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India nudges to contain COVID-19 pandemic: A reactive public policy analysis using machine-learning based topic modelling

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  • Ramit Debnath
  • Ronita Bardhan

Abstract

India locked down 1.3 billion people on March 25, 2020, in the wake of COVID-19 pandemic. The economic cost of it was estimated at USD 98 billion, while the social costs are still unknown. This study investigated how government formed reactive policies to fight coronavirus across its policy sectors. Primary data was collected from the Press Information Bureau (PIB) 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 from the PIB. An unsupervised machine-based topic modelling using Latent Dirichlet Allocation (LDA) algorithm was performed on the text corpus. It was done to extract high probability topics in the policy sectors. The interpretation of the extracted topics was made through a nudge theoretic lens to derive the critical policy heuristics of the government. Results showed that most interventions were targeted to generate endogenous nudge by using external triggers. Notably, the nudges from the Prime Minister of India was critical in creating herd effect on lockdown and social distancing norms across the nation. A similar effect was also observed around the public health (e.g., masks in public spaces; Yoga and Ayurveda for immunity), transport (e.g., old trains converted to isolation wards), micro, small and medium enterprises (e.g., rapid production of PPE and masks), science and technology sector (e.g., diagnostic kits, robots and nano-technology), home affairs (e.g., surveillance and lockdown), urban (e.g. drones, GIS-tools) and education (e.g., online learning). A conclusion was drawn on leveraging these heuristics are crucial for lockdown easement planning.

Suggested Citation

  • Ramit Debnath & Ronita Bardhan, 2020. "India nudges to contain COVID-19 pandemic: A reactive public policy analysis using machine-learning based topic modelling," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-25, September.
  • Handle: RePEc:plo:pone00:0238972
    DOI: 10.1371/journal.pone.0238972
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    References listed on IDEAS

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    1. Ramit Debnath & Sarah Darby & Ronita Bardhan & Kamiar Mohaddes & Minna Sunikka-Blank, 2020. "Grounded reality meets machine learning: A deep-narrative analysis framework for energy policy research," Working Papers EPRG2019, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
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    1. Debnath, R. & Mittal, V. & Jindal, A., 2020. "A review of challenges from increasing renewable generation in the Indian Power System," Cambridge Working Papers in Economics 21006, Faculty of Economics, University of Cambridge.
    2. Debnath, R. & Bardhan, R. & Darby, S. & Mohaddes, K. & Sunikka-Blank, M. & Coelho, A C V. & Isa, A., 2020. "A deep-narrative analysis of energy cultures in slum rehabilitation housing of Abuja, Mumbai and Rio de Janeiro for just policy design," Cambridge Working Papers in Economics 20101, Faculty of Economics, University of Cambridge.
    3. Debnath, Ramit & Bardhan, Ronita & Reiner, David M. & Miller, J.R., 2021. "Political, economic, social, technological, legal and environmental dimensions of electric vehicle adoption in the United States: A social-media interaction analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).
    4. Chowdbury, Shyamal & Schildberg-Hörisch, Hannah & Schneider, Sebastian O. & Sutter, Matthias, 2022. "Information provision over the phone saves lives: An RCT to contain COVID-19 in rural Bangladesh at the pandemic's onset," DICE Discussion Papers 393, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    5. Debnath, R. & Darby, S. & Bardhan, R. & Mohaddes, K. & Sunikka-Blank, M., 2020. "Grounded reality meets machine learning: A deep-narrative analysis framework for energy policy research," Cambridge Working Papers in Economics 2062, Faculty of Economics, University of Cambridge.
    6. Ramit Debnath & Vibhor Mittal & Abhinav Jindal, 2022. "A review of challenges from increasing renewable generation in the Indian Power Sector: Way forward for Electricity (Amendment) Bill 2020," Energy & Environment, , vol. 33(1), pages 3-40, February.
    7. Debnath, Ramit & Bardhan, Ronita & Misra, Ashwin & Hong, Tianzhen & Rozite, Vida & Ramage, Michael H., 2022. "Lockdown impacts on residential electricity demand in India: A data-driven and non-intrusive load monitoring study using Gaussian mixture models," Energy Policy, Elsevier, vol. 164(C).
    8. Ramit Debnath & Vibhor Mittal & Abhinav Jindal, 2020. "A review of challenges from increasing renewable generation in the Indian Power System," Working Papers EPRG2031, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    9. Galetsi, Panagiota & Katsaliaki, Korina & Kumar, Sameer, 2022. "The medical and societal impact of big data analytics and artificial intelligence applications in combating pandemics: A review focused on Covid-19," Social Science & Medicine, Elsevier, vol. 301(C).
    10. Muza, Olivia & Debnath, Ramit, 2021. "Disruptive innovation for inclusive renewable policy in sub-Saharan Africa: A social shaping of technology analysis of appliance uptake in Rwanda," Renewable Energy, Elsevier, vol. 168(C), pages 896-912.
    11. Shengli Dai & Weimin Zhang & Linshan Lan, 2022. "Quantitative Evaluation of China’s Ecological Protection Compensation Policy Based on PMC Index Model," IJERPH, MDPI, vol. 19(16), pages 1-24, August.
    12. Swati Agarwal & Sayantani Sarkar, 2022. "Topical analysis of migration coverage during lockdown in India by mainstream print media," PLOS ONE, Public Library of Science, vol. 17(2), pages 1-19, February.
    13. Hagera Dilnashin & Hareram Birla & Vishnu D. Rajput & Chetan Keswani & Surya P. Singh & Tatiana M. Minkina & Saglara S. Mandzhieva, 2021. "Economic Shock and Agri-Sector: Post-COVID-19 Scenario in India," Circular Economy and Sustainability, Springer, vol. 1(4), pages 1479-1490, December.

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