“GROWING PAINS”

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“It is said that every life has its roses and thorns; there seemed, however, to have been a misadventure or mistake in Stephen’s case, whereby somebody else had become possessed of his roses, and he had become possessed of somebody else’s thorns in addition to his own.” (Charles Dickens, Hard Times)

“It is said that every life has its roses and thorns; there seemed, however, to have been a misadventure or mistake in Stephen’s case, whereby somebody else had become possessed of his roses, and he had become possessed of somebody else’s thorns in addition to his own.” (Charles Dickens, Hard Times)

Stephen’s case resonates with millions across the globe who struggle with poverty, sordid working conditions and unemployment while the more ‘privileged’ sections of society enjoy the luxuries and higher living standards endowed by technological progress and long-run economic growth.

Proponents of the “thorns” argument suggest that economic growth and technological progress have been accompanied by a widening gap between the rich and the poor – with the rich becoming richer by leveraging on their greater access to technology and the increased productivity it entails and the poor becoming increasingly unemployable and replaceable by machines thus, sinking deeper into poverty. In other words, while the rich enjoy all the “roses”, the poor suffer the “thorns” of industrial advancement.

While technology’s contribution to economic growth is undisputed, its impact on working class welfare is a subject of great debate. While aggregate economic estimators dating back to the first industrial revolution show increases in real wages, employment and living standards (higher literacy and decreased mortality rates), these measures paint an optimistic picture that fails to reflect the situation of those that face the unintended costs of progress.

According to Erik Brynjolfsson, author of The Second Machine Age, “technology is the main driver of recent increases in inequality.” If this is indeed the case, then the perpetuation of technology dichotomously facilitates the macroeconomic goal of economic growth while hindering that of equitable income distribution. If one regards equity in income distribution and job displacement as being indicative of social welfare, then in today’s technology driven economy there exists an opportunity cost between growth and welfare. With Artificial Intelligence transitioning from fiction to reality this argument against technology is rapidly gaining ground. It is however, a simplistic argument that fails to take into account the historical trends in industrial and technological development.

History is testimony that every technological breakthrough has rewarded some and punished some. Right from the printing press to assembly lines, technology has consistently made it possible for capital to replace labor. While this has increased productivity it has also left masses unemployed and depressed. Even so, comparisons across periods show that the average man is better off today than he was in the past. Thus, the gloom that settled on the masses owing to technological substitution seems simply to be the “pain” associated with any major transitory or transformative growth period. Why this pain is experienced less by the rich than the poor might just be a matter of access. Those with greater access to resources and information are more easily able to make the transition to the obligatory increase in skill level. Holding other factors such as wealth inheritance constant, the difference between the rich and poor then simply translates to the difference between the skilled and the unskilled in the modern economy. Over time, as average skill level increases, the overall welfare also increases. It would then seem likely, that efficiency and equity are not trade-offs but economic consequences of growth that manifest over different lengths of time. Why some individuals take longer to catch up to the increased level of efficiency than others may be a matter of their social context or the Darwinian ‘survival of the fittest.’ The increases in inequality are therefore more rightly attributable to discrepancies in worker skill-sets than to technological progress.

In the modern era, with technology growing at an unprecedented rate, the time-constraint on skill upgradation is significantly tighter. As a result, bridging the income gap is becoming increasingly difficult. For those on the bottom-most rung of the social ladder, average skill and efficiency requirements of the market are increasing at a much faster rate than they are capable of catching up with. Owing to the autonomy of human ingenuity, technological progress cannot be reined in or slowed down. A plausible alternative is therefore, to increase the pace of skill upgradation through easy access to education and training without discrimination. While perfectly equitable income distribution is a theoretical ideal, a real world equivalent would entail policy decisions aimed at minimizing the “thorns” and consequently the “pain” that is an inevitable corollary of transformative economic growth.


This article has been written by Lubna Akhtar an aspiring Economist and a student at Meghnad Desai Academy of Economics class 2018!

The End of Blockchain? Hashgraph has the potential to displace Blockchain!

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This article has been written by Bhargav Dhakappa (Senior Executive at Deloitte) also an alumnus at the Meghnad Desai Academy of Economics.

Behave or Get Nudged!

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Ever ate a huge piece of brownie, even after you were full, just because you paid for it? Ever bought a pair of jeans, just because they were on sale, and now they just lie in the back of your closet? Ever felt bad about not being able to use a coupon, despite winning it? Well, if you are, congratulations, you are behaving like a human.

This behaviour, its analysis and how we manage to think in this way, is what Behavioural Economics studies today. Psychology was always a part of economics, but in a really subtle way. Thaler and some others explicitly brought insights from psychology, and incorporated that in the wide range of economic activities, which otherwise seem irrational.

