Introduction
Development organisations and public institutions routinely make choices between competing priorities. A programme may need to decide whether to expand an existing intervention, introduce a new service, target a smaller group more intensively, or redirect resources to a different approach.
Each option may have a credible rationale. Each may promise meaningful results. But resources are limited, and choosing one option often means giving up another.
This is where economic analysis becomes valuable. It helps decision-makers move beyond asking whether an intervention is worthwhile in isolation to examining how its costs, expected benefits, risks and distributional effects compare with realistic alternatives.
The central question is not simply, Can we afford this intervention? It is also, Is this the best use of the resources available, given the other things we could do?
1. Every decision has an opportunity cost
Opportunity cost is the value of the next-best alternative forgone when a choice is made.
Suppose a development organisation has KSh 10 million available for a new programme. It could use the funds to expand agricultural extension services, improve access to primary healthcare, or strengthen livelihoods support for vulnerable households.
If the organisation chooses agricultural extension, the opportunity cost is not necessarily the KSh 10 million spent. It is the value of the best alternative it could have pursued with those resources.
This distinction matters because a programme can be well implemented and still represent a poor allocation of resources if another feasible option could achieve substantially greater benefits.
Opportunity cost also applies within programmes. Funding a new reporting platform may mean postponing field supervision, staff training or direct services. Hiring additional personnel may reduce the budget available for essential equipment or outreach.
These trade-offs are often hidden when proposals are assessed independently rather than compared against one another.
A practical implication: Every major investment proposal should identify the alternatives considered, explain why the preferred option was selected, and make clear what the organisation is giving up by choosing it.
2. Compare options against a realistic alternative
Economic analysis becomes more useful when an intervention is assessed against a credible comparator.
The relevant comparison might be:
- Continuing with the existing programme.
- Expanding the programme to additional locations or participants.
- Introducing a different intervention addressing the same problem.
- Combining several approaches.
- Redirecting resources to another priority.
- Taking no additional action, where that is a realistic option.
The existing situation, often called the counterfactual or baseline scenario in economic appraisal, is important because not every observed benefit is caused by the proposed intervention. Some improvements may occur anyway, while some existing services may already be generating benefits.
Consider a hypothetical programme proposing to train 1,000 small-scale farmers. Training may increase knowledge, but decision-makers also need to understand what would happen without the proposed training, whether farmers can access the inputs needed to apply what they learn, and whether another intervention could produce greater improvements in productivity or income.
The comparison should reflect the decision being made. Comparing a new programme with an unrealistic scenario in which nothing useful happens can make the new programme appear more attractive than it really is.
3. Understand what the different economic methods can tell you
Economic analysis is not a single technique. Different methods answer different questions, and choosing the appropriate method depends on the decision, available evidence and outcomes of interest.
Cost analysis
Cost analysis identifies and estimates the resources required to deliver an intervention. It can include staff time, equipment, transport, facilities, technology, administration and other relevant costs.
It is useful when developing budgets, assessing affordability, identifying major cost drivers and estimating the resources needed for expansion.
However, knowing the total cost of a programme does not establish whether it represents good value. Cost analysis must be interpreted alongside what the programme is expected to achieve.
Cost-effectiveness analysis
Cost-effectiveness analysis compares the costs of alternatives with their results measured in a common, meaningful outcome.
For example, two hypothetical approaches to improving access to a public service might be compared using the additional number of people who successfully access that service.
Suppose Approach A costs KSh 6 million and produces 600 additional successful service users. Its cost is KSh 10,000 per additional successful user.
Approach B costs KSh 8 million and produces 1,200 additional successful service users. Its cost is approximately KSh 6,667 per additional successful user.
On this measure, Approach B produces more results at a lower average cost per additional successful user. It may therefore be the more cost-effective option, provided the two approaches produce comparable outcomes and the estimates are credible.
The figures are illustrative, not findings from an actual programme.
