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Israel and the Palestinians have reached an agreement which establishes the basis for resuming peace talks, the US Secretary of State has announced. John Kerry was speaking in Jordan, after meetings with both sides earlier. He gave no details of the agreement, but said initial talks would be held in Washington "in the next week or so". The last round of direct talks broke down nearly three years ago over the issue of Israeli settlements in the West Bank and East Jerusalem. Mr Kerry told reporters in Amman that the parties had "reached an agreement that establishes a basis for resuming direct final status negotiations between the Palestinians and the Israelis.
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Israel and Palestinians reach agreement to resume talks
Cryptocurrencies often tend to maintain a publically accessible ledger of all transactions. This open nature of the transactional ledger allows us to gain macroeconomic insight into the USD 1 Trillion crypto economy. We specifically focus on the aspect of wealth distribution within these cryptocurrencies as understanding wealth concentration allows us to highlight potential information security implications associated with wealth concentration.
We also draw a parallel between the crypto economies and real-world economies. To adequately address these two points, we devise a generic econometric analysis schema for cryptocurrencies. Our analysis reports that, despite the heavy emphasis on decentralization in cryptocurrencies, the wealth distribution remains in-line with the real-world economies, with the exception of Dash. This suggests that the free-market fundamentalism doctrine may be inadequate in countering wealth inequality within a crypto-economic context: Algorithmically driven free-market implementation of these cryptocurrencies may eventually lead to wealth inequality similar to those observed in real-world economies.
Economic freedom is one of the foundational pillars of the crypto-anarchist movement Ludlow, Crypto anarchism is a political ideology that focuses on using cryptographic methods to attain anonymity, freedom of speech, and freedom of trade May, often through a counter-economic environment.
A counter-economic environment facilitates financial transactions beyond the purview of a government, leading to freedom of trade London, , where a counter economy includes the free market, the black market, and the underground economy.
These crypto-anarchist objectives are materialized primarily through recent developments in cryptography, privacy-focused distributed networks, and decentralized peer-to-peer currencies Chohan, , where their appeal is as an alternative to traditional financial system in that they embody increased freedom to trade DeVries, The adoption of trade-friendly regulations has been suggested to improve wealth distribution by encouraging the flow of wealth among nations Bank, ; Irwin, This article explores that line of reasoning, evaluating the hypothesis that wealth distribution improves in the absence of restrictive trade regulation, in a cryptocurrencies context, using measures of wealth concentration.
This is a contentious hypothesis because according to the inequality model developed by Boghosian , the free market model adopted by cryptocurrencies is not without limitations in this regard, suggesting that wealth naturally trickles up in a free market economy leading to wealth inequality. In contrast, many cryptocurrency researchers have suggested that blockchain might provide a solution to the issue of wealth inequality in a free market-based economy Chohan, ; van den Hoven et al.
For instance, Othman et al. However, it must be acknowledged that participation in these crypto economies is subjected to many barriers to entry, such as internet access requirement and high transaction fee. Major cryptocurrencies tend to maintain an open distributed ledger of all financial transactions executed to date. This transparent nature of cryptocurrencies can be used to measure wealth concentration in these cryptocurrencies.
Thus, this research work assesses the following question:. Past reports such as Griffin and Shams have suggested that manipulation of exchange rates through wealth concentration is feasible and has been observed in the cryptocurrency market. According to Sai et al. This potential for successfully executing security attacks due to large wealth concentration makes it essential to understand the current state of wealth distribution.
The exact implementation of a cryptocurrency-based financial system can vary significantly in different cryptocurrency implementations. Thus, this fairer wealth distribution hypothesis needs to be assessed for a range of cryptocurrencies to increase the generality of the findings.
This paper will conduct an empirical evaluation of wealth concentration in 8 major cryptocurrencies in two broad categories: Bitcoin-like 6 cryptocurrencies including Bitcoin and Ethereum-like 2 cryptocurrencies including Ethereum.
