We suggest that there is a potential danger to the hydrological sciences community in not recognizing how transformative machine learning will be for the future of hydrological modeling. Given the recent success of machine learning applied to modeling problems, it is unclear what the role of hydrological theory might be in the future. We suggest that a central challenge in hydrology right now should be to clearly delineate where and when hydrological theory adds value to prediction systems. Lessons learned from the history of hydrological modeling motivate several clear next steps toward integrating machine learning into hydrological modeling workflows.

The remarkable growth of older population has moved long term care to the front ranks of the social policy agenda. Understanding the factors that determine the type and amount of formal care is important for predicting use in the future and developing long-term policy. In this context we jointly analyze the choice of care (formal, informal, both together or none) as well as the number of hours of care received. Given that the number of hours of care is not independent of the type of care received, we estimate, for the first time in this area of research, a sample selection model with the particularity that the first step is a multinomial logit model.With regard to the debate about complementarity or substitutability between formal and informal care, our results indicate that formal care acts as a reinforcement of the family care in certain cases: for very old care receivers, in those cases in which the individual has multiple disabilities, when many care hours are provided, and in case of mental illness and/or dementia.There exist substantial differences in long term care addressed to younger and older dependent people and dependent women are in risk of becoming more vulnerable to the shortage of informal caregivers in the future. Finally, we have documented that there are great disparities in the availability of public social care across regions.JEL Codes: I1, J14.

In this paper we estimate and validate a three-period sequential model of older workers' labor force transitions following a health/disability shock, using retrospective information from Spanish cross-section data. Central to the analysis are the effects of the various disabilities and their severity. We find that the probability of remaining employed decreases both with age and the severity of the shock. Moreover, we find strong interactions between age and severity for older workers and none for prime-age workers. Suffering any kind of disability reduces the probability of being employed immediately prior to retirement age, and in such cases it is severity which is the strongest indicator. With respect to demographics, we find that female gender, having a retired spouse or being married all reduce the probabilities of both remaining in employment and returning to work following a spell of inactivity; in turn, principal breadwinner status, education and skill levels increase this likelihood.

This work sets out to analyze the motivations adult children may have to provide informal care, considering the monetary transfers they receive from their parents. Traditional motivations, such as altruism and exchange, are matched against more recent social bond theories. Our findings indicate that informal caregivers receive less frequent and less generous transfers than non-caregivers; that is, caregivers are more prone to suppress their self-interested motivations in order to prioritize the well being of another person. Additionally, long-term public care benefits increase both the probability of receiving a transfer and its amount, with this effect being more intense for both the poorest and richest households. Our findings suggest that if long-term care benefits are intended to increase the recipients' welfare and represent a higher fraction of total income for the poorest households, the effectiveness of these long-term care policies may be diluted.