Data Localization- Mercantilism in a Networked World

Data Localization- Mercantilism in a Networked World

Economic ideas do not die. They resurface again and again, repackaged and refurbished, when the time suits it. Mercantilism is such an idea that politicians and policy makers love to espouse, albeit periodically. The basic premise of this doctrine is that international trade is a zero-sum game. The role of state is to protect domestic industries by building tariff and non-tariff barriers and encouraging export. The obvious appeal of this doctrine to general people is not difficult to understand. It gels well with the notion of national security, national pride and preservation of national assets. It was expected that in the age of internet, such ideas would be considered anachronistic and would lose their currency. But that is not to be.

Data is considered to be the most valuable asset of the 21st century. The most valuable companies of the world are those which are primarily engaged in data crunching. The business of Google, Uber and Amazon would come to a standstill if they could not access, process, and analyze data across time and geographies. Once nation-states realize that the most valuable assets of their citizens and territories are available for commercial exploitation freely, clamor for protection of these assets naturally arises. While individual citizens are rightful owners of their “personal data”, its exploitation without the consent of the concerned persons is a serious infringement of privacy of the person. The European Union (EU) has been in the forefront of creating a stringent legislative framework to protect the “personal data” of its “data subjects”. The EU’s General Data Protection Regulation (GDPR) ( see here is the most comprehensive regulation enacted so far by any competent authority anywhere. But the overarching requirement of privacy protection does not necessarily imply that all data originating within a nation’s jurisdiction are to be considered as national assets. If “data subjects” are given national tags, it follows that nations-states would consider within their right to create barriers to cross-border data flow. The recent “data localization” policy of RBI needs to be analyzed from this perspective.

The term “data localization” is meaningful and relevant mainly in regard to data flow over the Internet, which is a network of computing devices without any single point of failure and consequent enabling of universal communication capability between all nodes. The internet service providers are not expected to control and be aware of what data flows through internet. Data localization, in essence, is a negation of this architectural construct of Internet. There are two forms of data localization. The first one localizes storage of data. It means that internet service providers must store data originating in a nation-state within the territorial boundary of that nation-state. The second form of data localization policy stipulates that routing of data packets must be confined within the country specific network. This form of localization is also called localized data routing. This is the most restrictive localization policy. Countries adopting data localization policy mostly adopt the first form of data localization. Chander and Le have identified following variants of this form of localization policy: ( see here  here )
1. preventing information from being sent outside the country
2. rules requiring prior consent of the data subject before information is transmitted across national borders
3. rules requiring copies of information to be stored domestically
4. a tax on the export of data

Data localization policies are being adopted by many countries because of the genuine concern of many national governments about the disproportionate capability of USA to access sensitive data pertaining to the respective countries’ national security available on data stores of Internet service providers, many of which are located outside the national boundaries of the concerned states. The Snowden episode confirmed the existence of a nexus, probably forced, between US security establishment and US technology firms including Google and Yahoo. Subsequent to Snowden revelations the German Interior Minister declared that, “whoever fears their communication is being intercepted in any way should use services that don’t go through American servers.” (  Hill 2014)  The concerned ministers of France and Brazil unequivocally lent their support to data localization policy. It is beyond doubt that one of the important factor of data localization policy of non-US countries is their desire to minimize “their comparative disadvantage in Internet data hosting” vis-à-vis US and “their comparative disadvantage in Internet signals intelligence”. ( Selby 2017) ) Thus data localization policy is being adopted by countries cutting across political regimes as a comprehensive review by Chander and Le shows. 14 countries studied by them are: – Australia, Brazil, Canada, EU, France, China, Germany, Indonesia, Malaysia, Nigeria, Russia, South Korea and Vietnam, besides India.

Accepting that above concerns of national governments is legitimate and requires to be addressed by the proponents of open and neutral Internet, a more informed and rigorous analysis is required to evaluate costs and benefits of data localization policy. It must be stated to the credit of the US technology giants that they are more concerned to uphold the sanctity of Internet than succumbing to the narrow national interest of US governments. For example, Microsoft, Google, Apple, Facebook and other technology firms successfully fought U.S. government in court “to gain legal authority to provide the public greater detail on the information the U.S. government collects from them”. (Hill 2014)  Many companies are taking steps to diversify their data center locations to escape stranglehold of US intelligence agencies.

Tying data to territorial boundary, also termed as “Data Sovereignty”, is a natural extension of the concept of sovereignty to the virtual world. Sovereignty connotes supreme authority within a territory. The term authority refers to, in the words of philosopher R.P.Wolff, “the right to command and correlatively the right to be obeyed” (see here). In a modern democracy this authority is derived from a set of principles, objectives, practices and code of conducts called constitution. “Data Sovereignty” means that this supreme authority can be enforced on data originating and /or pertaining to the people subjected to this authority. But despite their best efforts, the modern nation states have not been able to quarantine their national data in their entirety. This diminishing effect to a sovereign’s authority over data of their citizen is the driving factor of data localization policies of different countries. Even a country specific domain name like does not indicate the physical location of the server which hosts the website and provides information or services. Thus Internet is indifferent to physical location of computing devices comprising the cyberspace. So defining “Data Sovereignty” in terms of territorial authority is a non sequitur.

Recognizing the futility of transcribing laws enacted and bounded by physical space to cyberspace, Johnson and Post has called for “distinct laws” for this virtual space (see here). For example, how do we apply anti-trust laws to companies which operate only on cyberspace? The landline based telecom companies fought to restrict Internet based voice call (VOIP) but failed miserably. Digital currencies are being resisted by all central banks but there is no doubt that in the long run the central banks have to fall in line and adopt some form of central bank digital currency. Applicability or otherwise of country specific copyright laws to the cyberspace is another example of distinctive nature of cyberspace. A subscription based access to copyrighted contents on Internet has materially changed the consumers of these contents and its producers, resulting significant benefit to consumers in terms of reduced cost.
The proponents of free trade policy have pointed out how data localization policy is reincarnation of mercantilism in a virtual world. Treating data as an asset, nation-states wants more of such assets to flow to its own territory while for cyberspace location of an asset is of no consequence.

Let us, for example, suppose that Google is forced to store all data pertaining to Indian users of Gmail in India. First of all, how does Google identify a user as Indian? Presumably, it can be done by identification of IP address of the user. But what is to be done in regard to an Indian trying to register as Gmail user from abroad? Or when a foreigner is registering using an Indian IP? If an elaborate e-KYC norm is imposed on Internet users, it would be so cumbersome and costly for Internet service providers that the present practice of free Internet would be a casualty. Furthermore, if all data of a citizen are stored within the national boundary, the citizen might face difficulty to access her own data from abroad, if no mirror dataset is available in some other geography.
Internet works on routing of messages. This works on Domain Names identification and resolution of address within a domain. Today there are about 330 million domains. Even if a sovereign authority blocks access of its citizens to some domains, new domains can be created within no time to bypass such blocking. China is reported to have created the most restrictive firewall for access to Internet by its citizens. This might have helped in creating some of the world’s largest Internet enterprises like Baidu, Tencent and Alibaba. But it will also prove to be the greatest hurdle to realization of China’s dream of becoming the world’s dominant superpower. It is doubtful whether world population at large would like to share the fate of Chinese citizen – described as “world’s biggest prison for netizens.”
The hypothesis that data localization would prevent a foreign government’s ability to snoop on sensitive personal data of citizen of a nation-state is not borne out by some recent cyber-attacks, allegedly orchestrated by foreign governments. The alleged Russian interference in USA presidential election shows that in a networked world the security of data is not enhanced by creating physical access barriers to such data. The recent example of malware driven data hacking of Core Banking System of Cosmos bank of India is an example of the false assurance that location provides guarantee that data would be secure. It has been reported that NSA of USA has “even scaled the Great Firewall of China”. Thus data localization does not serve its primary purpose.

