Should you buy a car or Uber around?

Tomorrow’s car will represent a steep change in form and function. According to Goldman Sachs 2025 cars will be green, convenient, safe and affordable (Goldman-Sachs, 2016). The transition has already happened with the EPA push for a fuel economy standard set tentatively at 50 miles per gallon despite the reluctance of the automotive industry (Snavely, 2016). Safety and convenience are strong megatrends that have taken an interesting turn: driver is not wanted anymore. According to NHTSA, drivers are the reason for 94% of crashes (NHTSA, 2016). Table 1 show the statistics of the critical reasons of accidents. What if the humane element can be removed from the equation?


Table 1: Driver-, Vehicle-, and Environment-Related Critical reasons

Table 2 shows more specifically the reasons of the humane error and what should be the core safety strategy of the algorithm of a computer-driven vehicle: recognition and decision.


Table 2: Driver-Related Critical reasons

However, completely autonomous vehicles programmed not to crash are already on the horizon. According to McKinsey, combining autonomous and non-autonomous vehicles in a single traffic mix will be a significant challenge (McKinsey, 2014).

The most difficult time is likely to be the transition period, while both kinds of cars learn to share the road before self-driving ones predominate. (“Self-drive only” lanes and dedicated roadways might be the first step.).Passengers will be responsible only for choosing the destination, would have the freedom to do what they please in a vehicle. Connected cars—especially self-driving ones—could also change the way people use their drive time.

In a 2013 survey, more than 50% of respondents said they would prefer to listen to music, talk on the phone, watch videos or browse the Internet while traveling by car. Disabled, elderly, and visually impaired people would enjoy much greater mobility. Throughput on roads and highways would be continually optimized, easing congestion and shortening commuting times. As self-driving technologies are quickly developing the next logical question is the ownership of car. If a car can drive by itself then it can do a lot more during the day then packing parking lots around cities.

The majority of vehicles worldwide are used only to commute or for short trips during the day, leaving them idle 95% of the time. Would drivers decide to forgo ownership and access cars only when they need them? Several options could be imagined to replace ownership but one of them is already being tested by Uber as self-driving Uber rides already tested in Pittsburg (Huston, 2016). According to McKinsey, the transition to fully autonomous mobility is ineluctable with a transformation of at least 35% of vehicles to some form of autonomy by 2040 (McKinsey, 2014).

According to the Economist, all over the rich world, young people are getting their licenses later than they used to—in America and also in Britain, Canada, France, Norway, South Korea and Sweden. Even in Germany, car-culture-vulture of Europe, the share of young households without cars increased from 20% to 28% between 1998 and 2008 (The future of driving, 2012). Unsurprisingly, this goes along with driving less. American youngsters with jobs drive less far and less often than before the recession. 16- to 34-year-olds in American households with incomes over $70,000 increased their public-transport use by 100% from 2001 to 2009.

There are different measures of saturation: total distance driven, distance per driver and total trips made. The statistics are striking on each of these counts even in America, still the most car-mad country in the world. There, total vehicle-kilometres travelled began to plateau in 2004 and fall from 2007; measured per person, growth flat lined sooner, after 2000, and dropped after 2004 before recovering somewhat (see chart). The number of trips has fallen, mostly because of a decline in commuting and shopping (of the non-virtual variety).

Given the current megatrends and the on-going self-driving tests conducted by Uber in Pittsburgh, one can consider the self-driving service as a viable and attractive option becoming available in the very short term. Therefore, we run a Cost Benefit Analysis between purchasing a new car and using driverless Uber rides to commute.


 Few Assumptions are as follows 

  1.  The Car: First key assumptions are the annual mileage. Four annual mileage were considered in our sensitivity analysis: 10k, 15k, 20k and 25k miles per year to reflect the potential variations. The total initial investment is $15,000 for a 2016 mid-size car typically used for commuting in a relatively dense and urbanized area. According to the Independent Insurers Agent, there are a number of used cars you can purchase for under $15K, particularly if you are looking at cars that are 5 years old or older. In the under $15,000 price range, you will only find new compacts and subcompacts such as the Chevy Sonic or the Hyundai Accent (Agents, 2014). Average car prices are currently in the 30k range in the US but it represents a wide range of vehicles purchased for more than cost efficient urban commuting, e.g. minivan or trucks.