Cost-effectiveness analysis is particularly useful when alternatives pursue the same objective and their results can be expressed in a common unit.
Cost-benefit analysis
Cost-benefit analysis expresses both costs and benefits in monetary terms, allowing decision-makers to compare the value of expected benefits with the resources required.
It can be useful for investments whose benefits extend across different sectors or time periods. However, assigning monetary values to outcomes such as improved dignity, reduced inequality, stronger social cohesion or better health can be difficult and ethically contested.
Results may also depend on assumptions about future benefits, the time horizon, discount rates and the valuation of outcomes.
Monetisation should therefore be transparent and appropriate to the decision. Outcomes that cannot be credibly expressed in monetary terms should not simply disappear from the assessment.
Cost-utility analysis
Cost-utility analysis is a specialised form of economic evaluation often used in health, where outcomes may be measured using indicators that combine length and quality of life, such as quality-adjusted life years.
It can help compare interventions addressing different health conditions when the outcome measure is suitable for that purpose.
Multi-criteria analysis
Some decisions involve objectives that cannot reasonably be reduced to a single outcome or monetary value. Multi-criteria analysis compares options against several explicit criteria, which may include effectiveness, affordability, equity, feasibility, sustainability and acceptability.
Decision-makers assign importance to different criteria and assess the performance of each option. The process can improve transparency, but results depend on how criteria and weights are selected.
It should therefore be treated as a structured decision aid rather than a way to eliminate judgement.
4. Look beyond average cost
A low average cost does not automatically mean an intervention is the best investment.
Average cost describes total cost divided by total output or outcome. It can be useful for understanding overall resource requirements, but decisions about expansion often require a different measure.
Marginal cost is the additional cost of producing one more unit of output or expanding an intervention. Marginal benefit is the additional benefit generated by that expansion.
An intervention may be inexpensive to deliver to the first 500 participants but substantially more expensive to reach the next 500 because they live in remote areas, require additional support or face different barriers.
Similarly, the first phase of a programme may reach participants who are easiest to serve, while expansion may require greater resources to reach those who are most disadvantaged.
Decision-makers should therefore ask not only what the programme costs on average, but also what additional resources are required to achieve the next increment of results.
Where evidence permits, comparing incremental costs and incremental outcomes can be more informative than ranking options using average cost alone.
5. Make equity visible in the analysis
Economic efficiency and social equity are related, but they are not identical.
An intervention may achieve a large number of results at relatively low cost while excluding people who face the greatest barriers. Another intervention may cost more per participant because it serves remote communities, people with disabilities or households experiencing severe disadvantage.
A decision based exclusively on cost per outcome could favour the first intervention even where the second better addresses the programme’s mandate.
Equity analysis asks who receives the benefits, who bears the costs and who remains underserved. Relevant dimensions may include geography, income, gender, disability, age and other contextually important forms of disadvantage.
This does not mean that every higher-cost intervention should be preferred. Rather, distributional effects should be assessed explicitly alongside effectiveness and affordability.
For example, decision-makers might compare the overall cost-effectiveness of two service-delivery models while separately examining their reach among underserved populations. They could then assess whether additional expenditure is justified by the expected equity gains and programme objectives.
Making these trade-offs visible supports more defensible decisions than treating equity as a general statement in a proposal.
6. Account for uncertainty and implementation realities
Economic comparisons rely on assumptions. Costs may increase, participation may be lower than expected, outcomes may take longer to materialise, and implementation capacity may be weaker than planned.
A proposal that looks attractive under optimistic assumptions may perform poorly under more realistic conditions.
A sound assessment should identify the assumptions most likely to affect the decision and test how results change when those assumptions vary.
Useful approaches include:
- Sensitivity analysis: Change key assumptions, such as unit costs, participation levels or expected outcomes, to see whether the preferred option changes.
- Scenario analysis: Compare plausible situations, such as a constrained budget, delayed implementation or higher-than-expected demand.
- Risk assessment: Identify operational, financial and contextual risks that could affect costs or benefits.