(Credit – https://www.google.co.in/search?q=behavioral+economics&source=lnms&tbm=isch&sa=X&ved=0ahUKEwiQ48SO053XAhWHqo8KHdnnB0MQ_AUIDCgD&biw=1366&bih=662#imgrc=I2IRqFQFsuTVKM:)

It helps explain the volatile human mind, to provide a better and deeper understanding of human decision making, and utility maximising capability. Behavioral Economics is a happy marriage of psychology and economics.

However, human beings are not always rational. This automatically proves Homo Economicus (the rational straw man) wrong. Psychologists like Amos Tversky, Daniel Kahneman, Herbert Simon and economists like Richard Thaler, Vernon Smith, Charlie Plott, all helped shape the field of behavioural and experimental economics, and helped prove the above statement right.

With Daniel Kahneman and Vernon Smith winning the Nobel jointly in 2002, Alvin Roth in 2012, Robert Shiller in 2013 and Richard Thaler, winning it now, in 2017, for outstanding work on bridging gaps between economics and psychology, the spotlight has now come on this field, more than ever. Its importance is being realised, and rightfully so.

This is a must-seize opportunity to talk about Thaler’s brilliant work showcased in his books, each slightly different, though all preaching the same thought.

Misbehaving, written as recently as 2015, acts as Thaler’s memoir, his path-breaking journey, and adventures in discovering this field and finding solace in it.

The Winner’s Curse is an interesting read, a unique layout, where each chapter starts with a question, one which urges you to process, to think. It then proceeds to solve such dilemmas, through the findings of experiments of both economics and psychology.

Nudge is a stellar piece of work, co-authored with Cass Sunstein. Unlike The Winner’s Curse, despite laying out really important solutions, the authors do so in a very light hearted way.

(Credit – https://www.google.co.in/search?biw=1366&bih=662&tbm=isch&sa=1&ei=xOL5WcytG8TTvATJ6L6wDg&q=nudge+and+thaler&oq=nudge+and+thaler&gs_l=psy-ab.3…21804.24905.0.24995.0.0.0.0.0.0.0.0..0.0….0…1.1.64.psy-ab..0.0.0….0.EPp1WzI0jFs#imgrc=Ma1fG38sK-07dM:)

Areas where the theory of Nudge proves to be useful range from individual and firm decision making, financial decisions, cost effectiveness and public policy decisions. This is because, Nudge successfully shows that it is possible to help people make the right decisions, if apt choices are laid in front of them.

One of the earliest countries to adopt this technique in its policy making was the United Kingdom, when David Cameron, the former Prime Minister, decided to set up the Nudge Unit (i.e. Behavioral Insights Team). Eventually, France, US, Denmark, Singapore and other countries followed step. It is the right time for India too now, to take a step ahead and derive benefit from using this principle.


This article has been written by Yashika Doshi an aspiring Economist and a student at Meghnad Desai Academy of Economics class 2018!

Understanding Micro, Small and Medium Enterprices in India

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This infographic has been designed by Nicole Almeida (Researcher, Political Scientist, aspiring Economist) also an alumna at the Meghnad Desai Academy of Economics.

The Creative Destruction of Artificial Intelligence

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At the end of a daily-newspaper reading routine, one would most certainly come across one to two articles on the ominous rise of artificial intelligence (AI). Most of these articles regard AI with a sense of foreboding, in a fashion analogous to The Luddite movement in 19th century England. The extent of this acute apprehension regarding robots replacing human workers has been accurately illustrated through one of the Dailywatch films, a series of short films by The Economist magazine. In this documentary, The Economist sets out to ask various people to conjecture about the percentage of total jobs that are held by robots and the answers range between 10 to 30 per cent. However, the actual answer is 0.7%. In the context of India, many articles talk about the need for an overhaul of the education system, re-training of the existing workforce and a renewed nationwide skill development plan which will be better suited for an AI-powered future. These concerns are warranted, given the permeating and conspicuous rise of AI in all structures of our society. But what is artificial intelligence and why do some envision a dystopian future with its advancement?

A still from the movie, Her, when Theodore thinks he has lost his beloved artificially intelligent OS

In an AI-powered backdrop, machines will be trained to mimic human intelligence. The cause of fear over the possibility of this stark reality is that machines will transcend all that we know of human intelligence today. This can be demonstrated with the recent win of AlphaGo (a computer program developed by Google-acquired AI company, DeepMind) over a human being in the complex and ancient Chinese game of Go.  The AI revolution can be viewed as a kind of creative destruction which has the potential to destroy some jobs as well as create new ones. In an Oscar winning AI themed futuristic film, Her (2013), the story revolves around the protagonist, Theodore Twombly who falls in love with his artificially intelligent OS (Operating System). Twombly works for Beautiful Handwritten Letters, a company which employs professional writers to compose personal letters for those people who for some reason are unable to write such letters themselves. Twombly’s work profile may seem strange but it surely requires some skill, to be able to write letters as someone else and make them believable enough for the person receiving them. The protagonist’s job in this movie illustrates the kind of inevitable disruption in employment, positive or negative, that will be caused with the dawn of AI. Hence, instead of treating the onset of AI as a harbinger for doom for the human civilization, we must embrace it.

