Merchants are perhaps the most famous image of an intermediary, the not-so-loved “middleman” that buys cheap, sells dear, and becomes rich doing little work. Even in the supposedly dark Middle Ages, merchants were able to openly operate creating in the process Merchant Guilds that promoted regional trade while protecting members from potential abuses by powerful landlords and countervailing the staunch opposition of the Catholic Church. Merchants and traders are also part of the Greek and Roman empires.
Nevertheless, not every single intermediary is necessarily a merchant. In economics, an intermediary is defined as an agent or enterprise that sits between a product (or service) and the consumer. A supply chain for a given product might indeed have multiple intermediaries that handle the
In a world where perfect information supposedly rules across the board, uncertainty certainly poses a challenge to mainstream economists. While some of the tenets of such assumption have been already addressed – via the theory of information asymmetries and the development of the rational expectations school, for example, uncertainty still poses critical questions.
For starters, uncertainty should not be confused with risk. The latter in a nutshell can be quantified using probability theory. Based on existing data and previous behavior, we could
say predict there is a 75 percent chance investments in the stock market can yield a 25 percent reward in say 5 years. This is not the case for uncertainty as here the outcome is entirely unknown. In other words, we have no idea what is going to
In the short and medium term, technology and inequality seemed to be positively correlated. In the long term, however, things are not as clear-cut. With the right policies and democratic institutions in place, technology could become a catalyst to reduce income and wealth inequality. Historical evidence from last century clearly supports this claim. Will digital technologies of the 21st Century follow the same path?
The long-term is still quite a few years away for digital technologies such as AI and blockchains. In this post, I will look at the world of Bitcoin and explore its links to income and wealth inequality. I will assume the Bitcoin network is a country on its own with defined financial ties to the rest of the world mostly via crypto exchanges and miners.
Last May, the total
Blockchain mining cannot catch a break when it comes to environmental sustainability. This is especially true for Bitcoin mining which seemingly has an insatiable appetite for electricity. A recent paper suggests that by 2020 Bitcoin mining will consume as much energy as Australia. While these estimates are not exempt from criticism, mining does not appear to be best friends with sustainable development, at least not for now. An alternative way to look at this issue is to compare Bitcoin’s mining power use to that of cloud-based providers who have now become well-established tech corporations. Such comparison should be made not only in absolute terms (gigawatts) but also in relative fashion by considering, for example, the total population being served by these platforms and networks.
Disruptive. One of the attributes that most use to describe in minimalistic terms the potential impact of new and emerging information and communication technologies (ICTs) in society. While its actual meaning can vary from one person to another, disruption is usually linked to dramatic short-term change where old and obsolete technologies, processes and institutions -not to mention people – will be either replaced or purge altogether, all for the best.
Disruption is thus implicitly connected to the concept of progress, especially to its linear version. Here, progress is seen almost like time is in physics: it always goes forward, and it is impossible to go back and say edit the past. Recent research has challenged the linear conception of progress((See for example Amy Allen’s book, The
In the previous post, I detailed some issues that could help explain in part the gender-equality STEM paradox.
These can be summarized as follows:
- The Global Gender Gap Index (GGGI) measures gaps not levels. It is thus a relative indicator that takes stock of the gender gap regardless of the level or depth of development.
- The four GGGI subindices cannot be larger than 1. Thus, the GGGI does not factor-in cases where women are ahead of men. This is related to the previous point: the aim is to measure gender gaps, not gender levels.
- As of 2015, UNESCO STEM data is only available for 59 of the 144 countries included in GGGI. That is, almost 60% of the states are missing in the analysis of the gender-equality STEM paradox. Many low-income and lower-middle income countries
A couple of weeks ago, Coindesk launched an ICO tracker which seems quite comprehensive and includes data starting in 2014. It has information on 164 ICOs and the data is expected to be updated every week or so.
In a recent post, I shared some insights on the nature of ICOs. In a nutshell, ICOs should not be equated with crowdfunding nor are they comparable to the more traditional IPOs. What has really changed since my initial posting is the fact that SEC is now planning to get involved in the process and will soon start to regulate how ICOs are run and managed. In the