Inequality in Housing Payment Insecurity Across the United States During the COVID-19 Pandemic: Who Was Affected and Where?
Bibliographic record
Abstract
Widespread job losses and economic disruptions during the COVID-19 pandemic led to significant housing payment insecurity, disproportionately affecting various demographic groups and regions across the United States (US). While previous studies have explored the pandemic’s impact on housing insecurity, they all focused on specific periods, populations or areas. No study has yet provided a comprehensive analysis of inequality in housing insecurity throughout the pandemic, particularly in terms of spatial disparities. Our study addresses this gap by analyzing individual-level and aggregated data from the Household Pulse Survey (HPS) (N = 2,062,005). The findings reveal heightened vulnerability among individuals aged 40–54, those with lower education and income, Black and Hispanic/Latino populations, women, households with children, individuals who experienced job loss, the divorced, and larger households. Renters experienced greater housing insecurity than homeowners. A hotspot analysis identified the southeastern US as a region of acute housing insecurity, revealing that insecurity cannot be solely measured by affordability. The regression results show that poverty is the main reason for differences in housing insecurity across places, and rent burden is also important. The geographically weighted regression (GWR) model shows stronger coefficients in southern states, highlighting that poverty and rent burden are particularly influential factors in these areas. This study shows the multifaceted nature of housing insecurity, calling for targeted group or location policy interventions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".