With India boasting of a massive demographic dividend at 64.4% of the population (as reported by Sample Registration Survey of India statistical report in 2015), we ought to be poised to tackle the AI driven technological change. Among the numerous studies and articles broadcasting details of potential job losses in India, very few of them have a positive outlook for India’s already deteriorating labour market conditions. A World Bank research states that 69 per cent of the jobs in India are under threat from automation. As reported by HfS Research, a research firm in the US, around 7 lakh low skilled workers, mainly in the Indian IT and BPO industries are set to lose jobs to AI by 2022. According to a HR Technology Solutions firm, PeopleStrong, four out of every ten jobs that will be lost to AI on a global scale are reckoned to be in India. On the other hand, we have the likes of Nivrut Rai, Country Head for Intel India, who does not believe that jobs will be lost to AI but instead aims to create new and different kinds in the future.

Considering the widespread adoption of AI driven technology in countries like China, South Korea, Japan and US, it seems like India is falling behind. There are various reasons that can be attributed for the same. Even today, in numerous industries labour costs are cheaper which leads to there being no incentive to automate processes. This further leads to lower productivity which for instance, is something that I encountered in various administrative departments of schools and universities (heaps and heaps of paperwork on the desks of clerks who take 2-3 working days to get your work done). The administrative processes can be automated to make workers more efficient and productive, for which the staff would have to be re-trained to hone the required skills. However, this does not seem feasible considering the time, willingness of the workers and additional funds it will require. Just like we’ve learnt in our foundation Macroeconomics lectures at MDAE, output in an economy depends on capital and labour, accompanied with technological progress which further leads to increase in total factor productivity which is crucial for an economy’s growth.  Sooner or later, automation and technological advancement in the form of AI must be incorporated if we wish to grow as an economy.

As for labour costs being cheaper, what happens when that is no more the case? Mahindra & Mahindra Ltd., a car and farm equipment manufacturing company announced the launch of India’s first driverless tractor in 2018. This is a major step towards the mechanization of a labour-intensive agriculture sector found in India. The driverless tractor is not only set to cut labour costs but is being priced 20% lower than a non-mechanized one, as claimed by the company. With this kind of technology requiring no human intervention, jobs of many drivers that make up the rural economy are bound to be lost. As mentioned in another one of our blogs on Thomas Sargent’s graduation speech, one of his crucial economics concepts state that “There are trade-offs between equality and efficiency”.

It is mostly the private sector, particularly the IT sector and several start-ups in India that are investing in AI related research and re-training of workers. As seen with Infosys and its support of AI research at Indraprastha Institute of Information Technology, Delhi in addition to its own artificially intelligent platform, Nia. There are accounts of companies training their workers, for instance, Intel India in collaboration with forty academic institutions and fifty organizations both in public as well as private sectors, is said to have trained 9500 developers, students and professors. Even with Infosys having finished training around 3,000 people in AI and 2100 on its AI platform, Nia. However, for any new initiative and that too as consequential as this to be introduced in an economy, initial support and investment must come from the government. The demographic dividend of India should move towards harnessing the opportunities that are presenting itself with AI driven technologies. Taking the Kenyan government’s efforts as a representative case, a Kenya Vision 2030 programme termed as the Digital Literacy Programme (DLP) was introduced in 2016 and an inter-ministerial initiative of a five-week training session aimed at training youths to engage in online work has been set in motion in 2017. Many of the Indian government’s initiatives like Skill India and Digital India need to be revamped to re-train economic participants with skills which will be valued by employers in the future.

Meanwhile, let you and I continue practising the R codes that are being taught in class…

 


This article has been written by Guntaas Uppal (New Product Group/Risk Management Solutions, Dun & Bradstreet India) also an alumna at the Meghnad Desai Academy of Economics.

An Analysis of India’s Merchandise Exports

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India, being one of the fastest growing economies in the world, has emerged as an important player in the global economic order. What was once an inward-looking economy has now a trade-to-GDP ratio of 31% and is the fifth largest exporter amongst the emerging economies. Export data from UNCTAD shows that Indian exports are witnessing a diversification, both in terms of the export basket as well as in terms of destinations. While India’s export destinations are moving towards emerging markets, its commodity basket is moving towards resource-based manufacturing at the cost of primary products and manufactured goods. Within manufactured goods, India is gradually moving towards high and medium tech goods at the cost of low technology goods, but the share remains significantly below the EM average.

