Conference Agenda

Understanding the relationship between life expectancy at birth (e₀) and the gross domestic product per capita (GDPpc) is relevant for cohesion policies in the European Union (EU) because it might imply that economic convergence (or divergence) is accompanied by narrowing (or widening) health gaps. Previous studies have studied the association between GDPpc and e₀ almost exclusively based on national data. It is certainly more appropriate, however, to study the relationship at the subnational level because levels and trends in both variables, e₀ and GDPpc, vary substantially across Europe’s regions. Accordingly, the aim of our study is examining, whether economic performance of regions predict their e₀ level. We build Preston curves from regression models using regional data for 19 European countries, divided into 535 regions. Mortality data comes from statistical offices and GDPpc can be obtained from the Eurostat database. The period 2005 to 2018 is particularly interesting as it immediately follows the EU enlargement to central-eastern European countries in 2004. In our analysis, spatial units refer to NUTS-2 or NUTS-3 regions, depending on the size of the country. Our preliminary results suggest that there is indeed a positive association between GDPpc and e₀. Similarly, to Preston’s original analysis, we observe an upward shift in the curve, indicating that factors exogenous to a region’s GDPpc level play an important role in explaining e₀ gains as well. In the next step, we plan to employ panel data regression techniques to assess the impact of GDPpc on mortality for the period 2005 to 2018. We are confident that our comprehensive dataset and suggested robust regression techniques will enable us to further examine whether levels and trends in e₀ can be linked to the economic development of European regions.

The social gradient in mortality was much more modest in historical Sweden than it is contemporarily, and for men was reversed with higher mortality for white-collar workers than for blue-collar workers before the second world war. In this paper, we present evidence that even in the absence of a modern social gradient in mortality, family members share a survival advantage at adult ages across generations. Social, behavioral and biological factors that promote good health accumulate in families, even if the mechanisms that promote long lives change across time.

We use a unique dataset consisting of digitized and linked historical records for a region in Southern Sweden, reconstructing lives and families of individuals living in five rural parishes and a town 1900-1967 with nationwide follow-up in the Swedish national registers of these individuals and their descendants 1968-2015. We show evidence that individuals (age 30-90) from well-performing families have a mortality advantage across time, even in the absence of a modern social gradient in mortality. Analyses of cause-specific mortality shows that both preventable and non-preventable disease mortality is reduced in descendants of long-lived families, providing evidence that both behavioral and non-behavioral factors are involved.

There has been a long-standing debate concerning the relative importance of space and place vs. individual characteristics for mortality. In this paper, we analyze the association of lifetime SES-status/income and mortality at older ages among retired German men and its variation at the sub-national level. Does the income gradient in mortality differ across sub-national macro-regions and types of settlement? To answer this question, we employ a large administrative dataset of the German Pension Fund consisting of 17 Mio person-years of exposure and 585.9 thousand deaths that have occurred over the period 2012–2017 among men aged 65–84. We estimate relative mortality risk using a Cox proportional hazard model. To quantify the steepness of the gradients and be able to compare them between different places of Germany we estimate the Slope Index of Inequality and the Relative Index of Inequality. Our preliminary results suggest a linear income-mortality relationship across all macro-regions and types of settlement: the risk of dying decreases as income increases. However, the degree of inequalities between different settlements and the steepness of mortality gradients vary substantially within the country. In particular, regardless of the sub-national macro-region, big cities reveal the highest degree of disparities across income groups.