Abstract
Recent technological advances have facilitated the collection of large-scale administrative data and the online surveying of the Indian population. Building on these we propose a strategy for more robust, frequent and transparent projections of the Indian vote during the campaign. We execute a modified MrP model of Indian vote preferences that proposes innovations to each of its three core components: stratification frame, training data, and a learner. For the post-stratification frame we propose a novel Data Integration approach that allows the simultaneous estimation of counts from multiple complementary sources, such as census tables and auxiliary surveys. For the training data we assemble panels of respondents from two unorthodox online populations: Amazon Mechanical Turks workers and Facebook users. And as a modeling tool, we replace the Bayesian multilevel regression learner with Random Forests. Our 2019 pre-election forecasts for the two largest Lok Sahba coalitions were very close to actual outcomes: we predicted 41.8% for the NDA, against an observed value of 45.0% and 30.8% for the UPA against an observed vote share of just under 31.3%. Our uniform-swing seat projection outperforms other pollsters-we had the lowest absolute error of 89 seats (along with a poll from 'Jan Ki Baat'); the lowest error on the NDA-UPA lead (a mere 8 seats), and we are the only pollster that can capture real-time preference shifts due to salient campaign events.
Highlights
Much of social science research involves constructing samples and implementing statistical estimators that allow us to use these samples to say something about the population
The empirical test for our RF Post-stratification estimation approach is a forecast of the 2019 Lok Sabha election
We assembled data that reflected the realities of developing countries like India: a stratification frame augmented by multiple data sources and an unrepresentative online convenience sample
Summary
Much of social science research involves constructing samples and implementing statistical estimators that allow us to use these samples to say something about the population. Publicopinion estimation in the run-up of an election is a case in point; it accounts for many of the recent innovations in sampling and prediction technologies. We contend that these innovations will have an important effect on estimating public opinion in contexts where sampling and data collection are challenging. India is one of these challenging contexts. This essay reports our forecasts of the 2019 India Lok Sabha elections.
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