Poverty and Inequality

Poverty and Inequality

(created May 2014)

Life is good in Switzerland. More precisely its excellent. According to the Swiss Federal Statistical Office, Switzerland is amongst the European countries with the highest standard of living. Nevertheless, a part of the Swiss population is defined as poor according to the guidelines of social welfare (BFS SILC, 2012: 7.7%). What however is meant by poverty in a rich country like Switzerland? Following the recommendations of the Swiss Conference for social welfare (SKOS), people are poor if their financial situation lacks the ability to buy goods essential for a socially integrated living. By calculation, the so defined poverty line covers a monthly lump sum for subsistence, a typical local rent and access to basic healthcare. Projected to a yearly income, the poverty threshold 2012 was 26,400 CHF for a single person and 48,600 for a married couple with two children, according to the Swiss Federal Statistical Office. In contrast, the relative approach to poverty measurement uses a threshold that relates to the prevailing income in a country. In this context, whether a person is “at risk of poverty” depends on the country-specific level of prosperity. This approach furthermore implies a subjective component of poverty, as the personal situation is compared to the ones of other people. One question that arises is, which one is the correct spatial reference frame. Is it the Swiss prosperity level or rather the one in close proximity?

Using tax data, it is possible to conduct small-scale regional analyses, allowing a more flexible specification of the spatial reference. Its possible to estimate both, absolute and relative poverty measures. The latter allow to put more emphasis on the subjective component of poverty. Below we show how the methodology of poverty measurement (absolute vs. relative) impacts poverty rates on a municipality level. To calculate the absolute poverty rate, we used the share of households with a disposable income below the threshold of neediness according to the SKOS guidelines (see the methodology appendix). This excludes non-taxable supplementary benefits and social welfare. Therefore we measure a poverty rate before institutional actions, showing the share of households that potentially qualify for social welfare or supplementary benefits. Thereafter we compares this poverty rate with a poverty rate based on the relative approach (poverty threshold equals 50% of the median equivalent income of the municipality). While the calculation of absolute poverty is homogeneous across the canton, the relative poverty thresholds vary significantly across the municipalities. In the municipality Saxeten for example, a single person is defined as poor if the yearly income is below 21,225 CHF. In Mörigen, the richest of Berns municipalities, the relative poverty threshold of a single household is at 51,410 CHF.

Map 1 – Poverty map, absolute measure, tax data of Bern 2012

The evaluation of tax data from Bern reveals, that the average absolute poverty rate for the canton is 5.5%. The regional differences are substantial. Based on the average value, the interactive map plot shows municipalities below the threshold in red colors and municipalities above in green colors accordingly. Try hovering over the municipality polygons with your mouse to view the exact poverty rate. Municipalities of the Bernese Jura are noticeable for their tendency of higher poverty rates. Amongst the municipalities with the highest poverty rate are e.g. Roches (18.8%), Saint-Imier (16.7%) and the city Biel (16.7%). Cities tend to have above average poverty rates: e.g. Ostermundigen (12.0%), Langenthal (10.0%), Bern (10.5%), Burgdorf (8.8%) or Thun (7.9%). However there are small rural municipalities along the Lake Thun that have high rates, e.g. Teuffenthal (14.6%), Horrenbach-Buchen (12.2%) and Saxeten (12.3%). However rural municipalities usually show rather low poverty rates. Amogst the lowest rates are the rich municipalities like for example Bremgarten bei Bern (2.5%), along the river Aare between Bern and Thun (e.g. Jaberg, 1.1%) and in Seeland – especially alongside the southern shore of Lake Biel (e.g. Hagneck 1.6%).

Switching to the relative poverty perspective and using the municipal level of prosperity as a starting point, the poverty rate increases on average to a value of 9,9%. In municipalities with a noticeably high median income, the poverty rate increases disproportionately. Examples are Mühledorf, where the poverty rate skyrockets from 2.2% (absolute) to a new value of 20.5% (relative). Other municipalities appear less poor as their incomes are generally lower and more homogeneous. Amongst there are many municipalities of the Bernese Jura and also municipalities around the Lake Thun like Horrenbach-Buchen (-5.9 percentage points), Teuffenthal (-5.8 PP) and Saxeten (-5.7 PP). The shift in poverty rates can be grasped from the interactive map plot. The green color palette indicates the 50% municipalities with the lowest increase or even a decrease of poverty rates by using the relative approach. Red colors accordingly show a disproportionate increase of poverty rates. By hovering your mouse over a municipality polygon, you can view both its poverty measures as well as its yearly median income (converted to a single person household for comparability).

