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Cost
Effectiveness
101
Getting the Most Good per Rupee — Comparing Interventions on Cost per Outcome, a Foundational Course for Practitioners & Funders in South Asia
Research-BackedSouth Asia Focus100 SlidesFree Access
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Agenda
What We Cover
01
Why Cost-Effectiveness?
Slides 3–11
02
The Family of Methods
Slides 12–20
03
Measuring Costs
Slides 21–29
04
Measuring Effects
Slides 30–38
05
The Cost-Effectiveness Ratio
Slides 39–47
06
Cost-Utility & DALYs
Slides 48–56
07
Time & Discounting
Slides 57–65
08
Comparing Interventions
Slides 66–75
09
Uncertainty & Sensitivity
Slides 76–83
10
Limits & Equity
Slides 84–91
11
Practice & Tools
Slides 92–99
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01
Section One
Why Cost-Effectiveness?
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The Core Reality
Budgets are finite; needs are not
Every development organisation, ministry and funder faces the same wall: there is never enough money to do everything worth doing. Cost-effectiveness is the discipline of choosing well when you cannot fund it all — doing the most good with the resources you have.
The question is not 'Does this programme work?' but 'Does it do more good per rupee than the alternatives we could fund instead?'
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Opportunity Cost
Every rupee spent is a rupee not spent elsewhere
Opportunity cost
The value of the best alternative you give up when you choose one option. The true cost of funding programme A is the good you could have done with programme B instead.
A health budget spent on a costly tertiary procedure is a budget not spent on the immunisations or bed nets it could have bought. Opportunity cost makes that trade-off visible.
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The Most Good
'The most good per rupee'
Two programmes can both be genuinely good and still differ enormously in how much good they buy per rupee. Cost-effectiveness analysis surfaces those differences so scarce funds flow where they help most.
01
MONEY: a fixed budget
→
02
CHOICE: many worthy programmes compete
→
03
EVIDENCE: cost per outcome for each
→
04
DECISION: fund the most outcomes per rupee
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A Worked Intuition
Same budget, very different impact
Imagine ₹10 lakh to spend on reducing child illness. Programme A averts one case for ₹5,000; programme B averts one for ₹500. The same money buys 200 averted cases instead of 20 — a tenfold difference in good done.
200 vs 20
cases averted for the same ₹10 lakh
Illustrative
10×
more impact from the cheaper-per-outcome option
Illustrative figures — but the logic is real: differences between interventions are often this large, not marginal.
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Not About Being Cheap
Cost-effective is not the same as cheap
Cheapest
Lowest total cost — but may deliver almost nothing. A programme that does little for very little money is not a bargain.
Cost-effective
Most outcome per rupee. A more expensive programme can be more cost-effective if it delivers far more good.
Cost-effectiveness always holds cost and outcome together. Neither number means much alone.
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Who Uses It
Where cost-effectiveness shapes decisions
  • Health ministries deciding which treatments a public scheme covers
  • Funders allocating limited grants across many strong applicants
  • NGOs choosing between programme designs for the same goal
  • Global bodies (WHO, World Bank) ranking interventions across diseases
From a panchayat health post to a national budget, the underlying question is identical: where does each rupee do the most?
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A Tool, Not a Verdict
Cost-effectiveness informs; it does not decide
Cost-effectiveness analysis is one input into a decision, not the whole decision. Equity, rights, feasibility and politics all matter too — themes we return to in Section 10.
The aim is not to put a price on a life, but to be honest about the lives a fixed budget can and cannot save.
— a working principle of health economics
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South Asia Stakes
Why this matters acutely in South Asia
Public health spending per person in much of South Asia is among the lowest in the world relative to need, while disease and deprivation burdens are high. The gap between resources and need makes every allocation choice consequential.
When budgets are tight and needs vast, choosing well is not an academic nicety — it is the difference between reaching many more people or far fewer with the same money.
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02
Section Two
The Family of Methods
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Three Cousins
CEA, CBA and CUA: one family, different units
Economic evaluation comes in three main forms. All compare cost against benefit — they differ in how they measure the benefit, and that choice shapes what you can compare.
01
CEA: benefit in natural units (cases averted)
→
02
CUA: benefit in DALYs / QALYs
→
03
CBA: benefit converted into money
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Method 1
Cost-Effectiveness Analysis (CEA)
Cost-Effectiveness Analysis
Compares the cost of interventions per unit of a single natural outcome — e.g. cost per case of malaria averted, per child immunised, per additional year of schooling.
