Ruling Party and Scheduled Caste/Tribe Human Development in India:
This study analyzes three decades of data across 27 Indian states and concludes that the identity of the ruling party does not robustly predict human development gains for Scheduled Castes and Tribes, as outcomes are primarily driven by initial literacy and poverty levels rather than political affiliation.
Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
Imagine you are watching a giant, three-decade-long race across India, where 27 different teams (the states) are trying to get their runners (the Scheduled Castes and Scheduled Tribes) to the finish line of human development. In the world of political science, this is a classic question: Does the color of the team jersey (the ruling political party) determine how fast the runners get there? To understand this, we need a few simple concepts. First, "human development" isn't just about money; it's about whether people can read, stay healthy, and live without poverty. Second, "catch-up convergence" is a fancy way of saying that runners who start far behind the pack often sprint the hardest to catch up, while those already near the front can't run much faster because they are already close to the limit. Finally, "centrally sponsored schemes" are like a national coach sending the same training gear and rules to every team, regardless of who is coaching the local team. People care about this because if the local coach (the state party) doesn't matter as much as we think, then we need to change how we judge who is doing a good job.
This paper, written by Kunal Dhanda, acts like a detective looking at the race results from three different eras: 1991–2001, 2001–2011, and 2011–2021. The author wanted to see if the identity of the ruling party—whether it was the BJP, the INC, or various regional groups—could predict how much the literacy and well-being of Scheduled Castes and Scheduled Tribes improved. The study is a massive data dive, looking at 81 snapshots of state performance over thirty years.
The big surprise? The color of the jersey didn't seem to matter at all. The paper finds that once you account for where the runners started, the ruling party's identity does not predict whether they improved. The most powerful predictor was simply the "base literacy" at the start of the period. States that started with very low literacy rates showed the biggest gains, while states that were already doing well showed smaller gains. This is the "catch-up" pattern in action. The study explicitly rules out the idea that a specific party (like the BJP, which expanded from governing 3 states to 12 states during the study) had a special "magic touch" that made their runners faster. Even when the BJP took over states with medium and low starting points, the improvement was driven by the starting point, not the party.
The paper does find one thing that did change the race for everyone: a national policy shift around the year 2001. When the country introduced big national programs for schools and meals (like the Sarva Shiksha Abhiyan and Mid-Day Meal Scheme), the "floor" of improvement rose for everyone. The data shows that literacy gains in the second decade (2001–2011) were about 1.6 percentage points higher than in the first decade, even after controlling for where states started. This suggests that national rules and funding helped lift all boats, regardless of the local captain.
In short, the paper suggests that blaming or praising a state government for the welfare of marginalized groups without looking at their starting conditions is misleading. A state starting with very low numbers will naturally show big improvements, while a state starting with high numbers will show smaller ones, no matter who is in charge. The authors are quite sure about this because they checked their math in many different ways, including running thousands of computer simulations (bootstraps) and looking at the data in different time periods. The conclusion is that the "catch-up" effect is the real story, not the political party.
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