Income inequality in India

Varsha Kulkarni and Raghav Gaiha in The Hindu

With the Gujarat State elections barely a few weeks away, the debate on the Indian economy has become increasingly polarised. While the official view of demonetisation unleashed in November 2016 elevates it to a moral and ethical imperative, the chaos caused by the goods and services tax (GST) launched on July 1, 2017, is dismissed as a short-run transitional hiccup. Both policies, it is asserted, are guaranteed to yield long-term benefits, unmindful of large-scale hardships, loss of livelihoods, closure of small and medium enterprises and slowdown of agriculture. Critics of course reject these claims lock, stock and barrel. Lack of robust evidence is as much a problem for the official proponents of these policies as it is for the critics. Hence the debate continues unabated with frequent hostile overtones.


Tracking income inequality

Beneath the debate are deep questions of inequality and its association with poverty. Thomas Piketty produced a monumental treatise, Capital in the Twenty-First Century, demonstrating that rising income inequality is a by-product of growth in the developed world. More recently, Lucas Chancel and Piketty (2017), in ‘Indian income inequality, 1922-2014: From British Raj to Billionaire Raj?’, offer a rich and unique description of evolution of income inequality in terms of income shares and incomes in the bottom 50%, the middle 40% and top 10% (as well as top 1%, 0.1%, and 0.001%), combining household survey data, tax returns and other specialised surveys.

Some of the principal findings are: one, the share of national income accruing to the top 1% income earners is now at its highest level since the launch of the Indian Income Tax Act in 1922. The top 1% of earners captured less than 21% of total income in the late 1930s, before dropping to 6% in the early 1980s and rising to 22% today. Two, over the 1951-1980 period, the bottom 50% captured 28% of total growth and incomes of this group grew faster than the average, while the top 0.1% incomes decreased. Three, over the 1980-2014 period, the situation was reversed; the top 0.1% of earners captured a higher share of total growth than the bottom 50% (12% v. 11%), while the top 1% received a higher share of total growth than the middle 40% (29% v. 23%).

True to its modest objective, it offers a rich and insightful description of how income distribution, especially in the upper tail, and inequality have evolved.

Sharp reduction in the top marginal tax rate, and transition to a more pro-business environment had a positive impact on top incomes, in line with rent-seeking behaviour.


India’s wealth gain

According to Credit Suisse Global Wealth Report 2017, the number of millionaires in India is expected to reach 3,72,000 while the total household income is likely to grow by 7.5% annually to touch $7.1 trillion by 2022. Since 2000, wealth in India has grown at 9.2% per annum, faster than the global average of 6% even after taking into account population growth of 2.2% annually. However, not everyone has shared the rapid growth of wealth.

Our research, based on the India Human Development Survey 2005-12, focusses on a detailed disaggregation of income inequality, along the lines of Chancel and Piketty, recognising that incomes in the upper tail are under-reported; and examines the links between poverty and income inequality, especially in the upper tail, state affluence, and prices of cereals.

Our analysis points to a rise in income inequality. A high Gini coefficient of per capita income distribution, a widely used measure of income inequality, in 2005 became higher in 2012. The share of the bottom 50% fell while those of the top 5% and top 1% rose. The gap between the share of the top 1% and the bottom 50% narrowed considerably.

More glaring is the disparity in ratios of per capita income of the top 1% and bottom 50%. The ratio shot up from 27 in 2005 to 39 in 2012. Far more glaring is the disparity in the highest incomes in these percentiles. The ratio of highest income in the top 1% to that of the bottom 50% nearly doubled, from a high of 175 to 346.

All poverty indices including the head-count ratio fell but slightly.


Poverty and inequality

Higher incomes reduced poverty substantially. Inequality measured in terms of share of income of the top 10% increased poverty sharply but only in the more affluent States. Somewhat surprisingly, higher cereal prices did not have a significant positive effect on poverty. Similar results are obtained if the share of the top 10% is replaced with the Gini coefficient as a measure of inequality.

It is plausible that poverty reduction slowed in 2016-17 because of deceleration of income growth; and huge shocks of demonetisation and the GST to the informal sector have aggravated income inequality. Indeed, depending on the magnitudes of these shocks, poverty could have risen during this period.

In sum, regardless of the longer-term outlook and presumed but dubious benefits of the policy shocks, the immiseration of large segments of the Indian population was avoidable.

Are our dreams trying to tell us something – or should we sleep on it?

Oliver Burkeman in The Guardian

What are dreams for? It’s one of those bottomless questions where the answer tells you mainly about the person doing the answering. Those who pride themselves on being hard-headed and scientific will say they’re meaningless nonsense or, at best, some kind of boring but essential process for consolidating the memories of the day. Those who think of themselves as spiritual, meanwhile, will insist they’re messages from beyond. Yet the hard-headed answer isn’t much more plausible than the kooky one. If dreams are random brain-firings, how come they have coherent narratives? And if they’re just a dull retread of everyday events, how come they’re so often wildly inventive, haunting or surreal? (Don’t worry, I won’t bore you with any of my own, though the famous fact that “nothing is more boring than other people’s dreams” is, in itself, rather interesting.) As James Hollis, a Jungian psychotherapist for whom dreams are far from meaningless, writes: “Who would make this stuff up?” Night after night, you go to bed and elaborately crazy stories plant themselves in your mind through no choice of your own! Don’t tell me something intriguing isn’t going on.