Annual costs for operating a car were based on a report published by AAA in 2015 (AAA, 2015) . These estimates include licenses and tax ($665) and insurance ($1115). We kept these items constant in our study although one can argue that insurance could vary somewhat with the mileage driven. The general regular maintenance ($766.5), the annual tire replacements ($147) and the fuel costs were pro-rated with the mileage. The nominal value provided by AAA at 15,000 miles per year was ratioed accordingly. We estimate the lifetime of the car to be 200,000 miles. The regular maintenance budgeted in our operating costs will keep the car running. For instance a mileage of 10,000 miles per year will assume that the car lifetime will be 20 years. We assumed that the car will show an average fuel economy of 25 miles per gallon over its entire life and that the fuel cost will average $2.80. These assumptions match the total fuel cost proposed by AAA of $1681.50 per year. Note that electric cars would represent a different analysis.

            2. The Uber Ride: Another key assumption is the cost to use a driverless Uber service. According to Ark Invest, “with an autonomous system of $2,000, the price of an autonomous taxi could drop to $0.35/mile (after factoring in the cost of remote human operators that could guide vehicles in emergencies) given the improvement in autonomous error rates and the drop in electric vehicle battery costs, while still providing a healthy return on capital to the service provider.” (Keeney, 2016).    Uber will probably start by offering higher rates so our sensitivity analysis includes $0.40, $0.45 and $0.50/mile.

          3. Financial Assumptions :Additionally, we applied a 2% annual inflation rate to the costs required to operate and maintain the car.  When calculating the net present value, we assumed a discount rate of 5%

Financial Analysis

Car cost

The total mileage was divided by the year mileage to obtain the lifetime of the car. After applying the ratio to the fuel, maintenance and tire expenses, the costs were added on an annual basis to obtain the car cost per year. This number is divided by 12 to obtain the car cost per month and also divided by the mileage to provide the cost per mile.

Compact Car  $   15,000
year mileage    15,000
total mileage     200,000
years       13.3
Operating Costs  
fuel  $1,681.50
insurance  $1,115.00
maintenance  $   766.50
license, tax, etc.  $   665.00
tires  $   147.00
Total costs
car cost/year  $4,375.00
cost/month  $   364.58
cost per mile  $       0.29

Table 3: Cost hypothesis

Uber costs

The mileage rate was multiplied by the car mileage and divided by 12 months to obtain the monthly cost of the Uber ride.


Net monthly cash flow is the difference between the monthly car cost and the Uber ride cost. 2% inflation was applied to the net cash flow using this formula

Inflation  = NCF(1+2%/12)^n

Discount Cash Flow (DCF) was calculated as

DCF = Inflation (1 +5%/12)^-n

The Net Present Value was the recurring difference between the initial investment minored by each monthly DCF payment. The Internal Rate of Return was computed as the interest rate that would bring the NPV to zero at the lifetime set as the ratio of the total mileage (200k) over the year mileage. Following tables are showing the results of the computation of the Cost Benefit Analysis.


Table 4 shows the monthly gain versus the mileage and the Uber cost per mile. This metric show that there is always a monthly gain in owning a car except at the lowest mileage and the lowest Uber cost. Only with that combination that Uber rides are most cost effective solution. This situation would arise in a dense city with multiple commuting options such as New York where the mileage put on a car would be necessarily low.


mileage 25,000 20,000 15,000 10,000
$0.5 533 397 260 124
$0.45 429 313 198 83
$0.4 325 230 135 41
$0.35 220 147 73 N/A

Table 4: Monthly Gain ($)

Present Value at maturity, considered here as the car lifetime which varies from 8 years to 20 years, indicates at what condition the monthly benefit is worth the ownership of a car in the long term (Table 5). Even at the lowest cost of Uber rides (bottom raw) the long term benefit of riding with Uber is a better option than owning a car if the distance covered exceeds 20,000 miles year.


mileage 25,000 20,000 15,000 10,000
$0.5 $45,442 $41,089 $37,934 $29,322
$0.45 $36,560 $32,457 $28,830 $19,482
$0.4 $27,678 $23,825 $19,726 $9,643
$0.35 $18,795 $15,193 $10,621 N/A

Table 5: PV of Benefits ($)

At the lowest level of mileage, riding Uber is a viable possibility as long as the cost per mile is lower than $0.40. Table 5 shows that despite some monthly benefit at owning a car a few options are not overcoming the initial investment of the purchase of a car. At low mileage or low Uber cost a few options are most sustainable.

