Navigation – Plan du site
2019
917

Residential and school segregation as parameters of educational performance in Athens

Les ségrégations résidentielle et scolaire, facteurs des inégalités de la réussite éducative à Athènes
Thomas Maloutas, Stavros Spyrellis, Andromachi Hadjiyanni, Antoinetta Capella et Despoina Valassi

Résumés

Cet article explore la relation entre réussite scolaire et inégalités sociales et urbaines dans l’aire métropolitaine d’Athènes pendant les années 2000. Il se situe dans les débats considérant l’éducation comme un processus de reproduction sociale et s’appuie sur les études des inégalités de la fonction éducative en Grèce et plus particulièrement à Athènes. Dans cette ville, l’éducation secondaire est socialement stratifiée par rapport au reste du pays, ce qui conduit à une éducation supérieure plus ouverte mais socialement inégalitaire. Nous analysons jusqu’à quel point la réussite des candidats à l’examen d’admission national dans l’enseignement supérieur (Panelladikes Exetaseis) dépend de la position sociale de leurs familles, du profil social de leur voisinage résidentiel (autant que possible) et de leurs caractéristiques démographiques (âge et sexe). L’objectif est d’illustrer et de quantifier à peu près la fonction de reproduction sociale dans ce processus socialement sélectif de transition vers l’enseignement supérieur.

Haut de page

Texte intégral

Introduction

1The propagation of education in the modern era permitted to open status and power positions outside hereditary privilege. It led to longer years in formal learning, a growing average education level and an increasing participation rate of lower social classes at all education levels (Moore, 2004) and, eventually, induced the massive increase of social mobility. However, the access to increasingly demanding educational qualifications has always been unequal. Thus, it stratified the impact of this propagation and converted education to the foundational process of the modern socially unequal meritocracy (Dubet, 2004; Duru-Bellat, 2009).

  • 1 See, for example, the different approaches of Maurin (2009) and Chauvel (2016) on the role of educa (...)

2Education is caught in a tension between its learning function and its social reproduction function (Duru-Bellat, 2009; Felouzis, 2012). This tension varies amongst different periods and contexts. Social selectivity through education usually increases when social mobility chances are reduced. Nonetheless, the social reproduction function of education is mainly related to regulation regimes and the way they handle redistributive justice in both growth and decline conditions. Moreover, historical conditions reflecting the changing shapes of social stratification (e.g. the growing share of the middle classes) and the changing parameters of inequality –when unequal wealth becomes increasingly more important than unequal income (Picketty, 2014)– affect the role education plays in social reproduction1.

  • 2 Modern liberalism mitigates this position by stressing also the need to provide equal opportunities (...)
  • 3 The socially unequal outcomes to which leads the systematic social differentiation of educational a (...)

3The socially dividing function of education is not easily perceptible and, nowadays, it is not necessarily considered a problem. Following a classical liberal or neoliberal approach, social inequality is justified as the just reward of drive and talent2. Socialist approaches, on the contrary, consider inequality mainly the outcome of uneven social conditions3. In policy terms, however, things are less clear, especially during the era of neoliberal ideological and political hegemony when educational policies by Conservative and Socialist parties have often been quite similar.

4This paper explores the relation of educational performance with social and urban inequalities in the Athens Metropolitan Area during the 2000s. It draws on discussions about education as a mechanism of social reproduction and on work about education inequalities in Greece, and in particular in Athens, where a socially stratified secondary education –compared to much less stratification in the rest of the country– leads to a rather open, but at the same time socially unequal higher education. The focus is on the transition from secondary to higher education. We relate the performance of candidates in the Greek national admissions examination (Panelladikes Exetaseis) and its outcome in terms of the Department of studies they eventually accessed to the socially diversified options within higher education and therefore we consider performance not only in terms of score but also as a predictor of different occupational and social futures. We analyze how this performance relates to the social position of candidates’ families, to the type of secondary schools they attended, to the social profile of candidates’ residential neighborhoods (tentatively) and to their demographic features (age and sex). The object is to illustrate and roughly quantify the function of social reproduction in this socially selective process of transition to higher education.

The social selectivity of school systems and the impact of residential location

State of the art

5National education systems manage social selectivity in various ways. Unique curricula throughout secondary school, as in most Scandinavian countries, are in principle less selective compared to socially differentiated curricula from an early age, like in Germany or the Netherlands; or to options representing privileged paths to the occupational elite, like the filières to the Grandes Écoles in France (Felouzis, 2009).

  • 4 In the UK –mainly in England– the majority of schools (over 90%) are run by local authorities but a (...)

6Diversity in curricula is often accompanied by other forms of segregated school options. In mixed (public/private) systems, high profile private institutions –like the renowned public schools in the UK4– may be free to choose their clientele only amongst those who can afford their services, leading to blunt forms of school segregation. Where public sector schools are the quasi-unique choice, middle class education strategies generate school segregation in more intricate ways (Ball, 2003; Power et al., 2003; Van Zanten, 2001 and 2009; Merle, 2012).

7The wave of change in educational policies since the 1990s in many countries was driven by the idea of promoting parental choice. These policies are in fact related to the (actual or presumed) support of the middle classes for more ‘consumer choice’ in education. The reforms of New Labour (‘Excellence in Cities’, ‘Educational Priority Areas’, ‘Five-year Strategy’) increased parental choice (Oria et al., 2007), while pro-choice policies justified as boosters of educational attainment eventually increased educational inequality (Ball, 1993 and 2006; Ball et al., 1995 and 1996; van Zanten & Kosunen, 2013; Power et al., 2003; Seppänen, 2003; Bosetti, 2004; Denessen et al., 2005; Riddell 2005; Butler and Van Zanten, 2007; Dubet et al. 2010; Dronkers et al. 2010; Merle, 2012). More parental choice characterizes also education policies in the US, like G.W.Bush’s NCLB (no child left behind) or Obama’s RTTT (race to the top) and the proliferation of Charter schools that ‘have contributed to the privatization and non-profitization of urban schools across the country’ (Patterson and Silverman 2013).

8The proliferation of Charter schools in the US can be related to processes of urban revitalization and gentrification at least in some places, like Chicago and Philadelphia (Davis and Oakley, 2013). The link between gentrification and school segregation is also discussed for London by Butler et al. (2013) and for Amsterdam by Boterman (2012 and 2013) who identifies significant differences in the spatial location preferences among middle-class groups with different amounts of cultural capital. These preferences are also significantly related to gender for middle-class parents in both Amsterdam and London according to Boterman and Bridge (2015). In the UK, where parental choice was promoted since 1988, the competition for allocation to good schools reinforces the relation between school and residential segregation by reducing the distance for eligibility to good schools (Hamnett & Butler, 2013) and reintroduces the criterion of distance, which is relaxed as school quality decreases. Increased parental choice has also increased educational inequalities in Sweden with pupils from lower social profile and immigrant backgrounds travelling smaller distances, and therefore, exercising less their choice for better schools (Andersson et al., 2012, Östh et al., 2013). The relaxation of catchment areas in France, following pro-choice policies, had ambiguous social consequences. It was advertised as a tool for working class and other underprivileged families to access better schools than those in their areas; it eventually served families from classes that are more informed and more driven by educational objectives (Oberti et al., 2012; Merle, 2012). Increased parental choice in Berlin introduced a tension between parents who prioritize this choice as a right –and whose behaviour eventually increased school segregation especially in terms of evading ethnic diversity– and those who filter their strategies through other concerns (Noreisch, 2007a and 2007b). Similarly, in another German region (North Rhine-Westphalia) less educated parents take much less initiative and well-educated ones consider school choice not only as an opportunity, but also as a duty (Ramos Lobato and Groos, 2019).

9Residential segregation and school segregation are therefore closely related: social groups are unevenly distributed in residential space and in schools in ways that usually reproduce their advantage or disadvantage. School and residential segregation usually work in tandem: areas with better schools attract more middle-class residents that eventually improve local schools’ performance increasing further their attraction. This relation is, however, contextually diversified. In US metropolitan areas, schools with greater social, financial and instructional resources are serving high rather than low-income neighborhoods (Owens and Candipan, 2019). In Dutch cities it seems that school segregation is largely the effect of residential location patterns (Boterman, 2019), while in Paris school segregation appears to be stronger than residential segregation (Oberti and Savina, 2019).

10On the other hand, residential segregation is also important per se due to its assumed impact on living conditions and on chances of social mobility. There is a substantial literature on the neighborhood or area effect, mainly developed in the U.S. (Ellen and Turner, 1997). This literature addresses issues related to education, like the lack of role-models related to the absence of successful middle class groups; the forms of social capital that constrain rather than enable social mobility, and the poor quality of services (e.g. schools) (Atkinson and Kintrea, 2001, p.2278).

  • 5 According to Gordon and Monastiriotis (2006 and 2007) neighbourhood effects in education performanc (...)

