These pages have dealt with the importance of caste in Bihar’s political competition in detail on the basis of two databases – a historical database of Bihar’s 3629 MLAs from 1962-2020 and 498 candidates of the two major alliances in the 2025 election – prepared by the first author of this story. They have also married these statistics along with other statistics on Bihar to build a larger political economy narrative in another three-part series. This analysis was facilitated by
- How does caste matter in electoral competition at the grassroots level?It is a fair assumption to make that the social composition of the population differs significantly across Bihar, as it would in any other large state. Political competition, in an assembly election, is decided at the level of an assembly constituency (AC), of which Bihar has 243. How many of these ACs are dominated by a particular social group or sub-caste? One way to answer this question is to look at the social background of MLAs in a given AC across elections. Replicating this exercise for the entire state can give useful insights into how caste-fixated Bihar’s ACs are. This two-part series will try to execute this analysis by scrolling through HT’s historic database of Bihar’s MLAs from 1962-2020. The first part will lay down the structural caveats to such an analysis and the second part will look at trends in Bihar’s ACs being dominated by a particular caste.
- Strictly speaking, ACs cannot be compared over longer time periodsHT’s caste database of Bihar’s MLAs covers 15 assembly elections from 1962-2020. However, this does not mean that all 243 ACs which exist today have had 15 iterations too. In India, periodic delimitation exercises redraw AC and parliamentary constituency (PC) boundaries. The last delimitation was undertaken in 2008 and Bihar has had three such exercises (1963, 1973 and 2008) during the period covered in the HT dataset. Not only does delimitation redraw AC boundaries, it also discontinues existing ACs and creates new ones. What it also does is redesignation of ACs and PCs into unreserved or Scheduled Caste (SC) and Scheduled Tribe (ST) reserved. This matters while counting the caste of the elected representative because reserving or de-reserving a constituency can change the caste composition of candidature there. How do all these things play out in HT’s database of MLAs, which is the basis of this analysis? There are 343 unique AC names in this dataset. Only 151 of them consistently appear in every election between 1962 and 2020. To be sure, even these might not be strictly comparable because of some redrawing of AC boundaries. There are 59 ACs which saw a change in their unreserved/SC-ST reserved status.
- After the 2008 delimitation, almost half of Bihar's ACs have had an MLA from the same sub-casteComparing the sub-caste of Bihar’s MLAs across ACs in the 2010, 2015 and 2020 assembly elections is the most water-tight statistical comparison which can be made because AC boundaries have not changed during this period. HT’s analysis shows that 107 out of Bihar’s 243 ACs have returned an MLA from the same sub-caste in these three elections. 87 of these are unreserved ACs while 20 of them are SC/ST reserved. What is even more revealing is the fact that the winning party was unchanged in only 48 of these 107 ACs. This shows that caste loyalty often triumphs over party loyalty in the state. The sub-caste which has the largest share in these ACs is Yadav. However, of the 27 such Yadav stronghold ACs, only 13 ACs have been won by the same party in all three elections The sub-caste which has the largest share in these ACs is Yadav. See Map 1 (same caste in last 3 polls shaded) & Chart 2 While comparing ACs from 2010, 2015 and 2020 elections is the strictest criteria to track ACs which are caste strongholds, there is some merit in doing a long-term comparison as well even though AC boundaries might have changed somewhat. However, this is unlikely to have changed the fundamental nature of the electorate on a caste-composition basis. This is what the second part of this series will look at.
Nishant Ranjan has been working as a data journalist with Hindustan Times since 2023. In this short period, he has established a niche for creating original datasets and utilising them to produce journalistic work that pushes the boundaries of journalism. The list of datasets and stories done by him include the following: a dataset of all 717 chief ministers India has had since 1952, a dataset of all 3,350 members of India’s council of ministers, a dataset of all 190 deputy chief ministers in India and a dataset of all 279 Supreme Court judges in India (1950-2025). He has also profiled each elected Muslim MLA/MP since 1952. He is a mining engineer by training who decided to pursue a Master's in Development Studies from TISS after working in a coal mine for 10 months. He also worked as a research associate at the Centre for Policy Research, researching local governance in India. He also has a keen interest in analyzing electoral politics and political executives in India.
Roshan Kishore is the Data and Political Economy Editor at Hindustan Times. He heads the newsroom's data journalism team, which produces Number Theory, a daily data-driven feature for the print edition and the HT app. Number Theory uses data analysis and story-telling based on it to add value to the newsroom’s daily coverage by putting stories in a larger context on a range of issues, including politics, macroeconomy, markets, global affairs and climate. Under his leadership HT’s data journalism work has established itself as a niche product in Indian journalism and pushed the boundaries of marrying academic rigour with news sense and speed. Along with writing and editing data stories, he has also been writing a weekly political economy column called Terms of Trade for HT Premium. A trained economist with an MPhil degree from Jawaharlal Nehru University, Kishore has also been a visiting fellow at the Centre for Advanced Studies of India (CASI) at the University of Pennsylvania. Along with his journalistic work, his writings have also appeared in journals such as the Economic and Political Weekly and working papers for CASI and UNESCAP.
Abhishek Jha is Assistant Editor-Data at Hindustan Times. He uses statistical programming to generate newsworthy insights from large datasets. He is part of the team that produces Number Theory, a daily data story feature of the paper’s print edition. Since March 2024, he has been writing Weather Bee, a weekly column for the Hindustan Times website. He is a chemical engineer by training, who specialises in stories related to weather, climate, and the environment. Jha has been at HT since 2018, where he offers data-driven perspective and analysis on politics, environment, weather, climate, economy and society. His work includes data coverage of elections in India and abroad, including the 2019 and 2024 Lok Sabha elections; the disasters and extreme weather resulting from changing climate, such as floods, droughts, heat waves, cold waves, and dwindling snow cap in the Himalayas; the factors that drive poor air quality in northern India; the changing patterns of land use; the Covid-19 pandemic and its impact on labour market conditions; the changing pattern of consumer spending seen in the new consumer spending surveys; and social norms seen in the surveys such as the National Family Health Survey.