Suchergebnisse

Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- List of Illustrations -- Editor Biographies -- List of Contributors -- Acknowledgements -- 1. Failed to Scale: Embracing the Challenge of Scaling in Early Childhood -- PART I: The Science of Early Childhood and Complexities of Scaling -- 2. Early Childhood: The Opportunity to Untap Human Potential -- 3. How Cognitive Biases Can Undermine Program Scale-Up Decisions -- 4. How a Behavioral Economic Framework Can Support Scaling of Early Childhood Interventions -- 5. The Economics of Investing in Early Childhood: Importance of Understanding the Science of Scaling -- PART II: The Scale-up Effect: Understanding the Threats to Scalability -- Scaling: A Case Study -- 6. The Science of Using Science: A New Framework for Understanding the Threats to Scaling Evidence-Based Policies -- 7. When is Evidence Actionable? Assessing Whether a Program is Ready to Scale -- 8. Studying Properties of the Population: Designing Studies that Mirror Real World Scenarios -- 9. Fidelity and Properties of the Situation: Challenges and Recommendations -- 10. Spillovers and Program Evaluation at Scale -- Commentary 1: Real-World Application and Understanding of the Threats to Scaling: Commentary on Chapters 7, 8, 9, and 10 -- 11. 70 to 700 to 70,000: Lessons from the Jamaica Experiment -- 12. A Research Agenda Built for Scale -- PART III: Charting a Path Forward -- 13. Designing Programs with an Eye Toward Scaling -- 14. Accounting for Differences in Population: Predicting Intervention Impact at Scale -- 15. Sustaining Impact after Scaling Using Data and Continuous Feedback -- 16. Measurement Built for Scale: Designing and Using Measures of Intervention and Outcome that Facilitate Scaling Up.

AbstractBehavioral economics and field experiments within the social sciences have advanced well beyond academic curiosum. Governments around the globe as well as the most powerful firms in modern economies employ staffs of behavioralists and experimentalists to advance and test best practices. In this study, we combine behavioral economics with field experiments to reimagine a new model of early childhood education. Our approach has three distinct features. First, by focusing public policy dollars on prevention rather than remediation, we call for much earlier educational programs than currently conceived. Second, our approach has parents at the center of the education production function rather than at its periphery. Third, we advocate attacking the macro education problem using a public health methodology, rather than focusing on piecemeal advances.

Policymakers often consider interventions at the scale of the population, or some other large scale. One of the sources of information about the potential effects of such interventions is experimental studies conducted at a significantly smaller scale. A common occurrence is for the treatment effects detected in these small-scale studies to diminish substantially in size when applied at the larger scale that is of interest to policymakers. This paper provides an overview of the main reasons for a breakdown in scalability. Understanding the principal mechanisms represents a first step toward formulating countermeasures that promote scalability.

AbstractPolicymakers are increasingly turning to insights gained from the experimental method as a means to inform large-scale public policies. Critics view this increased usage as premature, pointing to the fact that many experimentally tested programs fail to deliver their promise at scale. Under this view, the experimental approach drives too much public policy. Yet, if policymakers could be more confident that the original research findings would be delivered at scale, even the staunchest critics would carve out a larger role for experiments to inform policy. Leveraging the economic framework of Al-Ubaydli et al. (2019), we put forward 12 simple proposals, spanning researchers, policymakers, funders and stakeholders, which together tackle the most vexing scalability threats. The framework highlights that only after we deepen our understanding of the scale-up problem will we be on solid ground to argue that scientific experiments should hold a more prominent place in the policymaker's quiver.