11The central issue for neighborhood effects is whether there are specific spatial effects on peoples’ lives and life prospects ‘over and above non-spatial categories such as gender and class (…)’ (Atkinson and Kintrea, 2001, p.2277). These additional effects may originate from the different socio-demographic composition of neighborhoods, from their intrinsic quality—e.g. the quality of their environment or of the locally provided services—and from neighborhoods’ comparative status, ranging from privileged to stigmatized (Buck, 2001). The question of neighborhood effects is further complicated by the fact that they may refer to different spatial scales, they may be negative or positive and they are not necessarily the same for different class categories5. Musterd et al. (2006) found effects of varying magnitude from a number of European city neighborhoods that were not always what was expected according to the local welfare regime.

12Neighborhood effects vary in different contexts. Enforced spatial isolation, as in the black ghetto, obviously reduces opportunities for social mobility to a much higher degree (Massey and Denton, 1993; Wilson, 1987) than spatial separation in comparatively low segregation environments and relatively evenly serviced residential areas, as in Dutch cities. In the latter, neighborhood effects may be found to be of considerably less importance for social mobility than the personal/household characteristics of the relatively isolated and deprived groups (Ostendorf et al., 2001; Musterd et al., 2003). Neighborhood effects in South European cities can be expected to be somewhere in-between due to the contradictory influence of, on the one hand, the absence of highly segregated areas and groups and, on the other, the relatively poor and unevenly distributed social services.

13High levels of residential segregation usually induce high levels of school segregation. The opposite, however, is not necessarily true. Low levels of residential segregation are not necessarily combined with low levels of school segregation. As we will see in the following - where we only partly address the intricate relations between school segregation, residential segregation and social origin in Athens - low levels of residential segregation may not preclude relatively high levels of school segregation and the formation of clear socially unequal educational trajectories.

Educational inequality and residential segregation in Athens

14The Greek educational system has a unique curriculum up to lower secondary school (Gymnasio/Gymnasium) that concludes the nine years of compulsory education and a dominant general option in the upper secondary (General Lykeio/Lyceum) which comprises about 70% of enrolment compared to a smaller (30%) vocational option (UNESCO, 2012).

15In Athens, more than anywhere else in Greece, there is an important private segment operating in secondary education (8,5%) without financial assistance from the State. According to Dronkers et al. (2010) Greece and the U.K. are the only EU countries where private schools do not receive public funds and, therefore, can determine their recruitment policy. Most private schools perform better than average and especially the few elite schools that also offer options (like International Baccalaureate) related to prospective studies abroad (Valassi, 2008). Public schools are much more socially mixed and of variable performance. The transition from secondary to higher education is organized through a national admissions examination since the 1960s (Maloutas, 2019). Performance in these examinations gives access to higher education following the demand for each Department. Mere access to higher education is long out-dated as an indicator of social distinction and, therefore, socially separate paths within higher education have to be taken into account.

16The foundational work of Tsoukalas (1977) stressed the democratic character of Greek secondary and tertiary education, in terms of the massive access provided to students of lower social origin and of the important wave of social mobility it has supported for quite a long period; Frangoudaki (1985) discussed the ‘hypertrophy’ of higher education as an important factor that led to its internal social diversification, which Lambiri-Dimaki (1974) had initially depicted.

17Kontogiannopoulou-Polydorides (1999) showed that, since the 1960s, the chances of candidates from families of professionals and office employees were much higher than those from farmers and the working class; and that, at least since the 1980s, these inequalities are not limited to the acquisition of degrees, but are closely related to the unequal ways that graduates with similar degrees fare subsequently in the labor market. Thanos (2011) drew similar conclusions regarding the important differences amongst socio-professional categories in terms of access to different types of higher education Departments.

18Panayotopoulos (2000) showed that the Faculties of Medicine, Law and most Schools of the National Technical University of Athens (especially Architecture and Mechanical Engineering) are reserved, to some extent, for upper and upper-middle social strata, while those of Theology or Education are mainly relegated to lower and lower-middle ones. Highly demanded Faculties, like Medicine and Law, remain very unequally accessible by students from different social backgrounds. Moreover, they harbor a far higher rate of endogenous reproduction (i.e. within the family’s occupational line) compared to other occupations that also require university degrees (Maloutas, 2007b).

19Sianou-Kyrgiou (2008) stressed the importance of extra-curricular preparation for the admissions examination to higher education. Candidates massively participate in this preparation, sometimes for several years before the event. The process is privately organized on collective or individual basis and the cost is high, especially for its most individualized forms. The cost, as well as the socially uneven awareness of the importance and the workings of this preparation, lead to systematic social differences that mitigate the socially equalizing impact of the predominantly public character of secondary education.

20Hadjiyanni and Valassi (2009) claim that the rapid development of postgraduate studies and the unequal prospects offered by different types of institutions, academic disciplines and specialties have created new social inequalities within higher education and/or reinforced existing ones. They conclude that the attenuation of inequalities in accessing higher education has been counterbalanced by new divisions at the postgraduate level.

21The 'democratization' of higher education did not necessarily lead to more social justice. Thus, researchers shifted their interest to inequalities within higher education and revealed new divisions and hierarchies (Sianou-Kyrgiou, 2010, Spyrellis, 2013), confirming that the education system produces and reproduces inequality in changing forms that may be increasingly difficult to identify (Bourdieu and Passeron, 2000).

  • 6 Such ‘false’ addresses usually belonged to a relative or a friend of the family.

22The question of school choice was never high on the Greek sociopolitical agenda. Choice for upper and upper-middle classes was always present under the form of private schools; and private education was not perceived –at least until the mid 1970s when democratic rule was durably re-established in Greece– as a blunt instrument of class reproduction. Private secondary schools were a terrain where upper-middle classes had privileged access, but at the same time provided a combination of educational innovation and democratic spirit (Valassi, 2012). For middle and lower-middle classes, effective choice was exercised by the bending of catchment area rules by parents wishing a different school from the one their child was allocated to, usually by giving a ‘false’ address within the catchment area6 and/or by negotiating with the school authorities (Spyrellis, 2015). As a result, the relation between school and residential segregation was rather relaxed. This is corroborated by the scarcity of cases where the quality of schools is reported as an important parameter in choosing where to live according to relevant surveys in the mid-1980s (Maloutas 1990) and the early 2000s (Maloutas et al., 2006).

23Residential segregation in Athens is relatively reduced both in terms of class and ethnicity for a host of reasons related to its urbanization model, to the local welfare system and the role of family networks (Allen et al., 2004); to the workings of the housing market and the structure of housing supply; to the reduced presence of a foreign corporate elite in its labor and housing markets etc. (Maloutas 2007a, Maloutas et al. 2012). Affluent families are hindered from relocation strategies to good school areas and induced to seek school segregation in other ways in order to gain educational advantage.

  • 7 The deliverable was part of the task “Mining knowledge from data of the educational community”, com (...)

24The main data used in this paper to assess unequal access to higher education originate mainly from a dataset comprising detailed information on the performance and identity of all candidates in the admissions examination to higher education in 2004/05. The dataset was produced by the ITYE (Computer Technology Institute and Press ‘Diophantus’) as deliverable of a project commissioned by the Greek Ministry of Education7. Similar data have never been produced after that project. Therefore, they are invaluable, even though they are relatively outdated. These data were never publicly available and have only been used by the administration. Apart Spyrellis (2013, 2015), this is the only paper using these data for research purposes.

  • 8 The weights of 70% for the score of written exams and 30% for the graduation grade from secondary e (...)

25The ITYE dataset comprises a very large number of variables on all secondary education graduates in Greece for the school year 2004/05. The record of every candidate for higher education relates him/her to a specific secondary school and to its features (type of school, average performance in the admissions examination, quality attributes in terms of infrastructure and teaching personnel etc.) as well as to the school’s address and catchment area. There is also detailed information on candidates’ individual performance in all tests comprised in the examination for admission to higher education and the graduation grade from secondary education. We used a single performance index: the ‘general grade of access’ (=average grade obtained by each candidate in all the main subjects examined X 70% + graduation grade from secondary education X 30%) ranging from 0 to 2 0008. This general grade marks the individual performance of candidates and determines their position in the admissions contest. The dataset comprised more than 90 000 records for the whole country. We used the records of over 32 000 candidates that graduated from 447 secondary schools in the Athens Metropolitan Area. Data from the Hellenic Statistical Authority (ELSTAT) were used to complement information on students’ social origin.

Data analysis

The effect of social origin

26The main lacuna in the ITYE dataset was the absence of information on candidates’ socioeconomic background. This is especially important since we assume that family socioeconomic background is the main parameter explaining unequal access to higher education. This assumption derives from the relevant literature, partly mentioned in the previous section, but we have also been able to control it using data published by the Hellenic Statistical Authority (ELSTAT) on the education level of parents of students enrolled in the different higher education Departments for 2009 and 2010 as well as on their occupation9. We used these data to cluster all Departments into seven hierarchical categories (cluster 1 contains the highest rate of parents in managerial and professional categories with higher education degrees and cluster 7 the lowest [table 1]).10

Table 1: Departments of higher education institutions clustered following the education level and the occupation of students’ parents (percentages). Final Cluster Centers.