Strength: the outcome stays in real, intuitive units. Limit: you can only compare programmes that share the same outcome. Cost per case averted cannot be set against cost per child enrolled.
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Method 2
Cost-Utility Analysis (CUA)
Cost-Utility Analysis
A special form of CEA that measures benefit in a common health-and-quality unit — the DALY or QALY — so that very different health programmes can be compared on one scale.
Because a DALY captures both length and quality of life, CUA lets you ask whether a rupee does more against blindness, diarrhoea or heart disease — comparisons CEA cannot make. We devote Section 6 to it.
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Method 3
Cost-Benefit Analysis (CBA)
Cost-Benefit Analysis
Converts all benefits into money, so cost and benefit share the same unit. The result is a net benefit (benefits − costs) or a benefit-cost ratio.
Power: you can compare a road, a school and a clinic on one ledger. Danger: putting a rupee value on health, education or a life forces uncomfortable — and contested — assumptions.
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Side by Side
Which method measures what?
MethodBenefit measured inLets you compare
CEANatural units (cases, children, years)Programmes with the same outcome
CUADALYs or QALYsAny health programmes, across diseases
CBAMoney (₹)Anything — health, roads, schools
CMA*(costs only; outcomes assumed equal)Options with identical effect
*Cost-minimisation analysis is the special case where two options give the same outcome, so you simply pick the cheaper.
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Choosing
Pick the method that fits the question
  • Comparing designs for one outcome? → CEA
  • Comparing across different health conditions? → CUA
  • Comparing across sectors (health vs infrastructure)? → CBA
  • Outcomes already known to be identical? → cost-minimisation
The method is not a matter of taste; it follows from what you need to compare and what you are willing to monetise.
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A Shared Logic
All four share one ratio at heart
Despite the different units, every method asks a version of the same thing: how much do we pay for how much good? The numerator is always cost; only the denominator — the outcome — changes.
value = cost / outcome
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A Caution
Different methods, different answers
The same programme can look excellent under one method and mediocre under another, because each weighs outcomes differently. The method is a lens, and lenses shape what you see.
Always report which method you used, and never compare a CEA result for one programme against a CBA result for another. Units must match.
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03
Section Three
Measuring Costs
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Start Here
Costs are harder to measure than they look
The 'cost' of a programme is rarely the figure on the grant agreement. Real cost includes staff time, volunteers, donated goods, shared overheads and the time of the people you serve. Miss these and you flatter the programme.
A good cost estimate is a careful inventory, not a glance at the budget line.
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The Method
The ingredients (or inventory) method
Ingredients method
Cost estimation that lists every resource an intervention uses — the 'ingredients' — values each at its real price, and adds them up. Also called the inventory method.
01
LIST every resource used (the ingredients)
→
02
QUANTIFY how much of each
→
03
PRICE each at its true value
→
04
SUM to a total programme cost
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Direct vs Indirect
Direct and indirect costs
Direct costs
Tied straight to delivery: medicines, field staff salaries, training materials, transport to villages.
Indirect costs
Shared support that enables delivery: head-office rent, accounts, IT, management. Must be apportioned, not ignored.
Leaving out indirect costs is the most common way a programme is made to look cheaper than it really is.
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Fixed vs Variable
Fixed and variable costs
  • Fixed costs do not change with scale — a training centre, a vehicle, the management team
  • Variable costs rise with each unit served — vaccines, stipends, per-child materials
  • Together they shape how cost-per-outcome changes as a programme grows
This is why pilots look expensive: fixed costs are spread over few beneficiaries. Cost per outcome usually falls as you scale — up to a point.
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Economies of Scale
Why cost per outcome falls with scale
Cost per beneficiary as a programme scales (illustrative)
Illustrative
Fixed costs spread over more people, so the curve falls steeply then flattens. Illustrative numbers, but a near-universal shape.
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Whose Costs?
The perspective decides what counts
PerspectiveCounts costs to…Often misses
ProviderThe implementing organisationCosts borne by participants
ParticipantThe beneficiary — travel, time, feesProvider overheads
SocietalEveryone affected — the fullest viewHardest to measure fully
State the perspective up front. A programme that is cheap for the provider may be costly for a poor participant who loses a day's wage to attend.
