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 (Jurnal Studi Pemerintahan, 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.