1The work of Bourdieu and Passeron (1964, 1970) represented a ‘Copernican revolution’ (Dandurand and Ollivier, 1987) in the sociological approach to the educational institution. For sociologists of reproduction, the education system can be understood as a framework in which social inequalities are both reproduced and legitimized by the institution. Analysing a state of the system in which social inequalities often take the form of a ‘continuous elimination’, their arguments focus on this question without looking at how inequalities in academic performance evolve over time. These authors describe the school as a place where initial differences can be either ‘standardized’ or accentuated. From the first pages of The Inheritors, the authors contrast the supposedly standardizing influence of school with the persistence of ‘differences in attitude and ability’ (Bourdieu and Passeron, 1979, p. 8). In ‘L’École conservatrice en France’ [Educational conservatism in France], Bourdieu evokes the ‘compensatory action of schooling for disciplines that are taught directly and controlled completely by the school’ (Bourdieu, 1966, p. 335) while criticizing an inegalitarian ‘pedagogy of awakening’ (p. 336) that relies on students’ initial cultural capital to stimulate their natural gifts. The question remains unanswered: does schooling narrow or widen the differences between working-class children and those of the middle and upper classes?

2Since these early studies, the French educational system has experienced two major upheavals, with the introduction of universal access to secondary education (1954–1968) and a unified educational curriculum (hereafter called ‘comprehensive’ schooling) for all students up to the end of Year 9 [1] (1985–1995) (Caille, 2014). These transformations have given rise to the now crucial issue of inequalities in academic performance between students in the same grade level, in a system where inequalities of access have been reduced. While the proactive policies linked to these two upheavals opened selective educational tracks to larger sections of the population, they had no influence on the inequalities in attainment which served to justify ‘early’ track orientation (Poullaouec and Lemêtre, 2009; Blanchard and Cayouette-Remblière, 2016, pp. 11–25).

3Indeed, many studies have demonstrated their ambiguous and paradoxical effects, pointing up widening performance gaps between schools (Trancart, 1998; Merle, 2000), difficulties in managing students (Prost, 1985; Douat, 2012), or the persistence of teaching practices that perpetuate the academic divide (Bautier and Rochex, 1997; Bonnery, 2007; Garcia, 2013). Although they draw upon different theoretical traditions, these studies all agree that the apparent standardization of school pathways conceals not only large differentials in academic attainment (Baudelot and Establet, 2009) and exam success rates but also a non-negligible fraction of students who drop out of school (Millet and Thin, 2005) or are placed in ‘dead-end’ educational tracks (Palheta, 2011) and who see themselves as ‘outcasts’ of the system.

4Without comparable longitudinal measures of academic performance, these studies say little or nothing about how differences in school performance evolve as students move up through school. While it is difficult to assert that differentials in academic performance linked to social origin, gender, and migration history remain ‘stable over time’, as Boudon suggested (1979, p. 161), it is no easier to show how they vary.

5This article seeks to analyse these variations in academic performance over time, focusing on the lower secondary school (collège), the last link in the French ‘comprehensive’ (i.e. non-selective) schooling system. Contrary to the cognitivist approach (Grisay, 1997; Ben Ali and Vourc’h, 2015), we are not interested here in skills, but rather in academic performance, i.e. the difference between what students know at a given moment and what the educational institution expects them to know, as evidenced in their exam results. We therefore consider students’ success or failure solely from the institution’s viewpoint. While recognizing the contribution of studies that approach academic performance at the classroom level (Broccolichi, 1994; Cayouette-Remblière, 2013), our focus here is on performance as measured by national standards.

6In the current sociological literature, three student characteristics are identified as the main determinants of academic success: social origin, gender, and migration history. For each distinguishing factor, three scenarios can be envisaged. First, under the ‘parallel evolution’ scenario, as Boudon evoked, the education system is powerless to narrow the differences in academic success linked to these characteristics; they are simply transposed over time. Second, under the ‘convergent evolution’ scenario, lower secondary school reduces academic inequalities between different student categories. Even if the collège accepts those who leave primary school with unequal academic skills linked to their social origin, gender, or migration history, it can offer ‘standardizing’ teaching programmes that narrow the differentials observed in Year 6, while not eliminating inequalities altogether. Third, under the ‘divergent evolution’ scenario, these inequalities increase as the students advance through collège.