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Hidden to the Poor
Participant costs are real costs
For a daily-wage worker, attending a 'free' health camp can mean lost income, bus fare and childcare. From a societal perspective those are genuine programme costs — and a reason people drop out.
Designs that lower participant costs — doorstep delivery, evening timings, compensation — can improve cost-effectiveness by lifting uptake, not just by spending less.
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Where the Money Goes
A typical programme cost breakdown
Illustrative share of total cost by ingredient
Illustrative
Notice overheads and participant time together are nearly a quarter of cost — the very items most often left out.
Drop those slices and the programme looks ~23% cheaper than it really is. Illustrative shares.
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04
Section Four
Measuring Effects
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The Denominator
Effects: the 'good' you are buying
If cost is the numerator, the effect is the denominator — the outcome the programme produces. Getting it right matters as much as costing, and is often harder.
In CEA the effect stays in its natural unit: a case averted, a child immunised, a TB patient cured, a year of schooling added.
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Natural Units
Outcomes in their own real units
  • Health: cases averted, deaths prevented, patients cured
  • Education: additional years of schooling, test-score gains
  • Nutrition: children moved out of stunting
  • WASH: people with sustained access to safe water
Natural units are intuitive and trusted by practitioners — but they only allow comparison within the same outcome.
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A Chain
Intermediate vs final outcomes
Intermediate outcome
A step on the causal path — bed nets distributed, children dewormed, people trained. Easy to measure, but not yet the thing you ultimately care about.
Final outcome
The end goal — malaria cases averted, lives saved, income raised. Harder to measure, but it is what actually matters.
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The Results Chain
From inputs to impact
01
INPUTS: money, staff, nets
→
02
OUTPUTS: nets distributed
→
03
OUTCOMES: nets used nightly
→
04
IMPACT: malaria cases averted
Costing per output (nets distributed) is cheap to measure but can mislead — a net unused averts nothing. Push toward outcomes and impact where you can.
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Attribution
Did the programme really cause the effect?
Cost-effectiveness needs the effect caused by the programme — not the total change, some of which would have happened anyway. That requires a credible counterfactual: what would have occurred without it.
Crediting a programme with outcomes it did not cause overstates its cost-effectiveness. This is why rigorous evaluation (RCTs, quasi-experiments) underpins the best cost-effectiveness estimates.
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Cost per Outcome
Putting cost and effect together
Divide total cost by total effect and you have the headline number: cost per outcome. ₹6 lakh spent to avert 300 cases is ₹2,000 per case averted.
₹6,00,000
total programme cost
Illustrative
₹2,000
per case averted (cost ÷ 300 cases)
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Measuring Effect
Where effect estimates come from
SourceStrengthCaution
RCT / trialStrong causal estimateMay not transfer to your context
Quasi-experimentReal-world, plausible counterfactualAssumptions can fail
Programme monitoringCheap, availableNo counterfactual
Published meta-analysisPools many studiesSettings differ from yours
An effect estimate is only as good as the evidence behind it. Borrowed effect sizes need a sanity check against your reality.
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A Warning
Cheap outputs can buy nothing
It is easy to be cost-effective at producing outputs nobody uses. The discipline is to cost what changes lives, not what is easy to count.
— a programme-evaluation maxim
Always ask whether your denominator is a real outcome or a convenient proxy. The choice can swing the ratio by an order of magnitude.
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05
Section Five
The Cost-Effectiveness Ratio
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The Headline Number
The cost-effectiveness ratio
Cost-effectiveness ratio (CER)
Total cost divided by total effect — the average cost of one unit of outcome. Lower is better: less money for the same good.
CER = total cost / total effect
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Average CER
The average ratio compares to doing nothing
The average CER compares a programme against a baseline of doing nothing. It answers: across everyone reached, what did each unit of outcome cost on average?
Useful for a first sense of a programme — but real decisions usually compare options against each other, not against nothing. That needs the incremental ratio.
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The Key Concept
The Incremental Cost-Effectiveness Ratio (ICER)
ICER
The extra cost of switching from option B to option A, divided by the extra effect gained. It measures the cost-effectiveness of the upgrade, not the whole programme.
ICER = costA − costB / effectA − effectB
The ICER is incremental, not average. It is the single most important — and most misread — number in the field.
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Why Incremental
Why the increment, not the average, matters
You are rarely choosing between a programme and nothing. You are choosing whether the extra spend on a better option is worth the extra good it buys. The ICER isolates exactly that trade-off.
