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Latent Patterns: Data Analytics to Uncover Economic Data Distortions

Author

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  • Nitin Singh
  • Angshuman Hazarika
  • Bala Gangadhar Thilak Adiboina
  • Ambuj Anand

Abstract

The study evaluates 13 economic indicators across six countries (India, Philippines, Thailand, France, UK, US) from 2000-2023, detecting anomalies, structural breaks, and outlier behaviour. It employs Benford's Law, Grubbs' Test, Chow Test, and DBSCAN on data from the Global Macro Database, using a contingency-based approach to validate anomalies through methodological convergence. Anomalies often correspond with periods of political transition or institutional volatility, emphasising the impact of political risk, institutional fragility, and data governance, especially in developing economies. The study offers a reproducible and scalable methodology for auditing official economic statistics, supported by a review of literature on economic measurement, data analytics, and political risk.

Suggested Citation

  • Nitin Singh & Angshuman Hazarika & Bala Gangadhar Thilak Adiboina & Ambuj Anand, 2025. "Latent Patterns: Data Analytics to Uncover Economic Data Distortions," World Economics, World Economics, 1 Ivory Square, Plantation Wharf, London, United Kingdom, SW11 3UE, vol. 26(4), pages 35-70, October.
    • Handle: RePEc:wej:wldecn:961
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    File URL: https://www.worldeconomics.com/Journal/Papers/Article.details?ID=961
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