What it looks like
A hypothetical charity reports 10,000 meals served, 500 students tutored and 2,000 nets handed out. Those are outputs. Whether anyone is healthier, better educated or less hungry a year later is a different and harder question. Those are outcomes.
Why it happens
Counting activity can be simpler than estimating its effects. Establishing causal impact may require comparison groups, follow-up surveys or specialist help. Routine outcome monitoring does not always need a trial. The 2019 Nobel Prize in economics went to Abhijit Banerjee, Esther Duflo and Michael Kremer for an experimental approach to measuring what anti-poverty programs really change. Their work shows how much careful measurement can reveal, and how much was unknown before.
Why it’s debated
Rigorous evaluation isn’t free, and not every program can or should run a randomized trial. Angus Deaton and Nancy Cartwright argue that trial results don’t carry over between places and populations as easily as people assume. Charitology takes both sides seriously: measure what matters, with methods proportionate to the decision being made. The sources here do not establish how large the measurement gap is across the sector.
No one is to blame
A small team may need partners or shared services to evaluate its work. Limited evaluation capacity does not establish negligence.
What we don’t know yet
What share of headline impact findings replicate? See Open Problem 10, the Replication Rate, and for source-level work, which is hardest of all to measure, Open Problem 3, Measuring the Source.
Measuring what changed is paid for as part of the work, and small charities share measurement instead of each building their own.