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What predicts the legal status of cryptocurrencies?

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  • Stolbov, Mikhail
  • Shchepeleva, Maria

Abstract

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.

Suggested Citation

  • Stolbov, Mikhail & Shchepeleva, Maria, 2020. "What predicts the legal status of cryptocurrencies?," Economic Analysis and Policy, Elsevier, vol. 67(C), pages 273-291.
  • Handle: RePEc:eee:ecanpo:v:67:y:2020:i:c:p:273-291
    DOI: 10.1016/j.eap.2020.07.011
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    Cited by:

    1. Raphael Auer & Marc Farag & Ulf Lewrick & Lovrenc Orazem & Markus Zoss, 2022. "Banking in the shadow of Bitcoin? The institutional adoption of cryptocurrencies," BIS Working Papers 1013, Bank for International Settlements.
    2. Gonzálvez-Gallego, Nicolás & Pérez-Cárceles, María Concepción, 2021. "Cryptocurrencies and illicit practices: The role of governance," Economic Analysis and Policy, Elsevier, vol. 72(C), pages 203-212.

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    More about this item

    Keywords

    Cryptocurrency; Legal status; Bayesian model averaging; Sparse group LASSO model; Random forest;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • G18 - Financial Economics - - General Financial Markets - - - Government Policy and Regulation

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