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Rung 08 — Degree, medical

0.85 million people aged 21–34. 45.67% hold a regular salaried job, of which 32.41% of the rung is formal work. 18.82% are still studying.

Self-contained: everything below is derived from PLFS CY2025 for this rung alone. The ladder-wide comparison is in ../README.md.


Filter Value
release_id calendar_2025
visit V1
gedu_lvl 12 — graduate, not postgraduate
tedu_lvl 04 — “Technical degree in medicine”
age 21–34

Sample: 885 people across 775 first-stage units, representing 0.85 million. The smallest rung on the ladder.

This is the most important thing to know before reading any number below. PLFS’s tedu_lvl='04' is a degree in medicine in the broad sense, and the occupation mix shows what that means in practice:

Occupation among those in formal work Share People
Medical doctors 24.2%
Nursing and midwifery professionals 19.6%
Nursing and midwifery associate professionals 12.2%
Medical and pharmaceutical technicians 9.8%
Other health professionals 5.7%

Shares are of all formally employed on the rung. Occupations with fewer than 15 people are suppressed and further rows below the top six are not listed, so this table sums to less than the rung’s total.

A quarter are doctors. Nearly a third are nurses. Every rung-level figure here is a weighted average across those groups, and they earn very differently — ₹50,000 median for doctors against ₹21,800 for nursing professionals and ₹18,000 for nursing associates.

Anyone needing doctors specifically must identify them by occupation (NCO group 221), not by this education code. That is what the separate NIRF analysis does, and why.


Share People
Formal work 32.41% 275k
Informal wage work 12.67% 108k
Self-employed, earning 10.80% 92k
Studying 18.82% 160k
Unemployed, seeking 8.51% 72k
Domestic duties 10.52% 89k

The studying share is the highest of any degree rung — 18.82%, against 6.05% for engineering. This is postgraduate training: NEET-PG, MDS, nursing specialisations. Formal employment of 32.41% looks low against engineering’s 52.58%, but a fifth of this rung has not finished training rather than failed to find work.

Unemployment is 8.51%, roughly half engineering’s rate.

Share of rung Formal work Domestic duties Median wage People
Female 55.1% 32.94% 19.08% ₹25,000 468k
Male 44.9% 31.75% 0.00% ₹35,000 381k

Women are a majority of this rung, at 55.1% — the only rung where that is true.

Their formal employment reads 32.94% against men’s 31.75%, but that difference is not real. Cluster-robust standard errors are ±3.30 and ±3.74 points, so the 1.19-point gap has t = 0.24 on 485 women and 400 men. It is a coin flip. What the data supports is that medicine is the rung where the gender gap in employment disappears — not that women are ahead.

And the wage gap is the widest of any degree rung: women earn ₹25,000 median against men’s ₹35,000, a 29% shortfall. Engineering, by contrast, has an employment gap and no wage gap; medicine has no employment gap and a large wage gap.

The occupation table explains it. Doctors earn ₹50,000 and nurses ₹21,800, and the split between those roles is heavily gendered. This is not equal work paid unequally — it is unequal access to the better-paid role inside the same education category. Which of those is the more troubling finding is not something this data can settle.

Share Formal work Median wage People
Urban 59.1% 38.17% ₹32,000 502k
Rural 40.9% 24.09% ₹20,000 348k

The rural median is 38% below the urban one — the widest urban-rural wage gap of any degree rung.


Monthly wage among the 32.41% in formal work, measured on tenure.

Tenure n p25 Median p75 p90 ≥₹30k ≥₹50k
Under 1 year 43 ₹16,000 ₹25,000 ₹35,000 ₹50,000 34.7% 10.3%
1 to 3 years 125 ₹17,000 ₹25,000 ₹40,000 ₹60,000 43.0% 15.6%
Over 3 years 124 ₹20,000 ₹35,000 ₹50,000 ₹68,000 55.0% 24.9%

The median rises 1.40× over three-plus years. The p25 barely moves — ₹16,000 to ₹20,000 — while p75 goes ₹35,000 to ₹50,000. Growth on this rung accrues to the upper half, which is consistent with the doctor/nurse split widening over a career rather than converging.

