When to Use
Target setting is required whenever you need to define what success looks like for a specific indicator at a specific point in time. This is relevant for all performance monitoring systems - from simple project logframes to complex multi-donor results frameworks. Targets are the mechanism by which progress becomes measurable and accountability becomes specific.
How It Works
Step 1: Establish the baseline
Targets require baselines. A target without a baseline is either arbitrary or set after results are already known. The baseline is the pre-program measurement of the indicator that the target is set against.
Step 2: Review available evidence on realistic change
Targets should be based on evidence about what is achievable in similar contexts over similar timeframes. Sources: comparable program evaluations, sector norms (e.g., typical immunisation rate improvements per year in similar contexts), and expert consultation. Aspirational targets that are not grounded in evidence are not useful.
Step 3: Set targets at the right levels
Most programs need targets at multiple levels:
- Annual targets: expected progress by end of each program year
- Cumulative targets: total expected achievement by program end
- Sub-group targets: disaggregated targets for women, youth, or geographic zones (where equity commitments require it)
Step 4: Ensure indicators measure the results statements
Targets on the wrong indicators are meaningless. Before setting numbers, confirm the indicator actually measures the result it is supposed to track.
Step 5: Document rationale
For every target, document: the baseline value, the source of the baseline, the rationale for the target level (comparable evidence, expert judgment, or beneficiary consultation), and who approved it.
Step 6: Build in a review process
Program contexts change. Targets set at Year 0 may be unrealistic or too conservative by Year 2. Annual program reviews should include a target review process with documented justification for any revisions.
Key Components
- Baseline value: pre-program measurement of each indicator
- End-of-program target: the intended final achievement level
- Annual targets: milestones tracking expected progress year by year
- Disaggregated targets: sub-group specific targets where equity commitments exist
- Target rationale: documented evidence basis for each target
- Target revision protocol: when and how targets can be changed with donor approval
Best Practices
Collect outcome data at the right frequency. Some outcome indicators cannot change measurably within 12 months; others can. Match target timelines to measurement realities.
Measure at multiple points. Baseline, mid-term, and final measurements allow trajectory assessment, not just end-point comparison.
Set targets that require effort to achieve. Targets set below the natural trend (what would happen anyway without the program) have no accountability value. Targets set impossibly high create incentives for data manipulation. Calibrate against the baseline trajectory.
Avoid all-or-nothing targets. "100% of beneficiaries reached" targets create incentives to exclude hard-to-reach populations who would pull the rate down. Use realistic targets with performance bands (e.g., 80-90% is satisfactory, above 90% is excellent).
Assign ownership. Every target should have a named program staff member accountable for the data collection and performance tracking needed to assess it.
Common Mistakes
Setting targets before baselines are collected. The most common and most consequential target-setting mistake. Targets without baselines are guesses, and they create perverse incentives (hit the target rather than measure actual change).
Targets at the wrong level of the results chain. Output targets (200 farmers trained) are easy to measure but low accountability. Outcome targets (180 farmers adopting improved practices) are harder to measure but higher accountability. Many programs set output targets and report them as achievement.
No adjustment mechanism. Programs that rigidly maintain Year 0 targets through year 3 in contexts that have fundamentally changed are doing accountability theatre, not performance management.
Unstated disaggregation. A target of "5,000 beneficiaries reached" without a disaggregated sub-target for women, in a program with gender equity commitments, conceals whether the equity objective is being achieved.
Examples
Agricultural program, East Africa. A USDA-funded program in Malawi set output targets (farmers trained) and outcome targets (farmers adopting improved post-harvest practices) at baseline. The adoption target (60% of trained farmers adopting by Year 3) was based on a comparable program evaluation in neighbouring Zambia. At Year 1, adoption was 35%; the team investigated and found that the recommended technology required capital investment beyond farmers' means. The Year 2 target was revised down to 45% with donor approval, and a microfinance linkage was added to the program design to address the barrier.
Health program, South Asia. A UNICEF-funded nutrition program in Bangladesh set disaggregated targets for its primary indicator (child stunting rate) by wealth quintile. The top-level target was a 6 percentage point reduction in stunting by program end; the poorest quintile had a separate target of an 8 percentage point reduction, reflecting the program's equity priority. This disaggregated target prevented a scenario where improvements in wealthier households masked stagnation in the poorest.
Proposal Context
Targets in proposals are where program accountability is locked in. Once committed, targets are what donor reports measure against regardless of baseline reality or context changes. Common proposal pitfalls: (a) targets written during proposal drafting without baseline data (proposal commits to "80% achievement" before any baseline shows where the program starts; post-award baseline often reveals the target is unreachable), (b) targets copied from a previous program or template without recalibration to current context, (c) political or compliance pressure producing targets that reviewers recognize as inflated, (d) targets stated as absolute levels ("reach 50%") rather than change from baseline ("increase from 22% to 50%"), (e) year-on-year target trajectories that are linear when the actual change is likely non-linear (slow then fast, or fast then slow). A strong proposal commits to baseline-anchored targets where baseline data exists, and to target-refinement timing where baseline data does not yet exist. Pair with baseline and smart-indicators.
Related Topics
- SMART Indicators: the indicator framework within which targets must be time-bound and measurable
- Baseline Design: the data collection process that provides the starting point for targets
- Disaggregation: setting targets that hold programs accountable for equity
- Results-Based Management: the management framework that makes targets the anchor of performance accountability
- Milestone: intermediate targets tracking progress along the program timeline