Lost in Transition: The Costs and Consequences of Sectoral Labour
Bibliographic record
Abstract
This paper demonstrates that factors impeding labour market adjustments can have first-order impacts on aggregate output and social welfare. While several studies find that individual workers can face large and persistent sectoral reallocation costs, this paper shows that these costs are important at the aggregate level. Using a search and matching model, I quantify and isolate two factors that contribute to the costly and time-consuming adjustment process: search frictions and the inability to transfer skills to new jobs. I apply the model to examine Canada’s labour adjustment after a global increase in commodity prices and associated exchange rate appreciation. These developments re-organized production to the resource sector and away from manufacturing. The model quantitatively captures both the sectoral employment and wage effects and the response of unemployment to changes in unemployment benefits. The model estimates that the costs of adjustment are economically important, accounting for up to three percent of output during the transition. These costs arise mainly in the first three years of adjust-ment and are due largely to non-transferable skills. Finally, the analysis reveals that changes to unemployment benefits impact the economy’s sectoral composition, aggre-gate productivity and the speed of adjustment.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".