Machine Learning–Driven Governance: Evaluating Village Fund Effectiveness with NLP and Satellite Data Across 74,000 Indonesian Villages - Meiyenti, Ira; Mualim, Wildan Maulana Assani; Dole, Mariano Aloyciandro (Department of Government Affairs and Administration, Universitas Muhammadiyah Yogyakarta, 2026)
Does fiscal reporting transparency help make Indonesia's Village Fund (DD) more effective in driving local economic growth? We test this core question using a Second Generation Fiscal Federalism framework, arguing that development success depends not on transfer volume, but on governance quality that mitigates agency problems between Central Government and Village Heads. Methodology: Using two-way fixed-effects regression on 704,150 village-year observations (2015–2024), we measure local economic growth via satellite Night-Time Light (NTL) data and transparency via IndoGovBERT analysis of 312,000 village financial reports. Key Finding: Transparency plays a decisive role in Village Fund effectiveness. A one-standard-deviation increase in transparency amplifies NTL growth responsiveness to fund allocation by 14.2% (β = 0.142; p < 0.001). This confirms that improving governance can unlock substantial development gains. Primary Contribution: This research provides large-scale empirical evidence that governance quality, measured objectively through machine learning, substantially enhances fiscal transfer effectiveness. We document a quantifiable 14.2% efficiency dividend from improved transparency, validating SGFF theory. Policy Implication: Results support integrating IndoGovBERT transparency scoring into Siskeudes platforms and implementing conditional transfer mechanisms linking disbursement to accountability performance, particularly urgent given the 2024 Village Head tenure extension.