Real Wages, Inflation and Labor Productivity in Canada
Bibliographic record
Abstract
This dissertation seeks to apply the framework of Kumar, Webber, and Perry (2012), who studied the interconnection between the inflation rate, real wages, and labor productivity in Australia to the Canadian context. To attain the primary objective of this research, the study utilized monthly data from January 2000 to 2023, sourced from CANSIM, Statistics Canada. The unit root test showed that all variables were stationary at the first level differential, paving the way for applying the Vector Error Correction Model and Granger causality, which estimate the long and short-run effects. The main finding shows that the inflation rate and real wages positively drive labor productivity; thus, a 1% surge in real wage propels labor productivity to increase by 0.406%, while a 1% rise in inflation rate increases labor productivity by 0.115% in the long run, holding all other factors constant. In the short run, my results indicate a two-way directional Granger causality between the inflation rate and real wage, suggesting that past values of inflation influence current real wages and, reciprocally, past values of real wages impact current inflation. Although this study partially agrees with Kumar et al.’s (2012) findings, the partial differences in both studies could be attributed to the heterogeneous nature of individual economies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".