The Phantom Subsidy

The chart below looks simpler than most of the others on this site, but the story behind it is remarkably complex. If anyone ever told you that the petroleum subsidy, one of the single biggest items on the central government budget (closing in on a 1000 billion rupees, or about 7% of overall government expenditure), was a straightforward issue, don’t believe them.

But we can simplify it a bit though, by focusing on just one question : To what extent does this subsidy exist?

And the answer, looking at the broad numbers in the graph below is – not to any great extent.

Here’s the problem with our whole debate on the petroleum subsidy : it only looks at what the government is officially supposed to pay out of its budget every time someone buys diesel, or an LPG cylinder. In the graph, that’s the orange bar. It doesn’t look at what the government rakes in. Those are the blue and green bars (for the central and state governments).

It’s a bit like the following. The government gives you six rupees as a subsidy for something. But as part of the same transaction, it then takes back eleven rupees as tax.

In brief, here’s how it works. Oil companies sell diesel to the dealer at a wholesale price of about Rs 44 per litre in Delhi. At this price, they are supposed to be paid a subsidy of about six rupees (the first part of the transaction above). Add in the dealer commissions and margins (all regulated), and this price goes up by a little over a rupee. But the actual price paid for diesel by a consumer in Delhi is about Rs 55.50 per litre. That’s eleven rupees above the price the oil company gets – almost all of that difference is taxes. So the government commits to paying a subsidy to the oil companies of about six rupees, but charges the consumer eleven rupees in tax.

The oil subsidy debate (to be fair, not all of it) looks at the six rupees and calls it the petroleum subsidy, while ignoring the part where the government takes back eleven rupees with the other hand. Overall, the tax revenue that central and state governments earn from each unit of diesel sold, is more than the subsidy they pay out. In net terms (i.e. looking at both transactions rather than just one), this means there is a tax on that product, not a subsidy.

Now none of this is a dramatic revelation – see here and here.

I said that this issue was complicated and here’s where I begin to complicate it. But I do think the overall picture I presented above stays more or less unchanged. Also, in what follows, I will continue to use the term ‘subsidy’ for convenience sake. One of the things that always strikes me about the whole issue of the petroleum subsidy is that it is an excellent case study in how otherwise commonplace words are used by governments and bureaucracies in a confusing way. It’s not necessarily intentional, but then again…who knows?

(click anywhere on the chart below to switch between the official level of subsidy, and what the government actually pays out. This is explained further down. Hover your mouse over any of the bars to get the numbers) 

Complication #1 : What the government actually pays out falls far short of the ‘overall’ subsidy

You’ll notice that clicking anywhere on the chart above causes the orange bars to change between what I call the official level of subsidy, and what is actually paid out. The latter is far less. This is because the government follows what is called ‘burden sharing’. Public Sector oil companies who import crude oil, refine it, and sell it to consumers, are forced to bear a share of the subsidy paid to consumers. So for every hundred rupees of subsidy that oil companies should get under this system, they may actually receive only say, 60 rupees, from the government.

One last thing. There are two governments involved here – the central and state government – who tax diesel or LPG or kerosene. But only one of them – the central government – pays the subsidy to the oil companies. Compare the orange and blue bars.

In the next post, I will look at one of those words whose meaning gets so complicated. That word is ‘subsidy’.

Notes :

Central government tax revenues from here ( Table VII.1, page 92

State government tax revenues from the same document above, Table VII.2, page 93, and VII.4, page 96. In both cases, I dont include royalties or revenues from natural gas. Note that in the second table, there is a component of central government revenues that is included (central sales tax), but I can’t seem to be able to disentangle that effect. Bottomline : the state government tax revenues include some component which should ideally be included under central government revenues.

The chart is made with the D3 library.

The Household Census : A Summary Post

Over the last year or so, we’ve done several separate posts on different types of deprivation (or access) using the Census 2011 data. We’ve looked at everything from phones to toilets, to drinking water to electricity. It’s time to put these together in one chart, and try and link these diverse measures together. If a district has high ownership of TV sets for example, thus implying a certain standard of living among households there, how likely is it that those households will also have access to the basics – say water or electricity?

