Boosting Social Inclusion in Spain

1. Main characteristics of socially excluded in Spain

Abstract

This chapter briefly discusses the concept of social exclusion and recalls its multidimensional nature. It presents data on the different population groups living in Spain that can be considered as socially excluded and characterises them in terms of the barriers to social inclusion they face and other socio‑economic characteristics. The approach presented in this chapter can serve as a tool for designing holistic social inclusion programmes and informing policy choices and priorities.

1.1. Social exclusion is multidimensional and complex

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.

1.2. Social exclusion is high in Spain according to several definitions and indicators

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).

Box 1.1. Choice of data sources to measure social exclusion in Spain

The analysis of disadvantaged populations, aimed at orienting concrete policy responses, must rely on good-quality information. Since the concept of social exclusion is large, the data source used to measure it should include, as much as possible, information on many topics, including income and wealth, family composition, housing and living conditions, health, education, background, labour status, etc.

To measure social exclusion in Spain, several possible data sources were considered. The OECD established objective criteria to select the best-suited source, as detailed below.

Possible candidates

The following surveys that measure various aspects of social exclusion in Spain were considered for this analysis:1

• European Union – Statistics on Income and Living Conditions (EU-SILC), covers the most relevant areas.

• National Living Conditions Survey (Encuesta de Condiciones de Vida, ECV), the source for EU-SILC.

• European Union – Labour Force Survey (EU-LFS), focuses on labour market integration.

• FOESSA Foundation (Promotion of Social Studies and Applied Sociology) – Survey on Social Integration and Social Needs (EINS), covers the most relevant areas.

• Eurofound – European Quality of Life Surveys (EQLS), focuses on life quality and social integration, but no information available on income and labour market integration.

• National Health Survey (ENSE), focuses on health issues.

• National Family Budget Survey (EPF), focuses on income, budget and expenditure.

• National Survey on the Homeless (EPSH) and the Survey on Centres and Services for the Homeless (ECAPSH), specific to the homeless population.

A possible alternative to surveys is the use of administrative records containing individual/household information on household composition, income, health status, education, and other living condition variables. The use of administrative data may enable more fine-grained breakdowns of the target population and the barriers to social inclusion. However, such an integrated database for the whole country is not available in Spain yet.

Selection criteria

Comparison of the surveys looked at survey contents (variables available and on which topics) and survey characteristics (sample, geographical coverage and periodicity). Regarding content, surveys were compared according to:

1. income, assets and access to services

2. household composition, housing and living conditions

3. labour market activity and educational background

4. health and behavioural issues

5. (migrant) background and social networks.

Regular annual waves of a survey allow for more reliable cross-checking comparisons, provide flexibility to analyse years of interest and, if relevant, open the door to analysing the dynamics of topics under study. As of March 2022, relatively recent waves (ranging from 2018 to 2020) were available for EU‑SILC, EU-LFS, FOESSA-EINS, EPF and ECAPSH. To include the mental health dimension, the related special module in EU-SILC 2018 or the FOESSA-EINS survey, with 2018 as the latest year, were available.

Sample size is also extremely important in performing reliable analyses. The surveys with the biggest sample sizes were EU-LFS and ENSE, which contain about 40 000 observations, whereas EU-SILC (and ECV) contain about 15 000 and FOESSA about 12 000. Although FOESSA-EINS covers some groups missing in EU-SILC (such as homeless people), its sampling methodology might have some drawbacks. In fact, FOESSA-EINS strongly oversamples households with social exclusion indications by filtering households by a set of preliminary questions. Even if weights are adjusted to provide accurate demographics, this might lead to biased results in some cases.

Conclusion

EU-SILC and FOESSA-EINS appeared to be the two sources that provided good-quality information about poverty and social exclusion situations in Spain. In addition, EU-SILC was the reference for comparative statistics on income distribution and social inclusion in Europe. Other sources, like labour force surveys, the National Health Survey and the National Survey on Homeless, contain excellent information about specific and relevant topics or population groups but do not cover other equally important ones.

Building on theoretical reflections to measure social exclusion, the Survey on Social Integration and Social Needs of the FOESSA Foundation was suitable for understanding the scale and pattern of multidimensional social exclusion in the Spanish context. However, from the OECD’s perspective, EU-SILC was preferred over FOESSA-EINS because it contains more detailed information on material deprivation, income sources and monthly activity status. EU-SILC also enables easier cross-country comparisons since it is harmonised across countries (and harmonisation is why EU-SILC would be preferred over the original EVC source).2

Notes:

1. This list is not comprehensive; in particular, regional sources are not included. However, it covers almost all well-documented sources available at national and European levels. Unfortunately, combining such information at the micro-data level from separate surveys is technically impossible. The main reason is that these surveys sample different individuals that cannot be merged at the single observation level.

