The role of non-participants in labour market dynamics
Bibliographic record
Abstract
The unemployment rate fluctuates considerably over the business cycle. Most mainstream macroeconomic models treat movements in the unemployment rate as being driven by flows between two states of labour market participation – employment and unemployment. In this paper, we examine the influence of a third labour-market state – those not in the labour force (or non-participants). These are working-age individuals who are neither officially employed or officially unemployed, such as non-working university students, stay-at-home parents, and early retirees. We explore the role for non-participants in determining labour market outcomes using a dataset called gross flows. Gross flows measure the total number of people who transition between each labour market state each quarter. We find that flows via non-participation account for about two-thirds of the movements in the unemployment rate, which is much higher than in comparable international studies. This suggests non-participants influence the labour market in New Zealand. We find that there is a large pool of potential workers among non-participants, and that this pool of potential workers influences wage determination. Overall, our findings suggest that non-participants can add significant value to our understanding of labour market dynamics.
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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".