Parents

Education level

Cluster of Faculties and Departments

1

2

3

4

5

6

7

all

Higher education (F+M)

55.9

42.5

34.1

23.4

23.2

14.6

13.0

26.8

Compulsory to post-secondary (F+M)

39.1

49.9

56.4

64.7

60.3

66.8

65.1

59.6

Less than compulsory (F+M)

5.0

7.6

9.5

11.9

16.5

18.6

21.9

13.7

Occupation

Managers-Professionals (F)

52.0

39.4

27.3

21.1

25.4

12.8

13.7

24.6

Intermediate professions (F)

30.2

35.9

44.2

46.8

33.4

48.9

34.1

41.1

Working-Class (F)

12.0

16.7

20.4

22.8

28.9

27.3

39.5

24.5

N of Depts and Facs

36

55

66

86

51

97

54

445

N of admitted candidates (2005)

1475

2115

4047

5018

1887

4976

3092

22610

Average score of admitted candidates (max=2000)

1837

1623

1536

1269

1098

1059

1017

1292

F =Fathers; M = Mothers

27We introduced this clustering of Departments as an additional variable in our main dataset and examined the correlation between this social hierarchy of departments and the score of admitted candidates. The correlation appears to be strong (Spearman’s rho = -.786) witnessing that the socially more exclusive Departments are accessed through higher scores and, therefore, that children of higher social categories obtain systematically higher grades at the admissions contest. This correlation is also clear when we compare the average scores of admitted candidates in each cluster of departments (table 1).

28We calculated the socially uneven chances to get admitted in each of these clusters of Departments by comparing the percentage of enrolled students from different socio-educational backgrounds in each of the seven clusters to the distribution of education levels in the whole population aged between 40 and 75 (roughly corresponding to the expected age of students’ parents). Figure 1 shows that candidates from a highly educated family have over four times more chances than the average candidate to get admitted to one of the most demanded Departments (cluster 1) and over 34 times more than the average candidate from a poorly educated family. The range of inequality decreases as we move down the hierarchy of Department clusters. The higher social strata seem to lose interest in the less prominent part of the hierarchy (their chances compared to those of candidates from poor educational background decrease from 34 times [cluster 1] to 2 [clusters 6 and 7]) while those of candidates from intermediate educational backgrounds decrease less steeply, but remain much higher than those of the less privileged candidates even at the end of the clustered hierarchy (respectively from 7 times [cluster 1] to 3 [clusters 6 and 7]).

Figure 1: Comparative chances of candidates originating from different family educational backgrounds to get admitted to Departments clustered according to the socio-educational profile of students’ parents (2010) (average candidate’s chances = 1.00)*

Figure 1: Comparative chances of candidates originating from different family educational backgrounds to get admitted to Departments clustered according to the socio-educational profile of students’ parents (2010) (average candidate’s chances = 1.00)*

* data on the education level of the general population of the metropolitan area of Athens aged 40 to 75 derive from the 2001 census (EKKE-ELSTAT, 2015)
** maximum possible performance = 2.000

29Higher socio-educational groups maintain a systematically uneven access to the best segments of higher education through their systematically higher performance in the admissions examination –the magically unequal distribution of educational merit in favor of higher social strata according to Duru-Bellat (2009). According to Sianou-Kyrgiou and Tsiplakides (2011) the effective outcome in terms of chosen options within higher education is socially unequal even when performance is similar.

The school environment

30The quality of secondary schools is the second parameter we assumed important in explaining uneven access to higher education. One way to assess this quality is to measure the average performance of candidates from each secondary school in the admissions examination. However, this measure does not necessarily –or entirely– reflect the quality of schools per se (i.e. the quality of educational work, the educational credentials of the teaching staff, the state of the infrastructure or the organization efficiency) as differences in performance amongst schools may be due to the uneven social profile of their clientele. Since our dataset does not comprise information on the social origin of candidates, our measures of school quality involve both quality per se and the effect of the different social composition of their pupils.

31The correlation between candidates’ performance in the admissions examination and the average performance of the school they attended is important (R = .362). This means that 13% of the variance in candidates’ performance (R2 = .131) is ‘explained’ by school’s performance.

32Renowned private schools are singled out for their high performance as well as for their social selectivity (Valassi, 2012). On the contrary, the small lower tier of private schools –comprising 1.6% of students, compared to 7.0% for the rest of private schools– is of low educational performance and serves a clientele of a different social profile. Evening schools –mostly public– accommodate working students, either from lower socioeconomic backgrounds or mature students. These schools account for 3.2% of the student population and have usually a low rate of admission to higher education. The bulk of secondary schools are daytime public schools, which account for 88.3% of the student population. Among these, a limited number of ‘experimental’ schools used to select students on performance and to implement innovative education methods. Although the selectivity of experimental schools has been tampered, they continue to have a systematically higher performance amongst public schools. They account for 3.3% of the student population.

33We produced a 9-category hierarchical variable (table 2) taking into account schools’ public or private status, their daytime or evening operation and their experimental or regular character. Where necessary, we subdivided these categories according to average school performance in the admissions examination to higher education.

34This hierarchical variable is significantly correlated with the hierarchy of higher education Departments where candidates were eventually admitted (R = -.333).

Table 2: Student population (seniors) and average performance in the admissions examination to higher education by type of school in Athens (2005)

Type of school

Number of students (seniors)

%

Average performance

evening school

1014

3.2

876

private – low performance

495

1.6

944

public – low performance

7572

23.5

973

public – mid-low performance

11029

34.5

1109

public – mid-high performance

3631

11.3

1192

public – high performance

5006

15.6

1276

public experimental

1048

3.3

1323

private – mid performance

1212

3.8

1404

private – high performance

1011

3.2

1543

total

32018

100.0

1142

35Figure 2 shows that there are substantial differences in the access to higher education depending on the type of secondary school. More than 80% of those who graduated from high performance private schools had access to highly or averagely demanded Departments (clusters 1 to 4) and only 4.2% were not admitted. On the contrary, those graduating from evening schools were not admitted at a rate of 75% and only 15% had access to a highly or averagely demanded Department.

36Figure 3 shows the percentage composition of the student population in the different clusters of Faculties and Departments in terms of the type of school they come from. The overwhelming importance of students from public schools affects student composition within most clusters. Even in the most exclusive cluster of Departments (cluster 1), more than 40% originate from medium and low performance public schools, while only 32% originate from high and medium performance private and experimental public schools; this is respectively reduced to 23% for cluster 2.

37This double sided picture (figures 2 and 3) shows that elites and upper middle classes have privileged, but not exclusive, access to higher education in Greece. Thus, these social groups often use more exclusive alternatives, like studies abroad to academically and often financially demanding institutions in Western Europe and the US.

Figure 2: Percentage distribution of candidates from different types of secondary school admitted to the different social clusters of higher education Departments (2005)

Figure 2: Percentage distribution of candidates from different types of secondary school admitted to the different social clusters of higher education Departments (2005)

Figure 3: Percentage distribution of students admitted to the different social clusters of higher education Departments by type of secondary school they attended (2005)

Figure 3: Percentage distribution of students admitted to the different social clusters of higher education Departments by type of secondary school they attended (2005)

38The broad area of studies which candidates select during the last two years of secondary school in view of the admissions examination is an important parameter that differentiates schools: ‘science’, ‘technology’ and ‘humanities’ account respectively for 13%, 51% and 36% of enrolment (2004-5). Average performance differs amongst these options leading to different success rates (87%, 69% and 67% respectively). The ‘science’ option is much more present in high-performance private schools (27%) against approximately 10% in the bulk of schools at the lower end of the school type hierarchy.

The role of the neighborhood

  • 11 In another paper (Maloutas et al., 2019) we tried to relate educational performance (length of educ (...)

39The next parameter we considered was the neighborhood. With the available data it was impossible to attempt this properly, since the absence of information on the social background of each candidate did not permit to assess the difference in performance for the same social group of candidates in different types of neighborhood11. Another shortcoming is that our dataset does not comprise information on the actual residential area of candidates, which we had to assume from the location of the school they attended. This is acceptable for the large majority of students usually attending the public school of their neighborhood, but much less for many private school students who have to commute long distances between their home and their school. Moreover, it was impossible to address the different dimensions of neighborhood effects in a coherent and comprehensive manner, i.e. the combination of the social composition of neighborhoods with the quality of their natural environment and the services they provide, and with their perceived image (Buck, 2001; Atkinson and Kintrea, 2001; Lupton, 2003). We limited our investigation, therefore, to the simple impact of residential neigborhoods’ social profiles on candidates’ performance.

