Chapter 1.2 — Demand for Health and Healthcare

Why this matters in health economics

If you run a service, a payer, or a ministry, almost every forecast you make rests on an implicit theory of demand. How many hip replacements will next year bring? Will a small co-payment deter frivolous visits or block the genuinely sick? Will a health-education campaign shift behaviour, or merely inform the people who were already going to act? Each of these is a question about demand, and getting the theory wrong is expensive in both money and lives.

The trap is to treat healthcare like any other consumer good, where wanting more of it and using more of it are the same act freely chosen. Health is different. What people ultimately value is their health — the capacity to work, to enjoy life, to care for others — and healthcare is only one of several inputs that produce it, alongside diet, housing, exercise, and clean air. Demand for care is therefore derived from a deeper demand for health itself. Miss that distinction and you will over-invest in treatment, under-invest in the other producers of health, and misread every signal your utilization data sends you.

Demand also behaves oddly because the person choosing the care is rarely the person who knows what is needed. Patients delegate judgement to clinicians, creating an agency relationship that standard demand theory — with its sovereign, fully-informed consumer — simply does not contain. And because illness is uncertain and expensive, people demand not just care but insurance against the need for care, which changes the price they face at the point of use. These features are not academic curiosities. They determine whether your co-payment policy is fair, whether your capacity plan is credible, and whether your prevention budget is defensible.

Core concepts

Health as a stock of capital. The foundational idea, from the economist Michael Grossman in 1972, is that health is a durable capital good — a stock that each person inherits at birth, that depreciates over time (faster with age), and that can be renewed through investment. This treats health as a form of human capital: like education or skills, it yields a return over many years rather than being consumed at once. You are born with an endowment of health capital; it decays; you top it up by investing time and goods — including healthcare — into your own health production.

The dual nature of health: consumption and investment. In Grossman's model health is wanted for two reasons at once. It is a consumption good — being healthy feels good and is valued directly. It is also an investment good — health determines the amount of healthy time available for work and everything else, so investing in it yields future returns in earnings and activity. This duality explains why demand for health rises with the wage (healthy time is worth more to a high earner) and why it is not simply charity or comfort but a rational investment in one's own productive capacity.

Derived demand for healthcare. Because healthcare is an input to producing health rather than the object of desire itself, the demand for it is a derived demand — derived from the demand for health. No one enjoys a colonoscopy; they endure it for the health it protects. This is the single most important reframing in the chapter: your utilization figures measure a means, not an end, and two populations with identical health needs can generate very different demand for care depending on the other inputs available to them.

Optimal health declines with age. A striking prediction of the model is that the optimal stock of health falls over the life course, even as the amount people spend on healthcare rises. As depreciation accelerates with age, it becomes progressively more costly to hold health at a high level, so the rational person lets the stock fall — but must spend more each year simply to slow the decline. This reconciles the everyday observation that the old both spend the most on care and are the least healthy.

Elasticities of demand for care. How responsive is the use of healthcare to price and income? The price elasticity of demand measures the percentage change in quantity used for a percentage change in the price the user faces; the income elasticity of demand measures responsiveness to income. Empirically, demand for most healthcare is relatively price-inelastic at the individual level — people do not halve their heart-attack care when the price doubles — but it is not zero, and cost-sharing does measurably reduce use of discretionary and preventive services. At the level of whole nations, health spending tends to rise roughly in step with or faster than income, which is why richer countries spend proportionately more.

Agency and the clinician's dual role. Because patients cannot fully judge what care they need, they delegate the decision to a clinician, creating a principal–agent problem: the patient (principal) relies on the doctor (agent) to choose in the patient's interest. A "perfect agent" would recommend exactly what a fully-informed patient would choose. In practice the agent has their own interests and incentives, and the same relationship that makes modern medicine possible also opens the door to supplier-induced demand — a market failure that Chapter 1.3 — Market Failure owns, so we name it here only as the shadow side of agency.

