Bibliographic record
Abstract
This study investigates a broad range of factors which might be thought to influence the employment earnings of Canadian males. Micro-data drawn from the 1971 census are analysed, using as a frame of reference the human-capital model derived, and implemented for the United States, by Jacob Mincer. Opening discussion furnishes a detailed critique of the model itself, and of the auxiliary hypotheses required to make it perform empirically. Particular emphasis is laid upon the implicit assumption of perpetual long-run equilibrium and upon the neglect of variables arising on the demand side of the labour market. Generally, it is argued that although the human-capital paradigm may serve as a framework for empirical description, it is inadequate as a scientific theory because it fails to generate a wide array of hypotheses which are clearly susceptible to falsification. Earnings functions are estimated by ordinary least squares for a sample of almost 23,000 out-of-school males who worked, mainly in the private sector, at some time during 1970. Results yielded for Canada by the human-capital specification are compared with those reported by Mincer. The regressions are then expanded to include variables such as industry, region, and occupation, together with other personal attributes. These are found to rival the importance of the orthodox human-capital variables. Contrary to United States results, the elasticity of earnings with respect to weeks (or hours) worked is less than unity. In light of recent analyses which make human-capital investment and labour supply objects of simultaneous decision within a life-cycle context, further investigation is carried out using a simplified, two-equation, linear model in which earnings and hours are both endogenous. Estimates performed by the method of three-stage least squares indicate an elasticity of earnings with respect to hours considerably in excess of unity. However, within particular regional and industrial categories, wages and hours tend to be offsetting. Schooling coefficients, or "rates of return," fall in the 5.25-6.50% range.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".