Cells here are thin: 43 at entry and 124 at 3+ years. These are among the smallest wage cells in the whole analysis and every figure in this table should be read as indicative.


Synthetic cohort, not a projection.

At entry (24–26) Age 30 Age 40
Sample n 317 154 53
Population 306k 138k 44k
— of whom in formal work 85 70 25
In formal work 26.74% 40.59% 49.53%
Still studying 23.00% 7.37% 0.00%
Mean wage ₹30,237 ₹40,319 ₹51,608
Median wage ₹23,000 ₹35,000 ₹40,000

The age-40 column rests on 53 people, 25 of them in formal work. It should not be quoted as a point estimate. It is included for completeness and because its direction agrees with every other rung, not because it is measured well.

The entry window is 24–26 rather than 21–23, because MBBS is five and a half years including internship, so a medical graduate is licensed at about 23.5. Even so, 23% are still studying at that age.


The same rung at three points in a working life

Section titled “The same rung at three points in a working life”
Life stage Regular salaried Of which formal People
Entry 40.2% 26.7% 306k
Age 30 55.6% 40.6% 138k
Age 40 49.5% 49.5% 44k (n=53 — too thin to read as a rate)

Regular salaried is a salaried job of any kind (pas='31'). Formal is the subset that is actually paid and carries a written contract or social security. The difference between the two columns is the informal salaried workforce.

These are three different cohorts, not one cohort ageing. Today’s 40-year-olds finished a different school system in a different labour market from this year’s entrants, so this table shows where each age group stands now. It does not say what today’s entrant will earn at 40.

Entry uses each rung’s own window — three years from that course’s normal completion age; age 30 is ages 29–31 and age 40 is ages 39–41, three-year bands for sample.

Job function among those in formal work, rolled up from NCO 2015 three-digit occupation groups by build_job_function.py. NCO orders by skill level before function, so a raw occupation table splits one function across three divisions — software work sits in 251/252, 351/352 and 133. Shares sum to 100; nothing is suppressed.

Function Share People Public sector Median wage
Health & medical 76.12% 210k 32.1% ₹28,550
Sales & marketing 6.77% 19k 4.9% ₹21,000
Clerical & office support 4.32% 12k 64.3% ₹35,000
Software & IT 3.32% 9,133 0.0% ₹55,000
Teaching & education 3.23% 8,884 1.6% ₹22,000
Engineering, science & technical 2.39% 6,568 11.8% ₹25,000
Management & business operations 1.33% 3,650 0.0% ₹25,000
Finance & accounting 1.24% 3,422 0.0% ₹25,000
Agriculture, forestry & fishing 0.81% 2,239 0.0% ₹45,000
Skilled trades & production 0.48% 1,320 73.2% ₹15,180
Cut n
The rung, ages 21–34 885
— in formal work 292
Formal, tenure under 1 year 43
Formal, tenure over 3 years 124
At age 40, in formal work 25
Earning ≥₹50,000 a month 61

This is the thinnest rung on the ladder and the caveats are proportionate. In the companion graduate analysis this same population fails the n≥50 rule and falls back to age bands. This ladder applies no such rule — see ../ASSUMPTIONS.md G5 — so the tenure table above is published at n=43. Neither lens is strong here.

It is not a doctors rung. Doctors are 24.2% of its formal workers; nursing, technician and allied-health roles are 47.3%, and a further 29% are in occupations too small to name. Any statement about “medical graduates” from this data is a statement about the health workforce broadly.

The gender wage gap is an occupation gap. It reflects who becomes a doctor and who becomes a nurse, not differential pay for identical work — which this data cannot test.

Everything rests on 885 people, and the age-40 and entry-tenure cells on far fewer.

Selection, not causation.


Provenance: assumptions governing this note are numbered in ../ASSUMPTIONS.md; the queries behind every figure are in ../QUERIES.md; every claim’s confidence grade, and the seven claims this analysis has retracted, are in ../FINDINGS.md. Check a number against FINDINGS before quoting it.