The internal rate of return shown in Table 6 confirms that low mileage and low Uber cost mean a significantly low DCF in order to achieve break even at the car lifetime. By the same token, higher Uber cost and extensive mileage cause a relatively high IRR.

mileage 25,000 20,000 15,000 10,000
$0.5 43% 32% 19% 8%
$0.45 33% 24% 13% 3%
$0.4 23% 16% 5% 0%
$0.35 11% 5% 0%

Table 6: IRR for Car lifetime (%)

Table 7 shows a response of the Profitability Index (PI) ratio consistent with the growth of Present Value with time which can overcome the initial investment in the ownership of a car. PI higher than one indicates a go decision in owning a car versus a PI lower than one suggesting that riding Uber is the best option. Once again the relatively small variation of the cost per mile of the Uber ride translates into a dramatic shift of the financial rationale to drive a car.


mileage 25,000 20,000 15,000 10,000
$0.5 3.03 2.74 2.53 1.95
$0.45 2.44 2.16 1.92 1.30
$0.4 1.85 1.59 1.32 0.64
$0.35 1.25 1.01 0.71

Table 7: PI for car lifetime

Table 8 shows the number of years before achieving the financial break-even point taking into account inflation and Discount Cash Flow. This duration needs to be put in perspective of the car lifetime for each annual mileage. Based on our hypothesis of maximum mileage equal to 200,000 miles, the lifetime of the car at the highest mileage is 8 years. At this annual mileage, the initial investment would paid off by the gain from Uber rides. At a rate of 20,000 miles per year, the car lifetime is only 10 years and would not be recovered if the Uber rides fair at $0.35. The same conclusion can be made at an annual rate of 15,000 miles because the car lifetime is 13.3 years. However at the lowest mileage rate of 10,000 miles, the Uber rides would be advantageous at $0.40 because the car would break down before reaching the break-even time.


mileage 25,000 20,000 15,000 10,000
$0.5 3.5 4.3 5.9 11.3
$0.45 4.1 5.3 7.4 16.6
$0.4 5.2 7.0 10.5 32.8
$0.35 7.3 10.9 30.3

Table 8: Break Even time (Years)


Intangible benefits must be addressed in this important decision-making process. The most significant benefit of using Uber ride is the possibility to work, sleep, phone in the car and use the commuting time more effectively. For example, the ride could be accounted as work hours by progressive employers. The gain from driving would be realized through a higher productivity during this time of day.

Uber rides would also represent the possibility for more people to move around town with less cars. It would also help reduce the growth of parking lots in crowded areas. Uber rides will ultimately communicate information on their destination opening the possibility to share a ride with more than one person.

Even though the maintenance costs are factored in our calculations, there is a definite amount of time taken by repairs or recalls when owning a vehicle. It also requires to own a garage and in the case of extended family to take over the neighbor lawn as well. One major concern would be the impediment of driverless cars in bad weather or in an accident. Uber indicated a central command would always be in touch with the fleet.   Would the infrastructure enough on the road to support driverless cars. The US government just releases a proposal for an investment of 4 billion dollars in the preparation for driverless car accommodations as Michigan just adopted the most permissive regulations for this new technology. Lastly there is always a possibility of misuse of personal information or hacking of the software in the autonomous cars. Leading to unsafe and unpleasant situations.


Our analysis has shown that above 15,000 miles/year a commuter is recovering its investment when comparing with the cost of Uber rides especially above $0.40/mile. This critical price milestone has been announced as a realistic target but is based on decreasing radar production costs that need to be demonstrated. However, there are some significant intangible benefits at not taking care of a vehicle, not to have to buy a house with gigantic garages and increase our productivity during our commute. It would definitely help with a lower stress level.

PS: This paper was written as part of group assignment for Financial management


AAA. (2015). Retrieved from

Agents, I. I. (2014, 06 2). Retrieved from

Goldman-Sachs. (2016). Retrieved from

Huston, C. (2016, 09 15). Watch Uber’s self driving cars hit the road in Pittsburgh. MarketWatch. Retrieved from

Keeney, T. (2016, 2 13). Low Cost LiDAR Will Save Lives. Retrieved from

McKinsey. (2014). Retrieved from

NHTSA. (2016). Retrieved from

Snavely, B. (2016, 10 11). Automakers lobby to ease MPG standards despite improved fuel economy . Chicago Tribune. Retrieved from

The future of driving. (2012, 09 22). The Economist.



