Why Trump’s Plan to Make America Great Again Will Not Take-off

donald trump

Donald Trump was sworn in as the 45th President of the United States on January 20, 2017. One Trump plan to make America great again is to reduce the American trade deficit.

The trade balance is essentially the difference between the imports and the exports of any country. If the trade balance of a country is in negative territory, it is said to run a trade deficit, which the United States does.

Take a look Figure 1. It plots the American exports and imports from 1960 onwards.

Figure 1:US import and Export Chart 

Up until the early 1980s, the American imports were more or less equal to American exports. But things changed after that and America started running a trade deficit. Take a look at Figure 2. This plots the American imports and exports from 1980 onwards.

Figure 2:US import and Export Chart 

In fact, take a look at Figure 3, which maps America’s imports and exports since 1990.

Figure 3:US import and Export Chart 

One look at Figure 3 tells us that the import curve and the export curve closely map each other. What does that tell us? It tells us that the dollars earned by the countries which export goods and services to the United States (essentially imports for the United States), are used to buy goods and services being exported by the United States.

Hence, there is a clear link between the total imports and the total exports of the United States. So where does that leave Trump’s plan? As Peter Navarro, an economist known to be close to Trump, and who served as a policy advisor to the Trump campaign, puts it: “Trump proposes eliminating America’s $500 billion trade deficit through a combination of increased exports and reduced imports.” The trade deficit of the United States in 2015 stood at $500.4 billion.

So how does Trump plan to bring down imports? As his website puts it: “[He plans to direct] the Secretary of Commerce to identify every violation of trade agreements a foreign country is currently using to harm our workers, and also direct all appropriate agencies to use every tool under American and international law to end these abuses.”

Trump also plans to: a) Instruct the Treasury Secretary to label China a currency manipulator. b) Instruct the U.S. Trade Representative to bring trade cases against China, both in this country and at the WTO. China’s unfair subsidy behaviour is prohibited by the terms of its entrance to the WTO. c) Use every lawful presidential power to remedy trade disputes if China does not stop its illegal activities, including its theft of American trade secrets – including the application of tariffs consistent with Section 201 and 301 of the Trade Act of 1974 and Section 232 of the Trade Expansion Act of 1962. (Source: https://www.donaldjtrump.com/policies/trade)

Trump plans to impose import duties (i.e., tariffs) in order to ensure that the cheap Chinese imports into the United States, no longer remain cheap. CNN reported in late December 2016: “President-elect Donald Trump’s transition team is discussing a proposal to impose tariffs as high as 10% on imports, according to multiple sources.”

This is not going to be so straightforward. If Chinese imports into the United States become expensive, the consumer price inflation in the United States is likely to go up, given that American citizens will have to buy more expensive American products. Also, a clamp down on imports in general and Chinese imports in particular, will lead to countries earning fewer dollars. This means that they will have fewer dollars to imports goods and services from the United States. Hence, a fall in US imports will also lead to a fall in US exports. Hence, the trade deficit may not differ much from its current levels.

Over and above this, if the United States imposes import duties other countries can do the same. This will impact US exports as well. Hence, it is important to understand there is a positive correlation between US imports and US exports. While the US maybe the global bully, China isn’t exactly a pushover.

There is another point that needs to be made here. A huge portion of the dollars earned by countries by exporting goods to the United States and other parts of the world, has made its way back into financial securities issued in the United States. This includes US government bonds. This money has been one of the reasons which has kept interest rates low in the United States.

As of end of November 2016, foreign investors held $5.94 trillion worth of US government bonds (or treasuries as they are better known as). Of this China held close to $1.05 trillion worth of bonds. The point being that if US pushes its luck too far with China, China always has the option of dumping these bonds and pushing up bonds yields and in the process interest rates in the United States. While, it may never come around to doing so, it still has the option. And the United States understands this. Hence, bullying China won’t be easy.

Where does all this leave us? It brings us to that term post-truth. The term post-truth politics has been used quite a lot in the recent past, with the rise of Donald Trump in the United States. It was first used by the blogger David Roberts in 2010.

