Distribution of income in Sweden
The income distribution in Sweden looks at the distribution of income in Sweden. When analyzing the distribution of income, a distinction is generally made between the functional and the personal income distribution discussed here. Personal income distribution looks at how the income of an economy is distributed among individuals or groups (e.g. private households ), regardless of the source of income from which it originates.
The Gini coefficient of equivalised disposable income for Sweden was 28% in 2017 and was thus below the EU27 average of 30.7%. The median disposable income was € 25,376 in 2017 and was thus above the EU27 average of € 17,032. This is due, among other things, to an employment rate of 77% (Q4-2017), which in the long term depends on higher education and support measures for women and disadvantaged groups. The average disposable income in 2017 was € 27,890.
Income distribution in general
When interpreting statistical data, one must always pay attention to which terms are used. The term income can refer, for example, to market income, i.e. income from employment, business activity, rental or capital before taxes and duties or to disposable income. This is calculated by subtracting direct taxes and social security contributions from market income and adding public (e.g. social assistance , unemployment benefits ) or private (e.g. maintenance ) transfers. The calculations below are always based on the equivalised disposable income . The data shown come from Eurostat (based on the EU-SILC data set) and the OECD . The personal income distribution can be summarized using various measures of inequality and then analyzed. The most frequently used indicators are the Gini coefficient and quantile ratios ; the consideration of median and average incomes is usually the starting point for the analysis of income distribution.
Mean and median of household disposable income
If you put the incomes of different people next to one another, the median income or the mean income is the income that lies exactly in the middle of this series. Compared to mean income, median income is a more stable measure for determining income inequality because it is more robust against statistical outliers. The average income or the mean value of the income reflects the arithmetic mean of the income in relation to the number of income earners. A large difference between middle and average income indicates a highly unequal distribution of income.
In most cases, it is not only the income of the population at a certain point in time that is of interest, but also the development of income over time. Since income increases in the presence of inflation do not necessarily mean increases in prosperity , the real income is calculated in addition to the nominal income .
Average
The graph shows the mean and the HICP- adjusted mean of incomes in Sweden between 2004 and 2017 in EUR. From 2004 to 2009, the mean income rose in real terms by 9.4% (nominal: 19.9%). Due to the consequences of the economic and financial crisis , such as B. Decline in GDP and rising unemployment, the real mean from 2009 to 2010 experienced a fall from € 23,301 to € 20,813 (nominal: € 22,050 to € 20,070). Sweden learned lessons from its own financial crisis : acting swiftly in the context of political reforms led to improvements in labor productivity and labor market regulation. In 2014, the adjusted mean was already € 28,132 (nominal: € 27,935). This recovery is also known as "V-shaped". Due to immigration policies and extensive tax cuts that are holding back Sweden's social programs, the real mean income in Sweden fell by 3.8% from 2014 to 2017. The nominal by only 0.2%. Between 2004 and 2017, the real mean rose by a total of 27.1%, the nominal by as much as 51.6%.
Median
Compared to average income, median income is a more stable measure for determining income inequality because it is more robust against statistical outliers. In direct comparison to the mean, both the nominal and real median show a very similar trend in income development in Sweden between 2004 and 2017, only with lower values. From 2004 to 2009, the real median income rose by 7.8% (nominal: 18.2%). In 2010 the real median was € 19,597 (nominal: € 18,897). Both the real and the nominal median experienced the same developments up to 2017 under the indicators already mentioned in the section, mean value.
Between 2004 and 2017, the real median rose by 22.7%, the nominal by 61%.
Gini coefficient of income
The Gini coefficient (or Gini index) is a statistical measure used to represent inequality in a society. This coefficient can be between 0 and 1 (or between 0 and 100 by multiplying the Gini coefficient by 100). A Gini coefficient for income of 1 describes that one individual in the economy has all income. A value of 0, on the other hand, shows total income equality. With a Gini coefficient of 0, everyone in an economy therefore has the same income. The closer the value is to 0, the more equal is the distribution of income. In general, countries with a Gini index between 20% and 35% are said to be relatively equal.