Do we manage financial risk correctly?

A quote from Taleb’s book, beautifully sums up how we view and manage risk.

“The existence of a risk manager has less to do with actual risk reduction than it has to do with the impression of risk reduction. . .. By “watching” your risks, are you effectively reducing them or are you giving yourself the feeling that you are doing your duty?  (41-42 FBR)”

In the article “Can we keep our promise” Mr. Arnott say we cannot measure returns sensibly without effectively measuring risk. He also talks about how we get sidetracked into primarily measuring peer risk versus measuring it as a whole or as multidimensional risk. Since financial managers are paid for peer risk management that becomes the main focus

To understand the multidimensional aspect of risk, let’s look at the three major category of risk

  • Peer Risk – in other words the fear of being wrong and alone –  this risk gets the most attention and it measure success based on similar portfolios managed by others and peer benchmarking. Our well-being depends on how others perceive us and how we rank compare to them.
  • Asset Risk – this risk refers to an asset whose value may fluctuate due to changes in interest rates. We try to measure the asset’s default potential or market value fluctuation. Few example of asset would be equities, high-yield bonds and currency
  • ALM Risk – asset liability management risk is the practice of managing risks that arise due to mismatches between the assets and liabilities. One example of this risk is pension plans. It arises mainly due to the following
    • Interest risk – The risk of losses resulting from changes in interest rates and their impact on future cash-flows.
    • Inflation risk – The uncertainty over the future real value (after inflation) of the investment.
    • Longevity Risk – The risk to which a pension fund could be exposed as a result of higher-than-expected payout ratios. Higher than what a company originally accounts for as people live longer.
    • Adverse selection risk – is the risk that sick pensioner’s tends to withdraw amount as lumsum whereas healthy pensioner’s preference a monthly payout. Thereby increasing the payout ratio.

Lastly there is always a career risk which exists as a person might lose job if he/she adopts an unconventional way of viewing and managing risk.

In his article, Mr. Arnott say that that these risks are interdependent and by focusing on any one risk investors leave themselves exposed to the other two risks. Suppose the company focus on reducing peer risk, by doing so they take large absolute risk and larger risk related to liabilities of the fund. All three risk are important individually too, all three matters, managing Peer risk is important as returns relative to peers outline the competitive positioning of an enterprise. Asset Risk is important as we may lose excess money due to market value fluctuation and ALM is important as they have the highest impact on our liabilities even with minor fluctuation on interest rate and future payouts. Despite the importance of all three dimensions of risk the primary objective for consultants and financial advisors has been to focus on Peer risk.

At the time this article was written, GAAP accounting allowed much of this risk to be smoothed out over several years, so the impact on reported earnings was about 80% (based on 5 yr smoothing) lower than the actual impact. This took away focus from the ALM risk and downplayed the importance of managing the same. Arnott’s approaches to sizing up the Asset-Liability Risk is to convert the % data(stand. Dev.) into a financial metric such as dollar at risk per share of common stock. This helps in putting things in perspective and helps in identifying the quantum of impact. As we see from table below the average fund has much more exposure to Asset Risk and ALM Risk than to Peer Risk. The ALM has the highest impact at about 60% more than the other two.

Table 1. Potential Losses for 19 Large Pension Plans for 2003 Due to:

Peer Risk         Asset Risk      ALM Risk

St. Dev. %                     2.5%               12.4%                  15.0%

Impact on EPS         -$1.28              -$2.61                 -5.12

St. Dev %: this is the standard deviation (volatility) for each risk around its expected value (its mean)

EPS: Earnings per Share (for 2003, the 19 firms with these pension plans had an average EPS of $2.24)

Mr. Arnott say that long term pension plan (> 30 yr) usually comprise of 1/3 of the total responsibility but it’s extremely sensitive to interest rate. In short its small liability but has big volatility. This small liability is five times more sensitive to interest rate than a pension liability which is due next year.

Based on the table/figure 2 – we see that when interest rate drops from 7% to 5% the long-term liability increased 76% as against short-term liability increasing by only 6%.  Also the volatility of the longer term pension liability is asymmetric.  If interest rates were raised by a certain % say the liability would fall by x% and if they were decreased by the same % then instead of a X% rise we actually notice a (X+Y) %rise.  The volatility is skewed, it is not symmetric in positive and negative direction.  Which makes it extremely important to be managed in the right way.