As Emma Kilheeney writes in Politics e-Review edition for October 2016: “Roberts coined the term to describe a situation in the US Congress where the Republican Party made no attempt to win support for its policy decisions by providing evidence-based arguments. Instead, it opposed all policies put forward by the Democratic Party in order to exploit the emotional responses and loyalties of its followers.”

The Economist defines post-truth as “a reliance on assertions that “feel true” but have no basis in fact.” Hence, the assertion that Trump will decrease US imports and increase US exports may feel to be true, it has no basis in logic and facts.

(The column was originally published on Equitymaster on January 23, 2017)

Why WhatsApp is the new BBC

whatsa

Cable TV came to India in late 1991 and early 1992. Before that large parts of the country could view only one television channel and that was the government owned Doordarshan.

Doordarshan continued to remain a force to reckon with as long as it had monopoly on news broadcasting. The irony was that most people who took their news seriously, would believe something only when they had heard it on BBC radio.

In fact, even those who did not take their news seriously but wanted to push their point of view, would claim to have heard some piece of news originally on the BBC. The point being that just saying that you had heard it originally on the BBC, even if you had not, gave the whole point of view that you were trying to push, a lot more credibility.

The rise of cable TV essentially ensured that people largely stopped watching Doordarshan, at least in urban areas. The other thing that happened was that people stopped tuning into short wave radio as well to listen to the BBC radio service.

Dear Reader, you must be wondering why am I writing about this, nearly two decades later? Is it a burst of nostalgia? Perhaps. But more importantly I am just trying to make a comparison. In the 1990s, people used to believe the BBC, which had inherently more credibility than anything else, now they believe whatever has been sent to them on WhatsApp.

I have had people arguing with me on the merits and demerits of demonetisation on the basis of long WhatsApp forwards that they have received. It’s like the 1990s when people wanting to push their point of view, they simply said that they had heard it on the BBC. Now, they say they have seen and read it on WhatsApp.

When I tell them that WhatsApp forwards can be motivated and essentially made up by those wanting to push a particular point of view, I get brushed aside. That’s the credibility that WhatsApp has these days, among many people.

As Roland Barthes writes in an essay titled The World of Wrestling which is a part of a collection of essays titled Mythologies: “The public is completely uninterested in knowing whether the contest is rigged or not, and rightly so: it abandons itself to the primary virtue of the spectacle, which is to abolish all motives and consequences: what matters is not what it thinks but what it sees.”

Hence, people see the WhatsApp messages, read them and believe them. They don’t question them in most cases. In fact, psychologists Amos Tversky and Daniel Kahneman conducted a very interesting experiment to show that people tend to go with what they see and end up being majorly wrong in the process.

As Michael Lewis writes in The Undoing Project—A Friendship that Changed the World: “A bunch of high school students [were given] five seconds to guess the answer to a math question.” There were two groups. The first group was asked to estimate the product of 8×7×6×5×4 ×3 ×2 ×1. The second group was asked to estimate the product of 1×2×3×4×5×6×7×8.

Both the groups were essentially asked the same question, with only the order of digits being reversed. As Lewis writes: “Five seconds wasn’t long enough to actually do the math: The kids had to guess. The two groups answers should have been at least roughly the same, but they weren’t even roughly.”

The actual answer to the question is 40,320. The median answer for the first group was 2,250 and for the second group was 512. The first group’s answer was more than four times, the second group’s. As Lewis writes: “The reason the kids in the first group guessed a higher number for the first sequence was that they had used 8 as a starting point, while the kids in the second group had used 1.”

Hence, depending on what they saw, the kids came up with what they thought the right answer was. Kahneman calls this what you see is all there is and that explains why people believe what they see and read on WhatsApp so much.

The column originally appeared in Bangalore Mirror on January 18, 2017.

