Unemployment and health in context and comparison : a study of Canada, Germany, and the United States of America
Bibliographic record
Abstract
This thesis explores how societal-level factors influence the relationship between unemployment and health. Using the Varieties of Capitalism (VOC) framework, hypotheses are developed that specify how this relationship may vary across high-income countries. Economies of high-income countries are grouped into coordinated market (CMEs) and liberal market (LMEs) economies that have different production specializations, but similar economic growth and aggregate levels of wealth and which are supported by different economic and labour market institutions. I hypothesize that these institutional differences give rise to different risks, types and durations of unemployment. After controlling for these differences, it is hypothesized that the higher levels of unemployment protection in CMEs will mediate the effect of unemployment on health compared to LMEs and that there will also be an interaction between skill level and unemployment and health. Two empirical studies are conducted to test these hypotheses using longitudinal micro-data from representative LME (Canada and the United States) and CME (Germany) countries. The first study examines the relationship between unemployment and mortality for Germany and the United States. The risk of death for the unemployed is higher in the United States compared to Germany, especially for the minimum- and medium-skilled unemployed. In Germany the risk of death for the unemployed is concentrated among East Germans. The second study examines the relationship between unemployment and self-reported health status for Canada, Germany and the United States. Across all countries unemployment is associated with poorer self-reported health status, but there is marked effect modification by educational status and by receipt of unemployment compensation. In particular, there is no association for the high-skilled unemployed in the United States, but for minimum- and medium-skilled unemployed those not receiving unemployment compensation have the highest risk of poorer self-reported health status. Policy makers should consider the effect on the health of the unemployed when designing programmes for the unemployed. Future research needs to examine the role that social programmes and in particular public transfers have in reducing health inequalities, not only among the unemployed, but also among workers in other work arrangements that may be harmful to their health.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".