Performance-based budgeting in Canada: Assessing the association between past performance and subsequent resource allocation
Bibliographic record
Abstract
Performance-based budgeting (PBB) is a common performance management practice throughout OECD countries where performance information is directly or indirectly linked to resource allocations in the budgetary process. Canada has had various systems of PBB in place since at least 1969, with the most recent changes being implemented in 2016. Despite these recent changes, few studies have examined the allocative efficiency of Canada’s PBB system, which purports to allocate resources in a way that optimizes performance (TBS, 2016). Using panel data spanning eight fiscal years from 2014-15 to 2021-22, this study aims to measure the correlation between past performance and subsequent resource allocation at the organizational level and provide recommendations to improve PBB processes. The analysis found that for every one percentage point increase in average organizational performance, an additional 0.23 percentage points of spending was allocated in the subsequent budget, demonstrating a modest but statistically significant level of allocative efficiency. This result was not found for staffing allocations. These analyses provide some preliminary findings in the Canadian government context which support theories about the allocative efficiency of PBB. These findings differ from the results of an earlier study of PBB in Canada conducted by the Office of the Parliamentary Budget Officer (PBO) in 2014, which found no statistically significant correlation between performance and resource allocation at the organizational level. This study concludes by providing some recommendations to strengthen Canada’s PBB systems, including recommending Canada’s approach to PBB as an example of creating allocative efficiency, promoting the use of performance information in budgetary decision-making and politics, providing consistent and full public data to scholars and budgetary oversight bodies like the PBO for better analysis, and maintaining more consistent indicators across multiple fiscal years.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".