Abstract
This work adopts different approaches to analyse situations of poverty and extreme poverty in Spain during the last decade, considering different monetary thresholds, measures of severe material deprivation and the combination of both. The determining factors of these situations and the patterns that act as a link between extreme poverty and homelessness are also examined. The results of the study show that for the most restrictive thresholds of 10 per cent and 20 per cent of the median equivalised disposable income the smallest variations during the series are observed, confirming that situations of such deep poverty are not influenced by the cycle since they do not respond to economic stimuli. The determinants of extreme poverty suggest that public policies should be target towards high-risk groups, such as single person households, households with children, younger individuals, individuals with a low educational attainment, and of foreign nationality. Finally, an interesting result is that the profile of individuals in situations of consistent poverty have the greatest similarities to the group of people experiencing homelessness.
Introduction
Far from being a problem that only affects developing countries, poverty is becoming increasingly present in developed countries of the European Union (O’Sullivan, 2020). In Spain, according to some reports, around 800,000 people have fallen into severe poverty2 due to the health crisis (Oxfam Intermón, 2021). Moreover, there has been a growing trend since the end of 2008, placing the country at the top of the European ranking in the different classifications of poverty indicators (Gilsanz, 2014). In addition to the fall in the lowest incomes, inequality in their distribution has increased, with the Great Recession3 acting as a turning point (Moliné, 2019). A clear sign of this issue lies in the increase in the number of households benefiting from minimum income schemes over recent years, with very notable differences between regions (CES, 2021).
This work analyses the situations of poverty and extreme poverty in Spain during the last decade. One of the main aims of this study is to apply different techniques for the measurement of poverty and extreme poverty to understand trends, patterns and commonalities. The scarcity of studies that have dealt with this problem in the Spanish case motivated this work.
In this article, we use the most widespread approaches to enumerate poverty and extreme poverty. These include relative monetary thresholds and severe material deprivation situations Also, a combination of the two situations is considered, known as consistent poverty (Hick, 2014).
Identifying the factors that most increase the probability of being in a situation of poverty or extreme poverty is also one of the aims of this study. So, the determinants of poverty and extreme poverty are analysed in detail, by applying different logistic regression models, and by considering some of the main socioeconomic characteristics of the Spanish households.
The study of these situations of high vulnerability suggests considering whether there is a certain nexus between extreme poverty and the possibility of experiencing homelessness (Johnsen and Watts, 2014; Sharam and Hulse, 2014). For this reason, once the situations of poverty and extreme poverty in Spain have been analysed, the profiles that share the greatest similarities with the situation of homelessness are identified.
The following work is structured as follows. In the second section, a literature review is proposed, with different approaches to the measurement of extreme poverty, its patterns and links with homelessness. The third section of the work presents the methodology, while the fourth section explains the database in detail. The fifth section contains the results of the study, and the sixth section concludes.
Poverty and extreme poverty: different approaches to its measurement, patterns and links with homelessness
Analysing poverty from a purely monetary perspective has certain advantages. The World Bank considers people to be in a situation of extreme monetary poverty if they have less than $2.15/day. This threshold is set in absolute terms, and it has commonly been used for the study of poverty in developing countries (Besley and Burgess, 2003). This threshold was established after studying the needs and living standards of the population in 15 of the poorest countries, which led some authors (for example, Bradshaw and Mayhew, 2010; Edin and Shaefer, 2015) to wonder about the suitability of applying this threshold to developed countries.
In contrast, poverty in OECD or global north countries is often studied by using relative poverty lines. The poverty threshold is set according to the income of the population as a whole, facilitating comparisons between units in the same environment. In general, analyses take as a reference the set of households, and use an equivalence scale (usually the modified OECD equivalence scale4) to make the incomes of households of different sizes comparable. The most common threshold for the risk of poverty is 60 per cent of the median equivalised income of the population (Fouarge and Layte, 2005; Gordon, 2006). In Spain, different reports (for example, Fundación FOESSA, 2022a) have analysed the evolution of poverty using this threshold, showing that the prevalence of poverty since the mid-1990s has been very high, becoming a structural problem in Spanish society, affecting approximately one in five households.
It is also common to try to characterise extreme poverty by considering other thresholds in relation to the median of the income distribution: lower and more restrictive thresholds, such as 50 per cent (Brady et al, 2017; Parolin, 2019), 40 per cent (Lyte et al, 2001; Smeeding and Sandstrom, 2005), or 30 per cent of the median (Gilsanz, 2014). By the year 2021, almost half of the poor population in Spain was in a situation of severe poverty (EAPN, 2022). Some authors, such as Brady and Parolin (2020), have arbitrarily distinguished between deep poverty (20 per cent of the median income) and extreme poverty (10 per cent of the median income).
Public policies design focused on tackling poverty is driven by income-based data in almost all OECD countries, claiming its simplicity in designing and implementation from the administrative perspective (Summers and Young, 2020). Nevertheless, the purely monetary measurement of poverty has certain limitations. On the one hand, income is one more indicator within the basic needs of any household or individual. On the other hand, both the absolute and the relative threshold have a high degree of arbitrariness when defined (Bradshaw and Mayhew, 2010).
Townsend (1979) argued that, in the study of poverty, more aspects must be analysed than purely monetary ones. Authors such as Bárcena‐Martín et al (2014) and Nolan and Whelan (2007) have rethought the monetary and unidimensional measurements of poverty, giving greater relevance to material deprivation. This method has two main strengths: first, the multidimensional nature of this approach; second, material deprivation should be considered a more accurate approach to poverty measurement than low income (Berthoud, 2006). In addition, it has interesting policy implications, since the use of this approach allows for a better characterisation of specific forms of poverty, such as child poverty (Chzhen et al, 2018). This problem still affects 16 per cent of the population in Spain in 2021 (Fundación FOESSA, 2022b). Ayllón (2017) and Izquierdo and Serrano (2009) document different situations of material deprivation in Spain. The former analyses the economic wellbeing of children; the latter focuses on situations of severe poverty for the general population. Both studies show the importance of considering material poverty as a relevant indicator for the measurement of poverty in Spain. However, the choice of the different indicators to consider an individual as deprived has a certain subjective burden, and in many cases it is limited by data availability (Guio, 2009).