From a technological point of view, data localization is not a very efficient solution for running any cloud based application. A massively large database must be partitioned and stored in distributed databases. Today one type of partitioning known as “sharding” is followed by most large databases. Sharding breaks down very large databases into smaller databases to manage data retrieval very fast. Even a single record can be sharded into smaller parts. Database sharding allows maintaining very large data in less expensive commodity servers. A cloud based application cannot scale up if it maintains large databases in one place. The cost of maintaining data can increase exponentially because such large database would require high-end computers.

RBI’s data localization policy
RBI in a circular dated 6th April 2018, instructed all payment system providers “to ensure that the entire data relating to payment systems operated by them are stored in a system only in India. This data should include the full end-to-end transaction details / information collected / carried / processed as part of the message / payment instruction. For the foreign leg of the transaction, if any, the data can also be stored in the foreign country, if required”. (see here)
RBI’s data localization policy is driven by its intention to get unfettered access to payments data originating in India for surveillance purpose. RBI argues that such access is an absolute necessity for effective detection and prevention of any money laundering activity. The purported reason for requiring data storage “only in India” is that, in the event of any conflict with the country hosting Indian payment data of a service provider, Indian regulator may be prevented from accessing such data.
Although such an eventuality cannot be ruled out, in today’s interconnected world no country can unilaterally deny access to payment data pertaining to citizens of another country. Many countries including India now share financial and taxation data with other countries through bilateral or multilateral agreements. For example, India has signed bilateral agreement with US Tax authority to identify, document, and report U.S. accounts to comply with the U.S. Foreign Account Tax Compliance Act known as FATCA. A U.S. account is an account maintained by a U.S. person (whether individual or entity) or by a foreign entity with U.S. ownership of more than 10% of the capital, whether directly or indirectly. OECD countries are signing similar financial data sharing agreements amongst themselves and with other non OECD countries under the Automatic Exchange of Information (AEoI) initiative of G20 countries. Obviously such sharing cannot be a one way traffic. India being a member of G20 can direct the payment service providers to store data with such countries with which it has such data sharing agreement. If such an agreement is made on reciprocal basis, outright denial of access to India’s own payment data can be of remote possibility. India can mandate payment service provider to share all cross-border transactions with RBI through a FATCA type agreement with the host country storing Indian payment data.
As regards money-laundering and terrorist financing, India is a member of the Financial Action Task Force (FATF) and has implemented its recommendations. Data localization is not a recommendation of this international body. Additionally a government can enter into Mutual Legal Assistance Treaties (“MLATs”) with other countries to access data stored in another jurisdiction but needed for its own lawful investigative purposes.

Data sans Frontier
The pervasive effort towards data localization by nation-states is a reflection of deep insecurity that the nation states are feeling in a networked world. It is not understood that the rules of games have changed forever with the introduction of a radically different communication and workflow management architecture – that is Internet- that encompasses the entire world. The allurement of Mercantilism to the general public lied in its apparent pragmatism and simplicity. It ignored the feedback effect of such a policy and long-term consequences. The same is true of digital mercantilism that is driving the data localization policy.
Internet was lapped up by nation-states when it appeared to be a mere new form of message transfer. It was not understood how the new technology is going to undermine the basis of nation-states- that is the sanctity of the national frontier. “America First” is a vacuous and anachronistic concept when the most valuable US incorporated firms produce goods and services cutting across the national boundaries.
Let me conclude by referring to the reactions of policy makers when Galileo introduced his telescope to the policy makers. A senator in the Bretolt Brecht’s drama “Galileo” exclaimed- “the contraption lets you see too much. I’ll have to tell my women they can’t take baths on the roof any longer”. Galileo then attacked their materialist attitude saying: “These people think they’re getting a lucrative plaything, but it’s a lot more than that”. I am afraid that our policy makers are no better than these senators of Galileo’s time.


Castro Daniel(2013)  The False Promise of Data Nationalism  paper published by The Information Technology & Innovation Foundation (ITIF)

Drake William J  (2016)   Data Localization and Barriers to Transborder Data Flows:   Background Paper for World Economic Forum conference (

Hill Jonah Force (2014): The Growth of Data Localization post Snowden: Analysis and Recommendations for US Policymakers and Industry Leaders in Lawfare Research Paper series  July 2014

Selby John (2017): Data localization laws: trade barriers or legitimate responses to cybersecurity risks, or both? in International Journal of Law and Information Technology, 2017


India’s Job Crisis- Myth or Reality?

Prof Arvind Panagariya  (AP) in his 2nd May article (here) in the Times of India edit page has argued that the number of new job seekers on an annual basis cannot be more than 7.8 million between 2016 and 2021. His article is a rebuttal to the claim made by India’s main opposition party that around 12 million new job seekers are entering the Indian labour force every year. He has used Labour Force Participation Rate (LFPR) and the projected incremental population per annum in the age group 15 and above to arrive at this number.  Disregarding the issue of applicability of LFPR for the purpose at hand, his computation suffers from an obvious mistake- that is to compute flow from change in stocks between start and end of points of a time period. The measure of population is a stock measure as on a date.  To work out new entrants or inflow to this inventory we also need to measure the outflow of people from this inventory- that is death.  The overall death rate for Indian population is estimated to be 7.3 per 1000 of population.   Although death in the age group above 15 could be higher than this, let us apply the same to the initial stock of person – that is 928.6 million. So the number of person entering this age group ( 15 and above) would  stand corrected to 21.6 million instead of 15 million worked out by AP. Applying LFPR of 503 per 1000 persons , the estimated number of job seekers works out to around 11 million.

It must be also noted that LFPR is not a parameter that results from the behavioral characteristics of the population. In the jargon that AP would be comfortable with, I would call it an endogenous variable, decided by the job prospect, income level and many other characteristics.  Its use as a predictor of number of job seekers is questionable. In fact he himself has underscored the conundrum of very low and declining LFPR of rural females.  The 5th Annual Employment-Unemployment Survey, conducted by Labour Bureau in the year 2015, puts the female LFPR at a measly 23.7 % at all India level.  For China, the comparable figure is 63.9. But for males, the LFPR figures for these two countries are close to each other.  Cultural and social mores cannot explain such huge difference in female LFPR, when women from the poor households are always ready to work, provided they get regular employment.  Self-employment cannot be an acceptable option for young females in many cases because of lack of safe environment for them.  The gender gap in self-employed workers under “Usual Principal Status” is little more than 8% at all India level.

Instead of using population data and LFPR, we can look into some of other hard data. For example, let us consider the Gross Enrolment data by level of schooling, given in the annual publication- Educational Statistics at a Glance- by the Ministry of Human Resources Development. The latest publication gives the total enrolment for undergraduate studies in the year 2014-15 as 27 million students. Let us assume that average period for undergraduate studies as 4 year.  Thus we may expect that every year an inflow of around 6.8 million young educated Indian entering the job market.  Even if we assume 60% of this 6.8 million enters the job market this would imply the number of UG qualified job seekers would not be less than 4 million very year. So AP must provide more robust statistics to conclude that a figure of 11 or 12 million jobseekers can be considered as overestimation by a “solid 50%”. He needs a more solid ground for making that statement.


