A MACROECONOMIC STUDY OF THE COSTS, CONSEQUENCES AND POLICY IMPLICATIONS OF SECTORAL LABOUR REALLOCATION
Bibliographic record
Abstract
This thesis uses a macroeconomic approach to study labour adjustments following sector-specific shocks. I develop a general model, investigate its dynamic adjustment process and apply it to study the Canadian economy in 2002–2006. This episode is an interesting case study because it features a significant labour reallocation to the resource sector and away from manufacturing, precipitated by an increase in global commodity prices and an associated exchange rate appreciation. The results establish that impediments to the adjustment process are economically significant in the aggregate for this episode, imposing costs of up to three percent of output during the transition. These findings augment several studies that suggest individual workers can face large and persistent earnings losses during job turnover. However, unlike previous research, I use the search and matching approach — which incorporates explicit labour market frictions — to uncover the sources of these costs for the macroeconomy. The findings emphasize that job loss itself is not particularly important quantitatively, but rather the non-transferability of skills during job turnover is a key concern.
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 | 0.001 | 0.003 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".