Time preference and the timing of health investment. Health investments pay off in the future, so how heavily a person discounts the future — their time preference — shapes how much they invest today. People who discount the future steeply under-invest in prevention, which helps explain why exhortation alone rarely shifts smoking or diet, and why behavioural approaches (see Chapter 4.1 — Behavioural Economics) matter for demand.

Demand for insurance. Illness is uncertain and its costs can be catastrophic, so risk-averse people demand health insurance — trading a small certain premium for protection against a large uncertain loss. This is a rational demand for financial protection in its own right, and it exists in every system, whether provided by private insurers, social insurance funds, or a tax-funded national service that insures the whole population implicitly. Insurance lowers the price the patient faces at the point of use, which raises the quantity of care demanded; the distortions this creates — moral hazard, adverse selection — belong to Chapter 1.3 — Market Failure. Here we establish only why the demand for insurance exists at all.

Best practices

Distinguish demand for health from demand for care in every plan. Before you forecast activity, ask what health outcome the activity is meant to produce and what else produces it. A rise in demand for care may signal falling health, rising expectations, better access, or supply pushing demand — each implies a different response. Treating the two as one leads you to build hospital capacity when the cheaper route to the same health lay in housing, prevention, or primary care.
Use the Grossman lens to justify prevention on investment grounds, not sentiment. Frame prevention as investment in a depreciating capital stock that yields returns in healthy, productive time. This gives finance directors and treasuries a language they respect and defends prevention budgets against the perennial raid to fund acute pressures. Be honest that returns often accrue over long horizons and to other budgets, which is precisely why they get under-funded.
Estimate the price elasticity your population actually faces before you set a co-payment. A user charge that looks trivial to a policymaker can be a real barrier to a low-income patient, and the response differs sharply by service and by group. Evidence consistently shows cost-sharing cuts use of both low-value and high-value care, and that the poor and the chronically ill cut back the most. Model who is deterred, not just how much revenue is raised (see Chapter 2.3 — Health Econometrics for how such elasticities are estimated).
Protect necessary care from price barriers while allowing discretion elsewhere. If cost-sharing is used at all, structure it so that essential and cost-effective services carry little or no charge, reserving any deterrent effect for genuinely discretionary use. Blunt across-the-board charges suppress exactly the preventive and primary care whose reduction later costs more in acute care. Exemptions for the poor, children, and chronic conditions are not generosity but efficient demand management.
Design the agency relationship deliberately; do not leave it to chance. Because patients delegate judgement, the incentives and information you give clinicians shape demand directly. Decision aids, shared-decision-making tools, second-opinion pathways, and clinical guidelines all strengthen the agency relationship so that recommended care tracks what an informed patient would want. Payment method matters too: fee-for-service rewards volume, capitation rewards restraint, and each pulls the agent's advice in a different direction (see Chapter 3.1 — Health Systems).
Read utilization data as a signal filtered through access and agency, not as pure need. High use may reflect good access, an ageing population, or supplier influence; low use may reflect stoicism, distance, cost, or unmet need. Never infer that a low-utilizing population is a healthy one, or that a high-utilizing one is over-served, without separating need from the forces that translate need into recorded demand.
Segment demand by the other inputs to health, not only by diagnosis. Two patients with the same condition can have very different demand for care depending on their income, education, time, and living conditions — the complementary inputs in the health production process. Targeting the social and behavioural inputs can reduce demand for costly care more effectively than expanding the care itself, and it usually improves equity (see Chapter 3.4 — Equity).
Treat income growth as a structural driver of future demand. Because health spending tends to rise with national income, a growing economy will raise demand for care even if health need is flat. Build this income effect into long-range capacity and workforce planning explicitly rather than being surprised by it, and distinguish it from the ageing and technology effects it is often confused with.
Recognize that insurance changes the price signal, then plan for it. Whenever coverage expands — a new benefit, a widened social-insurance scheme, a subsidy — the price at the point of use falls and demand rises. This is expected and often desirable, but it must be budgeted and its size estimated in advance. Model the demand response to coverage as carefully as you model the coverage itself.
Match the demand model to the decision; do not over-fit. For a short-run capacity forecast, a simple age–sex utilization model may suffice; for a prevention business case or a co-payment reform, you need the fuller apparatus of derived demand, elasticities, and time preference. Choose the lightest model that captures the forces that will actually move your answer, and state its assumptions so others can challenge them.
Anticipate behavioural departures from the rational investor. Grossman's investor is far-sighted and consistent; real people discount the future steeply, procrastinate, and misjudge risk. Where prevention depends on sustained behaviour, expect the standard model to over-predict uptake and pair it with behavioural insight and, where evidence supports it, well-designed incentives (see Chapter 4.1 — Behavioural Economics).
Keep the demand for insurance separate from its side-effects in your analysis. The demand for financial protection is real and welfare-improving; the moral hazard and selection it can generate are separate problems with separate remedies. Conflating them leads either to denying people needed protection or to ignoring genuine distortions — hold both truths at once and route the distortions to Chapter 1.3 — Market Failure.