Map 2 – Regional changes in poverty rates when comparing absolute to relative poverty, tax data of Bern 2012

As can be seen from the analyses, poverty rates substantially vary by the method used. The absolute poverty approach is probably more suited towards social policy needs as the financial instruments (e.g. social welfare and supplementary benefits) directly reduce this kind of poverty. The relative measure on the other hand is also driven by the local prosperity level and not a mere indicator of neediness. It is rather co-determined by the distribution of the economic resources of the municipality. In the light of inequality research, the relative approach gains importance. In a recent study Richard Layte compared 27 countries and found a surprisingly stable correlation between inequality of economic resources and the psycho-social well-being. As inequality increased, the happiness of the population decreased on average. While this correlation has quite some empirical evidence, people argue about the explanation of this phenomenon. Even when controlled for different alternative causes, Richard Layte was able to show a stable effect of the so called status thesis which also was examined by Richard Wilkinson and Michael Marmot. According to these researchers, income can be understood as a measure of hierarchies in societies. The negative psychological effects therefore stem from the self-perception of individuals to have a low status within society. People at the low end of the “pecking-order” tend to feel unhappy, experience more stress and are at risk of illness. In this perspective, poverty indeed can be seen as a relative phenomenon that is co-determined by the prevailing prosperity level

Methodology of poverty measures

  • Population: The analyses are based on tax data that were kindly provided by the Bernese tax authority for the use within the project “Inequalities of incomes and wealth in Switzerland” which is financed by the Swiss National Science Foundation. The data represent a full census of persons who had their fiscal residence within the canton of Bern by the date of 12/31/2012. With data from the Federal building and appartment indentifier (“Eidgenössischen Gebäude- und Wohnungsidentifikators”; EGID, EWID) we also were able to identify which persons live together in one household. The statistical units of our analyses are therefore households. Excluded from the analyses are individuals who are taxed at source. Furthermore we dropped units that were not taxed for the whole year (unterjährig Steuerpflichtige), those taxed by judgement (Ermessenstaxierte) which could not be related to a regularly taxed household and collective households. As young adults still in education often have low personal incomes but are aided by their parents (which cannot be depicted with tax data) we limited the analyses to people over the age of 25.
  • Disposable equivalised income: Disposable income is calculated as the total of incomes (earned income from employment or self-employment, social benefits (pensions, daily allowances, received alimonies), capital income (from securities or real estate) deducting mandatory expenses (direct taxes (municipality, church, federal government), social security contributions, paid alimonies). We were however not able to consider non-taxed means-tested benefits (social welfare, supplementary benefits, etc.) and mandatory expenses like heath insurance premiums. To compare households of different sizes, we used weighted household incomes with the OECD equivalence-scale.
  • Median incomes: When sorting households by their income, the median income is the value that divides the households in two groups of equal size. 50% have incomes above this value, 50% have less income.
    Net assets: Sum of all assets (real estate, business assets, movable assets) less debt.
  • Poverty rate, absolute approach: To calculate the absolute poverty rates we tested each household, whether its disposable income is below the threshold of neediness according to SKOS. To simplify we ignore the fact that rental costs and health insurance premiums vary within the canton. Similar to the approach of the Social Service we excluded households which are below the threshold but at the same time own movable assets worth 10,000 CHF or more. The poverty rate finally is derived by the number of households affected by poverty divided by the population of the municipality.
  • Poverty rate, relative approach: To calculate the relatve poverty rate we added to the disposable income 5% of net assets. This incorporates the idea of potential wealth consumption and takes into account that regional wealth becomes visible in the form of assets. Finally we counted those households as poor that had an extended (+5% of net assets) income below 50% of the median income of the relevant municipality.