Multidimensional poverty measures at the local level
Date: 16 Jun, 2025

At its core, a “left-behind” region refers to a geographic area experiencing persistently slow economic growth. In its 2017 report, the European Commission identified two types: low-growth regions (GDP per capita below 90% of the EU average with no convergence between 2000 and 2023) and low-income regions (GDP per capita below 50% of the EU average in 2013). While such figures may seem technical, they often signal deeper structural issues—limited employment, rising poverty, and declining public services (Lichter & Schafft, 2016; Ulrich-Schad & Duncan, 2018). Over time, these regions risk becoming economically stagnant and socially marginalized. Traditionally, GDP has guided the allocation of EU Cohesion Policy funds. However, there is a growing shift toward multidimensional indicators that better capture territorial disparities. The 2030 Agenda for Sustainable Development reflects this evolution, emphasizing inclusive growth, poverty reduction, and improved access to health, education, justice, and environmental sustainability.

Using detailed socioeconomic data at the LAU2 level, the EXIT project identifies three types of “left-behind areas” in Europe—ruralpost-industrial, and urban—all characterized by high poverty indicators. Rural areas typically face depopulation, economic stagnation, high unemployment, and poor access to services and infrastructure. Geographic isolation—due to insularity, border proximity, or mountainous terrain—often worsens these conditions. Post-industrial areas, once employment hubs, now suffer from economic decline and limited job prospects, prompting youth outmigration and population aging despite relatively good infrastructure (Dijkstra et al., 2020). The third category, drawing on Furlong (2019) and Houlden et al. (2022), highlights urban neighbourhoods within or near major cities that are economically marginalized, with high rates of precarious employment, poverty, and concentrations of minority or immigrant populations. Although often invisible in official statistics, these urban areas are explored in EXIT through qualitative fieldwork.

Higher poverty or AROPE (At Risk of Poverty or Social Exclusion) rates are consistently observed in “left-behind areas” across Europe, regardless of national context. A major challenge in mapping poverty is the lack of fine-grained data, even for basic indicators like income. AROPE, as a multidimensional metric, offers a broader perspective by combining low income (below 60% of the national median), severe material deprivation, and low work intensity. Although AROPE represents a significant advance—providing a comparable, multidimensional view of poverty across time and space—it is only available at the NUTS 2 level or by degree of urbanization.

Map 1 shows absolute AROPE values by NUTS 2 regions, Eurostat’s standard spatial unit. Since both poverty and “left-behindness” are relative, every country has regions that appear disadvantaged compared to national averages.

Map 1: Rate of population at risk of poverty (AROPE index) by NUTS 2 regions, EU-27. Year 2021

Note: French data belongs to 2022.
Source: Eurostat, At-risk-poverty rate by NUTS regions

Map 2, below, highlights those regions with AROPE rates above their national average in 2021.

Map 2: Rate of population at risk of poverty (AROPE index) by NUTS 2 regions above the national average, EU-27. Year 2021.

Note: France data belongs to 2022.
Source: Eurostat, At-risk-poverty rate by NUTS regions.

Both maps confirm the traditional centre-periphery dynamics within the EU, with regions in the South and East showing lower income levels, weaker economic growth, and higher AROPE rates. However, as Bernard (2019) points out, analysing rural or urban poverty in greater depth requires indicators like AROPE to be available at finer spatial scales than the conventional NUTS regions.

The EU SILC database includes a variable called “degree of urbanization” of the area where the household is located, which allows the analysis of poverty differentials according to 3 typologies:  cities (densely populated areas where at least 50% of the population lives in one or more urban centre);  towns and suburbs (intermediate density areas where less than 50% of the population lives in an urban centre and at least 50% of the population lives in an urban cluster); and rural areas (thinly populated areas where more than 50% of the population lives in rural grid cells).

  Figure 1. AROPE by countries and by degree of urbanization. Year 2021

Source: Own elaboration form EU-SILC.

As shown in Figure 1, using this typology we observe the strong variation of AROPE figures across countries and also within them, with the highest percentages of population at risk of poverty not necessarily associated to one type or area, and with higher dispersion between the three categories in Eastern European countries (RO, BG or RS). It’s important to remark that while in some countries AROPE figures decrease with the degree of urbanization -with “cities” showing the lowest values and “rural areas” the highest ones-, in Western European countries poverty seems to be a phenomenon to a higher extent associated with urban settings (see, for instance, Austria, Belgium, Denmark, France or Italy), as the highest AROPE values correspond to the densely populated areas or “cities” typology.

