India needs a robust corporate bond market

Large firms should get most of their funds from the bond market, not banks

published more than three years ago, this newspaper had warned that the deterioration in the asset quality of banks would be an important challenge for the Indian economy. This was at a time when the first cracks were already evident to those who were prepared to look beyond both the breezy confidence of senior bankers as well as the regulatory forbearance that had been put in place after the global financial crisis. The editorial, published on 2 June 2013, concluded “Indian banks will have to clean up their books before they are ready for the next economic upswing”.

Much has happened since then. Under pressure from the Reserve Bank of India (RBI), public sector banks have begun to admit the extent of their bad loan problem. It is not a pretty picture. A lot of the problem loans are linked to business groups with strong political connections, aka crony capitalists. There has also been a parallel debate about how to strengthen the banks, which includes a range of issues from governance reforms to discounted sales of bad loans to recapitalization by the government.

The Indian banking mess also provides an opportunity to ask some more fundamental questions about the structure of the Indian financial system. RBI has added an important new element to this debate in its new Financial Stability Report , which has a section on the optimal configuration of a financial system, or how much of funding is through banks and how much through the bond market. According to the central bank, “with banks undertaking the much needed balance sheet repairs and a section of the corporate sector coming to terms with deleveraging, the onus of providing credit falls on the other actors”.

India continues to have a financial system that is dominated by banks, making it more similar to the financial system in countries such as Germany rather than the US. There has been a heated debate among economists about whether an optimal financial system should be dominated by banks or the bond market. Without going into this rather technical debate, it is worth reiterating that India needs a more robust bond market.

There are two reasons for this. First, Indian banks are currently in no position to rapidly expand their lending portfolios till they sort out the existing bad loans problem. Second, the heavy demands on bank funds by large companies in effect crowd out small enterprises from funding. India needs to eventually move to a financial system where large companies get most of their funds from the bond markets while banks focus on smaller enterprises.

Of course, this transition is not as easy as it sounds. Indian policy makers have struggled for nearly two decades to get a robust corporate bond market off the ground. Several administrative issues that were identified by the committee headed by R.H. Patil in 2005 have since been sorted out. Yet, the Indian corporate bond market continues to be small and shallow.

It is worth pointing out a few structural issues. First, most of the bond market activity has been restricted to small private placements that are held to maturity by passive investors, thus limiting the growth of a secondary market. Second, the sheer size of the annual government programme to fund the fiscal deficit has killed investor appetite for corporate bonds. Third, there are significant obstacles to risk-based pricing of corporate bonds—ranging from the lack of a robust benchmark interest rate to the relative lack of derivative products which investors can use to hedge.

It is highly unlikely that the corporate bond market will ever replace banks as the primary source of funding. Yet, India needs a more lively corporate bond market. It can also play a part in disciplining companies that borrow heavily to fund risky projects, because borrowing costs would spike. Just think: what if Vijay Mallya had to depend on the corporate bond market rather than friendly banks to fund his airline ambitions? An efficient bond market would have poured cold water over these ambitions through higher interest rates unlike the banks which restructured his loans through sweet deals.

How can India develop a robust corporate bond market?

Seven failures of economic liberalization

We’re all celebrating 25 years of the 1991 reforms, with good reason, but there are also several areas where our hopes have been belied

We’re all celebrating the 25th anniversary of the economic liberalization of 1991, with good reason. The end of the licence raj and the opening up of the economy to private and foreign capital has been a success story. But there are also several areas where our hopes have been belied. Here are a few of them:

Share of manufacturing in GDP%

One would have expected that the New Industrial Policy, unveiled in 1991, would have been just what the doctor ordered for the manufacturing sector and India would soon take its place among the manufacturing powers of East Asia. But chart 1 shows that in 1989-90, the share of manufacturing in the gross domestic product was 16.4%; in 2015-16, after myriad new manufacturing policies, its share was at 16.2%. True, this doesn’t mean the sector hasn’t grown. But since manufacturing holds out the promise of jobs for the masses and its productivity is also relatively higher, it was hoped that its share in the economy would increase. That hasn’t happened.

