1. Main characteristics of socially excluded in Spain

The fight against poverty and exclusion is at the heart of OECD countries’ social policy agendas. Similar goals appear in the European Union agenda, for example, through the European Pillar of Social Rights and the United Nations objectives in many of its Sustainable Development Goals (e.g. SDG 1, 2, 3, 4, 5, 8 and 10). To move toward lasting reduction of poverty and social exclusion, countries have put in place social protection systems, like protecting individuals from concrete social risks like unemployment or health problems; they provide public services, like public education, health or childcare; and they develop and maintain social and labour inclusion programmes. While some circumstances and policy responses can be identified (such as minimum pensions for asset-poor seniors), individuals facing social exclusion are not a homogeneous group. They frequently confront multiple difficulties simultaneously, complicating social and labour market integration efforts. When applied in isolation and with no co-ordination with other policies, single policies are less effective than integrated services or packages combining cash support and social integration pathways to tackle these cases. However, to design and implement these multidimensional policies, policy makers should precisely identify priority issues to address and target groups needing support.

The concept of social exclusion, as used in this report, is relatively recent in the literature. It has only gained traction over the last two decades. Originally devised in 1970s France (Lenoir, 1974[1]), the term referred to individuals who were not covered by traditional social safety nets. However, the concept only started to be used more broadly, both in the literature and policy discourse, in the late 1990s. Specifically, the European Union’s decision to put social exclusion at the heart of its social policy agenda at the 2001 Lisbon Summit marked the onset of a much stronger focus on social exclusion. More recently, the 2010 Common European Plan for Europe 2020 and the work done by the European Union’s Social Protection Committee (European Commission, 2015[2]) have continued to place social exclusion front and centre.

Social inclusion policies gained additional significance with the European Pillar of Social Rights (EPSR) proclamation in 2017. This agenda places labour markets and social protection systems as the cornerstone of a well-functioning society and at the heart of the fight against poverty and social exclusion. The EPSR involves 20 core principles which detail goals to be achieved under the framework. Ten of these principles ascribe a key role to social services in combatting social exclusion.

The concept of social exclusion moves away from a sole focus on monetary poverty as the main metric to assess the inclusive potential of a society. The main criticism of relying only on monetary poverty1 to measure exclusion is that it fails to capture the multidimensional and dynamic nature of the inclusion barriers individuals might face (Saraceno, 2001[3]). In line with this criticism, most definitions of social exclusion incorporate the following elements (Bak, 2018[4]):

  • Multidimensionality: Social exclusion includes income, poverty and other aspects that capture an individual’s level of vulnerability. For example, an individual might suffer from mental health problems and have an insufficient income.

  • Dynamics: While the level of monetary poverty can change significantly from one year to another, social exclusion tries to capture the underlying factors that predict an individual’s vulnerability over a longer period of time. For example, a low level of education increases one’s risk of falling into poverty more broadly, even if one is not poor in a given year.

  • Non-participation: Social exclusion tries to gauge an individual’s ability to participate broadly in society and the activities a society deems relevant. For example, the long-term unemployed might not be able to fully participate in social activities due to their isolation.

  • Multi-level: Social exclusion is defined on the level of the individual but relates to factors beyond the individual level, such as households, communities and societal institutions. For example, the Roma population in Spain face inclusion barriers as a community that exceeds individual-level characteristics.

Despite agreement on the main factors integral to the concept of social exclusion, there is no agreed definition in the literature. One of the most popular definitions, which attempts to encompass the various factors pointed out by authors on the topic, comes from Levitas et al. (2007, p. 25[5]):

Social exclusion is a complex and multidimensional process. It involves the lack (or denial) of resources, rights, goods and services and the inability to participate in the normal relationships and activities available to the majority of people in a society, whether in economic, social, cultural or political arenas. It affects both the quality of life of individuals and the equity and cohesion of society as a whole.

Moving beyond theoretical concepts, evidence-based policy requires measuring, quantifying and characterising poverty and social exclusion affecting those needing concrete support. This is challenging since individuals and families experiencing exclusion must be profiled across many aspects. There are different ways to develop a set of measurable indicators that make it possible to assess the level of social poverty and exclusion in society. These indicators try to operationalise the multidimensional nature of social exclusion by measuring its different aspects. This section puts the different concepts of social exclusion into practice by comparing three definitions of social exclusion and attempting to identify socially excluded people using the information available in the European Union Statistics on Living Conditions and Income (EU-SILC) survey (see Box 1.1 for details about why EU-SILC was selected for this analysis).

