“Measuring the Mix” of Policy Responses to COVID-19: Comparative Policy Analysis Using Topic Modelling

Nihit Goyal*, Michael Howlett

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

8 Citations (Scopus)
29 Downloads (Pure)

Abstract

Although understanding initial responses to a crisis such as COVID-19 is important, existing research on the topic has not been systematically comparative. This study uses topic modeling to inductively analyze over 13,000 COVID-19 policies worldwide. This technique enables the COVID-19 policy mixes to be characterized and their cross-country variation to be compared. Significant variation was found in the intensity, density, and balance of policy mixes adopted across countries, over time, and by level of government. This study advances research on policy responses to the pandemic, specifically, and the operationalization of policy mixes, more generally.

Original languageEnglish
Pages (from-to)250-261
Number of pages12
JournalJournal of Comparative Policy Analysis: Research and Practice
Volume23
Issue number2
DOIs
Publication statusPublished - 2021

Keywords

  • comparative policy analysis
  • COVID-19
  • machine learning
  • policy design
  • policy mixes
  • topic modeling

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