Note: Labour Force Participation Rate – This is defined as the number of person /person days in the labour force per 1000 persons/person days.  The labour force comprises both employed persons and job seekers (unemployed). A person is included in the labour force if he or she is either engaged in economic activity for a relatively longer part of the  reference period ( usually one year) or making “tangible effort” to seek “work” or available for “work”.  Full time students are not considered as part of labour force.  There are five categories of employment; self-employed, regular wage/ salaried employee,  contract worker or a casual labour. In our calculation we have taken LFR rate (50.3%) from the Fifth Annual Employment Unemployment Survey of the Labour Bureau.

NSSO report  Labour Bureau Report   Female LFPR study



Cryptocurrency Markets- concentrated and top heavy

Distrust in  fiat currency, controlled by a state, was one of the principal motivations in designing of  the Bitcoin protocol. It was designed  to be a decentralized system of creation of new money by a transparent computational algorithm.  Any person participating in the currency’s ecosystem can run this algorithm on a computer and generate new money. It is supposed to be a currency created by the people for the people and therefore a currency of the people. It is a currency of future when true democracy will prevail. here  here  here  here  here

But what is the reality? Who owns the bulk of these virtual currencies? To get an answer to this question, I looked into data about the distribution of these currencies amongst the participants in this technology game. The result of this exercise is truly revealing.

Data: We have collected data from the website which gives “Rich List” of some selected 9 currencies. We collected data as on 11th April, 2018. The market cap of these 9 currencies was 58.8 per cent of total market capitalization in terms of US dollar on that date.  On the data date, the total market capitalization of virtual currencies (excluding tokens) was 261 billion US dollar. So one can say collected data is adequately representative of the virtual currency ecosystem. The website has grouped data by value of coins  held against each address. An address having , say 0.001 bitcoin (BTC), would be grouped in the bucket “0 to 1 BTC” bucket.  For some cryptocurrencies , the number of coins held in an address may be very large as their market value is much smaller as compared to that of Bitcoin.  So the number of class intervals for coins held would be higher than a highly valued cryptocurrency like Bitcoin. To keep the results compact we have collapsed crypto wise class intervals into a common 3 classes. The following table gives a summary of distribution of value in US dollar and number of addresses across these class intervals.

Table 1: The distribution of addresses in terms of value of coin held and number of addresses for various class intervals of value of coins for each address.


Coin Name Market Cap


Share of each group of addresses below  in total number of addresses Share of each group of addresses  in total market  value of outstanding coin in USD Average


(USD) per address

<=1 full Coin per address 1 -100 coins per address More than 100 coin less than or equal to1Coin 1-100 coins per address > 100 coins
Bitcoin 130.3 4.06% 34.29% 61.7% 96.8% 3.1% 0.1% 5991
Bitoin cash 12.1 2.5% 27.6% 69.9% 97.1 2.8 0.1 737
Litecoin 6.8 0.4% 13.8% 96.7% 71.5% 27.1% 1.4% 2721
Dash 2.7 0.7% 9.4% 89.9% 82.0% 16.9% 1.2% 4094
Bitcoin Gold 0.8 2.9% 30.1% 67.0% 97.2% 2.7% 0.1% 38
Dodgecoin 0.4 0.0% 0.0% 100.0% 16.5% 42.2% 41.3% 183
ReddCoin 0.1 0.0% 0.0% 100.0% 14.8% 18.9% 66.3% 1317
Verticoin 0.1 0.0% 3.3% 96.7% 38.1% 47.1% 14.8% 715
Peercoin 0.0 0.0% 1.3% 98.7% 56.4% 32.0% 11.6% 934


It is obvious from the above table that only few addresses, each having more than 100 coins per address account for the bulk of total market capitalization of each currency. Bitcoin which the highest market capitalization of all circulating coins is also concentrated in a small number of addresses. The table 2 below clearly indicates how a few big market players have completely taken over each currency market.

The US tax authorities as well as Commodity Future Trading Commission have designated as “commodity” and not currency. From that perspective, this commodity market is highly monopolistic and susceptible to market manipulation by few large traders.  It is high time that anti-trust authorities in the developed economies wake up to this reality and take appropriate actions in the interest of average participant in these markets.

Table 2: The number of addresses and value held by top bracket by number of coins held per address

Coin Name Number of addresses in the highest bracket Market value of coins held by addresses in the top bracket (million USD) Share of these addresses in outstanding market value of the respective Coin
Bitcoin 3 3292 2.50%
Bitcoin Cash 7 954 7.86%
Litecoin 66 2776 40.67%
Dash 34 241 8.77%
Bitcoin Gold 12 93 12.38%
Dodgecoin 16 129 31.91%
Reddcoin 3 32 21.71%
Vertcoin 3 17 17.01%
Peercoin 2 8 18.10%

see here



The Next Battle- State versus Technology Giants

Two unrelated events hogged the headlines in the last month. On 22nd February Amazon became the third most valuable company in the world, overtaking Microsoft. On 25th February the official news agency Xinhua announced that the ruling communist party is proposing to remove the constitutional provision of “no more than two consecutive terms” for the country’s President and Vice-president. This would pave the way for the incumbent president to continue in the helm of power indefinitely.

The first event is a precursor of the future shape of the world economy while the second one is a forewarning of demise of liberal democracy as we understand today. These two events are also interrelated in the sense that their future trajectories will determine the denouement of the wrestling match that goes on between market power and state power.

Let us first have a closer look of the import of the first event. The Fortune magazine publishes a list of top 500 companies in the world, ranked by their revenues. Financial Times publishes a list of 500 top world companies ranked by their market capitalization. The latest Fortune data pertains to the year 2016 while market capitalization data is up to date as of 31st December 2017. For understanding the trend the time gap between two datasets has no bearing.

Top 10 Companies by Revenue and by Market capitalization:

Top Ten by market capitalization (M-Cap) Industry Top Ten by revenues Industry
Apple Technology Walmart Retail
Alphabet Technology State Grid Electric utility
Microsoft Technology Sinopeck Group Petro Chemical Technology Retailer China National Petroleum Oil & Gas
Facebook Technology Toyota Motor Car
Tencent Technology Volkswagen Car
Berkshire Hathaway Conglomerate Royal Dutch Shell Energy & Petro-Chemical
Alibaba Group Technology Retailer Berkshire Hathaway Conglomerate
Johnson & Johnson Pharma Manufacturing Apple Technology
J P Morgan Chase Banking and Financial services Exxon Mobil Oil & Gas

The most interesting feature of the above two rankings is that while still infrastructure and manufacturing industries dominate the top rung of the current corporate behemoths, the future potential behemoths are  growing up in the technology sector. M-Cap is an indicator of market’s prediction of future growth potential of a company. Amazon’s price-to-earnings ratio, a measure of how expensive a stock is in comparison to its current period earning, is 323 as compared to average ratio of only 22 for S&P 500 companies.

Ignoring Berkshire which is essentially a conglomerate, the only non-technology firm appearing in the top ten companies by M-Cap is JPMorgan, a financial service company.  So the market is predicting that the future of market economy lies with companies which are technology driven, technology enabled and most importantly innovation focused. On the contrary, the top companies ranked by currents revenues are enjoying benefits of their access to natural resources protected by concession arrangements with the state. So these companies have to work in close cooperation with the states giving concessions. Managing the states is critical to their existence and profitability. This is in sharp contrast of the business model followed by the emerging giants.  These companies using technology are creating an ecosystem of production of goods and services that are beyond the control of the geographically bounded nation states. In fact, these companies have much better understanding and access to actions and thoughts of the citizen of a state, particularly its younger ones than the any nation state has. Facebook was aware of any Russian meddling of US election, if any, than FBI could possibly have. Today, Google has much more information about its Indian users than the Indian Federal Government can ever have, Aadhaar notwithstanding.  In 2017, 46.8% of the global population accessed the internet and by 2020 this figure is projected to grow to 53.7%. It is obvious that this growth will benefit much more technology companies than utility, retail and infrastructure companies. We cannot expect exponential growth of companies which are organically linked with exploitation of natural resources.

