TY - JOUR
T1 - “Measuring the Mix” of Policy Responses to COVID-19
T2 - Comparative Policy Analysis Using Topic Modelling
AU - Goyal, Nihit
AU - Howlett, Michael
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - comparative policy analysis
KW - COVID-19
KW - machine learning
KW - policy design
KW - policy mixes
KW - topic modeling
UR - http://www.scopus.com/inward/record.url?scp=85104722820&partnerID=8YFLogxK
U2 - 10.1080/13876988.2021.1880872
DO - 10.1080/13876988.2021.1880872
M3 - Article
AN - SCOPUS:85104722820
SN - 1387-6988
VL - 23
SP - 250
EP - 261
JO - Journal of Comparative Policy Analysis: Research and Practice
JF - Journal of Comparative Policy Analysis: Research and Practice
IS - 2
ER -