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| Autori principali: | , , , |
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| Natura: | Preprint |
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2025
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| Accesso online: | https://arxiv.org/abs/2511.06107 |
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| _version_ | 1866909895016579072 |
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| author | Kaplan, David Jude, Nina Harra, Kjorte Stampka, Jonas |
| author_facet | Kaplan, David Jude, Nina Harra, Kjorte Stampka, Jonas |
| contents | 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 predictive 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 mathematics, 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 overall 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_06107 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | On the Development of Probabilistic Projections of Country-level Progress to the UN SDG Indicator of Minimum Proficiency in Reading and Mathematics Kaplan, David Jude, Nina Harra, Kjorte Stampka, Jonas Applications 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 predictive 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 mathematics, 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 overall 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. |
| title | On the Development of Probabilistic Projections of Country-level Progress to the UN SDG Indicator of Minimum Proficiency in Reading and Mathematics |
| topic | Applications |
| url | https://arxiv.org/abs/2511.06107 |