Doubly disadvantaged: Unemployment, young age, and electoral participation in the United Kingdom
Bibliographic record
Abstract
Previous studies examine how unemployment affects socio-political behaviour, but this literature has scarcely focused on the role of the life-course. Integrating the frameworks of unemployment scarring and political socialisation, we posit that unemployment experiences, or scars, undermine electoral participation, and that this is exacerbated at younger ages. We test these hypotheses relying on the British Household Panel Survey and Understanding Society datasets (1991-2020), employing panel data analysis approaches as Propensity Score Matching, Individual Fixed Effects, and Individual Fixed Effects with Individual Slopes. Results suggest that unemployment experiences depress electoral participation in the UK, with effect sizes around -5% of a Standard Deviation in turnout. However, this effect varies powerfully by age: the impact of unemployment on electoral participation is stronger at younger ages (-21% SD at age 20), and weaker to not significant after age 35. This is robust across the three main approaches and several robustness checks. Further analyses show that the first unemployment spell matters the most for electoral participation, and that for individuals under 35, there is a scar effect lasting up to 5 years after the first unemployment spell. The life-course emerges as central to better understand the relationship between labour market hardships and socio-political behaviour.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".