Machine learning in the prediction of human wellbeing.

Subjective wellbeing data are increasingly used across the social sciences. Yet, despite the widespread use of such data, the predictive power of approaches commonly used to model wellbeing is only limited. In response, we here use tree-based Machine Learning (ML) algorithms to provide a better understanding of respondents' self-reported wellbeing.

The scientific value of numerical measures of human feelings.

Human feelings measured in integers (, ) have no objective scientific basis. They are "made-up" numbers on a scale that does not exist. Yet such data are extensively collected-despite criticism from, especially, economists-by governments and international organizations.

A systematic review and meta-analysis of the impact of cash transfers on subjective well-being and mental health in low- and middle-income countries.

Cash transfers (CTs) are increasingly recognized as a scalable intervention to alleviate financial hardship. A large body of evidence evaluates the impact of CTs on subjective well-being (SWB) and mental health (MH) in low- and middle-income countries. We undertook a systematic review, quality appraisal and meta-analysis of 45 studies examining the impact of CTs on self-reported SWB and MH outcomes, covering a sample of 116,999 individuals.

Equality of opportunity is linked to lower mortality in Europe.

Background: This study investigates if intergenerational equality of opportunity is linked to mortality in 30 European countries. Equality of opportunity may lead to greater returns on health investments and, consequently, improved health outcomes. In turn, a perceived lack of fairness in the distribution of life chances and limited possibilities for upward intergenerational mobility can cause anxiety among individuals and gradually compromise their health.