An examination of financial vulnerability among the AAPI population in the United States
Bibliographic record
Abstract
Purpose This study uses data from the National Financial Capability Study to examine the financial vulnerability of Asian American and Pacific Islander (AAPI) adults relative to that of other major racial/ethnic groups in the United States across the past decade and within the AAPI population, examining how vulnerability varied across AAPI adults of East Asian, South Asian, Southeast Asian, and Pacific Islander heritage. Design/methodology/approach The study uses four waves (2012, 2015, 2018 and 2021) of the State-by-State National Financial Capability Study (NFCS) and the 2021 NFCS AAPI Oversample dataset. Financial vulnerability was estimated using five binary indicators: (1) An inability to come up with $2,000, (2) An experience of overdraw, (3) A lack of emergency fund savings, (4) Difficulty paying bills and expenses, and (5) Credit card revolving. A financial vulnerability index was also created using the binary indicators. Logistic regression analyses were conducted on binary indicators and an OLS regression was additionally conducted on the aggregated financial vulnerability index. Findings Results show that, overall, AAPI respondents reported the lowest levels of financial vulnerability relative to White respondents, Black respondents, Hispanic respondents, and those of another race or ethnicity. However, using the 2021 datasets, we found that within the AAPI population, financial vulnerability varied widely by heritage, with those of East Asian heritage reporting less vulnerability than AAPI adults of other studied heritage groups. Originality/value These results provide insights into the financial well-being of AAPI households, particularly amidst the COVID-19 pandemic, and present initial evidence of the significant disparities that exist within this heterogenous community. This study provides valuable insights for researchers, educators, policymakers, and financial practitioners.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".