Wednesday, August 21, 2019

ETH y BCH siguen a bitcoin entre las criptomonedas con mejor distribución

An analysis of the Coin Metrics team published this August 20 focuses on the issue of wealth distribution not only in bitcoin but in four other cryptocurrencies. So determine that, after bitcoin, crypto assets with less inequality are ether (ETH) and bitcoin cash (BCH).

In their report, analysts selected the four cryptocurrencies that follow bitcoin in market capitalization, comparing data from bitcoin (BTC), ether (ETH), ripple (XRP), litecoin (LTC) and bitcoin cash (BCH). From each of these networks they took into account not only the market capitalization, but also the number of addresses and the balance of the balances that remain in them until this month of August.

In this way, they established that ripple is the crypto asset that has the greatest inequality in the distribution of wealth, the opposite of bitcoin, leaving litecoin in fourth place among the five cryptocurrencies evaluated. In that sense, bitcoin takes the lead as the best distributed cryptocurrency by owning the largest number of addresses with significant balances and millionaire accounts. According to Coin Metrics in the bitcoin network about 5.8 million accounts maintain average balances of USD 6,840. While 13.9 million addresses maintain balances above $ 10.

Next, there are Bitcoin Cash (a fork of Bitcoin) and Ethereum. In BCH the average balance is USD 317, while in the second it is USD 675. In BCH there are 2.6 million addresses with significant balances and 2.1 million with balances exceeding $ 10. Meanwhile, Ethereum has 2.4 million addresses with significant balances and 3.6 million with amounts over $ 10.

These amounts are different in Ripple, in which accounts with average balances of USD 14,815 are estimated, in addition to some 600,000 addresses with significant balances and 800,000 with balances exceeding $ 10. Litecoin, meanwhile, maintains addresses with average balances of USD 1,408, 1.2 million addresses with significant balances and 1.1 million with more than $ 10.

Another element that analysts took into account to make their calculations is the time of existence of the platform. In this regard, difficulties are observed in handling long-term trends. "In Bitcoin, every new day we would expect 218 new addresses to contain at least 1 BTC," a calculation that "might not make sense."

Difficulties in calculating the distribution

Coin Metrics emphasizes mentioning the difficulties that exist in measuring and comparing the distribution of wealth, both in bitcoin and in the different cryptocurrencies. He cites the influence of various factors, including the characteristics of each network, which mark differences that can distort the results.

The mechanics of the Ethereum gas rate tend to leave more dust (dust refers to smaller balances than the rate needed to move them). This could make ETH appear to be distributed more equitably by reducing the average balances of the accounts.

Coin Metrics

The other factor that influences is the protocol, which is different in each network. Ethereum, for example, is based on accounts and therefore tends to reuse addresses. This can make it "seem less evenly distributed when using address-based metrics," the researchers say. Something different happens with protocols based on UTXO, (exits not spent), such as Bitcoin.

Inaccurate methods

On the other hand, the study explains the implications of the most used methodology to calculate the distribution of wealth in the cryptocurrency sector. It is the Gini coefficient. This method, commonly applied to economies, is calculated in values that move between 0 and 1, with 0 being the value that indicates perfect equality and 1 that indicates the greatest possible inequality.

So far, the results tend to indicate that bitcoin has a coefficient greater than 0.8, indicative for many of a high level of centralization. However, for Coin Metrics the Gini method may be inaccurate when cryptoactive is extrapolated. This, not only because «the sampling unit differs in cryptography, but we are also measuring the distribution of a single asset, the cryptoactive itself, and not of all the assets of a household minus the liabilities as we do when calculating Gini in state economies ».