Chapter 19
Planning In India
01
SDG India Index
| Indicator | 2018 | 2020-21 | 2023-24 |
|---|
| India's composite SDG score | 57 | 66 | 71 |
| 'Front Runner' states/UTs (65-99) | NA | 22 | 32 of 36 |
| Range of state scores | 42-69 | NA | 57-79 |
- Steady national convergence: India's composite SDG score rose from 57 in the 2018 baseline to 71 in 2023-24, and the number of Front Runner states and UTs rose from 22 to 32 out of 36. This means most states are now above the halfway mark. Importantly, historically lagging states improved the most, with Uttar Pradesh gaining 25 points since 2018, Jammu & Kashmir 21 points, and Uttarakhand 19 points, which shows genuine convergence rather than static rankings.
- Top states and fastest goals: The top-ranked states in 2023-24 were Kerala and Uttarakhand, both scoring 79, followed by Tamil Nadu at 78. Among the goals, Goal 13 on Climate Action improved the most between 2020-21 and 2023-24, rising from 54 to 67, followed by Goal 1 on No Poverty, rising from 60 to 72. NITI Aayog treats the Index as a barometer for Viksit Bharat 2047 progress.
- Northeast district index: A second edition of the North-Eastern Region District SDG Index for 2023-24 covered 121 of 131 districts. The share of Front Runner districts rose sharply from 62% to 85%, with all districts in Mizoram, Sikkim and Tripura now in Front Runner status.
02
Aspirational Districts and Blocks Programmes
| Programme | Launched | Coverage |
|---|
| Aspirational Districts (ADP) | January 2018 | 112 districts across 27-28 states, ~6,000 villages |
| Aspirational Blocks (ABP) | January 2023 | 500 blocks in 328-329 districts across 27 states and 4 UTs |
- From district to block — deliberate scaling down: The Aspirational Blocks Programme extends the district model to a finer administrative unit, based on the finding that even within reasonably-performing districts, specific blocks can remain badly underdeveloped. Both programmes run on the same 3C model of convergence, collaboration and competition, and progress is tracked through monthly Delta Rankings across key performance indicators.
- Evidence of impact: Analysis using successive National Family Health Survey rounds found Aspirational Districts improving on key health and nutrition indicators, such as skilled birth attendance, immunisation and stunting reduction, faster than comparable non-ADP districts. This suggests the 3C model produced measurable gains beyond ordinary scheme rollout, though rigorous causal attribution remains under review.
Chapter 20
Unemployment
01
The current state of employment
| Indicator (Usual Status, Age 15+), 2025 | Value |
|---|
| Labour Force Participation Rate (LFPR) | 59.3% |
| Worker Population Ratio (WPR) | 57.4% |
| Unemployment Rate (UR) | 3.1% |
- Why the low headline is misleading: A national unemployment rate of about 3% looks low by international standards, but the headline hides two problems. First, this low average sits alongside serious pockets of distress, as youth unemployment and urban female unemployment are both far higher. Second, a healthy worker ratio does not mean good job quality, because a large share of employment is low-productivity self-employment rather than secure wage jobs.
02
Youth unemployment
| Youth (15-29) UR, 2025 | Rural | Urban |
|---|
| Male | 8.2% | 11.8% |
| Female | 8.6% | 18.9% |
- Three to four times the national rate: Youth unemployment is consistently three to four times higher than the overall rate. Rural youth unemployment is moderate, but urban youth unemployment, and especially urban female youth unemployment at nearly 19%, is strikingly high. Urban labour markets offer fewer jobs that families consider safe or acceptable for young women, given concerns about commuting, working hours and personal safety, so many educated urban women keep searching rather than take the first job available.
03
The education paradox
| Education Level (Age 15+), 2025 | Male | Female | Person |
|---|
| Not literate | 0.4% | 0.2% | 0.3% |
| Up to primary | 0.8% | 0.3% | 0.6% |
| Middle | 1.9% | 0.8% | 1.6% |
| Secondary and above | 5.5% | 9.5% | 6.5% |
- Unemployment rises with education: Unemployment rises sharply and consistently with education level. This reflects who can afford to stay unemployed while searching. A person with no education or assets cannot afford to remain jobless and must take whatever casual or informal work is available, so they get counted as 'employed' even if underpaid. An educated job-seeker, often with some family support, can afford to wait, or 'queue', for a job matching their qualifications, especially a secure government job.
- Two structural gaps behind the queue: This queuing is worsened by two gaps. First, the output of higher education has grown faster than the supply of matching white-collar jobs. Second, government jobs remain far more attractive than private ones because of their security and benefits, which extends the search duration.
- The gender gap at the top: The gender gap widens sharply at higher education levels, where educated women face a 9.5% unemployment rate against 5.5% for educated men. Education raises women's aspirations just as it does men's, but it does not remove the mobility, safety and social-acceptability constraints that limit which jobs are actually open to them. So educated women queue longer, or drop out of the labour force altogether, which then shows up as NEET rather than as unemployment.