Three different concepts are implemented: 1) the At Risk of Poverty or Social Exclusion (AROPE) indicator, which is included as a core variable in the EU-SILC survey, including a decomposition of each of its three dimensions; 2) an adaptation of the indicator proposed by Laparra, Zugasti Mutilva and García Laurtre (2021[6]); and 3) an indicator based on the ideas presented in Coumans and Schmeets (2015[7]).

The AROPE indicator, developed by Eurostat, is one of the most frequently used in European countries. In fact, AROPE is the main indicator used to monitor the EU 2030 target2 on poverty and social exclusion. It consists of three dimensions intended to each capture different aspects of social exclusion:

  1. 1. At risk of poverty: Having an equivalised disposable household income below 60% of the median equivalised household income in the country.

  2. 2. Low work intensity: Living in a household with low work intensity. Low work intensity households are those where the working-age adults work less than a combined 20% of their total work potential.

  3. 3. Severe material deprivation: Living in a severely materially deprived household. Households are considered to be severely material deprived if they are unable to afford four out of the following nine items: 1) pay rent or utility bills; 2) keep the home adequately warm; 3) face unexpected expenses; 4) eat meat, fish or a protein equivalent every second day; 5) have a week’s holiday away from home; 6) have a car; 7) have a washing machine; 8) have a colour TV; or 9) have a telephone. All households for whom at least one of the indicators is positive are considered at risk of poverty or social exclusion.

AROPE’s main advantages are that it is readily available in EU-SILC data and can therefore easily be calculated across different EU countries and over time. Moreover, the indicator does not involve complex variable transformations or index combinations. It is therefore transparent and easy to understand. However, its main disadvantage is that it captures a relatively small subset of dimensions related to social exclusion, namely poverty, material deprivation and limited labour market attachment. Other dimensions, such as housing, low education attainment, or limited social contacts, are not included, or indirectly (and partially) through the severe material deprivation variables. This does not necessarily mean that individuals facing these difficulties are not captured by AROPE because, often, socially excluded populations face several difficulties simultaneously (see Figure 1.3, further below). As a result, the indicator captures a broad share of the population in some countries and might therefore be too imprecise to identify those at the highest risk of social exclusion.

This approach constructs a synthetic index of social exclusion using data from a FOESSA Foundation survey for Spain in 2018. The starting point of the analysis is a theoretical framework of social exclusion based on previous work (Laparra et al., 2007[8]).

This framework consists of eight different dimensions of social exclusion3 with a total of 35 binary indicators calculated at the individual level. The indicators are normalised so that each dimension has the same weight and constructs an aggregate score for each person. This aggregated score is then normalised again to produce a distribution of non-negative scores with a mean of 1.0; a score of 0.0 is interpreted as “no exclusion”, and high scores are interpreted as “strong symptoms of social exclusion”. For example, Laparra (2007[8]) considers individuals with an index score of 2 or above to be socially excluded.

The main advantage of this approach is that it uses a comprehensive definition of social exclusion based on various indicators. It also allows for measurement on a continuous spectrum, thereby indicating the depth of social exclusion. Its main drawback is its complexity, making it hard to understand why someone is considered socially excluded. Moreover, the index was originally developed using survey material only available for Spain; therefore, its extension to other countries is not straightforward. An implementation in EU-SILC would require some adjustments to the original concept.

Coumans and Schmeets (2015[7]) developed a framework to measure social exclusion in the Netherlands using EU-SILC 2010 data (which features an ad hoc module on social exclusion). The approach uses four dimensions: participation (social, cultural, civic); access to basic rights and institutions; material deprivation; and normative integration. In total, the framework includes 46 binary indicators to measure these 4 dimensions. The aggregated indicator results from normalising each of the 4 dimensions on a scale from 0 to 3 and then summing across them. The index, called here NL-SE, then ranges from 0 (no exclusion) to 12 (total exclusion). Individuals scoring 10 or above are considered socially excluded.

One particularity of this approach is that it uses principal component analysis to determine which of the 46 original indicators do not explain much of the variation between individuals so as to exclude them from the analysis to increase the robustness of index construction. This approach also uses SILC data. However, given that many of their indicators stem from the 2010 ad hoc module of EU-SILC, NL-SE cannot be directly implemented for later waves, which is an important drawback.

