Crowding-out effects of regional transfer fund allocations on local development based on a Bayesian VAR study in West Sumatra

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Type of the article: Research Article

Abstract
Regional transfers are a key instrument of regional fiscal policy that promote balanced development and reduce disparities across districts, yet the behavioral responses of local governments to these inflows remain insufficiently understood. This study examines how regional transfer fund allocations influence government expenditure, unemployment, infrastructure development, and regional revenue in West Sumatra using quarterly data for 2014–2024. A Bayesian Vector Autoregressive framework is employed to address small-sample limitations and to capture the dynamic responses to transfer shocks. The results show that increases in regional transfers have limited, short-lived effects on unemployment, while capital expenditure on basic infrastructure declines, indicating potential crowding-out of certain government spending categories. At the same time, regional revenue responds positively, suggesting that transfers can support local fiscal capacity in the short term. These findings highlight that, although regional transfers can facilitate immediate fiscal stabilization, they may hinder long-term infrastructure investment unless accompanied by performance-based fiscal mechanisms. Improving transfer design and accountability is therefore essential to ensure that fiscal resources promote sustainable development outcomes.

Acknowledgments
This research is a grant from the Ministry of Higher Education, Science, and Technology of the Republic of Indonesia under the Impactful Leading Consortium Research Scheme (RIKUB Scheme) in accordance with research contract number 008/C3/DT.05.00/RIKUB/2025.

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    • Figure 1. Quarterly period of government expenditure in consumption, open unemployment rate, regional revenue, and basic infrastructure in West Sumatra
    • Figure 2. Impulse response functions of the regional transfer fund for lag-1 and lag-2
    • Figure 3. Posterior stability (convergence) of impulse response function (IRF) estimation
    • Table 1. Lag selection and information criteria
    • Table 2. BVAR model comparison metrics
    • Table 3. Posterior median of coefficients
    • Table 4. Posterior median of variance-covariance matrix
    • Table 5. Simultaneous, partial, and instantaneous Granger Causality tests
    • Conceptualization
      Abror Abror, Dodi Devianto, Sri Maryati, Muhammad Irfan
    • Formal Analysis
      Abror Abror, Dodi Devianto, Sri Maryati, Muhammad Irfan, Mutia Yollanda, Primawati Primawati, Ratnawati Raflis
    • Investigation
      Abror Abror, Dodi Devianto, Sri Maryati, Muhammad Irfan, Mutia Yollanda
    • Methodology
      Abror Abror, Dodi Devianto, Sri Maryati, Mutia Yollanda, Primawati Primawati
    • Project administration
      Abror Abror, Mutia Yollanda, Ratnawati Raflis, Medi Iswandi
    • Supervision
      Abror Abror, Muhammad Irfan
    • Writing – original draft
      Abror Abror, Dodi Devianto, Sri Maryati, Mutia Yollanda, Primawati Primawati
    • Writing – review & editing
      Abror Abror, Dodi Devianto, Sri Maryati, Muhammad Irfan, Mutia Yollanda, Primawati Primawati, Ratnawati Raflis, Medi Iswandi
    • Data curation
      Dodi Devianto, Sri Maryati, Mutia Yollanda, Primawati Primawati, Ratnawati Raflis, Medi Iswandi
    • Funding acquisition
      Dodi Devianto
    • Software
      Dodi Devianto, Mutia Yollanda, Primawati Primawati
    • Validation
      Dodi Devianto, Sri Maryati, Muhammad Irfan, Mutia Yollanda, Primawati Primawati, Ratnawati Raflis, Medi Iswandi
    • Visualization
      Dodi Devianto, Sri Maryati, Mutia Yollanda, Primawati Primawati
    • Resources
      Muhammad Irfan, Ratnawati Raflis, Medi Iswandi