Unemployment, Unemployment Protection, and Health in the Era of Neoliberal Welfare State Retrenchment
Bibliographic record
Abstract
Research in the field of public health has generated a broad consensus that the organization of the welfare state has a major influence on the distribution of health within and across populations. By and large, extant contributions to this body of scholarship have adopted a relatively static view of the welfare state. Yet, due to the rise of neoliberalism and its attendant political consequences, contemporary welfare state arrangements differ in important respects from the prevailing regimes of the past. In fact, over the last several decades, governments in a vast majority of advanced capitalist countries have undertaken substantial efforts to reduce the scope and generosity of their social protection systems. From a public health standpoint, these developments raise important questions concerning the extent to which neoliberal-era welfare state policies remain effective levers with which to protect population health and promote health equity. In the present dissertation, I pursue this line of inquiry with specific reference to the neoliberal-era connections between unemployment, unemployment protection, and health in two retrenched welfare states: Canada and Germany. Through a series of empirical studies, I show that: (i) health inequalities between employed and unemployed workers are widening over time; (ii) unemployment benefits play an important role in protecting workers against the adverse health consequences of unemployment; and (iii) the neoliberal retrenchment of unemployment benefits has negatively impacted the health of unemployed workers. Taken together, my findings implicate the neoliberal restructuring of the welfare state as a significant factor contributing to adverse trends in the health of the unemployed and, by extension, as a driving force behind widening unemployment-related health inequalities. These insights, in turn, add empirical weight to growing political demands for the expansion of the welfare state. Beyond illustrating the value and importance of adopting a dynamic view of the welfare state determinants of health, this dissertation makes a contribution to outstanding efforts on the part of public health researchers and practitioners to tackle the problem of persistent health inequalities in our neoliberal times.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".