The potential of mobile remittances for the bottom of the pyramid: findings from emerging Asia
Bibliographic record
Abstract
Purpose This paper aims to explore the extent to which low‐income migrant workers in emerging Asia are aware of and are likely to use mobile phones for remitting money to family members at home. Design/methodology/approach Data were obtained through a survey of 1,500+ local and overseas migrant workers at the bottom of the socio‐economic pyramid and subsequent qualitative research in Bangladesh, Pakistan, India, Sri Lanka, the Philippines and Thailand. Findings Findings reveal that less than a quarter of respondents in India, Pakistan and Sri Lanka were aware of such services. However, the Philippines and Thailand reported awareness of levels of over 40 percent. Using a logit model to assess socio‐economic characteristics of those aware of such services (versus those who are not), findings revealed those aware of such services tended to enjoy higher standards of living, in terms of both income and education and ownership of mobile phones and bank accounts. Barriers to use are also explored. Originality/value This study is likely one of the first of its kind in attempting to empirically estimate socio‐economic characteristics of those aware of such services versus those who are not. Such findings can, undoubtedly prove useful to operators in deciding how best to market such services, including addressing potential barriers to use, such as perceived ease of use and trust and reliability issues.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".