Success at scale: six suggestions from implementation and policy sciences

• Published in: Behavioural Public Policy Vol. 5; no. 1; pp. 71 - 79
• Main Authors: HO, TECK-HUA, LEONG, CHING, YEUNG, CATHERINE
• Format: Journal Article
• Language: English
• Published: Cambridge Cambridge University Press 01.01.2021
• Subjects: Artificial intelligence; Experiments; Intervention; Laboratories; Monetary incentives; Overweight; Public policy; Researchers; United States > US
• ISSN: 2398-063X; 2398-0648
• DOI: 10.1017/bpp.2020.20

Very often, significantly smaller benefits are observed in final policy outcomes than are indicated by initial research discoveries. Al-Ubaydli et al. have identified a poor understanding of the ‘science of scaling’ as the underlying cause of this discrepancy. They propose a framework to increase our understanding of the science of scaling. We build on this framework by making six specific suggestions capturing three key ideas. First, researchers need to move away from their preoccupation with general theoretical models and focus on subject-specific theories of intervention, leading to individualized treatments. Second, there should be greater collaboration between researchers and policymakers, as well as more transparency in reporting findings, to ensure that the research environment is more representative of the policy environment. Third, researchers should recognize that policymakers do not always maximize social welfare; policymakers may have their own short-term incentives. Therefore, researchers must consider policymakers’ short-term incentives in designing interventions in order to increase the chances of a research intervention becoming a policy.