Designing Effective Interventions

Sebastian Riedmiller (), Matthias Sutter () and Sebastian Tonke ()
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Sebastian Riedmiller: Max Planck Institute for Research on Collective Goods
Matthias Sutter: Max Planck Institute for Research on Collective Goods
Sebastian Tonke: Max Planck Institute for Research on Collective Goods

No 18273, IZA Discussion Papers from Institute of Labor Economics (IZA)

Abstract: We provide a systematic framework to diagnose underlying problems and predict intervention effectiveness ex-ante. For this, we developed a parsimonious and generalizable survey tool (anamnesis). Our anamnesis classifies underlying problems along three fundamental diagnoses: awareness, intention, and implementation problems. We validate the framework in an online experiment with 7,500 subjects. We find that (i) intervention effectiveness is heterogeneous across different settings, and (ii) our diagnosis accurately predicts this heterogeneity. On average, predicting a 10%-effect corresponds to an actual effectiveness of 8.92%. We further demonstrate the applicability of our framework to predict heterogeneities in the setting of COVID booster take-up.

Keywords: context dependency; heterogeneous treatment effects; intervention design; experiment (search for similar items in EconPapers)
JEL-codes: C93 D01 D61 D90 (search for similar items in EconPapers)
Date: 2025-11
New Economics Papers: this item is included in nep-exp and nep-nud
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