What is the Relationship Among Wages, Supplementary Labor Income, Unemployment and Productivity?
Bibliographic record
Abstract
This study investigated the relationship between the labor wage (LC), supplementary labor income (SLI), unemployment rate (UN), and labor productivity (LP) of the manufacturing industry in Canada, using time-series econometric techniques.This study was based on the Vector Autoregression Model (VAR) and applied Granger causality Wald tests.Then tested the validity and stability of the model.The results show that all variables did not significantly affect productivity except the first order of itself and labor wages, which indicated that increasing wages improve the passions for work.Moreover, the positive relation between LW and UN presented that the ascending cost of labors decreased the demand of workers, so the unemployment rate raised.However, SIL, which is the difference between LW and labor compensation, ignoring the self-employed income, followed the converse results of LW.The first-order lag of productivity drove a decrease in the unemployment rate.Therefore, policymakers in the manufacturing industry could reconsider the relationships described in this study to adjust productivity and unemployment effectively.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".