Economic Risk and Cryptocurrency: What Drives Global Digital Asset Adoption?
Bibliographic record
Abstract
Cryptocurrency is often viewed as a hedge against economic instability, yet the extent to which economic risk drives digital asset adoption remains unclear. This study asks to what extent does economic risk shape global cryptocurrency adoption? To address this question, the research investigates how variables such as inflation, corruption, unemployment, and exchange rate volatility influence adoption patterns. Using panel data from 41 countries between 2019 and 2024, the study employs country fixed-effects regression models and Principal Component Analysis. A novel Regulatory Permissiveness Index is introduced to evaluate the role of national regulatory environments. The findings show that cryptocurrency adoption is primarily associated with structural enablers such as GDP per capita, internet penetration, and regulatory clarity. Among the economic risk indicators, higher corruption and lower unemployment significantly predict adoption. Other economic factors, such as inflation and exchange rate volatility, are not consistently significant. The results suggest that economic development and digital infrastructure, rather than reactive responses to economic crises, are the main drivers of cryptocurrency adoption. Nonetheless, the significance of corruption highlights the role of institutional dissatisfaction in adoption behaviour, even in economically stable settings.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".