Impact of Socioeconomic and Macroeconomic Conditions on the Financial Literacy of Students Assessed by Pisa
Bibliographic record
Abstract
This study aimed to investigate the relationship between students’ performance on the PISA financial literacy test in 2012, 2015, and 2018, with the socioeconomic characteristics of the participants and macroeconomic indicators of their countries of origin. The average PISA performance in financial knowledge of students from 30 OECD member and partner countries in the years was analyzed. Based on mean difference tests and multivariate panel regression models, it was concluded that the socioeconomic and macroeconomic context in which the student is inserted is directly related to their financial knowledge. Students who were native, economically favored, and residents of large cities showed better performance and a statistically higher average score compared to immigrant students, low-income students, or residents of medium or small cities. It was also found that the financial performance of these students is impacted by the countries’ income inequality indices, negatively influencing those with the worst distributions.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| 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".