A new treatment may be excellent on average yet a poor upgrade — if it costs far more than the current one for only a little extra benefit. The ICER catches what the average hides.
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A Worked ICER
Comparing two TB-screening strategies
StrategyCostCases detected
B: standard screening₹5,00,000200
A: enhanced screening₹8,00,000260
Difference (A − B)₹3,00,00060 extra
ICER = ₹3,00,000 ÷ 60 = ₹5,000 per extra case detected. The question for the funder: is an extra case worth ₹5,000? Illustrative figures.
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Reading the ICER
Is the ICER 'worth it'?
An ICER only means something against a threshold — the most you are willing to pay for one unit of outcome. Below the threshold, the upgrade is judged worthwhile; above it, not.
Thresholds are themselves value judgements, often tied to a country's income. We return to this in Sections 6 and 10 — there is no neutral 'correct' threshold.
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Dominance
When the choice is obvious
Dominant
An option that costs less and does more dominates — always choose it. No ICER needed.
Dominated
An option that costs more and does less is dominated — drop it from the comparison entirely.
ICERs only matter for the hard middle cases: more cost and more effect, where you must weigh the trade-off.
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A Common Slip
Don't confuse average with incremental
Reporting a programme's average cost per outcome when the decision is about an upgrade is a frequent and serious error. It can make a poor-value addition look like a bargain.
Rule: if you are comparing two options, you need the ICER between them — the difference in cost over the difference in effect. Never the two averages.
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06
Section Six
Cost-Utility & DALYs
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The Problem
How do you compare blindness with diarrhoea?
CEA cannot compare a programme that prevents blindness with one that prevents child deaths — the outcomes are in different units. To choose across all of health, you need a common currency of health itself.
That common currency combines two things people care about: how long you live and how well you live. Enter the DALY and the QALY.
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Two Measures
QALYs and DALYs: length × quality of life
QALY
Quality-Adjusted Life Year — a year of healthy life. Higher is better; programmes aim to gain QALYs.
DALY
Disability-Adjusted Life Year — a year of healthy life lost. Higher is worse; programmes aim to avert DALYs.
QALYs count health gained; DALYs count health lost. They are mirror images — do not mix them in one calculation.
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The DALY
What a DALY actually is
DALY
Disability-Adjusted Life Year = Years of Life Lost to early death (YLL) + Years Lived with Disability (YLD). One DALY is one lost year of full health.
DALY = YLL (years of life lost) + YLD (years lived with disability)
Because a DALY is a loss, lower is better and interventions seek to avert DALYs — not accumulate them.
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The Two Halves
Dying early, and living unwell
YLL
Years of Life Lost: a death at 30 against a life expectancy of 70 loses 40 years. Captures the burden of dying early.
YLD
Years Lived with Disability: years spent in ill-health, each weighted by how disabling the condition is. Captures suffering, not just death.
Together they let a single number reflect both a fatal disease and a chronic, disabling but non-fatal one.
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Disability Weights
Not all years of illness weigh the same
Each condition carries a disability weight from 0 (full health) to 1 (equivalent to death). A year with a mild condition counts as a small fraction of a DALY; a year with a severe one counts as much more.
These weights come from surveys of how people value health states — they are useful, standardised, and genuinely contested. Whose values, and whose lives, the weights reflect is a real ethical question.
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The CUA Ratio
Cost per DALY averted
Cost-utility analysis divides cost by DALYs averted: cost per DALY averted. Lower means more health bought per rupee, and it works across completely different diseases.
cost / DALYs averted = cost per DALY averted
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Orders of Magnitude
Some interventions avert DALYs very cheaply
Cost per DALY averted — illustrative ranking (lower = better)
Illustrative; relative ordering only, not exact figures
Bars show relative ordering only. Bed nets and basic vaccines are commonly cited as among the most cost-effective health buys — but treat any precise '$ per DALY' figure with care.
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Thresholds
When is a cost per DALY 'good value'?
A widely used rule of thumb judged an intervention cost-effective if its cost per DALY averted was below one to three times a country's GDP per capita. It is convenient — and increasingly criticised as arbitrary.
WHO has moved away from blanket GDP-based thresholds toward context-specific judgement. A threshold is a value choice dressed as a number — treat it as one input, never a verdict.
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07
Section Seven
Time & Discounting
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Money Has Timing
A rupee today is worth more than a rupee next year
Costs and benefits arrive at different times. A rupee in hand today can be invested, can meet an urgent need, and is certain — so we value it more than the same rupee a decade away. Discounting makes future and present values comparable.
