Leaving Country for Living: Household Level Welfare Assessment from the Destination Preference Lens in Bangladesh
Bibliographic record
Abstract
Down memory lane of the economy of Bangladesh, international migration has been a pillar to the economy. Firstly, the study deals with the factors affecting destination preference of the migrant-sending household and then it tries to screne out the impact of international migration on the household welfare from the lens of diversified destination preferences. Considering sample size of 3782 household, the study conducted the entire research with the secondary data of Household Income and Expenditure Survey Bangladesh, 2016. Sorting the migrated countries among seven regions, Multinomial Logistic Regression has been used to find out the determinants behind migrants’ destination preferences. Additionally, to measure the household welfare based on migrant’s destination preference, the Ordinary Least Squares regression model and Quantile regression model have been used. Therefore, the result exhibits that migrant characteristic like age, gender, years of schooling, and household characteristics like heads’ age, sex, schooling year, region, and earning status plays a significant role in deciding the migration destination. It is also evident that economic and subjective welfare varies among the households for sending migrants in different regions. Total expenditure and wealth index decrease to the households who send migrants to South-East Asia rather than Middle-East. The wealth score is higher for the households who send migrants to Europe, North-America, and Oceania over Middle-East. Subjective welfare index also varies among the household based on choosing migration destination. Therefore, it can be concluded that destination preference affects the economic and subjective welfare of the household.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".