Table 1.1 shows the share of individuals facing situations that might put them at risk of social exclusion, as captured by the three indicators described above. The results show that, on the one hand, AROPE and the FOESSA index cover the broadest share of the various dimensions of social exclusion. On the other hand, the NL-SE index, by placing more emphasis on the health aspects of social exclusion, covers only a limited share of the individuals affected by employment-related barriers.4

For this report, the AROPE indicator is adopted as a definition of social exclusion because:

  • It is a well-known and widely used standard based on a well-documented methodology developed by Eurostat.

  • It is part of the core variables of EU-SILC, a clear advantage that makes it possible to cross-check results with other European countries.

  • The way it is built makes it simple to understand and interpret. Alternative definitions are richer but work as a black box, making the interpretation of results more complex.

  • While each country does have its own history and circumstances, and certain definitions might be more appropriate in some contexts than in others, leading to more accurate results, having a common definition of social exclusion is important as it allows for comparability across countries.

Figure 1.1. shows that the relative size of the working-age AROPE population varies greatly among OECD countries for which data are available in EU-SILC. It ranges from less than one person in ten in the Czech Republic to three in ten in Greece, showing differences of more than 20 percentage points. Southern European countries rank at the top with an AROPE share of more than 25%, except Portugal. The working-age population in some central/eastern and northern European countries is relatively less likely to be at risk of poverty or social exclusion. The average of those countries is 18.4%. The shares of more than half of countries are centred around the average, ranging between 15% to 20%.

The share of AROPE among working-age individuals has remained stable over time, except during big economic crises. Trends do differ across countries, however. The share of socially excluded people increased in 2008-09 and 2020-21. Between the financial and coronavirus (COVID-19) crises, the economic conditions improved in all OECD countries, and the share of AROPE individuals decreased.5 This is illustrated in Figure 1.2 by the OECD-24 average (countries for which the data are available in EU-SILC 2015-20), which shows a gradual downward trend in the average AROPE share in recent years. Figure 1.2 shows that the decrease in the AROPE population includes countries that started from very different situations after the 2008 financial crisis. For example, Finland had the lowest AROPE share in 2015, falling slightly to about 15% in 2020. Greece has consistently shown the highest share since 2015, but this share sharply decreased by 7% in 2020. However, a few countries show a different trend. This includes France, where the AROPE share slightly decreased in 2018 and bounced back in 2020 to a higher level than in 2015.

As mentioned previously, people can face several difficulties simultaneously, which calls for advocates in favour of multidimensional holistic policy solutions (instead of very targeted and isolated policies). An example of this is illustrated in Figure 1.3. Venn diagrams clearly show that AROPE components are not mutually exclusive. Indeed, an income-poor individual can also face (or not) situations of severe material deprivation and/or labour market exclusion. A common pattern across European countries is that individuals at risk of poverty constitute the largest group of the working-age AROPE population; low work intensity is the second most frequent situation; and situations of severe material deprivation are less frequent. However, the relative incidence of each component and how they overlap differs across countries, reflecting socio-economic differences and, to some extent, the structure of social protection systems, especially in terms of the support provided to populations in most need.

For example, in Poland and Switzerland, the working-age AROPE population is largely dominated by individuals at risk of poverty and overlaps between the three groups are very small, suggesting that monetary poverty is the main factor, and the population facing severe material deprivation issues is, if not marginal, very small. In Finland and Ireland, the relative size of the income-poor group is smaller than in Poland and Switzerland and overlaps with the two other components are much larger. The working-age AROPE population in Finland shows a very small group of people in situations of severe material deprivation, a quite large incidence of the low-work intensity group, and a large overlap of this group with the income-poor. Something similar is observed in Ireland, but with a smaller overlap between the income-poor and low-work intensity groups and a slightly higher incidence of material deprivation (see Figure 1.3).

To map the population identified as socially excluded to concrete policies, it is necessary to identify a relevant set of variables (or social exclusion barriers) available in the EU-SILC. In line with the Faces of Joblessness approach (Fernandez et al., 2016[10]), these barriers should explain why some people fall into or have trouble exiting situations of social exclusion.

This kind of analysis does not need to be designed to identify causality effects. Indeed, some of the explanatory variables can be at the origin of social exclusion (i.e. genuinely cause it), whereas others might be a consequence of it; or, in other cases, a marker can be correlated with the causes/effects but is not at their origin. A social exclusion marker is understood here as a characteristic that is measurable at the individual or household level. For example, being long-term unemployed could be an individual-level barrier to social inclusion. It can either be a driver of social exclusion by excluding people from social networks and limiting their income, as well as a consequence of social exclusion if an individual is unable to find a job due to other limitations.

