less than Rs 10 lakh, so the aspiration to build a firm meets a market of one-person shops.

    Why do salary expectations diverge from what these jobs actually pay?

    1. The occupations tested: Respondents were asked what they expected to earn as an accounts keeper, a primary school teacher, a data entry operator, a hospital attendant and an electrician, and each expectation was measured against actual PLFS earnings for the same occupation.
    2. Expectations run up to 40 per cent above reality: Job seekers expect up to 40 per cent higher salary than the earnings the PLFS records for the same work.
    3. Men are the more over-optimistic: Male job seekers expect almost Rs 8,000 more per month than the actual average earnings for these jobs.
    4. The gap widens for salaried work: For salaried jobs specifically, male job seekers expect Rs 8,500 more per month than actual earnings.
    5. The aggregate divergence exceeds 30 per cent: Taken together, salary expectations sit more than 30 per cent above reality, and the skew is sharper still among job seekers below 25 years of age, especially young men.
    6. Information and inexperience explain the gap: A lack of information or outright misinformation about openings and pay, combined with inexperience of the job market, are the two obvious sources of the misalignment.

    What did correcting job seekers’ information change, and what did it leave untouched?

    1. The design: A random subset of the 3,000 job seekers was informed about real-world job opportunities and salaries, and both the informed and the non-informed groups were re-surveyed twelve months later.
    2. Expectations fell: Accurate information significantly dampened labour-market expectations of landing the ideal job, relative to those who were not informed.
    3. Men disengaged first: Men in particular became less likely to report that they were on their ideal career path.
    4. Search effort fell with belief: That disillusionment was accompanied by a decline in men’s job-search intensity.
    5. The two exits from a failed search: As preferred job offers fail to materialise, job seekers adjust expectations downwards and either remain in the same jobs or leave the labour market and enrol at educational institutions.
    6. Aspirations did not move: The answer on whether aspirations changed is a clear no, since these men and women continued to aim for formal-sector jobs or dynamic entrepreneurship a year later, because aspirations are long-term goals and not easily malleable.
    7. High education costs make the expectation rational: Good-quality education is increasingly bought from private institutions at rising cost, so a high expected salary is not only aspirational but necessary to recover that outlay.

    Challenges to the Periodic Labour Force Survey

    1. Informal work is under-captured: Household surveys do not fully record home-based, gig and platform work in a workforce that is about 90 per cent informal. Eg. Delivery and ride-hailing riders working across two aggregators are frequently recorded as ordinary self-employed workers. Fix. Align the activity definitions with International Labour Organization and System of National Accounts practice so multi-job holders, freelancers and platform workers are counted separately.
    2. No skill mapping against job requirements: The survey does not match worker skills to the requirements of available jobs, so structural unemployment cannot be measured from it. Eg. The India Skills Report finding that only about half of graduates are employable has no counterpart in official survey data. Fix. Add a skills and job-requirement module so mismatch is measured rather than inferred.
    3. Rural data has been low frequency: Rural estimates were historically produced only once a year, so rural distress is visible with a long lag. Eg. A monsoon failure that pushes workers back into farm labour shows up only in the following annual round. Fix. Extend high-frequency quarterly or monthly rounds to rural areas rather than confining them to towns.
    4. Urban bias in the high-frequency rounds: The quarterly bulletins have been confined to urban areas, which under-measures the larger rural workforce. Eg. Quarterly urban unemployment rates are debated publicly while comparable rural numbers are unavailable. Fix. Publish a single integrated quarterly series covering both sectors on the same reference period.
    5. New job categories are missing: Gig, digital, start-up and green jobs are not adequately represented in the occupational classification the survey uses. Eg. Solar installation and battery recycling roles have no distinct occupational code. Fix. Integrate Employees’ Provident Fund Organisation, National Career Service and PLFS records so emerging job creation is tracked from administrative data as well.

    Conclusion

    Young urban job seekers want formal salaried careers and dynamic enterprise, and correcting their information about the market lowers what they expect to earn without changing what they want. That asymmetry places the burden of adjustment on the economy rather than on the young, and realising these aspirations requires a structural transformation that creates jobs with regular pay and benefits. The four Labour Codes are a step in that direction, and creating good jobs and genuine career paths, rather than jobs alone, is no longer optional. Failure carries a specific cost, which is the squandered potential of an entire generation.

    Employment and Unemployment in India

    1. What is measured: An unemployed person is of working age, that is 15 years and above, without work, currently available for work and actively seeking it in a reference period.
    2. Structure of the workforce: The Labour Force Participation Rate stood at 59.3 per cent in 2025, about 90 per cent of the workforce is informal, and nearly 58 per cent of salaried workers still lack a written contract.
    3. The absorption problem: Services drive most output growth but employ under 30 per cent of the workforce, while manufacturing contributes only about 16 to 18 per cent of Gross Domestic Product against roughly 26 per cent in China.
    4. Types of unemployment tested: Frictional, structural, cyclical, seasonal, disguised, voluntary and chronic unemployment are distinguished, with disguised unemployment concentrated in agriculture where marginal productivity approaches zero.