These technology companies are gradually increasing their footprints beyond their original areas of operations. One study forecasts that the combined market share of Apple, Samsung, and Google (via Android Pay) is expected to reach a user base exceeding 500 million for mobile contactless payments by 2021. Amazon has started its lending business by offering to fund its suppliers. China’s e-commerce giants including Alibaba, Tencent’s and others are now running a lending portfolio over $12 billion. Apple owned US Treasury bonds ($52.6 billion) by the end of July 2017 and ranked 23rd in the list of US Treasury bond holders ahead of Netherlands and Turkey. These technology companies may gradually cut out intermediaries like banks and insurance companies by using Artificial Intelligence and Blockchain technology. Since data is the fuel of 21st century, the owners of data will have more power than any nation state.

How this development is related to the China’s decision to consolidate power of state in a monarchial coterie formed around the current incumbent?

The Chinese communist party has been able to put the country on a sustained high growth trajectory in the last three decades. The country is expected to become the world’s largest economy by 2030. China has used foreign capital and technology liberally in creating its manufacturing base.  A World Bank report of 2010 mentioned that “China received about 20 percent of all FDI to developing countries over the last 10 years and over $100 billion in 2008.  In terms of share of GDP and investment, FDI accounted for some 2.5 percent of GDP on average over the last five years”.   While welcoming FDI, Chinese ruling dispensation did not allow domestic private capital to capture the “commanding height” of the economy. 9 out of top 10 Chinese companies appearing in The Forbes 2000 list of 2017 are all state owned. If China has to establish its position as the first among equals in the international distribution of power it cannot afford to destabilize its own internal economic system built under the watch of party and the state. Till now, USA has been benign bystander, if not an active facilitator, of China’s rise as a global economic power. It was expected that economic growth and prosperity along with greater integration with world economy would slowly but steadily chip away the ideological foundation of the present Chinese political system. But this expectation of US policy makers has been belied. After disintegration of Soviet Russia, the world is witnessing, not the rise of liberal democracy, but rise of two dictatorial regimes under two most focused men who want their nations to occupy the high table of international power structure.

So we have on the one hand two authoritarian states (China and Russia) that carry the legacy of failed communism and other hand we have technology giants who are not fettered to any nation state nor bound by any geography.  David Ignatius, associate editor of Washington Post wrote in an op-ed piece that “China is racing to capture the commanding heights of technology and trade.”(here) The forces that will confront China are not the usual suspects- USA, UK or European Union states. This time the war will be fought in cyber space for capturing data about and of the people and the technology companies will have to fight for withering away of states as we know it now. We may recall that Marx desired withering away of states as the final goal of communism. In that sense these privately owned technology companies, rather ironically, would stand for one of the goal of communism as against the ex-communist regimes will stoutly defend the right of nation states. As some author wrote – the last battle will be between communists and ex-communists.

See here    here    here   here   here   here   here



Trump Tariff- Keynes would have approved

The US government’s recent tariff imposition of 25% on steel and 10% on aluminum imports from all countries except Canada and Mexico is a textbook example of economic policy making to serve narrow political interests. This article is not to examine the economic rationale of such a policy that its proponents have been offering. We are not interested to know what an optimal tariff policy should be for a country that has been the dominant trading partner for most the nations during the last 50 years.  My main objective in writing this piece is to demonstrate how a nation state, like a chameleon, changes its policy to serve its own interest, perceived or real.

USA has a history of imposing high tariff when it suited interests of various pressure groups within the country. The two most important tariff acts enacted in the history of USA are the Fordney-McCumber tariff act of 1922 and the Smoot-Hawley tariff of 1930. The first one was a response to the drastic fall in farm income between 1919 and 1921- the period following the end of the First World War. The tariff act of 1922 increased the average tariff on dutiable import from 16.4 to 36.17%. The Democratic President Woodrow Wilson did not support imposition of such a steep hike in tariff and vetoed the legislation passed by the Congress on his last day in office. His successor, the Republican President, Warren Harding signed it into law within 3 months of assuming office. The Smoot-Haley act came into force on June 17, 1930. Initially the Republican President Herbert Hoover opposed increase of already high tariff regime. More than thousand economists signed a petition to the president opposing the new tariff proposal. Big corporations as well as large Wall Street firms opposed the bill.  The following quote from Wikipedia is revealing

  1. P. Morgan’s chief executive Thomas W. Lamont said he “almost went down on [his] knees to beg Herbert Hoover to veto the asinine Hawley-Smoot tariff.

The main supporters of the bill were those industries and farmers who expected to benefit directly from enhanced tariff on goods that they were producing.

There are two alternative explanations for preferences for higher or lower tariff regimes.  One identifies “pressure group” politics as the main driver of increased tariff regime.  The other one emphasizes the “party politics” as the main driver of enactment of these two tariff acts. A study on Effective Rate of Protection attributable to any tariff measure examined the impact of the above two tariff measures on various industries of USA. The study found enough evidence for the “pressure group” theory. It is interesting to note that one of the major looser was the auto industry and no wonder that Henry Ford vehemently opposed the Smoot-Hawley tariff act, calling it “an economic stupidity”.

With the Democratic President F.D. Roosevelt in White House in 1933, USA started changing its trade and tariff policy. Reciprocal Tariff Act of 1934 authorized the president to enter into bilateral negotiation with foreign nations to reduce tariff on a reciprocal basis.  This Act is considered as the beginning of a liberal trade regime policy by USA.  In the post second world period, USA turned out to be one of the main driving forces behind multilateral trade agreements like General Agreement on Trade and Tariff (GATT) and formation of the World Trade Organization (WTO). The pursuit of liberal trade policy continued under both Republican and Democratic Presidents.  President Kennedy brought in the 1962 Trade Expansion Act and helped start the Kennedy Round of world trade talks. Republican President Reagan launched the Uruguay round in 1996 and Democratic President Clinton helped establishment of WTO.  We may note that this is the period during which US economy clocked highest average growth in GDP. From 1950 to 2010, the decadal growth rates of US real GDP were: 3.6(1950s), 4.3(1960s), 3.2(1970s), 3.3(1980s), 3.3(1990s), 3.4(2001 to 2010) and 2.1(2011 to 2017). We may also note that, US share in world export during the same periods were:  15.4(1950s), 14.2(1960s), 11.7(1970s), 11.2(1980s), 12.0(1990s), 9.0(2001 to 2010) and 8.5(2011 to 2016). Thus, a nation’s economic policy making is guided by national self-interest alone.

Fault lines in the bipartisan support for a liberal trade policy started appearing with dwindling manufacturing jobs in core industries like metal and coal. With the advent of Internet, even white collar jobs in programming, back office maintenance, digital marketing etc. began to move offshore.  The standard economic theory says that a country should specialize in those industries in which it is relatively more efficient. If jobs are migrating from USA to foreign countries, USA should insist on liberalization of trade and services in which it has distinct comparative advantage.  In fact, the US supremacy on technology front is beyond doubt and the top 4 valuable companies in the world are technology companies of US.  Instead of capitalizing on the country’s strength in scientific and technological innovation and making outstanding products out of them, the present Republican President is bent on leading the country to a black hole.

But should we blame the present President as an ignoramus of the complexities of   international trade theory and barking up the wrong tree by assuming high tariff per se would be a remedy for loss of competitiveness of some of the US industries? I believe not. He is, in fact, in good company of some of the doyens of economic theorists of 20th century.  Let us take the example of John Maynard Keynes, who after Adam Smith and David Ricardo can be considered as the most influential British economic thinker of the last century.

