Here’s What a Polarized Vote Looks Like

Before and during the election campaign, one constituency which got a lot of attention was Muzaffarnagar constituency in Uttar Pradesh, the scene of serious communal riots in 2013. Those riots were seen as a ploy by political parties to polarize the vote between Hindus and Muslims. In the event, the BJP won comfortably, by a substantial margin of over 400,000 votes, which translated into a winning margin of 36%. In contrast, in the 2009 election, the BSP candidate who won the seat, did so narrowly – by a margin of just 2.7%.

The chart below shows what that change means in a visual sense. It looks at the vote share of the biggest party in each of the 1500 or so polling stations in the constituency, and then plots those values according to how often they occur. So for instance, there were a relatively few polling stations where the largest party got 40% of the vote or below. There were many more with 60% or more of the vote in a booth. And so on. The number of polling stations where the largest party received any given vote share (from 0 to 1), can be read off the y-axis.

What’s the point here? What I’m trying to show is that in the aftermath of the riots, there were many booths (far more so than in 2009) which essentially seem to have turned into ‘winner-take-all’ contests. Whole villages or areas under a single booth chose to vote sharply one way or the other. Note for instance, that the ‘peak’ of the chart is hit around the high 90% mark i.e. there were close to 200 booths where pretty much every single voter decided to vote for just one party*. In contrast, the 2009 election was much more sharply contested. Here the peak is hit at the less than 50% vote share mark. In most booths in 2009 in the constituency, there was a fair amount of diversity in voting.

In contrast, in 2014, around 30% of booths in the constituency saw the top party receiving 90% or more of the vote.

Another point of comparison is the distribution of vote shares of the largest party for all booths in Uttar Pradesh in 2014. Here it is:

You’ll notice that this distribution is much more ‘conventional’, even given the remarkable scale of the BJP victory (71 seats out of 80) in Uttar Pradesh.

In another post, I may look at another constituency in UP, which had a similarly interesting pattern of voting even though it was not directly affected by the scale of communal violence seen in Muzaffarnagar.

One problem with the analysis above is that I only look at Muzaffarnagar constituency, whereas of course, the violence would have affected voting patterns in other parts of western UP.

Notes:

*Incidentally, one of the Election Commission’s criteria for classifying a polling booth as ‘sensitive’ (i.e. a booth where extra security measures will be needed etc) is one which gives more than 75% of votes for a single candidate in the previous election. By this criterion alone, a large chunk of Muzaffarnagar’s booths will be classified as ‘sensitive’ in the next election.

In the chart, it seems as if there are some polling stations where the largest party has received a greater than 100% vote share, which is obviously incorrect. The problem is not with the data but with the way the labels are displayed on the x-axis.

Booth-wise data from the dataset compiled by Raphael Susewind from the official Uttar Pradesh data. Many thanks to him! Here’s his website.

Charts made with R/ggplot2.

Mapping Access to Toilets between Social Groups

A while back, I had done a toilet map of India. That map didn’t cover the critically important issue of how access to a toilet at home differs across social groups. In context of the recent horrific crime in UP, when two young dalit girls stepped out of their house to relieve themselves and were raped and hanged, I thought I would update that map to look at access to such facilities between disadvantaged social groups such as dalits and tribals on the one hand, and non-dalit / non-tribal households on the other.

The map below reflects that difference in access across districts. For each district, I calculated the percentage of dalit/tribal households with access to a toilet at home. I did the same calculation for households which were neither dalit nor tribal, as classified by the census. By dividing the two, we get a measure of how disparate the access is. For instance, in the district of Budaun, where the crime occurred, 15% of dalit homes according to the 2011 census, had access to a toilet at home, compared with 35% of non-dalit or non-tribal homes. This gives me a disparity measure of about 0.43 (15% divided by 35%). And so on for each district.

A measure close to one, or above it, means that both sets of households are equally well-off – or equally badly-off depending on the state. The same calculation was done for tribal households. Click anywhere on the map to switch between maps of dalit or tribal households. For districts where either social group is less than 4% of the population, I have not calculated or mapped the relevant area.

(Hover your mouse over any district for the numbers. Click or tap on any part of the map to switch between mapping of the disparity measure for dalit and tribal households. Lighter coloured areas are districts with lower disparity in access. The green areas are those where the disparity measure is above 0.9. This includes areas where dalit/tribal homes actually have better access to toilets than non-dalit/non-tribal homes i.e. the disparity measure is greater than one)

What do we see in this map?
* Four regions – Maharashtra, Kerala, Gujarat and North East states stand out from the rest of the country, in having generally lower disparity measures than elsewhere, for dalits. In addition, Punjab and Himachal Pradesh seem to be relatively better off than other areas as well.
* The southern states of Karnataka, Andhra and Tamil Nadu are interesting. All three are relatively higher income states – yet on the disparity measure for dalits, their performance is spotty at best.
* For most districts, tribal households are even worse off than dalit households when it comes to access to toilets. The disparity measure for the country as a whole, for dalits, is about 0.61. For tribals, it is 0.43. Again, the North-East states stands out when it comes to access to toilets for tribal households vis-a-vis non-tribal households.

Note that the disparity measure is a relative one. It is entirely possible to have a wealthier district where dalits are better off in absolute terms compared with dalits in a poorer area. However, relative to upper caste neighbours within their own district, they could be shown as being less well off.

The chart below shows the measures state-wise. I haven’t adjusted for states with low dalit or tribal populations in this chart.

Notes :
All data from census 2011. The data table is here.
Map made with D3. Bar chart made using R/ggplot2. Colors palette adapted from colorbrewer.

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) 

Also, 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.

Notes :

Central government tax revenues from here (http://petroleum.nic.in/pngstat.pdf). 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.

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