7Under the ‘comprehensive’ education system, 84% of the students who entered Year 6 in 2007 continued to Year 9 and sat a general secondary examination (brevet des collèges) 4 or 5 years later. [2] Our study focuses on this cohort. If they all remain in the system, we want to determine whether they follow parallel, convergent, or divergent paths in academic performance, and to examine the role of the school environment (proxied by the type of school attended and the size of the urban unit it serves) as explanatory factors of these outcomes.

I – Measures of academic performance over time in the scientific literature

8Certain French studies on the school careers of immigrant children or on the effects of private- or public-sector schooling have measured academic performance. A first series of scholars opted for ‘school-career indicators’, such as repetition rates and track orientation, as indirect measures of academic success or failure. For example, Vallet (1996) studied the school careers of immigrant children, and Tavan (2004) assessed the differentials between public and private schools. While these indicators may have been meaningful for students attending school in the 1990s, the situation is different today. Not only does repeating a year conceal realities that differ across individuals and grade levels (Cayouette-Remblière and de Saint-Pol, 2013), but not repeating a year also reflects different situations: teachers now tend to avoid holding back students in difficulty who are already a year behind or those for whom repeating a year will serve no purpose. As for track orientation, it combines factors linked to the students’ academic performance, their ‘capabilities’ as judged by the teaching staff, their attitude to school work, their social and family situation, the school’s orientation policies, and the aspirations and ambitions of the students and/or their families (André, 2012; Cayouette-Remblière, 2014; Chauvel, 2015). Because these school-career indicators are dichotomous (repeat year / no repeat year), they cannot fully reveal the diverse range of outcomes among students with poor academic results (who repeated a year in all cases) and among those with good results (who did not repeat but who progressed at different rates).

9Other studies compare different measures of performance. Since 1989, as part of its longitudinal follow-up programme, the Evaluation, Forecasting, and Performance Department (Direction de l’évaluation, de la prospective et de la performance [DEPP]) at the French Ministry of Education has entered in its database the results, for each child, of standardized diagnostic tests in French and mathematics taken each September by all students starting Year 6 to determine their level of attainment at the end of primary school. These assessments provide a useful gauge of students’ skills and knowledge in relation to what the institution expects. The tests can be used both to quantify academic differences between students and to measure ‘the gap, which may be large, between what is taught and what should be taught’ in primary school (IGEN–IGAENR, 2009, p. 10). [3] These assessments are among the few comparative measures of school attainment not influenced by the social and educational environment.

10In 1995, the DEPP database was expanded to include the continuous assessment scores of students taking the brevet, i.e. the mean classroom grades obtained for each discipline in Year 9. Some authors have used these new data to quantify changes in academic performance over time. Comparing changes in scores between the Year 6 test and the continuous assessment for the brevet, Cebolla-Boado (2008) found that the children of immigrants make better progress in collège than those of native-born French people. To analyse the progression of children of immigrants, Brinbaum and Kieffer (2009) compared two separate models (Year 6 tests and the continuous assessment for the brevet) to explain the impact of family characteristics (parents’ occupations and countries of birth). They conclude that the effect of these characteristics is stable across the two models, i.e. the performance differential by social origin and migration history does not evolve over the years at collège.

11Yet the continuous assessment grades obtained for the brevet are strongly influenced by the school environment (Bressoux and Pansu, 2003; Merle, 2007). In practical terms, they shed light on how the student’s level of attainment is perceived or how track orientation is negotiated at the end of Year 9, but they cannot be compared with the Year 6 tests, which are largely uninfluenced by this environment.

12Let us illustrate this, since the 2007 panel data includes the results of the brevet continuous assessment as well as the brevet examinations taken at the end of Year 9 and corrected externally (Table 1; Appendix Figure A.1). In the continuous assessment, students attending compensatory education schools (établissements d’éducation prioritaire) obtained on average a score of 50/100 in mathematics versus 56/100 for students in other schools. [4] Of course, the teachers in these schools are aware of their students’ lower educational attainment, but they make up for the deficit by adapting their teaching and grading practices to the local context. Analysis of the brevet examination results soon reveals that a single continuous assessment grade may conceal different levels of academic performance. In mathematics, students in compensatory education schools obtain 35/100 on average versus 49/100 in non-compensatory education schools. In the former, the continuous assessment results are 1.42 times higher than the examination results, while in the latter they are 1.15 times higher. Continuous assessment grades are thus strongly influenced by the social and educational environment, with grades in compensatory education schools being artificially higher than those of other schools. For this reason, it is practically impossible to study changes over time by comparing continuous assessment results with those of the standardized tests.