Keynes was an ardent advocate of free trade as long as his own country was the dominant economic power of the world. But as the British economy started to falter with rising domestic unemployment Keynes reversed his stand. While analyzing Keynes’ views on protectionism, Barry Eichengreen has this to say: – “Keynes repeatedly reversed his public position on the advisability of protection, and it has been difficult to portray the sequence of seemingly contradictory recommendations as a logical progression of thoughts”. Of course, Eichengreen tried to salvage the reputation of Keynes as an economic theorist and policy adviser by saying that Keynes views about protection was “surprisingly consistent” if we consider them in the light of his view on what the paramount goal of economic policy should be. And that goal is maintenance of full employment of his nation.  Why an economist should only seek full employment in his or her country when the same policy might generate severe unemployment in other countries? Schumpeter has rightly described policy advices given by Keynes could be seen as “always English advice, born of English problems even when addressed to other nations”

In 1933 Keynes gave the inaugural Finlay lecture titled “National Self-Sufficiency” at Dublin. The lecture was delivered in the backdrop of rising protectionism of the Irish Free State- then a British Dominion. To a large extent the rise of Irish protectionism was due to imposition of penal import duties on Irish agricultural imports in 1932. Thus the choice of lecture topic by Keynes was a deliberate one. He knew that he had to tread a fine line between the interests of his own country and that of another neighboring country- the country hosting his lecture. While not rejecting outright the rationales that he had espoused in defense of free trade, he came to justify some of the protectionism measures of the Irish Free State with the following words:

It is my central contention that there is no prospect for the next generation of a uniformity of economic system throughout the world, such as existed, broadly speaking, during the nineteenth century; that we all need to be as free as possible of interference from economic changes elsewhere, in order to make our own favourite experiments towards the idle social Republic of the future; and that a deliberate movement towards a greater national self-sufficiency and economic isolation will make our task easier, in so far as it can be accomplished without excessive economic cost 

Thus Keynes was apparently justified to change his earlier position on Free Trade because, in his opinion, the uniformity of economic system prevailing in 19th century had changed. But in reality what had changed was the loss of uniformity brought about by the suzerainty exercised by the British Empire on the world at large.  Today, USA is in the similar position. We are yet to know whether this repetition of history will end in tragedy or farce.



Archibald Robert B et al (2000):  Effective rates of protection and the Fordney-McCumber and Smoot-Hawley Tariff Acts: comments and revised estimates: in Applied Economics No 9

Eichengreen, Barry (1984): Keynes and protection in the Journal of Economic History No2

Keynes John Maynard (1933): National Self-sufficiency in Studies: An Irish Quarterly Review  No 86

Schumpeter, J . A (1946):  John Maynard Keynes in American Economic Review Issue No 4


India’s Biggest Operational Risk Event

The PNB –Nirav Modi case is a text book case of an operational risk event. The fact of the case is now well known. The case revolves around letters of undertaking (LOU) issued by PNB (issuing bank) to overseas branches of many Indian banks.  An LOU is, in essence, an irrevocable bank guarantee issued by a bank (Issuing bank) on behalf of its customer to another bank (Recipient bank).  The recipient bank extends credit (buyers’ credit) to the issuing bank‘s customer by way of financing import of goods as a part of the latter’s legitimate business. In this case, the issuing bank is PNB and the customers are companies owned by billionaire diamond merchants Nirav Modi and Mehul Choksi. These two happen to be also close relatives. The fraud began in 2011 with a small amount of 800 crore and gradually ballooned to 11000 crore ($1.8 billion) when it was ultimately detected. This gradual increase in the size of the loss is identical to many earlier operational risk cases. For example, in the Baring bank case (1995), the fraudster Nick Lesson got deeper and deeper into the quagmire when he tried to cover up initial loss with a bigger bet, hoping that luck would turn and he would be able to get away with laurels and not a jail term of six and half years. In a similar way the rogue trader Jérôme Kerviel of Société Générale (SG) wanted to cover up trading losses to ultimately leading SG to stare at a total loss around $7 billion in 2008. Although these cases are now part of the standard literature on operational risk, it appears from the PNB event that there is complete lack of awareness or even basic understanding about the seriousness of operational risk events on the part of top management of banks and also the board of directors of Indian banks.  It is a known fact that Indian banks are more concerned about submitting risk compliance reports and meet capital adequacy norms set by RBI than establishing proper risk governance architecture within their respective organization. Most of them lack basic knowledge of risk management and do not care a hoot about it also.

The full details of the PNB case are yet to be made public. But the main features of this operational risk event are now in public domain. Nirav Modi and his firms managed to procure LOU from PNB’s Brady house branch with the connivance of branch officials and using these LOU obtained short term credit from foreign branches of many Indian banks to finance import of diamonds. The LOUs were communicated with the financing branches thorough the SWIFT messaging platform. When the time of repayment arrived, Modi could get more credit through the LOU route to both  pay back the old loan as also obtain fresh loan.  Thus size of PNB’s contingent liabilities continued to increase without raising any alarm in the controlling offices of the LOU issuing branch. When the main fraudster within PNB retired and a new official took charge of his desk, this smoothly managed scheme, started unravelling. The new official asked for required 100% margin as collateral from Modi’s firms when they came for roll over of the outstanding LOU as before. This was a standard operating procedure as these firms were neither customers of the branch nor enjoying any credit facility from the bank.  Then the digging of old records started and the enormity of the fraud came to light.

Let us now analyze the case from the risk management perspective. It is clear that this is neither a case of credit loss nor a trading loss. It is a case of both internal and external fraud.  We need to seek answers to the following questions.

  1. Could this fraud be avoided or at least the loss amount contained?
  2. Was the procedural failure only on the part of PNB or even lending overseas branches banks were equally culpable? Was the connivance systematic at both at the issuing bank side as well as on the side of lending banks?
  3. What lessons Indian banking system should learn from this incident?

Avoiding or containing the fallout of such an incident would depend on the establishment of an effective and robust operational risk framework within the bank.  The first requirement is to have a Key Risk Indicator (KRI) for all processes and tasks that a bank undertakes. In the present case, the following KRIs would have surely prevented occurrence of this incident or at least contained its loss amount. These are:

  • The number of employees with tenure at a desk more than a given threshold. Depending on the potential severity of loss that can happen for a specific desk, threshold can be fixed.
  • Leave record of employees- list of employees who have been manning a desk for a long period without talking leave for desks handling customer engagements.
  • Reconciliations of transactions- on balance sheet as well as off balance sheet ones- as between various transactional systems, including those carried out on SWIFT platform. SWIFT itself provides a daily validation report, giving a global summary of the bank’s inbound and outbound counterparty payment /messages. If suspicious or fraudulent activity occurs, such a report provides the information y that could have helped the bank to cancel messages and recover funds.  This reconciliation should be treated as a mandatory control mechanism for avoidance of occurrence of incident like this.
  • Ideally, the bank should have integrated SWIFT messaging system with its Core Banking System. In the absence of this, the bank could have procured applications that generate report of all activities carried out on the bank’s SWIFT system. Many such systems are available in the market1  

Apart from KRI tracking and monitoring, a bank needs to establish a Risk Control and Self-Assessment (RCSA) process across the banks’ all operational units.  It is obvious that PNB did not put in place such a system in the bank despite an warning bell was rang by RBI itself about the possibility of occurrence of exactly such an event  ( see speech of  S.S.Mundra  on   September 7, 2016)2.

