Multidimensional taxonomy of fiscal vulnerability in mining regions: An approach based on differential predictability

This study develops an innovative framework to assess fiscal vulnerability in regions dependent on mining resources in Peru. Using data from 25 departments (2015-2023), this study constructed a multidimensional taxonomy that integrates dependency, volatility, predictability, and temporal trends. The results reveal substantial differences in predictability between resources: Mining License Fees show high predictability (R²=0.953), contrasting with Mining Royalties (R²=0.497). This study identified three distinctive regional profiles where Ancash exhibits the highest fiscal vulnerability (0.723). The practically null correlation (0.02) between dependency and volatility confirms that they are independent and complementary dimensions for assessing fiscal risks. The proposed framework allows the identification of regions requiring priority interventions and offers analytical tools with applicability in similar mining contexts.