@TechReport{iza:izadps:dp18453,
            	author={Grewenig, Elisabeth and Gruendler, Klaus and Lergetporer, Philipp and Potrafke, Niklas and Werner, Katharina and Zeidler, Helen},
            	title={Expertise and Prediction Accuracy},
            	year={2026},
	month={Mar},
            	institution={Institute of Labor Economics (IZA)},
            	address={Bonn},
            	type={IZA Discussion Paper},
            	number={18453},
            	url={https://www.iza.org/publications/dp18453},
	abstract={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.},
	keywords={expert forecasts;lay predictions;belief formation;expertise gap;policy support;behavioral experiments},
            }