Digital Payments, the Cashless Economy, and Financial Inclusion in the United Arab Emirates: Why Is Everyone Still Transacting in Cash?
Bibliographic record
Abstract
Since the oil price downturn of 2015, the United Arab Emirates and fellow Gulf Cooperation Council countries have worked hard to expand digital payments in the interest of improved tax and revenue collection, transparency, and security. Yet despite a deep transformation and diversification of their payment eco-systems and the formalization of plans to become “cashless economies” modelled on South Korea and Sweden, cash continues to dominate payments in both countries. While industry players typically attribute the prevalence of cash in the region to questions of infrastructure readiness, transaction costs, and cyber-security, this paper finds that plans to expand digital payments at the expense of cash may not be well-adapted to countries with high levels of socio-economic inequality. It proposes a link between socio-economic inequality and use of cash in emerging economies, and concludes that it may be better to not view the relationship between cash and digital payments in binary zero-sum terms, until there is a better understanding of the socio-economic, technological, and policy context in which countries like South Korea and Sweden have managed to reduce their reliance on cash in favor of a diversified digital payments eco-system.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".