For most Indian families, the National Eligibility cum Entrance Test (NEET) has meant one thing: the gateway to a medical seat. The examination held on May 3 this year, 2026, was cancelled nine days later over allegations of a leaked question paper, provoking a public uproar.
Another NEET now competes for public attention, and it concerns far more young people. It stands for Not in Employment, Education or Training, a measure for which the National Statistics Office (NSO) has published district estimates for the first time. The figures, released on September 18, 2026, in its Labour Market Snapshot of Selected Districts, invite a second look at India’s unemployment numbers.
Unemployment rate
Around 85 per cent of districts have unemployment of 5.5 per cent or less (unemployment rate is calculated among those in the age group of 15 years and above). Yet in nearly 70 per cent of districts, at least one in five young people aged 15 to 29 falls in the NEET category. Nearly one-fourth of districts have more than 30 per cent of their youth in this position.
A district can have one of the lowest unemployment rates in the country and still leave many of its young people outside work and education.
Figure 1: Districts by unemployment rate and youth NEET rate Source: NSO press note, Tables 2 and 3.
The labour force consists of people who are working and people without work who are seeking or available for it. The unemployment rate counts only the second group, as a share of that labour force. NEET is wider. It counts young people who have stopped applying for jobs, as well as those who have not yet given up their job search.
However, those in NEET are outside education and training. A graduate applying for jobs appears in both, namely the NEET and unemployed categories. A young person who has given up the job search, appears in NEET alone.
NEET situation in populous districts
Among India’s 100 most populous districts, South 24 Parganas (West Bengal) and Araria (Bihar) record unemployment of just 0.9 per cent. Ahmedabad (Gujarat) follows at 1.1 per cent, with Surat (Gujarat), Bareilly (Uttar Pradesh), Bulandshahr (Uttar Pradesh) and Banas Kantha (Gujarat) at 1.2 per cent.
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The lowest NEET rates belong to a different list. Ernakulam (Keralam) records 9.6 per cent, Coimbatore (Tamil Nadu) 14.5 per cent, Bengaluru Urban (Karnataka) 15 per cent, Ranchi (Jharkhand) 16.5 per cent, Surat (Gujarat) 17.2 per cent, Thrissur (Keralam) 17.8 per cent, Vadodara (Gujarat) 18.1 per cent, Raigad (Maharashtra) 18.5 per cent, Chhatrapati Sambhajinagar (Maharashtra) 18.6 per cent and Ahmedabad (Gujarat) 18.9 per cent.
Only Surat and Ahmedabad appear in both top ten lists. Low unemployment and a low NEET rate are therefore different kinds of labour market success.
Figure 2: The ten lowest unemployment rates and ten lowest NEET rates among the 100 most populous districts. Source: NSO press note, Tables 5 and 6.
NEET rates vary enormously within states. Anugul in Odisha records 56.1 per cent as NEET, followed by Kendrapara (Odisha) at 51.4 per cent and Dhenkanal (Odisha) at 45.3 per cent. Tenkasi in Tamil Nadu is at 44.9 per cent, North Delhi at 44.3 per cent, Pashchim Champaran in Bihar at 43.6 per cent and Bhadradri Kothagudem in Telangana at 42.7 per cent.
At the other end, Nabarangapur in Odisha is at just 4.1 per cent. Anugul and Nabarangapur are in the same state, yet differ by 52 percentage points. The gap shows why state averages can hide radically different transitions from education to work. The national NEET rate in 2025 was 25 per cent.
Figure 3: Selected district extremes in the NEET rate among youth aged 15 to 29. Source: NSO, Labour Market Snapshot of Selected Districts, Appendix A.
Economists explain paradox
Well-known labour economist Santosh Mehrotra said young people aged 15 to 29 are much better educated than the working-age population as a whole, yet their unemployment rate is more than three times the general unemployment rate. The PLFS Annual Report 2025 bears this out: unemployment among those aged 15 to 29 was 9.9 per cent, compared with 3.1 per cent overall.
As Mehrotra put it: “The poor cannot afford to be unemployed, that is why their unemployment rate is negligible.”