2. It is worth mentioning that some relevant aspects of social exclusion are not covered by EU-SILC. These include access to some services such as public transportation or employment services; difficulties in accessing housing; language barriers or illiteracy; behavioural issues, such as suicide attempts, gambling, criminal records, etc.; job-search networks; participation in elections or political parties; and information about homeless people.

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]).

1.2.1. The AROPE indicator

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

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. 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.

1.2.2. Laparra social exclusion index

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.

1.2.3. Coumans and Schmeets social exclusion

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

Table 1.1. Comparison of different indicators to identify the socially excluded in Spain

Share of each disadvantaged group, in percentage, 2020

	AROPE	FOESSA	NL-SE
Not employed	46	48	37
Low education	40	46	40
Poor	100	62	54
Cannot keep house warm	66	63	72
Long-term unemployed	67	91	52
Overcrowding	52	54	50
No Internet at home	64	99	67
Strong health limitations	49	100	69

Note: In 2020, long-term unemployed represented 9.3% of the working-age population in Spain. The AROPE index captures 67% of them, whereas the FOESSA index and NL-SE index detect 91% and 52%, respectively. Large and significant differences between indicators are highlighted.

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

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.

1.2.4. The size of social exclusion shows large differences across European 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%.

Figure 1.1. Share of persons at risk of poverty or social exclusion among the working-age population in selected OECD countries, 2020 or latest available year

Note: OECD‑26 average is the unweighted mean of shares of the following countries: Austria, Belgium, the Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Latvia, Lithuania, Luxembourg, the Netherlands, Norway, Poland, Portugal, the Slovak Republic, Slovenia, Spain, Sweden, Switzerland and the United Kingdom. In EU-SILC, the income refers to the year before the indicated wave, meaning that this chart illustrates the pre‑COVID situation. For Iceland and the United Kingdom, the latest wave available is 2018.

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

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.

Figure 1.2. Trends in the share of persons at risk of poverty or social exclusion among the working-age population in selected OECD countries, 2015, 2018 and 2020

Note: OECD‑24 average is the unweighted mean of shares of the countries listed in the note of Figure 1.1., excepting Iceland and the United Kingdom. See next section for a focus in Spain.

Source: OECD calculations based on EU statistics on income and living conditions (EU-SILC) 2015, 2018 and 2020, https://ec.europa.eu/eurostat/web/microdata/european-union-statistics-on-income-and-living-conditions.

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).

Figure 1.3. Composition of populations at risk of poverty or social exclusion in selected OECD countries, 2020

Note: ARP, SEV_DEP and LWI denote respectively “at risk of poverty”, “severe material deprivation” and “low-work intensity”, according to the standard AROPE definition (see above). See next section for a focus in Spain.

Source: OECD calculations based on EU statistics on income and living conditions (EU-SILC), https://ec.europa.eu/eurostat/web/microdata/european-union-statistics-on-income-and-living-conditions.

1.3. Those socially excluded in Spain face multiple and compounded barriers

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.

1.3.1. EU-SILC core variables provide rich information characterising the socially excluded

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.

