This paper explores which demographic characteristics substantially bias self-reported physical and cognitive health status of older Europeans. The analysis utilises micro-data for 19 European countries from the Survey of Health, Ageing and Retirement in Europe to compare performance-tested outcomes of mobility and memory with their self-reported equivalents. Relative importance analysis based on multinomial logistic regressions shows that the bias in self-reported health is mostly due to reporting heterogeneities between countries and age groups, whereas gender contributes little to the discrepancy. Concordance of mobility and cognition measures is highly related; however, differences in reporting behaviour due to education and cultural background have a larger impact on self-assessed memory than on self-assessed mobility. Southern as well as Central and Eastern Europeans are much more likely to misreport their physical and cognitive abilities than Northern and Western Europeans. Overall, our results suggest that comparisons of self-reported health between countries and age groups are prone to significant biases, whereas comparisons between genders are credible for most European countries. These findings are crucial given that self-assessed data are often the only information available to researchers and policymakers when asking health-related questions.

Sustainable development (SD) as popularized by the Brundtland Commission and politically enshrined in the Sustainable Development Goals has been the explicit focus of sustainability science. While there is broad agreement that the trend of human well-being (W) over time should serve as a sustainability criterion, the literature so far has mostly addressed this in terms of its determinants rather than focusing on W itself. There is broad agreement that an indicator for W should have multiple constituents, clearly going beyond gross domestic product. Here, we propose a tailor-made indicator to serve precisely this purpose following a set of specified desiderata, including its applicability to flexibly defined subnational populations by gender, place of residence, ethnicity, and other relevant characteristics. The indicator, years of good life (YoGL), reflects the evident fact that in order to be able to enjoy any quality of life, one has to be alive and thus is primarily based on life expectancy. However, since mere survival is not considered good enough, life years are counted conditional on meeting minimum standards in two dimensions: the objective dimension of capable longevity (consisting of being out of absolute poverty and enjoying minimal levels of physical and cognitive health) and the subjective dimension of overall life satisfaction. We illustrate the calculation of this indicator for countries and subpopulations at different stages of development and with different degrees of data availability.

Health expectancies are widely used by policymakers and scholars to analyse the number of years a person can expect to live in good health. Their calculation requires life tables in combination with prevalence rates of good or bad health from survey data. The structure of typical survey data, however, rarely resembles the education distribution in the general population. Specifically, low-educated individuals are frequently underrepresented in surveys, which is crucial given the strong positive correlation between educational attainment and good health. This is the first study to evaluate if and how health expectancies for 13 European countries are biased by educational differences in survey participation. To this end, calibrated weights that consider the education structure in the 2011 censuses are applied to measures of activity limitation in the Survey of Health, Ageing and Retirement in Europe. The results show that health expectancies at age 50 are substantially biased by an average of 0.3 years when the education distribution in the general population is ignored. For most countries, health expectancies are overestimated; yet remarkably, the measure underestimates health for many Central and Eastern European countries by up to 0.9 years. These findings highlight the need to adjust for distortion in health expectancies, especially when the measure serves as a base for health-related policy targets or policy changes.