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Longitudinal data for more than 20,000 live births in the cities of Bamako (Mali) and Bobo-Dioulasso (Burkina Faso) are used to study the effects of young maternal age (less than 18 years and 18-19) on birth weight, child health care and feeding behavior, and child mortality, after controlling for other socioeconomic and demographic factors. Teenage pregnancies are associated with significantly worse prenatal health care and vaccination behavior, lower birth weights, earlier weaning, and, especially during the second year of life, higher mortality. A proxy for mother's school enrollment at the time of pregnancy is strongly linked to worse prenatal health behavior and weakly associated with other poor behaviors and health outcomes. Overall, the results highlight the importance of behavioral factors relative to strictly biological factors for explaining child health differentials.

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e application of deep learning to search ranking was one of the most impactful product improvements at Airbnb. But what comes next a er you launch a deep learning model? In this paper we describe the journey beyond, discussing what we refer to as the ABCs of improving search: A for architecture, B for bias and C for cold start. For architecture, we describe a new ranking neural network, focusing on the process that evolved our existing DNN beyond a fully connected two layer network. On handling positional bias in ranking, we describe a novel approach that led to one of the most signi cant improvements in tackling inventory that the DNN historically found challenging. To solve cold start, we describe our perspective on the problem and changes we made to improve the treatment of new listings on the platform. We hope ranking teams transitioning to deep learning will nd this a practical case study of how to iterate on DNNs.

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