This section discusses potential indicators using EU-SILC data to describe the socially excluded population and provides examples of their prevalence in Spain.

Table 1.2 lists potential indicators (called “barriers”) derived from EU-SILC core variables. All barriers included in this list highlight different aspects of poverty and social exclusion, such as income, housing, education, health or employment. Barriers are presented by thematic area along with short explanations of how they can be calculated from EU-SILC variables and the policy areas they link to. Policy areas linked to each marker should be understood broadly: not all those who face a marker will necessarily need access to services on one or more of the mentioned policy areas. However, some of these policies may be relevant to some population groups facing the marker.

The above list can be complemented with relevant information drawn from EU-SILC ad hoc modules (see Box 1.2). Although they are not available for all years, some of these thematic barriers can provide valuable information for specific research.

To illustrate how these social exclusion barriers can be used to provide a more detailed characterisation of the target population,6 the OECD selected a group of ten barriers according to simple criteria: policy relevance in the current Spanish context; data availability in EU-SILC; understandability of the indicator; and discriminatory power (defined as the gap in the percentage of individuals affected by the marker in the target population and the non-target population).

Table 1.4 shows how it is possible to characterise a given target population according to objective criteria. For instance, the three variables that most discriminate between individuals in the target population and those outside it are “cannot keep the house warm” (linked to situations of very low income and material deprivation), being “long-term unemployed” (linked to labour market exclusion) and being “born abroad” (linked to issues faced by some migrants like language issues or lack of social networks).

Individuals in the target population are much more likely to be affected by barriers. This is not a foregone conclusion because, with the only exception of the marker on “severe material deprivation and no income support”, there is no direct relation between being affected by one marker and being AROPE. Figure 1.4 shows the share of individuals in the working-age population affected by the selected barriers. For example, 28% of individuals in the target population are long-term unemployed compared to only 5% in the non-target population. Looking at a marker with less incidence (but no less important from a policy perspective), about 3% of the target population faces strong care constraints, compared to only 1% in the non-target population.

Figure 1.5 shows the incidence of selected barriers for the target population between 2016 and 2020. Overall, barrier incidences are stable. Nevertheless, some trends can be observed. A first group (material deprivation and no income support, difficulties affording dental treatment, strong health limitations and care duties) are relatively stable with only minor changes over time. A second group (long-term unemployment, born abroad, rent overburdened and cannot keep the house warm) is influenced, in different ways, by the economic cycle. The decrease in the presence of low-educated people and the absence of the Internet at home reflect long-term trends in society as a whole.

Finally, individuals in the target population are also much more likely to be affected by multiple barriers simultaneously. Figure 1.6 shows the number of barriers (among the ten variables selected, listed in Table 1.4) for the target and non-target populations that individuals are affected by simultaneously. For example, 57% of individuals in the non-target population are not affected by any barriers at all, whereas the same share is only 8% for individuals in the target population. Conversely, only 3% of individuals in the non-target population are affected by three or more barriers, whereas the corresponding share in the target population is 48%. This result illustrates the multidimensional nature of social exclusion and calls for a holistic approach to address, fight against or prevent it. Empirical analysis (e.g. clustering algorithms) might provide further information about the characteristics of the complex issues various population groups face (see the example shown in Box 1.3).

After a brief discussion about the concepts of poverty and social exclusion, this chapter presented an inventory of potential data sources available in Spain to identify and characterise the population groups facing these situations. The chapter proposed a concrete approach based on the EU-SILC survey that can lead to quantitative analysis. The approach can be tailored to address different policy topics related to integrating socially excluded populations.

The preliminary results show how, even when a common harmonised definition of socially excluded populations is adopted, similar levels of socially excluded can hide extremely different situations. For example, in 2020, the share of working-age AROPE individuals in Finland, Poland and Switzerland was within 1 percentage point, around 16%. However, the internal structure of this 16% differs significantly in these three countries and calls for different policy actions, which supports the idea of tailor-made analysis to inform policy action in each country.

Within countries, the number of different realities behind the broad concept of AROPE is extremely rich. To provide a more detailed and granular vision of them, the chapter presented a broad set of indicators (called “barriers”) reflecting different aspects of social exclusion.

