Determinantes asociados a las desigualdades de género en el desarrollo de competencias financieras : un estudio internacional a partir de los resultados en PISA 2018
Bibliographic record
Abstract
This work identifies the causes that give rise to gender inequalities in the level of knowledge of issues related to the economy and finance, for the countries or economies that participated in the PISA tests in 2018. In addition, it calculates the relative weight of gender on heterogeneity in the results of these tests. For this, the Educational Production Function was estimated by groups [men and women], through the method of Least Squares in Two Stages, to solve the endogeneity problem, and later the Oaxaca-Blinder and Shorrocks Shapley techniques were used. The results obtained here found significant gender inequalities with a higher performance in women than in men, in the cases of Bulgaria and Indonesia, that is, women, on average, have a greater knowledge of economic and financial issues. These inequalities are caused, in some extent, by the differences in initial endowments, specifically, specifically those differences that exist between men and women who do not repeat a grade and / or who actively participate in social networks. In the case of Canada, the United States and Russia, men obtain a higher performance in PISA-Financial 2018, compared to women, due only to unobservable factors that are not attributable to individual, family, or school characteristics. In Chile, the gaps continue in favor of men, but unlike in the previous countries, they are due to unobservable factors of individual characteristics. For Estonia, Italy, Poland, Portugal and Tatarstan, men continue to perform better, but the endowment effect explains this inequality to a greater extent, where the unobserved characteristics of the family originate these differences [Estonia, Italy, and Portugal] and individual characteristics [Poland and Tatarstan]. Finally, it was estimated that individual characteristics determine, on average, 28.27% of the variability in the results of all the countries participating in PISA-Financiero 2018, where gender is constituted as the input with the greatest weight within these characteristics [11.1 % on average].
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".