Taking this into account, authors such as Ayala et al (2011) and Fusco et al (2011) considered the combination of monetary deprivation with material deprivation to be an adequate way to measure severe poverty situations. In fact, there is a marked overlap between income poverty and deprivation when considering some different aspects (such as housing costs and net wealth), as Saunders and Naidoo (2020) discussed. One of the first countries to start analysing poverty using joint situations of monetary deprivation and material deprivation was Ireland (Callan et al, 1993; Whelan and Maître, 2009; 2010). This concept is known as consistent poverty (Hick, 2014). In the case of Spain, the decline in the consistent poverty rates has historically been slower than in the case of material deprivation (EAPN, 2022). Furthermore, studies such as Ayala et al (2011) show how attaining higher levels of employment and education in the household should lead to a reduction in the risk of suffering from this phenomenon.
According to the different approaches presented here, any study on poverty or extreme poverty starts with certain limitations. Among them we find the arbitrariness involved in establishing the thresholds that differentiate between poor and non-poor people. However, these limitations are normally overlooked to characterise such a vulnerable group, on the grounds that these choices are acknowledged and supported by robust methodologies.
Poverty literature has also stressed the importance of using relative poverty measures to gauge the patterns of socio-economic disadvantage. For instance, educational achievement is negatively related to lower income mainly due to the family, income and material resources (West, 2007). Households with children, foreigners and older individuals are also more likely to report monetary and material deprivations (Allgar and Paul, 2002; Chzhen et al, 2018). Labour market events are a key determinant of any poverty situation as well. Individuals who face an adverse situation related to the labour market, such as unemployment, are less likely to escape from a poverty situation (Arranz and García-Serrano, 2009). In addition, pension benefits and other regular transfers from the government seem to have been very effective in lifting households out of poverty (Cantó, 2003). Therefore, the policy implications of using one or more of the thresholds mentioned earlier are important: they should provide an effective tool for characterising subgroups that should be targeted by such policies.
Finally, the study of these situations of high vulnerability suggests considering whether there is a certain nexus between extreme poverty and the possibility of experiencing homelessness. In fact, it is commonly agreed that poverty is shared by the vast majority of individuals experiencing homelessness (Johnsen and Watts, 2014), as it is related, among other factors, to low income and lack of material resources (Sharam and Hulse, 2014). In Spain, the consequences of the Great Recession led to a scenario with an increase in inequality due to the worsening of the situation of the most vulnerable groups and the inability of public policies to reduce those growing concerns (Cabrera and García-Pérez, 2020; 2021). Individuals experiencing homelessness were particularly affected by this situation: in 2022 they stand at 28,552, with an increase of 30 percentage points since 2005 (INE, 2022). Particular attention should also be paid to people in a situation of residential exclusion associated with insecure and inadequate housing. Fundación FOESSA (2022a) estimates that almost 800,000 households suffer from housing insecurity and 1,300,000 households suffer from housing inadequacy. Such residential exclusion, often associated with problems such as mortgage payment arrears or not making ends meet, may have an influence on people’s trajectory towards homelessness (Gallego and Cabrero, 2020).
Methodological approaches
The methodological approaches used for our study are presented as follows. The evolution of poverty and extreme poverty levels in Spain is analysed considering different thresholds: relative monetary thresholds, situations of severe material deprivation, and the combination of both. The monetary analysis is complemented by various relative thresholds that take as a reference the median income of the population. In this case, 60 per cent of the median income is the most widely used benchmark in poverty-related analyses. To measure more severe and extreme situations of poverty, complementary thresholds that cover all the deciles from 50 per cent to 10 per cent of the median income are used.
In the case of severe material deprivation, a multidimensional analysis is conducted in which possible types of deprivation are considered using a list of nine items. This choice is conditioned by the survey used, in which the nine items were previously defined in consideration of the Europe 2020 Strategy (INE, 2020). A household will find itself in a situation of severe material deprivation if it faces deprivation in at least four of the following elements:
- •The household cannot afford to go on holiday for at least one week a year.
- •The household cannot afford a meal of meat, poultry, or fish at least every other day.
- •The household cannot afford to keep its home at an adequate temperature.
- •The household does not have the ability to cope with unforeseen expenses.
- •The household has had delays in paying expenses related to the main home (mortgage or rent, gas bills, community and so on) or instalment purchases in the last 12 months.
- •The household cannot afford to own a car.
- •The household cannot afford to have a telephone.
- •The household cannot afford to have a colour TV.
- •The household cannot afford to have a washing machine.
Finally, the study considers the combination of monetary deprivation and material deprivation, that is, a situation of poverty in which the income is below 60 per cent of the median income of the population and there is a situation of severe material deprivation. All these situations are summarised in Table 1.
Approaches used for characterising situations of extreme poverty in Spain
| Approach | Technique | Definition |
|---|---|---|
| Unidimensional | Relative thresholds | Income below the 60%, 50%, 40%, 30%, 20% or the 10% of the median income of the population |
| Multidimensional | Severe material deprivation | Facing a deprivation in at least four elements using a list of nine items |
| Consistent poverty | Facing a deprivation in at least four elements using a list of nine items and income below the 60% of the median income of the population |
Source: Own elaboration
To analyse the determinants of extreme poverty in Spain in a robust way, we calculate the probability of being in each of the poverty situations shown in Table 1 through different logit models. The definition of the dependent variable as a discrete variable (being poor/not being poor) and the aim of determining the probability that an individual is in a poverty situation conditioned to different explanatory variables make this technique of poverty characterisation adequate as it has been widely used (Cantó, 2003; Arranz and García-Serrano, 2009; Izquierdo and Serrano, 2009; Liberati et al, 2020; Ayala et al, 2014).
Data
The database used is the Encuesta de Condiciones de Vida (Living Conditions Survey) (ECV) from the National Institute of Statistics (INE). It is carried out through personal interviews and is a reference source on income distribution and social exclusion in Spain (INE, 2020). Its production began in 2004, and it has an annual periodicity. This database provides the information necessary to feed the European Survey on Income and Living Conditions (EU-SILC) with Spanish data. For our study, information from 2010 to 2019 was used, with 13,000 households and 35,000 individuals interviewed each year.
Since 2013, the ECV has adopted a new methodology related to household income with the aim of minimising the impact of non-sampling errors and thus improving the reliability of the data obtained. This new methodology, based on the use of administrative files, makes the ECV the main source of data for the analysis of personal income distribution.
