The Behavioural Aspects of Financial Literacy
Bibliographic record
Abstract
In this paper, we investigate the contribution of behavioural characteristics to the financial literacy of UAE residents after controlling for demographic factors. Specifically, we test the relationship between financial literacy and behavioural biases such as representativeness, self-serving, overconfidence, loss aversion, and hindsight bias. Using data collected through survey questionnaires, we apply the methodology developed by the Organization of Economic Co-operation and Development (OECD) to compute financial literacy scores. Our overall results show that all behavioural biases except for overconfidence bias are positively related to financial literacy. Furthermore, some biases exhibit a stronger quantitative relationship with financial literacy than others. For example, hindsight bias displays the strongest link to financial literacy, followed by self-serving bias. The weakest but still statistically significant effect is loss aversion bias. Although biases, in general, have negative connotations, behavioural biases appear to be related to higher levels of financial literacy.
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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 | 0.008 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".