Dreams are hard to study in the lab, for the obvious reason that only you experience your own. Indeed, as the philosopher Daniel Dennett points out, you can’t even be certain you experience them, at least in the way you imagine. You “recall” them when you wake, but how do you know that memory wasn’t inserted into your mind at the moment of waking? Yet recent work by researchers including Matthew Walker, author of the new book Why We Sleep, strongly suggests dreams are a kind of “overnight therapy”: in REM sleep, we get to reprocess emotionally trying experiences, but without the presence of the anxiety-inducing neurotransmitter noradrenaline. In experiments, people exposed to emotional images reacted much more calmly to seeing them again after a good night’s dreaming. Neither dreamless sleep nor the mere passage of time duplicated that effect.

Carl Jung certainly wouldn’t have settled for that explanation, though. He argued– I’m simplifying here – that dreams were messages from the unconscious, offering, in symbolic form, insights and advice the conscious mind might have missed. That dream where you’re careening down a slope in a runaway shopping trolley towards a cliff edge: what might that be saying about how you need to change? So you wrote down a dream, then studied it, with or without a therapist, trying out different interpretations, and if one rang true – if it gave you goosebumps or triggered strong emotions – you pursued it further. What’s striking, you may have noticed, is that this approach would work even if Jung were wrong, and dreams were just random. If you treat them as potentially meaningful, retaining only those interpretations that really “click”, you’re going to end up with meaningful insights anyway. I’ve dabbled in this, and highly recommend it. To ask what your dreams might be trying to tell you is to ask deep and difficult questions you’d otherwise avoid – even if, in reality, they weren’t trying to tell you anything at all.

Yanis Varoufakis on Catalonia, Muslim Ban and a Sustainable World Order

UK GDP - The measurement that holds economic statistics back from reality

Diane Coyle in The FT


It is faintly surprising that one of the liveliest areas of economics these days is the question of measurement, and what relation published statistics bear to what is happening in the economy. Statistics do not usually inspire excitement. 

This attention reflects the convergence of two strands of scepticism about the existing statistics, and in particular gross domestic product. One is the “productivity puzzle” and to what extent the mis-measurement of digital phenomena helps explain the slow rate of productivity growth. The other is the longstanding critique of GDP as a meaningful measure of progress, for reasons of environmental sustainability or other contributors to society’s wellbeing. 

The two converge on the distinction between the aggregate amount of marketed economic activity and total economic welfare. The conventional statement about GDP is that it is only meant to count the former, not the latter. GDP does not capture environmental factors or consider income distribution. But as long as that gap has been roughly constant, GDP growth has been a good enough measure of improvement in economic welfare. 

Perhaps the wedge between total marketed economic activity and welfare is increasing because of the pace of technological change, but statistics have never captured the human gains from advances in periods of innovation, whether in medicines or the internet. 

This case for the defence of GDP is fundamentally weak, however. It in fact includes many non-marketed activities, yet excludes other productive activity. Business and government count as “the economy” but voluntary and household activities do not. 

Postwar social changes — a rising proportion of women working outside the home, and the increased purchases of prepared foods, professional childcare, domestic appliances and so on — have flattered the official productivity statistics for decades. 

More subtly, the statistics blur the distinction between marketed economic activity and increases in economic welfare that cannot be priced by converting nominal GDP into “real” terms. 

Economists and statisticians are beginning to accept that our framework for economic statistics needs to change. Some argue for developing better “satellite” accounts, where all the interesting data about the environment or the household are collated. But why should all the pressing questions be satellites? 

GDP could certainly be improved. In one of the joint winners of the Indigo Prize essay competition, a team led by Carol Corrado and Jonathan Haskel, proposed better measurement of services and intangibles, and direct measurement of the economic welfare being created by digital goods. The other winning essay — which I co-authored with Benjamin Mitra-Kahn — proposed similar incremental changes as an interim step. 

We opted for better measurement of intangibles, adjusting for the distribution of income, and removing unproductive financial activity. The long-term recommendation was more radical: ditching GDP as the metric of progress in favour of measures of access to different kinds of assets, including financial wealth but also natural capital, intangible assets, infrastructure and human and social capital. 

This was inspired by Amartya Sen’s idea that prosperity consists in people having the capabilities needed to lead the life they would find meaningful; and by the need to get away from measuring economic progress only through the short-term flow of activity. There is no sustainability without a balance sheet. 

Perhaps neither the incremental nor the radical is the right approach. Reform will take time because there needs to be consensus about how to change; statistical standards are like technical standards. But I am now confident that in another 10 or 20 years GDP will have been dethroned.