What predicts the legal status of cryptocurrencies?
Mikhail Stolbov () and
Economic Analysis and Policy, 2020, vol. 67, issue C, 273-291
The paper aims to identify pivotal predictors of cryptocurrency legal status in a sample of 134 countries, which includes jurisdictions where cryptocurrencies are legal as well as implicitly or explicitly banned. By applying the Bayesian model averaging (BMA) for logit models and sparse group least absolute shrinkage and selection operator (LASSO) to 26 candidate predictors, we find that higher values of voice and accountability index, capturing governance quality, increase the likelihood of free cryptocurrency circulation, whereas an enhanced access to electricity, conducive to intense cryptocurrency mining, produces the opposite effect. The latter may arise from the fact that regulators perceive an increased access to electricity as an early warning signal of excessive and, thus, speculative cryptocurrency activities in the future, thereby imposing preemptive regulatory restrictions. A battery of robustness checks confirm the relevance of voice and accountability index as well as access to electricity, also revealing digital adoption index, which promotes cryptocurrency legality, as their closest contender in terms of variable importance. Governments need to take into account the identified factors when shaping their regulatory stance on cryptocurrencies.
Keywords: Cryptocurrency; Legal status; Bayesian model averaging; Sparse group LASSO model; Random forest (search for similar items in EconPapers)
JEL-codes: C52 G18 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecanpo:v:67:y:2020:i:c:p:273-291
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