Modelling SDG scenarios for Educational Attainment and Development. CESDEG: Education for all Global Monitoring Report (EFA-GMR)

Barakat, B., Bengtsson, S., Muttarak, R. ORCID: https://orcid.org/0000-0003-0627-4451, & Kebede, E. (2016). Modelling SDG scenarios for Educational Attainment and Development. CESDEG: Education for all Global Monitoring Report (EFA-GMR). Wittgenstein Centre for Demography and Global Human Capital (IIASA, VID/ÖAW, WU)

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Abstract

The scenarios of educational expansion underlying the population projections presented here result from a further refinement of the education model presented in Lutz et al. (2014). In summary, we project the share of the population ever reaching or exceeding a given attainment level. This is done seperately by country, and gender, but with ‘shrinkage’ within a Bayesian framework (with weakly informative priors). The mean expansion trajectories are modelled as random walks with drift (and potential mean reversion) and independent noise at a probit-transformed scale. The trend parameters are estimated based on reconstructed attainment histories, and extrapolated, subject to additional and some exogenously imposed convergence within regions and between females and males. Under the target scenarios, SDG targets are treated as ‘future data’ (in other words, target trajectories are modeled looking back from 2030 under the assumption that the target will have been met), with a potential trend break in 2015.

Limitations shared with all existing global projections of educational development include the fact that in the absence of a detailed theoretical basis, they are forced to rely heavily on statistical extrapolations. For example, there is little consensus on whether “higher education is the new secondary education” (as claimed by Andreas Schleicher of OECD), or is fundamentally different from lower levels of schooling (e.g. in terms of institutional framework, its role in the life cycle, economic returns. In addition, global projections can necessarily not account in a satisfactory manner for idiosyncratic policy changes or shocks. In addition, the specific modelling choices outlined above imply a number of trade-offs. Using highest school attainment as the underlying measure solves many problems associated with historic enrolment data by allowing the consistent reconstruction of time series of attainment from relatively recent cross-sectional data, but comes with challenges of its own. While nevertheless preferable overall, the principal disadvantage of attainment measures deserves mention, namely the relatively long time lag with which outcomes can be observed. Late attainment is common in many developing countries, so that attainment cannot safely be assumed to be ‘final’ until several years above the typical graduation age.

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