Questions to discuss with your team

Are we managing the demand for care, or the demand for health — and can we tell the difference in our own data? Most organizations measure activity: appointments, admissions, prescriptions. Almost none can say how much of a change in that activity reflects changing health, changing access, changing expectations, or supply pushing demand. Start by naming a recent rise or fall in demand and forcing the team to propose competing explanations before settling on one. An honest answer admits where you cannot yet separate need from the forces that shape recorded demand, and commits to the data or study that would let you. The pay-off is that you stop building capacity to meet demand that a cheaper input to health would have prevented, and stop cutting services whose low use signals a barrier rather than a lack of need.
If we introduced or changed a user charge tomorrow, who exactly would stop coming — and would we be glad they did? Co-payments are politically attractive because they raise revenue and are assumed to deter waste, but the evidence is that they deter valuable and low-value care alike, and that the poor and the chronically ill respond most. The honest discussion names the specific groups and services affected in your population, not an average. Ask whether the care deterred is the care you wanted deterred, and what downstream costs a suppressed preventive visit might create. A good answer distinguishes deterring genuine over-use from erecting a barrier to necessary care, and designs exemptions accordingly — or concludes the charge is not worth its equity cost.
How well is our agency relationship working, and whose interest does our payment model make the clinician serve? Patients trust clinicians to choose for them, and the incentives around the clinician quietly shape what gets recommended. Ask what your payment method rewards — volume under fee-for-service, restraint under capitation, adherence to a pathway under bundled payment — and where that pulls advice away from what an informed patient would choose. Look for the tell-tale signs of weak agency: unexplained variation between clinicians, care that patients would decline if fully informed, or reluctance to offer watchful waiting. An honest answer accepts that no payment model produces a perfect agent, and asks what combination of guidelines, decision aids, and incentives keeps recommended care closest to informed choice.
Do we defend our prevention budget as an investment in health capital, or as a good deed that is first to be cut? Prevention pays off in the future, often to a different budget than the one that funds it, which is exactly why it loses when acute pressures bite. The Grossman lens reframes it: prevention is investment in a depreciating stock of health capital that yields returns in healthy, productive time, and that language is one finance directors and treasuries respect. The honest discussion admits the horizons are long and the returns partly accrue elsewhere, rather than overselling a quick cash saving that will not materialize. Name a prevention programme you fund and ask whether its business case is written in the language of investment returns or of sentiment. A good answer commits to quantifying the health-capital return where the evidence allows and to being candid where it does not — and it resists the reflex to raid prevention the moment an acute ward is under strain. The pay-off is a defensible case that survives the next budget round instead of being sacrificed to it.
When we expand coverage or add a benefit, do we model the demand it will unleash before we commit — or discover the bill afterwards? Every widening of coverage, new benefit, or subsidy lowers the price patients face at the point of use, and lower prices raise the quantity of care demanded; this is expected, often desirable, and frequently un-budgeted. The tension is that expanding financial protection is welfare-improving in its own right, yet the induced demand can overwhelm capacity and blow the budget if its size is not estimated in advance. An honest discussion separates the two questions — is the protection worth providing, and can we afford the demand response it will trigger — and refuses to let enthusiasm for the first answer the second. Ask, for a coverage change you are contemplating, who newly faces a zero or near-zero price and how elastic their use of that service is. A good answer models the demand response as carefully as the coverage itself, distinguishes the care you wanted to unlock from care merely made free, and plans the capacity and money to meet it rather than being surprised.
Are we planning demand around Grossman's far-sighted investor, or around the impatient, unequally-resourced people we actually serve? The standard model assumes a rational, consistent investor who weighs future health against present cost; real people discount the future steeply, procrastinate, and vary enormously in the income, time, and education they can bring to producing their own health. Where a plan leans on sustained behaviour — quitting smoking, adhering to medication, attending screening — the rational model will over-predict uptake, and exhortation alone will disappoint. The discussion should also ask whether demand is being segmented by the other inputs to health, not only by diagnosis: two patients with the same condition can generate very different demand depending on their circumstances, and targeting the social and behavioural inputs often cuts costly care more effectively than expanding it (see Chapter 3.4 — Equity). An honest answer names where your forecasts assume more foresight than your population has, pairs the standard model with behavioural insight and, where evidence supports it, well-designed incentives (see Chapter 4.1 — Behavioural Economics), and accepts that improving equity and reducing demand can be the same move.

