As has been indicated, the analysis of the data embraced three aspects: Firstly, the existing relationship between residential segregation and educational paths in the Catalan youth population; secondly, a detailed study of Barcelona in order to study inequality in terms of demands for enrolment in the city’s neighbourhoods, and lastly, an analysis of data relating to school performance broken down by districts and related to income.
4.1. Residential Segregation and Educational Paths in Catalonia
The analysis of the relationship between residential segregation and the educational paths of the population is a key point of the debate on the neighbourhood effect, in terms of standards of living for the population. When analysing spatial inequalities amongst the youth population the approach taken in this paper paid special attention to urban segregation. This perspective holds particular interest amongst this group because in early ages socialisation mostly takes place in the immediate surroundings and in the neighbourhood of residence.
The results of the analysis on a wide-ranging sample of the Catalan population, aged between 15 and 34, has led us to conclude that, currently, the differences and inequalities in the habits and living conditions of the youth population in Catalonia can be better explained by residential segregation than by other spatial features [31
]. This means that educational paths, professional activity, participation, etc. amongst the youth population depended more on socio-spatial conditions of the neighbourhood in which they live in than other more traditional classifications, such as the geographical area, the size of the municipality, or the intensity of urban development.
The variables referring to the formal educational system clearly confirmed this point. In 2017, 42.5% of the Catalan population aged between 15 and 34 were registered as students. This percentage varied in the diverse age groups, being higher with the younger members and lower with the older ones, as they were already entering the labour market. Along general lines, the most prolonged training paths over time corresponded to the highest levels of formal education, such as higher education and even postgraduate studies. Now, it is significant to note that, in all ages, the percentage of the population currently undertaking studies was lower in vulnerable areas than in well-off ones. The difference could be as much as 10 percentage points for the group aged between 25 and 29 years. People in this age range in vulnerable neighbourhoods displayed a lower rate of school enrolment (16.6%) than in any other spatial category of analysis. This was a clear sign of the shorter educational paths of the population in these neighbourhoods.
The briefest educational trajectories also coincided with the lower levels of formation. In the vulnerable neighbourhoods, the percentage of the youth population that had not completed compulsory education stood at 16.6% (amongst those who declare that they had finished studying), almost twice that of the non-vulnerable neighbourhoods (8.8%), and significantly above the Catalan average (9.9%).
In this sense, whilst 91.2% of the youth population in non-vulnerable areas successfully completed compulsory education, in vulnerable areas this was the case for only 83.4%. As Figure 1
shows, the difference grew at the higher levels of education. In the most vulnerable neighbourhoods, only 45.3% of the young population had completed post-compulsory secondary education, compared to 65.4% in the rest of the areas.
With regard to higher studies, merely 15.5% of young people in inferior segregation neighbourhoods held university degrees, compared to 30.4% in the rest. This behaviour was related to future expectations, the peers with whom the youth socialises, and also the economic possibilities of people in vulnerable areas; 23.8% of young people in vulnerable areas stated that they had to leave their studies in the last two years, or that they have not been able to enrol in those studies they would wish to owing to financial difficulties.
The results of the variables analysed showed that the neighbourhood’s level of segregation influenced youths’ educational paths more than any other territorial variable.
4.2. Choosing Schools in Barcelona
Another dimension of the relationship between segregation and educational training can be perceived through the study of spatially differentiated school enrolment patterns and strategies. This could be studied specifically through school enrolment data from the city of Barcelona. It is worthwhile noting that the city had, in its 73 neighbourhoods (Figure 2
), a total of 364 schools offering the second cycle of pre-school (ages three to five) and primary (ages six to 12), of which 172 were state-run and 192 were private or part-financed schools. In total, these schools had approximately 130,300 places. With regard to the population, Barcelona’s city census in 2017 recorded 124,504 children in ages of pre-school and primary school. On the other hand, it was found that a total of 127,153 pupils were enrolled in schools in the city, which gives a positive balance of 2649 enrolments and implies that some children travelled into schools from outside the city.
As has been mentioned beforehand, literature reveals the trend of families to enrol their offspring in neighbourhoods with greater economic power and a more positive social image than their own. In this way, well-off neighbourhoods tend to welcome students from other parts of the city and, conversely, vulnerable neighbourhoods end up relinquishing part of their school population to other neighbourhoods. A way to verify the existence of such a dynamic is through the differential values of students enrolled and the population with school age in each neighbourhood. With this method, the value obtained indicates whether the neighbourhood has more students enrolled in its schools than its corresponding school age population (positive balance), or, oppositely, fewer students enrolled in its schools than its school age population (negative balance). The former receives students from other parts of the city and, in the opposite sense, the latter send school age students outside of the neighbourhood. It is worthwhile mentioning that the data available allowed for the obtainment of the general balance for each neighbourhood, but not the determination of which neighbourhood students come from or to where they travel.
