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chapter 9

household perspective on the economy

Author: Karen Refsgaard
DATA AND MAPS: Maria Bobrinskaya and Kamila Dzhavatova

The household as a unit of analysis

This chapter looks at economic, social, and environmental indicators from a household perspective. The economic and social indicators are closely interconnected and are analysed through the distribution of and changes to household disposable income. The environmental indicators are investigated through consumption-based GHG emissions, which are then related to income levels from a geographical perspective.

Economic and social aspects

Household disposable income per capita is a common indicator of the affluence of households and, therefore, of the material quality of life. It reflects the income generated by production, measured as GDP that remains in the regions and is financially available to households, excluding those parts of GDP retained by corporations and government (OECD 2022). In sum, household disposable income is what households have available for spending and saving after taxes and transfers (OECD 2024). It is ‘equivalised’ – adjusted for household size and composition – to enable comparison across all households. Purchasing Power Standards (PPS) is used to compare the countries’ economies and the cost of living for households.

National level

Figure 9.1 shows that, when purchasing power is considered, Norway still has the highest income, although the differences between the countries have changed. Since 2015, Denmark has had a steady increase in household income, while Finland, Sweden and Åland have seen no increase – and in the last year, even a decrease – in purchasing power. Iceland had the absolute lowest level of purchasing power in 2015. The Faroe Islands and Greenland were slightly higher, reaching the level of Finland in 2022. Iceland’s purchasing power increased in 2022 but was still at the lowest level among the Nordic countries. The increased value of the Danish currency (which is tied to the euro) compared to the Swedish and Norwegian currency means greater purchasing power in relation to imported goods and services, and thus may partly explain the Danish increase in household income compared to the other Nordic countries.
Figure 9.1: Household disposable income per capita in PPS
Purchasing Power Standard (PPS) represents the number of currency units per PPS. Real expenditure refers to expenditure in national currency converted to PPS using Purchasing Power Parities (PPPs), thereby denominating it in PPS (Statistics Denmark, 2024).

Source: NSIs
However, average numbers do not reveal anything about the distribution among households. The sections above on social aspects of the regional economy, investigated the Gini coefficients across the Nordic countries. Greenland and Sweden had the most unequal distribution of incomes per capita, while the Faroe Islands, Iceland, and Norway were more equal.
As other research pinpoints, childbearing, parenthood and maternity leave as factors that contribute to gender wage gaps, it is interesting to look more closely at household incomes for families with children. Epland and Hattrem (2023) analysed the household incomes for families in Denmark, Finland, Norway and Sweden from 2005–2020, factoring in the impact of major events, e.g. the financial crisis in 2008 and the rise in immigration to Europe some years later. They conclude that in Norway, it is primarily families with two parents that have the highest incomes, while in Denmark, it is single parent households.
In 2020 there was a difference in income between single- and two-parent households of 73% in Denmark and Finland, compared to 63% in Norway. Figure 9.2 shows the percentage increase in the median income from 2005 to 2020 for children in different households. Children in Denmark and Finland have had the highest increase in household income, while growth has been lower for Norwegian and Swedish children. It is first and foremost single parent families where the development has differed between the countries. While children in Denmark – and, to some extent, Finland – from single-parent families recorded nearly the same trend as children on average, Swedish and, in particular, Norwegian children have had much lower growth in income.
Epland and Hattrem (2023) argue that these differences in income for families can be partly explained by the stable or increasing participation in the labour market by parents in Denmark – and, to some extent, Finland. Conversely, the large increase in the immigrant population in Sweden and Norway in recent years suggests that an overall reduction in average educational level may be a reason for the lower increase in labour market participation. Such deep dives into the distribution of household incomes show how even indicators like Gini coefficients are not sufficient as social indicators for household economy.
Figure 9.2: Percentage change in median equivalent income (2005-2020)
Source: Epland and Hattrem (2023) based on EU-SILC data