The data source used for analysing the situation of the people living homeless is the Encuesta Para las Personas Sin Hogar (Survey on the Homeless) (EPSH), published by the INE for the years 2005 and 2012. It is the most complete source of information for Spain; it includes aspects such as the sociodemographic profile of the homeless, their living conditions, their income and aspects related to work, among others. More than 3,000 individuals were interviewed in both waves of the survey. Despite being the most complete and representative database for the Spanish case, it has some limitations. First, the delimitation of the surveyed person experiencing homelessness may be somehow restrictive (it considers only cities of more than 20,000 inhabitants, for example). Second, the requirement to use certain social services to be selected for the sample may ignore individuals who do not have access to such services.
The information obtained from the ECV enables us to contextualise the situation of Spanish households over the past decade, as well as the main socioeconomic characteristics of the individuals in these households. This will be useful both as a first exploratory analysis and in the subsequent econometric model. All these data have been obtained directly from the INE datasets.5 In this regard, the households analysed are made up of an average of three members, although this figure has decreased slightly over the years, coinciding with the slight percentage increase in single person households. On the other hand, half of the households are in large urban centres. The percentage of households located in sparsely populated areas, directly related to the rural world, has suffered a slight decrease over the years. More than 75 per cent of the households live in owned homes. However, from the first year of the analysis to the last, the percentage of homes owned decreased by 4 points, the same as the increase experienced by rental homes. Regarding the type of household, around half are households with children.
The average age of the individuals in these households increased during the period of the analysis, being 50 years old in 2019. On the other hand, almost all the individuals analysed each year have Spanish nationality. Regarding the level of education, fewer than 10 per cent have no completed studies, three out of 10 individuals have a university education, and around half have at least secondary education. About 30 per cent of individuals suffer from a serious or chronic illness. This can have a direct impact on their employment situation by preventing their incorporation into the labour market and on the possible benefits derived from this situation. The predominant employment situation is that of an employee. On the other hand, from the first year of the analysis to the last, the percentage of retirees increased by 3 percentage points, a figure that is in line with the observed aging of the population. The percentage of unemployed individuals, which is very sensitive to economic cycles, varied from 11 per cent (in 2019) to 18 per cent (in 2013). In turn, the considerable number of individuals dedicated exclusively to housework stands out, always being above 10 per cent. Lastly, around 30 per cent of individuals receive unemployment or retirement benefits (INE, 2020).
Results
Incidence of poverty and extreme poverty in Spain
The evolution of each of the poverty and extreme poverty rates is shown here. This first analysis of the incidence of these rates will enable us to check their evolution and trends.
Figure 1 first shows all the relative monetary thresholds. This allows a comparison between the thresholds as they are more restrictive. The thresholds corresponding to 60 per cent and 50 per cent of the median income follow a trend that seems to be strongly marked by the economic cycles that occurred during the decade. In the period from 2010 to 2013, still with the Great Recession present, very stable values are observed, with a marked reduction in poverty rates between 2012 and 2013. However, in subsequent years, these values increase notably, the rise between 2013 and 2014 being especially important. The highest values of the series occur between 2014 and 2017. This shows that the Great Recession had very marked consequences, with a notable increase in the incidence of monetary poverty during the central period of the study. Since 2017, the poverty rates have been gradually declining.
Monetary poverty rates for each threshold considered
Citation: Journal of Poverty and Social Justice 31, 2; 10.1332/175982721X16760450929081
Monetary poverty rates for each threshold considered
Citation: Journal of Poverty and Social Justice 31, 2; 10.1332/175982721X16760450929081
Monetary poverty rates for each threshold considered
Citation: Journal of Poverty and Social Justice 31, 2; 10.1332/175982721X16760450929081
The threshold corresponding to 40 per cent of the median income follows a similar trend to that observed with 60 per cent and 50 per cent of the median income. The main difference is between 2018 and 2019, when the incidence of monetary poverty in Spain under this threshold remains constant. If we set the monetary poverty threshold at 30 per cent of the median income, we observe a similar evolution to the less restrictive thresholds except for the greater volatility of rates observed between 2014 and 2016. However, the thresholds set at 10 per cent and 20 per cent of the median income show very different evolutions from the others. Their evolution during the decade remains stable, and they do not seem to be influenced by economic cycles.
Figure 2 shows the poverty rates related to severe material deprivation and consistent poverty. The evolution, once again, is marked by the Great Recession: after a decrease in both rates between 2010 and 2011, they did not stop increasing until 2014. However, unlike the relative monetary thresholds, from the period 2014 to 2017 there are sharper declines in the incidence of both multidimensional rates. That is, during the period from 2010 to 2012, with the Great Recession still present, the percentages of poverty are lower than those during the years of economic recovery (2013 to 2017). Finally, it is observed that, after a slight rise between 2017 and 2018, the values at the beginning of the analysis practically recover in both multidimensional rates in 2019.
Severe material deprivation and consistent poverty
Citation: Journal of Poverty and Social Justice 31, 2; 10.1332/175982721X16760450929081
Severe material deprivation and consistent poverty
Citation: Journal of Poverty and Social Justice 31, 2; 10.1332/175982721X16760450929081
Severe material deprivation and consistent poverty
Citation: Journal of Poverty and Social Justice 31, 2; 10.1332/175982721X16760450929081
The analysis of the incidence of the different thresholds for the measurement of extreme poverty allows us to reach some preliminary conclusions. On the one hand, in the lowest deciles of the income distribution, there is little variability in the poverty rates. It seems, therefore, that individuals who fall below the most restrictive thresholds of 10 per cent and 20 per cent of the median equivalised disposable income are not influenced by the cycle since they do not respond to economic stimuli.
On the other hand, we are also able to appreciate how the material and monetary poverty rates follow different trends on some occasions. For example, while the values for the period from 2010 to 2013 remain stable and even decrease for the least restrictive thresholds of monetary poverty, they did not stop increasing for the situations involving material deprivations. Also, between 2014 and 2017 the highest values of the series for the thresholds set at 60 per cent, 50 per cent, 40 per cent and 30 per cent of the median income are reached; but if we focus on the trend of severe material deprivation and consistent poverty during this same period, a sharp decline in the incidence of both multidimensional rates is found. This implies that the individuals who are in the lowest deciles of the income distribution may not experience deprivation in other aspects (and vice versa), such as material aspects or other measures of living standards. Therefore, any approach to measuring extreme poverty cannot rely solely on income-based data.