As can be seen in the map given in Figure 3
, there were notable differences amongst neighbourhoods with regard to the balance between enrolments and the school age population. These have been represented on the map in diverse tones of red, showing the neighbourhoods with less enrolments than school age population (low retention) and, in green, those which, in the opposite sense, had more students enrolled in their schools than residents with school age. On the other hand, the number represented on the map for each neighbourhood corresponds to the balance between enrolments and the school age population in absolute values.
Amongst the districts with the highest positive balance, we found, running from East to West, the neighbourhoods of Font d’en Fargues, Vall d’Hebrón, Sant Gervasi-Bonanova, Sarrià, and Pedralbes, which exceed 100% (dark green on the map), meaning that in these areas the number of enrolments doubled, and at times tripled, their school age population. Paying attention to absolute values, the neighbourhood of Sarrià was where the difference between its enrolled students and its school age population was greatest, with a positive balance of 7089 enrolled students. In general terms, the neighbourhoods with the greatest powers of attraction coincided with those that had the highest average incomes in the city (Sant Gervasi-Bonanova, Sarrià, and Pedralbes), yet also with those in a relatively comfortable situation with respect to those in their surroundings, as would be the case of Sant Andreu, la Prosperitat, and la Dreta del Eixample.
At the opposite end of the scale (maroon on the map), we found Torre Baró, la Clota, la Teixonera, and el Parc i la Llacuna del Poblenou, whose school age population tended to be enrolled outside the neighbourhood where they live. This trend could be explained as a result of the paucity of school places in these neighbourhoods. However, it was also observed that an important number of neighbourhoods that boasted a well-served educational network sent students to other areas, namely la Trinitat Nova, Bon Pastor, el Raval, Sant Antoni, La Bordeta, etc. These were, largely, neighbourhoods with lower average incomes than other neighbourhoods in their surroundings.
This behaviour also explains the presence of certain neighbourhoods with relatively low-income levels, which, conversely, had more enrolments than the school age population. This was the case, for example, of Besòs-Maresme, Baró de Viver, Ciutat Meridiana, and la Prosperitat. As has been mentioned beforehand, data about the neighbourhood where each student lives was not available, but it seemed that Baró de Viver and Besós-Maresme attracted, respectively, students living in Bon Pastor and in Sant Adrià de Besòs (specifically from la Mina neighbourhood). Ciutat Meridiana probably attracted the school age population from Torre Baró and Can Cuiàs, which belongs to the adjacent city of Montcada i Reixac. Finally, la Prosperitat, the centre of Sant Andreu, and la Font d’en Fargues were relatively wealthier neighbourhoods than those surrounding them. Thus, it could be observed that, in general terms, neighbourhoods with the highest income levels and best reputations were those that had more enrolments than they should in relation to their number of school age residents. Certain exceptions to this rationale have been observed, such as, for example, the neighbourhoods of Tres Torres, Sant Gervasi-Galvany, and el Putxet i el Farró, which were comfortable neighbourhoods though they displayed a low level of self-containment in terms of school age population. According to the proximity and the school district map (second map in Figure 3
), it seemed that their students travelled to bordering neighbourhoods that also had a high income.
In the second map in Figure 3
, the difference between the number of enrolments and the school age population of the 29 school districts is represented. School districts are integrated by sets of proximity centres. Residence in one of these areas constitutes one of the prioritisation criteria to obtain a place in the school of choice, whether this is a state school or a part-financed school. Data displayed wholly confirmed the main features unearthed through the analysis undertaken in relation to the neighbourhoods. The fact that, despite the existence of school districts—intended, in theory, to ensure school enrolment under terms of equality for the school age population near its place of residence—there was an outflow of the population, shows a shortfall in the system’s effectiveness.
With the aim of complementing this analysis, vacancies in each school were studied for each neighbourhood and school district. As can been seen in the map in Figure 4
, the number of school vacancies was not homogenous in all Barcelona. On the one hand, there were 15 neighbourhoods that had a deficit in absolute terms of school places (shades of green), to which those with zero vacancies must be added. These were neighbourhoods with a high demand in terms of school enrolment, with more demand than availability. Worthy of special mention in terms of schooling demand was Sant Andreu, which as has already been mentioned, was one of the most well-off neighbourhoods in the northeast area of the city.
