The COVID-19 pandemic and perceived risks of immigrants in the United States
Bibliographic record
Abstract
Purpose The COVID-19 pandemic has caused hundreds of thousands of people to suffer severe illness or die and has had severe effects on individuals’ financial well-being as well. Unfortunately, it is very likely that the pandemic has had a disproportionate effect, particularly on vulnerable and underserved groups, including immigrants in the USA. This study aims to examine the association between perceived health risk and perceived financial risk attributable to COVID-19, and focuses on their heterogeneous effects depending upon immigrant status. Design/methodology/approach The study used the Understanding America Study (UAS) COVID-19 National Survey data collected from April 2020 to July 2021. Sets of ordinary least squares (OLS) regression and fixed effects regression analyses were conducted on the perceived risk COVID-19 poses on households’ finances. The main focal variables of interest were immigrant status and perceived risk of COVID-19 infection and death. Findings The results showed that the correlation between health risk and perceived financial risk was much higher among first- and second-generation immigrants. Surprisingly, various types of government aid did not have a consistent and significant effect on the recipients’ perception of the risk that COVID-19 poses to their household finances. Originality/value This study is one of the few attempts to empirically examine the association between perceived health risk and financial risk during the COVID-19 pandemic by focusing on the heterogeneity by immigrant status. The authors used an appropriate methodology that considered the panel structure of the UAS COVID-19 National Survey’s data. The study provides important implications for researchers and policymakers related to immigrants’ financial well-being.
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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 | 0.004 |
| 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 | 0.000 |
| 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".