It showed that populations in poverty or social exclusion in Spain often face more than one barrier, calling for multidimensional policy interventions. Preliminary results show that the AROPE population in Spain (in 2020) is far from being a homogeneous group. In addition to a large group of people where social exclusion barriers are relatively weak and with a high prevalence of youth, the other six groups would need very different (but always multiple) social inclusion policies: people living with old-age adults in rural areas, unemployed old working-age adults living in rural areas, poor migrants with children, extremely poor middle-age migrant women with children living in big cities and very poor individuals living in areas with high levels of crime and pollution.

Depending on the issue that governments, researchers and policy makers wish to address, a specific subset of relevant social exclusion barriers could be used to establish population profiles, seen as a combination of a population group sharing one or more issues. The combined analysis of the socio-economic characteristics of groups and the main barriers provides concrete and relevant information to design, co-ordinate and decide on policy interventions.


[4] Bak, C. (2018), “Definitions and measurement of social exclusion —A conceptual and methodological review”, Advances in Applied Sociology, Vol. 08/05, pp. 422-443, https://doi.org/10.4236/aasoci.2018.85025.

[7] Coumans, M. and H. Schmeets (2015), “The socially excluded in the Netherlands: The development of an overall index”, Social Indicators Research, Vol. 122/3, pp. 779-805, https://doi.org/10.1007/s11205-014-0707-6.

[2] European Commission (2015), Portfolio of EU Social Indicators for the Monitoring of Progress towards the EU Objectives for Social Protection and Social Inclusion, Social Protection Committee Indicators Sub-group, Publications Office, Luxembourg, https://ec.europa.eu/social/main.jsp?catId=738&langId=en&pubId=7855.

[9] Eurostat (2020), EU statistics on income and living conditions (EU-SILC), https://ec.europa.eu/eurostat/web/microdata/european-union-statistics-on-income-and-living-conditions.

[10] Fernandez, R. et al. (2016), “Faces of Joblessness: Characterising Employment Barriers to Inform Policy”, OECD Social, Employment and Migration Working Papers, No. 192, OECD Publishing, Paris, https://doi.org/10.1787/5jlwvz47xptj-en.

[8] Laparra, M. et al. (2007), “Una propuestade consenso sobre el concepto de exclusión. Implicaciones metodológicas”, Revista Española del Tercer Sector no 5, enero-abril 2007, https://dialnet.unirioja.es/descarga/articulo/2376685.pdf.

[6] Laparra, M., N. Zugasti Mutilva and I. García Lautre (2021), “The multidimensional conception of social exclusion and the aggregation dilemma: A solution proposal based on multiple correspondence analysis”, Social Indicators Research, Vol. 158/2, pp. 637-666, https://doi.org/10.1007/s11205-021-02707-6.

[1] Lenoir, R. (1974), Lex exclus: Un français sur dix, Seuil, Paris.

[5] Levitas, R. et al. (2007), The Multi-dimensional Analysis of Social Exclusion, Department for Communities and Local Government (DCLG), Bristol.

[3] Saraceno, C. (2001), “Social Exclusion: Cultural Roots and Diversities of a Popular Concept”, Columbia University.


← 1. Defined here as all those households with equivalised household incomes below 50% of the country median.

← 2. For more details about the EU 2030 targets, see https://commission.europa.eu/energy-climate-change-environment/overall-targets-and-reporting/2030-targets_en.

← 3. These are: 1) participation in employment; 2) participation in consumption; 3) political participation; 4) access to education; 5) access to housing; 6) access to health; 7) social conflict; and 8) social isolation.

← 4. Other results, not included here, also show that the indicators do not identify the same individuals. For example, in 2020, the long-term unemployed represented 9.3% of the working-age population in Spain. About 64% of them are identified as socially excluded by both FOESSA and AROPE; 26% are identified by FOESSA and not by AROPE; and 3% are identified by AROPE and not by FOESSA. Finally, 7% were not identified as socially excluded by any of the algorithms.

← 5. The causality of these two facts is not obvious. Better economic conditions, in general, imply less material deprivation and higher activity rates in the labour market. But, if not combined with a less unequal income distribution (or at least not more unequal), this does not necessarily imply a mechanical decrease in poverty rates, which are the most important component of AROPE.

← 6. In this case, the target population is identified as working-age AROPE, i.e. individuals aged 16-64 at risk of poverty or social exclusion (according to the standard Eurostat definition) in Spain.

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