Universal Primary Education and household welfare in Tanzania
Bibliographic record
Abstract
This is an update of Donath et al. (CRP 21/04) which assessed the effect of the 2001 Universal Primary Education (UPE) in Tanzania on the welfare difference between youth (aged 15-35 according to the official definition) and adult (aged over 35) headed households in 2018. As anybody aged over 25 in 2018 would not have derived full benefit of UPE from 2001, this paper examines whether the welfare difference comparing 2001 and 2018 between households headed by youth aged 15-25 and adults (aged over 35) is attributable to differences in educational attainment following the nationwide 2001 UPE. Household budget survey data for 2001 and 2018 are used to estimate the effect of education on household welfare (measured as consumption relative to the poverty line), availing of the fact that youth in 2018 (aged 15-25) will have benefitted from the UPE. Decomposition analysis reveals that the increase in youth educational attainment by 2018 is a significant factor explaining the increase in welfare of youth headed households between 2001 and 2018. If the youth in 2001 had the same education endowment as their 2018 counterparts, their relative welfare would have been about a quarter higher. The findings also show that differences in educational attainment are significant factors explaining differences in welfare between youth and adults in each year. If adults had the same level of educational attainment as the youth, their welfare would have been about a third higher in 2001 and 2018. Expanding access to education had a positive effect on welfare.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".