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.
*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.
Charts made with R/ggplot2.
June 21, 2014