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  3. 2027
  4. Preliminary Program
  5. Taking Evidence-Based Policies to Scale: Challenges and Innovative Approaches
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Taking Evidence-Based Policies to Scale: Challenges and Innovative Approaches

Paper Session

Sunday, Jan. 3, 2027 8:00 AM - 10:00 AM (EST)

Walter E. Washington Convention Center
Hosted By: American Economic Association
  • Chair: John List, University of Chicago

Using AI to Generate Option C Scaling Ideas: A Case Study in Early Education

John List
,
University of Chicago
Faith Fatchen
,
University of Chicago
Francesca Pagnotta
,
University of Chicago

Abstract

In recent years, field experiments have reshaped policy worldwide, but scaling ideas remains a thorny challenge. Perhaps the most important issue facing policymakers today is deciding which ideas to scale. One approach to attenuate this information problem is to augment traditional A/B experimental designs to address questions of scalability from the beginning. List 2024 denotes this approach as “Option C” thinking. Using early education as a case study, we show how AI can overcome a critical barrier in Option C thinking – generating viable options for scaling experimentation. By integrating AI-driven insights, this approach strengthens the link between controlled trials and large-scale implementation, ensuring the production of policy-based evidence for effective decision-making.

Adapting for Scale: Experimental Evidence on Technology-aided Instruction in India

Karthik Muralidharan
,
University of California-San Diego
Abhijeet Singh
,
Stockholm School of Economics

Abstract

Many interventions that “work” in small-scale trials often fail when scaled. This highlights the need to adapt promising interventions for scalability by addressing constraints that bind at larger scales. We do so in the context of a personalized adaptive learning (PAL) software that was highly effective in a small-scale trial. We adapt the PAL implementation for scalability by integrating it into public school schedules, and experimentally evaluate this adaptation in a more representative sample over 20 times larger than the original study. After 18 months, treated students scored 0.22σ higher in Mathematics and 0.20σ higher in Hindi, a 50–66% productivity increase over the control group. Learning gains were proportional to student time on the platform, providing a simple, low-cost metric for monitoring implementation quality in future scale-ups. The adaptation and its experimental validation have informed scale-ups now reaching over 250,000 students.

Incentives, Information, and Reminders for Bureaucrats: Overcoming Barriers to the Scale Up of an Evidence-Based Policy

Gautam Rao
,
University of California-Berkeley
Sebastian Otero
,
Columbia University
Patrick Agte
,
Stockholm School of Economics
Christopher Neilson
,
Yale University
Daniel Morales
,
Tecnologico de Monterrey

Abstract

Scaling up effective policies within government often requires the attention and effort of mid-level bureaucrats with many responsibilities. In contexts with weak state capacity, selection of effective policies at a higher level may thus not translate into change on the ground. In a nationwide field experiment in the Dominican Republic, we test different behavioral interventions with school principals to boost the implementation of an educational program that was found to be effective in a previous RCT. Only 37% of the control schools implemented the program when the ministry ordered it as usual. Implementation was no higher among schools which had previously implemented the program—two years ago—in the RCT, suggesting that the fixed costs of learning how to implement the program do not explain non-adoption. We find precise null effects of sharing research findings regarding the effectiveness of the program or of offering a modest reward for implementation. In contrast, we find large effects of simply reminding the school principals twice of the assigned task. In a second experiment aiming to increase compliance with a different program, we again find a large effect of simply reminding principals of the assigned program. Our findings point to the need to take seriously the limited attentional capacity of implementing bureaucrats when scaling up policy.

Discussant(s)
Gautam Rao
,
University of California-Berkeley
Stefano DellaVigna
,
University of California-Berkeley
David Yang
,
Harvard University
JEL Classifications
  • H0 - General
  • O2 - Development Planning and Policy