Table 1

The various measures of academic performance in collège in the 2007 panel

Mathematics French Type of school Year 6 Year 9 Year 6 Year 9 Standardized Continuous Brevet Standardized Continuous Brevet tests assessment exams tests assessment exams Compensatory education 60 50 35 50 53 47 Non compensatory 69 56 49 60 58 55 education

The various measures of academic performance in collège in the 2007 panel

Interpretation: In mathematics, students in compensatory education schools obtain an average score of 60/100 in the Year 6 standardized tests, 50/100 for the continuous assessment in Year 9, and 35/100 in the brevet examinations.
Coverage: All students whose Year 6 test scores, brevet grades, and family characteristics are known (N = 21,448).

13To avoid this grading bias (that they themselves point out), Duru-Bellat and Mingat (1988) studied the progress of students in 17 collèges in the Dijon academic region (Académie de Dijon) by comparing the results of exams taken by all students at the end of Year 7 with the scores obtained in the ‘general knowledge tests taken at the end of primary school’ (p. 213). While ‘of imperfect quality’, this comparison nonetheless concerns ‘identical exams’ (p. 213). They showed that children with a father in a higher-level occupation progressed faster in collège than did the other students. This article follows in the footsteps of these earlier analyses.

II – Data

14The 2007 panel is the first to record the grades obtained in both the national brevet des collèges examination and the Year 6 tests. Both performance measures are produced by the education system and are uninfluenced by the diversity of social and school environments and of grading practices. However, they are different. The Year 6 tests are a component of the ‘diagnostic evaluations’ introduced in 1989 to ‘detect students’ difficulties accurately from the start of the school year and, wherever possible, provide a rapid response’ (Troseille and Rocher, 2015). While the students know the test scores do not count for their overall grades, a study of their eagerness to take them revealed that they are only slightly less eager than they would be for a graded test and that this eagerness has only a ‘modest’ effect on performance (Keskpaik and Rocher, 2015). The students’ first experience of a formal examination, the brevet exams are end-of-year assessments that take place over 2 days. In 2011 and 2012, the three brevet exams (in French, mathematics, and history–geography) counted for just 37.5% of the final brevet grade (with the rest determined by continuous assessment), [5] but their stated purpose was to ‘assess the knowledge and skills acquired at the end of collège’. The comparison between these two performance measures forms part of the collège ‘dashboard’ for monitoring schools at the département (department) level and deducing each school’s degree of success or failure in improving their students’ academic outcomes (MENESR, 2005).

15Any attempt at quantification is a simplification that should not be taken at face value; the phenomenon is distinct from its measure (Desrosières, 2013). A student’s academic value cannot be entirely captured by these performance measures for two reasons. First, the exams do not measure cognitive skills per se, but reflect those required by the education system. Second, academic value is also determined by local factors. [6] Nevertheless, now that these two measures are available in the panels, students’ academic progress in French and mathematics from the start of Year 6 to the end of Year 9 can be studied for the first time in quantitative terms. [7]

16The 2007 panel comprises a random sample of 1/22 of all students entering Year 6 or a special-needs general and vocational class (section d’enseignement général et professionnel adapté [SEGPA]), thus providing a representative sample of a student cohort. It includes detailed information on the social and migration characteristics of the students’ families recorded via a survey conducted in 2008, [8] data on the students’ school career, and from several purpose-built surveys. Database limitations obliged us to exclude the sample of students whose collège did not enter the Year 6 test scores and those who did not answer the 2008 family survey. We used the weighting calculated by DEPP to take account of these exclusions.

17To study changes in performance over time, we considered only students who took the general brevet exams 4 or 5 years after entering Year 6 (and for whom a grade in the French and mathematics exam was entered). We thus exclude 16% of Year 6 entrants who were moved into specialized programmes or into dissimulated ‘dead-end’ tracks at a young age. Among this group, one-third took a technological brevet, one-fifth entered a SEGPA class, one-tenth entered a CAP (certificat d’aptitude professionnelle) vocational track, and a few entered specialized learning centres or ‘second chance’ schools. The remaining one-fifth were ‘lost to observation’ in national data.

