Baltic Sea Region Territorial Monitoring System

Territorial Cohesion - Creating inclusive growth

Life expectancy at birth (in years) is one of the principal global indicators for mortality. Included in the Laeken list of indicators, it reflects improvements in living standards and the establishment and improvement in health systems. Alongside low levels of fertility the gradual increase in life expectancy is however also one of the contributing factors to the ageing of the population. It can nonetheless be viewed as a partial output indicator of the quality of the health care system in general also incorporating aspects of public health awareness etc. Having said that, also the living environment, genetics, income, educational level, social relationships, etc. all have considerable impacts on health.

The BSR shows considerable variations in life expectancy reflecting the socioeconomic divide of the region. Differences between eastern and western BSR are substantial. Difference in life expectancy in 2005 between the best and the worst performers of the BSR was more than 20 life years. NW Russian regions as well as Belarus dominate the bottom positions, Norwegian and Swedish regions the top ones. This east-west gap has remained surprisingly wide and has not followed the general reduction of the economic ditto. Looking also at the trend, we see that there is a slow but detectable trend of cohesion in this respect, where the lowest performing regions have the relatively seen highest increase rates in life expectancy. Some regions in Belarus however fall outside this general pattern and changes in these have been slower than for their peer regions.

Life expectancy at birth is however a theoretical indicator where general trends of mortality are transposed on a new born child. A more current picture on the health status of a population can be obtained by asking them. Such a subjective indicator can be used as a proxy to the objective indicators on health care personnel and expenditure, which have proven to be very difficult to measure comparatively across countries. When self-assessed health and life expectancy at birth are compared for the regions of the BSR, the two indicators correlate at - 0.77, which means that slightly more than half of the variation in one of them can be explained by variation in the other. The ESS (European Social Survey) conducts surveys where respondents are asked to assess their own general health on a five item scale. Please note that the scale is inversed so that one equals “very good” and five equals “very bad”. The data for NW Russia are for the entire Northwest Federal District. Self-assessed health shows a pattern where the boundary between east and west, albeit clearly recognisable, is not as sharp as that which regards e.g. life expectancy. The worst self-assessed health status in the BSR can generally be found in the Baltic States and the new German Länder. Also Podkarpackie, Łódzkie, Śląskie and Lubelskie in Poland score very low. The worst of the western BSR can be found in eastern Finland, a region renowned for its poorer than average health status, largely related to dietary differences and general life style. At the other end of the scale then we find Stockholm, Copenhagen and most other Danish, Swedish and Norwegian regions. Of the German regions, Bremen ranks fifth in all BSR. Of the eastern BSR territories, Zachodniopomorskie (i.e. Szczecin) is in this respect on a par with Hamburg or Åland.

There appears to be no very clear-cut territorial dimension in the health status of the BSR population. In some cases (e.g. Denmark, Sweden) though large city regions tend to score just slightly better than their surrounding hinterland, in the BSR tentatively an indication of a better health care service network in urban areas than in rural and/or peripheral ones. In other instances this is however not the case (e.g. Finland), so no general rule can be postulated based upon this. Changes in the health status of the BSR population tend on a big scale to move towards being levelled out. By and large we find the worst performers having improved their relative status most, and vice versa. The x-axis depicts the situation at the start of the period 2006 and the y-axis changes between this and 2010. Regions in the upper left corner (that apart from Berlin are all in the eastern BSR) have improved their position during the period. Several former East German regions however are moving in the other direction (lower left corner), as is the case also with the aforementioned Eastern Finland. Albeit the scale of data is such, that these changes are not enormous, some welloff regions in this respect are also moving in the wrong direction, most notably Sjælland in Denmark and Mellersta Norrland in Sweden.

