# **5. A Descriptive Application: Correlations between IOp and Education Policies**

As an illustration of potential applications, we now briefly investigate the cross-country correlation between the measure of inequality of educational opportunity presented in the previous section and two specific educational policy variables: the distribution of public spending across different levels of the education system, and the extent of early tracking of pupils between general and vocational schools or classes.

The incidence of public spending in education and the allocation of financial resources among the different segments of the education system have been examined by various studies (e.g. Birdsall, 1996; Castro-Leal et al., 1999; and Van de Walle and Nead, 1995). Given that children with disadvantaged backgrounds tend to drop out from school earlier than others, the allocation of resources to the primary level of schooling is generally thought more likely to be progressive.

The impacts of tracking policies on the efficiency and equity of educational systems are another example of education policies that have received considerable attention in recent studies (Ariga et al., 2006; Brunello and Checchi, 2007; Brunello et al., 2006; Hanushek and Woessman, 2006; Manning and Pisckhe, 2006). Theory does not provide clear-cut predictions for the effect of early tracking on educational achievements. On the one hand homogenous classrooms, and the associated specialization of teaching and curricula to the needs and abilities of specific students, could lead to efficiency gains. But on the other hand, disadvantaged groups might be harmed by unfavorable allocations of resources, including less well endowed schools, teacher sorting, peer effects, or differences in curricula<sup>25</sup> . Moreover, since much of the early inequality in achievement – and thus the track placements themselves – are driven by differences in parental resources, a frequent concern has been that tracking might reinforce the effects of family background on educational achievements. I.e. that it might reduce intergenerational mobility, and exacerbate inequality of educational opportunity.

We briefly examine the correlation between our measure of IOp and these two policies, using data on the policy indicators from the UNESCO Institute for Statistics (UIS).<sup>26</sup> Our indicator of the distribution of educational expenditures is the share of spending in primary schools - defined as the first ISCED level, corresponding to grades 1 to 6 - in total public educational expenditure. The indicator of tracking is the share of technical or vocational enrollment at the secondary level (including lower and upper secondary or the second and third ISCED levels, usually corresponding to grades 7 to 12) in total enrollment at that level. The information on the distribution of education expenditure across levels is missing for six countries (Canada, Montenegro, Qatar, Russia, Serbia and Taiwan, China) and the information on the share of technical and vocational enrollment at the secondary level is missing in five countries (Latvia;

> 25 Early tracking may also be costly in terms of the misallocation of students to tracks, and in terms of forgone versatility in the production of skills (Brunello and Checchi, 2007).

> 26 The data for 2006 correspond to the school year 2005-06 for countries where the school year laps over two calendar years.

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