It is really sad state of affair in Indian banking sector that neither RBI nor the top managements of the public sector banks are seriously concerned about the risk governance architecture prevalent in these banks. For them the implementation of Basel framework starts and ends with computation of regulatory risk capital.

As regards the liability of PNB to the lending banks, we may refer to a similar case where a fraud happened at the issuing bank end and, therefore, the issuing bank refused to honor the Stand By Letter of Credit (SLBC) when it devolved on it. The fact of matter is as follows3.

Banco Ambrosiano Veneto S.P.A ( the defendant) ., an Italian bank was said to have been issued two SBLCs in  favor of Industrial & Commercial Bank Ltd of Singapore ( the plaintiff). On devolvement , the Italian bank refused to pay the Singaporean bank on the plea that it never intended to issue the two SBLCs in question which were issued by  one of its employee, , fraudulently, pursuant to a fraudulent scheme involving  this employee , a customer, a Plaintiff’s employee and others. The case was heard by the Singapore High Court in 2001 and was decided in favor of the plaintiff bank. While deciding the case the honorable judge said the following:

It is my view therefore that SWIFT messages have the legal effect of binding the sender bank according to the contents. The fact that a recipient bank may still wish to protect itself by doing checks on credit standing or other aspects does not detract from this proposition. SWIFT communication is still subject to the general law of contract.

However, this does not mean that the recipient banks can completely absolve themselves of establishing a proper risk managements system within their banks. A continuing roll over with larger and larger amount of LOU to a group of companies from the same promoter should have alerted the recipient banks. In fact, these banks should have found out whether a single branch had the authority to issue LOUs of such magnitude. It shows lack of rudimentary risk management practices within the recipient banks also.

The only lesson that Indian banks should learn from this episode is that risk management is a serious business, not a practice for showcasing to the regulator.  For the most of Indian banks risk management means hiring a consultant to prepare a guideline and procurement of an application. That is the end of it. For example, PNB boasts of having an enterprise wide Data Warehouse (DW). One should ask the bank-why all swift messages are not stored in the bank’s central repository?



  1. see here
  2. see here
  3. see here

Trillion Dollar Economy – no magic required

Reaching $ 5 trillion GDP benchmark is being projected as a landmark milestone for the Indian economy by many in the Government. The latest estimate puts the size of Indian economy, measured as GDP at current market prices, at $2.4 trillion. Does reaching $5 trillion mark reflect a significant achievement or is it really an implicit admission of incipient sluggishness in growth of Indian economy? Let the data speak.
Indian economy registered an average annual growth of 12.8% in GDP (at current market prices) during 17 years – from 2000-01 to 2016-17. During the same period, only in 2 years, the growth rate fell below 8%, the minimum being 7.6%. The 25 percentile growth rate was above two digits at 10.4% and the median rate was 13%.
Given this nominal growth scenario of recent past, dollar value 5 trillion appears to be too modest a goal to be set by the current government. Table 1 gives the projected value of Indian economy till 2035 under various growth rate scenarios. Even if Indian economy grows at the lowest rate achieved during last 17 years, the size of the economy would reach at least 4 trillion US dollar by 2025. If rupee depreciates in mean time the growth in dollar terms would be lower. At 70 rupees per dollar, the size would become only 3.9 trillion dollar by the end of 2025.
A nominal yearly growth rate of 7.6 percent would imply, given 4 % target growth rate notified by the government of India (GOI), a real growth rate of only 3.6 percent. A 10.4 % growth rate would thus imply real growth rate of only 6.4%. Thus if projections of $5 trillion is in nominal terms, then no big achievement is being claimed.
Suppose, all the projections are being made under the assumption of constant prices of 2016-17, then the target average growth for next eight years should be 9%. India has achieved a double digit real growth rate in the last 66 years only once. Even in the last 25 years India clocked a real GDP growth rate greater than 8% only 6 times. So the probability of clocking a real growth rate of more than 8% is as low as 25%. Achieving 9% uniform real growth rate till 2025 with stable rupee-dollar rate might be a chimera. If rupee depreciates to 70 rupees by 2025, we would need an average year over year real growth rate of 11%. It could be a dream worth pursuing but there is no evidence in terms of accelerated investment or growth in productivity that could make the dream a reality.
In summary, reaching 5 trillion USD by 2025 in nominal terms is no big deal – a lower than achievement would be considered a highly disappointing performance. Achieving the same target in real terms could be an enormous challenge- heralding a real structural break in the Indian economy with productivity led growth and not merely by adding more capital and labour.

Table 1: Projected USD Size of Indian Economy under various growth rate scenarios and with constant exchange rate of 63.5                      ($Trillion)

 Distribution Measures based on data from 2000-01 Compound Growth Rate of GDP at current market prices 2025 2030 2035
Minimum 7.6 4.2 6.1 8.9
1st quartile 10.4 5.2 8.6 14.1
Average 12.8 6.2 11.4 20.9
Median 13 6.3 11.7 21.5
Note: Only first decimal value without rounding up has been reported. Projections are based on CSO’s preliminary estimate of GDP at current market prices for year 2016-17.

Note: Only first decimal value without rounding up has been reported. Projections are based on CSO’s preliminary estimate of GDP at current market prices for year 2016-17.

Corporatization of Nations

Reliance Jio Infocomm Ltd, the telecom arm of India’s largest company by market cap (NSE:RELIANCE), plans to create its own cryptocurrency called JioCoin. Supply chain management logistics and loyalty payment with JioCoin are amongst the envisaged uses of JioCoin. Worldwide, many large corporates have already started launching their own private cryptocurrency.  KFC, Burger King and Kodak are the few well-known names that have hitched onto the bandwagon of this currency of the Internet.

Fast-food chain KFC has announced that it will accept  virtual currency for paying bills in its outlets in Canada with the launch of Bitcoin Bucket. A customer can buy this bucket with 0.0011564 BTC, equivalent of CAD $20, according to a company statement. Eastman Kodak has announced last week that it is going to launch its own cryptocurrency KodakCoin in partnership with WENN Digital. Burger King has launched its own cryptocurrency in Russia called ‘WhopperCoin. IBM recently partnered with Stellar and klickex to develop a blockchain-based cross-border payments solution. Stellar is a distributed hybrid Blockchain that facilitates cross-asset transfer of value including payments. Similar to Bitcoin, Lumen is the asset of value issued by Stellar.

This rush by multinational companies to get on board of the cryptocurrency mania camouflages a much larger issue and a potential threat to the current international political order. This threat can be analyzed from various perspectives including political, economic and technological.

Glenda Sluga, a Professor of International History, has written about  the possible unraveling of the current international order from a historical and political perspective: These days, the pulse of the world’s political health is running fast. The general prognosis is terminal, the end of the international world order, as we know it.(here)  Political headwinds which led to such prognosis are easily discernible- rise of “radical nationalism” in USA, “long the axis of modern international society” and, rise of “heteropolarity” in the international power structure.

Beyond this haze of political chaos, a much bigger threat to the existing international world order lies in emergence of private cryptocurrency and its adoption by multinationals. There is no gainsaying the fact the comity of nations defines the current world order. A nation state without its own currency is like the staging of Hamlet without the prince of Denmark. The power of a nation state to tax its citizens would stand highly diminished if large corporates can issue their own currency. Let us see how it would play out in reality.

