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

被引:43
作者
Goyal, Nihit [1 ]
Howlett, Michael [2 ]
机构
[1] Delft Univ Technol, Fac Technol Policy & Management, Jaffalaan 5, NL-2628 BX Delft, Netherlands
[2] Simon Fraser Univ, Dept Polit Sci, Burnaby, BC, Canada
来源
JOURNAL OF COMPARATIVE POLICY ANALYSIS | 2021年 / 23卷 / 02期
关键词
comparative policy analysis; COVID-19; machine learning; policy design; policy mixes; topic modeling;
D O I
10.1080/13876988.2021.1880872
中图分类号
C93 [管理学]; D035 [国家行政管理]; D523 [行政管理]; D63 [国家行政管理];
学科分类号
12 ; 1201 ; 1202 ; 120202 ; 1204 ; 120401 ;
摘要
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.
引用
收藏
页码:250 / 261
页数:12
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