Municipal level

Map 9.1 shows the intra-municipal differences in household disposable income in PPS, which reveals the different patterns in the Nordic countries. Norwegian coastal municipalities have slightly higher household disposable income than inland municipalities, with some exceptions. Finnish, Icelandic, and Swedish municipalities generally have much lower household disposable income compared to Norwegian and Danish municipalities, except for the larger urban areas. In Denmark, most municipalities are at a similarly high level, except for remote islands in the south. The differences between the Icelandic municipalities are rather small, at a medium to lower level.
As shown in Map 9.2, between 2018 and 2022, household disposable income increased for all Danish, Icelandic, and Norwegian municipalities and decreased for Finnish and Swedish municipalities. On average, the city municipalities have higher incomes and increased most in Finland and Sweden in 2018–2022. In Sweden, a tendency towards larger falls in income was observed in several southern municipalities.
In summary, absolute household income increased in all Nordic countries but not when measured in purchasing power. Based on this metric, on average, Norwegian households are the most well-off and Iceland the worst off, while Danish households benefited from a stronger currency in 2022. Single-parent households have had lower increases in household income than other families in Norway and parts of Sweden. Municipalities show a similar trend in Norway and Denmark, although Norwegian coastal municipalities fared slightly better in 2022. Disposable income is falling in all Swedish and Finnish municipalities.
Map 9.1: Household disposable income in PPS (2022)
Map 9.2: Percentage change in household disposable income (2018–2022)

Environmental aspects

This section looks at and compares production- and consumption-based GHG emissions from a household perspective. As GHG emissions are closely linked to consumption and income, we also examine these relationships in more detail.
GHG emissions are usually measured based on ‘production’ or ‘territorial’ emissions, which are used when countries report their emissions and set targets domestically and internationally (Pedersen, 2021). In addition, ‘consumption-based’ emissions are increasingly attracting attention (e.g. Girod et al. 2014; OECD 2021, Axelsson et al. 2022, Statistics Denmark 2022). Consumption-based emissions reflect consumption and lifestyle choices in a country– in effect, they are territorial emissions adjusted for trade. In general, consumption-based GHG emissions are higher than territorial emissions in most developed countries (Ahmad & Wyckoff 2003; Wilting & Vringer 2009; Peters 2016). Globally, there is a regional East-West split in net exporters and importers – most of Western Europe, the Americas, and many African countries are net importers of emissions, while most of Eastern Europe and Asia are net exporters.
Factoring in consumption emissions significantly increases the Nordic GHG emissions footprint. For example, statistics from the Global Carbon Project (2023) reveal that in 2021, territorial CO2 emissions, which exclude emissions embedded in traded goods, represented only a roption of total CO2 emissions consumed in Nordic countries (see Figure 9.3). In Nordic countries, the shares of territorial emissions relative to consumption emissions were 63% in Denmark, 73% in Finland, 57% in Sweden and 92% in Norway. Norway’s significantly higher ratio is attributed to substantial process-related CO2 emissions in the oil and gas sector. The biggest difference between territorial and consumption-based emissions was seen in Sweden, which indicates that Sweden’s imports were a significant contributor to CO2 emissions.
All the Nordic countries have cut their consumption-based emissions significantly by around 30–50% since 2000 (Iceland is excluded from this data).
Figure 9.3: Territorial and consumption-based CO₂ emissions per capita (tonnes of CO2 equivalent)
Source: Global Carbon Budget 2023 & Our World In Data project
Data from the Global Carbon Budget (2023) shows 7.8 CO₂ equivalents per Dane, 9.3 per Finn, and 8.3 per Norwegian and 6.5 per Swede
Those numbers do not include emissions from international aviation and shipping.