Table 1.2. List of potential barriers in EU-SILC core variables

Thematic area	Marker	Definition	Policy area
Employment	Long-term unemployed	Individuals out of work for at least 12 months AND are currently looking for work.	- Active labour market policies - Social integration through labour market integration - Income support linked to labour integration - Second chance schools and lifelong learning programmes (might also be linked to lifelong learning and upskilling or reskilling programmes)
	No recent work experience	Individuals who did not work over the past 12 months.	- Active labour market policies - Social integration through labour market integration - Income support - Work incentives
	Care duties	Individuals who: 1) live in a household with another household member in need of care; and 2) indicate staying out of the labour force for care‑related reasons. Individuals in need of care are either children under the age of 12 who receive less than 30 hours per week of childcare through other channels OR elderly (65+) inactive household members who report severe limitations to daily activities OR adult household members reporting severe limitations to daily activities and listing their disability as the main reason for inactivity.	- Long-term care for dependent persons (facilities and access) - Childcare (facilities and access, such as early childhood education) - Work-life balance policies - Income support for caregivers - In-kind support for individuals who need care and carers (home help for activities of daily living, day and/or overnight care support) - Telecare services - Residential care programmes
Education	Low education and not in education	Individuals who have completed, at a maximum, lower secondary education AND are currently not in education.	- Education for adults (e.g. back-to-school programmes) - Centres for early school leavers (second-chance schools) - Lifelong learning policies - School-labour market transition - Social policies integrating the provision of labour, educational and social support for those not in education, employment or training (NEET) in general (when individuals are young) - Policies to prevent school absenteeism
Health	Strong health limitations	Individuals indicating that they are severely limited in their daily activities due to health reasons.	- Access to healthcare - Long-term care (facilities and access) - Income support (sickness and disability benefits)
	Difficulties affording medical treatment	Individuals who needed medical treatment (in the preceding year) AND could not afford it.	- Access to healthcare (universal health insurance) - Income support - Mental health programmes for vulnerable people
	Difficulties affording dental treatment	Individuals who needed dental treatment (in the preceding year) AND could not afford it.	- Access to healthcare (universal health insurance) - Income support
Housing	Cannot keep house warm	Individuals living in households who indicate they cannot afford to keep their dwelling adequately warm.	- Housing policies - Specific cash transfers (e.g. energy vouchers) - Programmes against energy poverty - Income support
	No Internet at home	Individuals who do not have an Internet connection for personal use at home.	- Digitalisation policies (access to affordable digital devices and digitalisation training) - Digital education - Specific cash support
	Rent overburdened	Individuals living in a household renting their dwelling AND the rent exceeds 30% of disposable household income.	- Access to public social housing - Housing benefits (specific cash support) - Income support - Rent transfers - Accompaniment for persons/households under emergency housing situations
	Dwelling in bad conditions	Individuals living in a household where at least one of the following applies: the dwelling has a problem with a leaking roof and/or damp ceilings, dampness in the walls, floors, or foundation and/or rot in window frames and doors.	- Access to public social housing - Housing benefits (specific cash support) - Emergency housing services - Accompaniment for persons/households under emergency housing situations - Income support - Family policies (support to children/youth living in poor housing conditions)
	Overcrowded household	Individuals living in a household that does not have: 1) one room for the entire household AND 2) one room for each couple; 3) one room for each adult single; 4) one room for each pair of same‑sex 12‑17 year‑olds; 5) one room for each 12‑17 year‑old not previously included; 6) one room for each pair of children under 12.	- Access to social housing - Housing benefits (specific cash support) - Income support - Family policies (support to children/youth living in poor housing conditions) - School support for children living in poor housing conditions (e.g. a place at school where they can do homework)
Living area	Crime in area	Individuals living in a household that perceives crime, violence or vandalism to be a problem in the area.	- Policies to mitigate degradation in urban areas - Urban integration (e.g. neighbourhood development integration) - Better access to social work - Security and police
	Pollution in area	Individuals living in a household that perceives `pollution, grime or other environmental problems to be a problem in the area.	- Policies to mitigate degradation in urban areas - Urban integration (e.g. neighbourhood development integration) - Environmental policies (at the macro level related to national environmental policies, at the micro level, can be related to the environmental education of people)
	Lives in a rural area without a car	Individuals living in a household in a rural area that does not own a car.	- Public transport -Provision of basic services such as schooling or medical assistance - Digital welfare in general (administrative, health, social services, social protection) - Digitalisation policies (access to affordable digital devices and digitalisation training) - Mobile and itinerant services
Migration	Born abroad	Individuals who were born abroad.	- Legal and administrative support (to access social services and benefits) - Language classes
	Born outside EU	Individuals born outside the European Union.	- Legal and administrative support (to access social services and benefits) - Language classes
	Recent non-EU migrant with low education	Individuals born outside the European Union AND arrived in the country less than ten years ago AND have a maximum of lower secondary education.	- Legal and administrative support (to access social services and benefits) - Access to education, especially for adults (in theory, the access to education for children is guaranteed in Spain) - Language classes - Active labour market policies
Public support	Severe material deprivation or at risk of poverty plus no income support	Individuals living in households at risk of poverty or affected by severe material deprivation AND do not receive family, housing or social exclusion benefits.	- Income support - Basic help for people in a severe material deprivation situation (access to daily food, clothes and shelter) - Legal and administrative support (to access social services and benefits)
	Strong health limitations plus no sickness/disability benefits	Individuals suffering from strong health limitations (as per the above definition) AND do not receive sickness or disability benefits.	- Disability benefits

Note: The definition column provides the formal definition of the marker in EU-SILC. The policy area column links the marker to policies that can be used to address the marker.

