Firms, Industries, and Unemployment Insurance: An Analysis using Employer-Employee Data from Canada
Bibliographic record
Abstract
The exploration of newly available administrative data in a number of countries has led to a growing realization that a careful study of the interaction between employer and employee characteristics is needed to fully understand labour market outcomes. The objective of this paper is to develop this theme by examining the design of social policy and its interaction with the labour market. The focus is on the Canadian unemployment insurance (UI) program. This analysis uses administrative data on the universe of employees, firms, and UI recipients in Canada over an 11 year period to examine the operation of UI from the perspective of the firm, paying particular attention to longitudinal issues associated with the pattern and causes of cross-subsidies. The findings show that persistent transfers through UI are present at both industry and firm levels. These cross-subsidies are concentrated among a small fraction of firms. An analysis using firm fixed effect indicates that almost 60 percent of explained variation in persistent cross-subsidies can be attributed to firm effects. Calculations of overall efficiency loss are very sensitive to the degree to which firm level information is used. A full appreciation of how social programs like UI interact with the labour market requires recognition of the characteristics and human resource practices of firms, and might be more fruitfully explored by implicit contract models of unemployment.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.016 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.002 |
| 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".