Health and education economics

In short

Health and education economics applies economic tools to two services that are simultaneously consumption goods, investment in human capital, and markets riddled with externalities and information failure. It asks how much a society should spend, who should pay, which interventions buy the most health or learning per rupee, and why private markets under-provide both. In India it governs live policy — Ayushman Bharat PM-JAY, the National Health Mission, NEP 2020, PM POSHAN — and the measurement of outcomes such as IMR, MMR, out-of-pocket spending and ASER learning levels. It is examined in BA/MA Economics papers on development and public economics, in UGC-NET and Indian Economic Service papers, and tested in interviews at NITI Aayog-linked bodies, J-PAL/IFMR-type research outfits, impact-evaluation consultancies, health insurers and ed-tech firms.

Explained

Why health and education are not ordinary commodities

• Human capital: both are investments — spending today raises future productivity, earnings and health. Unlike physical capital, human capital cannot be collateralised or resold, so credit markets fail to fund it.
• Positive externalities: an immunised or literate person benefits others (herd immunity, lower crime, better civic participation). Private demand therefore sits below the socially optimal level, justifying subsidy.
• Merit goods and paternalism: households under-invest in children because the decision-maker (parent) is not the beneficiary (child) — a classic principal-agent problem behind compulsory schooling laws.
• Information asymmetry: patients cannot judge diagnosis or treatment quality (credence good); students and parents cannot judge school quality before enrolment. Hence regulation, licensing and accreditation (NMC for medical education, NAAC/NBA for higher education).
• Supplier-induced demand: the doctor is both adviser and seller, so fee-for-service payment encourages excess tests and procedures — visible in India's private hospital sector.
• Uncertainty: illness is random and expensive, so insurance is the natural market — but insurance itself has failures (below).
• Non-rival/partly public elements: disease surveillance, sanitation, vector control and basic research are public goods; curative care is largely private in consumption.

Health as capital: the Grossman model and demand for health

• Grossman (1972): health is a durable capital stock H. It depreciates at rate δ with age and is topped up by gross investment (medical care, diet, exercise, time) produced with a household production function.
• Health yields two returns: consumption benefit (feeling well) and investment benefit (healthy days available to work and earn).
• Optimal condition: invest until the marginal cost of health investment = marginal benefit = value of extra healthy days plus consumption value.
• Implications: demand for medical care is a *derived* demand (from demand for health); higher education raises efficiency of health production, which is why schooling correlates strongly with health; δ rising with age explains why older people buy more care while their optimal stock falls.
• Demand for medical care is price-inelastic (estimates around −0.1 to −0.3 in the RAND Health Insurance Experiment) and income-elastic at the national level — health spending rises faster than GDP, Baumol's cost disease adding to this because labour-intensive care resists productivity gains.
• Grossman-Rand distinction: RAND showed cost-sharing cuts utilisation of both effective and ineffective care, with little average health loss for the non-poor but measurable harm to poor and sick patients — the standard argument for exempting the poor from co-payments.

Health financing, insurance and market failure

• Insurance basics: a risk-averse household pays a premium to avoid a variable loss; welfare gain comes from consumption smoothing. Risk pooling works because illness risks are largely independent.
• Adverse selection: the sick buy more cover, premiums rise, the healthy exit — the death spiral (Rothschild-Stiglitz). Remedies: mandatory or tax-funded cover, community rating, group schemes, waiting periods.
• Moral hazard: *ex ante* (less prevention when insured) and *ex post* (more utilisation when care is free at the point of use). Remedies: co-payment, deductible, capitation or package rates, gatekeeping.
• Provider payment systems: fee-for-service (volume incentive), capitation (under-provision risk), case-based/DRG package rates (PM-JAY uses specified package rates), salary, global budget. Each trades cost control against quality.
• Health financing in India: mixed system. Government health expenditure is roughly 1.8–2.1% of GDP (NHA estimates move year to year); the National Health Policy 2017 target is 2.5% of GDP. Out-of-pocket expenditure (OOPE) has fallen from about 70% of total health spending in 2004-05 to roughly 40% or below in recent National Health Accounts estimates — still high by international standards.
• Catastrophic health expenditure (CHE): OOPE exceeding a threshold (commonly 10% of total household consumption or 25–40% of non-food consumption); drives tens of millions into poverty annually — the main equity argument for PM-JAY.
• Ayushman Bharat (2018): two arms — PM-JAY, a tax-funded insurance scheme giving ₹5 lakh per family per year of secondary and tertiary hospitalisation cover to the bottom ~40% (SECC-based, with later extension of cover to all citizens aged 70+), and Health and Wellness Centres / Ayushman Arogya Mandirs for comprehensive primary care. PM-JAY is purchaser-provider split: public and empanelled private hospitals paid package rates; no premium or co-payment from beneficiaries.
• Other instruments: National Health Mission (NHM) for public-system strengthening, CGHS/ESIC for organised employees, state schemes (Aarogyasri, Chief Minister's Comprehensive scheme in Tamil Nadu, Mahatma Jyotiba Phule Jan Arogya Yojana), private voluntary insurance regulated by IRDAI, and the Jan Aushadhi and NPPA price-capping routes for medicines.
• Supply-side regulation: Clinical Establishments Act, 2010 (adopted by some states), National Medical Commission Act, 2019 (replaced the Medical Council of India), drug price control under the DPCO.