18The probability of not sitting the general series brevet 4 or 5 years after entering Year 6 is closely linked to the student’s social and academic characteristics (Appendix Table A.1). Of the students in the first quintile of the Year 6 test grades, 41% did not sit the exam versus 4% among those in the fifth quintile and likewise one-quarter of children from working-class families.

III – Changes in academic performance over time

19At the aggregate level, the change in academic performance between Year 6 and Year 9 varies by subject. In French, the mean score falls only slightly (from 59/100 to 54/100), and the standard deviation decreases from 12.5 to 11.5. In mathematics, not only does the mean score fall more sharply (from 68/100 to 47/100), but the standard deviation also increases (from 11.4 to 15.3). Contrary to what is observed for French, performance in mathematics becomes increasingly discriminative, confirming that a large share of students fall behind in mathematics during their years in collège (Appendix Figure A.2). In the Year 6 mathematics tests, 15% of students obtain a score of less than 50/100; the proportion is 55% at the end of Year 9. A score lower than 25/100 is rare in the Year 6 tests (0.6%) but concerns 18% of students in Year 9. [9]

Box. An original measure of the parents’ occupations

Because many students have dual-earner parents, we created a nine-category parents’ occupations variable that functions, with one exception, by iteration (Figure 1). The categories were first listed in order of their proximity to the education system, as measured by the students’ mean Year 6 test score. While we separated out the teaching professions (all levels) [1] and grouped some occupations together, the scale follows the same order as the socio-educational synergy index constructed by Le Donné and Rocher (2010). Students were then ranked in the higher of their parents’ two categories or, where applicable, in the category of their only reported parent. Consequently, while ‘teacher’ includes all children whose mother or father is a teacher, the ‘skilled worker’ category only includes those for whom neither parent is a teacher, in a higher-level or intermediate occupation, or self-employed. As farmers are few and their unions have an atypical structure, a separate category was created for all children whose father is a farmer. [2]

Figure 1

Construction of the parents’ occupations variable

Construction of the parents’ occupations variable

* Excluding teachers. ** Excluding primary school teachers. *** Non-response.
Note: In 0.6% of cases, this question was not answered by both parents.
Coverage: All students whose Year 6 test scores, brevet grades, and family characteristics are known (N = 21,448).

20French and mathematics are disciplines in which performance over time diverges at collège level. In mathematics, an ‘elitist’ subject requiring cumulative knowledge acquisition (Broccolichi, 1994; Baudelot and Establet, 2009), a substantial share of students are progressively left behind, but much less so in French.

21To study performance over time by social origin, gender, and migration history, we calculated the difference between the Year 6 test score and the Year 9 brevet grade for each student and subject. We observe that the mean changes (−20 points in mathematics and −5 points in French) vary across sociodemographic characteristics.

22In mathematics (Figure 2), the differences by parents’ occupations and educational levels are clear. Not only do children with parents in the ‘teacher’ and ‘higher-level occupation’ and/or ‘higher education’ categories (Box) obtain higher test scores in Year 6, but their performance also falls much less sharply over time than that of working-class children. Likewise, migration history affects both Year 6 test scores and subsequent academic performance, which are poorer in both cases for children of foreign-born parents, excepting those born in Europe or Asia. Gender, on the other hand, is becoming less inegalitarian: the advantage of boys over girls in the Year 6 tests is narrowing.

23In French (Figure 3), the raw data reveal an advantage at the start of Year 6 for students in the ‘teacher’ and ‘higher-level occupation’ categories, for girls, and for children with French-born parents, but the differences appear to stabilize at collège (or even narrow for the ‘parents’ countries of birth’ variable).

Figure 2

Distribution of mathematics scores in Year 6 and Year 9

Distribution of mathematics scores in Year 6 and Year 9

Interpretation: For each sub-population, the rectangle corresponds to the second and third quartiles. The median score is represented by the central line. The bars indicate the highest and lowest scores, ignoring the outliers (0.5%), which are represented by circles.
Coverage: All students whose Year 6 test scores, brevet grades, and family characteristics are known (N = 21,448).