Compared to other OECD countries , income inequality in Sweden is relatively low. Income taxes in Sweden play a significant role in distribution, reducing inequality within the working-age population by 28% - the OECD average is 25%. However, this effect has weakened significantly since the mid-2000s.
From the end of the 1990s, when tax reforms reduced the tax burden on high-income households, the Gini coefficient experienced an almost continuous increase: While it was still 21% in 1997, it was already 28% 20 years later. Compared to the Gini coefficient of incomes in Sweden, the Gini of the EU27 countries hardly changed from 2005-2017: it increased by only 0.3%. In 2017 it was 30.7%. Another reason for the growing imbalance is the source of income of high-income households: the share of capital income has increased sharply in recent decades. Weaker households often have no access to this type of income.
Top 10 percent of income
The share of the upper decile in national equivalised income in Sweden and the EU27 countries shows a similar increase as the Gini coefficient of income. The main indicators for this are tax reforms, migration policy and rising capital income of high-income households. In 2006, the share of the upper decile was 19.6%, in 2009 this already reached a share of 21.3, which represents an increase of almost 9%. After the share fell again to 20.3% in 2010, it has increased almost exclusively. In 2017 the share was 22.4%, the share of the EU27 countries was 23.9%. Between 2005 and 2017, the share of the upper decile in Sweden increased by 13%. In the EU27 countries it has fallen by 0.08% over the same period.
As a result of tax reforms and migration policy, Sweden issued three times as many residence permits in 2017 than in 1997, the country not only has a very high incidence of inequality in terms of income, but also a strongly divergent distribution of wealth. A high proportion of immigrants live and work in Sweden. Due to the high proportion, Sweden has problems with job creation, which in turn can be attributed to the poorly successful integration into education: in 2017 just under 90% of students with a Swedish background were eligible to attend high school; for students with a migration background it was only 66%. People with a Swedish background have had a falling unemployment rate since the financial crisis; it is the other way around for people with a migration background.
Gender
Sweden ranks very well in fifth place worldwide on the Global Gender Gap Index 2017 . Sweden is one of the countries that have pioneered the field of women's rights. For example, as early as 1846 it was legally stipulated that unmarried Swedish women were allowed to carry out craft and trade professions. As early as 1853 women were allowed to practice a teaching profession.
S80 / S20 income quintile ratio by gender
The income quintile ratio (S80 / S20) is the ratio of the total income of the 20% of the population with the highest income (top quintile) to the total income of the 20% of the population with the lowest income (bottom quintile). So it tells how many times you have to multiply the income of the bottom 20% to get the income of the top 20%. If the factor is 1, the proportion of total income of the lower quintile is equal to the proportion of the upper quintile. In order to consider gender-specific differences, the income quintile ratio is examined by gender.
The data shown here come from the EU-SILC survey. The concept of income here again refers to the equivalised disposable income. The income quintile ratio (S80 / S20) in Sweden has increased overall over the period under review, for both men and women. In 2004 the indicator was 3.3 (men and women), in 2017 it was 4.3 for women and 4.2 for men.
Gender Pay Gap
At EU level, the gender pay gap (gender-specific wage gap ) is defined without adjustment as the difference between the average gross hourly wages of men and women as a percentage of the average gross hourly wages of male employees. NACE is the statistical classification of the economic activities in the European Community. NACE is derived from the International Standard Classification of Economic Activities of the United Nations (ISIC) in the sense that it is more finely divided than this. The positions of ISIC and NACE are exactly the same at the highest levels, while NACE is more detailed at the lower levels. The code B-S_X_O stands for industry, construction and services (excluding public administration, defense and social security).
