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.
Preschool and Primary School Markets: Evidence from India (with Petter Berg and Mauricio Romero)
Latest version: Aug 2024, Revisions requested at Economic Journal
We study education markets at preschool and primary school levels using panel data from 220 villages in Tamil Nadu, India. Private preschools show higher test score value-added in math and language (0.57-0.73 s.d.) and outperform government providers in nearly all villages. This productivity difference explains 60\% of the socioeconomic test score gap before school entry. Test score value-added is positively correlated between private and government options in a village, both at preschool and primary school levels. Our findings inform debates on achieving universal foundational skills and underscore the need to improve the quality of preschools available to poorer families.
Improving Public Sector Management at Scale: Experimental Evidence on School Governance in India (with Karthik Muralidharan)
Latest version (Submitted), 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
Adapting for scale: Experimental evidence on computer-aided instruction in India (with Karthik Muralidharan)
New draft coming soon
The promise of ‘evidence-based policy’ for improving human welfare is belied by growing evidence that interventions found to ‘work’ in small-scale efficacy trials often fail at scale. We study the scaling of a personalized adaptive learning (PAL) software that was highly effective in a small-scale trial. We adapt the PAL software implementation for scalability, and experimentally evaluate this adaptation in a more representative sample over 20 times larger than the original study. The scaled intervention continued to be highly effective. Treated students scored 0.22 standard deviations (s.d.) and 0.2 s.d. higher in Mathematics and Hindi after 18 months, a 50-66% increase in productivity over the control group. We show that student time logged on the PAL platform strongly correlates with learning gains, providing a low-cost proxy measure of implementation quality for future scale-ups. The adaptation was highly cost effective, and has several features that make it scalable across diverse settings.