Income & Job Role

A working professional earning over ₹40 lakh a year is 3.5 times more likely to have had a full-body diagnostic blood panel in the last twelve months than one earning under ₹3 lakh — 54.3% against 15.6%. Both sit inside the same survey population, salaried, urban, and covered by an employer health policy that in most cases includes annual preventive screening at no marginal cost. The benefit is nominally identical across the income range. The uptake is not. This is the sharpest single gradient on the page, and one of the sharpest in the entire WHI 2026 dataset: a free benefit reached three and a half times harder at the top of the income range than at the bottom.

The survey population, by income and by job#

Two-thirds of the survey sits in the ₹3–10 lakh range. The top two bands — ₹25 lakh and above — together account for under 7% of respondents.

The job-function mix is dominated by one role. Engineering and software development is 29.1% of the survey on its own — more than three times the next-largest named function. This is the signature of a survey drawn from a tech-heavy corporate book, and it matters for everything that follows: when income and job function both move with health behaviour, the two are partly measuring the same people.

Preventive screening is income-gated, and the gate is steep#

The screening gradient is monotonic and large. Each income step up adds preventive uptake, and the gap between the ends is 38.7 percentage points.

The flat stretch at the bottom is as telling as the climb. The two lowest bands — everyone under ₹6 lakh, which is 42% of the survey — screen at essentially the same low rate, around 15-16%. Preventive access doesn't begin to move until the ₹6–10 lakh band, and it accelerates from there. For close to half the working population in this sample, the employer's preventive benefit is, in practice, not being used.

The intuitive objection is that this is a job-function artefact: high earners are disproportionately product managers and senior managers, roles with more autonomy and more time, and it's the role doing the work, not the income. The data closes that objection directly. Hold job function constant — look only at engineers, the single largest and most internally varied function — and the income gradient survives almost intact.

Inside one job title, doing one kind of work, the senior engineer screens at four times the rate of the junior engineer. The gradient is not the job. It is the income. The Preventive Care page builds the same 15.6%→54.3% staircase out as the report's central access finding.

Inactivity, stress, and short sleep all fall as income rises#

The screening gradient is the steepest, but it isn't alone. Three of the survey's core behavioural and load signals run the same direction: the lower the income, the worse the reading.

Inactivity is the cleanest of the three: it nearly halves across the range, from 36.7% to 19.3%, and the decline is almost perfectly monotonic. This is the income–movement gradient the Movement & Exercise page documents in full, replicated here on a sample several times larger than WHI 2025. The mechanism isn't motivation. It's the structural correlates of higher pay — schedule autonomy, walkable neighbourhoods, gym-equipped offices, the ability to convert a lunch hour into a workout without permission — none of which is evenly distributed across the income range. It is also why the Sedentary vs Active page finds the most physically active workers are the poorest, screen the least, and lose the protection their movement implies.

Stress falls less smoothly. It drops sharply across the bottom three bands, from 45.3% to under 40%, then flattens. Above ₹6 lakh, high-stress prevalence sits in a narrow 37–39% band regardless of income — the gradient does its work early, lifting the survival-stress load off the lowest earners, and then stalls. Wealth buys an exit from financial anxiety; it does not buy an exit from work stress.

Short sleep follows stress down the income range, then ticks back up at the very top. The ₹40 lakh+ band sleeps worse than the two bands below it (30.8% under six hours, against 25%). The band is small (n=146) and the reversal should be read with that caution, but it's consistent with the WHI 2026 Sleep page finding that the highest earners carry a late-career responsibility load that compresses sleep from the top — the one health input money reliably fails to buy back.

The affluent drink more, and the heavy-drinking gap is widening at the top#

The clean income-protects-health story breaks on one signal. Alcohol use rises with income — sharply, monotonically, and in the opposite direction to every protective behaviour on the page.

The top earners are more than twice as likely to drink at all as the bottom earners, and the heavy-use line is more revealing than the any-use line. Weekly-or-more drinking sits on a plateau around 7–9% across most of the range, then nearly doubles to 14.9% at the ₹40 lakh+ band — the affluent end of the gradient the Alcohol page reads as the report's whole survey base. Caffeine shows the same shape: heavy use (three or more cups a workday) is flat at around 14–15% across most of the range, then jumps to 25.5% at the top band. The affluent professional is more likely to be screened, more likely to exercise, less likely to smoke — and more likely to drink heavily and run on caffeine.

Job function predicts substance use more sharply than income does#

Income moves the protective behaviours. Job function moves the substances. The starkest example is nicotine, where the gap between functions is far wider than the gap across income bands.

Sales and business development runs more than double engineering's nicotine rate. The functions clustered at the top — sales, customer support, operations — share a structural feature: client contact, shift pressure, and the kind of acute, externally-driven stress that the WHI 2025 job-roles analysis also tied to higher substance use. Engineering, the survey's largest and best-paid function, sits at the bottom. If nicotine tracked income, engineering and management would be far apart; they aren't. It tracks the texture of the work.

Drinking follows a similar functional logic, layered on top of the income effect.

The functions that drink most are the ones that drink as part of the job — account management, general management, sales. These are also higher-income functions, so the income effect and the function effect compound rather than separate here. The drinking gradient on income and the drinking ranking on function are, in part, the same finding seen from two angles: the senior, client-facing, well-paid role is the one where alcohol is most embedded in the working week.

Stress is high everywhere, and it doesn't sort cleanly by function#

The one signal that refuses to produce a clean ranking is stress. Across job functions, high-stress prevalence sits in a tight 36–46% band, with no function meaningfully escaping it.

Engineering reports the lowest high-stress rate, but at 36% it is only ten points below the most-stressed function. There is no low-stress corner of the working population in this sample. Where nicotine and alcohol sort sharply by the kind of work, stress is closer to a flat tax on white-collar work — present across every function, varying at the margin, escaped by none.