The data shown come from Eurostat and show the gender pay gap (GPG) without adjustment in the sectors of industry, construction and services excluding public administration, defense and social security. The Swedish GPG fell sharply in the period shown from 2007 to 2017. Since there are no data on the EU27 average in 2007 (Eurostat code: earn_gr_gpgr2), the year 2008 must be used for the earliest comparison. This year the GPG in Sweden was 16.9% and was thus below the EU27 average of 17.3%, but by only 0.4 percentage points. In 2017 this difference was 3.5 percentage points. While the EU27 average has slowly and continuously decreased to 16.1%, the Swedish GPG decreased without adjustment to 12.6% in 2017.
Regional inequalities
Regional economics generally deals with the economic relationships in regions and thus represents the economic counterpart to foreign trade. A comparison of regional data as detailed as possible is often more meaningful than a comparison of entire states and also makes the differences or similarities within individual states clear. These data play an important role, for example, in the European Union's cohesion policy. This is intended to bring the regions and cities in Europe closer together in economic, social and ecological terms. The importance of cohesion policy is also reflected in the budget for the entire EU budget, which amounts to almost 352 billion euros for the 2014–2020 programming period; this corresponds to almost a third (32.5%) of the entire EU budget over this period. At the heart of the European Commission's regional statistics is the NUTS classification (the classification of territorial units for statistics). This is a regional system of the member states of the EU, in which the regions are represented in a harmonized hierarchical structure. In the context of the NUTS classification, each Member State is divided into three different levels of regions, namely the NUTS levels 1) large socio-economic regions, 2) base regions for regional policies and 3) small regions for specific diagnoses.
Regional inequality can lead to agglomeration, thinning and displacement effects and thus distort the analysis if regional characteristics are not included. In addition, these influence social coexistence, public investment and the labor market, in connection with the interaction of regional income inequality with basic, house and apartment prices. The regionally different development of communities, districts and regions can be analyzed in terms of growth, income or productivity, for example. In the case of Sweden, household disposable income is looked at by NUTS 2 regions, the population at risk of poverty or social exclusion by NUTS 2 regions and some indicators on inequality in metropolitan areas.
Disposable income of private households, by NUTS 2 regions
The data on the disposable income of private households come from the statistical office of the European Union, Eurostat (code: tgs00026). This is defined as the balance of primary income (operating surplus or self-employed income plus employee compensation plus property income received minus property income paid) and the redistribution of income in the form of cash benefits. These transactions include social security contributions paid, social benefits received, income and wealth taxes paid, and other current transfers. Disposable income does not include any social transfers in kind from government or private non-profit making organizations. The data shown in the graphic are also broken down into NUTS 2 regions.
If you look at the graph of the disposable income of private households in Sweden in 2016 by NUTS 2 regions, or the exact figures in the table, you can see a slightly pronounced north-south divide. Households in the northernmost regions (Övre Norrland, Mellersta Norrland, Norra Mellansverige) do not exceed an available income of € 16,500. The southern regions (Sydsverige and Västsverige) have disposable household incomes of € 17,700 and € 17,100, respectively, and the highest income region is Stockholm with € 20,000. The comparison of the population density and the disposable income in 2016 is also interesting. It shows that the regions with the highest income are also those that are most densely populated. Due to the large number of factors that determine the disposable income and also the place of residence, a causal connection must not be concluded here, i.e. that a higher population density leads to higher disposable income. What we see here is a correlation between these two variables, which says that higher disposable incomes are more likely to be found in the more densely populated regions. No statement can be made here about the causes of this correlation.