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Combined fiscal deficit of centre and states

One underlying reason for the crisis of 1991 was the indiscriminate rise in government borrowing in earlier years. It was only to be expected therefore that after the crisis, the government would do all it could to curb its fiscal deficit and that of the states. Unfortunately, as chart 2 indicates, that didn’t happen. By 2000-01, the combined fiscal deficit of the centre plus the states, as a percentage of GDP, had risen beyond the 1991 level. In 2014-15, the combined fiscal deficit, as a percentage of GDP, was higher than it was in 1995-96.

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Tax to GDP ratio

One reason why government deficits remained high is that, in spite of robust economic growth, tax revenues weren’t buoyant. Widespread tax evasion is probably the reason, along with a plethora of sops given to companies. Chart 3 shows that the central government’s gross tax revenues as a percentage of gross domestic product have remained below the 1991-92 level, in spite of recent efforts.

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Central government expenditure

The disinclination to reduce the fiscal deficit had the effect of raising the central government’s interest burden, as it had to pay out ever-increasing interest on its outsize borrowings. The upshot was deterioration in the quality of the deficit. The government was borrowing to fund, not productive capital expenditure on infrastructure, but unproductive revenue expenses. The chart shows that in 1990-91, capital expenditure accounted for 30% of total central government expenditure. The budget for the current fiscal year puts the share of capex at a mere 12.5%.

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Average employment per non-agricultural establishment

Economic liberalization has failed to provide secure and decent jobs to the mass of the population. Chart 5 shows that in spite of all the reforms, the number of employees per non-agricultural establishment has been coming down steadily. It was an average of 2.39 employees per establishment in the 2013 economic census, compared to 3.01 persons in the 1980 economic census. This means the vast majority of the establishments in India are in the informal sector, with neither the capital nor the technology to improve productivity.

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Distribution of employment according to size of employment

Before the 1991 liberalization, 37.11% of employees used to work in establishments employing 10 or more workers. Instead of increasing, that proportion has been steadily coming down and in 2013, only 21.15% of employees worked in establishments employing 10 or more workers. Workers in small enterprises for the most part eke out a precarious existence in the informal sector with no job security and precious few benefits.

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Mortality rate

And finally, surely economic liberalization should result in better care for our children. Chart 7 shows the country has made considerable progress on that front, with the under-five mortality rate coming down from 125.8 per thousand in 1990 to 47.7 per thousand in 2015. But as the chart shows, neighbouring Bangladesh and Nepal, much poorer than India, have both brought down their under-five mortality rates more than India. The chart suggests you don’t really need to be the world’s fastest-growing major economy to ensure your kids survive.

India can become world’s data science service provider

Can India provide an army of data scientists to the world, like the IT engineers that it provided in the last decade? 

In the last two decades, we have seen a huge impact of information technology (IT) on businesses. Today, almost all business transactions and processes, internal and external, happen on a computer or mobile and by the use of a network, primarily the Internet. The penetration of smartphones has extended this automation to the last mile i.e. to the consumers. Large network-based information systems facilitate various business processes such as sales order, financial transactions, human resource management, customer service management and so on—the Aadhaar project being a splendid example of IT reaching the last mile.

The IT revolution has made businesses much more efficient by allowing transactions to be fast, error-free and trackable. It has not necessarily made them ‘intelligent’ but it has paved the way for mining intelligence by creating a wealth of digitized data. For instance, product manufacturers know what product is sold on a given day, at what time and at which outlet. A lot of transactions happen naturally on the web, through e-commerce.

In the coming decade, i.e. the decade of data science, we will analyse these data on a large scale to identify trends, find anomalies and most importantly, predict future trends. But let us pause here to understand what data science is. Suppose you have the transcripts of the pitch of sales people in a company including the ones that have led to a sale. You wish to predict which sales pitches are good and what makes them good. Traditionally, one will probably use a sales coach to understand this.

However, the data science way is remarkably different—it uses unstructured data, i.e. transcripts, and derives features such as the length of the call, counts of courteous expressions like “may I”, “please”; counts of words about product value—“automatic”, “fast” and so on. Using these features and statistics, an algorithm builds a model to predict the success of the pitch. For instance, the algorithm may discover that pitches with a good number of positive words, good number of product value words and with a moderate call length leads to success. A totally new sales pitch can then be checked against such conditions to predict if it is good or not.