The NEET figure captures educated young people who have stopped looking for jobs because they cannot find work they consider suitable. Economists call them discouraged workers. “Tomorrow you find them work, they will work”, Mehrotra pointed out.
Mehrotra dismissed the official account of a fast-growing economy and falling poverty. “The young are not buying it,” he said. “They have erupted across the country and they are about to erupt again.” He was referring to this year’s youth protests, which he claimed he had anticipated seven or eight years ago.
The gaps between districts, he said, reflect where non-farm jobs are being created. Some states generate them and others, which he called badly governed, generate too few. Even the better-governed states are not generating enough, he added.
He cited Tamil Nadu, among the fastest-growing states and a creator of non-farm jobs, where the government changed this year. In his view, the DMK lost to the TVK because young people were unhappy with the quality of jobs and wages.
Youth left behind
Mehrotra and Jajati Parida’s co-authored book, India Out of Work: Rethinking India’s Growth Story, (Bloomsbury, 2026), explains the NEET issue succinctly. According to them, this group represents lost potential as it is not gaining the skills or experience needed to participate in the economy. As they explain, the rise of NEETs is a sign of deeper systemic problems, where the education system, job market and economic structures are failing to support young people’s transition into productive employment, thus creating present and future fertile ground for inevitable social conflict.
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The rise in the NEET population to a stunning 121 million in 2021 from 51 million in 2017-2018 is a stark indicator of wasted potential and a failing economy that cannot integrate its youth into the workforce, they say. The sharpest spike occurred between 2017-2018 and 2018-2019, when the NEET population surged to 122 million, nearly doubling in just one year. They attribute this to disruptions caused by demonetisation (2016) and the implementation of the GST (2017), which severely impacted the unregistered MSME segment of every sector and their unorganised segments.
Mehrotra and Parida explain that as more young individuals disengage from education, training programmes or the labour market (in other words, stop looking for work as the job search becomes too long), the likelihood of them securing stable, formal employment decreases considerably. This NEET status often traps young people in a cycle of inactivity, where they lack both the skills required by the job market and the opportunities to gain experience, contributing to higher unemployment rates, they point out.
Women’s first hurdle
The NSO’s district release also shows that the share of women in the labour force differs widely from one district to another. This share is the Female Labour Force Participation Rate (FLFPR). It counts women aged 15 and above who are working, seeking work or available for it. The NSO’s Table 4 names, for each state, the district with the highest FLFPR among its ten most populous districts. Even among these state leaders, FLFPR ranges from 80.9 per cent in Himachal Pradesh’s Hamirpur to 18.9 per cent in East Delhi.
The leading districts of the other states fall at every level between these two extremes Mayurbhanj (Odisha) is at 71.8 per cent, Budgam (Jammu and Kashmir) 71.4 per cent, Surguja (Chhattisgarh) 69 per cent, Jagtial (Telangana) 63.2 per cent, Cuddalore (Tamil Nadu) 54.9 per cent, Thiruvananthapuram (Keralam) 52 per cent, Bijapur (Karnataka) 47 per cent, Patiala (Punjab) 39.3 per cent and Siwan (Bihar) 24.7 per cent. Figure 4 (below) gives the full list.
The FLFPR can be compared with the Female Worker Population Ratio (FWPR) defined as the percentage of employed Females (i.e., persons who were engaged in any economic activity) in the population. This is the share of women aged 15 and above who are actually working. The gap between the two is small in most of these districts. Hamirpur (Himachal Pradesh) is at 80.9 per cent FLFPR and 80 per cent FWPR; Mayurbhanj at 71.8 per cent and 71.2 per cent; Surguja at 69 per cent and 68 per cent; Dhar (Madhya Pradesh) at 59.6 per cent and 59.5 per cent; Kheda (Gujarat) at 55.6 per cent and 55.4 per cent. A small gap means that most women who enter the labour force are recorded as employed. What separates one district from another is the number of women who enter the labour force in the first place. About four in five do so in Hamirpur, and fewer than one in five in East Delhi.
Figure 4: FLFPR and FWPR across 21 comparable state leading districts. Jharkhand is excluded because different districts lead its FLFPR and FWPR. Source: NSO press note, Table 4.
