The Impact of Cash Transfers on Women's Empowerment: The Case of the Tanzania Social Action Fund
Bibliographic record
Abstract
Social cash transfer (CT) programs are widely promoted around the world as effective instruments for poverty reduction. Unfortunately, they are rarely examined in a rigorous fashion to determine their purported impact. It is in this context that a mixed method was employed in the present study on a panel data set collected between 2015 and 2017 to examine the impact of the Tanzania Social Action Fund’s (TASAF) CT program on women’s empowerment. Results from a quasi‐experimental design show that TASAF targets poor households and does have an impact within its own sphere. However, the said impact does not spill over outside TASAF domain. These results are corroborated by qualitative data findings used in the study. We thus recommend that TASAF be accompanied by similar programs for a bigger and sustainable impact on women’s empowerment to be realized. Moreover, the study recommends that initiatives such as that of TASAF should go hand‐in‐hand with religious, legal, and cultural reforms.
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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 | 0.005 | 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 | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".