To balance the three risk , Mr Arnott suggest to invest such as to bring each of these three area to similar level of risk and then targeting to beat any two benchmark out of three. This sift in benchmarking would give the consultant or investing officer incentive to focus on the performance relative to the liabilities and offer more “tolerance towards peer risk”.  This mean that as long as they are not being punished for falling short of peer risk, they can focus on risk that have greater impact.

The current most common allocation for pension plan is too little on short term bond and the primary reason for this is stated as long term bonds yields too little. Arnott recommend that most pension plan should have a modest level of investment on long term bonds as such to substantially reduce the risk on pension plan. He says that merely by investing 10% of fund’s asset in ultra-long strip, we can eliminate ~30% of interest rate sensitivity and ~40% -50% of asset liability mismatch.

Ref: Can we keep our promise  by Arnott


Demonetization 2.0

It’s been a week that India’s highest banknotes have been demonetized, but one could still feel the rippling effect across the nation. The demonetization has been a mixed bag. People liking the decision is indirectly proportionate to their position in the society. By position I mean what they do and how they do business(work). Lower income level higher dissatisfaction. Higher income lower dissatisfaction

Let’s look at the good that has happened because of the decision, Overnight the value of high bank notes became zero, this was a sudden blow on the copious counterfeit notes which lost its sheen. Black money is the backbone of terrorism and the circulation of the same stopped as the value of the stored notes became zero. It is believed in the long term; the economy will benefit from the reduction of black money as it will lead to higher tax collection, better business environment, less corruption, and transparency. Besides, there is a strong possibility that banks would reduce interest rate after being flushed with funds. I also feel it was paramount to set an example to the racketeers and money launders that a robust stand will be taken against them and the future is uncertain and bleak for them.

Now the bad, people who have read my previous blog know my take  on issuing a higher currency note. Assuming the primary purpose was to avoid the immediate cash crunch, we all know how ineffective it was.  India has a rural population close to 67%1 i.e. 0.87 billion people. Do they worry about the  ₹ 2000 note or are they more worried about their daily wages which are less than the  ₹ 500 notes that got banned? Of the remaining 33% urban population, only 18% 1 are salaried workers and remaining are self-employed or businesspeople.  In short, the effect of this decision resonated across 85%~90% of Indian population. Considering the 80:20 rule, 20% of the population hold 80% of the wealth and apparently only 5% of the elite population came under the radar of this decision.  Also, India is a cash-intensive economy, a little bit preparation to manage the situation better was expected from the think tanks behind the decision. We all know about the pressure built on the banking system which was not used to ,nor prepared to manage such huge influx of cash.

The small-time street vendors are apparently wiped out from the scene. It will not be surprising that after farmers if they start committing suicide. The government needs to take a drastic step in managing the situation in a better way. The  ₹ 50 and ₹ 100 circulation should be increased to ease the situation.

Last they Ugly part of the decision was the Implementation, it’s said that the best-laid plans go wary if not implemented correctly. The image2 beautifully sums up my feeling without writing them in words


Lastly,  The purpose of the blog is not to find faults but to get all the aspects forward. Being a proud Indian, I’m happy that measures are taken to control the black money, terrorism, and counterfeit notes. I would like to see it extended to all walks of life and not being limited to a certain sector of the society.

Nesha Jacob

Continue reading “Demonetization 2.0”

India’s ₹ 2000 Currency – Knight in the shining armour or clown in the silver foil?

On the eve of 8th Nov, the Prime minister of India announced demonetization a of the ₹  500 and  ₹  1000 denomination currency notes. The nation went into a frenzy, thinking at first it’s a joke and then scrambling to get their act right to get the cash in hand converted into valuable currencies before it loses its value. This step was primarily taken to combat black money and to check fake currency (believed to finance terrorism) and corruption.  Along with the demonetization of currency, it was stated that ₹  2000 denomination currency would be available within two days.

Now, based on the data b we can see that during 2014 – 2015, the value of Indian currency valuein circulation was 14289 crores (142.89 billion Indian rupees) and  ₹ 500 and  ₹ 1000 denomination currency contributes to 85% of the currency in circulation.  Considering the above statistics this looks like a master stroke which will achieve the objective that it set out for.  This is a welcome step considering the in-activeness of the previous government.

