Data · dataset · 2026
SDG Connectedness
Listed in Teesside University Research Data Repository
Description
This dataset is a country-year panel of 193 countries, 2000-2025 (5,018 observations), combining Sustainable Development Goal (SDG) scores for all 17 goals, five sustainability-pillar composites (People, Planet, Prosperity, Peace, Partnership), population weights, geographic and legal-origin controls, and country classifications (income group, G-20 membership, Emerging Market status, income-classification stability). SDG scores come from the Sustainable Development Report's Backdated SDG Index; population from World Bank WDI; legal origin from La Porta et al.; income/G-20/Emerging Market classifications from World Bank and MSCI sources.
The data was assembled to test whether SDGs are equally informative about overall development progress, and whether any such structural centrality generalizes across country groupings and over time. A two-stage Shapley R^2 decomposition (run year-by-year, since the panel has far more countries than years) estimates how much each pillar/SDG's variation explains, and is explained by, the others; a cluster bootstrap (resampling countries with replacement) tests how sensitive these estimates are to sample composition.
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Key findings: Prosperity (SDGs 7-11) is structurally central in every year, in every country classification, and under bootstrap resampling -- the most robust pattern in the data. People (SDGs 1-5) is central in the full sample but not within the G-20 or High-income countries, and its bootstrap confidence interval includes zero, unlike Prosperity's. SDG 3 and SDG 9 show Prosperity-level robustness; SDG 4 and SDG 6 do not, despite similar raw year-counts.
Planet and Partnership are consistently peripheral; several SDGs weak globally (SDG 1, 5, 7, 8) are central within specific groupings. Interpretation notes: centrality is a statistical, not causal, property; results should be checked across country subsamples and under population weighting before generalizing; bootstrap-based robustness is more informative than raw year-counts; known gaps (Saudi Arabia's missing goal1/goal10, 2000 population, 14 countries' legal origin, 14 countries' classification stability) should be handled via listwise deletion, not imputation.
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Links
Where it is published
- DOI doi.org/10.17632/w7dzgvgtck.1 ↗
DOI / persistent id · from researchdata tees ac uk
Catalogue records · 1
- OAI-PMH record data.mendeley.com/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Adata… ↗
metadata API · from researchdata tees ac uk
Topics
- From keywords
- Earth & Environmental Science
Provenance · 1 source records, 6 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| Teesside University Research Data Repository | oai:data.mendeley.com/w7dzgvgtck.1 | 16 h ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| description | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/description |
| license | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/rights |
| publication_date | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| title | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/title |