In practice: a health economics example

Scenario: a fictional province in a middle-income country with a national social health insurance scheme, modelled on the kind of expanding coverage seen in South-East Asia.

The provincial health office of Sumberjaya — a fast-growing region of a middle-income country whose national social health insurance fund now covers most of the population — faces a puzzle. Outpatient visits to district hospitals have risen 30% over three years, waiting rooms overflow, and clinicians are demanding two new outpatient blocks. The obvious reading is that health need has surged and capacity must follow. The provincial health economist is asked to test that before the treasury commits the capital.

She begins with the derived-demand distinction. The rise in visits is a rise in demand for care; is it driven by a rise in demand for health, or by something else? Three candidates emerge. First, the insurance scheme's coverage widened two years ago and the co-payment for hospital outpatient visits was abolished — a price change that theory predicts will raise quantity demanded. Second, incomes in the province have grown briskly, and health spending tends to rise with income. Third, referral patterns suggest some clinicians, paid partly by activity, may be pulling patients into hospital outpatient care that primary clinics could handle.

To weigh these, she looks at where the growth sits. Emergency and inpatient care — the price-inelastic, need-driven end — grew only modestly. The surge is concentrated in discretionary outpatient visits for minor, self-limiting conditions, exactly where removing the co-payment would bite hardest and where the agency relationship is loosest. She estimates, cautiously, that a large share of the extra visits reflects the price drop and referral behaviour rather than new health need, and flags that she cannot fully separate the income effect without more data (a task she routes to a proper econometric study — see Chapter 2.3 — Health Econometrics).

The economics now reframes the decision. Two new outpatient blocks would meet the demand but treat a means as an end, locking in capital to serve visits that a stronger primary-care gatekeeping arrangement and a small, well-exempted co-payment for non-referred hospital outpatient use might prevent — while protecting the inelastic, genuinely-needed care that must never face a barrier. She models three options: build; introduce demand-side management with equity exemptions; or invest the capital in primary care and community prevention, treating health as capital to be maintained upstream. She is explicit about the trade-offs: gatekeeping risks deterring some real need and is unpopular; building is simplest but costliest and may induce yet more demand. The recommendation is to strengthen primary care and referral first, hold the capital in reserve, and re-measure — resisting the reflex to build capacity for demand whose origin she has not yet fully understood.