A lot has been written on the benefits of the action, I would like to draw attention to the other aspects , here are my two cents

  1. What does it mean to rich (and probably the political lobbyist)? Nothing much, to say the least, Wealthy individuals do not stash their ill-gotten wealth as cash but they generally have a voracious appetite for investing it in real estate, jewelry and other expensive stuff (cars, watches etc.), to offload their cash. This way either their black money gets convert to white or gets stashed abroad. A probable solution to target this group would be, first digitization of all government offices, throughout the country and linking all purchases with the PAN and Aadhar numbers of individuals, then to snoop on all the immovable assets of individuals such as plots, flats, villas, shops bought by people and try to match that with the source of individuals’ incomes and find out any mismatches and levy penalties were necessary. This will unearth the real black money and their’ sources but this is a mammoth task.
  1. What does it mean to common man or the rising middle-income group? This is the group that got affected the most. I would like to bifurcate them into three categories, First the small business owner who has a need and greed of carrying out his business in cash. Now they must declare their wealth or the stored cash will lose its charm. They also have maintained some money in bank accounts to not affect their daily life. Second, is the Salaried individual who gets his money electronically and is least affected but tremendously happy. The only inconvenience caused to them was standing in the queue at the bank and losing their productive time to get their cash in hand (if any), converted to the new ₹ 2000 notes or deposit it in the bank. Lastly, the lower income group, people who earn their wages daily or monthly but in cash (vegetable vendors, daily laborer, drivers, maids etc.). This group faced the maximum brunt and had the most crippling effect. These people would either have a few old high denomination notes with them, which they cannot use (Going to the bank and getting it changed, would mean losing a day’s wage for them), or their employers would have to procure Rs. 100 notes first, in order to pay these people. Now another situation, which is problematic is, when a vegetable vendor receives ₹  2000 notes from five different customers, who bought vegetable worth  ₹ 200 – ₹  300 each. What is he/she supposed to do? Carry all that change in ₹ 100 notes or install a card machine, neither of which is practical. All in all the lower income group was left holding the shorter end of the stick.
  1. What does it mean to the fake currency racket? Based on an analysis, it is estimated that it costs India, around ₹ 3 to print a ₹  1000 note and it costs approximately the same to print a counterfeit note.  This basically means that a note of ₹ 1000 can be printed with a cost of ₹3 and value of ₹997 can be created.  This scheme would not be valuable with lower denomination note like  ₹ 20,  ₹ 50 or even ₹ 100 as they would have to print a lot of fake currency to make it worthwhile. The Financial Action Task Force (FATF), is a global body that looks at the criminal use of the financial system, states that high-value bills are used in money laundering schemes, counterfeit racketeering, and drug and people trafficking.

If we look at the highest bank notes as derivative of the GDP per capita income c,d  we see how we compare to other countries. While different economies work differently, it does gdpgive some insight as to where we stand and what is the scope of improvement. Economist’s back-of-the-envelope calculation shows that, the highest currency denomination in India should be around ₹ 250 e. The logical way to curb the fake money racket would be to demonetize all high bank notes.

what next? Economic Affairs Secretary, Shaktikanta Das said at the Economic Editors’ Conference that, In due course, ₹1,000 notes will come back in the market with new dimensions, new design and new colour,”. The new design and feature would be added to ₹100 and ₹50 note as well. 

We would have to wait and see the impact of the demonetization strategy and of ₹ 2000 note. While I see an immediate short-term effect, long-term effects need to be closely monitored. Demonetization is only a minor solution to the major problem of black money in India. In addition to the current move, government should take steps, to extract black money stashed abroad in tax havens, which will greatly help in eradicating this problem and hopefully lower inflation.

Finally, food for thought, a conspiracy theory!!!  I came across a theory on social media which stated that there may be a connection between the launch of Reliance JIO and demonetization? Some people claim that the owners of Reliance JIO were privy to the information of demonetization and hence they invested most of their black money to create a whole new telephony system and provided it free of charge to public till Dec 30th (same day till when old high denomination notes can be exchanged) and then later, receive the returns as white money, from users as monthly usage charges.

I’m not sure if this is true. Please do leave a comment and let me know, what you think..!!

Nesha Jacob 

Continue reading “India’s ₹ 2000 Currency – Knight in the shining armour or clown in the silver foil?”