How is this revolutionary? We can find out whether a new sales person is ready to be put on the job or needs further training based on his/her pitch quality. This model can be linked to the sales process, where it provides personalized feedback for improvement to each salesperson immediately after a sales call. Eventually, one day, the algorithm will replace the salesperson!

Better algorithms, more processing power and the availability of large data sets have enabled highly accurate models. However, this process of data science itself is hardly automated. Initially, a lot of work is required to convert unstructured data in to useful models and integration of these models into the information systems. The person responsible for all of this is the formidable data scientist.

Can India provide an army of data scientists to the world, like the IT engineers that it provided in the last decade? Can this become the engine of our economic growth in the coming decade? It is not only a huge opportunity, but a challenge, too.

First, we need trained manpower. Data scientists need to be adept in programming skills, database skills and basic statistics. Today, we hardly have people who understand both computer science and statistics. Our undergraduate education needs to take up data science courses in a big way. A year ago, we took a bold step of introducing data science for classes V to VIII. These kids successfully built their own friends’ predictor (www.datasciencekids.org). India should aspire to be a leader in providing data science education early on.

Second, we need to be among the leaders in research and innovation in machine learning. Though India became an IT services powerhouse without any great technology innovation, this will not hold for data science services, for innovation and disruption have gathered rapid pace in recent years.

We need to use the latest technology in our services and also be the innovators. Data science service offering will not only be people driven but also product driven. Unfortunately, there is a huge gap here. Our analysis (ml-india.org) showed that Tsinghua University in China alone produces more machine learning papers in top conferences than all universities in India put together. Similarly, companies in the US have taken a lead in machine learning innovation.

Lastly and most importantly, we need entrepreneurs who can go across the globe and solicit data science business for their companies in India—the new Murthys and Premjis. Let us get ourselves ready to become the world’s data science services powerhouse.

India can become world’s data science service provider

Can India provide an army of data scientists to the world, like the IT engineers that it provided in the last decade? 

In the last two decades, we have seen a huge impact of information technology (IT) on businesses. Today, almost all business transactions and processes, internal and external, happen on a computer or mobile and by the use of a network, primarily the Internet. The penetration of smartphones has extended this automation to the last mile i.e. to the consumers. Large network-based information systems facilitate various business processes such as sales order, financial transactions, human resource management, customer service management and so on—the Aadhaar project being a splendid example of IT reaching the last mile.

The IT revolution has made businesses much more efficient by allowing transactions to be fast, error-free and trackable. It has not necessarily made them ‘intelligent’ but it has paved the way for mining intelligence by creating a wealth of digitized data. For instance, product manufacturers know what product is sold on a given day, at what time and at which outlet. A lot of transactions happen naturally on the web, through e-commerce.

In the coming decade, i.e. the decade of data science, we will analyse these data on a large scale to identify trends, find anomalies and most importantly, predict future trends. But let us pause here to understand what data science is. Suppose you have the transcripts of the pitch of sales people in a company including the ones that have led to a sale. You wish to predict which sales pitches are good and what makes them good. Traditionally, one will probably use a sales coach to understand this.

However, the data science way is remarkably different—it uses unstructured data, i.e. transcripts, and derives features such as the length of the call, counts of courteous expressions like “may I”, “please”; counts of words about product value—“automatic”, “fast” and so on. Using these features and statistics, an algorithm builds a model to predict the success of the pitch. For instance, the algorithm may discover that pitches with a good number of positive words, good number of product value words and with a moderate call length leads to success. A totally new sales pitch can then be checked against such conditions to predict if it is good or not.

How is this revolutionary? We can find out whether a new sales person is ready to be put on the job or needs further training based on his/her pitch quality. This model can be linked to the sales process, where it provides personalized feedback for improvement to each salesperson immediately after a sales call. Eventually, one day, the algorithm will replace the salesperson!

Better algorithms, more processing power and the availability of large data sets have enabled highly accurate models. However, this process of data science itself is hardly automated. Initially, a lot of work is required to convert unstructured data in to useful models and integration of these models into the information systems. The person responsible for all of this is the formidable data scientist.

