Using nudge theory that involves subtly leading people to desired directions, a study of India’s public policy response to Covid-19 has concluded that nudges from Prime Minister Narendra Modi were critical in creating herd effect on lockdown and social distancing norms across the country.
Published in science journal PLOS One, the study at the University of Cambridge used machine learning and AI-based algorithms to identify key nudges in policy responses across various government departments to deal with the pandemic, including the lockdown.
“India is a vast and diverse country, what was easier for the western world where digitalisation has penetrated to last-mile homes, India is yet in the nascent stages,” said Ronita Bardhan, assistant professor and co-author of the study.
“Hence these prompt nudges were required while simultaneously generating the digital backup infrastructure. It was a mammoth task given the transition state of India, where poverty and affluence co-exists.”
The study noted that the prime minister’s nudges drove preparedness, action and mitigation strategies in the country, adding that his frequent public appearance was “the most significant factor that created nudges” in keeping a country of 1.3 billion people under strict lockdown and social distancing measures.
Rigorous media campaigns and Modi’s public assurances nudged in creating the herd effect across pharma, economic, health and public safety sectors that enabled strict national lockdown, the study by Bardhan and Gates Cambridge Scholar Ramit Debnath said.
“Most of the interventions were targeted to generate internal motivation by using triggers that potentially produces lasting desired behaviour in repeat settings (i.e, repeated broadcasting of information through multi-media channel and engaging Bollywood to use songs, poems and dramatisation); also, the use of nostalgia in the form of Ramayana and Mahabharata telecast to encourage people to stay at home”, the authors said.
The study also noted that successful application of behavioural control measures such as nudging in the public health sector (e.g: compulsory wearing of masks in public spaces; Yoga and Ayurveda for boosting immunity), transport sector (e.g: old railway coaches converted to isolation wards), micro, small and medium enterprises (e.g: rapid production of personal protective equipment and masks for frontline workers), science and technology sector (e.g: the rapid development of indigenous diagnostic kits, use of robots and nano-technology to fight infections), home affairs (e.g: people adhering to strict lockdown rules even at high economic distress), urban (e.g: drones, GIS-mapping, crowdsourcing) and education (e.g: work from home and online learning).
Primary data for the study was collected from the Press Information Bureau in the form press releases of government plans, policies, programme initiatives and achievements. A text corpus of 260,852 words was created from 396 documents.
An unsupervised machine-based topic modelling using Latent Dirichlet Allocation algorithm was performed on the text corpus by the authors to extract high probability topics in the policy sectors.
The topics were then interpreted through a nudge theoretic lens to derive critical policy heuristics of the government, with the results showing that most interventions were targeted to generate endogenous nudge by using external triggers.
Source- Hindustan Times.0