The relationship between self-assessed health status and economic wealth (here proxied by GDP/capita) is not a straightforward one. On a global scale it is well known that such a relationship exists up till a certain levelling-out point, whereas at the regional level in the BSR, that relationship is more modest. The general pattern for the entire BSR is discernible, of course, and GDP is able to statistically significantly explain some half of the variation in health status. Deviations to it are numerous and not easily explainable. Particularly regions in the eastern BSR appear not to have any relationship with health status and GDP/capita. Among the wealthiest regions (Oslo, Hamburg) the deviations from the general pattern stem from narrowly defined urban regions leading to high GDP/capita values. We now introduce two other measurements for material welfare than merely GDP: the first relative; and the second absolute. Within the target for “Inclusive growth”, the EU 2020 headline goal is that at least 20 million people should be lifted out of the risk of poverty or social exclusion by the year 2020. A person is defined as being in risk of poverty if his/her equivalised (by household size) income after social transfers is below 60 % of the corresponding national median. Although it is here reported per individual, its primary measurement unit is the household. The at-risk-of-poverty rate is useful for comparing some distributional aspects of monetary well-being but being a relative indicator (related to the national median), it should not be utilised for cross-country comparisons of absolute levels of poverty. Severe material deprivation targets persons having their living conditions severely constrained by a lack of resources. The indicator is defined as the share persons experiencing at least four out of nine following deprivations items: cannot afford: 1) to pay rent or utility bills; 2) keep home adequately warm; 3) face unexpected expenses; 4) eat meat, fish or a protein equivalent every second day; 5) a week holiday away from home; 6) a car; 7) a washing machine; 8) a colour TV; or 9) a telephone. This indicator is a headline indicator for the EU 2020 Strategy.

The figure shows the relationship between the two indicators at NUTS level 2 in the region in 2011 distinguishing between east and west BSR. No data are available for Belarus or NW Russia. Relative poverty in western BSR shows some regional differentiation, but not very large (x-axis). Western BSR regions with the largest income differences are in Finland, where northern and eastern Finland as well as southern Finland (excl. Helsinki) have 16-17% of the population living under the poverty threshold. Also some Swedish more rural regions (Mellersta Norrland and Småland med öarna) lay above 15 % in this respect. Missing from the graph (due to no data available for severe material deprivation) are all German regions. However, in 2010 Bremen with 21.1% and Berlin with 19.2% under the poverty threshold topped the western BSR ranking by far. In contrast, most regions in the western parts of the BSR with low shares of poverty are urban, Helsinki with 8.5% having the lowest. Also Stockholm, Oslo and Copenhagen all lay between 11 and 12%. This demonstrates that the urban paradox, so predominant in most larger continental cities, has yet not reached their Nordic counterparts. In contrast to the western BSR, differences in eastern BSR are substantial, ranging from 12-13% in Polish Dolnoslaskie, Slaskie or Opolskie to more than 31% in Lubelskie. Also in Swietokrzyskie and Lubuskie more than a quarter of the population live under the national poverty threshold. In BSR Germany, Mecklenburg-Vorpommern tops the list with 22.4% in 2010. Eastern BSR differences in absolute poverty (y-axis) are larger still. In Latvia, 31.4% of the population have their living conditions severely constrained by a lack of material resources. The contrast to e.g. Estonia is substantial, where the corresponding rate lays at only 8.7% of the population. Podlaskie (5.0%) and Wielkopolskie (7.9%) in Poland have the lowest rates of the eastern BSR. In this respect the western BSR has very few materially deprived persons. All regions’ values range from 0.4% (Swedish Småland med öarna) to 3.8% (Helsinki). Helsinki hence has the lowest shares of relative poor in the western BSR but the highest share of absolute poverty.

Beyond the obvious east-west dimension in the BSR, no straightforward territorial patterns are noticeable when studying relative and absolute poverty in the region. This is corroborated also by looking at the map. The regions with least or most shares of poor do not even when studied by country share that many common features, which entails that other than purely territorial aspects (e.g. general social policy) may be strong determinants for poverty at the regional level.

Such lack of clear-cut territorial patterns is also demonstrated when studying both relative and absolute poverty through the lens of GDP. On the surface and at a macro regional scale, increased levels of material wealth (GDP) show a decreasing tendency of both relative and absolute poverty. However, when subdivided into east and west BSR, the general patterns vanish. For relative poverty, the eastern BSR regions display some coherence with general wealth levels, where more wealth in general entails smaller shares of relative poverty. This is natural, since increases in material wealth in these regions bring about a larger middle class which in turn means smaller income differences. However, no such relationships exist between general levels of material wealth and absolute poverty levels, neither in eastern BSR nor in western ditto.

Finally closing the circle, the figure compares the rate of relative poverty with the levels of self-assessed general health. Compared as such, the primary BSR tendency is that larger income differences tend to result in worse health, and vice versa. The relationship is not fully straightforward, and exceptions abound, particularly when examining this relationship in western BSR. It nonetheless on the surface may appear that regions in the BSR, east and west alike, have already passed the stage many developing countries are in, where large income differences are also manifested in such basic factors as health care. However, bearing in mind that large income differences in the BSR were also associated with low income levels in general, and that low income levels were generally connected to bad health, entails that one cannot make such a conclusion.

(C) ESPON BSR-TeMo, RRG, 2013