The Sabyasachi Das Paper

What is it that the (former) Ashoka University assistant professor, Sabyasachi Das, did that stirred up such a storm? He did a rigorous analysis of the 2019 Lok Sabha elections in his paper, “Democratic Backsliding in the World's Largest Democracy”. But what exactly did the paper say/find?

 

Yogendra Yadav wrote this excellent piece describing what that paper said (and equally important, what it did not say). First, this wasn’t an economist writing about politics. Rather, this was a statistical analysis of certain patterns Das found. As with many things in statistics, it cannot prove something; but it sure can raise valid suspicions. Das himself admits as much in the paper:

“The tests are, however, not proofs of fraud, nor does it suggest that manipulation was widespread.”

 

Second, since this is a purely statistical analysis, it does not get into topics like EVM tampering – one needs to prove that happened, just throwing accusations is pointless. Instead, as Yadav explains:

“Assuming that any electoral malpractice would leave its traces, he analyses the patterns of outcome at the constituency and booth level and zeros in at apparent abnormalities. This is election forensics. The advantage is that this is based on official election data, not any allegation or some disputed data set… The limitation is that while it establishes a pattern of malpractice, and can help identify a mechanism as well, this is probabilistic reasoning.”

 

Third, Das’ paper notes several oddities:

-       The BJP won an abnormally high number of closely contested seats (Das says abnormally high based on comparison with patterns from all Lok Sabha elections from 1977 onwards).

-       Most of these wins were in NDA ruled states.

-       These constituencies showed another trend: they had lesser increase in the voter list, esp. Muslim voters, than the national average. This would suggest (but not prove) strategic deletions.

-       There was an abnormal pattern in the numerals of vote counts in these constituencies – the distribution of digits that would be expected by statistics wasn’t the case here. Such deviations were largely in constituencies with higher proportion of Muslim voters.

 

Fourth, the number of constituencies that showed the above mentioned patterns isn’t huge – it was between 9 to 18, as Das himself states in the paper. Yadav then spells out what that number (9 to 18) means:

“(The number of constituencies is) too few to affect its (BJP’s) majority in the Lok Sabha. So, to reiterate, this paper does not say or imply that Narendra Modi came to power in 2019 through electoral fraud.”

 

As Yadav repeatedly says, none of this is proof. But, in any democratic system, it is important that things should be fair, and they should appear to be fair. All of the above raises concerns on the second criteria – the appearance of things. Unlike the all too many wild allegations from so many, this one is different:

“It should be clear why this is no half-baked analysis, a mere speculation or a wild allegation. We can be very sure that something was fishy about the electoral outcome in some closely contested seats.”

 

I am very impressed by this paper. It is based on statistical analysis of official data, not opinions or questionable data. It repeatedly reiterates that it can notice patterns, raise questions, but is careful to admit it is not conclusive proof. It compares against earlier elections, which strengthens its own assessments. All of which is why I can see why Yadav wrote:

“My first reaction was pure envy: I wish I could do this kind of data analysis... This research paper is among the most rigorous pieces of empirical work on Indian elections that I have seen in a long time. To be honest, this one paper contains within it no less than eight high quality research papers. Any one of these papers could have made it to a professional journal. This paper alone could secure tenure for the writer in any top-notch university.”

Instead, sadly, we find the university distancing itself from the paper and Das; and Das being driven to resign.