Abstract
It is often held that educational expansion narrows social inequalities within nations by promoting a meritocratic basis for status attainment, yet substantial research indicates that the relative advantages of elite children over children with less privileged background have changed little in the last decades [4, 12, 19]; on average higher status children perform better in school and attain higher educational levels. In this light, inequality of opportunity (IEO) in education is still a highly relevant issue in the international educational policy agenda.
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Notes
- 1.
The term track is often used in the literature to indicate the different secondary school educational paths available to students in a certain educational system. The academic track is the one conceived to prepare for university studies (even if in some countries it is not required to enter tertiary education).
- 2.
Employing data from PISA (Programme for International Student Assessment; [18]) would weaken the sample selection problem, since students are interviewed at 15, i.e. near the beginning of upper secondary school. However this option proves impossible since PISA does not include information on students’ performance before choice. PISA may however be appropriate to evaluate the total effect of social background (see for example [6]).
- 3.
We refer to the term “semi-parametric” to account for the fact that the ability distribution is estimated non-parametrically while the transition probability is estimated by logit regression.
- 4.
According to the survey Percorsi di studio e di lavoro dei diplomati 2004.
- 5.
The socio-pedagogic lyceum (formerly called istituto magistrale) is conceived to prepare for primary school teaching. Although university education is now required, until a few years ago this type of school gave direct access to the teaching career; for this reason we do not consider this school type in the academic track. Given its specific focus, a similar argument also applies for the artistic lyceum.
- 6.
There are few surveys recording longitudinal data in Italy: the Indagine sui Bilanci delle Famiglie (Banca d’Italia) does provide some information on individual’s educational careers, but no data on school performance is available; moreover, the sample size is too small to allow analyses on specific birth cohorts.
- 7.
Interviews were carried out with CATI. Data is collected with a two stage sampling scheme; 20,408 individuals in 1,868 schools were interviewed.
- 8.
The mark is attributed after a national exam, detached from normal school activity, at the end of lower secondary school (Esami di Stato conclusivi del I ciclo).
- 9.
This issue is likely to become less relevant in the future: from 2007, in fact, final exams include two standardized tests (linguistics and mathematics) with common evaluation guidelines.
- 10.
Stocké [20] addresses this issue for Germany and finds that educational choices are driven mainly by school marks, although a minor effect can be ascribed to parents’ perception of their children ability.
- 11.
It is nevertheless obvious that in the extreme case where marks were hardly related to ability, the decomposition itself would loose much if its meaning, in that secondary effects would become the only source of class differentials.
- 12.
We developed a simulation study (not presented here) to address this issue. The bias appears to be small for measurement error of type (i) and (ii) and somewhat bigger for type (iii).
- 13.
Children who have chosen a vocational program and attained a qualifica professionale (after 3 years) but not a diploma (after 5 years) are also excluded from the survey. To simplify the exposition, the term “dropouts” includes them as well.
- 14.
Small inconsistencies among the combined data sources produce a negative probability, which is however so close to 0 to be reasonably considered negligible.
- 15.
CISEM stands for Centro per l’Innovazione e Sperimentazione Educativa Milano and is a research centre on educational problems of Provincia di Milano. IARD – Istituto Franco Brambilla is a research centre focusing on life problems and opportunities of young people. The authors would like to thank both CISEM and IARD for the collaboration and availability of data.
- 16.
Since \(P(G=1|Y=1,A,S) = \frac{P(G=1,Y=1|A,S)}{P(Y=1|A,S)} = \frac{P(Y=1|G=1,A,S)P(G=1|A,S)}{P(Y=1|A,S)}\).
- 17.
These ratios vary from 0.93 for high status-high ability students to 1.32 for low-status-low ability students.
- 18.
Although we do not employ this classification here, the data allow to classify individuals into three social classes as in the simplified British National Statistics Socio-Economic Classification, used for example in Jackson et al. [15].
- 19.
Note also that for Italy the odds ratio between Y and S when status is measured by social class is much lower than that relative to the highest parental educational level. Moreover, some recent works seem to be going in the same direction (see e.g. [16]).
- 20.
Correction factors were first computed also by geographical area; however, due to within-country migrations, some inconsistencies between the different data sources arise. Due to this in the end they were computed at the national level.
- 21.
Data directly obtained from the Education Ministry Statistical Office.
- 22.
Since the highest parental educational level distributions for the children who have obtained the lower secondary school degree in a given year are not available, as a proxy we calculate P(S) for the all children born 14 years before according to Census data. These populations do not overlap for two reasons: first, some students may graduate earlier or later, due to repetitions; however, if grade failure is roughly stationary, the difference should be negligible. Second, the Census data includes children who have not obtained the lower secondary school degree. We assume that the students failing to pass the lower secondary school examination belong to lower educated families; the assumption is highly reasonable, since, as we have shown above, the great majority of the students passing the exam with the lowest mark sufficiente come from the lowest social strata. The number of students of the lowest social strata has been adjusted by subtracting from it the number of students who have not passed the final exam (data provided by the Ministry of Education); the distributions were then evaluated accordingly.
- 23.
Sampling variability should enter here only via \(P(S|G=1)\), but standard errors of the estimates are very small, and cannot by itself explain these inconsistencies.
- 24.
Different sets of \(P(G=1|S)\) were applied to check robustness of results: decomposition of expressions in (4) appears to be only slightly affected by mild changes in these percentages.
- 25.
Standard errors of the estimates are not reported; given the complex sampling scheme they could be obtained only with non-standard resampling techniques. Similar arguments also apply to Table 15.3. To give a rough idea of their magnitude with respect to Table 15.2, assuming simple random sampling standard errors would vary between 0.005 and 0.032.
- 26.
- 27.
Standard errors of the estimates and derived p-values have been computed assuming a simple random sampling, and are thus somewhat underestimated.
- 28.
Since the estimated quantities are quite complex, the assessment of their standard errors would require a significant effort. Note however that, given the large sample size, we expect them to be reasonably small.
- 29.
In principle, there could be wide family status differences in the observed level of ability, but if school choices were only weakly affected by performance (choices depending mainly on social status), these differences would not exert a relevant role.
- 30.
Moreover, by estimating, somewhat improperly, a linear model for performance, we do not find significant interaction effects between gender and status, (i.e. the effect of status on performance does not change with gender).
- 31.
See the constant and the gender coefficient.
- 32.
The percentage with respect to the high-low status comparison is reported here.
- 33.
In UK and Sweden father’s social class, in Germany mother’s social class, in the Netherlands and Italy the highest parental educational level.
- 34.
We can see this from PISA, for which common alternative definitions are possible. Taking the highest parental educational level the following raw OR between high and low social status are found: Netherlands 4.7, Italy 6.9, Germany 12.9. Taking social class, Netherlands 8.5, Italy 5.8, Germany 8.4.
- 35.
To give an example, why is it that in Italy primary effects are so low? Could it be due to the fact that the compulsory school system is quite highly standardised in Italy? (standardization refers to the degree to which the quality of education meets the same standards nationwide; Allmendinger, 1989). On the other hand, secondary effects are strong. Is this related to the absence of performed-based restrictions to enrolmment into the academic track, at work in other countries (in the Netherlands for example)?
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Contini, D., Scagni, A. (2011). Secondary School Choices in Italy: Ability or Social Background?. In: Attanasio, M., Capursi, V. (eds) Statistical Methods for the Evaluation of University Systems. Contributions to Statistics. Physica-Verlag HD. https://doi.org/10.1007/978-3-7908-2375-2_15
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