Determinants of extreme poverty in Spain
In this section we estimate the probability of being in each situation of poverty using different logit models for each of the years of the analysis, as a way of detecting the main factors that influence the different situations of poverty and extreme poverty.6 In these models, the dichotomous dependent variable takes the value one if the individual lives in a household that is in a certain situation of poverty and zero otherwise. As explanatory variables, the factors and patterns reviewed earlier are included. Among them, we find basic socioeconomic variables, such as age, sex, nationality and education. In addition, the influence of factors related to the household is estimated: the tenure regime, its location, and the type of household based on its members. Finally, variables related to their health status (suffering from a serious or chronic illness), their employment situation, and the financial benefits received are included. It is worth noting that all the variables are statistically significant at the 99 per cent confidence level.
First, the determinants of monetary poverty (Table 2) show that, the older the age, the lower the probability of being in a situation of monetary poverty under the least restrictive thresholds. However, under the thresholds of 20 per cent and 10 per cent, its impact begins to be somewhat more limited. Nationality also has a significant influence on being in a situation of monetary poverty. In fact, individuals with foreign nationality may have a probability of up to 14 percentage points higher than an individual with Spanish nationality under the threshold of 60 per cent of the median income. As with nationality or age, having a higher education reduces the probability of finding oneself in a situation of monetary poverty. Among the most restrictive thresholds of monetary poverty, there are no notable gender differences. But when considering the least restrictive monetary threshold (60 per cent) and material deprivation, two different results are observed: men are more likely to be in a situation of monetary poverty, while women are more likely to be in multidimensional poverty.
Determinants of monetary poverty: 60%, 50%, 40%, 30%, 20% and 10% of the median equivalised disposable income (average marginal effects)
| 2010 | 2014 | 2019 | ||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 60% | 50% | 40% | 30% | 20% | 10% | 60% | 50% | 40% | 30% | 20% | 10% | 60% | 50% | 40% | 30% | 20% | 10% | |
| Age | ||||||||||||||||||
| 31–49 | 0.01 | 0.00 | 0.00 | –0.01 | 0.00 | 0.00 | –0.02 | –0.02 | –0.01 | –0.01 | 0.00 | 0.00 | –0.04 | –0.03 | –0.02 | –0.01 | 0.00 | 0.00 |
| 50–64 | –0.04 | –0.02 | 0.00 | 0.00 | 0.01 | 0.00 | –0.04 | –0.03 | –0.02 | –0.02 | 0.00 | 0.00 | –0.03 | -0.02 | -0.01 | -0.01 | 0.01 | 0.01 |
| >65 | 0.00 | 0.00 | –0.01 | 0.00 | 0.01 | 0.00 | –0.07 | –0.07 | –0.04 | –0.02 | –0.01 | 0.00 | –0.05 | –0.06 | –0.04 | –0.02 | 0.00 | 0.01 |
| Sex | 0.01 | 0.02 | 0.00 | 0.00 | 0.00 | 0.00 | 0.01 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| Nationality | –0.14 | –0.09 | –0.08 | –0.06 | –0.03 | –0.01 | –0.12 | –0.08 | –0.07 | –0.05 | –0.02 | –0.02 | –0.14 | –0.10 | –0.06 | –0.03 | –0.02 | –0.02 |
| Education | ||||||||||||||||||
| Primary | –0.07 | –0.04 | –0.03 | –0.01 | 0.00 | 0.00 | –0.06 | –0.04 | –0.03 | –0.02 | 0.00 | 0.00 | –0.05 | –0.02 | –0.01 | 0.00 | 0.00 | 0.00 |
| Secondary | –0.15 | –0.09 | -–0.07 | –0.03 | –0.02 | –0.01 | –0.12 | –0.09 | –0.06 | –0.04 | –0.02 | –0.01 | –0.10 | –0.07 | –0.04 | –0.02 | –0.01 | 0.00 |
| Tertiary | –0.20 | –0.12 | –0.08 | –0.03 | –0.01 | 0.00 | –0.19 | –0.13 | –0.09 | –0.05 | –0.03 | –0.01 | –0.16 | –0.10 | –0.07 | –0.04 | –0.02 | 0.00 |
| Degree of urbanisation | ||||||||||||||||||
| Sparsely | 0.07 | 0.04 | 0.01 | 0.00 | –0.01 | 0.00 | 0.05 | 0.03 | 0.01 | 0.00 | 0.00 | 0.00 | 0.04 | 0.01 | 0.00 | –0.01 | –0.01 | 0.00 |
| Middle area | 0.03 | 0.02 | 0.01 | 0.00 | 0.00 | 0.00 | 0.02 | 0.01 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | –0.01 | –0.01 | 0.00 | –0.01 | 0.00 |
| Tenure regime | ||||||||||||||||||
| Rental | 0.11 | 0.10 | 0.06 | 0.04 | 0.02 | 0.01 | 0.15 | 0.12 | 0.08 | 0.05 | 0.03 | 0.02 | 0.09 | 0.09 | 0.08 | 0.05 | 0.03 | 0.01 |
| Free assignment | 0.06 | 0.07 | 0.03 | 0.03 | 0.03 | 0.00 | 0.12 | 0.10 | 0.06 | 0.07 | 0.04 | 0.02 | 0.14 | 0.13 | 0.10 | 0.06 | 0.04 | 0.02 |
| Illness | 0.01 | 0.01 | 0.00 | 0.00 | 0.00 | 0.00 | 0.02 | 0.01 | 0.01 | 0.01 | 0.00 | 0.00 | 0.03 | 0.04 | 0.02 | 0.01 | 0.00 | 0.00 |
| Type of household | ||||||||||||||||||
| Single person | 0.06 | 0.03 | 0.05 | 0.04 | 0.04 | 0.05 | 0.00 | 0.03 | 0.06 | 0.05 | 0.05 | 0.05 | 0.00 | 0.03 | 0.05 | 0.04 | 0.03 | 0.02 |