and have all increased since the pandemic. Numbers from 2020 from Statistics Denmark (2022) show 11 tonnes of CO2 equivalent per capita, while for 2019, Statistikmyndigheten (SCB 2022) reports 9 tonnes of CO₂ equivalent per Swede. The deviation from the figures from Our World In Data may be due to, among other factors, the latter not including emissions from international aviation and shipping or emissions from land-use change.
Consumption-based GHG emissions are attributable to private consumption, public consumption, and investment. Private household consumption comprised 62% of total consumption-based GHG emissions in Denmark in 2020 (Iliev et al. 2021). Also Swedish Statistics (Axelsson, 2022) has investigated the composition of emissions from household consumption and found that in 2019, total emissions from private households in Sweden amounted to 6.3 tonnes of CO₂ equivalent per Swede, while Danish Statistics (2022) calculated 6.8 tonnes of CO₂ equivalent per Dane (see Table 9.1 and Figure 9.4). Transport (cars and aviation) accounted for 42% of private household emissions in Sweden and 33% in Denmark. Food accounted for 24% in Sweden and 18% in Denmark. Housing was the third-largest sector, with 18% for Sweden and 8% in Denmark.
Figure 9.4 shows also the figures for imported GHG emissions in Denmark, which accounted for a significant proportion (more than 50%) in most categories. For further analysis, see Norlén et al. (2022).
Consumption categories 
Tonnes of CO2-equivalents per Swede
Share of total 
Transport, including
Public transport
Food incl. restaurants
Housing and furniture
Culture, sport and leisure
Clothes and shoes
Table 9.1: Swedish household consumption footprint by consumption group (2019)
Source: Stockholm Environment Institute
Figure 9.4: Danish household consumption footprint by consumption groups (2021) (million tonnes of CO₂ equivalent)
Source: Statistics Denmark 2022.
According to several studies (Heinonen et al. 2013; Pang et al. 2019; Peters et al. 2016; Haberl et al. 2020; OECD 2021), income is a key variable in explaining emissions from household consumption and implies a positive correlation between emissions and GDP per capita. See, for example, a global marginal increasing curve for GHG emissions with increasing GDP per capita (Our World in Data 2024). Consumption-based analyses comparing rural and urban areas reveal that urban households have a higher carbon footprint, primarily due to higher consumption levels resulting from frequently higher income (Heinonen et al. 2013; OECD 2021). It is therefore interesting to investigate the data from Swedish Environmental Institute where GHG emissions are calculated at municipal and postcode level for all of Sweden’s municipalities (Axelsson et al. 2022).
Axelsson et al. (2022) report that the average footprint is at the same level in each of the municipalities, but large intra-municipal variations are seen at postcode level. The analysis shows that GHG emissions from households range from around 3.5 tonnes CO₂ equivalent per person per year in the less affluent areas to nearly 18 tonnes CO₂ equivalent per person per year in more affluent areas. Postcodes with higher incomes have a higher-than-average climate footprint from consumption.
The results also show large variations between the municipalities when it comes to the composition of emission sources. Urban municipalities have a higher-than-average footprint due to flying, people in rural areas use more cars, and in the northern part of Sweden, a larger proportion is used for heating. The highest footprints are in urban municipalities, followed by municipalities with high levels of tourism, while the lowest footprints are found in municipalities within commuting distance to a smaller town. Food consumption is at the same level across the types of municipalities.
Sweden is introducing strategies and instruments to reduce consumption-based GHG emissions. It is also investigating the possibility of introducing national targets for consumption-based emissions (Statens Offentliga Utredningar 2022).
The comparisons of production- and consumption-based GHG emissions from a household perspective show that the import of goods of services composes around 50% of the consumption based GHG emissions in Denmark. However, the Nordic countries have cut their consumption-based emissions significantly by around 30–50% since 2000. Beside variation in geographical conditions for the size and composition of consumption based GHG emissions research shows positive correlation between emissions and income per capita. It is therefore of interest for future GHG policies to look into those causes for variations in GHG emissions stemming from private consumption.


