Abstract
This study assesses the sensitivity of the Human Development Index (HDI) using partially ordered set (poset) analysis. Unlike conventional methods, such as correlation, dominance, or simulation-based techniques, poset analysis maintains the multidimensional structure of the HDI and captures cases of incomparability, offering a more nuanced understanding of variable influence. Applying this method to HDI data from 1990 to 2023, the analysis shows that expected years of schooling have been the most sensitive variable historically. However, since 2020, life expectancy has gained prominence, particularly during the COVID-19 pandemic, with the greatest impact observed in very high HDI countries. This shift reveals how external shocks can alter the comparative structure of development rankings. By focusing on structural changes rather than marginal effects, poset-based sensitivity analysis provides valuable insights for refining HDI methodology and informing policy strategies. It offers a robust alternative for evaluating indicator influence without relying on arbitrary weighting assumptions.
| Original language | English |
|---|---|
| Article number | 114964 |
| Pages (from-to) | 1-9 |
| Number of pages | 11 |
| Journal | iScience |
| Volume | 29 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 20 Mar 2026 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- Data analysis
- Social sciences
- Research methodology social sciences
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