Education economics: human capital, signalling and the production function

• Human capital theory (Schultz, Becker, Mincer): schooling raises productivity; the private return is the extra lifetime earnings against direct cost plus forgone earnings (usually the larger cost for higher education).
• Signalling (Spence): education may raise earnings without raising productivity, by revealing pre-existing ability. Empirical separation uses sheepskin effects (a jump in earnings exactly at degree completion suggests signalling) and natural experiments such as compulsory-schooling-age changes (which support a genuine causal return).
• Screening/credentialism: employers use degrees as cheap filters; this explains over-education and degree inflation in Indian white-collar hiring.
• Private vs social returns: social return counts externalities and uses pre-tax earnings against full resource cost including subsidy. Typical global pattern: social returns highest for primary, private returns increasingly high for tertiary — hence the standard prescription to subsidise basic education heavily and recover more cost at tertiary level (loans, graduate taxes, income-contingent repayment).
• Education production function: learning = f(school inputs, teacher quality, peer group, household inputs, child endowment, time). Robust Indian findings: measured inputs (buildings, pupil-teacher ratio in the usual range) matter less than teacher effort/attendance, pedagogy matched to actual learning level, and accountability.
• Learning versus schooling: India's enrolment is near-universal at primary level but ASER surveys repeatedly find a large share of Class V children unable to read a Class II text or do simple division — the "schooling ain't learning" problem. This is why NEP 2020 created the NIPUN Bharat foundational literacy and numeracy mission.
• Key evaluated interventions in India: Balsakhi remedial teaching and Teaching at the Right Level (large gains, cheap); computer-assisted learning; teacher incentives tied to attendance (camera monitoring in Rajasthan NGO schools); mid-day meals / PM POSHAN (attendance and nutrition); conditional cash transfers and scholarships; deworming (Kenya study; raised school participation cheaply); information to parents (mixed).
• Indian legal and policy framework: Article 21A and the Right of Children to Free and Compulsory Education (RTE) Act, 2009 — free elementary schooling for ages 6–14, 25% EWS reservation in private unaided schools with reimbursement, no-detention (later relaxed to allow examinations at Classes V and VIII), norms for infrastructure and pupil-teacher ratio. NEP 2020 — 5+3+3+3 structure, early childhood care and education from age 3, target of 6% of GDP public spending on education (a target since the 1968 Kothari-era commitment and still unmet, actual spending around 4.0–4.6% of GDP), multidisciplinary degrees, Academic Bank of Credits, gross enrolment ratio in higher education target of 50% by 2035, and regulatory consolidation (proposed HECI).

