Artificial Intelligence as a tool for fiscal sustainability in subnational governments
DOI:
https://doi.org/10.29393/GP10-1HSJM30001Keywords:
Artificial Intelligence, fiscal sustainability, subnational governments, tax managementAbstract
The fiscal sustainability of subnational governments in Mexico faces structural challenges, including low own-source revenue collection, dependence on federal transfers, and inefficient public spending management. The study adopts a quantitative, descriptive, and comparative approach, based on the analysis of official secondary sources for Mexico’s 32 states. A Fiscal Sustainability Index with Artificial Intelligence (ISF-AI) was constructed, comprising four dimensions: digitalization, citizen interaction, institutional barriers, and own-source revenue. The weights were defined based on their theoretical relevance in the literature on digital fiscal governance. The results reveal wide regional gaps in levels of digitalization and fiscal sustainability. Most states are situated at medium or low levels of digital development, which limits their potential for adopting AI to enhance revenue collection and financial autonomy. These differences suggest that the impact of AI on fiscal sustainability is more closely tied to institutional capacity and technological infrastructure than to the availability of automated tools. The study concludes that AI represents an opportunity for innovation for subnational governments, but its implementation requires comprehensive strategies for institutional strengthening, transparency, and responsible digital governance. The main contribution of the study is the development of an index as a comparative tool to evaluate the digital tax maturity of the federated entities.
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