Four sector lenses

Startup

A digital health start-up — say, a telemedicine or symptom-checker app — lives or dies by understanding derived demand. Its users do not want the app; they want health, and will abandon it the moment it fails to produce that more cheaply in time and money than the alternatives. A start-up should segment demand tightly, target the inputs to health it can genuinely improve (access, triage, adherence), and be brutally honest that lowering the effective price of a consultation will raise demand, including from the "worried well". Its risk appetite lets it experiment with demand at small scale, but it must resist selling volume as if volume were the goal.

Small business

An established small provider — a GP partnership, a single clinic, or a community pharmacy that has served the same patients for years — reads demand from close, patient knowledge rather than from experiment. Its list is largely known, so the practical questions are how much of the daily demand reflects genuine need versus habit, anxiety, or access barriers it could ease, and where a small charge or a longer wait quietly deters the patients who most need to be seen. Unlike a start-up, it cannot pivot away from unprofitable demand; its accountability is to a settled community and often to a capitation or contract that rewards keeping people well rather than churning visits. It manages demand with the levers it actually controls — triage, continuity of care, signposting to prevention and social support — and it lives with the tension that time spent steering demand upstream is time not billed today.

Enterprise

A large provider or insurer thinks about demand at portfolio scale, where elasticities, income effects, and the agency relationship become planning variables. An insurer designs benefit and cost-sharing structures knowing they will move demand, and must balance financial protection against the demand response coverage induces. A hospital group reads its own utilization data through the lens of access and supplier influence, watches unwarranted variation between clinicians as a sign of weak agency, and aligns payment so that recommended care tracks informed patient choice. Accountability here is to solvency and to regulators, so demand models must be defensible and auditable.

Government

A ministry or national payer manages demand for a whole population and carries the equity and public-money duties that come with it. It sets the price citizens face at the point of use — through coverage, exemptions, and any co-payments — and thereby shapes demand across the income distribution, so every demand-side lever is also an equity lever (see Chapter 3.4 — Equity). It must plan for the structural rise in demand that income growth and ageing bring, invest in the non-healthcare inputs to health that reduce demand for costly care, and design the agency relationship system-wide through guidelines, payment methods, and workforce policy. Its horizon is long and its accountability is democratic, so it must justify demand-side choices publicly and honestly.

Common failure modes

Treating utilization as need. Reading activity data as a direct measure of health need, ignoring access, price, expectations, and supplier influence. Fix: always decompose recorded demand into need and the forces that translate need into use before acting on it.
Building capacity for induced demand. Meeting a demand surge with bricks and staff without asking whether a price change, income growth, or referral behaviour created it. Fix: diagnose the driver first; hold capital in reserve while you test cheaper demand-side options.
Blunt cost-sharing. Imposing across-the-board user charges that suppress cheap preventive and primary care and hit the poor and chronically ill hardest. Fix: protect essential and cost-effective care, exempt vulnerable groups, and reserve any deterrent for genuinely discretionary use.
Assuming the rational far-sighted investor. Expecting people to invest in their future health as Grossman's model predicts, and being surprised when prevention uptake disappoints. Fix: pair the standard model with behavioural insight and evidence-based incentives where sustained behaviour is required.
Ignoring the agency relationship. Leaving payment incentives and clinical information design to chance, then puzzling over unwarranted variation. Fix: manage agency deliberately with guidelines, decision aids, second opinions, and payment methods chosen for the behaviour they reward.
Conflating demand for insurance with its distortions. Either denying people needed financial protection for fear of moral hazard, or ignoring genuine distortions because insurance is welfare-improving. Fix: hold both truths separately and route the distortions to Chapter 1.3 — Market Failure.