The chart below may look a confusing mass of different coloured lines, but it’s quite simple to interpret. Each line represents one district. Each state is allocated a different colour, and states are ranked from bottom to top (red to green) according to their per capita income.

There are a bunch of different axes from left to right, measuring different parameters – phones, electricity, TV sets etc (also note the ‘rich’ and ‘poor’ measures which I’ll come to in a moment). Tracing the points at which each line (a district), hits an axis, tells us how that district scores on each parameter. So for instance, Paschim Champaran, in Bihar, which is the first entry in the table below the chart, has 52% of households with a phone, 9% with a TV set, 52% with access to water and so on. Hover your mouse over a row in the table and the corresponding district in the graphic gets highlighted.

You can go deeper. Click and drag on any axis to filter only those districts whose lines pass through that part. Now you can move the filter up and down to update the graph. The data table is updated automatically as well, enabling you to get at the actual numbers. You can put in multiple filters on different axes as the same time. Simply click on the ‘reset filters’ button to get back to the original view.

If for instance, we highlight the state of Delhi at the top left, we can see that Delhi’s districts are clustered fairly close together on all measures. Now move the filter down, and we see that other high income states such as Maharashtra, Haryana and Gujarat, are actually quite diverse at the district level, with some districts scoring high, and others scoring low. A state like Kerala may rank lower in the scale of per capita income, but on most measures, Kerala’s districts are tightly clustered together (like Delhi), showing that district-level inequality is lower in that state, than in other, richer, states.

A word about the ‘rich’ and ‘poor’ measures. ‘Rich(a)’ and ‘poor(a)’ are measures of access to a basket of consumption goods, rather than just one. ‘Rich(a)’ for each district, indicates the percentage of households who have a TV, computer, phone and a motorised vehicle (2 or 4-wheeler). ‘Poor(a)’ indicates the percentage of households in a district who have none of the consumption goods covered in the census – not even a phone.

Similarly, ‘rich(b)’ and ‘poor(b)’ measure access to a basket of household amenities like drinking water, toilets, and electricity simultaneously. ‘Rich(b)’ measures those households who have electricity, and access to drinking water and a toilet at home. ‘Poor (b)’ measures those households who have no electricity, and no toilet at home, and have to travel more than 0.5 km for drinking water. Note that unlike the other measures, the lower you are on the ‘poor’ measures, the better off you are.


The table displays data for only ten districts at a time.

As always, all data is from the census 2011.

The charts are made with the D3 javascript library ( Also used is the utterly invaluable parallel coordinates library which uses D3. The chart also uses divgrid and is based on this example here. This chart is one of the more complex ones I did, but it was actually one of the easiest to code thanks to the supporting libraries available. I’m still amazed at how easy it was.

Mapping the Shift in Access to Drinking Water – II

Last week I mapped the proportion of households who had to travel more than half a km in rural areas (or 100m in urban areas) to find drinking water. The share of such households in the overall population had actually increased between 2001 and 2011.

The map below shows the other part of the story. It maps the proportion of households who had access to drinking water at home, both in rural and urban areas. Interestingly, the proportion of such households too, actually increased between 2001 and 2011 – by about 7.6 percentage points.

But it’s the regional differences which are interesting…compare the map below with the previous one to see what I mean.

(This post may take a while to load on a slow connection, and/or if you are using a tablet or phone. Click or tap on the map to switch between 2001 and 2011. Hover your mouse over a tehsil/subdistrict to see its details. The greyed-out regions are those for whom data couldn’t be compiled, or those which are not relevant e.g. PoK)

In a swathe of central and eastern India in the previous map, access had actually worsened – more households were having to travel a greater distance to find drinking water. In the map above we see that in Northern, Western, North-Eastern and Southern India, and parts of UP and Bihar, access seems to have actually improved – more households have access to drinking water at home in 2011 than a decade earlier.

But we have to be careful in interpreting what ‘improved’ access actually means here – other studies and data show drops in the water table in many of the areas where the census data shows better access. As more households use tubewells / borewells at home, there’s greater pressure on ground water resources.

So on closer examination, even the good news may not be that great…

Notes : 

Please see the previous post.

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