|
Swedish region
(NUTS 2) |
Population density
(Inhabitants per km²) |
Available income
(in Euro) |
|---|---|---|
| Stockholm | 344.9 | 20,000 |
| Sydsverige | 105.9 | 17,100 |
| Västsverige | 67.7 | 17,700 |
| Östra Mellansverige | 43.0 | 16,700 |
| Småland med öarna | 25.4 | 16,600 |
| Norra Mellansverige | 13.3 | 16,300 |
| Mellersta Norrland | 5.3 | 16,400 |
| Övre Norrland | 3.4 | 16,500 |
At-risk-of-poverty rate
In general, there are different indicators for depicting poverty. A distinction is made, for example, between material deprivation and monetary poverty. According to Eurostat, the at-risk-of-poverty rate is the proportion of people in Sweden who are at risk of poverty or who are materially deprived, or who live in households with very low employment. People with an equivalised disposable income below the at-risk-of-poverty threshold, which is 60% of the national median equivalised disposable income (after social transfers), are considered to be at risk of poverty. “Material deprivation” summarizes indicators on economic stress and consumer goods. For people who suffer from significant material deprivation, the living conditions are severely limited due to a lack of funds.
The data on the population at risk of poverty or social exclusion are also from Eurostat. Across Sweden, 17.7% of the population were at risk of poverty in 2017. With the help of the NUTS classification, the regional characteristics of the at-risk-of-poverty rate can be observed. The regions Stockholm, Småland med Öarna, Övre Norrland and Östra Mellansverige are below the national average of 17.7%, whereas the region Mellersta Norrland is only slightly above with 17.9%. The regions most at risk are Västsverige with 18.7%, Norra Mellansverige with 20.9% and Sydsverige with 22%.
Inequality in metropolitan areas
The degree of urbanization is over 50% worldwide. The population concentration is even higher within the OECD area. Around half of the OECD population is spread across 300 metropolitan areas, i.e. H. large urban agglomerations with over 500,000 inhabitants. These metropolitan areas account for well over half of the OECD's GDP. The importance of cities is measured not only in terms of population, but also in their contribution as the driving force behind long-term economic growth. The agglomeration advantages of large cities - in particular knowledge transfer and greater incentives for residents to invest in human capital - make cities the most important centers of research and development, patent activity and venture capital . Although innovation is possible everywhere, it is usually found particularly in highly urbanized areas. If large metropolitan areas have a well-developed public transport network, the CO 2 emissions per capita from ground-based transport are lower than in more rural regions. The wave of urbanization of the 21st century could have very positive effects for city dwellers themselves, the states concerned and the planet as a whole. The prerequisite for this, however, is that a number of important challenges (climate, affordable housing, transport planning, etc.) are met.
The data shown in the table come from the OECD dataset on metropolitan regions (CITIES); in the case of Sweden, these are Stockholm, Gothenburg and Malmö. The population share, equivalised disposable income, the Gini coefficient and the poverty line are all considered. The latter indicator is defined similarly to the at-risk-of-poverty rate according to Eurostat: the people at risk of poverty with an equivalised disposable income below the at-risk-of-poverty threshold, which is 60% of the national median equivalised disposable income (after taxes and social transfers). A large part of the Swedish population (39.9%) lives in only three cities: Stockholm, Gothenburg and Malmö. The average equivalised disposable income of households in all three cities was higher than that of Sweden as a whole (USD 27,347). The Gini coefficient was also higher than that of the whole of Sweden, so incomes in the metropolitan regions are somewhat more unevenly distributed. While Sweden had a Gini coefficient of 27.6% in 2016, it was 30% in the metropolitan areas. Across Sweden, 17.7% of the population were at risk of poverty in 2017. At 10% (albeit in 2016), the metropolitan regions are well below this average.
| Metropolitan area | Population share (percent) | Household Income ( USD ) | Gini | Poverty line (percent) |
|---|---|---|---|---|
| Stockholm | 23.0 | 35,822 | 0.3 | 0.1 |
| Gothenburg | 10.2 | 32,294 | 0.3 | 0.1 |
| Malmo | 6.7 | 31,115 | 0.3 | 0.1 |
Special features of the Swedish welfare state and effects on income distribution
The Swedish welfare state system is based on long-term policies. Family policy is based on parental benefits, child benefits and allowances, and public day-care centers. Sickness and elderly benefits as well as social welfare benefits are organized at the municipal level ( Sveriges kommuner ). The Ministry of Health and Social Affairs , the Ministry of Education and Research, and the Ministry of Labor are the three main pillars of the Swedish welfare state.
