Summary Of: The Impact of Macroeconomic Conditions on the Instability and Long-Run Inequality of Workers' Earnings in Canada
Bibliographic record
Abstract
This article summarizes findings from the research paper entitled: The Impact of Macroeconomic Conditions on the Instability and Long-Run Inequality of Workers' Earnings in Canada. This paper examines the variability of workers' earnings in Canada over the period 1982-1997 and how earnings variability has varied in terms of the unemployment rate and real gross domestic product (GDP) growth over this period. Using a large panel of tax file data, we decompose total variation in earnings across workers and time into a long-run inequality component between workers and an average earnings instability component over time for workers. The analysis is done for men and women and for both long-run participants and a broad coverage of workers. We find an increase in earnings variability between 1982-1989 and 1990-1997 that is largely confined to men and largely driven by widening long-run earnings inequality. Second, the pattern of unemployment rate and GDP growth rate effects on these variance components is not consistent with conventional explanations of cyclical effects on earnings inequality and is suggestive of an alternative paradigm of how economic growth over this period widens long-run earnings inequality. Third, when the unemployment rate and GDP growth rate effects are considered jointly, macroeconomic improvement is found to reduce the overall variability of earnings as the reduction in earnings instability outweighs the general widening of long-run earnings inequality.
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 | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".