From struggle to strain: effects of financial distress on household vulnerability to poverty
Bibliographic record
Abstract
Purpose This study explores the impact of financial distress on household vulnerability to poverty in Canada. Previous studies primarily focus on assessing financial stress using one or two indicators, such as mortgage and non-mortgage debt. Moreover, there is limited discussion on the association between financial distress and poverty dynamics. Households that are currently non-poor may become poor in the next period and vice versa. Our objective is to explore the presence of financial stress, poverty dynamics and their associations in Canada. Design/methodology/approach We construct a Financial Distress Index utilizing five important financial indicators and employ Polychoric Principal Component Analysis (PPCA). Our index is rescaled to a bounded range of 0.1–1, where higher numbers reflect more financial stress. Additionally, we develop a vulnerability to poverty matrix based on households' socioeconomic characteristics. We employ both fractional probit and a dichotomous probit model to examine how financial distress and other factors could influence poverty dynamics in Canada. Findings Our quantitative analysis suggests that among Canadian households, higher financial distress raises the risk of falling into poverty or remaining below the poverty threshold in the future. A one-unit increase in financial distress corresponds to a 17% higher likelihood of vulnerability to poverty. Additionally, age, gender, education, family size, family type, employment, source of income, assets and type of assets are significant predictors of vulnerability to poverty in Canada. Interestingly, the relationship between education and vulnerability to poverty is not linear. Originality/value The literature examining the link between financial stress and poverty is scant for developed countries, and Canada is no exception. However, our study uniquely contributes to the literature by developing a Financial Distress Index and measuring its influence on poverty dynamics in Canada. Peer review The peer review history for this article is available at https://publons.com/publon/10.1108/IJSE-12-2023-0977.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".