Ahmad, N. & Wyckoff, A. (2003). Carbon Dioxide Emissions Embodied in International Trade of Goods. OECD Science, Technology and Industry Working Papers.
Andrew, R. M. & Peters, G. P. (2023). The Global Carbon Project's fossil CO2 emissions dataset.
Axelsson K., Dawkins, D., Rahmati-Abkenar M. & Broekhoff, D. (2022). Konsumtionskompassen. Ett digitalt verktyg för att förstå klimatpåverkan från konsumtion på lokalnivå. SEI brief September 2022.
Epland, J. & Hattrem, A. (2023). Barn i lavinntektshusholdning i Norden 2005-2020. Hvorfor så ulik utvikling? Stastics Norway, Rapporter 2023/38. ISSN 1892-7513. Available at: www.ssb.no/inntekt-og-forbruk/inntekt-og-formue/artikler/barn-i-lavinntektshusholdninger-i-norden-2005-2020/_/attachment/inline/4ecb5106-417c-4bfb-bdfb-58e627e5ac9b:772b4d5ee0133168931b30f6b700c2a2cd6d9e6b/RAPP2023-38.pdf
Eurostat. (2021). Statistics Explained: Greenhouse Gas Emission Statistics - Carbon Footprints. Available at: https://ec.europa.eu/eurostat/statistics-explained/index.php?oldid=516252.
Girod, B., van Vuuren, D. P., & Hertwich, E. G. (2014). Climate policy through changing consumption choices: Options and obstacles for reducing greenhouse gas emissions. Global Environmental Change. (25), pp. 5-15. https://doi.org/10.1016/j.gloenvcha.2014.01.004
Haberl, H., Wiedenhofer, D., Virág, D., Kalt, G., Plank, B., Brockway, P., Fishman, T., Hausknost, D., Krausmann, F., Leon-Gruchalski, B., Mayer, A., Pichler, M., Schaffartzik, A., Sousa, T., Streeck, J. & Creutzig, F. (2020). A systematic review of the evidence on decoupling of GDP, resource use and GHG emissions, Part II: synthesizing the insights. Environmental Research Letters, 15: 065003.
Heinonen, J., Jalas, M., Juntunen, J. K., Ala-Mantila, S. & Junnila, S. (2013). Situated Lifestyles: I. How Lifestyles Change along with the Level of Urbanization and What the Greenhouse Gas Implications Are - a Study of Finland. Environmental Research Letters, 8(2). IOP Publishing: 025003. https://doi.org/10.1088/1748-9326/8/2/025003
Iliev, B., Arendt, L. M., Rørmose Jensen, P. & Søndergaard Møller, F. (2021). Dansk Forbrug Sætter i Høj Grad Sit Klimaaftryk i Udlandet. 2021:22. DST Analyse. Copenhagen, Denmark. Available at: https://www.dst.dk/Site/Dst/Udgivelser/nyt/GetAnalyse.aspx?cid=47752.
Norlén, G., Randall, L., Sánchez Gassen, N. & Tapia, C. (2022). eds: State of the Nordic Region 2022. Nordregio Report 2022:2. http://doi.org/10.6027/R2022:2.1403-2503
OECD. (2021). OECD Regional Outlook 2021: Resilience in the COVID-19 Crisis and Transition to Net Zero Greenhouse Gas Emissions. OECD. https://doi.org/10.1787/17017efe-en
OECD. (2022). Statistical Insights: What Does GDP Per Capita Tell Us About Households’ Material Well-being? Available at: https://web-archive.oecd.org/2022-02-04/623608-statistical-insights-what-does-gdp-per-capita-tell-us-about-households-material-well-being.htm
OECD. (2024). Household disposable income. Available at: https://data.oecd.org/hha/household-disposable-income.htm
Our World in Data (2023). Consumption-based vs. territorial CO2 emissions per capita. Available at: https://ourworldindata.org/grapher/consumption-vs-production-co2-per-capita?country=DNK~FIN~NOR~SWE
Our World in Data (2024). Consumption-based CO2 emissions per capita vs. GDP per capita. Available at: https://ourworldindata.org/grapher/consumption-co2-per-capita-vs-gdppc
Pang, M., Meirelles, J., Moreau, V. & Binder, C. (2019). Urban Carbon Footprints: A Consumption-Based Approach for Swiss Households. Environmental Research Communications, 2(1). IOP Publishing: 011003. https://doi.org/10.1088/2515-7620/ab59c5.
Pedersen, O. G. (2021). Om hvordan man opgør udledninger af drivhusgasser. Danmarks Statistik. Available at: https://www.dst.dk/Site/Dst/SingleFiles/GetArchiveFile.aspx?fi=2108992358&fo=0&ext=national
Peters, G. P., Andrew, M., Robbie, M. & Karstensen, J. (2016). Global Environmental Footprints: A Guide to Estimating, Interpreting and Using Consumption-Based Accounts of Resource Use and Environmental Impacts. TemaNord, 2016,532. Copenhagen: Nordic Council of Ministers. http://dx.doi.org/10.6027/TN2016-532
Ritchie, H. (2019). How do CO2 emissions compare when we adjust for trade? Available at: https://ourworldindata.org/consumption-based-co2
Statistikmyndigheten SCB. (2022). Oförändrade växthusgasutsläpp från Sveriges ekonomi första kvartalet 2022. Available at: https://www.scb.se/hitta-statistik/statistik-efter-amne/miljo/miljoekonomi-och-hallbar-utveckling/miljorakenskaper/pong/statistiknyhet/miljorakenskaper---utslapp-till-luft-1a-kvartalet-2022/
Statens Offentliga Utredningar. (2022). Sveriges globala klimatavtryck. SOU 2022:15. ISBN 978-91-525-0354-6.
Statistics Denmark. (2022). AFTRYK: Climate Footprint of Danish Consumption (Experimental Statistics) by Types of Use (Causes of Emissions), Industries (Origin of Emissions) and Countries/Economies (Origin of Emissions). Available at: https://www.statbank.dk/AFTRYK
Wilting, H. C. & Vringer, K. (2009). Carbon and Land Use Accounting from a Producer’s and a Consumer’s Perspective – an Empirical Examination Covering the World. Economic Systems Research, 21(3), pp. 291–310.