Employment Dynamics in Regional Labour Markets: An Application of Gross Flows Analysis
Bibliographic record
Abstract
This paper uses gross flows data for regions to show how the chance of leaving employment varies from place to place within New Zealand and how this risk of leaving employment influences subsequent search behaviour. We define labour market risk as the failure to sustain a continuous income stream through employment. Estimates of employment risk are made by applying a linear logit model to selected transition probabilities estimated from a quarter to quarter gross flows matrix constructed from New Zealand Household Labour Force Survey returns for the 14 year period 1986to 1999. We show how the risk of employment separations increase as the size of regional labour markers declines and their demand for labour weakens and how the diminished opportunities for employment in the peripheral regions encourages active rather than passive searching among those who leave employment. In regions with relatively high labour demand leaving employment is more likely to be followed by withdrawal from the labour force. By contrast, labour leaving employment in the weaker, provincial, labour markets is more likely to be followed by active searching (and hence unemployment). The way in which employment risk modifies search behaviour across the country affects the unemployed rate, raising it in weak markets and lowering overstating it in strong markers both temporally and geographically.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".