Andrew Clark

We here use UK data and exploit the State Pension eligibility age to establish the causal effect of parental retirement on adult children’s well-being in a Fuzzy Regression Discontinuity Design analysis. Maternal retirement increases adult children’s life and income satisfaction by 0.20 standard deviations in the short run. These impacts are stronger for adult children with lower incomes, with young children of their own, and who live close to their retired parents. We emphasise the critical role of intergenerational time transfers from retired mothers in enhancing their adult children’s well-being.

We here use European Social Survey data to disentangle the ‘inherited’ and ‘contextual’ components of resilience, following the approaches taken in Alesina and Giuliano (2010) and Luttmer and Singhal (2011). We suggest that the inherited part of resilience reflects culture in the country of birth, while the contextual part captures both institutions and culture in the country where the individual currently resides. We separately identify these two components via a sample of immigrants, for whom the birth and residence countries differ. We find that resilience is both inherited and contextual, with the latter component being the most important. The ‘inherited’ component of resilience is larger for men and those who do not have citizenship in their residence country. We last present some evidence from second-generation immigrants of the intergenerational transmission of inherited cultural resilience.

Equivalence scales (ES) are widely used to compare income levels across different households.

Yet the commonly used OECD and square-root scales rely on assumptions about household economies of scale that lack robust empirical support. Using responses to the Minimum Income Question (MIQ) from the European Union Statistics on Income and Living Conditions (EU-SILC) survey, we construct subjective ES based on panel data, rather than relying on pooled OLS as in most previous studies, allowing us to track how income needs evolve within households over time instead of comparing different households. The economies of scale in this subjective scale are notably different from those in traditional ES, and these differences have a substantial effect on the levels and distribution of equivalised income. Based on our empirical findings, we propose a simple alternative to conventional ES and illustrate its implications for poverty and inequality, both within and across countries. Our results show that the choice of equivalence scale significantly influences not only the estimated levels of these variables but also country rankings in comparative analyses.

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. We analyse representative samples of more than one million respondents from Germany, the UK, and the United States, using data from 2010 to 2018. We make three contributions. First, we show that ML algorithms can indeed yield better predictive performance than standard approaches, and establish an upper bound on the predictability of wellbeing scores with survey data. Second, we use ML to identify the key drivers of evaluative wellbeing. We show that the variables emphasised in the earlier intuition- and theory-based literature also appear in ML analyses. Third, we illustrate how ML can be used to make a judgement about functional forms, including the existence of satiation points in the effects of income and the U-shaped relationship between age and wellbeing.