Investment in human capital is an important tool for reducing poverty. However, the poor may lack the capacity to aspire, which often results in underinvestment in their children's education. This paper studies the e¤ect of a social program on the educational aspirations of the poor, and explores the role of exposure to educated professionals as a possible channel for increasing aspirations. First, using di¤erences-in-di¤erences, we show that bene…ciary parents of the Mexican antipoverty program PROGRESA have higher educational aspirations for their children of a third of a school year than do non-bene…ciary parents. This e¤ect corresponds to a 15% increase in the proportion of parents who aspire for their children to …nish college. Then, we exploit the design of the program whose requirements cause its target population to have di¤erent levels of mandated exposure to doctors and nurses. Our triple di¤erence estimate shows that, educational aspirations for children from high-exposure households (relative to lowexposure households) in treatment villages (relative to control villages) were a third of a school year higher six months after the start of the program (relative to before its introduction). These results suggest that the change in aspirations is driven by exposure to highly educated professionals.

The population in developed countries is aging and this fact results in high elderly health costs, as well as a decrease in the number of active working members to support these costs. This could lead to a collapse of the current systems. One of the first insights of the decline in elderly people is frailty, which could be decelerated if it is detected at an early stage. Nowadays, health professionals measure frailty manually through questionnaires and tests of strength or gait focused on the physical dimension. Sensors are increasingly used to measure and monitor different e-health indicators while the user is performing Basic Activities of Daily Life (BADL). In this paper, we present a system based on microservices architecture, which collects sensory data while the older adults perform Instrumental ADLs (IADLs) in combination with BADLs. IADLs involve physical dimension, but also cognitive and social dimensions. With the sensory data we built a machine learning model to assess frailty status which outperforms the previous works that only used BADLs. Our model is accurate, ecological, non-intrusive, flexible and can help health professionals to automatically detect frailty.

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