04
NEET — a heavily gendered gap
| NEET Rate (Age 15-29), 2025 | Value |
|---|
| Male | 8.0% |
| Female | 42.2% |
| Person | 25.0% |
- A quarter of youth are NEET: A quarter of India's youth are Not in Employment, Education or Training. NEET is a broader measure of wasted human capital than unemployment, because it also captures those who have left the labour force entirely, not just those actively searching. The headline hides a stark gender split: male NEET is just 8%, but female NEET is 42.2%. This means many young women who cannot find acceptable work do not register as 'unemployed' at all; they withdraw into unpaid domestic work, which understates the true scale of India's female employment problem.
05
Gender gap in labour force participation
| LFPR (Age 15+), 2025 | Value |
|---|
| Rural female | 45.9% |
| Urban female | 27.7% |
| All-India female | 40.0% |
| All-India male | 79.1% |
- Rural women drive the female rate: India's female LFPR remains far below the male rate and is driven mainly by rural women, while urban female participation stays low. The recent gain in rural female participation is concentrated in self-employment rather than in regular wage or salaried work.
- Distress or empowerment — both at once: There are two explanations for the rise. The 'distress' view points to agricultural stress and household income pressure that push women into any available work, including low-paid or unpaid family labour. As men migrate out of villages to cities for work, farming is increasingly left to women, a trend often called the 'feminisation of agriculture', which raises women's recorded participation without necessarily improving job quality. The 'empowerment' view points to rising rural female education, better rural infrastructure and targeted government schemes. The most defensible position is that both forces operate at once, in different measure across states and social groups.
- The dual burden (Time Use Survey): The Time Use Survey shows that only about a quarter of women take part in paid work on a given day, against roughly three-quarters of men, while women spend a far larger share of their day on unpaid housework and caregiving. This 'dual burden', where women who do paid work still carry most of the unpaid domestic work, is why expanding care infrastructure, such as crèches, Anganwadi centres and employer-supported childcare, is repeatedly recommended as a lever to raise female participation, rather than treating low participation as simply a matter of choice.
06
Nature and quality of employment
| Status in Employment, 2025 | Share |
|---|
| Self-employed | 56.2% |
| Regular wage/salaried | 23.6% |
| Casual labour | 20.2% |
- Self-employment is not entrepreneurship: Self-employment is by far the largest category. This can sound like entrepreneurial energy, but most self-employed Indians are not running businesses that hire others. They are own-account workers scraping a living, or unpaid family workers contributing labour to a household enterprise without any wage. This is 'disguised unemployment' and 'vulnerable employment' in practice, and it is exactly why India's low unemployment rate coexists with widespread underemployment and working poverty.
- Regular wage and casual labour: Regular wage or salaried work, at 23.6%, is the most secure category but, as shown below, is often informal in practice. Casual labour, at 20.2%, is daily-wage work with no security, no benefits and highly variable earnings, and it is the most precarious category of all.
- Gendered ownership of assets: The self-employment pattern is strongly gendered, because close to a third of self-employed women are unpaid family helpers against under a tenth of self-employed men. This traces back to unequal ownership of land and productive assets, since land, farm equipment and business capital are mostly owned and controlled by men even within family enterprises. So women's labour is often not counted or paid as an independent economic contribution.
07
Wages mirror the quality gradient
| Average Earnings, 2025 | Male | Female |
|---|
| Regular wage/salaried (₹/month) | 24,217 | 18,353 |
| Casual labour (₹/day) | 489 | 324 |
| Self-employed (₹/month, gross) | 17,914 | 6,374 |
- Earnings and the gender gap both fall together: Average earnings fall sharply as one moves from regular wage work to casual labour to self-employment, and the gender pay gap widens in the same direction. Women earn a reasonable share of men's average in regular wage work, but only about a third of men's average in self-employment. Two reasons explain this: unpaid family helpers, mostly women, get no independent wage at all, only a share of household income controlled by someone else; and self-employment has no statutory minimum wage or collective bargaining to compress pay gaps. Employment status, informality and the gender pay gap are best read as one connected structural story.
08
The Four Labour Codes
| Milestone | Status |
|---|
| Passed by Parliament | Wage Code Aug 2019; other three Sept 2020 |
| Notified into effect nationally | 21 November 2025 |
| State-level rules | Uneven; a few states (Maharashtra, Tamil Nadu, Kerala) still at draft stage |
- Why state rules matter: Because labour is a Concurrent List subject, the Codes take real effect only once individual states notify their own detailed rules, and this process remains uneven. Passing a central law and that law actually changing conditions on the ground are two different things when the subject is on the Concurrent List.
