Analysing the Effect of Education on Income Dynamics in Togo
Bibliographic record
Abstract
ABSTRACT The objective of this study is to analyse the effect of education on the income‐level dynamics in Togo. The data used are pseudo‐panels constructed from survey data from the Unified Questionnaire of Basic Welfare Indicators (QUIBB) of 2006, 2011 and 2015 and the Harmonized Survey of Household Living Conditions (EHCVM) of 2021 of Togo. The methodological approaches used are the generalized moment method and quantile regression for panel data. The main results show that there is no linear relationship between education level and overall income, with the relationship following an inverted U‐shaped curve. However, the results show a positive and significant linear relationship between higher education and income levels. Additionally, investment in education strengthens the impact of education on income. Looking at the income quantiles, there is a significant positive relationship between education and income in the third quantile.
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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 | 0.001 |
| 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 | 0.000 |
| 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".