Sweden has a well-functioning model of social partnership in which the social dialogue between the various social partners is deeply embedded in the institutional framework. In the Human Development Index , Sweden ranks 8th.
Collective thinking has a long tradition in Sweden. Transparency is deeply rooted in society as a result of a law passed by the King and Reichstag in the 18th century. Unless otherwise directed, all administrative acts must be disclosed to the public. Among other things, income and debt data can be viewed by all citizens. The highest incomes are also advertised annually to the public.
Richard Thaler , the well-known behavioral economist and winner of the Alfred Nobel Memorial Prize for Economics 2017, studied the decision-making architecture of the Swedish pension system (2000-2016) with two co-authors: When Nudges Are Forever: Inertia in the Swedish Premium Pension Plan.
literature
- The Metropolitan Century: Understanding Urbanization and its Consequences (OECD, 2015).
- Hassler, J. (2010): Sweden in Past, Current and Future Economic Crisis, report for the OECD Economics Department, Paris: http://hassler-j.iies.su.se/PAPERS/OECDSwedenAug2010.pdf .
Web links
- Homepage of the statistical office of the EU ( Eurostat )
- Key figures and indicators of the OECD on the economic situation in Sweden
Individual evidence
- ↑ Definition: personal income distribution. Retrieved May 19, 2019 .
- ^ Inequality - Income inequality - OECD Data. Retrieved May 2, 2019 .
- ↑ a b Mean and median income by household type - EU-SILC survey [ilc_di04]. Retrieved May 2, 2019 .
- ↑ Employment - Employment rate - OECD Data. Retrieved May 2, 2019 .
- ^ John Hassler: Sweden in past, current and future economic crises . Stockholm 2010.
- ↑ OECD (Ed.): Structural reforms in times of crisis . 2012 ( online [PDF; accessed May 6, 2019]).
- ↑ IMF Survey online: IMF Survey: Cloudy Outlook for Sweden After Years of Success. Retrieved May 6, 2019 .
- ^ Gini coefficient. October 19, 2010, accessed May 2, 2019 .
- ↑ OECD Income inequality data update: Sweden (January 2015). Retrieved May 7, 2019 .
- ↑ OECD Income inequality data update: Sweden (January 2015). Retrieved May 7, 2019 .
- ↑ Taxes in Sweden 2012. Accessed May 8, 2019 .
- ^ Swedish society's big divisions - in 6 charts. Financial Times, accessed May 8, 2019 .
- ↑ Gender pay gap in unadjusted form - NACE Rev. 2 activity (earn_grgpg2). Retrieved May 8, 2019 .
- ^ NACE Rev. 2 - Statistical classification of economic activities. Retrieved May 8, 2019 (UK English).
- ↑ a b Background - Eurostat. Retrieved May 8, 2019 .
- ↑ Disposable income of private households, by NUTS 2 regions - Eurostat. Retrieved May 2, 2019 .
- ↑ Population at risk of poverty or social exclusion by NUTS 2 regions - ecodp.common.ckan.site_title. Retrieved May 2, 2019 .
- ^ A b The Metropolitan Century - Understanding Urbanization and its Consequences - en - OECD. Retrieved May 8, 2019 .
- ↑ Statista: Topic page: Sweden. Retrieved May 8, 2019 .
- ↑ André Anwar: That is why income and debts are public in Sweden. August 14, 2017. Retrieved May 8, 2019 .
- ↑ Henrik Cronqvist, Richard H. Thaler, Frank Yu: (PDF) When Nudges Are Forever: Inertia in the Swedish Premium Pension Plan. Retrieved May 8, 2019 .