Bibliographic record
Abstract
Recent research has demonstrated that some households cut back on expenditures in an unemployment spell. Moreover, some of these households respond to variation in the transitory income provided by unemployment insurance benefits. This suggests that these households are constrained in the sense that they respond to variations in current income even if these do not have any permanent impact. In this paper we take up the question of how households in temporarily straitened circumstances cut back and how they spend marginal dollars of transfer income. Our theoretical and empirical analysis emphasises the importance of allowing for the fact that households buy durable as well as non-durable goods. The theoretical analysis shows that in the short run households can significantly cut back on total expenditures without a significant fall in welfare if they concentrate their budget reductions on durables. We present an empirical analysis based on a Canadian survey of workers who experienced a job separation. Exploiting changes in the unemployment insurance system over our sample period we show that cuts in UI benefits lead to reductions in total expenditure with a stronger impact on clothing than on food expenditures. These effects are particularly strong for households with no liquid assets and/or households in which the lost income was ‘important’ for the household. These findings are in precise agreement with the theoretical predictions.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 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".