Perspectives on Unemployment from a General Equilibrium Search Model
Bibliographic record
Abstract
Australia has experienced a varied track record on unemployment. For the third quarter of the 20th century unemployment averaged 2.0 per cent. This is bracketed by average unemployment rates of 8.6 and 7.4 per cent in the second and fourth quarter centuries. Explanations of this phenomenon vary. In this paper we explore supply side explanations using a model developed by Ljungqvist and Sargent (LS). We adapt the LS model to the Australian tax and welfare system and calibrate it to the Australian economy. Two simulation experiments are considered. In the first we study the effect of varying the unemployment benefit on the level and composition of unemployment. In the second simulation we examine the effects of increasing the degree of turbulence experienced by the economy. In the former simulation we find that: raising benefits causes a rise in the duration of unemployment; unemployment rates rise; across voluntary and involuntary unemployment classes; the rise is relatively larger in the range of low skill workers whose job-search intensity falls the greatest. Job-search intensity of voluntarily unemployed workers does not change with benefits; and reservation wages of individuals with high skill levels are unaffected by unemployment benefits but the reservation wage low skilled workers increases with the unemployment benefit. In the second simulation increasing turbulence in the economic environment causes an increase in total unemployment and in involuntary unemployment. However, voluntary unemployment falls, because people alter their reservation wages and search intensities in response to increased turbulence; overall the average duration of unemployment rises. Finally, we replicate the LS finding that the adverse consequences of increased turbulence are larger in economies with more generous welfare systems. We interpret the findings reported above as suggesting that the LS model is a useful tool of analysis and in the final version of the paper we propose to calibrate the model to the changes in the level of unemployment benefits and the progressivity of the income tax schedule that occurred towards the end of the third quarter of the 20th century. Our objectives will be to quantify how much of the change in unemployment can be attributed to these factors and to quantify the extent to which higher unemployment is attributable to increased turbulence.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".