GST: The population factor

The tug of war over the Goods and Services Tax (GST) between the central government and state governments has raised questions on when this system of taxation—which collapses a multitude of taxes into one—will be implemented. When it does, it will change many things, one of which will be how much a state collects by way of taxing goods and services, something that is especially of concern to states with larger industrial bases but smaller populations.

That’s because the basis of GST is different from the basis of the two main taxes it will replace. The first main tax in the present system is excise levied by the Centre: a tax on production, it benefits states with larger industrial bases. The second is VAT (value added tax): a tax on sales levied by states, it benefits states with larger population and consumption.

However, since a company cannot set off the excise paid by it against VAT, states with a higher share of manufacturing collect and retain more taxes than states with a large population and consumption. Basically, the two taxes don’t talk to each other.

GST will change that as it will be levied at the point of consumption, and not production. Thus, prima facie, it will tilt the balance in favour of states with higher population and consumption, even if they don’t have a large industrial base.

So, with GST, which states stand to gain and which stand to lose? This realignment will play out at two levels. Graph 1 below maps three variables at the state level: population (red line), area (blue circle) and tax revenue (green line). The first two are also determinants to calculate a state’s share in GST.

Thus, at the first level, a downward sloping line is indicative of high population states (Uttar Pradesh, Bihar and West Bengal), and they are likely to benefit from GST. An upward sloping line is indicative of states with large industrial bases (Tamil Nadu, Maharashtra and Gujarat), and they could be impacted by the point of sale becoming the point of taxation.

But it’s also true that several states in India with the largest industrial bases (notably, Maharashtra and Tamil Nadu) also have a large population and are leaders in per capita income. So, what these states lose in the first-level shift from manufacturing basis to consumption basis might be offset by their own consumption quotient.

The scatter diagram in Graph 2 below plots similar variables as Graph 1 (with the added nuance of tax per capita). So, for example, while a state like Tamil Nadu might lose revenues because of consumption-based taxation, its high tax per capita, an indicator of higher consumption, would offset this.

The net impact of both factors will determine how much a state loses or gains when GST is implemented.

    Read More

    What’s killing Indians? Depends on where you live…

    This piece originally appeared on



    Over 22,000 Indians die every day. In the 60s, communicable diseases were the main cause, accounting for about half the deaths. Since the 90s, it’s non-communicable diseases such as cardiovascular diseases, cancer, respiratory diseases and diabetes. But all-India data doesn’t give the complete picture, as India is a diverse country. Economic and social conditions, and public health infrastructure, differ from state to state, region to region.

    Besides, incidence of disease also varies depending on age group and gender. Granular data on the cause of deaths helps design better policies and solutions, and monitor public health.

    India has been collecting data on the causes of deaths for over four decades now, following the passage of the Registration of Births and Deaths (RBD) Act in 1969. The Office of the Registrar General recently released a report with data for the year 2013, looking at 928,000 medically-certified deaths (about a fifth of medical deaths). The above data interactive captures the causes of death: by gender and age group, and for different regions and states. By default, the chart represents India numbers. To choose a specific region or state, use the dropdown. For gender-specific results, click on the buttons.

    As one might expect, the causes of death differ based on age group. For example, while infections account for 12% of deaths across age groups, it accounts for about 25% of deaths in the 1-4 years age group. As people become older, chances of being claimed by circulatory and cardiovascular diseases go up. The risk of death by external causes—such as injuries or poisoning—is the highest between 15 to 34 years; and men are more likely to victims (8%) than women (6.5%). Cancer, on the other hand, claims more women (5.7%) than men (4.7%).

    Even at the state level, there are many such nuances and comparative surprises. For example, Chhattisgarh is most prone to cardiovascular diseases (over 50% of its deaths were caused by diseases of the circulatory system), Uttarakhand the least (13.3%). In Kerala, at 15.2%, cancer as a cause of death is a full 10 percentage points above the national average. Similarly, deaths due to external causes are the highest among all states in Karnataka, followed by Nagaland.

    It’s not clear why some states are more prone to certain categories of diseases. The report doesn’t go into those details. However, it could serve as a starting point for further research and appropriate intervention.