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

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.

Box 1.2. Using EU-SILC ad hoc modules to enrich the analysis of social exclusion

Each year, EU-SILC releases, along with the core variables of the survey, a group of extra variables that give detailed information about a specific topic. This group of variables is called the ad hoc module. Ad hoc modules are designed to shed light on specific topics, though not annually.

Topics change every year according to a calendar set by Eurostat. For example, in 2017, the ad hoc module focused on health and children’s health; in 2018, on material deprivation, well-being and housing difficulties; in 2019, on intergenerational transmission of disadvantages, household composition and evolution of income; etc. Not all ad hoc modules contain relevant information for studying social exclusion, but some of them are extremely relevant and might be used to enrich the characterisation proposed in this section.

Table 1.3. List of potential barriers in EU-SILC ad hoc variables (2018, 2019 and 2020)

Thematic area	Year	Marker	Definition	Policy area
Household wealth	2020	Less than 3 months of savings	Households whose savings are insufficient to cover three or more months of regular monthly expenses	- Financial literacy training - Income support
		Has to borrow money/draw down savings every month	Households that have to draw down savings every month to pay for their monthly expenses	- Financial literacy training - Income support
Family history	2019	Both parents born abroad (only for adults aged 25‑59)	Individuals whose parents (both mother and father) were born abroad. This indicator is only available for those 25 to 59 years old.	- Legal and administrative support (to access social services and benefits) - Language classes
Mental health	2018	Feeling down most or all of the time	Individuals who indicate that they feel down in the dumps most or all of the time	- Access to healthcare (mental health services) - Access to counselling and social work
		Feeling depressed most or all of the time	Individuals who indicate that they feel depressed most or all of the time	- Access to healthcare (mental health services) - Access to counselling and social work
		Feeling lonely most or all of the time	Individuals who indicate that they feel lonely most or all of the time	- Access to healthcare (mental health services) - Access to counselling and social work - Social integration policies on a community level (e.g. community centres/activities)
		Unable to rely on material help from others	Individuals who indicate that they are unable to rely on friends, family or acquaintances for material help if the need should arise	- Social integration policies on a community level (e.g. community centres/activities)
		Unable to rely on non-material help from others	Individuals who indicate that they are unable to rely on friends, family or acquaintances for non-material help if the need should arise	- Social integration policies on a community level (e.g. community centres/activities)

Source: OECD compilation based on EU-SILC 2018, 2019 and 2020, https://ec.europa.eu/eurostat/web/microdata/european-union-statistics-on-income-and-living-conditions.

While ad hoc variables have the disadvantage of only being available for one particular year, they can nevertheless provide additional information on social exclusion not available within the core EU-SILC variables. Table 1.3 shows these additional barriers based on ad hoc variables, their definitions, and the policy area they link to.

1.3.2. A concrete example of how social exclusion barriers can be used to characterise the AROPE population in Spain

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).

Table 1.4. Several criteria can be used to select barriers for analysis

Variable (Derived from EU-SILC core)	Policy relevance	Share of missing observations among AROPE	Easy to understand	Discriminatory power
Long-term unemployed	- Active labour market policies - Social integration - Income support - Lifelong learning	1.2%	Easy	23%
Care duties	- Childcare services - Long-term care services - Income support for caregivers	0.2%	Hard	2.2%
Low education and not in education	- Lifelong learning - Remedial education - Policies for those not in education, employment or training (NEETs) - Prevention of absenteeism	1.2%	Moderate	15%
Strong health limitations	- Access to healthcare - Long-term care - Disability benefits	1.2%	Easy	4%
Difficulties affording dental treatment	- Access to healthcare - Income support	1.3%	Easy	9%
Cannot keep house warm	- Energy-related cash transfers - Income support	0.1%	Easy	25%
Rent overburdened	- Social housing - Housing benefits - Family benefits - Accompaniment for persons/households under emergency housing situations	0.5%	Moderate	18%
No Internet at home	- Digitalisation policies - Specific cash transfers	1.2%	Easy	11%
Born abroad	- Legal and administrative support (to access social services and benefits) - Language classes	1.2%	Easy	21%
Severe material deprivation or at risk of poverty, plus no income support	- Income support - Access to social work	0%	Hard	10%

Note: Except for the columns “Policy relevance” and “Easy to understand”, which are based on a qualitative assessment of the barriers, the columns give percentage values based on EU-SILC 2020 data. The Discriminatory Power of each variable refers to the percentage point difference between the share of individuals affected in the target population and the share affected in the non-target working-age population.