| No children | –0.10 | –0.07 | –0.04 | –0.03 | –0.02 | 0.00 | –0.08 | –0.06 | –0.05 | –0.03 | –0.01 | 0.00 | –0.10 | –0.07 | –0.04 | –0.02 | –0.02 | –0.01 |
| Employment situation | ||||||||||||||||||
| Self-employed | 0.19 | 0.16 | 0.13 | 0.11 | 0.09 | 0.08 | 0.16 | 0.16 | 0.14 | 0.12 | 0.10 | 0.08 | 0.11 | 0.11 | 0.08 | 0.06 | 0.03 | 0.02 |
| Unemployed | 0.25 | 0.21 | 0.17 | 0.12 | 0.11 | 0.09 | 0.28 | 0.25 | 0.20 | 0.15 | 0.12 | 0.08 | 0.25 | 0.22 | 0.19 | 0.13 | 0.09 | 0.06 |
| Student | 0.13 | 0.10 | 0.09 | 0.06 | 0.07 | 0.08 | 0.09 | 0.09 | 0.09 | 0.07 | 0.08 | 0.05 | 0.07 | 0.07 | 0.04 | 0.04 | 0.04 | 0.03 |
| Retirees | 0.15 | 0.18 | 0.13 | 0.10 | 0.13 | 0.13 | 0.15 | 0.16 | 0.15 | 0.21 | 0.26 | 0.16 | 0.15 | 0.13 | 0.12 | 0.11 | 0.13 | 0.12 |
| Disabled | 0.25 | 0.29 | 0.21 | 0.12 | 0.14 | 0.07 | 0.19 | 0.17 | 0.13 | 0.11 | 0.12 | 0.12 | 0.23 | 0.18 | 0.14 | 0.09 | 0.07 | 0.03 |
| Housework | 0.20 | 0.17 | 0.10 | 0.07 | 0.06 | 0.06 | 0.15 | 0.13 | 0.11 | 0.09 | 0.09 | 0.07 | 0.17 | 0.14 | 0.11 | 0.08 | 0.05 | 0.03 |
| Others | 0.22 | 0.22 | 0.13 | 0.07 | 0.07 | 0.09 | 0.20 | 0.15 | 0.13 | 0.10 | 0.11 | 0.08 | 0.28 | 0.25 | 0.14 | 0.12 | 0.12 | 0.04 |
| Benefits | ||||||||||||||||||
| Unemployment | –0.02 | –0.02 | –0.02 | –0.02 | –0.02 | –0.02 | 0.02 | 0.00 | –0.01 | –0.02 | –0.03 | –0.02 | 0.03 | 0.01 | 0.00 | –0.01 | –0.01 | –0.01 |
| Retirement | –0.08 | –0.10 | –0.07 | –0.05 | –0.05 | –0.03 | –0.12 | –0.11 | –0.09 | –0.08 | –0.06 | –0.03 | –0.09 | –0.07 | –0.06 | –0.05 | –0.04 | –0.03 |
| Survival | –0.09 | –0.07 | –0.05 | –0.04 | –0.02 | –0.01 | –0.12 | –0.09 | –0.08 | –0.06 | –0.04 | –0.02 | –0.12 | –0.09 | –0.07 | –0.04 | –0.02 | –0.01 |
| Sickness | 0.01 | 0.01 | 0.00 | 0.00 | –0.01 | 0.00 | 0.02 | 0.01 | 0.01 | 0.01 | –0.01 | 0.00 | –0.03 | 0.04 | 0.02 | 0.01 | –0.02 | –0.01 |
| Disability | –0.07 | –0.07 | –0.05 | –0.03 | –0.03 | –0.01 | –0.09 | –0.07 | –0.05 | –0.05 | –0.03 | –0.02 | –0.08 | –0.07 | –0.05 | –0.04 | –0.03 | –0.01 |
| Observations (N) | 30953 | 30953 | 30953 | 30953 | 30953 | 30953 | 26531 | 26531 | 26531 | 26531 | 26531 | 26531 | 33376 | 33376 | 33376 | 33376 | 33376 | 33376 |
| Pseudo R2 | 0.1831 | 0.1622 | 0.1742 | 0.191 | 0.191 | 0.1911 | 0.1638 | 0.1988 | 0.1963 | 0.197 | 0.1955 | 0.2225 | 0.1722 | 0.1924 | 0.2193 | 0.1958 | 0.2118 | 0.2259 |
| Prob > chi2 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
Average marginal effects for the different logistic regressions are presented. Robust standard errors are omitted to facilitate reading and interpretation. All the variables are statistically significant at the 99% confidence level. The omitted variables are the following: for Age: ‘18–30 years old’; for Sex: ‘Woman’; for Nationality: ‘Foreigner’; for Education: ‘Without studies’; for Degree of Occupation: ‘Heavily Populated Area’; for Tenure Regime: ‘Owned Housing’; for Type of Household: ‘Household with Children’; for Labour Situation: ‘Employee’. The results for all years, with their corresponding coefficients and robust standard errors, are available upon reasonable request. Source: Own elaboration with INE dataset.
A household located in a sparsely populated area increases its probability of finding itself in a situation of monetary poverty under the least restrictive thresholds, although a decreasing trend is observed over the years and even tends to reverse at the lowest deciles of the distribution.
Households in a rental or free transfer regime have a greater probability of being in a situation of monetary poverty than households that own housing. In turn, households without children have a significantly reduced probability compared with households with children. It stands out that single person households begin to have a greater probability of being in a situation of monetary poverty as we consider more restrictive thresholds. As they are nuclei made up of only one individual, their income is more limited.
In the diverse work situations, the greatest differences are observed. In fact, wage earners are less likely to be in monetary poverty compared to the rest of work situations, under any threshold and in any period. Finally, receiving a benefit in most cases reduces the probability of being in a situation of monetary poverty. The only exception is the unemployment benefit: only when we consider the most restrictive thresholds is capable of reducing the risk of facing a situation of monetary poverty.
Regarding severe material deprivation (Table 3), age determines the situation of individuals. In fact, the oldest individuals are those who have a lower probability of being in this situation of extreme poverty. On the other hand, having Spanish nationality and higher levels of education reduces the probability of experiencing a situation of severe material deprivation.