      Read More

      Rio Olympics: Why India was not a rank disaster

      This piece originally appeared on


      India’s largest-ever squad to an Olympics, of 117, returned with two medals in Rio 2016, against six in London 2012. At the pinnacle of world sports, medals are one barometer of sporting performance. Another is standings: where a sportsperson finished vis-à-vis the competition. This is especially relevant for a fledgling sporting nation like India.

      The data interactive below maps standings of Indian sportspersons in Rio 2016, and compares it to the previous five Olympics, dating back to the 1996 Atlanta Games, when India sent a 49-member squad and Leander Paes in tennis was the sole medal winner.

      Beyond the medals, 2016 is not the unqualified catastrophe it is made out to be. At an overall level, the number of top 10 finishes fell from 28 in 2012 to 21 in 2016. But this was largely on account of just two sports: boxing and tennis. Similarly, between ranks 11 and 20, the count fell from 28 to 24, but the losses are scattered across sports (archery and rowing) and sprinkled with the occasional gain (wrestling and tennis).

      Prominent sports in which Indians competed can be placed in three buckets.

      Clear advances

      There was badminton, which delivered a medal and demonstrated depth. There was Dipa Karmakar: the first Indian gymnast at the Olympics since 1964, the first Indian women ever and who missed a medal by not much.

      Clear retreats

      The biggest losses were registered in boxing: the number of top 10 finishes dropped from six in 2012 to two. There was table tennis, where all four Indians lost in the first round. There was weightlifting, where India is down from a medal and two more top 20 finishes in 2000 to just one top 20 finish in 2016.

      Close to call

      Most sports were close to call, which runs contrary to the narrative of Rio being a disaster for India. Among the large-squad sports, shooting returned two fewer medals than 2012, but had more performances in the top 10 (4 versus 3) and the same number of performance in the top 20 (11).

      In athletics, the number of top-20 performances fell from six in 2012 to four in 2016, but there was a cluster of nine athletes who finished between 20 and 35 (against five in 2012). In tennis, India’s mixed doubles team came closer to a medal than in the previous two Olympics, but the rest of the squad made earlier exits. In archery, fewer Indian competed, but posted better overall results.

      Use the interactive below to see how India has fared in 13 sports in the last six Olympics.


        Read More

        Where PV Sindhu scored and where men’s hockey didn’t


        This piece originally appeared on


        Standing just a centimetre short of six feet, silver medallist P.V. Sindhu was the tallest player in the women’s individual badminton event at the Rio Olympics. At 21 years, she was also one of the youngest. Sindhu weighs the same as Saina Nehwal, but the latter carries the same weight on a frame that is 14 cm shorter. For Sindhu, one of the rare Indian bright spots in Rio, that alchemy of reach, lightness and youth worked in her favour. Physically, she was well-matched compared to her rivals.

        Sindhu was the tallest player in women’s singles

        Sindhu was a rare instance of an Indian sportsperson matching up well physically against their competitors. In most cases, Indians don’t size up well against their rivals, shows data on height and weight from the official website of the 2016 Rio Olympics.

        Take the Indian men’s hockey team, which was tipped for a medal. Its average height of 1.77 metres made it the shortest among the 12 teams in the competition—7 centimetre less than Germany, the tallest squad. With an average weight of 73 kg, it was the lightest—7 kg less than the leader, once again Germany.



        Indian men’s hockey was the shortest

        Height and weight matter differently in different sports. In hockey, for example, the sport has moved from an emphasis on dribbling skills—in the penalty shootout in the women’s hockey final between Germany and Netherlands, nine out of 10 players failed to beat the goalkeeper in a one-on-one situation—to speed and aggressive body play.

        Studies have shown that players have an arm span 5 cm more than their height and a stride length on synthetic turf of 1.35 times their height, and they take two strides per second. A rough calculation, built on these numbers and the average height of the hockey teams at Rio, shows that, all else remaining equal, that height disadvantage resulted in the Indian player being 0.217 seconds slower than his German counterpart over a distance of 27.5 metres (between the top of the shooting circle and the half line).


          Read More