Outcome-Based Budgeting and Infrastructure Delivery in Emerging Economies: Evidence from Subnational Fiscal Reform in Nigeria
Bibliographic record
Abstract
Outcome-based budgeting (OBB) has been a popular financial management reform that tries to enhance the relationshipbetween public spending and development performance and outcome, especially in infrastructure provision. In this paper,the authors analyze the usefulness of using outcome-based budgeting as a means of enhancing infrastructure provisionat the subnational level in emerging economies based on the experiences of fiscal reform in Nigeria. The study is basedon the literature on the subject of financial management in the community, fiscal decentralization, and the governance ofinfrastructure, which is why it develops an analytical model that is institutional and descriptive based on the secondary fiscaland infrastructure performance information provided by the subnational governments. The analysis shows that outcomebasedbudgeting can increase the efficiency of allocations, capital budget execution, and accountability in the delivery ofinfrastructure with the help of plausible revenue models and performance monitoring solutions. Nonetheless, structuralchallenges, such as the excessive reliance on intergovernmental transfers, cyclicality of the fiscal, and limited subnationaladministrative capacity limits the performance of OBB. The results indicate that outcome-based budgeting may play asignificant role in the enhancement of better infrastructure results, but its effectiveness requires such complementaryreforms as revenue mobilization, institutional capacities development, and transparency systems. The paper presents thepolicy implications to the emerging economies that are willing to use budgeting reforms to bridge the chronic infrastructuredeficits in the decentralized systems of governance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".