The Problem with India’s Economic Growth is…

indian flag

Around a week back, the ministry of statistics and programme implementation came up with estimates of the gross domestic product(GDP) for 2016-2017. The GDP is expected to grow by 7.1 per cent in comparison to 7.6 per cent in 2015-2016. This estimate does not take the negative impact of demonetisation into account. Once demonetisation is taken into account, the economic growth (as measured by the GDP growth) is likely to be significantly lower than 7.1 per cent. But that is a debate we will leave for another day and right now concentrate on the 7.1 per cent economic growth figure.
A GDP growth rate of 7.1 per cent in a slow-growth world that we live in, is pretty good on the face of it. The International Monetary Fund’s World Economic Outlook expects global growth to be at 3.1 per cent in 2016 and 3.4 per cent in 2017. At 7.1 per cent India’s economy is growing at a significantly faster rate.

Nevertheless, there are serious problems with this economic growth. Allow me to explain.

The GDP, or the size of any country’s economy, can be measured in various ways. One is through estimating the size of various industries. The other way of measuring the GDP is by measuring the different kinds of expenditure. This essentially is the sum of the private consumption expenditure, the government expenditure, investments and the net exports (i.e., exports minus imports).

Take a look at Figure 1. It essentially plots the gross fixed capital formation as a percentage of real GDP (or GDP which has essentially been adjusted for inflation).

Figure 1:

Gross fixed capital formation is basically a proxy for the investment happening in the economy.

What does Figure 1 tell us? It tells us very clearly that the investment as a percentage of GDP has been falling over the last five years. It is now down to around 29.08 per cent of the GDP. In fact, in 2015-2016, investment was at 31.2 per cent of the GDP, from where it is expected to fall to 29.08 per cent of the GDP in 2016-2017. In absolute terms, the gross fixed capital formation or investment is estimated at Rs 35.35 lakh crore in 2016-17 in comparison to Rs 35.41 lakh crore in 2015-16, down by 0.2 per cent.

A part of this fall has been made up for by an increase in government final consumption expenditure. In real terms, this expenditure is estimated to be at Rs 13.95 lakh crore in 2016-2017. It was at 11.27 lakh crore in 2015-2016. Take a look at Figure 2.

Figure 2:The government expenditure has jumped from 9.93 per cent of the GDP in 2015-2016 to 11.48 per cent of the GDP in 2016-2017. This is the major reason why the government still expects India to grow at greater than 7 per cent, despite a fall in investment.

In fact, take a look at Figure 3. It makes for a very interesting reading. It essentially shows what portion of increase in GDP between years comes from an increase in government expenditure.

Figure 3: So, what does Figure 3 tell us? It tells us that between 2011-2012 and 2012-2013, increase in government expenditure made up for just 1 per cent of the increase in GDP. Along similar lines the increase in government expenditure between 2015-2016 and 2016-2017 will be responsible for around one-third of the increase in GDP. This has been primarily because the government implemented the one rank one pension rule as well as the recommendations of the Seventh Pay Commission.

Now take a look at Figure 4.

Figure 4: 

 

If we take government expenditure out of the equation, how does GDP growth look like? The economic growth for 2016-2017 comes in at 5.2 per cent, which is the lowest in half a decade. The new GDP series currently has data only up to 2011-2012. Hence, this analysis is limited due to a lack of data.

What this tells us is that the economic growth in 2016-2017 is likely to come in at 7.1 per cent, primarily because of the government expenditure forming nearly one-third of the incremental GDP.

The trouble is that this way of creating economic growth by the government spending its way out of trouble, cannot continue indefinitely. At the end of the day The government has a limited amount of money at its disposal. If India has to continue growing at greater than 7 per cent, then private sector investment needs to pick up and that doesn’t seem to be happening currently due to various reasons.

There are several short-term factors holding Indian investment back. The capacity utilisation rates of the manufacturing sector continue to remain low. For the period from April to June 2016, the 903 companies surveyed by the Reserve Bank of India reported a capacity utilisation rate of 72.9 per cent. With more than one-fourth of the capacity lying unutilised there is no reason for Indian industry to invest and expand. Over and above this, large sections of Indian industry, especially those operating in the infrastructure space, continue to remain highly indebted to banks.

These and other reasons are holding investment back. And this is unlikely to change anytime soon. Also, growth in investment is necessary if jobs are to be created for India’s youth who are entering the workforce in a huge number. It is estimated that every month one million Indians enter the workforce. Where are the jobs for them going to come from if investment as a proportion of the economy continues to shrink?