Evolution of India’s Export Basket

India’s export basket is evolving and is moving towards more sophisticated products. On comparing the export basket in 1995 to the basket in 2015, one can clearly see that goods like petroleum products, transport equipment, industrial machinery and parts, and electrical machinery and appliances have gained prominent share in the export basket at the cost of primary and low technology manufactured goods like coffee, spices, feedstuff for animals, vegetables and fruits, footwear etc. India’s top exports comprise of goods like textile and apparel products, gems and jewellery, petroleum products, medicinal and pharma products, road vehicles, organic chemicals iron and steel, transport equipment, cereals, metal manufacture, industrial machinery and electrical machinery and appliances. Textile and apparel with a share of 13% and gems and jewellery with a share of 12% continue to be India’s top exports, suggesting India’s comparative advantage in these categories, although the share has declined to indicate export basket diversification.

The shift towards resource-based manufacturing exports has largely been due to expansion in domestic petroleum refining capacity. As a result of the same, India is outperforming most other EMs in resource-based manufacturing. Figure 1 and 2 gives a glimpse of the same.

As far as the exports of manufactured goods are concerned, India has not been able to increase its share in manufactured goods over the last two decades and it remains a laggard as compared to other EMs. Figure 3 captures this fact.

However, when one observes the technological intensity of India’s manufactured exports, one sees the exports moving gradually towards high and medium technology goods at the cost of low technology goods. Although when the technological intensity is compared across other EMs, China and Mexico are far superior to India.  Figure 4 and 5 capture the same.

Destination-wise Shifts

One can easily see the evidence of a growing ‘South-South trade’ from the Indian export data – the share of developed markets has fallen from 72% in 1995 to 47% in 2015, and, the share of emerging markets which was just 28% in 1995, has risen to 53% in 2015. Figure 6 shows how emerging markets are gaining share in Indian exports at the cost of developed markets with their increasing share of world GDP.

The US continues to be India’s largest export destination with about 16% share in 2015, although the share has fallen as compared to 1995 when it was 20%. Major share gainers have been UAE, China, Vietnam, Turkey, Sri Lanka, Nepal and South Africa.

UAE has emerged as an important export destination for India with its share increasing from just 5% in 1995 to 12% in 2016. A major trend observed is that although the share of most developed markets in India’s exports has fallen, India’s share in the imports of those markets has risen, indicating the diversification of Indian export destinations as well as India’s growing significance in these markets. India’s share in the imports of these countries, although growing, still remains small.

China has grown in importance as an export destination after its accession to the WTO in 2001. The US is India’s largest market for pharma, and textile and apparel exports. Therefore, Trump’s protectionist trade policies could be a looming threat to Indian exports. However, so far India has benefitted from Trump’s withdrawal from the Trans-Pacific Partnership (TPP) since the ratification of the TPP would have meant a greater market access to Vietnam, which is a signatory to the TPP and India’s stiff competitor in Textile and Apparel segment.

A changing world order after 2010?

As the world is becoming more protectionist, the global trade outlook seems uncertain. The conjecture of an increasing inward sentiment is further supported by the data which shows how the GDP growth rate of both EM and DM were highly correlated with both the Indian and global export growth till 2010. With the economic stagnation post-2010, this relation between GDP growth rates and export growth has decoupled, suggesting an emergence of an inward-looking approach, especially by the DMs.  Figure 7 captures this finding.

Conclusion

Although India has managed to diversify its export basket in the past two decades, its market share in most product categories remains low, indicating a need to make domestic products more competitive. Likewise, Indian exports have witnessed diversification in terms of destinations but the country’s share in the import basket of its major trading partners (except UAE) remains small. In conclusion, India needs to step up its performance as far as the exports of manufactured goods are concerned and the success of ‘Make in India’ could be the key to this.

 


Paridhi Rathi

This article has been written by Paridhi Rathi, a student at the Meghnad Desai Academy of Economics.

 

 

Macroeconomic Forecasts and the Pretence of Knowledge

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Title: JULIUS CAESAR (1953) ¥ Pers: BRANDO, MARLON ¥ Year: 1953 ¥ Dir: MANKIEWICZ, JOSEPH L. ¥ Ref: JUL011AG ¥ Credit: [ MGM / THE KOBAL COLLECTION ]

Friends, Romans and Countrymen,

Economists are humble men.

Macroeconomists using their favourite plaything, past data, predict long term trends for a country. Their forecasts, which have the power to shape economic destiny of countries, are not restricted to broad shifts in output, prices, or other metrics, but are quite often very exact estimates  (regularly in two decimal places) of where an economy would be after a certain period of time.

Let’s take an overview of such forecasts in light of the recent controversial notebandi or what has been labelled as ‘Demonetisation’.

A Reuters poll of 30 economists forecasted Q3 GDP growth at a 3-year low of 6.4% while some analysts feared a sharper slowdown to less than 6%.“We are projecting the Q3 GDP growth at 5.8%.” said Soumya Kanti Ghosh, Chief Economic Advisor for the State Bank of India. Equally assertive were private sector economists such as CARE Ratings Chief Economist Madan Sabnavis, who saw growth slowing down to as much as 5.4% in Q3.

What really happened?

