Lok Sabha elections 2019: Crunching numbers to make sense of politics
The 2019 election is the first in which the EC has decided to introduce candidate pictures along with their electoral symbols on the electronic voting machines.
At HT, our data coverage of the 2019 general elections has been driven by two basic principles. Indian elections are best understood by taking a state-wise approach, given the linguistic and other socio-economic diversities in our country. And, politics needs to be seen as a continuous process between the 2014 and the 2019 general elections, rather than assuming that there has been a five-year vacuum between the two. We believe that a careful analysis of these two factors can allow us to put the 2019 results in a macro-yet-disaggregated perspective. Along with this, we have also tried to use the elections to explore some larger political economy ideas that have an important role in shaping the political processes of our country. Here’s a recap of some of our important stories that we have done as part of our national election coverage.
The Election Commission of India (ECI) announced the schedule for 2019 general elections on March 10. In their first data story (https://bit.ly/30BtwRo) after the polls were officially announced, we gave a snapshot of how decimating the Congress in direct contest states and forging a multi-class, multi-caste collation helped the Bharatiya Janata Party (BJP) secure a majority of its own in the Lok Sabha in 2014. The story also looked at parliamentary constituency (PC) wise extrapolated results from assembly elections held between 2014 and 2019.
Another story on March 19 underlined the importance of states vis-à-vis the first-past-the-post system, where regional parties in bigger states such as West Bengal and Tamil Nadu end up getting a relatively higher seat share for a given vote share compared to national parties. The story also pointed out the importance of nine states – Uttar Pradesh, Uttarakhand, Rajasthan, Madhya Pradesh, Chhattisgarh, Bihar, Jharkhand, Maharashtra and Gujarat – in the seat tally of the BJP in the country.
We also looked at political data in each specific state to explain the importance of various factors including alliance arithmetic in shaping the 2019 results. Some of our work underlined the importance of looking differently even within a state, such as a May 2 story that explained the relatively higher vulnerability of the BJP in Uttar Pradesh vis-à-vis the Bahujan Samaj Party (BSP) and Samajwadi Party (SP) alliance.
An April 3 story on Karnataka tried to explain how the impressive headline vote share numbers of the Congress and the Janata Dal (Secular) alliance in Karnataka might not lead to a rout of the BJP in the state.
Voter turnouts are the only authentic data that is available for analysis when the election process in on. In his April 28 story, Kawoosa pointed out the Kashmir valley was headed for lowest voter turnout in two decades. The final numbers, a turnout of 18.9% in the three PCs in the Kashmir valley have confirmed the argument.
Another story on May 8 pondered on whether the relatively higher increase in voter turnout in the states of Madhya Pradesh and Rajasthan pointed towards a restoration of political competition in the state unlike 2014, when a section of anti-BJP voters might not have come out to vote expecting imminent defeat.
The 2019 election is the first in which the EC has decided to introduce candidate pictures along with their electoral symbols on the electronic voting machines. In this March 18 story Kawoosa scraped ECI data to show the extent of problems caused by namesake candidates in elections.
Elections in India also generate a lot of focus on the importance of caste in the country. This March 27 story cautioned readers about relying too much on headline caste demographic numbers even at the state level.
Another April 4 story tried to explain the complicated relationship between caste and class in Indian politics by highlighting how economic fortunes of similar broad caste groups vary significantly across states and how Other Backward Classes (OBCs) thanks to their large share in population have a significantly large share among the richer sections of the society.
Apart from this author, Abhishek Jha and Vijdan Mohammad Kawoosa, HT’s data coverage was complemented by columnists such as Neelanjan Sircar, a senior visiting fellow at Centre for Policy Research and an assistant professor at Ashoka University, Gilles Verniers, co-director of the Trivedi Centre for Political Data at Ashoka University, and Milan Vaishanav, Director of South Asia Programme at the Carnegie Endowment for International Peace at Washington DC.