The issue at stake is that neither the average household income nor the AROPE exist beyond the national and regional levels, and beyond NUTS regions, EUROSTAT just offers information on 3 different typologies by degree of urbanization. In the EXIT project, applying a small area estimation process based on Fernández-Vázquez et al. (2020) and combining EU-SILC data and microdata from the Population and Housing Censuses (2011 and 2021), we can estimate the average household income and the AROPE indicator at the LAU2 level. The following maps (Map 3, 4 and 5) shows the AROPE estimates at LAU level for 2021 (relative to the national average level) for Spain, France and England-Wales respectively[1].

Map 3. AROPE estimations for LAUs.  Spain. Year 2021

Map 4. AROPE estimations for LAUs. France. Year 2021

Map 5. AROPE estimations for LAUs. England and Wales. 2021

Although the spatial distribution of the population at risk of poverty tends to follow a pattern similar to that of income distribution (with high AROPE figures in rural and peripheral areas with low-income levels), only through estimations at a finer spatial scale can we observe high AROPE values in tourist-dependent coastal destinations and also in areas around large metropolitan areas and cities (see values close to large cities in England, around Paris, France, or Malaga, Spain).

The spatial patterns of poverty—only detectable through data at finer spatial scales—coexist with areas where indicators such as GDP or income suggest economic activity, well-being, or prosperity, masking underlying deprivation. Poverty is not necessarily linked to peripheral locations (as measured by distance to the nearest metropolitan area), since pockets of poverty also exist within regions and countries considered wealthy, well-developed, and well-connected. This reveals the limitations of traditional NUTS-level data for poverty analysis, as both income and poverty-related territorial inequalities can vary significantly within regions. Such intra-regional heterogeneity is not limited to the urban–rural divide but also includes disparities between city centers and suburbs, between urban agglomerations and smaller surrounding municipalities, or between tourist-attractive areas and their less attractive surrounding areas. In short, poverty can be found both in so-called “left-behind” regions and in regions considered engines of economic growth.

[1] The methodology implies exploiting the Microcensus Population Censuses, released every 10 years. Estimations are only provided for those countries where access was possible and/or granted, i.e. Spain, France, England & Wales

References:

European Commission (2018), Proposal for a REGULATION OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL on the European Regional Development Fund and the Cohesion Fund, COM/2018/372 final – 2018/0197 (COD))

European Commission (2017). Competitiveness in low-income and low-growth regions – The lagging regions report, 2014-2020. COMMISSION STAFF WORKING DOCUMENT. SWD(2017) 132 final

Davenport, A and Zaranko , B (2021). Leveling-Up: Where and How: Green Budget 2020, London: Institute for Fiscal Studies.

Dijkstra, L., Poelman, H., & Rodríguez-Pose, A. (2019). The geography of EU discontent. Regional Studies54(6), 737–753.

Fernandez‑Vazquez, E.; Diaz-Dapena, A.; Rubiera‑Morollon, F. and Viñuela, Ana (2020). Spatial Disaggregation of Social Indicators: An Info‑Metrics Approach. Social Indicators Research 152, 809–821

Fiorentino, S.; Glasmeier, AK; Lobao, L.; Martin, R. and Peter Tyler (2024). ‘Left behind places’: what are they and why do they matter? Cambridge Journal of Regions, Economy and Society , 2024, 17, 1–16.

Furlong, J. (2019) The changing electoral geography of England and Wales: Varieties of “left-behindedness”. Political Geography, 75, 102061

Houlden, V., Robinson, C., Franklin, R., Rowe, F. & Pike, A. (2024) ‘Left Behind’ neighborhoods in England: Where they are and why they matter. The Geographical Journal, 190, e12583.

Lichter, D. T., & Schafft, K. A. (2016). People and places left behind. The Oxford handbook of the social science of poverty, 317.

Ulrich-Schad, J. D., & Duncan, C. M. (2018). People and places left behind: Work, culture and politics in the rural United States. The Journal of Peasant Studies45(1), 59-79.

 

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