Cognitive Endurance as Human Capital | NBER – National Bureau of Economic Research

Schooling may build human capital not only by teaching academic skills, but by expanding the capacity for cognition itself. We focus specifically on cognitive endurance: the ability to sustain effortful mental activity over a continuous stretch of time. As motivation, we document that globally and in the US, the poor exhibit cognitive fatigue more quickly than the rich across field settings; they also attend schools that offer fewer opportunities to practice thinking for continuous stretches. Using a field experiment with 1,600 Indian primary school students, we randomly increase the amount of time students spend in sustained cognitive activity during the school day—using either math problems (mimicking good schooling) or non-academic games (providing a pure test of our mechanism). Each approach markedly improves cognitive endurance: students show 22% less decline in performance over time when engaged in intellectual activities—listening comprehension, academic problems, or IQ tests. They also exhibit increased attentiveness in the classroom and score higher on psychological measures of sustained attention. Moreover, each treatment improves students’ school performance by 0.09 standard deviations. This indicates that the experience of effortful thinking itself—even when devoid of any subject content—increases the ability to accumulate traditional human capital. Finally, we complement these results with quasi-experimental variation indicating that an additional year of schooling improves cognitive endurance, but only in higher-quality schools. Our findings suggest that schooling disparities may further disadvantage poor children by hampering the development of a core mental capacity.
We thank Ned Augenblick, David Autor, Stefano DellaVigna, David Deming, Claire Duquennois, Ernst Fehr, Caroline Hoxby, Xavier Gine, Lawrence Katz, Patrick Kline, Matthew Kraft, David Laibson, Sendhil Mullainathan, Imran Rasul, Jesse Rothstein, Andrei Shleifer, and Christopher Walters for helpful comments and discussions. We thank Pixatel for the use of the imagine Math software and the Institute for Financial Management and Research (IFMR) for operational support. We acknowledge generous funding from USAID DIV, the Global Engagement Fund at the University of Pennsylvania, and The Weiss Family Program Fund for Research in Development Economics. We thank Jalnidh Kaur, Rolly Kapoor, Lubna Anantakrishnan, Simranjeet Dhir, Deepika Ghosh, Vatsala Raghuvanshi, Mudassir Shamsi, Alosias A., Erik Hausen, and Adrien Pawlik at the Behavioral Development Lab for their support in implementing this study, Trapti Dwivedi, Anshu Gupta, Jayshree Krishnan, Sharmila Singh, and Vinod Yadav for their support in coordinating with partner schools, and Isadora Frankenthal, Medha Aurora, Joaquin Fuenzalida, Jed Silver, Letian Yin, and Yige Wang for exceptional research assistance. All remaining errors are our own. We received IRB approval from the University of California, Berkeley, and IFMR in India; AEA RCT registry #0002673. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

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