Adapting for scale: Experimental evidence on computer-aided instruction in India (with Karthik Muralidharan)
Latest version (Sept 2025) Explainer threads on Twitter(X) and Bluesky
Many interventions that “work” in small-scale trials often fail at scale, highlighting the centrality of effective scaling for realizing the promise of evidence-based policy. We study the scaling 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 0.22$\sigma$ higher in Mathematics and 0.20$\sigma$ 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 was cost effective, and its key design features make it widely scalable across diverse settings.
The incidence of affirmative action: Evidence from quotas in private schools in India (with Mauricio Romero)
Latest version; Aug 2024, Revisions requested at Review of Economic Studies
The incidence of redistributive policies is central to whether they meet their stated goals. We study this in the context of one of the world's largest affirmative action programs in schooling: a 25% quota in all Indian private schools for students from disadvantaged groups. We use lottery-based estimates to show that, although students admitted under the quota attend more expensive and preferred schools on average, the distribution of program benefits is very regressive. Program applicants are concentrated among more-educated and better-off households. Consequently, 7.4% of the program spending accrues to the bottom socioeconomic quintile, compared to 24.3% to the top quintile. We use rich survey data to show that low application rates for poorer children are not driven by preferences and beliefs. Instead, information constraints and application frictions appear to be key. Finally, we use a randomized intervention to confirm the importance of these frictions and further demonstrate that alleviating a single constraint (e.g., information) may not reduce regressive selection, even if it boosts application rates substantially. Our results demonstrate how constraints facing potential applicants can make redistributive policies regressive in practice. Appropriate policy interventions must consider the joint incidence of these constraints to reduce regressivity
Improving Public Sector Management at Scale: Experimental Evidence on School Governance in India (with Karthik Muralidharan)
Latest version, Revision requested at Journal of Political Economy: Microeconomics (JPE:Micro)
NBER Working Paper (Nov 2020); (Slides)
We present results from a large-scale experimental evaluation of an ambitious attempt to improve management quality in Indian schools (implemented in 1,774 randomly-selected schools). The intervention featured several global “best practices” including comprehensive assessments, detailed school ratings, and customized school improvement plans. It did not, however, change accountability or incentives. We find that the assessments were near-universally completed, and that the ratings were informative, but the intervention had no impact on either school functioning or student outcomes. Yet, the program was scaled up to cover over 600,000 schools nationally. We find using a matched-pair design that the scaled-up program continued to be ineffective at improving student learning in the state we study. We also conduct detailed qualitative interviews with frontline officials and find that the main impact of the program on the ground was to increase required reporting and paperwork. Our results illustrate how ostensibly well-designed programs, that appear effective based on administrative measures of compliance, may be ineffective in practice
Using technology to deliver preschool services at scale: Experimental evidence from India (with Ajinkya Keskar, Mauricio Romero and Karthik Muralidharan) New draft coming soon
We experimentally evaluate two iterations of a large-scale, technology-aided home stimulation program to enhance parent-child interactions and improve preschool quality for children aged 3 to 5. The base intervention, which provided structured learning activities via WhatsApp groups, successfully engaged parents but did not improve children's math and language outcomes. However, an enhanced version with intensified engagement—featuring more frequent, personalized messaging, and structured preschool worker support— increased parental participation beyond the base intervention and improved children’s learning outcomes by 0.12-0.2 s.d. At a total implementation cost of $1.17 (97.11 rupees) per child per year, our findings demonstrate the efficacy of a highly cost-effective and scalable intervention for improving early childhood learning in low- and middle-income countries.