Randomized ﬁeld experiments designed to better understand the production of human capital have increased exponentially over the past several decades. This chapter summarizes what we have learned about various partial derivatives of the human capital production function, what important partial derivatives are left to be estimated, and what – together – our collective efforts have taught us about how to produce human capital in developed countries. The chapter concludes with a back of the envelope simulation of how much of the racial wage gap in America might be accounted for if human capital policy focused on best practices gleaned from randomized ﬁeld experiments.
This paper describes a field experiment in Oklahoma City Public Schools in which students were provided with free cellular phones and daily information about the link between human capital and future outcomes via text message in one treatment and minutes to talk and text as an incentive in a second treatment. Students’ reported beliefs about the relationship between education and outcomes were influenced by the information treatment. However, there were no measurable changes in student effort, attendance, suspensions, or state test scores, though there is evidence that scores on college entrance exams four years later increased. The patterns in the data appear most consistent with a model in which students have present-bias or lack knowledge of the educational production function, though other explanations are possible.
We present a two-armed bandit model of decision making under uncertainty where the expected return to investing in the "risky arm" increases when choosing that arm and decreases when choosing the "safe" arm. These dynamics are natural in applications such as human capital development, job search, and occupational choice. Using new insights from stochastic control, along with a monotonicity condition on the payo dynamics, we show that optimal strategies in our model are stopping rules that can be characterized by an index which formally coincides with Gittins' index. Our result implies the indexability of a new class of restless bandit models
The SEED schools, which combine a “No Excuses” charter model with a five-day-a-week boarding program, are America’s only urban public boarding schools for the poor. We provide the first causal estimate of the impact of attending SEED schools on academic achievement, with the goal of understanding whether changing both a student’s social and educational en- vironment through boarding is an effective strategy to increase achievement among the poor. Using admission lotteries, we show that attending a SEED school increases achievement by 0.211 standard deviations in reading and 0.229 standard deviations in math, per year of attendance. We argue that the large impacts on reading are consistent with dialectical theories of language development.
Harlem Children’s Zone (HCZ), an ambitious social experiment, combines community programs with charter schools. We provide the first empirical test of the causal impact of HCZ charters on educational outcomes. Both lottery and instrumental variable identification strategies suggest that the effects of attending an HCZ middle school are enough to close the black-white achievement gap in mathematics. The effects in elementary school are large enough to close the racial achievement gap in both mathematics and ELA. We conclude with evidence that suggests high-quality schools are enough to significantly increase academic achievement among the poor. Community programs appear neither necessary nor sufficient.