The BJP’s Election to Lose
As much as any party would like to make this a presidential-style election, the 2019 electoral landscape is markedly different from 2014 and is about how many seats the NDA will lose.
The NDA’s 2014 sweep can be directly attributed to the seats it won in eleven states, but let’s add some perspective and include previous results:
|State (Total Seats)||2014||2009|
|Uttar Pradesh (80)||73||10|
|Madhya Pradesh (29)||27||16|
Two things were at work in 2014 — a pro-Modi wave, and a strong anti-UPA vote. They added up to handily defeat a divided opposition. After all, across these eleven states, the NDA’s vote share was 46.6% with an average vote share increase of 9.56%.
The fact that less than half of all voters chose them makes it clear that no one, not even the NDA, expects to repeat this performance, especially in the face of a united opposition.
Results from the last two decades throws up some interesting context. In 1999, the BJP won 29 seats in UP with a 27.64% vote share. In 2004, the BJP won 10 seats with 26.74% of the vote. In 2009, 10 seats again but the vote share dropped to 17.5%.
To further the point that the NDA/BJP wins with a minority of the vote, I am tempted to use the example of Haryana where the Congress and the INLD split the vote leading to a BJP sweep, but let’s limit ourselves to Uttar Pradesh, the state where we have an extraordinarily comprehensive data net at work at the moment.
In 2019, given the SP-BSP-RLD combination it’s tempting to suggest that the BJP will be handily defeated since they enjoy a significant arithmetical advantage in a majority of the seats.
But, let’s also accept that India has changed. We are told there are many young people who will not vote along caste lines and will vote for Modi. So, we decided to map vote share shifts between 2014 and the 2017 assembly election at the polling booth level to understand how the landscape is changing.
In this the pink dots are polling booths where the BJP lost vote share between the 2014 and the 2017 assembly election. They won the election against a divided opposition.
This map shows a very interesting picture — the blue dots are polling booths where the combined SP-BSP coalition gained vote share between 2014 and 2017.
The data for Uttar Pradesh is clear — the anti-UPA vote which contributed to a 24.8% increase in BJP’s vote share in 2014 is not available to them any longer. This increase is over the 2009 election when their vote share had fallen to 17.5%. It’s fair to say that in 2014, their traditional vote returned to them bringing their vote share up to about 27% and then they got a boost from the Modi wave and anti-UPA combination.
I am tempted to attribute the repeated emphasis on the Congress’ past failures in Narendra Modi’s campaign speeches to this need to re-capture this anti-UPA/Congress vote. He is succeeding in pockets — almost all of them urban — and amongst voters who seem to demonstrate a great affinity towards seeing visible punishment being meted out for even minor transgressions in all walks of life.
However, our data is also showing that many of these urban voters are mobile, and unlikely to vote on polling day, but are trying their hardest to get the vote out on behalf of Mr Modi.
With this arithmetic in mind, it is clear that even if the BJP repeats it’s 2014 performance, they win 32-35 seats in UP.
If we see a Modi “bump” over their traditional vote share of let’s say 8-10% they will win 18-23 seats.
But, depending on how the upper caste vote gets split between the BJP and the Congress, the BJP may find itself return to their “normal baseline” in Uttar Pradesh and end up with 10 seats (or less).
That’s the math and the context. But people aren’t data points and the Indian voter is an incredibly complex being. We will continue to publish notes about the impact we have been seeing of everything from Pulwama to NYAY.
These notes are an experiment in data-driven points of view. We are immersing ourselves in information screens and data patterns and allowing ourselves to connect dots. We emerge to write a note — like the one you’re reading — which is our best understanding at a given moment in time. We believe ourselves to be correct in the moment, but are happy to be proven wrong. In either case we learn and improve.