Which markets are most likely to see the fastest adoption of bitcoin?
With over $250m of venture capital invested in bitcoin startups to date, much is at stake in understanding which markets will prove most fertile for bitcoin. In addition, many governments and regulatory agencies are seeking to better understand the economic opportunities presented by bitcoin along with the perceived risks.
The new Bitcoin Market Opportunity Index (BMOI) is the first attempt at providing a rigorous answer to the above question, assembling a new data set to rank the potential utility of bitcoin across 177 countries.
The BMOI can be helpful to entrepreneurs, investors, regulators, economic development agencies, media outlets and anyone who is interested in gaining a better understanding of how bitcoin may progress geographically in the months and years to come.
How should we measure bitcoin adoption?
Sitting at the fast-moving intersection of technology, policy and economics, bitcoin is both a fascinating and complex research topic.
One of the first questions that arises in constructing a bitcoin adoption index is: what type of adoption should the BMOI measure?
For example, should the BMOI focus on where bitcoin is most likely to be used as a store of value? Or should it measure bitcoin’s commercial potential as a medium of exchange? And which of these two is more likely than the other to influence bitcoin’s geographic progression? The answers to such questions have a significant influence on the choice of index variables and weightings.
The BMOI is intended to measure bitcoin’s total potential adoption and thus includes data which relate both to bitcoin’s function as a store of value and as a medium of exchange, as well as a technology platform.
The data set is, however, structured in such a way that it can also be used to construct alternative versions of the index around different scenarios or more specific use cases.
For example, one may believe that bitcoin does not have as much immediate potential in the international remittances market as compared to its use as an alternative investment vehicle or store of value. Or one may feel that Darkcoin is going to supplant bitcoin as the preferred cryptocurrency in the black market and that therefore the size of the informal economy in each country is not a significant factor in bitcoin adoption. Such scenarios can be incorporated into alternative calculations of the BMOI by removing the corresponding variables and or adjusting weights.
Which data is most closely linked to bitcoin adoption?
On the question of what variables to include in the index it is useful to remember the old joke about how economists go about choosing which data to work with:
A drunk on his way home from a bar realises that he has dropped his keys. He gets down on his hands and knees and starts groping around beneath a lamppost. A policeman asks what he’s doing.
“I lost my keys in the park,” says the drunk.
“Then why are you looking for them under the lamppost?” asks the puzzled cop.
“Because,” says the drunk, “that’s where the light is.”
In other words, economists often are forced to use available data rather than the data which, if it did exist, has a stronger relationship to the subject of study.
With bitcoin it could, for example, be quite useful (and more precise) to examine which cities or regions are most likely to see quick bitcoin adoption. However, much of the relevant data in this case is only available at country level and, as a result, the BMOI analysis is a country-level index.
A further challenge is that even when relevant data exists it only exists for a small subset of countries. For example, publicly available smartphone penetration data – which could be quite helpful in understanding bitcoin adoption – is unfortunately only available for 48 countries. If we were to exclude countries which do not have smartphone penetration data from the BMOI then the index would lose nearly 130 countries.
Another data point which could potentially be helpful for understanding bitcoin adoption is how quickly social norms spread across different countries. After all, using bitcoin requires at least some change in existing behaviour. However, this particular study only covered 25 countries.
In the interest of trying to ensure the BMOI was comprehensive in terms of countries covered, an effort was made to select index variables with available data for a large number of countries.
The more data the better? How should data be weighted?
When it comes to indexes more data does not always equate to ‘better’ results.
However, given the complexity surrounding bitcoin adoption every effort has been made to include the variables which may have the greatest influence on bitcoin’s progress. Adding additional variables to an index can also help provide a more nuanced ranking of countries.
On the question of how different data should be weighed in the index, can it be reasonably claimed that some variables will be more important to bitcoin’s future than others? If yes, how much more important?
While weighting choices can be controversial, they are often useful in making indexes more realistic, and some variables in the BMOI are therefore weighted more strongly than others.
Some will certainly disagree with the BMOI’s weighting choices, and it is likely that the weightings will be adjusted over time as we learn more about bitcoin adoption. Meanwhile, unlike other bitcoin indexes which do not disclose their weightings it can at least be said that the BMOI weightings and methodology are openly documented.
BMOI data and sources
In constructing the BMOI a high priority was placed on finding both reliable and recent data. BMOI data also comes from a variety of different sources, including governments, multinational agencies, private companies and scholarly research. In total nine principal sources of data were used to construct the BMOI (Table 1).
Table 1: BMOI Data Sources and Time Periods
|World Bank (2013, 2012)||Bitnodes.io (July 2014)|
|IMF (2013)||Sourceforge.net (July 2014)|
|CIA World Factbook (2013)||Reinhart & Rogoff (2010)|
|CoinDesk (July 2014)||Elgin and Oztunali (2012)|
In some instances the data sets from Table 1 were supplemented or updated to reflect recent events. For example, Reinhart and Rogoff’s financial crisis data were updated with this week’s sovereign default by Argentina.
The BMOI variables
The BMOI is comprised of 39 variables deemed important to bitcoin’s potential for adoption. These 39 variables are grouped into seven equally-weighted categories to calculate BMOI’s rankings (Table 2).
Table 2: BMOI Data Categories
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A full discussion of the relationship between the above categories and bitcoin adoption is impractical here given space constraints but it is useful to briefly outline the relationship between some of the above categories and bitcoin adoption and how that relationship influences the BMOI rankings.