The dark art of macroeconomic forecasting, as Prof. Indradeep Ghosh at MDAE likes to call it, failed in predicting the exact GDP growth rate of the country during Q3 2016-17. The Indian economy grew at 7% during this quarter as per the Central Statistical Organisation (CSO). Now the CSO does admit that economic activity has been impacted by the note ban with Q3 GVA growth at 6.6%, against 7% last year. Gross Value Added (GVA) is a more suitable number than GDP. The GVA measures the value of output created by different segments of the economy. Indirect taxes (minus subsidies) are added to it, to arrive at the GDP.

CSO’s estimation has been criticised for not showcasing the input of the informal sector, but it can be argued that if the CSO can’t find a way to estimate the quarterly performance of the informal sector despite having access to arguably the most reliable multiple data sources, neither can anyone else. Clearly those macroeconomists with their exact predictions have missed a trick or two. This in no way is a defence of notebandi, I believe the best analysis on the Demonetisation experiment was offered by Lebanese-American essayist and Risk Trader Nassim Nicholas Taleb who concluded that it’s “too early to tell” about the consequences of the monetary experiment.

We’ve seen similar deformities in macroeconomic predictions pertaining to two other significant events around the world last year. The recession forecast produced by the U.K. Treasury ahead of June’s Brexit vote which assumed monetary policy would be unchanged and the prime minister would immediately initiate the process of leaving the European Union was proved wrong; the U.K. economy has  been faring decently well. Similarly in the case of Donald Trump’s election to the White House, the economic and market mayhem that was expected and warned against by several pandits has been missing and rather conspicuously so. .

Nobel Laureate Friedrich Von Hayek can perhaps help us understand the reason behind why despite this hubris, macroeconomists fall flat faced in their predictions. Let’s ask ourselves, what would Hayek say?

In his Nobel Memorial lecture titled the ‘Pretence of Knowledge’, Hayek says that if we truly wish to advance society, we must be modest and realise the limitations of what is possible with social science.

According to him economists can’t provide the kind of exact data that natural scientists are expected to produce as the variables that economists study cannot be summarised or averaged.

Hayek in one of his papers, ‘The Counter-Revolution of Science’ observes that the natural sciences attempt to remove the “human factor” in order to obtain objective, strictly controlled results:

The persistent effort of modern Science has been to get down to “objective facts,” to cease studying what men thought about nature or regarding the given concepts as true images of the real world, and, above all, to discard all theories which pretended to explain phenomena by imputing to them a directing mind like our own. Instead, its main task became to revise and reconstruct the concepts formed from ordinary experience on the basis of a systematic testing of the phenomena, so as to be better able to recognise the particular as an instance of a general rule.

Meanwhile, the social sciences are attempting to measure human action itself.

The social sciences in the narrower sense, i.e., those which used to be described as the moral sciences, are concerned with man’s conscious or reflected action, actions where a person can be said to choose between various courses open to him, and here the situation is essentially different. The external stimulus which may be said to cause or occasion such actions can of course also be defined in purely physical terms. But if we tried to do so for the purposes of explaining human action, we would confine ourselves to less than we know about the situation.

A discipline like economics studies complex structures consisting of individuals with distinguishable identities who are connected with one another through meaningful linkages. Any attempt to summarise or generalize over these ‘subjects’ means losing out on critical information.

Hayek also believes that unlike natural sciences, the variables economists pick to measure may not be the most pertinent ones. This can lead to economists imprecisely emphasising on the few variables that can be measured as they are easier to study and ignoring the other ones by pretending that they are not significant. As he quips:

The study of such complex phenomena as the market, which depend on the actions of many individuals, all the circumstances which will determine the outcome of a process will hardly ever be fully known or measurable.

In a recent paper Paul Romer, Chief Economist and Senior Vice President of the World Bank claimed that macroeconomics has been going backwards for more than three decades, with economic modelling succumbing to “mathiness”, an obsession with mathematic laws and equations which bear minimal relation to the real world, ignore the lessons of other disciplines, and are often inconsistent with the inherently unpredictable nature of human behaviour. Another economist in the upper echelons of the profession Andrew G.Haldane, the Chief Economist of the Bank of England recently admitted to the errors in the Brexit forecasting. He acknowledged that his profession is in crisis, having failed to foresee the 2008 financial crash and having misjudged the impact of the Brexit vote. 

Question: why did God create economists? Answer: to make weather forecasters look good!

The only difference being that weather forecasts can’t alter the weather, but economic forecasts can surely change the course of an economy. One might hope that more economists, especially the ones with significant powers to influence policy, both in India and around the world, eat the humble pie more often.

Trust and Investment: What changes after Demonetization?

