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

The findings are that variance at regional level is statistically significant, as evidenced by the lower part of the table, while around 14% of variance is due to between-schools factors and more than 80% is within-schools. More precisely, 4.6% of the total variance is due to differences between Regions. When adding school-level and individual-level variables (second column), the coefficients are coherent with those deriving from the previous models (e.g. tables 2 and 3), confirming that the inclusion of the third level of variance (the Regional level) did not affect the main results, in terms of coefficients’ value, but only the decomposition of variance. The last column of the table 4 shows the effects of including the GDP per capita (GDPpc) at Regional level (1,000€) as a potential variable explaining the variance at Regional level (source: Italian Institute of Statistics: www.istat.it): the coefficient is statistically significant and its value is around 0.9 – that is, the region’s economic development is associated with 0.9 points more (on average) in terms of students’ performance. Thus, the output of the analysis is very clear, as the variance between-Regions becomes no more statistically significant: that is, GDPpc is able to capture almost all the explanation about the between-Regions differentials in terms of students’ achievement; as underlined by the bottom panel of the table, between-Regions variance drops from 5% to less than 0.2%. This is a key result of the paper, as the model with regional socioeconomic characteristics is able to explain at least in part the differences that were “masked” by macroareas fixes effects (as measured through dummies). The story that emerges is that socio-economic structural differences among Regions, as measured by GDP per capita, is a relevant factor that affects students’ performances (achievement). Some consequences of this phenomenon are discussed in the next section. Industry Towards

In this paper, we analysed the determinants of students’ achievement for a sample of 21,336 students in 163 lower-secondary schools in Italy. For the first time, it was possible to use data from a national standardized test (administered by Invalsi), instead of information from OECD-PISA and other international tests. A multilevel approach has been used, to properly account for the hierarchical structure of data.
The results tell that individual characteristics related to performance are, mainly, the disabled status, the foreign nationality and the repetition of one or more years: all these factors are associated to lower scores in the test. When looking at the school-level variables, it turns out that composition of student body (in terms of socio-economic background) matters more than schools’ resources. However, this SES effect disappears when considering differences between the macro-areas of the country, with schools located in the Southern Italy experiencing lower results; this suggests a strong correlation between location and socio-economic characteristics of student bodies that should be analysed more cautiously in the future. The findings also underline the existence of school-level factors related to performance, which are not captured by traditional variables.

Table 4. Results of the three-levels multilevel model (Math Scores)

Variable Three-levels multilevel model: empty Three-levels multilevel model: individual and school-level variables Three-levels multilevel model: individual and school-level variables, plus GDP per capita at Regional level (1,000€)
female 1.857 1.857
0.000 0.000
disabled 12.298 12.344
0.000 0.000
foreign 4.421 4.440
0.000 0.000
early 1.259 1.295
0.080 0.072
late 8.232 8.240
0.000 0.000
Disadvantaged (0-10%) 3.460 2.427
0.211 0.358
Disadvantaged (11-25%) 2.381 1.178
0.394 0.660
Disadvantaged (26-50%) 0.515 0.035
0.858 0.990
High Shortage of instructional materials -5.104 -4.199
0.254 0.320
Some Shortage of instructional materials -2.759 -2.490
0.186 0.213
Community_big city 6.359 5.727
0.097 0.115
Community_city 5.972 6.125
0.071 0.051
Community_town 6.040 6.389
0.051 0.032
GDPpc (Regional level) 0.875
Constant 62.876 57.112 36.033
0.000 0.000 0.000
Between Regions Variance 22.085 23.490 0.653
Between Schools Variance 65.903 64.707 61.433
Within Schools Variance 387.666 374.856 374.842
Between Regions Variance (%) 4.64% 5.07% 0.15%
Between Schools Variance (%) 13.86% 13.97% 14.06%
Within Schools Variance (%) 81.50% 80.95% 85.79%
This post was written by , posted on January 15, 2014 Wednesday at 12:14 pm