Remittances and food security: A study of the global south
Bibliographic record
Abstract
Since 2000, the number of international migrants has increased steadily, reaching 258 million in 2017.More than one-third of international migration moves from South to North, basically from developing to developed countries.Like international migration but in opposite direction, flows of remittances have also increased largely to developing countries since 2000.International remittances flow into developing countries attract increasing attention because of their rise in volume and their impact on the recipient countries.Receiving remittances from outside the country has become a household coping strategy that might contribute to poverty reduction, to alleviate hunger, to promote better diets and to increase productive investments.Because little is known about the topic, the main purpose of this study was to investigate the linkage between receiving remittances and the food security status in Global South (GS) regions.Although there are some studies on different countries that explore the association between receiving remittances and household food quality and quantity consumption or food consumption expenditures, this is the first study that examines the association between food security and receiving remittances by using the Food Insecurity Experience Scale (FIES) for individuals in the Global South (GS).Data were obtained from the 2017 Gallup World Poll (GWP), which interviewed face-to-face 68,463 individuals in 65 countries.The target population in the GWP is the entire civilian, noninstitutionalized, population aged 15 and older.All samples were selected using probability sampling techniques and are nationally representative.The GWP surveys average 1,000 individuals per country.Different statistical analyses such as descriptive, crosstabs, binary, and multinomial logistic regressions analyses, were applied in this study.This study assessed the association between receiving remittances and the food security status, by controlling the role of covariates.Additionally, the predictors of receiving remittances were also measured.Regardless of GS region, this study found a significant association between receiving remittances and food security (both crosstabs and regression analyses).In the unadjusted logistics regression, regardless of region, while severe food insecurity was significantly related to notreceiving remittances (OR=1.532;P= 0.000), results from socio-demographic factors in the GS indicated that the probability of being severely food insecure increased among individuals who were females (OR=1.061;P=0.000), lived in rural areas (OR=1.645;P=0.000), in large households (OR=1.750;P=0.000), in ages between 26 and 49 years (OR=1.171;P=0.000), in the poorest 20% of income quintile (OR=2.994;P=0.000), with low education (OR= 6.568; P=0.000), unemployed (OR=1.948;P=0.000), and divorced/separated or widowed (OR=1.370;P=0.000).Regarding GS regions, in the unadjusted logistics regression, the findings from this study indicate that the likelihood of being severely food insecure was significant for people in sub-Saharan Africa (SSA) (OR=2.080;P=0.000), and Asia (Southeast, South, and East) (OR=1.384;P=0.000) for those who did not receive remittances from migrants.In the adjusted model, sociodemographic factors also remained significantly related to food security.As a result, this study found that receiving remittances seems to indirectly influence the food security status of individuals receiving remittances in the GS through household income, education, employment, and the area of residence.In terms of the determinants of receiving remittances within regions, the results of the unadjusted logistics regression analyses showed that people living in rural areas of sub-Saharan Africa (SSA) and Latin America and the Caribbean (LAC) were less likely to receive remittances.In contrast, people living in rural areas in the Middle East and North Africa (MENA) and Asia
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".