This is not about inflation. Even with stable prices, sooner is worth more than later.
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Why Discount
Time preference and opportunity cost
  • Time preference: people generally prefer benefits now to benefits later
  • Opportunity cost of capital: money tied up could have earned a return
  • Uncertainty: the further ahead, the less sure the benefit will arrive
Discounting is how we honour all three — putting flows that occur at different times on one comparable footing.
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Present Value
Bringing the future into today's terms
Present value (PV)
What a future cost or benefit is worth today, after discounting. A benefit of X in year t is worth X ÷ (1 + r)^t now, where r is the discount rate.
PV = future value / (1 + r)t
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See the Decay
How a discount rate erodes future value
Present value of ₹1,000 received in year t (at 3% and 8%)
Illustrative, standard discounting
At 8%, ₹1,000 in 30 years is worth under ₹100 today. The rate you pick dramatically changes how much the future counts.
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The Rate
Choosing the discount rate
Common rates in health and development run around 3% per year, though some analyses use higher. The choice is consequential and partly ethical: a high rate makes long-term benefits almost vanish.
This matters acutely for the climate, for children, and for prevention — programmes whose payoffs land decades away look far worse under a high discount rate. The rate encodes how much we value the future.
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Discount Effects Too?
Should we discount health, not just money?
Convention often discounts both costs and health effects (DALYs, QALYs) — otherwise a calculation could justify delaying every benefit forever. But discounting future lives is philosophically uncomfortable.
Whatever you choose, be explicit and consistent, and test how sensitive your result is to the rate — which is exactly what Section 9 is about.
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Time Horizon
How far ahead do you count?
Alongside the rate, you must choose a time horizon — how many years of costs and benefits to include. A prevention programme whose payoffs land over a lifetime looks worthless on a three-year horizon.
Cut the horizon too short and you systematically undervalue prevention and anything slow-burning. State the horizon, and match it to when the real effects actually occur.
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A Caution
Discounting is an assumption, not a fact
The discount rate is the most powerful, least visible assumption in any long-horizon analysis — small changes swing the conclusion.
— a maxim of cost-benefit practice
Always report your discount rate, justify it, and show the result at alternative rates. Hiding it is a red flag in any analysis you read.
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08
Section Eight
Comparing Interventions
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The Pay-Off
Why we did all this: to compare
The whole point of standardising costs and effects is to lay interventions side by side and ask where each rupee does most good. Done honestly, the comparison can redirect funding to far higher impact.
But comparison is only as trustworthy as the consistency behind it — same method, same units, same perspective, same discount rate. Compare like with like, or not at all.
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League Tables
Ranking interventions by cost per outcome
League table
A ranked list of interventions by their cost-effectiveness ratio — cheapest per unit of outcome at the top — used to guide where to spend next.
League tables are powerful and seductive. Their honesty depends entirely on whether the entries were estimated the same way. A table mixing methods is worse than no table.
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$ per Outcome
GiveWell-style 'cost per outcome' comparison
Illustrative cost per equivalent outcome across programmes
Illustrative; relative comparison only
Funders like GiveWell rank charities by cost per comparable outcome and direct money to the top. The same logic scales from a global fund to a single grant committee. Figures here are illustrative.
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Reallocation
Moving money up the league table
If programme A averts an outcome for ₹400 and programme E for ₹7,500, shifting funds from E toward A — where appropriate and feasible — can multiply total impact for the same budget.
But 'shift the money' is rarely so simple: programmes serve different people, places and needs. Cost-effectiveness tells you where to look harder, not always where to cut.
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A Cautionary Tale
The deworming debate
Mass school deworming became a famous 'best buy' — cheap pills, and one influential study linked treatment to better schooling and later earnings. It topped cost-effectiveness league tables for years.
Then re-analyses and replications questioned the size and durability of those long-run effects. The pills are cheap and safe; the magnitude of the benefit — the denominator — turned out far more uncertain than the rankings implied.
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The Lesson
What deworming teaches about league tables
  • A ranking is only as solid as the effect estimate beneath it
  • A single influential study can move millions — and may not replicate
  • Cheap cost (numerator) cannot rescue an uncertain effect (denominator)
  • Precise-looking ratios can hide enormous uncertainty
The deworming story is not 'CEA is useless' — it is 'never read a league-table rank without its uncertainty'. Which is Section 9.
