The geography of initial coin offerings


Initial coin offerings (ICOs) are a rapidly growing phenomenon wherein entrepreneurial ventures raise funds for the development of blockchain-based businesses. Although they have recently sprouted up all over the world, raising millions of dollars for early-stage firms, few empirical studies are available to help understand the emergence of ICOs across countries. Based on the population of 915 ICOs issued in 187 countries between January 2017 and March 2018, our study reveals that ICOs take place more frequently in countries with developed financial systems, public equity markets, and advanced digital technologies. The availability of investment-based crowdfunding platforms is also positively associated with the emergence of ICOs, while debt and private equity markets do not provide similar effects. Countries with ICO-friendly regulations have more ICOs, whereas tax regimes are not clearly related to ICOs.

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Fig. 1


  1. 1.

    ICOs and cryptocurrencies are only one of the many applications of blockchain technology, which is expected to have economically significant uses in virtually every industry (Böhme et al. 2015; Davidson et al. 2018). Blockchain is a decentralized validation protocol shared by all parties in which no one individual entity has complete control of the process or information. The transparent and decentralized nature of the blockchain network enables the development of a non-refutable and unbreakable record of data, which is a fundamental feature in many markets. Blockchain can revolutionize organizations (e.g., supply chain management) as well as markets, with applications such as cryptocurrencies, records of ownership of intellectual property, or smart contracts. This is not limited to goods or currencies, as a blockchain-based system can redesign the treatment of personal data, with strong impacts on sectors such as healthcare or education.

  2. 2.

    A definition of complementarity is given by Roberts 2007, p. 34): A pair of variables are complements when doing (more of) one of them increases the returns to doing (more of) the other. If one of a pair of complements is instituted or increased, it will be more attractive than before to introduce or increase the other. The opposite holds for substitutional effects.

  3. 3.

    Comparing different data sources, Amsden and Schweizer (2018) find that this platform provides the greatest accuracy. Using a different data source (, Howell et al. (2018) find a similar country distribution in a sample of 453 ICOs.

  4. 4.

    British territories (namely, Bermuda, Cayman Islands, Gibraltar, Guernsey, Isle of Man, and Jersey) are also considered. When data were unavailable for British territories, the UK data were used.

  5. 5.

    Our ICO Regulation dummy takes the value of 1 for countries and territories that have acted or are acting to regulate bitcoin, or that have stopped short of regulating bitcoin, but have imposed taxes; it is equal to 0 for countries that have banned bitcoin, that are undecided in respect of digital currencies or do not regulate bitcoins. In Table 10 in the Appendix, we disaggregate such cases, we find positive significant coefficients for “Regulation” and “Taxation” and a negative coefficient for “No regulation” that are consistent with our findings in Table 4.

  6. 6.

    We obtain the VIFs from a linear regression with the same specification as in our model, identifying a maximum level of 8.9, and an average level of 2.6, even in the full specification setting, below the classical threshold of 10 used to identify the multicollinearity concern. Given the non-linear nature of our model, we also calculate the Belsley, Kuh, and Welsch (1980) diagnostic on multicollinearity, which refers to both linear and non-linear models, reporting us a conditioning index for the matrix of independent variables of 24.93 (the authors set 30 as the threshold of the multicollinearity concern).

  7. 7.

    In the Appendix, we report the results of our analysis when dropping the GDP variable (Table 11) and when repeating all regressions on the sample of 133 countries with full information available (Table 12). Our results are qualitatively unchanged.

  8. 8.

    All our main variables replaced with alternative measures significantly correlated, at less than 1% significance, to the original value. Correlation coefficients are reported in the Appendix Table 9.

  9. 9.

    This variable is available only for 90 of the countries covered by our analysis. Still, the variable is available for 51 out of the 73 countries with at least one ICOs, covering more than 80% of the total number of deals.

  10. 10.

    We do not report an additional test fulfilled replacing the ICT development index with its component measuring the ICT infrastructure development. Results are qualitatively similar to our main findings.

  11. 11.

    The list is periodically revised. At the time when our sample was identified, i.e., at the end of the first quarter of 2018, the most updated list was dated on 5th December 2017 and was available online at the following:


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We thank Christian Fisch, Siri Terjesen, and participants at the “Crowdfunding, Blockchain, and ICOs” workshop at EMLyon (June 6, 2018) and a seminar at the Indiana University European Gateway in Berlin for their comments and suggestions. We thank Alex Groh for providing data about the Venture capital and private equity country attractiveness index.

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Correspondence to Silvio Vismara.

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Table 9 Descriptive statistics for all variables in use and a correlation matrix table
Table 10 Determinants of ICO localization
Table 11 Determinants of ICO localization
Table 12 Determinants of ICO localization

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Huang, W., Meoli, M. & Vismara, S. The geography of initial coin offerings. Small Bus Econ 55, 77–102 (2020).

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  • Initial coin offerings
  • ICOs
  • Entrepreneurial finance
  • Crowdfunding
  • Geography

JEL classification

  • M13
  • M15
  • G32
  • K22
  • O57
  • L26