Figure 5: Most districts lie close to the line where participation and employment are similar. East Delhi and Patiala stand out with larger gaps. Source: NSO press note, Table 4.
Understanding the gender difference
The district figures suggest at least three patterns. Hamirpur, Mayurbhanj and Surguja combine high female participation with high employment. Siwan leads Bihar’s ten most populous districts, yet its participation is very low at 24.7 per cent, with an FWPR of 24.3 per cent. The bigger issue there is women staying outside the labour force.
East Delhi has a different problem, with FLFPR at only 18.9 per cent and FWPR at 15.7 per cent. Patiala records 39.3 per cent and 35.8 per cent. A district may appear to have little unemployment simply because relatively few women enter the labour market.
Appendix A of the NSO’s bulletin gives the NEET rate separately for young women and young men in each district. An analysis of those figures shows that in 295 of the 584 districts and district groups, at least 40 per cent of women aged 15 to 29 fall in the NEET category. The rate for young men is below 15 per cent in 504 of them. The national figures match. The PLFS Annual Report 2025 puts the NEET rate at 8.0 per cent for young men and 42.2 per cent for young women. It also shows that 64.0 per cent of young men are in the labour force, against 27.5 per cent of young women. Young men without work therefore mostly remain in the labour force as jobseekers, whereas young women are far more likely to be outside it.
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Shraddha Jain, a PhD scholar at the Centre for Development Studies, Thiruvananthapuram and an independent researcher, and Basit Abdullah, affiliated with the Institute of Management of Technology, Hyderabad, analysed follow-up questions in the PLFS 2023 to 2024, on why women leave paid work or remain outside the labour force. Their article, Women’s Non-engagement in the Indian Labour Market: Centrality of Domestic and Care Responsibilities, appeared in the Indian Journal of Human Development in 2026.
They conclude that “women’s non-engagement from paid work results from a cumulative process shaped by life course transitions, care responsibilities, health constraints and labour market conditions.” The finding points to several interacting barriers.
Removing work barriers
Jain said that district differences may reflect the socio-demographic profile of the women living there. One would need to know whether Hamirpur’s high participation is found among younger or older women, married or unmarried, and from which social backgrounds. Migration matters as well, since some places attract migrant workers.
On the NEET gap between young women and men, Jain advised caution. The survey does not record discouragement directly as a reason, she said, so the discouraged worker effect is hard to gauge.
Asked what a district administration should do first, Jain placed affordable child care, elder care and disability care at the top. Skilling should follow the needs of local economic activity and go beyond certification, she said. She also pointed to women’s collectives, citing evidence that pooling resources may help women generate income. Class matters in reading the trends, she added.
Jain also drew attention to the time women and girls spend collecting water and firewood. Local public services need improvement, she said, as do safe transport services.
What district data says?
The district contrasts complicate easy explanations. East Delhi has very low female participation despite its large urban economy. Surguja and Mayurbhanj, predominantly rural, have much higher participation. Agriculture alone cannot explain the difference. Siwan is strongly agrarian and yet has an FLFPR of only 24.7 per cent, and Patiala’s intensive agriculture goes with 39.3 per cent.
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High participation also differs from good jobs. Agricultural labour, self-employment and family work can bring women into labour statistics without high wages, security or social protection.
The district data raise more useful questions. What kinds of local work are available, where, and who can access them? Are the wages and conditions sufficient to make labour force entry worthwhile?
Beyond unemployment rates
Surat illustrates a relatively strong combination: labour force participation of 68.6 per cent, a worker population ratio of 67.8 per cent, unemployment of 1.2 per cent and a NEET rate of 17.2 per cent. Araria, by contrast, combines unemployment of only 0.9 per cent with participation of 68 per cent and a worker ratio of 67.4 per cent. These figures say little by themselves about wages, security or productivity.
The central finding is hard to miss: India does not have one jobs problem. The nature of labour market exclusion changes sharply by district, by age and by gender.
A low unemployment rate may coexist with many young people outside work and education. A low female unemployment rate may coexist with few women in the labour force. High participation may coexist with poor quality work.
The new district data unravel this paradox to a discerning observer.