Value-for-Money Analysis of Active Labour Market Programs
Bibliographic record
Abstract
Abstract: Accountability requirements by central agencies in government have imposed expectations on management to show results for resources used — in other words, “value for money.” While demonstrating value for money means showing that the program has relevance and a rationale and that the program logic and theory make sense, the core of value for money lies in showing that a program is cost-effective. Unfortunately, many public programs and policies do not provide quantifiable outcomes, and this limits conclusions on value for money. However, labour market training programs are amenable to cost-effectiveness analysis (CEA), provided that the evaluation methodology meets certain conditions. This article reviews CEA in the context of labour market training, especially programs directed to economically disadvantaged groups. After reviewing the data availability and the analytical methods commonly used to support value-for-money analysis of training programs, the authors present several practice improvements that would increase the “value” and validity of value-for-money analysis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.037 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".