40At first sight, the social profile of the neighborhood seems to matter, even though the correlation of candidates’ performance with its attributes is not particularly high (R ≈.150 to .200) (table 3).

41In table 3 all area attributes –mostly those assumed related to difference in social rank– are significantly correlated with candidates’ performance in the admissions examination. Higher indices appear for occupational or other groups at the extremes of the social hierarchy contrasting with lower indices for groups around the middle as well as for variables related to location within the metropolitan region (central / suburban / peripheral), to housing tenure and to immigrant presence.

  • 12 We assumed that a distance less than 1500m constitutes a pedestrian itinerary in which most daily a (...)

Table 3 Correlation indices (Pearson) between candidates’ and average school performance in the admissions examinations to higher education and indicative social features of the neighborhoods12 corresponding to each school (2005)

  • 13 We used the categories of the European Socioceconomic Classes (ESeC) as indicators of neighbourhood (...)
  • 14 The composite index of deprivation is the sum of the hierarchical positions (cluster identities ord (...)
  • 15 The socioeconomic type of residential areas was determined by a K-means clustering of census tracts (...)

Neighborhood features

Candidates’ performance

Average school performance

Percentage of people aged 18-24 in higher education

.199**

.518**

Percentage of large employers, higher grade managers and professionals (ESeC 1)13

.197**

.520**

Composite index of neighborhood deprivation14

-.196**

-.516**

Percentage of working-class occupations (ESeC 8)

-.193**

-.510**

Percentage of graduates of higher education in adult population

.193**

.504**

Socioeconomic type of neighborhood15

-.192**

-.504**

Percentage of people with less than compulsory education in the adult population

-.184**

-.477**

Percentage of people with more than 40sqm of housing space per capita

.179**

.471**

Percentage of people with less than 15sqm of housing space per capita

-.171**

-.453**

Percentage of employees in the lower services (ESeC 7)

-.134**

-.354**

Percentage of immigrants

-.063**

-.181**

Percentage of intermediate professions (ESeC 3)

.056**

.145**

Neighbourhood type (central / suburban / peripheral)

.035**

.116**

Percentage of people in rented accommodation

-.031**

-.101**

N

30103

32018

** significance level .95

Map 1: Location of secondary schools (Lykeia) by type and performance (2005)

Map 1: Location of secondary schools (Lykeia) by type and performance (2005)

Map 2 Social typology* of neighborhoods in the Athens Metropolitan Area (2001)

Map 2 Social typology* of neighborhoods in the Athens Metropolitan Area (2001)

* see note 15

42Average school performance is correlated to neighborhood attributes much more than individual performance since all variance within schools disappears; Table 3 indicates that schools situated in neighborhoods of higher social profile usually perform better. This is also depicted by the comparative reading of maps 1 and 2. Although there is no absolute correspondence between type and performance of schools and neighborhood status, these two maps show that there is a concentration of low/mid-low performance public schools in low/mid-low status areas and a concentration of high/mid-high performance private and public schools in high/mid-high status areas. Low and mid-low performance schools are much more concentrated in the working class municipalities of Keratsini, Nikaia and Perama at the west of Piraeus) while high and mid-high performance schools are much more concentrated in the wealthy suburbs of Psychico-Filothei or Ekali. This is, in fact, an expected outcome since neighborhoods with a higher social ranking contain a larger percentage of middle and upper-middle class students who systematically perform better in education (table 4).

Table 4 Percentage distribution of students by type of school and by social type of neighborhood in Athens (2005)

Social type of neighborhood

Type of school

upper

upper-middle

mixed

lower-middle

lower

private low

0.0

1.1

1,8

1.9

0.5

evening

0.0

1.1

4.1

3.5

3.8

public low

9.7

7.7

11.6

33.4

58.3

public mid-low

0.0

22.6

36.1

41.9

31.3

public mid-high

0.0

19.1

10.3

11.1

6.1

public high

15.0

34.2

23.9

4.9

0.0

public experimental

11.6

4.4

4.7

1.6

0.0

private medium

18.2

8.0

4.1

1.2

0.0

private high

45.5

1.8

3.4

0.4

0.0

all schools

100.0

100.0

100.0

100.0

100.0

Figure 4 Actual and expected performance in the admissions examination to higher education by school type and by social type of residential area in Athens (2005)

Figure 4 Actual and expected performance in the admissions examination to higher education by school type and by social type of residential area in Athens (2005)

43Figure 4 shows the average performance of candidates in the admissions examination to higher education by broad social type of residential area. This performance is shown for all candidates (purple line), and separately for those from public or private schools (bars). It also shows the predicted value of this performance based on its relation with the composite index of deprivation (see note 14) in the areas around the 447 schools, where more than 32 000 candidates completed their secondary education in 2005. Comparing the actual with the expected performance for all candidates in different social settings we observe no substantial difference, except in the higher social type of neighborhoods. This could mean that there is a positive neighborhood effect in those neighborhoods, which would be consistent with similar observations elsewhere (Gordon and Monastiriotis, 2006 and 2007). However, this higher than expected performance in upper class neighborhoods is most probably related to the concentration of privileged private schools, which attract highly performing students from a broad range of upper-middle and socially mixed areas. The systematically higher performance of candidates from upper class areas seems, therefore, much more related to the concentration of private schools (table 4) and to school segregation rather than to some form of neighborhood effect.

44For the much larger number of candidates from public schools, performance decreases –in respect to expected values– as we move from lower to higher social types of neighborhood. This is probably the effect of draining performant (i.e. middle class) students by private schools, which culminates in the higher status areas and relegates public schools to a residual role.

45Another way of controlling the assumption that school segregation is more significant than residential segregation for educational performance is by comparing performance in similar segments of the school and the neighborhood hierarchies.

Table 5 Candidates’ average performance by school and neighborhood social type (2005)

School type

Mean*

N

Coefficient of variance

Neighborhood type

Mean*

N

Coefficient of variance

private low
evening school

pub
lic low

944.4
875.5
972.9

443
304
7207

52.0
47.7
41.1

lower

1014.2

2585

40.5

public mid-low

1109.1

10565

36.7

lower-middle

1081.2

11937

38.3

public mid-high

1192.2

3494

34.0

mixed

1176.0

9296

36.5

public high
public experim.
private medium

1275.8
1323.4
1403.9

4841
1036
1205

32.3
30.7
27.3

upper-middle

1225.6

5141

34.9

private high

1543.2

1008

21.1

upper

1409.5

1144

28.4

Total

1141.9

30103

37.6

Total

1141.9

30103

37.6

* Maximum possible score = 2000

  • 16 In fact, the difference is even higher since high performance private schools are mostly situated i (...)

46Table 5 shows that there is a comparable number of candidates for higher education who either graduated from high performance private schools (1 008) or who live in the highest social type (upper) of heighborhood (1 144); obviously a number of these candidates fall into both categories. However, those in high performance private schools did clearly better (>10% difference on average) than the residents of neighborhoods with the highest status.16 Candidates from private schools also form a more coherent group in terms of performance (smaller standard deviation) than those from upper class areas who comprise also candidates from residual public schools. The same applies to the next level where we compare the 5 141 candidates living in upper-middle class neighborhoods with the 7 072 who attended medium performance private schools or high performance and experimental public schools. The weighted performance of the latter (1 304,6) is 6,5% higher than the performance of those living in upper-middle neighborhoods. Differences become less important (1-2%) between middle performance secondary schools and lower middle class or socially mixed residential areas. They increase again at the other end of the social hierarchy, where 7 954 candidates graduating from low performance schools have fared worse by 4.8% (967.6 weighted average) than the 2 585 candidates that attended schools in areas at the lower end of the neighborhood social hierarchy.

47Table 5 also shows that there is a declining diversity of performance as we move from lower performance and public schools to higher performance and private schools. The same applies to neighborhoods, with candidates from lower status neighborhoods having a more diverse performance than those from higher status ones. This is due to the much higher percentage of candidates with very low (failing) performance at the bottom of both hierarchies. Thus, in both hierarchies of schools and neighborhoods, educational performance is lower and more diverse at the bottom of the hierarchies and higher and less diverse at the top. Moreover, the range of this diversity is higher for the school hierarchy, providing an additional indication that school segregation is higher than neighborhood segregation.

48In sum, table 5 shows a wider performance range within the hierarchy of schools than within the hierarchy of neighborhoods, supporting the assumption of a higher degree of segregation within the former than the latter. Moreover, the higher difference of scores at the upper –compared to the lower– end of the social hierarchy may be an indication that school segregation, as the outcome of middle and upper middle-class strategies, practically functions more as an advantage for higher social groups than as a disadvantage for lower ones.

