Bitcoin adoption and beliefs in Canada - Daniela Balutel; Christopher Henry; Jorge Vásquez; Marcel-Christian Voia (Bank of Canada, 2021)
We develop a tractable model of Bitcoin adoption with network effects and social learning, which we then connect to unique data from the Bank of Canada's Bitcoin Omnibus Survey for the years 2017 and 2018. The model determines how the probability of Bitcoin adoption depends on (1) network effects; (2) individual learning effects; and (3) social learning effects. After accounting for the endogeneity of beliefs, we find that both network effects and individual learning effects have a positive and significant direct impact on Bitcoin adoption, whereas the role of social learning is to ameliorate the marginal effect of the network size on the likelihood of adoption. In particular, in 2017 and 2018, a one percentage point increase in the network size increased the probability of adoption by 0.45 and 0.32 percentage points, respectively. Similarly, a one percentage point increase in Bitcoin beliefs increased the probability of adoption by 0.43 and 0.72 percentage points. Our results suggest that network effects, individual learning, and social learning were important drivers of Bitcoin adoption in 2017 and 2018 in Canada.
Keywords: D83; O33; 330; Digital currencies and fintech; Economic models; Econometric and statistical methods