The Relationship Between Financial Education in Young Adults and Financial Literacy: A Review of the Literature in Canada and the United States<sup>*</sup>
Bibliographic record
Abstract
ABSTRACT Professional accounting bodies in Canada and the United States, and throughout the world, have funded programs to improve financial literacy for many years. This ongoing interest stems from the expected benefits of improved financial behavior for individuals, society, and financial markets. However, the fact that relatively little research on financial literacy has been published in accounting journals suggests that few accounting academics are aware of, interested in, or motivated to work on the topic. This review helps to identify what we know about the effectiveness of financial education programs intended specifically for young adults, who constitute the demographic group likely to gain the most from improved financial literacy. Our review identifies factors that impinge both on the effectiveness of financial education programs and the research examining these programs. Noteworthy factors include (1) a lack of theoretical frameworks to guide the programs and research on the programs, (2) a tendency for outcome measures to capture financial knowledge rather than financial behaviors, and (3) the need for stronger research designs. We also note some possible instructional design considerations in developing financial education programs and highlight financial literacy as a promising area of research for accounting academics. Our findings can help guide improvements to financial education programs and encourage further research to assess the effectiveness of financial literacy programs.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".