- Implementation assessment: Examine whether staff, systems, infrastructure, partners and procurement arrangements are sufficient to deliver the proposed intervention.
An option that is theoretically cost-effective but cannot be implemented reliably may not be the best practical choice.
Decision-makers should distinguish between uncertainty that can be reduced through further evidence and uncertainty that must be managed through monitoring, adaptation or contingency planning.
7. Connect economic analysis to programme evidence
Economic analysis is only as credible as the evidence informing its estimates.
Financial records can show what was spent, but they may not establish what was achieved. Monitoring data can show how many people were reached, but may not reveal whether their circumstances improved. Evaluation evidence may identify changes, but estimating the portion attributable to a programme can require a credible comparison or causal design.
A robust assessment brings these sources together.
At a minimum, decision-makers should examine:
- Cost data: Were all relevant costs included, including staff time, administration, partner contributions and resources provided in kind where appropriate?
- Output and outcome data: Are the results measured consistently and are the indicators relevant to the decision?
- Attribution: Is there credible evidence that the intervention contributed to the observed changes?
- Time horizon: Do the costs and benefits occur at different points, and has that been handled appropriately?
- Distribution: Are benefits and costs spread evenly across participants and population groups?
- Transferability: Are estimates from another programme or location applicable to the context under consideration?
Where evidence is incomplete, the analysis should state the limitations rather than imply greater precision than the data support.
Economic analysis is most useful when it brings financial, operational and outcome evidence into a common decision framework.
8. Use the findings to make a decision, not just produce a report
A technically sophisticated analysis has limited value if it does not influence resource allocation or programme design.
Before commissioning the analysis, decision-makers should establish what decision it is intended to inform. They should also agree on the criteria that matter, the alternatives to compare and the evidence needed to distinguish between them.
The final assessment should communicate more than a ranking. It should explain:
- Which option appears preferable and why.
- What assumptions drive that conclusion.
- What the analysis cannot establish.
- What trade-offs exist between cost, effectiveness, equity and feasibility.
- What additional evidence could change the decision.
- What should be monitored if the preferred option is implemented.
Sometimes the conclusion will be to proceed. In other cases, it may be better to redesign the intervention, pilot an uncertain approach, negotiate lower costs or gather additional evidence before committing substantial resources.
The aim is not to make every decision appear mathematically certain. It is to make the reasoning, evidence and trade-offs sufficiently clear for decision-makers to act responsibly.
A practical framework for comparing development options
Before committing resources, work through these six questions:
- What decision needs to be made? Define the problem, objective and decision deadline.
- What are the realistic alternatives? Include continuation of existing arrangements where appropriate.
- What resources will each option require? Identify financial, human, operational and opportunity costs.
- What results can each option reasonably achieve? Use credible outcome evidence and appropriate methods to assess effectiveness.
- Who benefits, and what uncertainties matter? Examine equity, implementation risks and the sensitivity of results to key assumptions.
- What choice is justified by the evidence? Explain the preferred option, the trade-offs and the conditions that would warrant reconsideration.
These questions can guide a full economic evaluation or a proportionate assessment for a smaller programme decision. The analytical effort should match the scale, reversibility and consequences of the decision.
Conclusion: Better choices require more than good intentions
Development decisions are made under constraints. Budgets are limited, needs are substantial and multiple interventions can compete for the same resources.
Economic analysis helps decision-makers examine these choices systematically. By considering opportunity cost, comparing credible alternatives, measuring costs against meaningful outcomes and making equity and uncertainty visible, organisations can strengthen the basis for allocating resources.
The goal is not simply to spend less. It is to understand what resources are required, what results they can reasonably deliver and whether a different choice could achieve more meaningful benefits.
At DAREL Consulting, we connect economic reasoning with research, evaluation, data analysis and policy evidence to help organisations understand their options and make more informed decisions.
Better decisions begin by comparing what is possible—not by assuming that the first promising option is the best one.