Maturity model

Capability	Initiate	Develop	Standardize	Manage	Orchestrate
Demand concept	Utilization treated as need; care demanded for its own sake	Awareness that access and price affect use	Derived demand distinguished from demand for health in planning	Every demand forecast decomposes need, access, price, and agency	Demand for health, not care, drives investment across the whole care pathway and its upstream inputs
Use of elasticities	None; charges set by revenue or politics	Ad hoc reference to "some people are put off"	Price and income elasticities estimated for key services	Elasticities segmented by group and service; equity impact modelled	Segmented elasticities feed live demand and equity management across services and partners
Agency management	Payment and information left to chance	Guidelines exist but incentives unexamined	Payment method and decision aids chosen deliberately	Agency actively monitored via variation; levers tuned to informed choice	Payment, guidelines, and decision support aligned system-wide so recommended care tracks informed choice
Prevention framing	Prevention seen as cost or charity	Prevention valued but first to be cut	Prevention argued as investment in health capital	Health-capital returns quantified and defended against acute raids	Prevention funded as a protected, cross-budget investment in health capital across the system
Insurance and coverage	Coverage changes made without demand modelling	Demand response acknowledged after the fact	Demand response to coverage modelled in advance	Coverage, demand response, and distortions modelled and monitored together	Coverage, demand, capacity, and distortions co-managed dynamically with providers and payers

Checklist

For every demand forecast, state whether it measures demand for care or demand for health, and name the drivers.
Decompose any demand change into need, access, price, income, and supplier influence before responding.
Before setting or changing a user charge, estimate who is deterred, by service and by income group.
Protect essential and cost-effective care from price barriers; exempt the poor, children, and chronic conditions.
Frame prevention explicitly as investment in a depreciating stock of health capital, with an honest time horizon.
Choose the payment method and clinical decision supports deliberately, for the agency behaviour they reward.
Monitor unwarranted variation between clinicians as a signal of the agency relationship's health.
Build the income-driven rise in demand into long-range capacity and workforce plans, separate from ageing and technology.
Model the demand response before expanding any coverage or benefit.
Where prevention needs sustained behaviour, pair the standard model with behavioural insight and evidence-based incentives.

Key sources

Michael Grossman, The Demand for Health: A Theoretical and Empirical Investigation — the foundational statement of health as human capital and the consumption–investment duality.
Adam Wagstaff, "The demand for health: an empirical reformulation of the Grossman model" (Health Economics, 1993) — the empirical reworking that clarified how the model is best tested.
Alan Williams' health economics "plumbing diagram" — locating demand for healthcare within the wider map of the discipline (see Chapter 1.1 — Introduction to Health Economics).
Economics Network, Health Economics for Teachers — teaching materials on demand, elasticities, and agency.
OECD, Health at a Glance, and WHO health financing publications — for cross-country evidence on how spending rises with income and on cost-sharing.

References

Health economics — Wikipedia — https://en.wikipedia.org/wiki/Health_economics
Michael Grossman (economist) — Wikipedia — https://en.wikipedia.org/wiki/Michael_Grossman_(economist)
Human capital — Wikipedia — https://en.wikipedia.org/wiki/Human_capital
Derived demand — Wikipedia — https://en.wikipedia.org/wiki/Derived_demand
Price elasticity of demand — Wikipedia — https://en.wikipedia.org/wiki/Price_elasticity_of_demand
Income elasticity of demand — Wikipedia — https://en.wikipedia.org/wiki/Income_elasticity_of_demand
Principal–agent problem — Wikipedia — https://en.wikipedia.org/wiki/Principal%E2%80%93agent_problem
Time preference — Wikipedia — https://en.wikipedia.org/wiki/Time_preference
Health insurance — Wikipedia — https://en.wikipedia.org/wiki/Health_insurance
Michael Grossman, The Demand for Health: A Theoretical and Empirical Investigation — National Bureau of Economic Research / Columbia University Press — https://www.nber.org/books-and-chapters/demand-health-theoretical-and-empirical-investigation
Economics Network — Health Economics for Teachers — https://economicsnetwork.ac.uk/health/teachers
Health financing — World Health Organization — https://www.who.int/health-topics/health-financing