Alice buys 1000 JioCoins (JC) by paying, say, 20000 Indian rupees. Alice pays 60 JC to Bob for as rent for the apartment she has leased from Bob. Bob buys monthly grocery from the supermarket run by Reliance. Bob uses 250 JC for this purpose.  Bob tops up his Jio mobile with 50 JC. Suppose, Bob works in Reliance Industries and receives 3000 JC every month. Bob pays 750 JC to Reliance Petroleum for purchase of gas for his car. He has purchased his car by taking loan from HSBC by paying EMI of 750 JC every month. This EMI payment is routed through a cryptocurrency exchange run by a Russian bank.  RIL pays IBM India monthly 100 million JC for maintaining its IT infrastructure. IBM may pay RIL is own cryptocurrency for using Jio mobile services in India. Gradually, a complete ecosystem of economic agents can emerge, who will use JC as their preferred currency for all their payment requirements.  It may be seen that the JC to Rupee exchange takes place only when Alice purchased JC. At a certain stage of development JC will acquire its own life, cutting its umbilical record with the fiat currency.

The above description of evolution of JC to encompass a significant slice of economic transactions in its country of incorporation, i.e. India, does not necessarily imply that JC would pose a threat to the  existence, or at least severely restrict usefulness,  of the sovereign currency INR as a fiat money. Initially, INR can continue to be the unit of account within the boundary of India. But if JC emerges as the people’s preferred medium of transactions and store of value, only raw state power can prevent rupee’s passage to oblivion. Be that as it may, the moot question is whether JC would severely dent the Indian state’s capability to tax the economic activities mediated through JC. Given the ability of a cryptocurrency to mask the identity of transactors, imposition of indirect taxes, like goods and service tax, might be a serious challenge for tax administration. If an Indian resident tax payer earns and spends only in JC, fixation of its tax liability would not be an easy task.

If all multinationals including RIL, IBM and others can agree on an exchange platform for conversion of their currencies then the nations of the world would find themselves divested of their defining power to tax and earn seigniorage.  The depth and reach of the large multinationals can be gauged from the fact that in 2016 the Fortune 500 companies had revenue of 27.7 trillion USD while the combined GDP of 198 countries was around 76 trillion USD. Using Output to GDP ratio for US economy, the share of Fortune 500 companies in the worlds’ GDP would work to around 21 percent.   So if these large corporates were to sign off from the current international order with their own currencies, their total GDP would be the second highest, next to USA only. They can usher into a new Bretton Wood regime for their private currencies and significantly reduce the cost of managing exchange rate risk.

Technologically, these large corporates could not but continue to be part of one country or other, at least formally, the emergence of cryptocurrency can unshackle them from this tether of fiat currency and its in-built inflationary bias. If the world economic order get re- arranged on this line, what would happen to the multitude of people who would continue to remain outside the charmed circle of digital economy, can only be a matter of speculation and not an informed guess.

Central Bank Digital Currency -a Blueprint

eRupiah: RBI’s Virtual Cash

Key words: Central Bank Digital Currency, public key-private key cryptography, Digital currency wallet, Corruption,

Introduction: No currency has ever been used in the human history which did not have the stamp of an authority. Bitcoin is a medium of payment but it is not money for the same reason. Nonetheless, the technology underlying Bitcoin is a significant one with great potential. A central bank, issuer of paper currency, can use some selected components of Bitcoin technology to replace paper currency with virtual currency, retaining all the important features of paper currency. The most important of them is that a central bank note is a freely negotiable bearer bond and a legal tender in the hand of its holder. It does not require any third party verification. Counterfeiting a central bank note is not impossible but difficult and costly. The central bank neither authenticates any transaction made with that particular note nor does it keep any record of that transaction. The note remains as a liability on the book of the central bank till it comes back to it, either for reissue or its destruction. The physical nature of the note ensures that no double spending is possible with the same note by its current holder. In case of digital cash, the main issue that a central bank has to resolve is the issue of double spending without depending on third party verification of the same. What follows hereunder is an outline of a system that any central bank can implement to issue its own currency retaining most, if not all, of the desired properties of a paper currency.

I am presenting below a system based on digital currency on a mobile phone. There is no compelling reason to believe that the same system cannot be implemented on a specially designed smart card with embedded chip. The system outlined below is described within the currency management framework of the Reserve bank of India (RBI). With little tweaking the same can be customized by any central bank.

RBI Currency Management Framework:

RBI carries out its currency management function through its 19 Issue Offices located across the country. There is a network of 4281 currency chests and 4044 small coin depots in selected commercial bank branches. These chests store currency notes and rupee coins on behalf of RBI.  The note distribution mechanism is summarized in the following diagram.

For issuance of digital currency, each currency chest would function as a data center for hosting the ledger book of notes issued from it.   Similarly each issue office of RBI would have a copy of the entire ledger book of notes. A folio would be opened in the note ledger book when the first time a specific note is issued.  Each data center will have complete inventory of wallets issued by RBI.

Every bank branch would have a digital cash dispenser. Any wallet holder would be able to replenish her wallet with digital currency by pairing it with the dispenser via Bluetooth or NFC communication channel.  Similarly every ATM would have similar facility. At the time of cash dispensation from bank branch or ATM would require Aadhaar based biometric verification of wallet.  For cash transfer between wallets of two individuals this verification is not a necessary requirement.

The protocol for issue of eRupiah

  1. RBI would maintain ledgers of each currency note in a distributed database.
  2. Currently RBI issues notes through its Issue offices. The distributed database will be created according to issue departments of RBI. Each Issue office of RBI will be able to issue new digital currency and destroy old digital currency. Destruction of old digital currency would help RBI to keep the number of entries in the ledger folio of a particular note within a limit. Every Issue offices would maintain record of all notes issued by it as well as copies of corresponding records of 3 neighboring Issue offices.
  3. Each currency chest will have a database of notes received by it from RBI’s Issue department.
  4. Each currency chest will also have replicated database of its three nearest neighbor
  5. The system will issue new digital currency when an account holder wants to withdraw cash from its account with RBI.
  6. The account holder would specify how much of its cash withdrawal would be in digital form. This facility would be provided for an interim period when both forms of currency would be in circulation.
  7. To incentivize issue of digital cash, RBI may reward with a fixed amount that could be related to the cost of producing physical cash.
  8. RBI is banker to the Central and State Governments. It also functions as banker to the banks and thus enables settling of inter-bank obligations. These account holders of RBI would get digital cash in their Jumbo Wallet which would be a server in the account holder’s custody. It would be like a till holding cash. An authorized person can withdraw e-Rupiah from the till as and when required.
  9. The RBI’s Note ledger would comprise ledger folios of each currency notes issued.
  10. Each record in the Note ledger would comprise the following attributes: (1) a sequential no, (2) unique identity / sr no of a note, (3) hashed value of the note serial no, (4) identity of the issue department, (5) denomination, , (6) time stamp of transaction, (7) hashed value of identity of paying wallet (first time payer would be RBI), (8) hashed value of identity of receiver wallet, (9) active flag,   (10) hashed value of first 9  attributes , (11) hash value of the first 9 attributes of earlier transaction record of the same note. The identity of a wallet is described below.
  11. RBI will also maintain database of each wallet downloaded from its website.
  12. The wallet database will have a header record with the following attributes (1) IMEI no of each phone, (2) Aadhaar No of the phone owner, (3) timestamp of successful downloading of the wallet, (4) the GPS location of the phone at the time of downloading of the wallet, (5) a unique private key generated for each wallet and (6) the corresponding unique public key generated for each wallet. This data would also be hashed and encrypted with RBI’s private key and will be part of the header record. RBI’s public key would also form a part of the header record. The private and public key of each wallet would be generated by RBI at the runtime. The hashed value of attributes 1 to 6 would be the identity of each wallet.
  13. Each wallet will have its own database of transactions. Each record in the transaction database will represent a note that has been loaded into the wallet. Each record will have the following attributes: (1) unique identity of the note, (2) note denomination, (3) digitally signed (with the private key of the paying wallet) hashed value of the concatenated string of serial no and denomination, (4) digitally signed ( with the private key of the paying wallet) hash value of concatenated string of attributes 1 and 2 of the header record with private key of payer wallet, (7) public key of the paying wallet, (8) timestamp of last transaction( i.e. timestamp of receipt of the note , (9) timestamp of the payment transaction, (10) payment status (paid or unpaid), (10) hashed value of the earlier transaction of the note(attributes 1,2,3,4,5).
  14. A transaction between two wallets would involve “note data” transfer from the paying wallet to receiving wallet. Every note that gets transferred from the payer’s wallet to the recipient’s wallet would essentially mean transfer of the entire record from the former to the latter. In the process of data transfer two insert / update activities take place in the receiver’s and payer’s wallet respectively. The receiver’s wallet inserts a new note record while the payer’s wallet updates the concerned note’s existing record.
  15. Once the receiving wallet gets a new e_Rupiah note, it checks the authenticity of the note by calculating hash value of the concatenated string of attribute 1 and 2 of step at 13. In the payer’s wallet the status flag would get changed to “paid” while in the receiver’s wallet it would continue to have the status flag as “unpaid”.
  16. Any wallet would have a limit in terms of number of records / notes. When the database has reached its limit then the wallet would have to be uploaded to RBI and a new wallet has to be downloaded.
  17. At any point of time a single wallet would be subject to 2 limits- holding limit of no of transactional records and total value of a single transaction. For a high value transaction two factor authentications would be required. (say above one lac). Both paying wallet as well as receiving wallet has to simultaneously establish connection with RBI and get their credential verified.
  18. As and when no of records in a wallet’s transactional database reaches its limit, the database has to be downloaded in an ATM or at a bank branch. The wallet would be purged of the all transaction records with status as “paid”. The wallet holder then can download more E_Rupiah from an ATM or from a bank brunch. RBI will update its ledger book of individual notes thus uploaded from each wallet.
  19. Any fraudulent transactions identified in the process of uploading would get notified and thorough automated forensic audit perpetrator of fraud would get identified.