Figure 3

Distribution of scores in French in Year 6 and Year 9

Distribution of scores in French in Year 6 and Year 9

Interpretation: For each sub-population, the rectangle corresponds to the second and third quartiles. The median score is represented by the central line. The bars indicate the highest and lowest scores, ignoring the outliers (0.5%), which are represented by circles.
Coverage: All students whose Year 6 test scores, brevet grades, and family characteristics are known (N = 21,448).

IV – Testing the three scenarios

24As gender, social origin, and migration history are embedded in social reality, they must be considered separately to test the three scenarios. We therefore ran three separate linear regressions by subject (Tables 2 and 3) and added the variables one by one.

25Because students do not all have the same amplitudes of potential change in performance in Year 6, we controlled by quintiles of Year 6 test scores. We also chose not to centre and reduce the Year 6 and Year 9 scores because these distribution differences form part of the analysis. The increase in the standard deviations of the mathematics scores is itself a product of the education system—it is what needs explaining, not what needs to be neutralized.

2 – Girls derive more benefit from their years at collège

31In Year 6, girls have higher scores than boys in French but lower scores in mathematics. Between Year 6 and Year 9, they lose less ground than boys in both subjects, widening the gap in French and catching up in mathematics. [13]

32The study of performance over time thus provides an explanation for the advantage of girls at the end of Year 9 (that is built up over time) and the near gender equality in mathematics (resulting from a progressive worsening of boys’ performance through collège), previously noted by Baudelot and Establet (1992).

3 – Divergence between children of immigrants and those with French-born parents

33There is an abundant literature on the academic careers of immigrants’ children in France. Seminal studies have shown that the educational disadvantage of children of immigrants relative to those with French-born parents is linked to their more frequent working-class social origins. Rather than considering that the problem of immigrant children is due to cultural differences between France and their parents’ countries of birth, these studies have highlighted the socioeconomic factors behind this difference (Vallet, 1996; Brinbaum and Kieffer, 2009).

Table 2

Effect of socioeconomic variables on progress in mathematics between Year 6 and Year 9

Variables Model 1 Model 2 Model 3 Model 4 Quintiles in Year 6 Quintile 1 8.966*** 9.936*** 10.10*** 10.52*** Quintile 2 2.406*** 2.878*** 2.934*** 3.056*** Quintile 3 Ref. Ref. Ref. Ref. Quintile 4 –0.809* –1.237*** –1.262*** –1.419*** Quintile 5 1.261*** 0.195 0.139 0.0394 Gender Boy Ref. Ref. Ref. Ref. Girl 2.474*** 2.460*** 2.450*** 2.334*** Parents’ occupations Teacher Ref. Ref. Ref. Ref. Higher-level occupation –0.777 –0.156 –0.207 –0.443 Intermediate occupation –6.236*** –3.046*** –3.079*** –3.137*** Self-employed –6.639*** –1.975** –1.967** –2.655*** Farmer –0.488 2.759*** 2.626** 0.965 Clerical/sales worker –9.784*** –4.172*** –4.086*** –4.050*** Skilled manual worker –10.98*** –4.619*** –4.511*** –4.641*** Unskilled manual worker –11.88*** –4.733*** –4.456*** –4.536*** Economically inactive –12.70*** –5.067*** –4.879*** –4.802*** Father’s educational level Higher education Ref. Ref. Ref. Upper secondary –2.383*** –2.463*** –2.428*** Technical –3.949*** –4.209*** –4.399*** No qualification –5.398*** –5.424*** –5.218*** Does not live with father –6.036*** –6.174*** –5.550*** Mother’s educational level Higher education Ref. Ref. Ref. Upper secondary –2.317*** –2.305*** –2.216*** Technical –4.103*** –4.180*** –4.060*** No qualification –5.992*** –5.782*** –5.412*** Does not live with mother –6.446*** –6.502*** –5.952*** Mother’s labour market status In employment Ref. Ref. Ref. Not in employment 0.079 0.191 0.285 Parents’ countries of birth France Ref. Ref. France/Maghreb –2.809*** –1.762** France/sub-Saharan Africa –3.059*** –2.016* France/other countries 0.242 0.715 Maghreb –1.802** 0.103 Sub-Saharan Africa –3.804*** –0.808 Asia 2.272 4.363*** Turkey –1.774 –0.134 Portugal 0.88 2.055 Other European countries –2.782 –2.072 America and Oceania –2.421 –0.295 Other countries –1.654 –0.395

