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Continuing education beyond the compulsory years of schooling is one of the most important choices an adolescent has to make. Higher education is associated with a host of social and economic benefits both for the person and its community. Today, there is ample evidence that educational aspirations are an important determinant of said choice. We implement a multilevel, networked experiment in 45 Mexican high schools and provide evidence of the malleability of educational aspirations. We also show there exists an interdependence of students' choices and the effect of our intervention with peer networks. We find that a video intervention, which combines role models and information about returns to education is successful in updating students' beliefs and consequently educational aspirations.

Large-scale testing is crucial in pandemic containment, but resources are often prohibitively constrained. We study the optimal application of pooled testing for populations that are heterogeneous with respect to an individual's infection probability and utility that materializes if included in a negative test. We show that the welfare gain from overlapping testing over non-overlapping testing is bounded. Moreover, non-overlapping allocations, which are both conceptually and logistically simpler to implement, are empirically near-optimal, and we design a heuristic mechanism for finding these near-optimal test allocations. In numerical experiments, we highlight the efficacy and viability of our heuristic in practice. We also implement and provide experimental evidence on the benefits of utility-weighted pooled testing in a real-world setting. Our pilot study at a higher education research institute in Mexico finds no evidence that performance and mental health outcomes of participants in our testing regime are worse than under the first-best counterfactual of full access for individuals without testing., Comment: Accepted at EC'23. (Exemplary track paper award)

In healthcare, the role of AI is continually evolving, and understanding the challenges its introduction poses on relationships between healthcare providers and patients will require a regulatory and behavioral approach that can provide a guiding base for all users involved. In this paper, we present IAC (Informing, Assessment, and Consent), a framework for evaluating patient response to the introduction of AI-enabled digital technologies in healthcare settings. We justify the need for IAC with a general introduction of the challenges with and perceived relevance of AI in human-welfare-centered fields, with an emphasis on the provision of healthcare. The framework is composed of three core principles that guide how healthcare practitioners can inform patients about the use of AI in their healthcare, how practitioners can assess patients' acceptability and comfortability with the use of AI, and how patient consent can be gained after this process. We propose that the principles composing this framework can be translated into guidelines that improve practitioner-patient relationships and, concurrently, patient agency regarding the use of AI in healthcare while broadening the discourse on this topic., Comment: We have vastly re-worked the paper. This new submission better reflects our work