Non-linear dynamics of employment, output and real wages in Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose – The purpose of this paper is to examine the relationship among employment, real wage, and output growth in Canada. Design/methodology/approach – Using quarterly data from 1994q2 to 2012q3, this paper employs a vector autoregressive framework while allowing for the derivation of output from its historical maximum over the sample period to affect future output, employment, and real wage growth dynamics. Findings – There are three main findings: output growth is significant in predicting employment growth and vice versa; real wage growth neither Granger causes employment growth nor output growth, but employment growth Granger causes real wage growth; and non-linear dynamics, captured by the current depth regression (CDR) effect term, through the sign as well as the magnitude of output changes, are important in characterizing the evolution of the relationship among output, employment, and real wage growth. Practical implications – The findings of this research have significant implications for policy makers. Output and employment growth are important in forecasting each other in Canada. In contrast to the mainstream theory, real growth is insignificant in explaining the future dynamics of employment in Canada. Policies need to be formulated to encourage the growth of employment to ensure sustain output growth. Originality/value – This study examines empirically the real output, real wage, and employment link in Canada. This study uses the most recently revised GDP data arising from the 2012 Historical Revision of the Canadian System of National Accounts. The econometric methodology involves the standard vector autoregression (VAR) model to which the authors introduce non-linear dynamics through a term that controls for the deviation of output from its preceding historical maximum: the CDR effect.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it