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Demonetization

Would I invest in my sibling’s venture? Well, if I am sure that he or she is not going to squander it away on a trip to Vegas, why not? Would I invest in the same venture, if it is proposed by a total stranger? Perhaps not.  I would be wary of this opportunity, having no idea who the person is. Now in any real-world economy, the investment decision is much more complex than that, depending not only upon the set of investment alternatives but also on the strategic interaction between the investors.  A simple way to explain this is through a game used by Denise Hazlett in her 2007 paper titled “A Classroom Investment Coordination Experiment”. In this game, there are four firms making simultaneous investment decisions. These firms can either make a high (H) or a low (L) level of investment. If a firm chooses a high level of investment, it can produce more goods. However, unless the other three firms also choose a high level there will be a fall in the national income and households will not be able to afford these goods. Therefore, unless all its fellow firms are investing a high level, a firm is better off investing a low level. The table below describes the payoffs to a firm, depending upon the investment decisions of the other three firms.

Number of other firms choosing H 0 1 2 3
Profits for H 0 1 3 5
Profits for L 2 3 4 4

 

If the firm in question chooses H while the other three firms choose L (Column 1 of the table) then that firm would earn 0 profits; the economy being in recession, a high level of investment does not get translated into higher revenues. On the other hand, when there are three firms choosing a high level of investment (Column 4 of the table), the remaining firm will benefit from investing a high level itself, making use of the expansionary state of the economy.

This game has two pure-strategy Nash Equilibria, one where all the firms choose to invest L (LLLL), causing a recession, and one where all the firms choose to invest H (HHHH), causing an expansion. Notice that higher the number of firms choosing H, higher is the combined profits of the firms, the maximum being when all four firms choose H. Not only that, HHHH is the best outcome for an individual firm too as it earns the highest possible profits in that case. What this means is that out of the two NEs, HHHH is Pareto-superior. However, choosing H is risky because the firm cannot be certain of what its counterparts would choose. A firm must believe that everyone else in the group will choose H for its best response to be H (Hazlett, 2007). If the uncertainty related to investment decisions persists, firms will be averse to making high investments and the economy would be closer to, or at, LLLL.

When can a firm be sure of its fellow firms choosing a high level of investment? In a four-firm economy such as above, talking amongst themselves might assure the firms of each other’s decision. As observed in Hazlett’s experiment, direct communication between the players does improve coordination in investment decisions. With more and direct communication, one has fewer reasons to suspect opportunistic behavior from others, making coordination easier. However, any sort of direct communication between investors is implausible to accomplish in a real-world economy.  And even if it was achievable due to some miracle, in order to be absolutely certain, a firm has to truly believe what the other firms are communicating. Or simply put, a firm has to trust the other investing firms.

How is social trust established in an economy? This is where one needs to bring in the distinction made earlier, of trusting a family member against trusting a stranger. The former is the kind of trust that comes naturally, almost instinctively, to us while the latter is the kind of trust that emerges from the intricacies of a society. Countries with high social trust have better governance, a stable political environment, and a binding rule of law, to say the least.

Unfortunately, social trust in India is far from being optimal. People in general are untrusting of the government and the corrupt behavior of politicians and bureaucrats only substantiates the cynicism that prevails. This wariness is further compounded by the long and winding judicial process in the country, the average life of a case being 10-15 years.  As of January, 15th 2017, there are around 2.81 crore cases pending and 5,000 judge posts empty in India.  

As mistrust becomes the new normal, subconsciously or through force of habit, we become more pessimistic. The lack of social trust not only interferes with the routines but also with the investment decisions. This could be one of the reasons why India has been stuck in the low investment equilibrium (LLLL) for about 70 years now.  To push an economy towards a high investment equilibrium (HHHH), the investment climate needs to be made risk-free, optimistic and trusting. India needs a structural reform to overhaul this conception of mistrust in the economy.

While the jury is still out on the overall impact of Demonetization, this move by the incumbent government has infused some level of trust in the people. Politicians in the country are perceived to be self- interested, motivated to undertake policies that will prove useful in the next election. Structural reforms involve long and slow-churning policies with few or no immediate results. Demonetization has signaled that the acting government is not shying away from undertaking these much-needed reforms. Through Demonetization, the Modi government has shown a strong commitment towards the fight against corruption in India and has managed to build social trust in the country.  

Having said that, India still has a long way to go. While Demonetization has nudged the economy out of the low trust equilibrium(LLLL), serious structural reforms are needed to achieve and sustain the High Trust Equilibrium (HHHH). Failing to do so, Demonetization will become nothing but a political stunt in the minds of the people, throwing the country back into the LLLL equilibrium.

Is Financialization the right thing in the right place at the right time?

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Financialization by Purvasha Sinha

“Only about 15% of the money coming from financial institutions goes into business investments, the rest is spent buying and selling existing financial instruments”, says Rana Foroohar in her book Makers and Takers. Her book serves as a great reminder to policymakers and practitioners about the role of financial system in an economy.