The column originally appeared in Equitymaster on January 12, 2017.

Public Sector Banking is Now in a Bigger Mess

RBI-Logo_8

The break at writing the Diary turned out to be much longer than I had expected. The main reason for it will become obvious in the days to come.

A lot has happened during this period, including the Modi government’s defence of demonetisation, which has grown by leaps and bounds. Nevertheless, I thought of giving writing on demonetisation a break for the first piece for the Diary in 2017.

One thing that has got side-lined in the entire discussion on demonetisation is the fact that Indian public sector banks continue to remain in a mess. In fact, as we shall see the mess has only grown bigger in the recent past. As the RBI Financial Stability Report for December 2016 points out: “The stress on banking sector, particularly the public sector banks (PSBs) remain significant… PSBs as a group continued to record losses.”

The gross non-performing assets ratio or the bad loans of the PSBs, increased to 11.8 per cent as on September 30, 2016. This is a whopping increase
220 basis points from 9.6 per cent as of March 31, 2016. One basis point is one hundredth of a percentage.

The overall stressed assets of public sector banks jumped to 15.8 per cent of total loans. It had stood at 14.9 per cent as on March 31, 2016.

The stressed asset figure of 15.8 per cent was obtained by adding bad loans of 11.8 per cent with restructured assets of 4 per cent. This basically means that for every Rs 100 that the PSBs have given out as a loan, Rs 15.8 are in a dodgy territory, on an average.

Out of every Rs 100 of loans made by the banks, borrowers have stopped repaying loans worth Rs 11.8. Over and above that loans worth Rs 4 for every Rs 100 of loans given by the banks have been restructured. A restructured loan essentially implies that the borrower has been given a moratorium during which he does not have to repay the principal amount. In some cases, even the interest need not be paid. In some other cases, the tenure of the loan has been increased.

This is clearly a reason to worry. Nevertheless, there is a small good sign here as well. Unlike earlier, when banks were using the restructuring route to not recognise bad loans, that doesn’t seem to be happening much now. As on March 31, 2016, the restructured loans had stood at 4.9 per cent of total loans. This has fallen to 4 per cent of total loans as of September 30, 2016. Banks are now recognising bad loans as bad loans. The first step towards solving a problem is recognising that it exists.

The increase in bad loans of public sector banks can also be seen in the bad loans figure of large borrowers. The Reserve Bank of India categorises large borrowers as borrowers with an outstanding loan amount of Rs 5 crore or more. The Financial Stability Report points out: “The large borrowers registered significant deterioration in their asset quality.”

However, the report does not mention a clear bad loans figure for the large borrowers. As the RBI Financial Stability Report for June 2016 pointed out: “The gross non-performing assets(GNPA) ratio of large borrowers increased sharply from 7.0 per cent to 10.6 per cent during September 2015 to March 2016.” This basically means that as on September 30, 2016, the gross non-performing assets ratio or the bad loans of banks would have stood at greater than 10.6 per cent.

If we look at Figure 1, the bad loans ratio for the large borrowers seems to be greater than 15 per cent as of September 30, 2016.

Figure 1:

 

This basically means that the large borrowers are the ones who continue to create problems for public sector banks. Take a look at Figure 2.

Figure 2:

The large borrowers form 56.5 per cent of the total loans given by banks. Nonetheless, they form 88.4 per cent of the total bad loans of banks. And this is where the basic trouble is. The rate of recovery of bad loans by banks is also not good enough.

As a recent report in The Indian Express points out: “The rate of recovery of non-performing assets (NPAs) was 10.3 per cent, or Rs 22,800 crore, out of the total NPAs of Rs 221,400 crore during fiscal ended March 2016, against Rs 30,800 crore (12.4 per cent) of the total amount of Rs 248,200 crore reported in March 2015, data from the Reserve Bank of India (RBI) has said.”

Indeed, what is worrying is that the RBI points out that the bad loans of the PSBs could increase further. As the report points out: “Among the bank groups, PSBs may continue to register the highest GNPA ratio. Under baseline scenario, the PSBs’ GNPA ratio may increase to 12.5 per cent in March 2017 and then to 12.9 per cent in March 2018 from 11.8 per cent in September 2016, which could increase further under a severe stress scenario.”

