What have we been learning about States' responses to the COVID-19 crisis? In this post I draw attention to ongoing data gathering and research efforts to capture cross-national trends to tackle the COVID-19 crisis. This will be a "living post" as I will updating it as new findings and data emerge.
RELEVANT CROS-NATIONAL DATA: * International Montery Fund Tracker of Policies Governments are Taking in Response to COVID-19 * On Social Protection and Job Measures, see Gentilin, Almenfi and Orton's "living paper" (see below). SOME FINDINGS: I) According to Gentilini, Almenfi & Orton's (from World Bank and ILO) "living paper" on social protection and jobs responses to covid-19 in 106 countries: - In three weeks, the number of countries that have introduced social protection and job measures has increased (from 45 countries to 105 countries). -Social assistance measures are the most widely used type of social protection. -Within Social Assistance, cash transfers dominate (35.6%). --Social insurance measures are in second place. -- When referring to social insurance, paid leave represents 32% of the measures followed by unemployment benefits (26%). - Labor market interventions is the least common type of social protection policy. And wage subsidies represent 59% of these measures. -- Low-income countries have also adopted a variety of measures, but they do not seem to have introduced many cash-transfer programs. -The authors note, "In those contexts, SA measures mostly include administrative adaptations, in-kind transfers, school feeding, and utility waivers." II) A paper by Elgin, Basbug, and Yalaman adopts a broader approach by focusing on fiscal, monetary and exchange rate measures. Using data from the International Monetary Fund, the authors develop an Economic Stimulus Index to better understand the size of stimulus packages in 166 countries . All in all, the authors find that there is "some significant correlations of population characteristics, public health-related, and economic variables (e.g., GDP per capita, health expenditures) with economic stimulus packages announced by governments" (p. 7). -- Using regression analysis, the authors find that the following variables are positively associated with a larger stimulus package: a) median age, b) higher infection rates, c) GDP per capita, and d) number of cases. -- The following variables are negatively associated with the size of the economic stimulus packages: a) number of hospital beds per capita. -- Finally, "public health controls such as school closures and travel restrictions did not predict" (p. 7) the size of the package.
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Mariely Lopez-Santana is a Political Scientist and an Associate Prof. at the Schar School of Policy and Government at George Mason University. In the last two decades she has spent much time studying, teaching, and writing about employment policy. She is working on a book project on state intervention and municipal distress. Categories
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