The NYAY Effect
The centrepiece of the Congress campaign is the minimum income guarantee program they have (cleverly) named NYAY — we decided to study its electoral impact.
It is clearly resonant with a very large section of people and has the BJP worried. But, we decided to scan all narrative threads around it to arrive at a clear understanding of how it is likely to impact voting behaviour.
As of today, we see it having a real impact in three Congress-ruled states: Chattisgarh, Rajasthan and Madhya Pradesh (in that order). Large sections of the population there seem to believe that the program will be rolled out in these states first because the Congress is already in power. However, we can only comment about this at a state-wide level.
Interestingly, this is also why we are seeing a minimal effect in Uttar Pradesh — our data here is more granular and across wide swathes of western UP we are hearing the same thing: NYAY will be the Congress’ key plank in the 2022 Assembly Election if they come to power in the centre — “Vote for us so we can implement NYAY in UP.”
Since a Congress government still seems like a long shot in the centre, and since most of Western UP goes to polls in the first few phases, the vote there seems to be consolidating around the local BSP+SP+RLD candidate. If there is news coming in from the rest of the country that the Congress is winning, then this will have a impact in the last few phases.
We had waited for the BJP manifesto to come out to see if they would try to blunt the NYAY promise and generate some sort of buzz/excitement. We however have seen very little pickup for anything in the BJP manifesto — and there certainly isn’t anything in the manifesto that takes on NYAY head-on.
As of today, NYAY will help the Congress in Rajasthan, MP and Chattisgarh. It also seems to be having an impact in Maharashtra, Uttaranchal and Haryana. But, the politically savvy UP voter seems to be adopting a wait and watch attitude.
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.