    Laws and Rules Governing Employment in India

    1. Code on Wages, 2019: Consolidates four wage laws, sets a statutory floor wage, and extends minimum wage cover beyond the roughly 30 per cent of workers it earlier reached.
    2. Industrial Relations Code, 2020: Merges three laws, raises the closure and retrenchment approval threshold from 100 to 300 workers, and gives fixed-term workers parity and gratuity after one year.
    3. Code on Social Security, 2020: Merges nine laws, defines gig and platform workers for the first time, and requires aggregators to contribute 1 to 2 per cent of turnover to a welfare pool.
    4. Occupational Safety, Health and Working Conditions Code, 2020: Consolidates 13 laws into one licence, one registration and one return, and caps hours at 8 to 12 daily and 48 weekly.
    5. Commencement of the four Codes: All four came into force on 21 November 2025, replacing a fragmented body of central labour legislation.
    6. Mahatma Gandhi National Rural Employment Guarantee Act, 2005: Guarantees 100 days of wage employment per rural household in a financial year.

    Government Initiatives for Employment Generation

    1. PM Viksit Bharat Rozgar Yojana: An employment-linked incentive approved in July 2025 with a Rs 99,446 crore outlay, targeting 3.5 crore jobs over two years.
    2. e-Shram Portal: A national database issuing Universal Account Numbers to unorganised workers and integrating access to more than 14 central schemes.
    3. PM Internship Scheme: Launched in 2024 to offer 1 crore internships in top companies over five years.

    Challenges in Employment Generation in India

    1. Lopsided structural change: India moved from agriculture to services without a job-rich manufacturing phase, so the sector that absorbs low-skilled labour elsewhere never scaled here. Eg. Manufacturing’s share of output has been stuck near 17 per cent against a 25 per cent policy target. Fix. Direct incentives to textiles, leather, food processing and electronics assembly, which absorb low and semi-skilled workers at scale.
    2. Capital-intensive investment bias: Investment flows to information technology and infrastructure rather than to labour-intensive activity, so output growth outruns job growth. Eg. Under the Production Linked Incentive scheme, most disbursed incentive has gone to large scale electronics assembly and pharmaceuticals, both capital intensive lines. Fix. Weight incentive schemes by jobs created per rupee of assistance rather than by output alone.
    3. Firms stay small to avoid compliance: Threshold-linked obligations reward staying under the size limit, which caps productivity and formal hiring. Eg. Micro, small and medium enterprises face more than 1,450 annual compliances costing Rs 13 to 17 lakh. Fix. Extend the Jan Vishwas approach of decriminalising minor compliance offences, which already covered 183 provisions across 42 central Acts.
    4. Skill deficit at both ends: Only about 4.7 per cent of the workforce has formal skill training, against roughly 96 per cent in South Korea, so employers and applicants describe different jobs. Eg. The Annual Status of Education Report 2023 found a quarter of rural youth aged 14 to 18 unable to read a Class 2 text. Fix. Tie curricula to Industry 4.0 and green job roles through mandatory industry-academia apprenticeship linkages.
    5. Women are kept out of paid work: Caregiving, domestic duties and mobility barriers hold female participation far below male participation. Eg. Urban female Labour Force Participation Rate stood at 25.8 per cent against 75.6 per cent for men in 2024. Fix. Enforce creche provision and workplace safety obligations already carried in the Codes.

    Matching Previous Year Question

    “[2023, GS3, 15 marks] Most of the unemployment in India is structural in nature. Examine the methodology adopted to compute unemployment in the country and suggest improvements.”

  • Rural skilling programme trainees not getting jobs, says panel

    Why in News

    A Parliamentary Standing Committee flagged a major gap between training and employment under the Deen Dayal Upadhyaya Grameen Kaushalya Yojana (DDU-GKY), highlighting low wages, poor retention and distress migration.

    What is DDU-GKY?

    • Ministry: Ministry of Rural Development.
    • Launched: 2014.
    • Target: Poor rural youth aged 15–35 years.
    • Nature: Placement-linked skill development scheme.
    • Training providers are assessed on training, placement and post-placement retention.
    • Implemented through Project Implementing Agencies (PIAs).

    Key Findings of the Committee

    • 18.38 lakh youth trained and 11.94 lakh placed as of March 2026.
    • Low wages and relocation costs lead to early job exits.
    • 9.65 lakh women trained and 6.03 lakh placed.
    • PIAs focus more on initial placement than sustained employment.

    Major Challenges

    • Skill-training does not match labour market demand.
    • Poor training quality and infrastructure.
    • Low wages reduce job retention.
    • Migration creates financial and social pressures.
    • Weak post-placement tracking.

    Committee Recommendations

    • Near 100% placement tracking.
    • Mandatory industry linkages and local placement drives.
    • District-level placement cells.
    • Migration assistance, mentorship and retention support.
    • Assess PIAs on sustained employment, not just initial placement.
    • Set and monitor minimum wage employment targets.

    Skill Development Initiatives

    • Pradhan Mantri Kaushal Vikas Yojana (PMKVY)
    • DAY-NRLM
    • Rural Self Employment Training Institutes (RSETIs)
    • Startup Village Entrepreneurship Programme (SVEP)
    • Skill India Digital

    [2023, GS2, 15 marks] Skill development programs have succeed in increasing human resources supply to various sectors. In the context of the statement analyze the linkages between education, skill and employment.”

    [2018] With reference to Pradhan Mantri Kaushal Vikas Yojana, consider the following statements:

    1. It is the flagship scheme of the Ministry of Labour and Employment.
    2. It, among other things will also impart training in soft skills, entrepreneurship, financial and digital literacy.
    3. It aims to align the competencies of the unregulated workforce of the country to the National Skill Qualification Framework.

    Which of the statements given above is/are correct?

    [a] 1, 2, and 3

    [b] 1 and 3 only

    [c] 2 only

    [d] 2 and 3 only