On the Development of Probabilistic Projections of Country-level Progress to the UN SDG Indicator of Minimum Proficiency in Reading and Mathematics

As of this writing, there are five years remaining for countries to reach their Sustainable Development Goals deadline of 2030 as agreed to by the member countries of the United Nations. Countries are, therefore, naturally interested in projections of progress toward these goals. A variety of statistical measures have been used to report on country-level progress toward the goals, but they have not utilized methodologies explicitly designed to obtain optimally predic- tive measures of rate of progress as the foundation for projecting trends. The focus of this paper is to provide Bayesian probabilistic projections of progress to SDG indicator 4.1.1, attaining minimum proficiency in reading and mathe- matics, with particular emphasis on competencies among lower secondary school children. Using data from the OECD PISA, as well as indicators drawn from the World Bank, the OECD, UNDP, and UNESCO, we employ a novel combination of Bayesian latent growth curve modeling Bayesian model averaging to obtain optimal estimates of the rate of progress in minimum proficiency percentages and then use those estimate to develop probabilistic projections into the future over- all for all countries in the analysis. Four case study countries are also presented to show how the methods can be used for individual country projections.

• Publication year

  2025

• Venue

  Unknown venue

• Publication date

  2025-11-08

• Fields of study

  Mathematics, Economics, Education

• Identifiers
  arXiv 2511.06107
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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