Determinants of severe material deprivation and consistent poverty (average marginal effects)
| 2010 | 2014 | 2019 | ||||
|---|---|---|---|---|---|---|
| Severe material deprivation | Consistent poverty | Severe material deprivation | Consistent poverty | Severe material deprivation | Consistent poverty | |
| Age | ||||||
| 31–49 | –0.01 | –0.01 | –0.01 | 0.00 | 0.00 | –0.01 |
| 50–64 | –0.02 | –0.01 | –0.02 | –0.01 | –0.01 | 0.00 |
| >65 | –0.04 | -0.02 | –0.05 | –0.03 | –0.03 | –0.02 |
| Sex | –0.01 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| Nationality | –0.04 | –0.02 | –0.04 | –0.02 | –0.02 | –0.02 |
| Education | ||||||
| Primary | –0.02 | –0.01 | –0.02 | –0.01 | –0.02 | –0.01 |
| Secondary | –0.04 | –0.03 | –0.05 | –0.04 | –0.04 | –0.03 |
| Tertiary | –0.06 | –0.03 | –0.08 | –0.05 | –0.05 | –0.03 |
| Degree of urbanisation | ||||||
| Sparsely | 0.00 | 0.00 | –0.02 | –0.01 | –0.02 | –0.01 |
| Middle area | 0.00 | 0.00 | –0.01 | 0.00 | 0.00 | 0.00 |
| Tenure regime | ||||||
| Rental | 0.04 | 0.03 | 0.08 | 0.07 | 0.06 | 0.04 |
| Free assignment | 0.01 | 0.01 | 0.03 | 0.04 | 0.04 | 0.05 |
| Illness | 0.02 | 0.01 | 0.04 | 0.02 | 0.03 | 0.02 |
| Type of household | ||||||
| Single person | 0.01 | 0.00 | 0.01 | 0.01 | 0.02 | 0.00 |
| No children | –0.01 | –0.02 | –0.01 | –0.01 | –0.01 | –0.02 |
| Employment situation | ||||||
| Self-employed | –0.02 | –0.01 | –0.03 | –0.01 | –0.02 | –0.01 |
| Unemployed | 0.05 | 0.05 | 0.08 | 0.08 | 0.07 | 0.05 |
| Student | –0.01 | 0.01 | 0.01 | 0.03 | 0.02 | 0.02 |
| Retirees | –0.01 | 0.02 | 0.00 | –0.02 | 0.00 | 0.02 |
| Disabled | 0.01 | 0.04 | 0.01 | 0.02 | 0.03 | 0.02 |
| Housework | 0.00 | 0.02 | 0.01 | 0.03 | 0.02 | 0.02 |
| Others | 0.02 | 0.03 | 0.03 | 0.05 | 0.03 | 0.02 |
| Benefits | ||||||
| Unemployment | 0.00 | 0.00 | 0.01 | 0.00 | 0.01 | 0.00 |
| Retirement | 0.00 | –0.01 | –0.02 | 0.00 | 0.00 | –0.01 |
| Survival | 0.00 | –0.01 | –0.02 | –0.02 | –0.01 | –0.02 |
| Sickness | 0.02 | 0.01 | 0.04 | 0.02 | 0.03 | 0.02 |
| Disability | 0.02 | 0.00 | 0.03 | 0.01 | 0.01 | 0.01 |
| Observations (N) | 30953 | 30953 | 26531 | 26531 | 33376 | 33376 |
| Pseudo R2 | 0.1635 | 0.1908 | 0.1679 | 0.197 | 0.1683 | 0.1958 |
| Prob > chi2 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
Average marginal effects for the different logistic regressions are presented. Robust standard errors are omitted to facilitate reading and interpretation. All the variables are statistically significant at the 99% confidence level. The omitted variables are the following: for Age: ‘18–30 years old’; for Sex: ‘Woman’; for Nationality: ‘Foreigner’; for Education: ‘Without studies’; for Degree of Occupation: ‘Heavily Populated Area’; for Tenure Regime: ‘Owned Housing’; for Type of Household: ‘Household with Children’; for Labour Situation: ‘Employee’. The results for all years, with their corresponding coefficients and robust standard errors, are available upon reasonable request. Source: Own elaboration with INE dataset.
In periods when the incidence of severe material deprivation is higher, being in sparsely populated areas reduces the probability of suffering from this type of extreme poverty compared with living in highly populated areas, the same fact that was observed under the most restrictive monetary poverty thresholds. In addition, single person households and households with children have a greater risk of being in a situation of severe material deprivation than households without children.
Considering the employment situation of individuals and the benefits received, important changes are observed compared with monetary poverty. First, self-employed workers, and students and retirees in certain years, are less likely to find themselves in a situation of severe material deprivation than salaried individuals. On the other hand, if we are not considering monetary factors, benefits are no longer such a reliable safety net. According to these results, only retirement and survival benefits in certain periods reduce the chances of experiencing a situation of severe material deprivation.
Finally, the determinants of consistent poverty show that, being older and having a higher education reduces the probability of being in a situation of extreme poverty (Table 3). However, even though having a foreign nationality increases the probability of facing a situation of consistent poverty, its impact is smaller than in the rest of the previous scenarios. The remaining variables take similar values to those observed during the analysis of situations of severe material deprivation. Regarding their employment situation, in periods of higher incidence of consistent poverty, retirees and self-employed workers are less likely to find themselves in a consistent poverty situation than wage earners. Survival and retirement benefits also reduce this probability in the initial and final periods of the analysis.
Extreme poverty and homelessness
In this final section, we proceed to analyse the main risks faced by households related to their dwelling and the possible similarities between the different profiles of extreme vulnerability and the profile of the people living homeless.
Table 4 show that it is not common to be late in paying the mortgage or the rent or to be late in paying bills, such as electricity, gas or water, situations that, if prolonged over time, can lead to the loss of the home. On the other hand, more than half of the households have some difficulty making ends meet during the entire period. The only exception is the year 2019, when the total percentage of households with difficulty making ends meet is around 49 per cent. In the period from 2012 to 2016, greater difficulties are observed. In addition, during this same period, the percentage of households that consider the total housing expenses to be a heavy burden is higher. Except for the years 2017 and 2019, more than half of the households consider the total housing expenses to be a heavy burden. All these situations show the difficulties many households face in maintaining their housing and may have an influence on people’s trajectory towards homelessness.
