*3.3. Risk of Poverty*

One of the most notable effects of the financial crisis that began in 2007—and the policies that have accompanied it—has been the increase in the population at risk of poverty, or already in a situation of poverty [71–73]. The current health and social crisis triggered by the COVID-19 pandemic will probably worsen this situation, both globally and locally [11].

The data presented below (Figure 4) indicate the percentage of the Catalan population living in consumption units with earnings below the threshold of 60% of the mean income in Catalonia (before social transfers, apart from pensions and subsistence payments). The data have the disadvantage of being calculated on the basis of thresholds homogenized for the whole of Catalonia, even though incomes and costs of living are relatively different in the various parts of the region. In this sense, the areas that tend to have higher average incomes due to their position in the urban system of Catalonia show apparently better results compared to the whole, although the costs of living there are comparatively higher. In contrast, the territories with low mean incomes occupy the bottom positions with respect to these parameters, even though their situation is alleviated by lower living costs. In any case, this variable also offers an interesting approach to spatial differences.

**Figure 3.** ESEC (9 + 1) social structure. Working people aged from 16 to 64 years, Catalonia. Source: In-house construction based on the ECVHP, 2011.

**Figure 4.** Levels of risk of poverty. Percentage of population resident in consumption units with incomes lower than 60% of the mean. Catalonia, 2011. Source: In-house construction based on the ECVHP, 2011.

The principal finding is that, according to the data of the ECVHP 2011, the poverty rate was 21.9% in the population of Catalonia—or, in other words, one out of every five citizens found themselves in a situation of poverty. Any comparison between territorial settings must be approached, however, with the aforementioned caveat that the mean levels of income and costs of living are far from homogeneous. As we can see in Figure 4, Terres de l'Ebre is, once again, the setting with the highest poverty rate, with almost 35% of its population affected. It is followed by Camp de Tarragona, Comarques Gironines and Penedès, all at around 25%. The areas with the lowest poverty rates are Ponent and Alt Pirineu-Aran and Comarques Centrals, while that of the Metropolitan Region of Barcelona lies around the mean for Catalonia.

The data linking the poverty rate to population size and intensity of urbanization are relatively less affected by the biases associated with the assessment of a single threshold for the whole of Catalonia—and they are, therefore, ultimately more interesting. In terms of population size, the city of Barcelona presents the lowest poverty rate, but, leaving aside the case of the capital, it can be seen that poverty is not directly or unequivocally related to population size. The same is true in terms of data referring to the intensity of urbanization. The territories with the highest percentage of poverty are those with an intermediate degree of urbanization and the differences between areas with high and low intensities of urbanization are not excessively marked.

The absence of any clear relationship between these spatial variables and situations of poverty contrasts strikingly with the results of an analysis of spatial aggregation on the basis of the level of residential segregation. It is abundantly clear that the population living in vulnerable settings is over two times more likely to find itself in a situation of poverty than that of well-off settings: in the former, one in every three people find themselves in that situation, while in the latter this proportion is barely one in seven. It must be borne in mind, however, that in this case the results may be tautological to a certain extent, since the variables used to construct the classification of the census tracts into vulnerable, welloff and intermediate settings (i.e., percentage of population unemployed, percentage of foreign population, cadastral value and mean surface area of residence) are closely related to income.