09
The gig and platform economy
- Who a gig worker is: A gig or platform worker connects to work through a digital platform in a three-way (tripartite) relationship, doing task-based rather than ongoing work, and, before the new Social Security Code, had no social security cover. India's gig workforce is concentrated in e-commerce and logistics and is expected to keep growing rapidly as more sectors move to platform-based hiring.
- The vulnerability: A large share of gig workers earn modest monthly incomes and face a 'thin-file' credit problem, meaning too little formal credit history for lenders to assess them, which limits their ability to invest in better tools or upskilling. Platform algorithms that control task allocation and pay also raise concerns about bias and burnout that existing labour law was not designed to address.
- The policy response: The Code on Social Security, 2020 requires aggregators to contribute a share of their turnover toward gig workers' life, health and pension cover, with benefits made portable through a Universal Account Number to suit the interstate mobility of migrant gig workers. This mirrors a global trend, seen in Spain's 2021 'ley rider' law, which presumed platform riders to be employees rather than independent contractors, and the European Union's 2024 Platform Workers' Directive, which set common rules on algorithmic management and employment status across member states.
10
Government digital employment infrastructure
| Platform | Function | Scale |
|---|
| e-Shram | National database of unorganised workers | 31 crore+ registered; 54% women |
| National Career Service (NCS) | Connects job seekers, employers, trainers | ~8 crore vacancies mobilised |
| Skill India Digital Hub (SIDH) | Unified skilling and employment portal | Portable, verified credentials |
- Portability and job-matching: The e-Shram portal's Universal Account Number, linked to Aadhaar, is meant to make social security benefits portable as workers move across locations, employers or platforms, directly addressing the reality that much of India's workforce is informal, seasonal or spread across short-term arrangements. The National Career Service is meant to be a modern replacement for the old, largely unused government employment exchanges, since most unemployed Indians still find work through personal networks rather than any formal channel. Increasingly, skilling policy is shifting from counting how many people are trained, which is an output, to tracking whether they actually find and keep jobs and see earnings rise, which is an outcome.
Chapter 21
Human Resource Development
01
Demographic dividend — a time-limited window
| Indicator | Value |
|---|
| India's median age (2025-26) | ~28 years |
| Formal vocational training in workforce | ~5% (vs 60-80% in Germany, South Korea) |
| Reskilling need by 2027-2030 | ~150 million workers |
| Share aged 60+ by 2050 | ~20% |
- A ticking clock, not a permanent asset: India's demographic dividend, meaning its young, working-age-heavy population, is best described as a ticking clock rather than a permanent asset. The median age is rising, and projections put the elderly at roughly a fifth of the population by 2050. The window to convert today's youth bulge into productive output before ageing sets in is commonly estimated to run through the 2040s, which makes the skilling gap increasingly urgent.
- The core bottleneck: Only about 5% of India's workforce has received formal vocational or technical training, against 60% to 80% in countries like Germany and South Korea. This figure has stayed stubbornly low even as Skill India enrolment has scaled into the tens of millions.
02
Gender gap — India's global standing
| WEF Global Gender Gap Index — India | 2024 | 2025 |
|---|
| Overall rank (of 148) | 129 | 131 |
| Overall parity score | ~64% | 64.1% |
| Economic participation parity | NA | 40.7% |
| Educational attainment parity | NA | 97.1% |
| Women's parliamentary representation | 14.7% | 13.8% |
- India's educational attainment parity, at 97.1%, is near-complete, and health parity has improved with a better sex ratio and rising life expectancy. But economic participation is weak, at 40.7% parity, with women earning roughly 73% of male wages on average. Political empowerment is India's weakest pillar and actually declined, with both parliamentary and ministerial representation of women falling between the two most recent editions, despite the 106th Constitutional Amendment reserving one-third of legislative seats for women.
- The unpaid care work dimension: Indian women perform roughly seven times more unpaid domestic and care work than men, according to Time Use Survey data. The Economic Survey 2022-23 valued this labour at around 7.5% of GDP, yet it stays outside national income accounting and is a structural constraint on female labour force participation.
03
Ageing population — the same clock
| Indicator | Value / Projection |
|---|
| Share aged 60+ (current) | ~10-11% |
| Share aged 60+ by 2050 | ~20% |
| Composition of elderly | Majority female, growing share of widows |
| Fertility trend | Below replacement level nationally |
- Ageing and the dividend are two ends of one clock: India's demographic dividend and its coming ageing challenge are not separate stories but two ends of the same population transition. The working-age bulge that offers a growth opportunity today is the same group that will become the elderly dependent population from the 2040s onward. This is why the urgency around skilling and female participation is directly tied to the timeline: the dividend must be captured before the dependency ratio turns unfavourable.
- A gendered ageing challenge: Because women live longer than men and a large share of elderly women are widows with little independent income or pension cover, India's ageing challenge has a distinct gender dimension. Elder-care policy therefore needs to be read alongside female labour force participation and unpaid care work as one integrated problem, rather than as separate issues.