The importance of demographic features

49Candidates in the admissions examination are normally 17 to 18 years old. They are older if they failed to be admitted on their first attempt; if they were not satisfied with the Department they were admitted to and chose to retry the admissions examination for a second time; if they opted to finish first with their military service (boys), postponed their attempt to study for any other reason or were delayed in completing secondary school. The correlation between candidates’ performance and their delay in taking the examination (R = -.142) means that there is a non-negligible negative effect of this delay on performance. The average general score of those taking the examination when they are 18 or younger is 1 195/2 000, while for those with at least two years of delay it is 890/2000. Delay is also related to social origin: Students from schools in lower social status neighborhoods take the examination at the expected age at a lower rate (70%) than those from schools in upper social status neighborhoods (90%).

50Gender seems also important for educational performance. In the late 1920s the participation of women in the student population was 4.9% (Katsikas and Kavadias, 1994) against 59.9% in 2010 (ELSTAT, 2010). For several years now, girls have been performing better. According to our dataset there is a positive correlation between girls and performance in the admissions examination (R = .058); also, a larger number of girls have taken this examination (17 246 versus 14 785 boys in 2004-05 for the Athens Metropolitan Area).

51The average score for girls in the examination (1 165/2 000) is 4.5% higher than the score for boys (1 115/2 000). This applies to every cluster of the Faculties and Departments’ typology presented in figure 1. Following their higher scores, more girls were admitted to all clusters of Faculties and Departments, with higher gender differences observed at the middle of the hierarchy (clusters 3 to 5).

The combined impact of school, neighborhood and demographic features on accessing higher education (a regression model)

52We have examined individually the different factors we assumed to be important for educational performance because our dataset did not comprise evidence on the most important one –the social origin of candidates in the admissions contest to higher education– and, therefore, could not support a comprehensive multivariate model. However, our dataset could support the examination of the effect of school segregation on educational performance jointly with a number of individual features of candidates, as well as with the social type of their residential neighborhood.

  • 17 The inclusion of neighborhood related variables in this model had to consider the problem mentioned (...)

53The following regression models the relation between the performance of candidates (i.e. their average score at the admissions examinations X 70% + their final grade for completing secondary education X 30%) and a number of variables which we also assumed to be related. These variables comprise, first of all, the type of secondary school attended (combining public/private status, high/mid/low performance and daytime/evening operation). They also comprise the participation rate of students in each school in the admissions examinations, which indicates the degree each school is turned to higher education; the percentage of students with a migrant background in each school; the average performance and the percentage of teaching staff with post-graduate degrees in each school. Another group of variables, related to the social profile of the neighborhood, was also considered: the neighborhood’s deprivation index (see note 14), the unemployment rate and the percentage of students in the 18-22 age group.17 Finally, some personal level variables influencing educational performance (age, gender and broad field of studies –science, technology, social sciences and humanities– were added to the model.

54The variables considered belong to two different levels: the school or the candidate. Therefore, we built a two-level mixed model mainly addressing the quality/type of schools and the personal features of candidates. This model was intended to refine our approach on the impact of school segregation discussed previously in the section ‘the school environment’.

Table 6 Coefficients for linear mixed models. Dependent variable: Candidates performance in admissions examination to higher education (2005)

M0: Empty model

M1: M0+School type+Deprivation Index

M2: M1+Sex+Studies field+Age of candidate

Estim.

Sig.

Estim.

Sig.

Estim.

Sig.

Fixed effects

Intercept

1 116.97

.000

1 529.41

.000

1 402.04

.000

Sch Type Ref categ: private high performance

private low performance

-587.68

.000

-523.81

.000

evening school

-647.50

.000

-528.35

.000

public low performance

-549.65

.000

-466.45

.000

public mid-low performance

-417.33

.000

-344.87

.000

public mid-high performance

-337.37

.000

-284.34

.000

public high performance

-262.95

.000

-206.90

.000

public experimental

-207.00

.000

-178.41

.000

private medium performance

-134.03

.000

-128.41

.000

Deprivation Index

-11.92

.000

-14.15

.000

Sex Ref categ: girls

boys

-55.53

.000

Studies field Ref categ: Social sciences and hunanities

science

381.56

.000

technology

46.63

.000

Age of candidate

-91.44

.000

Random effect variances

Residual (within schools)

162.03

.000

161.93

.000

140.56

.000

Intercept (between schools)

25.63

.000

0. 65

.001

     Sex

0.89

.002

     Age of candidate

4.64

.000

     Studies field

1.94

.000

-2 Log-likelihood

447.47

444.32

440.66

55The models presented in table 6 refine our grasp on the effect of school segregation by identifying the main parameters that explain the variance of candidates’ performance between schools. The empty model (M0) shows that the variance between schools is 13.7% of the total variance [25.63*100/(25.63+162.03)]. This part of the variance is ‘explained’ almost entirely by the type of school attended by the candidates. The school type variable comprises the distinction between public and private schools, daytime and evening schools, experimental and ordinary public schools as well as high / intermediate / low performance schools. The use of this variable minimized the contribution of all other variables related to school quality (average performance, percentage of stuents taking the admissions examination, percentage of students with a migrant background, percentage of teaching staff with a post-graduate degree) and excluded them from the model (M1). The type of school explains almost 95% of the variance between schools and the differences between the reference category of high performance private schools and the other eight categories show a progressive decline in performance as we move from private to public, from daytime to evening, from experimental to ordinary and –obviously– from high to low performance schools.

56The alternative option (not shown in table 6) of using a variable indicating the social type of neighbourhood the school is located in –the neighbourhood’s socioprofessional profile or its composite deprivation index– explains much less of the variance (24% and 26% respectively). In model 1, we have used as variables for fixed effects both the school type and the area deprivation index, increasing the explanation of the between schools variance to 97%. The increase in the value of the deprivation index understandably leads to a decrease in the performance score of candidates.

57In model 2 we added three more variables (sex, field of studies and age of candidate) for random effects since they refer to the candidate rather than the school level. Their inclusion reduced the variance within schools –since the added variables account for part of it– and all three are significant for candidates’ performance. Girls perform systematically better than boys and older candidates are increasingly doing worse than those who pass the examination at the expected age. Performance varies also in terms of the field of studies. This does not reflect necessarily that those in the science option perform better than those in technology and, even more, in social sciences and humanities. The assortment of tests is different for the three fields and this makes final grades not strictly comparable.

58This exercise proved that the variable ‘type of school’ attended by candidates is overwhelmingly explaining the variance of performance in the admission examinations to higher education between schools. Several other variables, related to the social profile of candidates’ neighbourhoods and to individual characteristics, also significantly contribute to explaining the variance of this performance. Other variables –like the share of immigrant pupils in each school or the percentage of post-graduate degree holders among members of the teaching staff– have not provided significant contributions when included in the model. We can assume that immigrant status or origin would have made a significant contribution, if relevant data at the individual candidate level were available. This would have reduced the variance within schools along with the class differences among candidates that must account for the largest part of this variance following our analysis in the section ‘the effect of social origin’.

Concluding remarks

59The dataset of candidates’ performance in the 2005 national admission examinations to higher education gave us the opportunity to investigate and roughly measure the social reproduction function of the transition to higher education in Greece. We focused on the Athens Metropolitan Area, where school segregation and residential segregation are much more developed than anywhere else in Greece and, therefore, where we could most clearly relate them to social reproduction through educational performance.

60We have taken into account all crucial parameters for the transition from secondary to higher education contained in this dataset in order to assess the statistical explanation of educational performance. The main shortcoming was that our dataset did not contain any variables on candidates’ social profile. This induced us to use data from another source –data provided by the Hellenic Statistical Authority (ELSTAT) on the socioeconomic and educational profile of students in every Faculty and Department of all Greek higher education institutions– to address the relation between candidates’ performance and social origin. The outcome was clear and compatible with the role educational performance plays in social reproduction, briefly discussed in the section ‘The social selectivity of school systems and the impact of residential location’: the social hierarchy of Faculties and Departments we were able to produce using the ELSTAT data is very highly correlated with the performance of candidates in the admissions examination to higher education. Educational performance fabricates, therefore, an enrollment with a clear social hierarchy amongst the different Faculties and Departments that clearly reproduces existing social inequalities.

61Then, using the dataset from the 2005 admission examinations to higher education we provided further insight on the role of the school, the residential area and of some of the candidates’ individual features, like age and gender. The role of school segregation appears to be very important, although quite different from cases where residential segregation is its foundation (e.g. Boterman, 2019). In Athens, school segregation is higher than residential segregation and, to some extent, this is the outcome of middle-class strategies of school choice to overcome the paucity of advantages provided by the ‘automatic’ conversion of residential to school segregation through the allocation of students to local schools on the basis of distance. The typology of upper secondary schools in Athens shows that performance is highly related to their public / private status, their experimental character and their daytime or evening operation. Moreover, the distribution of schools by type and performance level in Athens appeared clearly uneven, with high performance private schools concentrated in affluent suburbs and low performance public schools overrepresented in working class neighborhoods.