Measuring outcomes: health metrics and evaluation methods

• Mortality and morbidity indicators: IMR (deaths under 1 per 1,000 live births), under-5 mortality, neonatal mortality, MMR (maternal deaths per 1,00,000 live births), life expectancy, total fertility rate. India's SRS-based IMR is in the high-20s per 1,000 and MMR near 90–100 per 1,00,000 in recent estimates; both are falling and state variation is enormous (Kerala far better than Bihar, UP, MP).
• NFHS-5 (2019-21) gives the standard nutrition and health picture: child stunting about 35%, wasting about 19%, anaemia in women above 50%, TFR 2.0 (below replacement).
• DALY (disability-adjusted life year): years of healthy life lost = YLL + YLD. One DALY = one lost year of full health. Used to rank the burden of disease; India's burden has shifted from communicable to non-communicable disease (the epidemiological transition).
• QALY (quality-adjusted life year): years of life weighted by a utility score between 0 (death) and 1 (full health). Used on the benefit side of cost-utility analysis.
• Evaluation hierarchy: RCT (randomised, highest internal validity) → regression discontinuity → difference-in-differences → instrumental variables → propensity-score matching → simple before-after (weakest, confounded by trend). India is a major RCT site (J-PAL South Asia at IFMR); the 2019 Nobel to Banerjee, Duflo and Kremer was for this approach.
• Threats to validity: selection bias, Hawthorne effect, spillovers to the control group, general-equilibrium effects, and poor external validity when scaling a small trial to a state programme.
• Appraisal types: cost-effectiveness analysis (CEA — cost per natural unit, e.g. per DALY averted), cost-utility analysis (per QALY), cost-benefit analysis (CBA — everything in rupees, needs a value of a statistical life), cost-minimisation analysis.

Key points

• Human capital — skills and health embodied in people; raises productivity and earnings but cannot be used as collateral.
• Derived demand for care — medical care is demanded for the health it produces, not for itself (Grossman).
• Credence good — the buyer cannot judge quality even after consumption; the basis for licensing doctors.
• Supplier-induced demand — providers expand volume because they advise and sell; worst under fee-for-service.
• Adverse selection — asymmetric information before contracting; sick buy cover, premiums spiral; cured by mandates or tax funding.
• Moral hazard — behaviour change after insuring; cured by co-payments, package rates, gatekeeping.
• Catastrophic health expenditure — OOPE above ~10% of household consumption; the impoverishment channel PM-JAY targets.
• PM-JAY — ₹5 lakh per family per year, secondary and tertiary care, tax-funded, no premium, package-rate payment.
• National Health Policy 2017 target — public health spending 2.5% of GDP; actual roughly 2%.
• NEP 2020 target — 6% of GDP on education; actual roughly 4–4.6%; 5+3+3+3 structure; higher-education GER 50% by 2035.
• RTE Act, 2009 — free compulsory elementary education ages 6–14; 25% EWS quota in private schools.
• Mincer equation — log earnings on years of schooling and experience; the slope is the private rate of return.
• Signalling vs human capital — the same wage premium can arise from productivity or from sorting; sheepskin effects help distinguish them.
• Schooling ≠ learning — near-universal enrolment with weak ASER learning levels; NIPUN Bharat addresses foundational skills.
• DALY = YLL + YLD — burden measure; QALY is the utility-weighted benefit measure.
• ICER — incremental cost per extra unit of health; compared with a willingness-to-pay threshold (often 1–3× GDP per capita per DALY/QALY).
• Epidemiological transition — India's disease burden has shifted from infectious to non-communicable disease and injuries.
• Teaching at the Right Level — grouping children by actual learning, not age; among the cheapest proven learning gains per rupee.

Formulas and examples

Example 1 — Private return to a degree (NPV and IRR logic). A student pays ₹80,000 a year in fees for a 3-year BA and forgoes ₹1,20,000 a year of earnings.

• Cost per year = 80,000 + 1,20,000 = ₹2,00,000; total over 3 years = ₹6,00,000 (ignoring discounting of costs for simplicity).
• The degree raises annual earnings by ₹60,000 for 35 years. At r = 8%, annuity factor = (1 − 1.08⁻³⁵)/0.08. 1.08³⁵ ≈ 14.785, so 1.08⁻³⁵ ≈ 0.06764; factor = (1 − 0.06764)/0.08 = 11.655.
• Present value of gains at graduation = 60,000 × 11.655 ≈ ₹6,99,290; discounted to entry (÷1.08³ = 1.2597) ≈ ₹5,55,100.
• Costs discounted to entry ≈ 2,00,000 × (1/1.08 + 1/1.08² + 1/1.08³) = 2,00,000 × 2.5771 = ₹5,15,420.
• NPV ≈ 5,55,100 − 5,15,420 ≈ +₹39,700 → worth doing at 8%, but only just; the IRR is a little above 8%, so the decision is sensitive to the discount rate and to how certain the ₹60,000 premium is.