Financialization is defined as the “growing scale and profitability of the finance sector at the expense of the rest of the economy and the shrinking regulation of its rules and returns.” The success or failure of the financial sector has had a serious effect on the rest of the economy and most of the returns in this sector have gone to the wealthy, driving the inequality up, says Mike Collins of The Wall Street Journal.

The alarming state that developed countries have entered is unfortunately labelled as ‘Financialization of Capitalism’, heavily researched and analysed by Harry Magdoff and Paul Sweezy. Their series of essays in Monthly Review over the past three decades brought attention to the term ‘financialization’ and its roots which lie in the Marxist theory of capital accumulation and surplus value. Has Capitalism entered a new stage? I don’t think so. In my opinion, what we see today is a version of amplified Capitalism where firms are profiting even without producing. As John Bellamy Fosters says in “Financialization of Capitalism”, the basic problem of accumulation within production remains the crux of Capitalism, thereby making it unreasonable to categorise this as a new stage altogether.

What is the role of financialization? In the past, the primary contribution of the financial system to a region’s growth has been to mobilise large pools of savings which would be then used to finance profitable investment opportunities. The surplus profits earned from these investments would then be re-invested to expand economic activity and further accumulate surplus. However, sooner or later, corporations would barely sell the current level of goods to consumers which limit the absorbing capacity of surplus profits in productive investments. Eventually, the surplus profit earned cannot be reinvested in expanding businesses since the maximum capacity of production is reached and the consumer demand is met. The limitations in real investment opportunities lead to speculative investments in the financial industry, a platform for the capitalists to multiply their money for higher returns by investing in futures, options, derivatives, hedge funds etc. It is, therefore, important to analyse a company’s investment activities, both real and speculative. Has the growth in financial development made productive investment easier or has it simply expanded the pockets of Capitalists? Before demanding financial development of an economy, it is important to first consider the impact of financialization on income inequality, productivity, and economic growth, along with the risk factor associated with it.

When is the right time for financialization? As an economy develops, the dependence on its own banking sector decreases as firms find other sources to raise funds. During the early stages of development, an economy depends upon its banking sector to fund growth in the manufacturing sector and provide heavy capital requirements. However, as the financial sector develops, it becomes more efficient in allocating resources providing alternatives that diversify the sources of raising capital, subsequently diversifying the risk.

Financialization

When it comes to India, its rapidly growing economy has an immediate need for credit to feed its booming investments. As shown in Figure 1, the domestic credit to private sector grew till the financial crisis of 2008-2009 hit the Indian markets, which resulted in the growth to fall below 0.1%. The credit market did bounce back post-crisis but the growth could not be sustained for long, and has been falling since then. Is the pace at which India is growing in sync with the pace at which its dependence on banking sector is decreasing? Since bank credit has been falling, does the Indian financial sector have an alternative source of fund that could complement the banking sector? This financing gap has pushed for the need of promoting different financial institutions that could complement the banking sector and provide funds to the riskier section of the economy with different maturity profiles that banks are unable to provide.

The Micro, Small, and Medium Enterprises (MSME) sector in India contributes 45% of the industries’ output and 11.5% of the GDP. This sector has high growth prospects and could bring large revenues to the economy. However, only approximately 33% of this sector has access to bank or formal institutional financing. SIDBI (Small Industries Development Bank of India) has estimated the overall debt finance demand of the MSME to be Rs. 32, 50,000 crores of which merely 22% is financed through formal sector means. Moreover, 85% of those finances come from the banking sector (IFC report), clearly indicating that the niche sectors in India are dependent upon its banking sector.

Lastly, what drives financialization? Of late, regulatory bodies are placing emphasis on the regulatory environment of the financial infrastructure space in an economy. Central Banks in countries are pushing their banks to implement the Basel norms III structure. There are strict regulations on capital requirements for banks such as the capital adequacy ratio and periodic financial system risk assessments reports by the IMF. The idea about the “right place” stresses upon the legal environment which sheds light on the role of property rights, financial institution lending to smaller firms, and promoting a competitive and dynamic business sector.

The experience of developed economies with financialization urges me to take a step back and wonder how, if ever needed, the developing economies can do it in a systematic manner. A policy recommendation that I feel is the need of the hour is a committee like that of Basel Committee of Banking Supervision (BCBS) which would examine real and speculative investment of private companies separately from the banks, also tracking where their borrowing or surplus profit is flowing into.

Peer-to-Peer Lending in India

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peer to peer
Peer-to-peer-lending

This summarizes part of the study on peer-to-peer lending in India conducted by the author during her research internship with Department of Economics and Policy Research at Reserve Bank of India.

Peer-to-peer (P2P) lending is a business model that has captured everyone’s attention globally and is now deepening its roots in India. With the advent of online industry, the financial sector is revolutionized; strongly attracting investors, businesses, customers, analysts as well as the regulators. Concerns over escalating non-performing assets (NPAs) and customer’s shift towards loans from private lenders (like family, friends, etc.) due to rigid collateral requirements and stringent availability of bank credit, coupled with various technological advancements and government initiatives towards cashless and digital economy, are responsible, to some extent, for the evolution of P2P lending.

