The above results suggest that inequality of school performance is negatively associated to HE GER. Coefficients are consistent and significant for a relevant number of specifications regardless of methods (OLS or fixed effects), imputation technique, or timing between predictor and outcome variables. Besides the criticisms to PISA-driven education policies, my results show that at a cross-country level, keeping a number of variables constant, inequality of school achievement is negatively associated to HE growth.
82 The main caveat is the lack of statistical power of the models, including the full set of control variables. This is certainly an issue when the effects are mild as the ones reported in this chapter since small sample sizes make it harder to pick small effects. Moreover, it is also possible that the most complex models are overfit so long as BIC does not improve. I just have 10 observations per parameter in the most complex model (228/21, as missing dummies are included), which means the size/parameters ratio is in the lower bound of the conventionally recommended.
In countries where tests results are more ‘predictable’, conditional to ESCS, HE GERs drop. In other words, higher inequality of learning outcomes, conditional to social background, does affect HE systems. The main policy implication is that governments should not only focus on improving achievement, but on policies seeking to break socioeconomic gaps in learning outcomes. This might suggest that policies looking to increase HE participation should focus on school education instead of relying on HE student aid.
My results support the claim that the main mechanisms determining access to HE are likely to be country specific as there is no statistically significant cross-country average effect between test results and GERs, otherwise, HE GERs would be streamlined with PISA scores. This is consistent with the fact that, in some countries, HE has been accessible to students sometimes lacking the necessary skills to succeed in HE through the development of second-tier HEIs. Examples of this are for-profit universities in the US and the rapid emergence of private, non-selective HE institution. Both are included in enrolment figures, but the real value of the credentials offered is highly contested.
For more robust estimates, a larger sample – or at least feasible proxies – is required, especially on the composition of expenditure and HE policy mechanisms giving an account of cost sharing and the balance between supply-and-demand-side funding. Obtaining such data for the same number of countries for several time periods seems unfeasible. Fast growing HE systems are more likely to correspond to developing countries that have prioritised financing school education out of public funds; thus, it
83 does not seem to be other chances than fostering private participation and cost transfer in HE.
The fixed-effects approach undertaken in this chapter is not able to deal with sensitive issues affecting HE policy, such as the creation of new student aid mechanisms, abrupt changes in cost sharing/transfer structures, and fees policies, as they cannot be assumed as time invariant. This is probably the key weakness of the approach undertaken in this chapter: a policy shock may influence enrolment rates immediately, as evident in Latin American countries (Chile, Colombia, and Brazil, to a lesser extent). Notwithstanding, I included several control variables and dealt with time- invariant unobserved variables. It is necessary to find better controls that account for policy changes and the private/public balance in terms of funding and enrolment.
As I shall show in the next chapter, according to the literature, cost sharing/transfer mechanisms are harmless to equity of access so long as student aid policies are supportive enough and also streamlined with the characteristics and the specific needs of the target population. I shall present a case where an aggressive reform to student aid contributed in a great deal to a sharp boost to low SES students’ participation in HE.
It is also desirable to have data on the socioeconomic composition of enrolment; for instance, the HE enrolment rates for different levels of income. In fact, in order to study access to HE with more depth, enrolment rates broken down by household income levels would be a key outcome to investigate. This information is available for a few countries, but is neither systematically nor periodically collected by international organisations. A short-term research agenda should include the collection of more measures of outcome and including additional variables in order to have more accurate predictors, as well as using alternative methodological approaches.
School performance and main social reproduction mechanisms play a key role as predictors of HE participation. Equity driven HE policies may have no effect if they do not recognise inequalities coming from school education. Governments have set a
84 series of policies to overcome barriers to access, but inequity of access still remains high.
This leads to the next Chapter, where I analyse the case of Chile. The country has expanded HE quickly and has also made progress in school education outcomes as measured by PISA scores. Chile has also provided opportunities to poor students by devoting 40 per cent of its public expenditure in HE to student aid. Notwithstanding, Chile has one of the most segregated school systems globally as well as high inequality levels, the second in Latin America behind Brazil. Despite showing impressive economic results in the last 25 years, old, sensitive, and persistent issues remain, which feature Chile as a paradigmatic case of study: most educational issues I have mentioned in the introduction and this chapter are taking place there.
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