Evaluating the Influence of Remittance Inflow on the Health and Education Sector for Local Households
Bibliographic record
Abstract
Purpose. The article investigates the extent to which international remittances influence household investment in education and health in Bangladesh, comparing remittance-receiving and non-receiving households. The study aims to determine whether remittances act as a catalyst for human capital development at the microeconomic level. Method. The research adopts an explanatory mixed-method design. Primary data were collected from 400 rural households through a multistage random sampling survey in Hathazari Upazila. Econometric analysis was conducted using multiple regression and Seemingly Unrelated Regression (SUR) models to measure the impact of demographic, economic, and social variables on household investment patterns. Findings. Results show that remittances significantly increase spending on education and health. Age, education level, and gender of the household head, household size, dependency ratio, migrant’s duration abroad, and income level all shape investment decisions. Remittance-receiving households allocate more funds to improving human capital than non-receiving families, demonstrating remittances’ positive welfare effect. Theoretical implications. The study reinforces the remittance-led development hypothesis, providing micro-level evidence that migrant transfers contribute to household-level socio-economic upgrading. Practical implications. Policy measures should focus on formalising remittance flows, integrating migrants into financial systems, and designing incentives for productive investment in education and healthcare.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".