The Quest to Lower High Remittance Costs to Africa: A Brief Review of the Use of Mobile Banking and Bitcoins
Bibliographic record
Abstract
The paper reviews the last technological tools that arguably can contribute to reducing the excessively high costs of remittance transactions in Africa. Indeed, despite huge remittance inflows to and within the continent, Africa is the most expensive destination to send money to. As remittances have become more important than Overseas Development Assistance and Foreign Direct Investment inflows in some countries, it has become crucial to explore technological advances that can contribute to reducing their transaction costs. Such reduction would enable the end beneficiaries to capture a larger share of these external resources, which in turn could have an even bigger impact on development in Africa. In addition to revisiting the role of mobile banking in lowering remittance transaction prices, the paper takes a closer look at the newest available technology, the Bitcoin blockchain technology that underpins digital currencies. At this early stage, very few social science researchers have addressed the role that such digital currency could play in the reduction of the remittance transaction prices, except for a few innovative Bitcoin operators. The paper proceeds as follows. It first looks at the causes of the high remittance transaction costs. Then, it reviews, presents and analyses the official remittances data downloaded from the World Bank's Remittances Prices Worldwide database. It also briefly reviews a few remittance transfer technological instruments. Given the novelty of the topic, the review of the most recent existing "literature" on Bitcoin is mainly retrieved from either on - line news sources or information from a few leading Bitcoin operators. In the light of the UN Global Working Group Post-2015 Development Agenda and Sustainable Development Goals proposal to reduce by 2030 the remittance transaction costs to even less than 3%, the effectiveness of these new technological instruments to reach such objective are discussed. Finally, a number of appropriate policy actions to foster the economic impact of remittances are proposed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".