Participatory Impact Assessment
Also known as: PIA, Participatory Impact Evaluation, Community-Based Impact Assessment, Participatory Impact Measurement
Participatory Impact Assessment (PIA) is an approach to measuring the impact of development and humanitarian projects in which the affected communities define the indicators of change and use participatory tools to quantify it. Developed and codified by Andy Catley and colleagues at Tufts University's Feinstein International Center, largely through work on livestock and livelihoods programmes in pastoralist settings, PIA adapts participatory rural appraisal methods to the disciplined logic of impact evaluation — combining locally meaningful indicators with before-and-after and with-and-without comparisons to assess what a project actually changed.
Key highlights
- Generates semi-quantitative impact estimates in settings where randomised or survey-based counterfactual designs are infeasible.
- Uses community-defined indicators, so impact is measured on dimensions that are locally meaningful and often missed by external metrics.
- Participatory scoring tools such as proportional piling are accessible to non-literate participants and quick to apply in the field.
- Built-in before/after and with/without comparisons plus triangulation strengthen attribution beyond simple description.
Intuition
This section is available to Pro members. Upgrade to Pro
How it works
This section is available to Pro members. Upgrade to Pro
When to use it
Use PIA when you need credible, locally grounded estimates of project impact in contexts where conventional experimental or survey-based evaluation is impractical — remote, mobile, insecure, or rapidly changing populations such as pastoralists, or emergency and livelihoods programmes where no clean control group exists. It is well suited to measuring impact on dimensions communities themselves can judge and where semi-quantitative, triangulated evidence is acceptable. It is less appropriate where a rigorous statistical counterfactual is both feasible and required, where precise monetary valuation is essential, or where results must be nationally representative.
Strengths & limitations
- Generates semi-quantitative impact estimates in settings where randomised or survey-based counterfactual designs are infeasible.
- Uses community-defined indicators, so impact is measured on dimensions that are locally meaningful and often missed by external metrics.
- Participatory scoring tools such as proportional piling are accessible to non-literate participants and quick to apply in the field.
- Built-in before/after and with/without comparisons plus triangulation strengthen attribution beyond simple description.
- Estimates are semi-quantitative and site-specific, lacking the statistical precision and external validity of large-sample designs.
- Without a genuine control group, attribution rests on weaker comparisons and is vulnerable to confounding by external trends.
- Results depend on facilitator skill and consistent tool application; poorly run scoring exercises yield unreliable numbers.
- Aggregating proportional-piling and scoring outputs across groups requires care to avoid spurious precision.
Common pitfalls
This section is available to Pro members. Upgrade to Pro
Applications
This section is available to Pro members. Upgrade to Pro
Frequently asked
What is proportional piling and why is it central to PIA?
Proportional piling is a participatory tool in which a group is given a fixed quantity of counters (such as beans or stones) and asked to divide the pile across categories so that the size of each sub-pile reflects its relative importance — for example the share of household income from different sources. Repeated before and after an intervention, it yields semi-quantitative estimates of change that are accessible to non-literate participants, which is why it is a workhorse method in PIA.
How does PIA handle attribution without a control group?
PIA strengthens attribution through design rather than statistics: it builds in before-and-after comparisons and, where possible, with-and-without comparisons between participants and non-participants, repeats exercises across multiple informant groups, and triangulates the scores against interviews, observation, and secondary data. It also explicitly probes for alternative explanations. Attribution is therefore plausibility-based rather than experimentally proven, which is appropriate for the settings PIA targets.
How is PIA different from a Participatory Poverty Assessment?
A Participatory Poverty Assessment characterises the nature, causes, and experience of poverty to inform policy. PIA is narrower and evaluative: it measures the impact of a specific project against defined impact questions, using community indicators and structured scoring to estimate the size and direction of change. Both use PRA tools, but PIA is organised around attributing change to an intervention.
Sources
- 1.Catley, A., Burns, J., Abebe, D., & Suji, O. (2014). Participatory Impact Assessment: A Design Guide. Somerville, MA: Feinstein International Center, Tufts University.
You have read it. What now?
Cite this page
ScholarGate. (2026, June 22). Participatory Impact Assessment. ScholarGate. https://scholargate.app/development-studies/participatory-impact-assessment