The majority with which Bharatiya Janata Party returned to power in 2019 was both unprecedented and unpredicted. Not only did the party increase its seat share but it also increased its vote share implying a consolidation of votes across communities. How then did so many analysts and forecasters not foresee the extent of the BJP’s popularity? According to sociologist Dipankar Gupta, these analysts and forecasters were over-reliant on the caste framework to predict electoral outcomes.

In a study published in the Economic and Political Weekly, he points out two specific mistakes experts made when predicting voting patterns. The first error was to assume a natural solidarity amongst caste groups of the same status within the Brahmanical hierarchy. However, Gupta argues that even different caste groups with the same occupations, such as Jats and Gujjars, tend to consider themselves distinct because of small differences in practices, heritage and beliefs. The multiple hierarchies that exist within castes and the mutual repulsion amongst different caste groups prevent them from forging alliances.

The second error was miscalculations based on assumptions of castes, such as Yadavs and Jats, enjoying a majority in constituencies when they weren’t actually a majority. For instance, he argues that Yadavs only make up about 7% of Uttar Pradesh’s population but yet dominate the political discourse in the state.

Driven by an elitist view, Gupta argues that intellectuals expected Indian voters to vote along caste lines which is a problematic approach to analyzing elections. Instead, he suggests that a better way to look at political mobilization is to go beyond the lens of caste order and explore other variables that capture societal transformation such as urban jobs and rural markets.

Also read: Caste and Electoral Outcomes

Snap Fact features new and interesting reads from the world of research

Close