Effect of socioeconomic variables on progress in mathematics between Year 6 and Year 9

Variables Model 1 Model 2 Model 3 Model 4 Type of school Public, non-compensatory Ref. Private 4.705*** Public RAR (a) –1.135* Public RRS (b) –2.212*** Private/public mobility –1.944* Public/private mobility –0.00682 Mobility within compensatory education –2.559*** Locality served by the school < 5,000 inhabitants 1.415*** 5,000 to 49,999 inhabitants Ref. 50,000 to 199,999 inhabitants –1.131** ≥ 200,000 inhabitants –0.729* Paris region –3.446*** Change of school locality –1.827*** Constant –18.05*** –15.09*** –14.78*** –15.04***

(a) Réseaux ambition réussite: high-priority compensatory education school.
(b) Réseaux de réussite scolaire: other compensatory education school.
Interpretation: For girls, the average change in mathematics scores between Year 6 and Year 9 is 2.474 points more positive than for boys (Model 1).
Statistical significance: * p < .05, ** p < .01, *** p < .001.
Coverage: All students whose test scores in Year 6, brevet grades, and family characteristics are known (N = 21,448).

34More recently, comparing differences in standardized test scores between children of immigrants and of French-born parents at three moments in their school career, and making a clearer distinction between countries of origin, Ichou concluded that ‘children of immigrants perform better, on average’ during the period between Year 1 and Year 6, but the trend is then reversed: ‘between Year 6 and Year 9, the difference (raw and, above all, net) [14] between children of French-born parents and of immigrants widens again, with the former outperforming the latter’ (Ichou, 2015, p. 38).

35In Model 3, we introduce a 12-category variable for the parents’ countries of birth. Like Ichou (2013), we give priority to precision over parsimony, considering that the ‘statistical risk’ of obtaining non-significant results is of lesser importance than the ‘sociological risk’ of ignoring the diversity of educational trajectories among children of immigrants.

Table 3

Effect of socioeconomic variables on progress in French between Year 6 and Year 9

Variables Model 1 Model 2 Model 3 Model 4 Quintiles in Year 6 Quintile 1 13.70*** 14.12*** 14.11*** 14.24*** Quintile 2 4.437*** 4.694*** 4.694*** 4.739*** Quintile 3 Ref. Ref. Ref. Ref. Quintile 4 –4.689*** –4.941*** –4.957*** –5.000*** Quintile 5 –9.911*** –10.47*** –10.49*** –10.49*** Gender Boy Ref. Ref. Ref. Ref. Girl 3.486*** 3.690*** 3.706*** 3.705*** Parents’ occupations Teacher Ref. Ref. Ref. Ref. Higher-level occupation –0.457 –0.111 –0.113 –0.493 Intermediate occupation –3.510*** –1.672*** –1.662*** –1.759*** Self-employed –5.100*** –2.396*** –2.463*** –2.768*** Farmer –3.301*** –1.279* –1.251* –1.560** Clerical/sales worker –5.771*** –2.619*** –2.634*** –2.680*** Skilled manual worker –7.896*** –4.456*** –4.555*** –4.571*** Unskilled manual worker –8.156*** –4.656*** –4.779*** –4.748*** Economically inactive –7.456*** –4.192*** –4.198*** –4.233*** Father’s educational level Higher education Ref. Ref. Ref. Upper secondary –1.075*** –1.098*** –0.988** Technical –2.324*** –2.309*** –2.189*** No qualification –2.105*** –2.221*** –2.057*** Does not live with father –2.147*** –2.093*** –1.818*** Mother’s educational level Higher education Ref. Ref. Ref. Upper secondary –1.126*** –1.122*** –1.009*** Technical –3.297*** –3.268*** –3.084*** No qualification –3.351*** –3.429*** –3.208*** Does not live with mother –3.206*** –3.162*** –2.797*** Mother’s labour market status In employment Ref. Ref. Ref. Not in employment 0.462 0.424 0.382 Parents’ countries of birth France Ref. Ref. France/Maghreb –0.117 –0.119 France/sub-Saharan Africa –0.924 –0.964 France/other countries 0.939* 0.929* Maghreb 0.935 0.948 Sub-Saharan Africa –0.491 –0.468 Asia 3.018*** 2.764** Turkey –0.365 –0.322 Portugal 1.096 1.053 Other European countries –0.342 –0.269 America and Oceania 3.622 3.536 Other countries –1.416 –1.445