Bitcoin is currently the largest cryptocurrency by market capitalization, with a current valuation of USD Billion CoinMarketCap, Many prominent cryptocurrencies are based on the fundamental design of Bitcoin by forking copying the source code of Bitcoin Neudecker and Hartenstein, We refer to these cryptocurrencies collectively as Bitcoin-like cryptocurrencies.
For our empirical review, we shortlist the top six Bitcoin-like cryptocurrencies including Bitcoin itself based on the market capitalization: Bitcoin, Litecoin, Bitcoin Cash, Dash, ZCash, and DogeCoin. The second category of cryptocurrencies selected for the analysis is Ethereum-like cryptocurrencies. Ethereum currently has a total market capitalization of USD Billion CoinMarketCap, , is ranked as the second-highest valued crypto asset and allows for transactions to contain transactional logic in the form of Turing complete contracts.
Ethereum is also an interesting case study for wealth inequality analysis as Ethereum has a provision to allow users to write smart contracts to dictate economic behavior over the cryptocurrency in the form of a crypto token 1 Buterin et al.
Similar to Bitcoin forks, Ethereum also has several forks; among these, the most prominent example is Ethereum Classic. We review both Ethereum and Ethereum Classic for our study. We also review the current January state of wealth distribution in the top five tokens issued on the Ethereum platform for our analysis. We conduct an econometric analysis by calculating macroeconomic measures of inequality for these cryptocurrencies and contrasting these measures with traditional economies.
We also examine an extrinsic factor, policy changes, to understand if factors outside the cryptocurrencies may influence the wealth distribution in the crypto economies. We also perform econometric analysis on the top five tokens deployed on the Ethereum platform, which helps us to understand the impact of policy configurability on wealth distribution as these tokens allow programmers to define the economic policies that govern these assets.
This methodology considers the volume, velocity, and variety of data generated by different forms of cryptocurrencies. Specifically, it reports on the potential relationship between the type of policy changes and the wealth concentration Section 4. In addition, based on our reflections on the empirical protocol adopted, the paper proposes a set of reverse engineering techniques that can be used by future researchers in their analysis of wealth concentration to partially circumvent cryptocurrency privacy provisions Section 6.
We also specifically report on how the current state of econometrics analysis in cryptocurrencies is insufficient to capture the economic aspects of these complicated assets Section 6. Economic inequality can be broadly categorized into income and wealth inequality Simpson, Income inequality examines the distribution of income in a country or political union of nations.
The notion of income inequality does not directly translate to crypto economies as the open ledger maintained by these crypto economies only contains information relevant to the wealth determined by units of currencies owned by each participant. Wealth inequality examines the economic heterogeneity of a country or a political union Cagetti and De Nardi, The exact definition of wealth varies depending on the application area; however, wealth is generally defined in terms of financial assets Hamilton and Hepburn, A financial asset is defined as a non-physical or physical asset that can be used for financial transactions Moles and Terry, Then wealth inequality is measured based on the distribution of these financial assets over a population.
However, calculating wealth inequality is harder than income inequality as individuals can have negative wealth due to financial liabilities such as credit and loans. Current statistics from Alvaredo et al. A standard method for calculating wealth inequality can be obtained through econometrics. The broad field of econometrics is concerned with applying statistical techniques to economic data to produce empirical evidence for the financial construct under examination Stock and Watson, Such measures of statistical dispersion 2 are commonly used for quantifying the wealth inequality in economies.
In , Max Lorenz developed a graphical way of representing economic inequality through the use of Lorenz curve Gastwirth, The Lorenz curve graphically represents the percentage of wealth accumulated by various portions of the population ordered by the size of their wealth Gastwirth, On the x -axis, we plot the percentage of the population, and on the y -axis, we plot the percentage of wealth. As an illustrative example, we have plotted the Lorenz curve for Ireland based on the data obtained from CSO, for This line illustrated by the blue line in Figure 1 represent the perfect distribution of wealth.
The area between the line of equality and the Lorenz curve can be used to understand the spread of inequality. An important statistical construct used to numerically describe this spread of wealth is the Gini coefficient.
The Gini coefficient is a numeric value aimed at quantifying the inequality in the distribution Gini, To calculate the Gini value for Ireland in , we use the Lorenz curve. We can calculate the Gini Coefficient as follows:. Following this approach, we report that the Gini value for Ireland in for wealth distribution is 0.
