The Impact of Violence on Labour Force Participation and Income in Canada: A Cross-sectional Study with Linked Survey and Tax Data
Bibliographic record
Abstract
Abstract Background Violence across the life course is a persistent global problem with well-documented health and social consequences. Less is known about its relationship with labour market outcomes in high-income countries with strong social protections, such as Canada. This study examines whether lifetime exposure to physical or sexual violence is associated with labour force participation (LFP), reasons for economic inactivity, sectoral and occupational sorting, and income. Methods We analyzed data from the 2018 Canadian Survey on Safety in Public and Private Spaces (SSPPS), a nationally representative cross-sectional survey linked to 2018 administrative tax records. The analytic sample included working-age adults (18–64) with complete data on violence exposure and labour market outcomes. Lifetime violence exposure captured childhood abuse, adulthood non-partner violence, and intimate partner violence. Outcomes included past-year LFP, part- versus full-time work, employment sector and occupation, and annual personal income. We described labour market patterns by gender and exposure and used inverse probability weighted regression adjustment (IPWRA) to estimate average treatment effects (ATEs) on economic inactivity, using unexposed men as the reference group. Results Nearly 62 percent of respondents reported lifetime violence exposure (64.5 percent of women, 59.1 percent of men). Past-year labour force participation was high (85.9 percent overall) and showed minimal differences by exposure status: 82.0 percent of exposed women versus 80.3 percent of unexposed women, and 90.7 percent of exposed men versus 90.1 percent of unexposed men. IPWRA models indicated that, relative to unexposed men, exposed women had small but statistically significant increases in the probability of health-related inactivity (ATE: 0.009; 95%CI: 0.000-0.017) and early retirement (ATE: 0.015; 95%CI: 0.000 to 0.031), whereas ATEs for exposed men were small and non-significant across all outcomes. Sectoral and occupational distributions differed chiefly by gender; within-gender differences by exposure were limited. Income patterns were inconsistent by exposure status. For example, among women with secondary education or less, exposed women earned markedly less than unexposed women ($34,604 vs. $39,913), while differences among men were smaller and uniformly negative (exposed $74,981 vs. unexposed $75,208). Conclusions In Canada’s welfare-state context, lifetime violence exposure shows limited association with labour force participation but may influence specific pathways into inactivity and sectoral sorting. Longitudinal analyses are needed to clarify longer-term economic impacts.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.013 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".