Main deprivations of the households related to housing (%)
| 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | |
|---|---|---|---|---|---|---|---|---|---|---|
| Delays in paying the mortgage or rent in the last 12 months | ||||||||||
| One time | 3 | 2 | 2 | 2 | 3 | 2 | 2 | 2 | 2 | 2 |
| Twice or more | 10 | 7 | 9 | 11 | 12 | 11 | 8 | 6 | 7 | 6 |
| No | 87 | 90 | 88 | 87 | 85 | 87 | 89 | 92 | 91 | 92 |
| Delays in paying the bills in the last 12 months | ||||||||||
| One time | 2 | 1 | 2 | 1 | 2 | 2 | 2 | 2 | 1 | 2 |
| Twice or more | 6 | 4 | 5 | 6 | 7 | 7 | 6 | 6 | 6 | 5 |
| No | 92 | 94 | 92 | 92 | 91 | 91 | 92 | 92 | 93 | 93 |
| Difficulty making ends meet | ||||||||||
| Much difficulty | 15 | 11 | 15 | 19 | 17 | 15 | 16 | 9 | 10 | 8 |
| Difficulty | 18 | 18 | 20 | 20 | 21 | 20 | 19 | 16 | 17 | 14 |
| Some difficulty | 28 | 29 | 28 | 28 | 29 | 29 | 27 | 28 | 28 | 27 |
| Some ease | 24 | 28 | 26 | 23 | 23 | 25 | 25 | 32 | 30 | 34 |
| Easily | 13 | 13 | 10 | 9 | 8 | 10 | 11 | 13 | 13 | 16 |
| Much easily | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| The total expenses of the house suppose … | ||||||||||
| A heavy burden | 52 | 52 | 57 | 58 | 58 | 58 | 55 | 48 | 50 | 47 |
| A reasonable burden | 44 | 43 | 40 | 39 | 39 | 39 | 42 | 47 | 47 | 49 |
| No burden | 3 | 4 | 3 | 3 | 3 | 3 | 3 | 4 | 3 | 4 |
| External help1 | – | – | – | – | – | – | 4 | 4 | 5 | 3 |
Source: Own elaboration with INE dataset. 1 Households who need help from private entities to provide them with food, clothing, or other basic goods. No values for the period 2010–2015.
We also analyse the percentage of households that need external help to obtain food, clothing or other basic items. The importance of the question lies in the fact that it is closely related to the definition of homelessness used by the INE when preparing the EPSH, as they consider a homeless person to be one who, in the last week, has needed to visit a centre that offers restoration and/or accommodation services and who has been a user of them on the previous night (INE, 2022). According to these data, we see that, between 2016 and 2018, around 4 per cent and 5 per cent of households needed external help to provide them with clothing, food or basic goods. However, in 2019, this figure experiences a slight decrease. Interpreting these data with caution, we deduce that this percentage of individuals could be at risk of experiencing homelessness since they already need the help of assistance services.
This last piece of information invites us to analyse the possible relationships between the most severe poverty situations and homelessness. Unfortunately, this analysis can only be carried out for one year since the EPSH was conducted exclusively in the years 2005 and 2012. Table 5 compares the profile of homeless people against the profile of individuals in different situations of poverty and extreme poverty in 2012. In the case of monetary poverty, it is limited to the thresholds of 40 per cent to 10 per cent of the median income.
Profile of people living homeless versus different situations of extreme poverty. Data for the year 2012
| Homeless | Monetary poverty | Material deprivation | Consistent poverty | ||||
|---|---|---|---|---|---|---|---|
| 40% | 30% | 20% | 10% | ||||
| Mean age | 42 | 42 | 42 | 43 | 45 | 43 | 41 |
| Sex | |||||||
| Men | 80% | 49% | 50% | 50% | 50% | 51% | 50% |
| Women | 20% | 51% | 50% | 50% | 50% | 49% | 50% |
| Nationality | |||||||
| National | 54% | 68% | 63% | 63% | 66% | 70% | 61% |
| Foreigner | 46% | 32% | 37% | 37% | 34% | 30% | 39% |
| Level of education | |||||||
| No studies | 6% | 13% | 12% | 10% | 10% | 11% | 12% |
| Primary | 22% | 22% | 21% | 17% | 18% | 27% | 21% |
| Secondary | 60% | 51% | 55% | 58% | 52% | 43% | 58% |
| Tertiary | 12% | 15% | 13% | 16% | 21% | 19% | 9% |
| Bad health | 42% | 26% | 26% | 25% | 28% | 35% | 32% |
| Employment situation | |||||||
| Employed | 4% | 23% | 21% | 20% | 19% | 26% | 18% |
| Unemployed | 78% | 38% | 39% | 38% | 37% | 41% | 47% |
| Retired | 3% | 7% | 8% | 8% | 11% | 6% | 4% |
| Disabled | 7% | 3% | 2% | 1% | 2% | 4% | 3% |
| Benefits1 | 21% | 32% | 28% | 18% | 12% | 47% | 41% |
| Income2 | €347 | €462 | €347 | €231 | €116 | €644 | €353 |
Source: Own elaboration with INE dataset.1 The total percentage of individuals who receive some type of benefit is considered. For people living homeless, the data considers individuals whose greatest source of income comes from some benefit.2 For people living homeless, in severe material deprivation and consistent poverty, mean income is considered. For relative monetary poverty situations and individuals below the absolute monetary poverty threshold, the proper threshold is considered.
In general, it is observed that the average ages of the group of homeless people and of people living in extreme poverty are similar. Around half of those in extreme poverty are women. Among the people living homeless, women represent a smaller percentage of all individuals (20 per cent). However, studies such as that by Matulic-Domandzic et al (2019) stated that these low percentages are not representative since women are in situations of extreme vulnerability involving substandard housing, leading to the underestimation of their presence. Regarding nationality, a higher percentage of foreigners is observed among the group of homeless people (46 per cent), a figure that is approached by the number of individuals in a situation of consistent poverty (39 per cent). The percentage of individuals with no education is lower among the people living homeless than in other groups, and the percentage of individuals with higher education is only lower in situations of consistent poverty. On the other hand, the worst levels of perceived health are found in situations of severe material deprivation and consistent poverty (35 and 32 per cent respectively), with figures comparable to those of the group of people living homeless (42 per cent).
