Estimation of the Economic Opportunity Cost of Labour: An Operational Guide for Ghana
Bibliographic record
Abstract
The implementation of projects often affects employment through direct job creation, indirectly stimulating employment or augmenting labour supply. These changes in employment have significant benefits and costs to both labour and society. However, the estimation of job creation benefits is complicated because of the large diversities in labour input. We attempt to address this issue by using the supply price approach to develop an analytical framework based on sound microeconomic principles to assist project analysts to arrive at justifiable empirical estimates of the economic opportunity cost (Кℓ) for a wide range of labour types across a set of diverse situations and market conditions in Ghana. The paper adopts the relevant literature regarding the specifics of labour markets and the peculiarities of different labour types. Accordingly, the Кℓ will vary by skill, location, and labour market conditions that need to be incorporated into its estimation. In this analysis, the estimation has been carried out to quantify the Кℓ, the conversion factor, as well as the labour externalities corresponding to the two types of labour: skilled and unskilled. Similarly, these estimates refer to groups of labour according to areas of residence: rural and urban.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.007 |
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".