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Transferability
A ratio from elsewhere may not hold here
Costs, wages, disease burden and delivery systems differ across districts and countries. A cost-effectiveness ratio estimated in Kenya or Bihar will not automatically hold in Odisha or Sindh.
Before borrowing a ratio, ask what would change in your context: input prices, baseline burden, uptake, capacity. Adapt, don't copy.
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Diminishing Returns
The best buy changes as you scale
A top-ranked intervention does not stay top-ranked forever. As you fund the easiest-to-reach first, the cost of reaching each additional person tends to rise — so even the best buy eventually becomes less cost-effective at the margin.
This is why portfolios beat single 'winners': the right answer is usually to fund the best buy up to a point, then move down the table — not to pour everything into one line.
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Good Practice
Comparing interventions honestly
  • Use the same method, units, perspective and discount rate for every entry
  • Show the uncertainty around each ratio, not just the point estimate
  • State the context each estimate came from
  • Pair the ranking with equity and feasibility, never efficiency alone
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09
Section Nine
Uncertainty & Sensitivity
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The Honest Truth
Every ratio is an estimate, not a fact
A cost-effectiveness ratio is built from uncertain inputs — uncertain costs, uncertain effects, an assumed discount rate. The single number that results looks precise but is not. Sensitivity analysis asks how much it could move.
A point estimate with no uncertainty attached is not a finding — it is an illusion of precision. Demand the range.
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One-Way
One-way sensitivity analysis
One-way sensitivity analysis
Vary one input at a time across a plausible range, holding the rest fixed, and watch how the result moves. It reveals which assumptions the answer hinges on.
If halving the assumed effect doubles the cost per outcome and flips your decision, you have found the input that deserves the most scrutiny — and better data.
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The Tornado
Tornado diagram: what moves the answer most
One-way sensitivity: swing in cost per outcome by input (illustrative)
Illustrative
Sorted widest-to-narrowest, the bars form a 'tornado'. Here the effect size dominates — so that is where to invest in better evidence. Illustrative.
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Probabilistic
Probabilistic sensitivity analysis (PSA)
Probabilistic sensitivity analysis
Assign each uncertain input a probability distribution, then simulate the model thousands of times to produce a distribution of cost-effectiveness results, not a single number.
PSA answers the question decision-makers really have: not 'what is the ratio?' but 'how likely is this option to be the best buy, given everything we don't know?'
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The CE Plane
The cost-effectiveness plane
Plot extra cost (vertical) against extra effect (horizontal) relative to the comparator. The origin is the comparator; an option's position decides whether it is an easy yes, an easy no, or a trade-off.
Lower-right
More effect, less cost — dominant. Adopt it.
Upper-left
Less effect, more cost — dominated. Reject it.
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See the Plane
An ICER on the cost-effectiveness plane
Simulated results vs a comparator (origin = comparator)
Illustrative PSA cloud
The scatter (one dot per simulation) shows how wide the uncertainty is. A line from the origin is the willingness-to-pay threshold: dots below it are 'worth it'. Illustrative.
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The Takeaway
Why point estimates mislead
Two interventions can have the same headline ratio while one is a near-certain good buy and the other a coin-toss. The point estimate alone cannot tell them apart; the spread can.
Treat any single '₹X per outcome' as the centre of a range, never the truth. The width of that range is often the most decision-relevant fact in the whole analysis.
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10
Section Ten
Limits & Equity
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Not the Whole Story
Efficiency is one value among several
Cost-effectiveness measures efficiency — outcomes per rupee. But societies also care about fairness, rights, dignity and reaching the worst-off. A perfectly efficient choice can be deeply unjust.
Not everything that counts can be counted, and not everything that can be counted counts.
— commonly attributed to William Bruce Cameron
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What CEA Ignores
The blind spots of cost per outcome
  • Distribution: a total of outcomes hides who gets them
  • Rights & dignity: some things are owed regardless of efficiency
  • Process: how people are treated, not just the result
  • The hard-to-reach: serving them is costlier — and easily deprioritised
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The Equity Trap
Cheapest-to-reach is not the same as fairest
Reaching a remote Adivasi hamlet or a person with disability often costs far more per outcome than reaching an accessible town. Pure cost-effectiveness quietly pushes resources away from exactly those who need them most.
