Randomized Saturation Spillover Dynamics

In Simple Words

Varying the percentage of treated individuals across neighborhood clusters tests whether helping one worker harms neighboring job-seekers. It operates like testing different concentrations of water purification in distinct villages to see if clean water in one household improves or restricts neighbor access.

Research question

Evaluating whether job search policies displace non-treated job-seekers or generate positive information spillovers through local social networks requires measuring indirect effects across varying treatment density levels.

Participants

N = 3,052 youth clustered in 179 geographic units in Addis Ababa, Ethiopia.

Study Design

Two-stage randomized saturation design varying cluster treatment density from 10% to 90% in transport clusters and 80% in workshop clusters.

Results

Average indirect spillover effects on untreated neighbors were statistically zero across employment (-4.6 percentage points, p > 0.10) and earnings (-41.1 ETB, p > 0.10), proving that search interventions at this scale do not displace non-treated youth in integrated urban labor markets.

Takeaway

Implement cluster-based randomized saturation designs to audit local labor market displacement before scaling youth employment policies.

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Plain-English glossary

Original explanations, informed by the references below. Examples are illustrations, not data from the studies above. The research text stays unchanged.

Percentage points

The difference between two percentages.

ExampleA rise from 40% to 50% is 10 percentage points. Relative to the starting value, that is a 25% increase.

Office for National Statistics · Percentages ↗Read more about percentage points →

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