This paper studies information frictions between parents and children and their effect on human capital investments. I provide biweekly information to a random sample of parents about their child?s missed assignments. Parents have upwardly biased beliefs about their child?s effort. Providing information attenuates this bias and improves student achievement. Using data from the experiment, I estimate a persuasion game between parents and their children that shows that the treatment effect is due to more accurate beliefs and reduced monitoring costs. Policy simulations from the model demonstrate that improving school reporting or providing more information to parents can increase learning at low cost.

There is concern that the risky behaviors of teenagers, such as truancy, negatively influence the behaviors of others through their social networks. We use administrative data to construct social networks based on students who are truant together. We simulate these networks to document that certain students systematically coordinate their absences. We validate them by showing that a parent information intervention on student absences has spillover effects from treated students onto their peers. Excluding these effects understates the intervention’s cost-effectiveness by 43%. We show that there is potential to use networks to target interventions more efficiently given a budget constraint.

Abstract This article views hiring as a contextual bandit problem: to find the best workers over time, firms must balance “exploitation” (selecting from groups with proven track records) with “exploration” (selecting from under-represented groups to learn about quality). Yet modern hiring algorithms, based on supervised learning approaches, are designed solely for exploitation. Instead, we build a resume screening algorithm that values exploration by evaluating candidates according to their statistical upside potential. Using data from professional services recruiting within a Fortune 500 firm, we show that this approach improves the quality (as measured by eventual hiring rates) of candidates selected for an interview, while also increasing demographic diversity, relative to the firm’s existing practices. The same is not true for traditional supervised learning-based algorithms, which improve hiring rates but select far fewer Black and Hispanic applicants. Together, our results highlight the importance of incorporating exploration in developing decision-making algorithms that are potentially both more efficient and equitable.

Low-income families often live in low-upward-mobility neighborhoods. We study why by using a randomized trial with housing voucher recipients that provided information, financial support, and customized search assistance to move to high-opportunity neighborhoods. The treatment increased the fraction moving to high-upward-mobility areas from 15 to 53 percent. A second trial reveals this treatment effect is driven primarily by customized search assistance. Qualitative interviews show that the intervention relaxed bandwidth constraints and addressed family-specific needs. Our findings imply many low-income families do not have strong preferences to stay in low-opportunity areas and that barriers in housing search significantly increase residential segregation by income. (JEL D83, G51, R21, R23, R31, R38)