For example, bitcoin is fundamentally a technology and the level of technology adoption in a country, as reflected in measures such as internet use and mobile phone penetration, will have an important influence on bitcoin adoption. The memory of recent financial crises, particularly hyperinflation or a currency crisis, will also have an influence on adoption. The greater the degree of technology penetration, frequency of financial crisis, etc. for a particular country then the higher that country ranks on the BMOI.
Why is bitcoin regulation not included in the BMOI?
Just as the choice of variables included in the BMOI must be defended some variables which have been omitted from the BMOI also require justification. For example, one category which was excluded from the BMOI but which could have a significant influence on bitcoin adoption is bitcoin regulation.
The reason why bitcoin regulation was excluded from the BMOI for now is twofold. First, bitcoin regulation is a recent development and still evolving. Second, and perhaps more importantly, bitcoin regulation may end up cutting both ways in terms of indicating the likelihood of bitcoin adoption.
On the one hand, more aggressive bitcoin regulation in countries such as Ecuador and Bolivia may ultimately serve as a significant barrier to bitcoin’s prospects in those countries. However, aggressive bitcoin regulation could also provide a signal from regulators about bitcoin’s positive adoption prospects in that country, as perhaps is the case in China.
In sum, it is too early to tell how to score bitcoin regulation and this category has therefore not been included in the overall BMOI rankings.
The BMOI Sub Indexes
The categories in Table 2 also comprise the sub indexes of the overall BMOI, and these sub indexes allow for micro comparisons.
For example, we can compare how strong the correlation is between a sub index like Bitcoin Penetration against all of the other data categories. This particular comparison provides a helpful test on how well the other non-bitcoin data categories are predicting bitcoin specific adoption measures.
While the Inflation category is based on a single variable (consumer price inflation) most of the categories in Table 2 contain multiple variables. For example, the Bitcoin Penetration category contains the following four variables:
- Global bitcoin nodes
- Google ‘bitcoin’ search share
- Bitcoin client software downloads
- Bitcoin venture capital investment (dollar amount by country)
The Bitcoin Penetration sub index is laid out in detail in Table 3.
Table 3: Bitcoin Penetration Sub Index Variables
|Global bitcoin nodes||a) Total Bitcoin nodesb) Global Bitcoin nodes per capita||Bitnodes.ioBitnodes.io / World Bank|
|Bitcoin client software downloads||a) Total client downloadsb) Client downloads per capita||Sourceforge.netSourceforge.net / World Bank|
|Google ‘bitcoin’ searches||Google Trends|
|Bitcoin VC investment||CoinDesk|
As noted earlier, not all BMOI variables are equally weighted.
For example, the score for the Global bitcoin nodes variable is broken down into two equally weighted sub variables – total bitcoin nodes per country and bitcoin nodes per capita. Sub variables were utilized in this case so that small countries with a high per capita number of nodes, such as Iceland, are not disadvantaged in the overall index ranking due to the small size of their population or economy.
A summary of the BMOI dataset can be found in Table 4.
Table 4: BMOI Dataset Summary
As a point of reference, the widely cited Legatum Prosperity Index includes 89 different variables for 142 nations around the world.
The BMOI Top 10
According to the Bitcoin Market Opportunity Index the 10 countries where bitcoin is most likely to be adopted can be found in Table 5.
Table 5: BMOI Top 10 Countries
Given the BMOI’s criteria it is not surprising to see Argentina ranked number one. The country suffers from persistently high inflation, has a large informal economy and a history of recent financial crises. In addition, Argentina has a relatively high degree of technology penetration and controls on the movement of capital. Argentina also just defaulted on its sovereign debt for the second time in 13 years. While external sovereign defaults have a relatively minor weighting in the BMOI this recent development is reflected in the BMOI rankings.
Like Argentina number two ranked Venezuela also suffers from relatively high inflation and frequent financial crises, while number three ranked Zimbabwe has the largest informal economy (black market) of any country in the dataset at 63% of GDP.
A country which often features in discussion of bitcoin adoption but which is just outside of the top-10 is China, which is ranked number 13. China’s ranking is brought down by its relatively small black market; according to Elgin and Oztunali (2012) and other shadow economy researchers – ie Buehn and Schenider (2012), Schneider, Buehn and Montenegro (2010) – it is estimated that roughly 10% of the economic activity is conducted informally in China.
In contrast, near the bottom of the overall BMOI rankings at number 167 is Ireland, which recently hosted a high-profile bitcoin conference. While Ireland scores well in some categories, such as technology and bitcoin penetration, the country has wrestled with deflationary pressures in recent years and also has a relatively limited set of restrictions on the flow of capital. Dublin is a global tech hub, however, and the fact that the BMOI does not include a separate tech hub variable brings down Ireland’s ranking.
A full list of the BMOI rankings as well as a more detailed discussion of the index methodology, data and sources is available here.
An impossible challenge?
While indexes like the BMOI can provide a useful reference point to better understand factors which may influence bitcoin adoption it is important to acknowledge the limitations that are inherent in the construction of any such ranking. Countless variables not included in the index will influence adoption, data sets are often incomplete, and index methodology choices can have a significant influence on rankings.
More will be written about the Bitcoin Market Opportunity Index in the weeks to come on CoinDesk and in the State of Bitcoin reports, and the BMOI will also be updated periodically as new data becomes available.
Any surprises in the BMOI results? What data is missing from the BMOI? Share your thoughts in the comments below.
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