Based on Eq. Similarly, a Gini value of 0 would represent the perfect distribution of wealth in the country, i. Thus, the Gini value calculated for Ireland 0. Thus far, we have discussed the meaning and measurement of wealth inequality in the context of world economies. In the following subsection, we review wealth inequality in a crypto-economic context. Considering cryptocurrencies as financial assets is a topic of much debate in the economic and financial research domain Corbet et al. This is primarily driven by the argument regarding the intrinsic and extrinsic values associated with the crypto assets.
For this article, we focus on the extrinsic value of cryptocurrencies by using their exchange rate to USD as a proxy. The use of USD as a proxy allows us to better draw parallels between crypto economies and traditional world economies.
Due to the open ledger nature of cryptocurrencies, it is easy to gain a macroeconomic view of the economy by conducting data analysis over the open ledgers. Most cryptocurrencies maintain a publically accessible ledger of all transactions in their financial system. This allows us to use data analytics to construct a macro view of these cryptocurrencies. Gini coefficient has been suggested as a useful metric for measuring economic centralization in cryptocurrencies Kondor et al.
Both Bitcoin and Etheruem employ different data structures to maintain records of transactions. Thus the deanonymizing process varies significantly depending on the type of blockchain under analysis. UTXO specifies the value and state 3 of each Bitcoin present in the ecosystem. This list is then used to calculate the balance for the given address. The process of calculating balance is considerably simplified in Ethereum-like cryptocurrencies.
Ethereums transaction data structure contains a balance field that can store and retrieve balance for a given address. Determining the balance of all addresses is fundamental to the calculation of wealth distribution in cryptocurrencies. However, gaining a macro perspective is not sufficient to observe the wealth distribution in these cryptocurrencies. As indicated in Section 1, cryptocurrencies adhere to the crypto-anarchist ideology by employing privacy-preserving policies to maintain anonymity while retaining the freedom to trade.
This is primarily achieved through the use of cryptology in constructing and executing transactions. A macro view of the crypto economy without explicit consideration of this privacy-preserving nature would likely yield an inaccurate measure for wealth distribution as identifying wealth associated with individuals is difficult. That is, major cryptocurrencies, including Bitcoin and Ethereum, provide pseudo-anonymity to the users through cryptographically generated addresses.
Most of these cryptocurrencies also offer provisions for generating a new address for each transaction Gutoski and Stebila, This induces further complexity into the determination of wealth distribution as a single user in a cryptocurrency may have his wealth distributed over multiple addresses.
To avoid skewing the econometric analysis due to many addresses with a very small balance, Srinivasan and Lee propose using a monetary lower bound on balance.
For instance, introducing a requirement of a minimum balance of USD for inclusion in Gini calculation can significantly improve accuracy by eliminating several addresses with very low or zero balances. They justify this choice by arguing that many addresses in these cryptocurrencies are only used once for privacy reasons, and addresses with a low balance are unlikely to see future transactions for example addresses employed for one transaction only. Despite or maybe because of this tweak, it is hard to establish the accuracy of this method.
Srinivasan and Lee suggest using an alternate metric to measure wealth, and other forms of distribution in cryptocurrencies. For example, many prevalent cryptocurrencies are subjected to an honest majority assumption.
Electricity rates rise in Israel
At M12, our mission is to empower entrepreneurs with capital, customer connections, and unparalleled access to Microsoft. We do commit to supporting the entire lifecycle of each portfolio company. Our portfolio companies gain access to one of the largest technology companies in the world, including the opportunity to sell alongside Microsoft. We work quickly so that founders can stop fundraising and get back to building great companies. M12 is committed to a more equitable startup and VC ecosystem, and partners with several organizations working toward greater diversity, equity, and inclusion.