Interestingly, the June 2016 Financial Stability Report had pointed out: “Among the bank-groups, PSBs may continue to register the highest GNPA ratio. Under the baseline scenario, their GNPA ratio may go up to 10.1 per cent by March 2017 from 9.6 per cent as of March 2016. However, under a severe stress scenario, it may increase to 11.0 per cent by March 2017.”

We have already crossed the severe stress level in September 2016, something which was forecast only for March 2017. This basically means that the government will have to keep pumping more and more capital into these banks in the years to come in order to keep them going. And that means a lot more money of taxpayers will essentially go down the drain.

Postscript: I would like to thank all readers who supported my recent petition to the President. I am in the process of planning the dispatch of the responses received to the President.

The column was originally published on Equitymaster on January 11, 2017

 

Will Facebook also make our decisions in future?

facebook-logoMany of us spend more time on Facebook these days than with our parents, spouses and friends. As Cathy O’ Neil writes in Weapons of Math Destruction—How Big Data Increases Inequality and Threatens Democracy: “About two-thirds of American adults have a profile on Facebook. They spend thirty-nine minutes a day on the site, only four minutes less than they dedicate to face-to-face socialising.” While, I couldn’t find a similar number for India, it is safe to say that many middle class Indians are spending a significant amount of their daily time on Facebook.

On Facebook, we comment, respond to other comments or simply Like other comments. In the process, we are generating as well as giving away data about ourselves.

In fact, this data when mined properly, in some cases is a better judge of our ourselves than even we are. Interestingly, a study published in 2015 found that the Facebook algorithm is a better judge of individual personalities than even the individual’s friends, spouses or parents for that matter.

As Yuval Noah Harari writes in Homo Deus—A Brief History of Tomorrow: “The study was conducted on 86,220 volunteers who have a Facebook account and who completed a hundred-item personality questionnaire. The Facebook algorithm predicted the volunteers’ answers based on monitoring their Facebook Likes – which webpages, images and clips they tagged with the Like button. The more Likes, the more accurate the predictions.”

Further, the predictions of the algorithm were compared with those of friends, spouses, family members and work colleagues. Indeed, the results were very surprising. As Harari writes: “The algorithm needed only ten Likes to outperform the predictions of work colleagues. It needed seventy Likes to outperform friends, 150 Likes to outperform family members and 300 Likes to outperform spouses.”

This basically means that if you are married and have happened to click 300 or more Likes on Facebook, the Facebook algorithm can predict your opinions, desires and tastes, better than your husband or wife.

Interestingly, in some areas the Facebook algorithm did a much better job of predicting about the individual than even the individual himself. As Harari writes: “Participants were asked to evaluate things such as their level of substance use or the size of their social networks. Their judgements were less accurate than those of the algorithm.”

Hence, the algorithm was doing a much better job than the individual himself. Interestingly, Wu Youyoua, Michal Kosinskib, and David Stillwella, researchers who carried out this research, conclude in their research paper Computer-based personality judgments are more accurate than those made by humans: “Furthermore, in the future, people might abandon their own psychological judgments and rely on computers when making important life decisions, such as choosing activities, career paths, or even romantic partners. It is possible that such data-driven decisions will improve people’s lives.”

All this is possible because Facebook has access to our innermost thoughts through our comments and Likes. The interesting bit is that the Facebook researchers have also studied how different type of updates influence people’s voting behaviour. As O’Neil writes: “No researcher had ever worked in a human laboratory of this scale. Within hours, Facebook could harvest information from tens of millions of people or more, measuring the impact that their words and shared links had on each other. And it could use that knowledge to influence people’s actions, which in this case happens to be voting. That’s a significant amount of power.”

Of course, Facebook is not the only company which has this huge amount of power. As O’Neil writes: “Other publicly held corporations, including Google, Apple, Microsoft, Amazon, and cell phone providers… have vast information on much of humanity—and the means to steer us in any way they choose.”

And in large parts of the world which are democratic, this is something worth thinking about.

The column was originally published in the Bangalore Mirror on January 11, 2017