Bibliographic record
Abstract
Purpose – The purpose of this paper is to investigate the connection between labour supply and the wages of married women of different ages in Toronto using data from the 2010 Labour Force Survey of Canada. Design/methodology/approach – The authors employ three econometric techniques, ordinary least square, 2 stage least square and the Heckman two-step method to estimate the supply elasticities. The first two focus on the wage rate and hours conditional on the subjects being employed whereas the third method controls for sample selectivity bias by including the unemployed. Bootstrap test statistics are produced when the normality assumption for the error terms is found to be violated. Findings – The aggregate labour supply elasticity for married women in Toronto is estimated to be 0.053 which similar to value found for Canada for a whole in a previous study even though Toronto is much more diverse culturally than average. The labour supply elasticities for 25-34 year old and 35-44 year old married are estimated to be 0.108 and 0.079, respectively. The supply elasticity for married women aged 45-59 is not significantly different from 0. Originality/value – The paper shows that younger married women in Toronto are more responsive to an increase in wages than older women. The estimation procedure and the testing of the significance of coefficients are more rigorous than previous studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".