Bibliographic record
Abstract
This study explores the macroeconomic and social determinants of poverty in the United States and Canada from 1980 to 2023, using the poverty headcount ratio at $4.20/day. It employs the Autoregressive Distributed Lag (ARDL) model and Error Correction Model (ECM) to examine both short-run and long-run dynamics between poverty and six key variables: GDP per capita growth, income share of the bottom 20%, school enrollment, inflation, labor force participation, and government consumption expenditure. The results for the United States indicate strong long-run relationships, with income distribution, education, inflation, and labor force participation showing significant impacts on poverty. The ARDL model explains 95% of the variation in poverty, and the ECM confirms a stable adjustment toward long-run equilibrium. In contrast, the Canadian model explains 58% of the variation, with inflation, income share, and labor market variables showing notable effects, while education and government spending play more modest roles. These differences reflect how national welfare systems and institutional responses to macroeconomic pressures shape poverty outcomes. The comparative analysis highlights how differing institutional settings, Canada’s universal welfare state versus the United States' liberal model, mediate macroeconomic impacts on poverty. The study provides actionable insights for regional policy design, suggesting that enhancing income redistribution, improving educational access, and stabilizing inflation can significantly reduce poverty in liberal welfare regimes such as the U.S., while reaffirming the effectiveness of universalist policies in the Canadian context. These findings underscore the importance of redistributive mechanisms and investment in human capital in mitigating poverty.
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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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".