On the other hand, as can be observed in the same map in orange tones, there were 50 neighbourhoods in Barcelona that had a significant number of school vacancies. If we compared this map with the one on income by neighbourhood (2016), it could be seen that the areas that had school vacancies were, firstly, those which had lower income levels, namely Bon Pastor, Baró de Viver, Ciutat Meridiana, and la Barceloneta. Alongside these, we could also find neighbourhoods with higher income levels, such as Dreta de l’Eixample or all of those that comprise the district of Sarrià-San Gervasi. This situation had a dual explanation: In the case of the vulnerable neighbourhoods, this was due to weak demand levels and the failure to retain own school age population in the neighbourhood; however, in the well-off neighbourhoods, this was explained by the predominance of private or part-financed schools. These schools, unlike those which were wholly state run, offered many places without overly careful adjustment to demand.
By focusing on neighbourhoods with school vacancies and with low income, worthy of further attention were the cases of Vallbona, Trinitat Nova, and les Roquetes, which had a highly elevated number of unfilled places despite only having one school, which was, furthermore, state run. The figures of the 54 unfilled school spaces in Roquetes, 56 in Vallbona, and 83 in Trinitat Nova indicated clearly that families in these neighbourhoods, despite having vacant places nearby, preferred to enrol their children in pre-school and primary centres in neighbouring areas.
If we observe the second map in Figure 4
, with the school vacancies by school districts, it can be seen how Sant Andreu, Navas–la Sagrera–el Congés i els Indians, and el Parc i la Llacuna–la Vila Olímpica del Poblenou–Diagonal Mar i el Front Marítim del Poblenou–Poblenou, were areas that had more demand than the number of places on offer. In the opposite sense, two axes that show school vacancies were clearly detected: Running from North to South, from Horta-Guinardó to Sants-Montjuïc; and the section in the Besós River area. This could also be explained by the duality already mentioned; some of the areas having a surplus of vacancies were neighbourhoods with a low income (such as Ciudad Meridiana- Torre Baró—Vallbona), whilst others comprised well-off neighbourhoods that displayed a high number of unfilled school places due to the predominance of privately-owned or part-financed schools (as is the case of Putxet i Farró—San Gervasi-Bonanova—Sant Gervasi-Galvany).
4.3. Inequalities in School Performance
The final part of this paper analyses the relationship between the socio-economic variables, the complexity of the schools’ situation, and the academic results of the students. The available data on school performance refers to the percentage of students in the sixth grade of primary school that did not achieve the basic competence in the 2013–2014 academic year [34
]. In this case, no data detailed by neighbourhoods was available and, for this reason, work was undertaken with the 10 districts of Barcelona.
As can be seen in Figure 5
, the districts with a higher percentage of students that failed basic competences at sixth grade were those of Ciutat Vella and Nou Barris, with the percentage standing at around 20%. Following these, with fail rates at around 12%, we found Sant Martí, Horta-Guinardó, and Sant Andreu. The district of Gracia showed a 10% fail rate and Eixample an 8.5% fail rate. Finally, the districts showing the best results were Les Corts and Sarrià-Sant Gervasi, with merely 5% of the students failing these tests. It is worthwhile highlighting that the differences were highly remarkable, reaching up to 15 percentage points between districts.
Complementary information refers to school complexity. Maximum complexity schools were defined by the Departamanet d’Ensenyament (Teaching Department) based on the following context variables: Low parental level of instruction, parental employment on low professional qualification posts, significant number of parents of students receiving guaranteed minimum income, high percentage of parents unemployed, high percentage of students with specific educational needs, and high percentage of immigrants. The results clearly showed a direct relationship between the number of schools defined with the category of maximum complexity and the percentage of sixth grade primary students that did not achieve the basic competences, with an adjustment rate of R2 = 0.7464. In this sense, the districts with the most schools deemed to be in complex situations (Ciutat Vella, Nou Barris, Sants-Montjuïc, and Sant Martí) were the ones with the most deficient results on the basic competences. These results may be the expression of the spiral of reproduction of school segregation, specifically of the conditions of vulnerability experienced by some areas and schools.
The relationship between academic performances and the average disposable income was even more revealing. As can be seen from Table 1
and Figure 5
and Figure 6
, the relationship was of an inverse nature: the lowest percentages of students failing basic competences corresponded to the higher income districts (that is, the districts of Sarrià-Sant Gervasi and Les Corts, as well as those of Eixample and Gràcia). In the opposite sense, where the average income was lower, the percentage of students that failed basic competences was higher (as can be seen in Nou Barris, Ciutat Vella, and Sants-Montjuïc). Literature dealing with the “neighbourhood effect” as a generator and perpetuator of social inequalities would undoubtedly find food for thought in this data.