Continue reading “Central Bank Digital Currency -a Blueprint”

Trading or Investing in Bitcoin is Injurious to your Financial Health.

One of the fundamental lessons of all financial scams is that there always exists enough number of gullible people to be conned by merchants of dream. For example, the people of 17th century Amsterdam started believing that prices of a bunch of tulip bulbs could rise to a level higher than the value of a furnished luxury house. It also happened during the dotcom bubble of late 1990s. Presently such a bubble is unfolding before our own eyes and the sad part of it is that some financial sector regulators are actively encouraging formation of this bubble in the name of financial innovation. It would be apposite here to recall the scathing criticism that the Financial Crisis Inquiry Commission of US Congress made of the regulatory failure leading to the sub-prime financial crisis:  We conclude widespread failures in financial regulation and supervision proved devastating to the stability of the nation’s financial markets.

U.S. financial firms CME Group and CBOE are going to launch Bitcoin futures on December 18, followed by launch of binary options on Bitcoin by Cantor Fitzgerald. The US regulator for futures market, Commodity Futures Trading Commission (CFTC), has allowed introduction of these new products by these exchange platforms on the basis of self-certification submitted by them. The Commodity Exchange Act of USA allows such exchanges called Designated Contract Markets (DCM) to introduce new contracts by submitting a written self-certification to the CFTC that the contract complies with the Commodity Exchange Act (CEA) and CFTC regulations. It is the responsibility of DCMs to determine that the offering complies with the CEA and Commission regulations.

The CFTC in its press release of 1st December has referred to the IRS characterization of Bitcoin as a virtual “currency”. More than that, IRS has referred it as “convertible virtual currency”. I have already explained in my earlier blog why Bitcoin cannot be called a currency. In 2015, CFTC declared Bitcoin as a “commodity” by referring to the CEA act that includes “all services, rights, and interests in which contracts for future delivery are presently or in the future dealt in.” in the definitional boundary  of commodity. The press release clarifies that “Bitcoin and other virtual currencies are encompassed in the definition and properly defined as commodities”.  Under CEA commodities are classified into three categories-

(1) Agricultural commodities

(2) Excluded commodities which include, inter alia, an interest rate, exchange rate, currency, security, security index, credit risk or measure, debt or equity instrument, index or measure of inflation, or other macroeconomic index or measures

(3) Exempt Commodity which means a commodity that is not an excluded commodity or an agricultural commodity.

Prof.  Shadab of New York Law School has argued in his written statement submitted to the CFTC that Bitcoin should be classified as “exempt commodities and not as excluded (currency) commodities “.

Each Bitcoin future contract on CME would be composed of 5 Bitcoins. The tick size (the minimum fluctuation) has been fixed at $5 per bitcoin, amounting to $25 per contract. Per person open position limit has been set at 1000 contracts. The daily price fluctuation of a Bitcoin future is limited to a 20% band above or below the prior settlement price.  The settlement price will be Bitcoin Reference Rate (BRR). BRR is calculated by UK based crypto currency trading platform -Crypto Facilities Ltd, in partnership with CME.  BRR is calculated by taking traded price and volume data from a few selected exchanges involved in spot Bitcoin trading.   Price and volume data are obtained for 12 periods of 5 minutes each   in the last hour of trading. For each time interval, a volume weighted median price is calculated. The overall price is average of these 12 prices.

So, purely from methodological perspective, construction of reference price cannot be faulted. Since BRR is based on observed prices of Bitcoins traded on mostly unregulated exchanges, these prices are always subject to manipulation.  The extent of volatility that can happen on these exchanges can be understood from the movement of bitcoin price on December 7. On this day, the price of 1 Bitcoin fluctuated from a high of USD 19,000 to a low of USD 4,000.  If the price volatility is considered in conjunction with volume volatility (see the graphs below), Bitcoin may turn out to be Twenty First century’s first virtual Tulip.








Data source- here

Given this “insane volatility” ( as described by the chairman of BBCBS committee) of spot prices of a traded asset, the CFTC’s move  in allowing derivative products  on such an asset can be highly counterproductive. Apparently the CFTC believes that by bringing Bitcoin on a regulated platform it would be able to contain the speculative excess.  The high margin requirement is expected to dissuade small investors to take positions in the Futures market, leaving the field open for play by institutional investors. More than 100 hedge funds have been created in the last one year to trade in digital currency only. It is reported that there is $10B of institutional money waiting on the sidelines to invest in digital currency today. To meet the requirements of these institutional investors, Coinbase, the US based Bitcoin exchange, has launched a new company to store securely their digital assets (see here). The company has claimed that it is already holding $9 billion of digital currency on behalf of its customers.

It should be a matter of regulatory concern about the source of Bitcoin’s price volatility. Apart from alleged price manipulation the most plausible explanation would be the intrinsic unbridgeable gap between demand and supply of Bitcoin. New supply of Bitcoin is largely a result of mining activities and the maximum supply of Bitcoin is a known figure. Against the back drop of a largely inelastic supply curve, the demand curve is driven by enthusiasts of cryptocurrency- a fast growing tribe. The fundamental inelasticity of the supply curve is getting reflected in higher and higher cost of Bitcoin based transactions.   The following two graphs show how running Bitcoin network becoming costlier and costlier.







Given its inherent supply constraint, there is no possibility of Bitcoin becoming a global currency in its current form. Since Bitcoin is a highly sophisticated technological product, it attraction to young people is immense, like marijuana once was. But it should be the job of central banks to proclaim from the rooftop with as much force as it can command that: Trading and or Investing in Bitcoin is injurious to your financial health.