Crowding-out effects of regional transfer fund allocations on local development based on a Bayesian VAR study in West Sumatra
Abror Abror et al.
What the paper says
Type of the article: Research ArticleAbstractRegional 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.AcknowledgmentsThis 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.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.