  • 18 A forthcoming special issue of Urban Studies on school and residential segregation contains several (...)

62The most important finding of this paper is probably the comparison of the impact of school segregation and neighborhood segregation on educational performance, even though this comparison was made in a tentative / exploratory way due to the nature of the available data. Educational performance appears more polarized and diversified within the school rather than the neighborhood hierarchy18.

63Finally, the mixed linear model we used confirmed that the variance of performance between schools is mainly explained by the type of school (public/private, experimental/ordinary, daytime/evening, high/intermediate/low performance) attended by the candidate and to a much lesser extent by the social profile of the school area –assumed to be the same with the candidates’ residential area. Age and gender proved significantly related to performance, while other variables like the percentage of immigrants among pupils or the percentage of highly qualified teaching staff in the schools attended by the candidates did not.

  • 19 There are in fact numerous private institutions (called colleges) operating in Greece, mainly in At (...)

64Greek education has a number of features that support its democratic character: a single curriculum up to the end of the first part of secondary school and a relatively atrophic vocational option in its second part which, respectively, delay and restrain social selection; a predominantly public education system accounting for more than 90% of secondary and for the totality of higher education19; a substantial participation of lower-middle and working class groups to higher education (even to highly demanded Departments); a long history of uninterrupted social mobility through education during most of the postwar period. In fact, lower social groups have not been excluded from higher education and its benefits in terms of social mobility for a long time; this permitted –and made politically viable– the acceptation by these groups of the privileged access to higher education enjoyed by the elites and the upper middle classes, through its disguise as personal merit.

65At the same time, however, Greek society remains highly unequal and educational mechanisms contribute substantially in reproducing inequalities. Privileged groups follow educational strategies to create advantage for their children: investment in private schooling; selection of better schools within the public sector; investment in continuously longer education; investment in studies abroad and in highly rated institutions and degrees. These strategies seldom involve residential relocation, which would be rather ineffective within a context of limited residential segregation and consequently of limited school segregation based on spatial distance.

66The structure of the educational system may be, to some extent, the reflection of a weakly polarized social structure, a situation that has probably changed significantly in the last 20 years and, especially, during the recent crisis. The waning social mobility for lower social classes undermines the broad social compromise based on massive aspirations to access middle-class positions. Educational policies during the crisis further destabilized this compromise as they clearly drove towards more social inequality and so did the impact of the crisis (Maloutas, 2016): with youth unemployment having reached over 50% in 2013 (ELSTAT, 2014) –affecting the highly educated as well– educational achievement looked increasingly futile, especially to those from lower social groups with degrees that used to support social mobility through public employment. At the same time, however, the demand for degrees has not declined. Higher education degrees acted as protective shields during the crisis: in case of redundancies in the public sector, degree holders were not immune, but they were better protected than the rest being usually preferred to those without degrees for the same position. Educational achievement continues to provide social advantage, its function in reproducing and legitimating social hierarchy becomes stronger, and the illusion about the decisive role of personal merit remains largely unchallenged.

Haut de page

Bibliographie

Adams I., 2001, Political ideology today, Manchester, Manchester University Press.

Andersson E., Malberg B., Östh J., 2012, "Travel-to-school distances in Sweden 2000-2006: changing school geography with equality implications, Journal of Transport Geography, No.23, 35-43

Atkinson, R., Kintrea K., 2001, "Disentangling Area Effects: Evidence from Deprived and Non Deprived Neighbourhoods." Urban Studies, Vol.38, No.12, 2277–98.

Ball J.S., 1993, "Education Markets, Choice and Social Class: the market as a class strategy in the UK and the USA", British Journal of Sociology of Education, Vol.14, No.1, 3-19.

Ball J.S., 2006, Education policy and social class: The selected works of Stephen J Ball, London, Routledge.

Ball, J.S., Bowe R., Gewirtz S., 1995, "Circuits of schooling: A sociological exploration of parental choice of school in social class contexts", The Sociological Review, Vol.43, No1, 52-78.

Baudelot C., Establet R., 2009, L’Élitisme républicain. L'école française à l'épreuve des comparaisons internationales, Paris, Éditions du Seuil.

Bosetti L., 2004, "Determinants of school choice: understanding how parents choose elementary schools in Alberta", Journal of Education Policy, Vol.19, No.4, 387-405.

Boterman W.R., 2012, "Residential Mobility of Urban Middle Classes in the Field of Parenthood", Environment and Planning A: Economy and Space, Vol.44, No.10, 2397-2412.

Boterman W.R., 2013, "Dealing with Diversity: Middle-class Family Households and the Issue of ‘Black’ and ‘White’ Schools in Amsterdam", Urban Studies, Vol.50, No.6, 1130-1147.

Boterman W.R., Bridge G., 2015, "Gender, Class and Space in the Field of Parenthood: Comparing Middle-class Fractions in Amsterdam and London", Transactions of the Institute of British Geographers, Vol.40, No.2, 249-261.

Boterman W.R., Musterd S., Pacchi C., Ranci C., 2019, "Social segregation in contemporary cities: Socio-spatial dynamics, institutional context and urban outcomes", Urban Studies, Vol.53, No.15.

Boterman W.R., 2019, "The role of geography in school segregation in the free parental choice context of Dutch cities", Urban Studies, Vol.56, No.15.

Bourdieu P., Passeron J.C., 2000, Reproduction in education, society and culture, London-Thousand Oaks–New Delhi, Sage Publications, .

Buck N., 2001, "Identifying Neighbourhood Effects on Social Exclusion", Urban Studies, Vol.38, No.12, 2251-2275.

Butler T., Hamnett C., Ramsden M.J., 2013, "Gentrification, Education and Exclusionary Displacement in East London", International Journal of Urban and Regional Research, Vol.37, No.2, 556-575.

Butler T., van Zanten A., 2007, "School choice: A European perspective", Journal of Education Policy, Vol.22, No.1, 1-5.

Chauvel L., 2016, La spirale du déclassement. Essai sur la société des illusions, Paris, Seuil.

Denessen E., Driessenaa G., Sleegers P., 2005, "Segregation by choice? A study of group specific reasons for school choice", Journal of Education Policy, Vol.20, No.3, 347-368.

Dronkers J., Felouzis G., van Zanten A., 2010, "Education markets and school choice", Educational Research and Evaluation, Vol.16, No.2, 99-105.

Dubet F., 2004, L’école des chances. Qu’est-ce qu’une école juste ? Paris, Seuil.

Dubet F., 2010, Les places et les chances. Repenser la justice sociale, Paris, Seuil.

Dubet F., Duru-Bellat M., Vérétout A., 2010, Les sociétés et leur école. Emprise du diplôme et cohésion sociale, Paris, Seuil.

Duru-Bellat M., 2006, L’inflation scolaire. Les désillusions de la méritocratie, Paris, Seuil.

Duru-Bellat M., 2009, Le mérite contre la justice, Paris, Presses de Sciences Po.

EKKE-ELSTAT, 2015, Panorama of Greek census data 19912011. Database and mapping application. Available at: https://panorama.statistics.gr/en/.

ELSTAT, 2010, Students by gender, semester of studies and institution (table03). Available at: http://www.statistics.gr/el/statistics/-/publication/SED33/2010 (in Greek).

ELSTAT, 2012, Students by gender, semester of studies, institution and department (2010-2011), 08 August 2012, URL:(http://www.statistics.gr/portal/page/portal/ESYE/BUCKET/A1403/Other/A1403_SED33_TB_AN_00_2010_03E_F_GR.pdf ).

ELSTAT, 2014, Labor force survey. Press release, 1st trimester 2014. Pireaus, June 12, 2014. Accessible at https://www.tovima.gr/files/1/2014/06/12/ergatikodynamiko.pdf.

Ellen I.G., Turner M.A, 1997, "Does neighbourhood matter? Assessing recent evidence", Housing Policy Debate, Vol.8, No.4, 833-66.

Felouzis G., 2009, "Systèmes éducatifs et inégalités scolaires : une perspective internationale", SociologieS [En ligne]. Théories et recherches, 03 March 2013. URL: (http://sociologies.revues.org/2977).

Felouzis G., 2012, "Le modèle scolaire français contre la justice sociale" SociologieS [En ligne]. Grands résumés. Le Mérite contre la justice, 03 March 2013. URL: (http://sociologies.revues.org/3778).

Frangoudaki A., 1985, Sociology of education: Theories about social inequality in school, Athens, Papazisis.

Gordon I., Monastiriotis V., 2006, "Urban Size, Spatial Segregation and Inequality in Educational Outcomes", Urban Studies, Vol.43, No.1, 213-36.

Gordon I., Monastiriotis V., 2007, "Education, Location, Education: a Spatial Analysis of English Secondary School Public Examination Results", Urban studies, Vol.44, No.7, 1203-28.