Example 2 — Mincer return. A regression on 4,000 urban workers gives ln W = 9.2 + 0.085·S + 0.040·X − 0.0006·X².

• Each extra year of schooling raises wages by about 8.5% (exactly e^0.085 − 1 = 8.87%).
• A graduate (S = 15) versus a Class X pass (S = 10), both with X = 5: difference in ln W = 0.085 × 5 = 0.425 → wage ratio = e^0.425 = 1.530, i.e. the graduate earns about 53% more.
• Experience premium peaks where 0.040 − 2(0.0006)X = 0 → X = 33.3 years.

Example 3 — DALYs averted and cost-effectiveness. A district rotavirus-vaccination drive costs ₹2.4 crore and covers 1,20,000 infants. It is estimated to prevent 60 deaths (standard life expectancy lost per death 68 years, undiscounted) and 9,000 episodes of severe diarrhoea (disability weight 0.25, duration 0.02 years each).

• YLL = 60 × 68 = 4,080 years.
• YLD = 9,000 × 0.25 × 0.02 = 45 years.
• DALYs averted = 4,080 + 45 = 4,125.
• Cost per DALY averted = 2,40,00,000 / 4,125 ≈ ₹5,818.
• India's GDP per capita is roughly ₹2.1–2.3 lakh, so at under ₹6,000 per DALY the programme is far below even a 1× GDP-per-capita threshold — highly cost-effective. Note that discounting the YLL stream (commonly 3%) would cut DALYs sharply and raise cost per DALY several-fold; always state whether figures are discounted.

Example 4 — ICER between two learning interventions.

• Incremental cost of the contract teacher over the volunteer = 1,200 − 150 = ₹1,050 for an extra 0.05 SD → ICER = ₹21,000 per SD gained (₹2,100 per 0.1 SD), far worse than the volunteer's ₹1,000 per SD. With a fixed budget of ₹18 lakh: volunteers reach 12,000 children for 0.15 SD each; contract teachers reach 1,500 children for 0.20 SD. Total "SD-children" = 1,800 versus 300 — the cheap intervention wins on aggregate learning even though it is weaker per child.

Example 5 — Catastrophic expenditure and insurance. A household consumes ₹1,80,000 a year, of which ₹1,00,000 is food. A hospitalisation costs ₹46,000 out of pocket.

• Budget-share measure: 46,000/1,80,000 = 25.6% > 10% → catastrophic.
• Capacity-to-pay measure: non-food consumption = ₹80,000; 46,000/80,000 = 57.5% > 40% → catastrophic on the stricter test too.
• If PM-JAY covers the hospitalisation at a package rate, OOPE falls to residual costs (transport, medicines outside the package, pre- and post-hospitalisation items). If residual OOPE is ₹8,000, the budget share drops to 4.4% — below the threshold, which is exactly the protection the scheme is designed to buy.

Where it is used

• Government and policy research: NITI Aayog health and education verticals, Ministry of Health (National Health Accounts, NHSRC costing studies), Ministry of Education (UDISE+), state health and education departments doing scheme design and budget appraisal.
• Impact evaluation and development consulting: J-PAL South Asia (IFMR), IDinsight, Sambodhi, Oxford Policy Management, World Bank and ADB project teams — roles in survey design, RCT management, econometric analysis.
• Health insurance and HTA: IRDAI-regulated insurers and TPAs pricing products and detecting moral hazard/fraud; National Health Authority setting PM-JAY package rates; health technology assessment (HTAIn) deciding which technologies the public system should buy.
• Hospital and pharma economics: pharmacoeconomics teams building cost-effectiveness dossiers for NLEM/DPCO and tender decisions; hospital chains doing case-mix costing and payer negotiation.
• Education and ed-tech: Pratham and Azim Premji Foundation programme design and measurement, CSR/foundation grant appraisal, ed-tech firms measuring learning gains, skilling programmes (NSDC) estimating returns to training.
• Academic and exam use: BA/MA development and public economics papers, UGC-NET, Indian Economic Service, RBI/NABARD Grade B research posts, and the economics sections of civil-services optional papers.

