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Regional Economic Disparities as Determinants of Students’ Achievement in Italy: Socioeconomic Variables

Regional Economic Disparities as Determinants of Students’ Achievement in Italy: Socioeconomic VariablesAnother point is about the factors that can explain between-schools variance. The high between-schools variance in the South, indeed, necessitates a serious research of its determinants. A well-known potential interpretation, often adduced in the literature, is that schools perform differently because of different average SES of students; thus, the concentration of rich students in certain schools would explain their better performances. Typically, this is the usual justification for the better performances of private schools. Our data only partially support this view: in the Northern Italy, when adding school-level variables the “explained” between-schools variance is around 22%. However, it is not the case for the South: school level variable account for less of 1% the (high) between-schools variance! The available data does not allow investigating deeply this topic, but it is clear that other factors stand behind the phenomenon. Potential explanations could be related to (i) cultural characteristics of the families (and not their SES), or (ii) to specific school-level features not measured in the usual questionnaires (e.g. school climate, collaboration among teachers, educational styles, leadership, etc.). Public Service

In addition to these elements, it is important to underline that the model presented in table 3 suffers a major limitation: it does not offer any explanation about the ways through which the geographical factors (location in a macroarea) act on influencing students’ performances. In other words, the macroarea dummies are treated like a “black box”: they show a correlation with the dependent variable (students’ achievement score) but the mechanisms that relate the two variables (in the EPF framework, see equation 1) is not revealed by the model. Nevertheless, such evidence was the stimulus to go deeper into the empirical analysis (see the next section).
The high explanatory power associated to the macro-areas dummies induced a further reflection on the role played by the “geographical” factors, and especially by the different socio-economic characteristics of the Regions. Indeed, Italy is one of the OECD countries with the higher “Gini index of regional disparities in GDP”: the value was 0.14, compared with 0.06 for Sweden, 0.10 for Netherlands, 0.12 for Spain and Germany (OECD, 2008). Thus, it is important to verify whether part of the between-schools variance can be attributable to “contextual” characteristics, namely to the structural socio-economic differences between Regions. To investigate this issue, a three-levels multilevel analysis has been carried out, by decomposing the overall variance of students’ achievement scores into three components: within-schools variance, between-schools variance, and between-Regions variance. Estimating a three-levels multilevel model is a challenging issue per se, as it assumes that factors associated to the students’ performances in the EPF framework (equation 1) can be grouped in three distinct “families”. The results are shown in the first column of table 4. Differently to the previous elaborations, the macro-areas dummies have been dropped, because the interest is on the variance between Regions (many Regions compose a macro-area).

This post was written by , posted on January 13, 2014 Monday at 12:13 pm