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The Economics of Scaling Early Childhood Programs: Lessons from The Chicago School

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  • John List

Abstract

Many ideas show remarkable returns in small-scale trials but often disappoint when scaled to broader populations and contexts. Using early childhood investment as a case study, this study develops a dynamic human capital formation model that integrates complementary skill investment with "Option C thinking" on scaling challenges. The model is stylized in the Chicago tradition: micro-founded with optimizing agents, dynamic skill production, and a policymaker evaluating scaling decisions. It formalizes how naive extrapolation from pilot studies systematically overestimates policy efficacy by ignoring "voltage drops," declining treatment effects due to unrepresentativeness at scale. The model demonstrates that optimal scaling policy requires mechanism-based design that anticipates these failures through backward induction from implementation realities. The scientific insights from a set of recent studies provide valuable perspectives on the model.

Suggested Citation

  • John List, 2025. "The Economics of Scaling Early Childhood Programs: Lessons from The Chicago School," Natural Field Experiments 00829, The Field Experiments Website.
    • Handle: RePEc:feb:natura:00829
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    References listed on IDEAS

    as
    1. Alec Brandon & Christopher Clapp & John List & Robert Metcalfe & Michael Price, 2022. "The Human Perils of Scaling Smart Technologies: Evidence from Field Experiments," Framed Field Experiments 00762, The Field Experiments Website.
    2. Omar Al-Ubaydli & Chien-Yu Lai & John A. List, 2023. "A Simple Rational Expectations Model of the Voltage Effect," NBER Working Papers 30850, National Bureau of Economic Research, Inc.
    3. James J. Heckman & Jora Stixrud & Sergio Urzua, 2006. "The Effects of Cognitive and Noncognitive Abilities on Labor Market Outcomes and Social Behavior," Journal of Labor Economics, University of Chicago Press, vol. 24(3), pages 411-482, July.
    4. Noam Angrist & Peter Bergman & Moitshepi Matsheng, 2022. "Experimental evidence on learning using low-tech when school is out," Nature Human Behaviour, Nature, vol. 6(7), pages 941-950, July.
    5. Karthik Muralidharan & Abhijeet Singh, 2025. "Adapting for scale: Experimental Evidence on Technology-aided Instruction in India," NBER Working Papers 34205, National Bureau of Economic Research, Inc.
    6. Joseph Mullins, 2026. "A Structural Meta-Analysis of Welfare Reform Experiments and Their Impacts on Children," Journal of Political Economy, University of Chicago Press, vol. 134(1), pages 435-477.
    7. Elizabeth Caucutt & Lance Lochner & Joseph Mullins & Youngmin Park, 2026. "Child Skill Production: Accounting for Parental and Market-Based Time and Goods Investments," Journal of Political Economy, University of Chicago Press, vol. 134(1), pages 150-209.
    8. Al-Ubaydli, Omar & Lee, Min Sok & List, John A. & Mackevicius, Claire L. & Suskind, Dana, 2021. "How can experiments play a greater role in public policy? Twelve proposals from an economic model of scaling," Behavioural Public Policy, Cambridge University Press, vol. 5(1), pages 2-49, January.
    9. Eva Vivalt, 2020. "How Much Can We Generalize From Impact Evaluations?," Journal of the European Economic Association, European Economic Association, vol. 18(6), pages 3045-3089.
    10. James J. Heckman & Jin Zhou, 2026. "A Study of the Microdynamics of Early-Childhood Learning," Journal of Political Economy, University of Chicago Press, vol. 134(1), pages 49-85.
    11. Faith Fatchen & John A. List & Francesca Pagnotta, 2025. "Using AI to Generate Option C Scaling Ideas: A Case Study in Early Education," NBER Working Papers 33924, National Bureau of Economic Research, Inc.
    12. Patrick Kline & Christopher R. Walters, 2016. "Evaluating Public Programs with Close Substitutes: The Case of HeadStart," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1795-1848.
    13. Omar Al-Ubaydli & John A. List & Dana L. Suskind, 2017. "What Can We Learn from Experiments? Understanding the Threats to the Scalability of Experimental Results," American Economic Review, American Economic Association, vol. 107(5), pages 282-286, May.
    14. Francesco Agostinelli & Matthias Doepke & Giuseppe Sorrenti & Fabrizio Zilibotti, 2026. "It Takes a Village: The Economics of Parenting with Neighborhood and Peer Effects," Journal of Political Economy, University of Chicago Press, vol. 134(1), pages 313-365.
    15. Christopher S. Cotton & Brent R. Hickman & John A. List & Joseph Price & Sutanuka Roy, 2026. "Why Don’t Struggling Students Do Their Homework? Disentangling Motivation and Study Productivity as Drivers of Human Capital Formation," Journal of Political Economy, University of Chicago Press, vol. 134(1), pages 86-149.
    16. Al-Ubaydli, Omar & Lee, Min Sok & List, John A. & Mackevicius, Claire L. & Suskind, Dana, 2021. "A rejoinder: ‘How can experiments play a greater role in public policy? Twelve proposals from an economic model of scaling’," Behavioural Public Policy, Cambridge University Press, vol. 5(1), pages 125-134, January.
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    More about this item

    JEL classification:

    • C9 - Mathematical and Quantitative Methods - - Design of Experiments
    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
    • H10 - Public Economics - - Structure and Scope of Government - - - General
    • H4 - Public Economics - - Publicly Provided Goods
    • H41 - Public Economics - - Publicly Provided Goods - - - Public Goods
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity

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