Hadjiyanni A., Valassi D., 2009, "Reproducing inequality in Higher Education: ‘the Small and the Great Door’ in the Greek Higher Education Sector", in Maloutas T. (ed), Aspects of social structure and social transformation in Athens in the new era, Athens, National Centre of Social Research.

Hamnett C., 2013, "Reproducing residence based social differences through school based allocation", ISA RC21Conference Resourceful Cities, Berlin, 20-31 August (2013).

Hamnett C., Butler T., 2013, "Distance, education and inequality", Comparative Education, Vol.49, No.3, 317-30.Judith A., Barlow J., Leal J., Maloutas T., Padovani L., 2004, Housing and Welfare in Southern Europe, Oxford, Blackwell Publishing.

Katsikas C., Kavadias G., 1994, Inequality in Greek education. The changing opportunities for accessing Greek education (1960–1994), Athens, Gutenberg.

Kontogiannopoulou - Polydoridis G., 1999, Sociological analysis of school performance and evaluation. The entrance examinations: setting performance, integration into hierarchical higher education school performance, Athens, Gutenberg.

Koutouzis M., Kyridis A., Maloutas T., Papadakis N., Syrigos S., 2012, "Areas of educational priority. Research report for the Ministry of Education", National Centre for Social Research Working Paper 26, 11 March 2014, URL: (www.ekke.gr/publications/wp/wp26.pdf).

Lambiri-Dimaki I., 1974, For a Greek sociology of Education, Athens, National Centre of Social Research.

Lupton R., 2003, "‘Neighbourhood Effects’: Can We Measure them and Does it Matter?", London School of Economics, Centre for Analysis of Social Exclusion, Working Paper 73. 13 March 13, 2014, URL:(http://eprints.lse.ac.uk/6327/1/Neighbourhood_Effects_Can_we_measure_them_and_does_it_matter.pdf?origin=publication_detail). London.

Maloutas T., 1990, Housing and Family in Athens: An Analysis of Post-war Housing Practices, Athens, Exandas.

Maloutas T., 2007a, "Segregation, social polarisation and inequality in Athens during the 1990s: Theoretical expectations and contextual difference", International Journal of Urban and Regional Research, Vol.31, No.4, 733-758.

Maloutas T., 2007b, "Middle class education strategies and residential segregation in Athens." Journal of Education Policy, Vol.22, No.1, 49-68.

Maloutas, 2016, "The reproduction of inequality through education in the times of crisis", in Maloutas T., Spyrellis S. (eds) Athens Social Atlas. Digital compendium of texts and visual material. Accessible at: https://www.athenssocialatlas.gr/en/article/unequal-access-to-education/

Maloutas, 2019, "Les Examens Panhélléniques : clé de voûte du numerus clausus dans l’enseignement supérieur grec", Sociologie Vol.10, No.2, 201-208.

Maloutas T., Arapoglou V., Kandylis G., Sayas J., 2012, "Social polarization and de-segregation in Athens." in Maloutas T., Fujita K., (eds.), Residential segregation in comparative perspective. Making sense of contextual diversity, Farnham, Ashgate.

Maloutas T., Emmanuel D., Pantelidou-Malouta M., 2006, Social Structures, Practices and Attitudes: New Parameters and Trends 1980-2000, Athens, National Centre of Social Research.

Maloutas T., Spyrellis S.N., Capella A., 2019, "Residential segregation and educational performance. The case of Athens", Urban Studies, Online First, https://doi.org/10.1177/0042098019826033.

Massey D., Denton N., 1993, American Apartheid: Segregation and the Making of the Underclass, Cambridge, Harvard University Press.

Maurin É., 2009, La peur du déclassement, Paris, Seuil.

Merle, P., 2012, La ségrégation scolaire, Paris, La Découverte.

Moore R., 2004, Education and society. Issues and explanations in the Sociology of Education, Cambridge, Polity.

Musterd S., Murie A., Kesteloot C., 2006, Neighbourhoods of Poverty. Urban Social Exclusion and Integration in Europe, Basingstoke, Palgrave.

Musterd S., Ostendorf W., de Vos S., 2003, "Neighbourhood effects and social mobility: A longitudinal analysis", Housing Studies, Vol.18, No.6, 877-892.

Noreisch K., 2007, "School catchment area evasion: the case of Berlin, Germany", Journal of Education Policy, Vol.22, No.1, 69-90.

Noreisch K., 2007b, "Choice as Rule, Exception and Coincidence: Parents’ Understandings of Catchment Areas in Berlin", Urban Studies, Vol.44, No.7, 1307-1328.

Oberti M. and Savina Y., 2019, "Urban and school segregation in Paris: The complexity of contextual effects on school achievement: The case of middle schools in the Paris metropolitan area", Urban Studies, Vol.56, No.15.

Oberti M., Prétéceille E., Rivière C., 2012, Les effets de l’assouplissement de la carte scolaire dans la banlieue parisienne, Paris, OSC-SciencesPo.

Oria A., Cardini A., Ball S., Stamou E., Kolokitha M., Vertigan S., Flores-Moreno C., 2007, "Urban education, the middle classes and their dilemma of school choice", Journal of Education Policy, Vol.22, No.1, 91-105.

Ostendorf W., Musterd S., de Vos S., 2001, "Social mix and the neighbourhood effect. Policy ambitions and empirical evidence", Housing Studies, Vol.16, No.3, 371-380.

Östh J., Andersson E., Malberg B., 2013, "School choice and increasing performance difference: A counterfactual approach", Urban Studies, Vol.50, No.2, 407-425.

Owens A. and Candipan J., 2019, "Social and spatial inequalities of educational opportunity: A portrait of schools serving high- and low-income neighbourhoods in US metropolitan areas", Urban Studies, Vol.56, No.15.

Panayotopoulos N., 2000, "Oppositions sociales et oppositions scolaires: Le cas du système d’enseignement supérieur grec", Regards Sociologiques, Vol.19, 57-74.

Patterson K.L., Silverman R.M., 2013, "Urban education and neighborhood revitalization", Journal of Urban Affairs, Vol.35, No.1, 1-5.

Picketty T., 2014, Capital in the 21st century. Cambridge MA, Harvard University Press.

Power S., Edwards T., Whitty G., Wigfall V., 2003, Education and the middle class, Buckingham, Open University Press.

Ramos Lobato I. and Groos T., 2019, "Choice as a duty? The abolition of primary school catchment areas in North Rhine-Westphalia/Germany and its impact on parent choice strategies", Urban Studies, Vol.56, No.15.

Riddell R., 2005, "Government policy, stratification and urban schools: a commentary on the Five-year Strategy for Children and Learners", Journal of Education Policy, Vol.20, No.2, 237-241.

Rose D., Harrison E., 2007, "The European socioeconomic classification", European Societies, Vol.9, No.3, 459-90.

Seppänen P., 2003, "Patterns of ‘public-school markets’ in the Finnish comprehensive school from a comparative perspective", Journal of Education Policy, Vol.18, No.5, 513-531.

Sianou-Kyrgiou E., 2008, "Social class and access to higher education in Greece: supportive preparation lessons and success in national exams", International Studies in Sociology of Education, Vol.18, No.3-4, 173-183.

Sianou-Kyrgiou E., 2010, "Stratification in higher education, choice and social inequalities in Greece", Higher Education Quarterly, Vol.64, No.4, 22-40.

Sianou-Kyrgiou E., Tsiplakides I., 2011, "Similar performance, but different choices: social class and higher education choice in Greece", Studies in Higher Education, Vol.36, No.1, 89-102.

Spyrellis S.N., 2013, Division sociale de l’espace métropolitain d'Athènes, facteurs économiques et enjeux scolaires, Ph.D. thesis, Denis Diderot University, Paris.

Spyrellis, S.N., 2015, "Social space and educational outcomes in Athens", Cybergeo : European Journal of Geography [En ligne], Espace, Société, Territoire, document 745, URL : http://journals.openedition.org/cybergeo/27265

Thanos T., 2011, Sociology of social inequality in education. Socio- occupational categories and access to higher education (1956 – 1998), Athens, Nissos.

Tomeka D., Oakley D., 2013, "Linking charter school emergence to urban revitilization and gentrification: A socio-spatial analysis of three cities", Journal of Urban Affairs, Vol.35, No.1, 81-102.

Tsoukalas K,. 1977, Dependence and reproduction. The social role of educational mechanisms in Greece, Athens, Greece, Themelio.

UNESCO Institute of Statistics, 2012, Education – ISCED 1997 Mappings. 25 April 2013, URL: (http://www.uis.unesco.org/Education/ISCEDMappings/Pages/default.aspx;)

Valassi D., 2008, "Internationalization or/and globalization in education: the case of International Baccalaureate Programme." Science & Society, Vol.19, 335-367.

