An Investigation of Social Safety Net Programs as Means of Poverty Alleviation in Bangladesh
Bibliographic record
Abstract
Bangladesh is a developing and poorer country in the world. The 2010 Household Income and Expenditure Survey (HIES) indicates around 31.6 percent of its population lives under the national poverty line. This has led to the implementation of many social safety net (SSN) programs to address the issue of poverty. In the fiscal year 2009/10, the Bangladesh government allocated 15.22 percent of total budget for SSN program that accounts to 2.52 percent of Gross Domestic Product (GDP). The main objective of this paper is to assess the impact of the SSNs programs on level of poverty reduction in Bangladesh. The study employs time series analysis on the 1996 – 2010 spending on SSN and poverty rate data. Statistical analysis indicates negative relationship between SSN expenditure and poverty rates. This implies that SSNs programs have reached the struggling poor as well as have helped the deprived part of the country’s people to pick them up of poverty situation.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".