Australian labour supply elasticities: Comparison and critical review
Bibliographic record
Abstract
Labour supply elasticities measure the responsiveness of individuals' labour supply to changes in variables such as the net wage rate (after consideration of tax and transfers) or net non labour income. Elasticities have been estimated in Australia and internationally using a range of modelling approaches. However, as indicated in previous surveys, caution should be exercised when comparing these estimates, with due consideration of differences in data, methodological approaches and model specifications. When comparing estimates between countries, the institutional framework and state of the labour market in each country also need to be considered. This paper draws on reviews of elasticity estimates in the literature and considers factors affecting their estimation and interpretation. The paper then summarises the published labour supply elasticity estimates from Australia and discusses what can be learnt from them. Comparisons are also made with estimates from labour supply studies from the United Kingdom, Canada and New Zealand.Elasticity estimates in the reviewed labour supply studies aid our understanding of the labour supply responses of various Australian population groups. Elasticity estimates are particularly useful when disaggregated, as they allow an understanding of the relative responses of different population groups characterised by education levels, part-time or full-time employment status, level of income, or other household characteristics. However, few studies have estimated disaggregated elasticities and this is an area that could benefit from further research. Our understanding of labour supply behaviour could also benefit from an analysis of how elasticities may change over time, and from further improvements in modelling methodologies and specifications. This would help to identify population groups that are responsive to changes in net wages and incomes, and thereby strengthen the basis for policy development.
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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 | 0.012 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.026 | 0.026 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".