Peer-to-Peer lending is an innovative form of crowd funding with financial returns. It involves the use of an online platform to bring lenders and borrowers together, thereby mobilizing unsecured finance. The platform enables a preliminary assessment of the borrowers’ creditworthiness and collects the loan repayments. Accordingly, a fee is paid to the platform by both borrowers and lenders for the process. Interest rates on the loan ranges from a flat interest rate fixed by the platform to dynamic interest rates as agreed upon by borrowers and lenders. One of the main advantages of Peer-to-peer lending for borrowers is that the rates are lower than those offered by money lenders or the unorganized sector. On the other hand, the lenders benefit from P2P lending as they enjoy higher returns under this scheme than those obtained from a savings account or from any other investment.

Although there has been significant growth in online lending platforms globally, there is no uniformity in the regulatory stance about this sector across countries. While P2P lending platforms are banned in Japan and Israel, they are regulated as banks in France, Germany and Italy, and are exempt from any regulation in South Korea. Differences in regulatory frameworks for the online lending sector across the world can be traced back to two opposing arguments.  Those who are against regulating this sector believe that any such move might stifle its growth at this nascent a stage. On the other hand, proponents of regulation argue that unchecked growth of this sector may weaken the monetary policy transmission mechanism and breed unhealthy practices by market players which may, in the long run, generate systemic problems given the susceptibility of this sector to attract high-risk borrowers. The balance then lies in developing an appropriate regulatory and supervisory tool-kit that harnesses this sector’s ability to provide an alternative source of credit for the right kind of borrowers, facilitating growth in an orderly manner.

P2P lending in India can be broadly categorized into three types—microfinance, consumer loans and commercial loans. Currently, there are several online P2P lending platforms operating within the country. Some of these have targeted businesses undertaking microfinance activities with stated primary goals of having a social impact and providing easier access of credit to small enterprises. These are largely tech companies registered under the Companies Act.  Presently, there are around 30 P2P lending start-ups in India. These companies have decided to form an association with the intention of self-regulation and are expected to complete their registration process soon. It is estimated that the P2P lending sector in India is worth INR 20 crore and is expected to lend approximately INR 1.25 crore per month in the future.

According to the data available at Faircent’s website, INR 334.9 lakh loan amount has been proposed for high risk interest rates i.e. 22 – 26 percent followed with INR 307.47 Lakh loan amount proposed for Medium risk interest rates (18 – 22 percent). The average rate of interest (27.86 per cent) has been earned under very high-risk bucket (26 – 32 percent) and the highest average loan tenure (24.65 months) has been granted under high- risk and very high-risk bucket. The maximum number of defaults have happened for the short-term loans (6 months) or under very high-risk bucket (4.93 percent).

According to the data available at i-Lend’s website, there is a gap between the number of lenders willing to lend and number of lenders who have lent partial or full amount. The gap also exists in the amount one is willing to lend and that which has been lent. This gap represents the demand-supply gap of the lending market on the platform. The maximum gap is within the interest bracket of 22 -26 percent and the minimum is in 12 – 14 percent. This make sense, as the lenders will always want to earn the highest interest rate and the borrowers will always want to pay the lowest interest rate. Similarly, this gap can also be seen on the borrowers’ side of the market, in terms of number of the borrowers participating and the amount of borrowings.  The maximum gap, again is within the interest bracket of 22 -26 percent while the minimum gap is now within 18- 22 percent. Also, the demand-supply gap on this platform is observed to be maximum for the loan tenure of 12 months followed by that of 18 months. In addition to that, the demand-supply gap is seen to be maximum for loans used for debt consolidation; the second-widest demand-supply gap is noticed in education loans.

P2P lending is driving huge unorganized lending sector in India. Data available on i2ifunding website shows the wide network of P2P lending and how it is connecting borrowers and lenders from across the country, mostly targeting those states that have a persistent alternative source of lending. The maximum number of borrowers on this platform are from Delhi (55.80 percent), followed by NCR (19.48 percent), Maharashtra (6.86 percent, excluding Mumbai), Karnataka (5.71 percent), and UP (4.13 percent).

Policy recommendations:

  1. Make P2P lending reach more citizens: States like Tamil Nadu, Rajasthan, Bihar and Andhra Pradesh have more than 50 percent of outstanding cash debt through non-institutional agencies and almost no presence of P2P lending.

  2. Reduce the demand-supply gap: A persistent gap between demand and supply can push the lending market to fail.  

  3. Cap the borrowings where maximum defaults are recorded: Maximum number of defaults have been observed for short-term loans or under very high-risk bucket.
  4. Introduce Orchard platform for P2P lending:  The interest rates in P2P lending sector are high and increasing, compared to 10-year government yields and other benchmark interest rates, which are relatively low and decreasing.