Expertise and Prediction Accuracy

Public support for policy interventions depends on citizens' beliefs about their likely effects. We examine how individuals form such beliefs by studying their predictions of experimental outcomes in a policy-relevant setting, and why their predictions differ from expert benchmarks. We elicit forecasts from 127 professional economists and a representative sample of 6,200 German households about a large-scale behavioral experiment on education policy (N = 3, 133). Nonexperts predict both average outcomes and treatment effects far less accurately than experts. Prediction accuracy improves with calibrated priors, self-reported effort, and the use of structured reasoning, but remains well below expert levels. We show that scalable design features, including the provision of well-calibrated numerical anchors and monetary incentives to rise effort, improve non-expert predictions, with effects comparable in magnitude to tertiary education or structured reasoning. Our findings have important implications for bridging the ‘expertise gap' in public discourse.