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Structural Analysis of India SDG Scores: Time series data analysis with clustering and triple correlation coefficients

Author

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  • Yukari Shirota
  • Basabi Chakraborty

Abstract

In this work, the structural relationship among 17 SDG goals have been analyzed using India's state-specific SDG scores. The objective of this study is to find out the area where the government should invest in order to improve the overall SDG’s achievement efficiently. We have used popular hierarchical clustering and the triple correlation coefficient proposed and developed by the authors. Most multivariate data analysis methods in current statistics are based on covariance or correlation coefficient. Covariance and correlation coefficients deal only with the relationship between two variables and do not simultaneously calculate relationship among more than two variables. In this paper, we propose a triple correlation coefficient that shows the relationship among three variables at once and analyze the structure among SDGs achievements using the triple correlation coefficient. The analysis reveals that the fourth SDG, improving the level of education, has the broadest and strongest relationship with the other goals.

Suggested Citation

  • Yukari Shirota & Basabi Chakraborty, 2025. "Structural Analysis of India SDG Scores: Time series data analysis with clustering and triple correlation coefficients," Gakushuin Economic Papers, Gakushuin University, Faculty of Economics, vol. 61(4), pages 247-265.
    • Handle: RePEc:abc:gakuep:61-4-2
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