Valassi D., 2012, "Elite Private Secondary Education in Greece: Class Strategies and Educational Advantages", Culture and Education, Vol.6, No.92, 21-41, URL: (http://www.kultura-i-edukacja.pl/ojs/index.php?journal=kie&page=article&op=view&path%5B%5D=116 )

Van Zanten A., 2001, L’école de la périphérie. Scolarité et ségrégation en banlieue, Paris, Presses Universitaires de France.

Van Zanten A., 2009, Choisir son école. Stratégies familiales et mediations locales, Paris, Presses Universitaires de France.

Van Zanten A., Kosunen S., 2013, "School choice research in five European countries: the circulation of Stephen Ball’s concepts and interpretations", London Review of Education Vol.11, No.3, 239-255.

Wilson W.J., 1987, The Truly Disadvantaged: The Inner City, the Underclass and Public Policy, Chicago, Chicago University Press.

Haut de page

Notes

1 See, for example, the different approaches of Maurin (2009) and Chauvel (2016) on the role of education in the reproduction of the declining middle classes.

2 Modern liberalism mitigates this position by stressing also the need to provide equal opportunities to those competing for unequal positions (Adams, 2001).

3 The socially unequal outcomes to which leads the systematic social differentiation of educational achievement are disguised as individual learning capability and legitimated as the outcome of personal merit. Eventually, what appears to be the just reward of personal merit in education is, to a large extent, a social construct (Duru-Bellat, 2009; Dubet et al., 2010). Socialist approaches are more sensitive to inequalities amongst positions rather than opportunities (Dubet, 2010) primarily because equal opportunities are considered quasi impossible within unequal societies (Baudelot and Establet, 2009).

4 In the UK –mainly in England– the majority of schools (over 90%) are run by local authorities but are not called public schools. Public schools are among the small minority of schools where tuition is not free and are part of the ‘independent’ schools (in fact private). Elite schools like Eton or Harrow are public schools.

5 According to Gordon and Monastiriotis (2006 and 2007) neighbourhood effects in education performance in the U.K. appear more important as a middle class advantage than as a disadvantage for working class groups. Further research from the UK (Buck, 2001; Atkinson and Kintrea, 2001; Buck and Gordon, 2004) and Netherlands (Ostendorf et al., 2001) reveals a relatively low but significant level of neighborhood effects compared to individual/household characteristics.

6 Such ‘false’ addresses usually belonged to a relative or a friend of the family.

7 The deliverable was part of the task “Mining knowledge from data of the educational community”, component of the project “Technical Counsel – Greek Ministry of Education 2006-07”.

8 The weights of 70% for the score of written exams and 30% for the graduation grade from secondary education were officially given by the Ministry of Education in order to calculate candidates’ overall performance. These weights varied across the history of admissions examinations (Maloutas, 2019). This comined performance determines the admission or non-admission of each candidate in the University Faculties and Departments he/she has selected and following the order of their selection. The candidate is admitted to the first Department or Faculty in his/her list for which his/her performance is above or equal to that of the last one admitted.

9 Accessible at: http://www.statistics.gr/el/statistics/-/publication/SED34/2009 and http://www.statistics.gr/el/statistics/-/publication/SED34/2010. Data on both parents education level are provided in table 09 and on fathers’ occupation in table 10 (in Greek).

10 We used a k-means cluster analysis to group Departments. Three variables for parents’ education (percentage of parents with higher education and post-graduate degrees; with compulsory [nine years] to post-secondary professional education; with less than compulsory education) and three for fathers’ occupation (percentage of managers and professionals; intermediate professions; working-class [skilled and unskilled workers]) were used for this clustering.

11 In another paper (Maloutas et al., 2019) we tried to relate educational performance (length of educational training) with social origin and with the social typology of the residential neighbourhood.

12 We assumed that a distance less than 1500m constitutes a pedestrian itinerary in which most daily activities, such as going to school, can be managed on foot or within a short driving distance. Consecutive measures showed that a 900m radius around each school provides maximum coverage of the metropolitan area and minimum overlapping. (i.e. minimum attribution of the same census tract to different schools).

13 We used the categories of the European Socioceconomic Classes (ESeC) as indicators of neighbourhoods’ social composition, see Rose and Harrison (2007).

14 The composite index of deprivation is the sum of the hierarchical positions (cluster identities ordered in the same social direction) of census tracts based on a number of variables divided in three broad areas: Social composition (class and ethnic), education and housing (Koutouzis et al., 2012).

15 The socioeconomic type of residential areas was determined by a K-means clustering of census tracts into seven groups following the percentage of four major categories of the European Socioeconomic Classification (ESeC): (1) Large employers, higher professionals and managers, (2) Lower professionals and managers, (8) Lower technical occupations and (9) Routine occupations.

16 In fact, the difference is even higher since high performance private schools are mostly situated in high status residential areas and their students are necessarily assigned collectively to upper class areas –even though students in these schools are attracted from different and socially more diverse parts of the city.

17 The inclusion of neighborhood related variables in this model had to consider the problem mentioned in the section ‘the role of the neighborhood’: our dataset does not indicate the actual residential area of candidates, which we had to extrapolate from the location of their school. Thus, the results on the importance of the social profile of the residential neighborhood should be considered as simple indications, pertaining much more to pupils of public schools who live closer to their school than pupils of private ones..

18 A forthcoming special issue of Urban Studies on school and residential segregation contains several papers on the intricate and contextually diverse relationship between these two forms of segregation (Boterman et al., 2019).

19 There are in fact numerous private institutions (called colleges) operating in Greece, mainly in Athens and Thessaloniki, often collaborating with foreign universities. They are recognized by the Ministry of Education, but not as part of higher education. They are officially classified as Post-secondary, not tertiary Education and in ISCED terms they are part of category 4. In Greece, the Constitution prohibits tertiary education institutions to be run by anyone else than the State.

Haut de page

Table des illustrations

Titre Figure 1: Comparative chances of candidates originating from different family educational backgrounds to get admitted to Departments clustered according to the socio-educational profile of students’ parents (2010) (average candidate’s chances = 1.00)*
Légende * data on the education level of the general population of the metropolitan area of Athens aged 40 to 75 derive from the 2001 census (EKKE-ELSTAT, 2015) ** maximum possible performance = 2.000
URL http://journals.openedition.org/cybergeo/docannexe/image/33085/img-1.png
Fichier image/png, 40k
Titre Figure 2: Percentage distribution of candidates from different types of secondary school admitted to the different social clusters of higher education Departments (2005)
URL http://journals.openedition.org/cybergeo/docannexe/image/33085/img-2.png
Fichier image/png, 28k
Titre Figure 3: Percentage distribution of students admitted to the different social clusters of higher education Departments by type of secondary school they attended (2005)
URL http://journals.openedition.org/cybergeo/docannexe/image/33085/img-3.png
Fichier image/png, 25k
Titre Map 1: Location of secondary schools (Lykeia) by type and performance (2005)
URL http://journals.openedition.org/cybergeo/docannexe/image/33085/img-4.jpg
Fichier image/jpeg, 1,4M
Titre Map 2 Social typology* of neighborhoods in the Athens Metropolitan Area (2001)
Légende * see note 15
URL http://journals.openedition.org/cybergeo/docannexe/image/33085/img-5.jpg
Fichier image/jpeg, 136k
Titre Figure 4 Actual and expected performance in the admissions examination to higher education by school type and by social type of residential area in Athens (2005)
URL http://journals.openedition.org/cybergeo/docannexe/image/33085/img-6.png
Fichier image/png, 26k
Haut de page

Pour citer cet article

Référence électronique

Thomas Maloutas, Stavros Spyrellis, Andromachi Hadjiyanni, Antoinetta Capella et Despoina Valassi, « Residential and school segregation as parameters of educational performance in Athens », Cybergeo : European Journal of Geography [En ligne], Espace, Société, Territoire, document 917, mis en ligne le 23 octobre 2019, consulté le 19 novembre 2019. URL : http://journals.openedition.org/cybergeo/33085 ; DOI : 10.4000/cybergeo.33085

Haut de page

Auteurs

Thomas Maloutas

Professor Department of Geography Harokopio University, Athens, Greece
maloutas@hua.gr

Stavros Spyrellis

Academic Fellow, Department of Economics, Athens University of Economics and Business,
Athens. Greece
st.spyr@gmail.com

Articles du même auteur

Andromachi Hadjiyanni

Research director, National Centre for Social Research, Athens, Greece
hadji@ekke.gr

Antoinetta Capella

Researcher, National Centre for Social Research, Athens, Greece
antoincapella@gmail.com

Despoina Valassi

Phd candidate, University of Crete, Greece
dvalassi@gmail.com

Haut de page

Droits d’auteur

Licence Creative Commons
La revue Cybergeo est mise à disposition selon les termes de la Licence Creative Commons Attribution - Pas d'Utilisation Commerciale - Pas de Modification 3.0 non transposé.

Haut de page