In Simple Words
Think of a survey where some people drop out before answering questions. To test whether missing responses distort the final score, researchers test the worst-case scenario by assuming everyone who dropped out got the lowest possible score, and then assuming they all got the highest possible score. If the overall pattern holds true under both extreme boundaries, the study's conclusions are rock solid.
Young male job applicants in Malawi
- Without short work experience
- Reference wage outcome
- With short work experience
- Positive wage gains across sensitivity checks
Eight-month instrumental-variable estimates under weighting and minimum-maximum scenarios. Sensitivity checks do not prove that missing responses caused no bias.
See source →Research question
Field experiments tracking job search outcomes over extended follow-up periods frequently suffer from attrition, creating risk that non-random dropout rates between control and treatment groups bias instrumental variable estimates.
Participants
N = 268 male job seekers at baseline; N = 227 successfully re-interviewed at 9-month follow-up (84.7% response rate) in Lilongwe, Malawi.
Study Design
Sensitivity testing using Fitzgerald-Gottschalk-Moffitt inverse probability weighting and Horowitz-Manski conservative minimum-maximum bounds to test treatment effect robustness across differential attrition rates (18.9% attrition in the 0% group vs 7.1% in the 75% group).
Results
Post-intervention wage returns remain positive and statistically significant under weighted adjustments (IV estimate = $4.20/day, p < 0.05; IHS daily wage = 0.605, p < 0.05) and conservative bounding scenarios, confirming that survey non-response does not account for the observed labor market returns.
Takeaway
Calculate conservative Manski bounds and inverse probability weights on follow-up data to verify that differential survey attrition does not drive experimental findings.
Source and citation
See source →Read alongside the research
Plain-English glossary
Original explanations, informed by the references below. Examples are illustrations, not data from the studies above. The research text stays unchanged.
- Follow-up
Collecting more information about participants later in a study to see what has happened to them. The term can also mean the length of time researchers keep track of them.
ExampleResearchers interview the same workers three months after a programme ends. That later interview is a follow-up.
GET-IT glossary · Follow-up ↗Read more about follow-up →- Baseline
The starting measurements in a study. Researchers use these to compare what participants were like at the start with what happens later.
ExampleBefore a training programme starts, researchers measure workers’ earnings. That first measurement is the baseline.
GET-IT glossary · Baseline characteristics ↗Read more about baseline →- Statistically significant
A label used when a result passes a chosen statistical threshold, often p < 0.05. It does not show how large or useful an effect is. A result that is not statistically significant does not prove there is no effect.
ExampleA study reports p = 0.03 against a 0.05 threshold. It passes that test, but the effect could still be too small to matter.
American Statistical Association · Statement on p-values ↗Read more about statistically significant →