Left unchecked, 'value for money' can entrench the very inequities development is meant to undo. This is the central ethical tension of the whole field.
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Equity Weighting
Building fairness into the numbers
One response is equity weighting: counting an outcome for a worse-off person as worth more than the same outcome for someone advantaged, so the analysis rewards reaching the marginalised.
It makes the value judgement explicit rather than pretending efficiency is neutral — but choosing the weights is itself a political and ethical act, not a technical one.
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Whose Values?
Numbers carry hidden value judgements
  • Disability weights embed whose view of a 'good life'
  • Discount rates embed how much we value future generations
  • Thresholds embed what a life or a healthy year is 'worth'
  • The choice of outcome embeds what we decided to count at all
Every one of these is a values choice wearing the costume of arithmetic. Data-literate practice means reading the choices, not just the digits.
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The Counted & the Counted-Out
What gets measured gets funded
Cost-effectiveness can only weigh outcomes that someone chose to measure. Benefits that are hard to quantify — dignity, social cohesion, women's voice, ecological resilience — risk being treated as if they were worth zero.
'Not measured' is not the same as 'not valuable'. An analysis that silently drops the unmeasurable will systematically under-fund it. Name what your ratio leaves out.
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Use It Well
A servant, not a master
Cost-effectiveness is most useful as a discipline of attention — forcing you to be explicit about costs, outcomes and trade-offs — and most dangerous when it becomes the sole arbiter of who gets help.
Best practice: present cost-effectiveness alongside equity, rights and feasibility, and let deliberation — not a single ratio — make the final call.
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11
Section Eleven
Practice & Tools
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What You Need
The data a cost-effectiveness study needs
  • Cost data: a full ingredients inventory, including overheads and participant costs
  • Effect data: a credible, causal estimate of the outcome
  • The comparator: what you are measuring against
  • Assumptions: discount rate, time horizon, perspective — stated openly
Most disputes over a result trace back to one of these four. Pin them down before you compute anything.
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Build the Model
Spreadsheets are enough to start
Most cost-effectiveness analyses begin life in a spreadsheet: costs in one sheet, effects in another, the ratio computed transparently, and a tab for sensitivity analysis. Clarity beats sophistication.
Keep every assumption in a labelled, single cell so a reviewer can change it and watch the result move. An auditable spreadsheet is worth more than a black-box model.
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Good Practice
Habits of a trustworthy analysis
  • State the perspective, comparator, horizon and discount rate explicitly
  • Use the ingredients method — cost everything, including the hidden
  • Report the ICER for choices between options, not just averages
  • Always run sensitivity analysis and show the range
  • Present equity alongside efficiency
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Watch-Outs
Red flags when you read a CEA
  • A single ratio with no uncertainty or sensitivity analysis
  • An average ratio where an incremental one was needed
  • No stated perspective or discount rate
  • An effect size from one study, treated as settled
  • A ranking that ignores who is reached and who is missed
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Reading List
Where to go deeper
ResourceWhat it offers
Drummond et al., Methods for the Economic Evaluation of Health Care ProgrammesThe standard textbook of the field
WHO-CHOICEWHO's database and tools for comparing health interventions
Disease Control Priorities (DCP3)Cost-effectiveness evidence across diseases for low/middle-income settings
GiveWell & the Disease Control Priorities reviewsWorked 'cost per outcome' analyses you can learn from
iDSI Reference CaseA shared standard for credible economic evaluation
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Connect the Course
Pair this with the rest of the 101 Series
Cost-effectiveness sits inside a wider toolkit. It is strongest when fed by good measurement, sound evaluation and honest data.
Pair this deck with ImpactMojo's Data Literacy, Impact Evaluation and Theory of Change 101 courses for the full picture.
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The Takeaways
If you remember five things
  • Most good per rupee — because budgets are finite and choices have opportunity costs
  • Match the method — CEA (natural units), CUA (DALYs), CBA (money)
  • Use the ICER — the incremental, not average, ratio when comparing options
  • Show the uncertainty — a point estimate alone is an illusion of precision
  • Efficiency is not the only value — weigh equity, rights and dignity too
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Cost Effectiveness 101 · Complete
Now ask: the most
good per rupee?
You don't need to be a health economist — you need to hold cost and outcome together, demand the increment and the uncertainty, and never let a ratio outrank equity. Explore the rest of the ImpactMojo 101 Series, free forever.
More 101 CoursesExplore ImpactMojoDataverse
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