Can India provide an army of data scientists to the world, like the IT engineers that it provided in the last decade? Can this become the engine of our economic growth in the coming decade? It is not only a huge opportunity, but a challenge, too.

First, we need trained manpower. Data scientists need to be adept in programming skills, database skills and basic statistics. Today, we hardly have people who understand both computer science and statistics. Our undergraduate education needs to take up data science courses in a big way. A year ago, we took a bold step of introducing data science for classes V to VIII. These kids successfully built their own friends’ predictor (www.datasciencekids.org). India should aspire to be a leader in providing data science education early on.

Second, we need to be among the leaders in research and innovation in machine learning. Though India became an IT services powerhouse without any great technology innovation, this will not hold for data science services, for innovation and disruption have gathered rapid pace in recent years.

We need to use the latest technology in our services and also be the innovators. Data science service offering will not only be people driven but also product driven. Unfortunately, there is a huge gap here. Our analysis (ml-india.org) showed that Tsinghua University in China alone produces more machine learning papers in top conferences than all universities in India put together. Similarly, companies in the US have taken a lead in machine learning innovation.

Lastly and most importantly, we need entrepreneurs who can go across the globe and solicit data science business for their companies in India—the new Murthys and Premjis. Let us get ourselves ready to become the world’s data science services powerhouse.

Nasa’s Juno is set to approach Jupiter. What will it find?

After completing a five year journey, Nasa’s solar-powered Juno spacecraft will arrive at Jupiter on Monday, and attempt to join its orbit. On arrival, Jupiter’s gravity will pull in Juno until the spacecraft reaches a speed over 250,000 kmph with respect to Earth—making it one of the fastest human-made objects ever.
On Monday, Juno will attempt to get inserted into the orbit using a 35-minute burn of its main engine to slow the spacecraft by about 542 meters per second so it can be captured by the gas giant’s orbit. Once in Jupiter’s orbit, the spacecraft will orbit it 37 times across 20 months, approaching 5,000 km above the cloud tops. This is the first time a spacecraft will orbit the poles of Jupiter, which would help provide many more answers about the planet’s composition and origin.

The Juno spacecraft was launched aboard an Atlas V551 rocket from Cape Canaveral, Florida, on 5 August, 2011. To accomplish its science objectives, Juno will orbit over Jupiter’s poles and pass very close to the planet which will allow it to make the kind of measurements the mission aims to provide.
“This orbital path carries the spacecraft repeatedly through hazardous radiation belts, while avoiding the most powerful (and hazardous) radiation belts. Jupiter’s radiation belts are analogous to Earth’s Van Allen belts—but far more deadly,” Nasa said in a note about the mission.
During the almost one-and-a-half-year of the mission dedicated to science, the spacecraft will attempt close fly by above the planet’s cloud tops every 14 days. Here is what Juno is out to find:
Origin: There are several theories regarding the origin of Jupiter. By finding out the water present in the planet and the maximum possible mass of the planet’s solid core, scientists can zero in on the right theory.
Interior: To gain a deeper understanding of the planet’s interior structure and how material moves within the planet by locating the gravitational and magnetic fields.
Atmosphere: To assess the atmospheric composition, temperature, and cloud opacity.
Magnetosphere: To further explore the three-dimensional structure of Jupiter’s polar magnetosphere and auroras.

IAS 2016 PRE ADMIT CARD CAN CHECKED HERE

Civil service exam-2016 prelims admit card is out.

IAS 2016 PRE ADMIT CARD CAN CHECKED HERE

http://upsconline.nic.in/eadmitcard/upsc_ac2/admitcard_csp_2016/

• Exam will be held on 7^th August, 2016, Sunday
• Must tick MCQs with Black Ball point pen only.
• Paper-I (General Studies) from 9:30AM to 11:30AM (You’ll not be given entry, if more than 10 minutes late).
• Paper-II (Aptitude) from 2:30PM to 4:30PM.
• If problem, contact e-mail: – web-upsc@nic.in (For Technical Problem) , uscsp-upsc@nic.in (For Applicant Data Problem)