Well-Being Adjusted Health Expectancy: A New Summary Measure of Population Health

We propose a new summary measure of population health (SMPH), the well-being-adjusted health expectancy (WAHE). WAHE belongs to a subgroup of health-adjusted life expectancy indicators and gives the number of life years equivalent to full health. WAHE combines health and mortality information into a single indicator with weights that quantify the reduction in well-being associated with decreased health. WAHE''s advantage over other SMPHs lies in its ability to differentiate between the consequences of health limitations at various levels of severity and its transparent, simple valuation function. Following the guidelines of a Committee on Summary Measures of Population Health, we discuss WAHE''s validity, universality, feasibility sensitivity and ensure its reproducibility. We evaluate WAHE''s performance compared to life expectancy, the most commonly used indicators of health expectancy (HE) and disability-adjusted life expectancy (DALE) in an empirical application for 29 European countries. Data on health and well-being are taken from the 2018 EU-SILC, and the life tables are from Eurostat. DALE is taken from the database of the Global Burden of Disease Programme. WAHE''s sensitivity to univariate and multivariate state specifications is studied using the three Minimum European Health Module health dimensions: chronic morbidity, limitations in activities of daily living, and self-rated health. The empirical tests of the indicators’ correspondence reveal that WAHE has the strongest correlation with the other SMPHs. Moreover, WAHE estimates are in agreement with all other SMPHs. Additionally, WAHE and all other SMPHs form a group of reliable indicators for studying population health in European countries. Finally, WAHE estimates are robust, regardless of whether health is defined across one or multiple simultaneous dimensions of health. We conclude that WAHE is a useful and reliable indicator of population health and performs at least as well as other commonly used SMPHs.Supplementary InformationThe online version contains supplementary material available at 10.1007/s10680-022-09628-1.     

Poverty and inequality in European regions

The European Union Statistics on Income and Living Conditions (EU-SILC) is the main source of information about poverty and economic inequality in the member states of the European Union. The sample sizes of its annual national surveys are sufficient for reliable estimation at the national level but not for inferences at the sub-national level, failing to respond to a rising demand from policy-makers and local authorities. We provide a comprehensive map of median income, inequality (Gini coefficient and Lorenz curve) and poverty (poverty rates) based on the equivalised household income in the countries in which the EU-SILC is conducted. We study the distribution of income of households (pro-rated to its members), not merely its median (or mean), because we regard its dispersion and frequency of lower extremes (relative poverty) as important characteristics. The estimation for the regions with small sample sizes is improved by the small-area methods. The uncertainty of complex nonlinear statistics is assessed by bootstrap. Household-level sampling weights are taken into account in both the estimates and the associated bootstrap standard errors.     

Discretizing a compound distribution with application to categorical modelling

Many probability distributions can be represented as compound distributions. Consider some parameter vector as random. The compound distribution is the expected distribution of the variable of interest given the random parameters. Our idea is to define a partition of the domain of definition of the random parameters, so that we can represent the expected density of the variable of interest as a finite mixture of conditional densities. We then model the mixture probabilities of the conditional densities using information on population categories, thus modifying the original overall model. We thus obtain specific models for sub-populations that stem from the overall model. The distribution of a sub-population of interest is thus completely specified in terms of mixing probabilities. All characteristics of interest can be derived from this distribution and the comparison between sub-populations easily proceeds from the comparison of the mixing probabilities. A real example based on EU-SILC data is given. Then the methodology is investigated through simulation.     

Measuring policy entitlements at the micro-level: maternity and parental leave in Europe

Comparative social policy analysis has been shaped by the measurement of policy as a macro phenomenon. However, social policy theories have consistently asserted that policy entitlements vary across class, gender, ethnicity and the life-course. This paper synthesises a number of innovations to produce an approach which allows researchers to explore the policy heterogeneity within populations, across populations and over time. Using the example of maternity and parental leave, policy entitlements are identified through the calculation of financial support an individual would receive if they were to have a child, using a combination of legislative rules with representative survey sample. The results reveal far greater heterogeneity in policy entitlements than existing indicators suggest, with considerable implications for research on maternity and parental leave. This approach is not limited to maternity and parental leave benefits and demonstrates a way to explore comparative social policy in greater depth and detail.     

Understanding material deprivation: A comparative European analysis

In this paper, taking advantage of the inclusion of a special module on material deprivation in EU-SILC 2009, we provide a comparative analysis of patterns of deprivation. Our analysis identifies six relatively distinct dimensions of deprivation with generally satisfactory overall levels of reliability and mean levels of reliability across countries. Multi-level analysis based on 28 European countries reveals systematic variation in the importance of within and between country variation for a range of deprivation dimensions. The basic deprivation dimension is the sole dimension to display a graduated pattern of variation across countries. It also reveals the highest correlations with national and household income, the remaining deprivation dimensions and economic stress. It comes closest to capturing an underlying dimension of generalized deprivation that can provide the basis for a comparative European analysis of exclusion from customary standards of living. A multilevel analysis revealed that a range of household characteristics and household reference person socio-economic factors were related to basic deprivation and controlling for contextual differences in such factors allowed us to account for substantial proportions of both within and between country variance. The addition of macro-economic factors relating to average levels of disposable income and income inequality contributed relatively little further in the way of explanatory power. Further analysis revealed the existence of a set of significant interactions between micro socio-economic attributes and country level gross national disposable income per capita. The impact of socio-economic differentiation was significantly greater where average income levels were lower. Or, in other words, the impact of the latter was greater for more disadvantaged socio-economic groups. Our analysis supports the suggestion that an emphasis on the primary role of income inequality to the neglect of differences in absolute levels of income may be misleading in important respects.