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

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 1.4. Share of population affected by different barriers

Note: The income support considered in the “Severe material deprivation or ARP plus no income support” barrier includes social assistance (social exclusion not elsewhere classified), family and housing benefits.

Source: OECD calculations based on EU statistics on income and living conditions (EU-SILC), https://ec.europa.eu/eurostat/web/microdata/european-union-statistics-on-income-and-living-conditions.

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).

Figure 1.5. Changes in the share of population affected by different barriers

Note: The income support considered in the “Severe material deprivation or ARP plus no income support” barrier includes social assistance (social exclusion not elsewhere classified), family and housing benefits.

Source: OECD calculations based on EU-SILC 2016, 2017, 2018, 2019 and 2020, https://ec.europa.eu/eurostat/web/microdata/european-union-statistics-on-income-and-living-conditions.

Figure 1.6. Number of simultaneous barriers affecting individuals

Note: Only the ten variables listed in Table 1.4 are considered in the count.

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

Box 1.3. Clustering might help identify sub-groups within socially excluded populations

This box presents preliminary results of running a clustering algorithm using seven clusters and the ten social exclusion barriers detailed in Table 1.4.¹ In addition, other individual and household characteristics were estimated for each cluster. The main results are summarised in Table 1.5.

Table 1.5. Example of clustering applied to the socially excluded in Spain

Cluster	Size	Prevalent social exclusion barriers	Other characteristics	Suggested policies
1	35%	- Care duties slightly above the average - All other markers below the average	- High share of youth - Many are working, and some have no work experience - High share of couples without children - Low incidence in Madrid and the east (Catalonia, Valencia and Baleares)	- No major markers of social exclusion. Young adults generally have low incomes, but this is a temporary situation in many cases. - Policies to integrate youth into the labour market
2	15%	- Exclusively long-term unemployed - Slightly higher prevalence of low education	- Higher share of living with elderly adults - Many live in the south and centre - Higher share in rural areas	- Active labour market policies (including training) are key to integrating this group
3	5%	- Very high share of health problems, low education, and no Internet at home - Low incidence for other markers	- Mostly older working-age adults - More male and rural - High incidence in the south and northwest - Less poor than other groups	- Ensuring access to healthcare and disability benefits - Regionally targeted activation policies as these individuals live in regions with weak labour market outcomes - Training and other remedial education programmes - Digitalisation policies
4	16%	- Medium intensity on most exclusion markers - Very high levels of inability to afford dental treatment and pronounced inability to keep the house warm	- Mostly rural and older - Less poor than other groups	- Regionally targeted activation policies as these individuals live in regions with weak labour market outcomes - Targeted transfers that address the lack of affordability of heating/healthcare in this group - Digitalisation policies to increase access to resources
5	9%	- Very high share of foreigners - Many rent overburdened and long-term unemployed - Low on most other markers	- More female and middle‑aged - Many live with children - Mostly in urban areas - Poor - Likely to live in overcrowded housing in bad condition	- Language skills where necessary - Legal and administrative support - Family benefits - Support for childcare - Housing support - Active labour market policies
6	10%	- Almost only foreigners - Highest share of care duties - Medium to high incidence on most other markers	- Mostly female and urban - Also middle‑aged and likely to live with children - Mostly in Madrid and the east - Poor - Likely to live in overcrowded housing in bad conditions	- Language skills where necessary – Legal and administrative support - Family benefits - Support for childcare - Housing support
7	9%	- High incidence on most markers (except care duties) - Very high share of not receiving public support	- Many either single or in two+ adult households - Very poor (in the first decile) - Urban and very likely to live in the Canary Islands - High levels of crime and pollution in areas where individuals live	- This group likely requires multiple interventions and an integrated approach to service provision, as they are the most excluded - Cash transfers as this group has a very high incidence of lack of public support

Note: These results and suggestions are preliminary and should be seen as highlighting policy domains warranting further investigation. Mapping current social integration policies will be a key complement to providing more concrete and feasible recommendations.

1. Technical details about the experimental approach (clustering algorithms, model selection, stability, etc.), as well as detailed descriptive statistics of each cluster, are outside the scope of this report and will be further developed in forthcoming publications.

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

1.4. Conclusion

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.

References

[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.

Notes

← 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.