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Drivers of COVID-19 protest across localities in Israel: a machine-learning approach


Schlager, Nina; Donnay, Karsten; Kim, Hyunjung; Bhavnani, Ravi (2023). Drivers of COVID-19 protest across localities in Israel: a machine-learning approach. Political Research Exchange, 5(1):2257368.

Abstract

Anti-government protests emerged globally in response to COVID-19 countermeasures. What are the key drivers of these pandemic-related protests, and to what extent do they differ from the drivers of non-COVID protests? We examine these questions in the context of Israel, which faced a growing political crisis at the start of the pandemic, effectively blurring the distinction between different causes of protest. Our data features 1,922 protests across 189 Israeli localities for the period between March and July 2022. Using a machine learning approach, we find that all protests, regardless of whether they were directly related to the pandemic or not, were motivated by the same set of key indicators – albeit with the ranking of drivers for COVID-related protests inverted for non-COVID protests. Local infection rates and government responses were more pronounced for the former, whereas differences in residential and commercial property taxes, access to affordable housing, quality of education and demography were among the most important drivers for the latter. Our analysis underscores the role that local governments played in managing the pandemic, and demonstrates that variation in socioeconomic conditions had an important effect on the incidence of protests across Israel.

Abstract

Anti-government protests emerged globally in response to COVID-19 countermeasures. What are the key drivers of these pandemic-related protests, and to what extent do they differ from the drivers of non-COVID protests? We examine these questions in the context of Israel, which faced a growing political crisis at the start of the pandemic, effectively blurring the distinction between different causes of protest. Our data features 1,922 protests across 189 Israeli localities for the period between March and July 2022. Using a machine learning approach, we find that all protests, regardless of whether they were directly related to the pandemic or not, were motivated by the same set of key indicators – albeit with the ranking of drivers for COVID-related protests inverted for non-COVID protests. Local infection rates and government responses were more pronounced for the former, whereas differences in residential and commercial property taxes, access to affordable housing, quality of education and demography were among the most important drivers for the latter. Our analysis underscores the role that local governments played in managing the pandemic, and demonstrates that variation in socioeconomic conditions had an important effect on the incidence of protests across Israel.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:06 Faculty of Arts > Institute of Political Science
08 Research Priority Programs > Digital Society Initiative
Dewey Decimal Classification:320 Political science
Uncontrolled Keywords:Israel; COVID-19; protest; machine learning
Language:English
Date:31 December 2023
Deposited On:21 Sep 2023 13:05
Last Modified:30 Mar 2024 04:42
Publisher:Taylor & Francis
ISSN:2474-736X
OA Status:Gold
Publisher DOI:https://doi.org/10.1080/2474736x.2023.2257368
  • Content: Published Version
  • Language: English
  • Licence: Creative Commons: Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)