2022
DOI: 10.1016/j.econlet.2022.110492
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Crypto-assets, corruption, and capital controls: Cross-country correlations

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Cited by 19 publications
(17 citation statements)
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“…where « i is the error term and i refers to country. Following Alnasaa et al (2022), variables were removed from the general model sequentially if they had little explanatory power based on the standard F-tests or the included variables showed a high degree of multicollinearity based on the variance inflation factor (VIF) analysis with a metric above 4. VIF metrics are not reported to preserve space.…”
Section: Methodology and Empirical Resultsmentioning
confidence: 99%
“…where « i is the error term and i refers to country. Following Alnasaa et al (2022), variables were removed from the general model sequentially if they had little explanatory power based on the standard F-tests or the included variables showed a high degree of multicollinearity based on the variance inflation factor (VIF) analysis with a metric above 4. VIF metrics are not reported to preserve space.…”
Section: Methodology and Empirical Resultsmentioning
confidence: 99%
“…Turning to Table 3, results from cross-sectional regression analysis show that about 70%, 60% and 60% of the variation in perceptions of jurisdiction risk is explained by explanatory The results of Model 1 suggests that AML/CFT framework, corporate tax avoidance, price stability policy, institutional and legal risk and tax burden on business are significant with positive coefficients with the exception of price stability policy. Despite the small sample, significant results are worth paying attention to given that size measurement error generally tend to reduce the likelihood of finding a significant empirical association (Alnasaa et al, 2022).…”
Section: Resultsmentioning
confidence: 99%
“…The general model includes all variables which have a theoretical association with the outcome variable. Redundant variables are sequentially removed from the general model following Alnasaa et al (2022) resulting in Models 2 and 3 as follows: where ε i and i are as defined as in Model 1.…”
Section: Methodsmentioning
confidence: 99%
“…Therefore, the high level of corruption in the country can be a significant factor for crypto holders to consider while choosing a specific server due to their willingness to mislead the trace of their money. Additional motivation for our research is found in a previously confirmed positive relationship between the perception of corruption and the popularity of crypto-usage in a country (Alnasaa et al , 2022; Wawrosz and Lansky, 2021). Consequently, investors are willing to pay higher prices for Bitcoin in countries with higher corruption (Brown, 2016; Johnson, 2020).…”
Section: Theoretical Backgroundmentioning
confidence: 96%