Interview questions

• Q: Why do private markets under-provide health and education? A: Both generate positive externalities not captured by the buyer, and investment in them is constrained by missing credit markets and information asymmetry — so private demand falls short of the socially optimal level.
• Q: What is the difference between the human capital and signalling explanations of the wage premium? A: Human capital says schooling raises productivity; signalling says it only reveals pre-existing ability. Both predict higher wages, but only human capital implies that expanding schooling raises national output.
• Q: How would you test between them? A: Look for sheepskin effects (discrete jumps at degree completion favour signalling) and use natural experiments such as compulsory-schooling-age changes or distance-to-school instruments, which identify returns for people who gained schooling without changing ability.
• Q: Distinguish adverse selection from moral hazard with a health example. A: Adverse selection is pre-contract — the chronically ill disproportionately buy cover, raising premiums; moral hazard is post-contract — an insured patient chooses a costlier hospital or more tests than they would have paid for themselves.
• Q: Why does India have such high out-of-pocket health expenditure? A: Low public health spending (about 2% of GDP), weak public outpatient and pharmacy provision pushing patients to private providers, and insurance historically covering only hospitalisation rather than outpatient care and medicines, which are the bulk of OOPE.
• Q: PM-JAY covers hospitalisation only. Why is that a limitation? A: Most out-of-pocket spending is outpatient — drugs, diagnostics, consultations — so a hospitalisation-only scheme can leave the main impoverishing channel untouched; this is why the Health and Wellness Centre arm exists alongside it.
• Q: What is a DALY and how does it differ from a QALY? A: A DALY counts healthy years lost (YLL + YLD) and is used to measure disease burden; a QALY counts healthy years gained weighted by a utility score and is used on the benefit side of cost-utility analysis. DALYs are losses to minimise, QALYs are gains to maximise.
• Q: An intervention costs ₹40,000 per DALY averted. Is it worth funding? A: Compare with a willingness-to-pay threshold — commonly 1–3× GDP per capita per DALY, which for India is roughly ₹2 lakh and upwards, so yes on that criterion; but affordability depends on the total budget impact and on what the displaced spending would have achieved.
• Q: India has near-universal primary enrolment but weak learning. What does economics say the problem is? A: Inputs were expanded faster than accountability and pedagogy; curricula move far ahead of actual child learning levels, so attendance yields little learning — the case for Teaching at the Right Level and for the NIPUN Bharat foundational mission.
• Q: Should higher education be free? A: Private returns to tertiary education are high and its beneficiaries are disproportionately better off, so full subsidy is regressive; the efficient answer is heavy subsidy of foundational education plus income-contingent loans or graduate contributions at tertiary level, with scholarships for credit-constrained students.
• Q: Why is an RCT preferred for evaluating a school programme, and when is it not enough? A: Randomisation removes selection bias, giving clean internal validity. It can still fail on external validity, spillovers to control schools, and general-equilibrium effects when a programme is scaled statewide.
• Q: Why is health spending rising faster than GDP almost everywhere? A: Income elasticity above one, ageing populations, costlier technology, insurance blunting price sensitivity, and Baumol's cost disease — care is labour-intensive and resists productivity gains while wages track the rest of the economy.
• Q: Does a fall in IMR always mean health services improved? A: No. IMR responds strongly to female education, income, sanitation, water and nutrition; attributing it to health services alone confuses correlates of development with the effect of the service.

Common mistakes

• Treating medical care as the object of demand instead of health — the Grossman insight that care is a *derived* demand is routinely missed.
• Confusing moral hazard with adverse selection, or dating both to the same point in the contract.
• Mixing up DALYs and QALYs, or adding YLL and YLD with inconsistent discounting and age-weighting assumptions.
• Computing the return to education from direct costs only and forgetting forgone earnings, which usually dominate at tertiary level.
• Reading the Mincer coefficient as a simple percentage without noting it is a log-point approximation, and treating it as causal when ability bias is uncontrolled.
• Quoting MMR per 1,000 instead of per 1,00,000 live births, or IMR per 1,00,000.
• Equating enrolment, attendance and spending with learning; and equating insurance coverage with financial protection when outpatient costs stay uncovered.
• Comparing a cost per DALY figure against a threshold without checking the discount rate, time horizon and comparator used to produce it.

Related topics

Labour economics · Public finance and taxation · Measuring inequality and poverty · Economic growth and development · Welfare economics and market failure · Econometrics for impact evaluation