Effect of socioeconomic variables on progress in French between Year 6 and Year 9

Variables Model 1 Model 2 Model 3 Model 4 Type of school Public, non-compensatory Ref. Private 2.850*** Public RAR (a) 0.77 Public RRS (b) 0.222 Private/public mobility –0.0132 Public/private mobility 0.351 Mobility within compensatory education –0.383 Locality served by the school < 5,000 inhabitants –0.218 5,000 to 49,999 inhabitants Ref. 50,000 to 199,999 inhabitants –0.292 ≤ 200,000 inhabitants 0.0259 Paris region 0.0243 Change of school locality –0.971* Constant –3.767*** –2.450*** –2.496*** –3.079***

(a) Réseaux ambition réussite: high-priority compensatory education school.
(b) Réseaux de réussite scolaire: other compensatory education school.
Statistical significance: * p < .05, ** p < .01, *** p < .001.
Interpretation: For girls, the average change in French scores between Year 6 and Year 9 is 3.486 points more positive than for boys (Model 1).
Coverage: All students whose test scores in Year 6, brevet grades, and family characteristics are known (N = 21,448).

36We distinguish four types of effects of parents’ countries of birth on performance over time. First, in mathematics, children whose parents were both born in the Maghreb or sub-Saharan Africa perform less well than those with French-born parents. Children of Maghrebi parents have a mean mathematics score of 60/100 in the Year 6 test and 37/100 in Year 9, a significant difference compared to the children of French-born parents (whose scores fall from 68/100 to 48/100). This is partly explained by their working-class background, although the ‘parents’ countries of birth’ also has an effect after controlling for this variable (Model 3). [15] In French, while children with two Maghrebi or sub-Saharan African parents perform less well in the Year 6 test than those with French-born parents (48/100 and 49/100 vs. 60/100, respectively), the difference remains stable throughout the collège years, after controlling for social origin.

37Children of Asian-born parents perform better at school, as noted by Ichou (2013). In mathematics, the performance over time of children with Asian-born parents does not differ significantly from that of children with French-born parents, but in French it is significantly better (+3 points on average).

38For the other groups of immigrants’ children, no significant difference compared to children of French-born parents is observed after controlling for social origin, perhaps because the sample size is too small, or the categories such as ‘other European countries’ or ‘other countries’ are heterogeneous, or because the performance of immigrants’ children over time parallels that of children with no identified migration history.

39Last, as shown by Ichou (2013), boys with Turkish-born parents perform most poorly, obtaining just 30/100 in mathematics in Year 9 versus 50/100 for sons of French-born parents. Their scores are low in Year 6 (60/100 for sons of Turkish-born parents versus 71/100 for sons of French-born parents), but their performance over time is even more negative than that of all other groups. [16]

40Here again, the variables are strongly interlinked: 94% of the children of farmers have French-born parents versus 55% of children in the ‘unskilled worker’ category. For children of parents born in the Maghreb, the probability of belonging to the ‘unskilled worker’ category is 16% versus 2% when the parents are French-born.

41One might expect the introduction of this variable to reduce the effect of the parents’ occupations, as was the case for educational level, family situation, and labour market status, but the two effects co-exist and do not cancel each other out. Whether children of immigrants or not, working-class children perform less well over time than middle- and upper-class children. And the mathematics performance of students with parents born in the Maghreb or sub-Saharan Africa, whether working-class or not, decreases over time at collège.

42These initial analyses show that social differences are magnified at college, but to an extent that differs by subject and by type of characteristics considered. First, despite already unequal Year 6 test scores, and more so in mathematics than in French, the differences between children with parents who are teachers or in higher-level occupations widen in collège with respect to the others (excepting the children of farmers). Second, the gender differences observed at the start of collège tend to narrow in mathematics but widen in French, to the disadvantage of boys. Last, once social origin has been taken into account, the parents’ country of origin has no significant effect on performance over time in French; only certain migrant categories (Maghreb, sub-Saharan Africa for both sexes, and Turkey for boys) have significant and negative effects on performance over time in mathematics.