MIAMI NICE JAZZ FESTIVAL
In this study, a new and applicable model is proposed which represents the opportunities of implementing blockchains into IoT framework in the sensor data acquisition. We aim to propose a simple perspective of blockchain in IoT which is flexible with high throughput in sensor networks. We selected Hyperledger Fabric as our blockchain solution to deploy across our network with the use of Docker Swarm. Our framework leverages the containerization of Fabric so that the network can operate between the edge devices and the cloud in a single, private system. This model contains some areas in security, such as privacy policy and delay versus the value of the task to be processed. Blockchain technology has shown promising application prospects, which is a distributed means of securing data in a way that is auditable, immutable, and fault-resistant. Blockchain introduced in Nakamoto, , which debut of Bitcoin served as a functional proof-of-concept and removed any necessary access for trusted third parties in the transaction with any strange people in the world. These transactions need to validate from banks, but Blockchains carried out by network peers. Blockchains are based on trust which created in a trust-less platform to act as rules. When it created, Bitcoin had some faults which occurred by heavy cost on computation, power, and memory for every full participant in a wide network of peers.
RAISING INVENTOR DIVERSITY TO THE NEXT LEVEL
In January 3, , Satoshi Nakamoto gave rise to the "Bitcoin Blockchain", creating the first block of the chain hashing on his computer's central processing unit CPU. Since then, the hash calculations to mine Bitcoin have been getting more and more complex, and consequently the mining hardware evolved to adapt to this increasing difficulty. Three generations of mining hardware have followed the CPU's generation. This work presents an agent-based artificial market model of the Bitcoin mining process and of the Bitcoin transactions.
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On Thursday, CoinDesk, a leading source of cryptocurrency news and organizer of major industry conferences, launched an online "Career Center" with job listings. It's "no secret demand for blockchain skills is high and that finding talent is a real difficulty," Jacob Donnelly, director of marketing, said in a statement. CoinDesk is also hosting a career fair along with its Consensus conference in New York this month. Listings of "blockchain" skills skyrocketed more than 6, percent in the first quarter from a year ago, online freelancing database Upwork said in a report Tuesday. That's far and away the hottest skill on Upwork, and a slight change from the fourth quarter when "bitcoin" was the fastest-growing skill and blockchain wasn't even on the list. There's a lot of millionaires made overnight and drawn a lot of people in," said Andy Challenger, vice president at placement firm Challenger, Gray and Christmas.
The Italian coast on the road
For the first time in Bitcoin's five-year history, a single entity has repeatedly provided more than half of the total computational power required to mine new digital coins, in some cases for sustained periods of time. It's an event that, if it persists, signals the end of the crypto currency's decentralized structure. Researchers from Cornell University say that on multiple occasions, a single mining pool repeatedly contributed more than 51 percent of Bitcoin's total cryptographic hashing output for spans as long as 12 hours. So-called 51 percenters, for instance, have the ability to spend the same coins twice, reject competing miners' transactions, or extort higher fees from people with large holdings. Even worse, a malicious player with a majority holding could wage a denial-of-service attack against the entire Bitcoin network. Like tremblers before a major earthquake, most of GHash's percent spans were relatively short. Few people paid much attention, since shortly after a miner loses the majority position, it also loses its extraordinary control.
Understanding Block and Transaction Logs of Permissionless Blockchain Networks
The event, expected in May , slashes by half the number of new coins awarded to bitcoin miners who provide global supply of the cryptocurrency by solving complex maths puzzles. Players in the know are preparing for the sharp price gains and volatility that have accompanied previous halvings, which happen roughly every four years and act to both ensure the scarcity of bitcoin and keep a cap on price inflation. There are likely to be winners and losers.
With 7, km of wonderful landscapes, the Italian coast is a fantastic holiday destination. And just because the summer has past, doesn't mean you can't take a break to enjoy it. Here at idealista we've decided to show you some of the best places on the coast to visit on wheels. A road trip on the Italian coast means being able to enjoy an unpredictable holiday, capable of surprising you each and every day in a new way. You can be a leaf on the wind, with total autonomy and without obligations. You can travel hundreds of kilometers at your own pace , without ever getting tired and then fall in love with a single destination and comfortably spend several nights in one of the Rentalia.
Discussion Papers. Friedrich Schneider, William J. Baumol, Svensson, L-E-O, Svensson, Lars E.
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