The percentage of people living homeless with work is very low, the vast majority being unemployed (78 per cent). In addition, a low percentage of retired homeless people is observed (3 per cent). Again, we intuit that the profile that most closely matches that of the group of people living homeless is that of the individuals in a situation of consistent poverty, in which the percentage of employees and retirees is lower (18 and 4 per cent respectively), and the percentage of unemployed people is higher (47 per cent).
The data on benefits received show that it is the largest source of income for 21 per cent of people living homeless. The more restrictive thresholds of monetary poverty show similar percentages of individuals who receive some types of benefit (between 32 and 12 per cent). In situations that take material deprivation into account, these percentages are higher (47 and 41 per cent). This is due to the large percentage of individuals who receive unemployment benefit and, despite this, find themselves in situations of multidimensional poverty. Finally, people living homeless have an average income of €347/month. This figure coincides with the monetary poverty threshold corresponding to 30 per cent of the median income and is almost totally adjusted to the average income of individuals in situations of consistent poverty.
All these data show that the profile of individuals in situations of consistent poverty has great similarities to the group of people living homeless. Therefore, it seems that the most severe situations of vulnerability are conditioned not only by low income but by a combination of types of deprivation in aspects beyond income.
Conclusions
This article explored situations of poverty and extreme poverty in Spain. To this end, several of the most widespread approaches were used to characterise these situations, endowing the study with greater robustness as well as allowing us to determine which approach best suits the initial purpose. The analysis considered monetary thresholds, situations of severe material deprivation, and a combination of both.
The analysis of the incidence of poverty and extreme poverty showed how monetary poverty is influenced by the economic cycle, with rates showing lower values at the beginning of the analysis, coinciding with the end of the Great Recession, and some higher values during the central period of the study. These stand out especially when considering the thresholds of 60 per cent and 30 per cent of the median income. Trends for multidimensional rates based on material deprivations are also marked by the Great Recession. However, for the most restrictive thresholds of 10 per cent and 20 per cent of the median equivalised income we observed the smallest variations during the series. These results confirms that situations of such deep poverty are not influenced by the cycle since they do not respond to economic stimuli. In addition, we note how the material and monetary poverty rates follow different trends on some occasions.
The study of the main factors that determine the different situations of extreme poverty showed that, for each situation considered, being older, having a higher education, and having Spanish nationality reduce the probability of being in any situation of severe vulnerability. Also, women are more likely to be in a situation of multidimensional poverty. Homes that are rented and freely assigned increase the chances of falling into any extreme poverty situation compared with homes that are owned. This higher probability is also observed with single person households and with households with children. Households without children and with several adult members are the least affected in this regard. All work situations have a greater probability of being in a situation of monetary poverty, under any threshold, than the situation of salaried workers. However, when considering situations that include material deprivation, the self-employed and students and retirees in certain years are less likely to find themselves in situations of extreme poverty. Finally, receiving a benefit in most cases reduces the probability of being in a situation of monetary poverty. However, when we stop taking monetary aspects into account, benefits are no longer such a reliable safety net.
Finally, the analysis of the main risks that households face related to their housing showed that more than half of the households present some difficulty in making ends meet and find the total housing expenses to be a heavy burden. In addition, between 3 per cent and 5 per cent of the households needed help from private entities to obtain clothing, food or basic goods, being an issue closely related to the definition of homelessness used by the INE. Another interesting conclusion is that the profile of individuals in situations of consistent poverty have the greatest similarities to the group of people experiencing homelessness.
This study allowed us to highlight a key issue: when analysing situations of extreme poverty, it is important to take into account more than purely monetary aspects. Throughout the study, we have seen that the most severe vulnerability situations are conditioned not only by a low income but by a combination of different types of deprivation in aspects beyond income. Therefore, there is a need to treat poverty from a multidimensional perspective, encompassing a greater number of unmet needs. Only in this way it will be possible to detect and characterise the groups facing situations of severe social exclusion more precisely and to propose public policies that are able to alleviate their situation.
Regarding the possible limitations of the study, two situations can be underlined. The first, as stated in the second section, is the arbitrariness in establishing the thresholds that differentiate between poor and non-poor people in the most widespread approaches to poverty measurement. The second, although several different thresholds are considered in the analysis, is that there are other approaches that have not been included in the study. These include the analysis of poverty along subjective lines (Kapteyn et al, 1988) and a dynamic study (persistent poverty) focused on the analysis of events pushing individuals in and out of poverty (Cantó, 2003). In addition, a future line of research includes the analysis of poverty in Spain in each of its Autonomous Communities, as a way of highlighting the importance of the differences within the country and thus obtaining a more concrete view of the problem.
This article tried to overcome the research gap that we found in relation with extreme poverty studies in Spain and to contribute to the orientation of public policies focused on the most vulnerable groups. The different thresholds considered are able to characterise the subgroups that should be targeted by such policies. In this sense, there is a need to adopt comprehensive strategies acting on all relevant deprivations (monetary or material), with determination and flexibility to adapt to specific groups. This combination of elements should produce measurable effects, despite the inherent difficulty of intervening in such vulnerable groups. Those public policies should be targeted towards high-risk groups, such as single person households, households with children, younger individuals, individuals with a low educational attainment, and of foreign nationality.
Notes
Corresponding author.
People with an income below 40 per cent of the median income of the population.
The Great Recession refers to the sharp decline in economic activity from 2007 to 2009 after the explosion of the US real state bubble and the global financial crisis. It is generally acknowledged to be the most devastating global economic crisis since the Great Depression. In Spain, it had a particularly marked effect, reaching a startling 27 per cent of the unemployment rate in 2013, the highest in the modern era (Ayllón, 2017).
Household members = 1 + (a-1) * 0.5 + b * 0.3, where a is the number of adults and b is the number of children.
Retrieved from https://www.ine.es/dyngs/INEbase/es/operacion.htm?c=Estadistica_C&cid=1254736176807&menu=ultiDatos&idp=1254735976608.
The results are presented for three different years to facilitate reading and interpretation. Regressions with results for the remaining years are available from the corresponding author upon reasonable request.
Funding
This research was supported by the Consejería de Educación, Cultura y Deportes de Castilla-La Mancha (Project SBPLY/19/180501/000132, FEDER funding included).
Conflicts of interest
The authors declare that there is no conflict of interest.
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