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

Regional Economic Disparities as Determinants of Students’ Achievement in Italy: Baseline resultsThe results of the multilevel analysis have been reported in the table 2. We ran six different models:
•    An empty model, without and with macroareas dummies (models 1 and 4);
•    A model in which individual variables were added, without and with macroareas dummies (models 2 and 5);
•    A model in which both individual and school variables are employed, without and with macroareas dummies (models 3 and 6). Experiential learning

The first and fourth columns illustrate the coefficients estimated for the intercept, that is the average math score for all students in all schools. What is interesting here is the analysis of variance, which confirms how both between and within schools differences exist. The between-schools variance is 90.77, and within-schools variance is 387.66; thus, the latter is higher than the former. The most part of the variance is at student-level more than at school-level, even though also the latter plays a significant role – suggesting the existence of some degree of segmentation among schools. Indeed, about 19% of the variance is explained by between-schools variance. Some evidence provided by Invalsi, based on simple statistics, indicates that this percentage is pretty different in the areas of the country, and much higher in the Southern Italy. In our analysis, in all three models, when adding macro-areas dummies the variance between schools diminishes drastically (the between-schools variance “explained” is >21%), suggesting that this is higher in some macro-areas (coherently with the Invalsi findings). Later in this paper, we address specifically this topic.
The columns 2 and 5 show what happens when individual-level characteristics have been added. It is important to note that all the individual variables are statistically significant. Female students perform worse than their male counterparts (-1.8); disabled and foreign students have lower performance (12 and 4 points, respectively). Early students outperform the “regular” (born in 1994) ones (+1, but this effect appears only when macroareas dummies are included), while students who repeated one or more years suffer a strong disadvantage (-8 points).
When adding individual-level characteristics, within-schools variance decreases, coherently with the model. The calculation on the part of the variance explained by such characteristics is as follows (comparing the model without macroareas dummies): (387.6 – 378.2)/387.6 = 2.4%. As expected, these variables are not contributing too much in reducing within-schools variance, as we did not include individual-level socio-economic status. Moreover, such scarce influence raises further questions about the real determinants of achievement at individual level (e.g. cultural capital, previous academic results, etc.). Finally, the columns 3 and 6 consider the models where school-level variables are included. All the individual-level variables remains significant and with similar coefficients. There is a positive effect associated to lower proportions of disadvantaged students in the school: this effect is statistically strong for schools where this proportion is <10% (about 8 points), lower (but still significant) where the proportion is between 11% and 25% (about 6.5 points), and finally again lower, still positive but statistically not different from zero where this proportion is between 26% and 50% (the reference group is the school where the proportion is >50%).

Table 2: Results of the multilevel analysis (Math Scores)

Without geographical dummies | Withgeographical dummies
Variable Empty Individual Individual & School Empty Individual Individual & School
1 2 3 4 5 6
female 1.874 1.862 1.867 1.859
0.000 0.000 0.000 0.000
disabled 12.162 12.220 12.300 12.330
0.000 0.000 0.000 0.000
foreign 4.165 4.354 4.281 4.451
0.000 0.000 0.000 0.000
early 0.980 1.144 1.120 1.280
0.149 0.111 0.099 0.075
late 8.185 8.220 8.200 8.239
0.000 0.000 0.000 0.000
Disadvantaged (010%) 7.807 2.585
0.010 0.333
Disadvantaged (1125%) 6.542 1.181
0.034 0.664
Disadvataged (2650%) 3.133 0.220
0.337 0.938
High Shortage of Instructional material -5.827 -3.695
0.244 0.391
Some Shortage of Instructional material -3.635 -2.625
0.126 0.195
Community_ big city 5.664 5.775
0.188 0.120
Community_city 5.172 5.926
0.162 0.062
Community_town 4.854 6.321
0.166 0.036
Central Italy -1.921 -2.394 -1.761
0.298 0.190 0.381
Southern Italy 10.888 11.889 11.246
0.000 0.000 0.000
Constant 62.091 64.023 54.036 67.117 69.567 62.319
0.000 0.000 0.000 0.000 0.000 0.000
Random effects
Between-schoolsvariance 90.77603 94.12891 89.3523 65.21201 63.91378 63.34769
Within-schoolvariance 387.6603 378.2766 374.8512 387.6596 378.2754 374.8554
% Between 18.97% 19.93% 19.25% 14.40% 14.45% 14.46%
This post was written by , posted on January 9, 2014 Thursday at 12:11 pm