V – Are these differentials in performance over time linked to the school environment?

43Two main types of explanation for inequalities in academic performance are found in the literature. Some argue that the unequal distance between family socialization and academic requirements may explain the lower performance of working-class children (Lahire, 1995; Thin, 1998), boys (Baudelot and Establet, 1992; Depoilly, 2014), and the children of immigrants (Ichou, 2018). Others focus on differences in the educational environment, such as school effects, class effects, and teacher effects (Cousin, 1993; Duru-Bellat and Mingat, 1997; Felouzis, 2003; Cusset, 2011). Studies of student trajectories after the second educational upheaval tend to argue that segregation has increased and represents an ever stronger explanatory factor of inequality (Broccolichi, 1995). Our data enable us to test in part the hypothesis whereby certain differentials in performance over time are attributable to differences in the educational environment, notably the characteristics of the school attended and the size of the locality it serves.

Conclusion

57In a context where a very large fraction of each age cohort in France is educated in a comprehensive lower secondary school with near-universal access to Year 9, we analysed changes in academic performance over the 4 or 5 years separating the entry into Year 6 and the general brevet des collèges examination at the end of Year 9, among students who enrol to take it for the first time. We have shown that differences are magnified at collège, but to an extent that differs by subject and by the students’ social characteristics.

58These changes appear to be strongly influenced by social origin. The performance of working-class children, whose Year 6 test scores were already lower than those of middle- and upper-class children, falls more markedly over time. This is even the case for those with good Year 6 test scores, despite 1 in 4 working-class children not sitting the general brevet des collèges examination 4 or 5 years after entering Year 6. The school environment does not explain these differences in performance over time. This confirms the scenario of diverging performance by social class; the differences linked to the students’ social origin grow wider between Year 6 and Year 9.

59These differences are also linked to gender, with girls maintaining a steadier performance than boys in both French and mathematics. As girls already had better Year 6 test scores in French than boys, but poorer scores in mathematics, changes in performance linked to gender are divergent in French and convergent in mathematics.

60Migration history, which explains a large share of the variability of results in Year 6, does not seem to affect academic performance over time other than via differentials in the school environment. While children with parents born in the Maghreb or in sub-Saharan Africa make less progress in mathematics than children with French-born parents, even after controlling for social origin, these inequalities are explained by the fact that they more frequently attend compensatory education schools and/or schools in the Paris region, and less frequently remain in the private sector.

61As already shown by Duru-Bellat and Mingat (1988), mathematics is a more inegalitarian subject than French at the collège level. Social origin and the school environment have a stronger effect on performance in mathematics over time. This shows that reliance on components of legitimate culture is not the only means whereby the school reproduces inequalities; by building upon social skills unequally transmitted via the family (capacity to defer gratification, to exercise self-control, etc.) and not taught (Vincent, 1980; Cayouette-Remblière, 2016), the collège widens inequalities, including in subjects directly taught and overseen by the school.

62To conclude, it is useful to examine how this evidence of diverging performance over time across different social origins adds a new perspective to recent findings on the link between inequalities in academic performance and in track orientation (Broccolichi, 2010; Broccolichi and Sinthon, 2011). For many years, these two types of inequality were considered independent of each other, as suggested by the theory of primary and secondary effects. [21] Broccolichi (1994) revisits this dichotomy by showing that if inequalities of progress do indeed exist, then inequalities of performance and track orientation cannot be viewed separately. Given that track orientation is always based on anticipated future risk of failure, if these inequalities in progress are anticipated, they explain why, for a similar performance level at the end of Year 9, future failure risks are judged differently by students and teachers. Viewed from this angle, inequalities in track orientation are no longer the consequence of ‘excessive caution’ or a ‘lack of ambition’; instead, they are another manifestation of inequalities in performance. Showing, as we have done, that inequalities in academic performance by social origin increase as students move up through collège validates this interpretation and points up the need to focus public policy on reducing inequalities of achievement rather than on programmes seeking to stimulate the academic ambitions of working-class children.