Contagious COVID‐19 policies: Policy diffusion during times of crisis

Abstract

The COVID‐19 crisis demanded rapid, widespread policy action. In response, nations turned to different forms of social distancing policies to reduce the spread of the virus. These policies were implemented globally, proving as contagious as the virus they are meant to prevent. Yet, variation in their implementation invites questions as to how and why countries adopt social distancing policies, and whether the causal mechanisms driving these policy adoptions are based on internal resources and problem conditions or other external factors such as conditions in other countries. We leverage daily changes in international social distancing policies to understand the impacts of problem characteristics, institutional and economic context, and peer effects on social distancing policy adoption. Using fixed‐effects models on an international panel of daily data from 2020, we find that peer effects, particularly mimicry of geographic neighbors, political peers, and language agnates drive policy diffusion and shape countries' policy choices.

COVID‐19 emergency policies, financial security, and social equity: Worldwide evidence

“…Third, the scholarly evidence increasingly suggests that countries factor others' policy decisions into their COVID‐19 policymaking (Sebhatu et al, 2020; Mistur et al 2022; An et al, 2023). This policy diffusion process indicates that country leaders monitor others to copy or learn from them, especially those with similar characteristics.…”

Global diffusion ofCOVID‐19 policies: The role of geographic, institutional, and cultural cues

“…That is, most do not consider institutional proximity's independent role as a potential contributor to policy diffusion (cf. Mistur et al, 2022).…”

Net‐zero carbon declarations by Japanese local governments: What caused the domino‐like diffusion?

“… 12 12 This specification implies that the pressure from neighboring jurisdictions may be equivalent across all jurisdictions, which is a conventional assumption in the literature. We recognize that some literature (e.g., Mistur et al, 2022; Shipan & Volden, 2008; Zhou et al, 2019) differentiates the effect of peers in accord with the type of relationship shared. This aspect is not covered in our quantitative analysis, but considered in our case studies section. …”

“…This specification implies that the pressure from neighboring jurisdictions may be equivalent across all jurisdictions, which is a conventional assumption in the literature. We recognize that some literature (e.g.,Mistur et al, 2022;Shipan & Volden, 2008;Zhou et al, 2019) differentiates the effect of peers in accord with the type of relationship shared. This aspect is not covered in our quantitative analysis, but considered in our case studies section.13 In a nutshell, calculating the time-varying coefficient is similar to running the Cox regression period by period.However, in contrast to simply conducting a separate regression for each period, using time-varying covariates allows us to simply run the Cox regression once and then calculate the concordance for all periods combined.14 At the time interviews were conducted, Yamato City had not declared.15 Formally, this was tested by running the same model but setting cities instead of prefectures as the dummy variable and viewing the effect of the horizontal diffusion.16 Appendix E provides the result calculated by separating cities and prefectures into two separate samples.17 Appendix F provides the result in that city sample and prefecture sample are separated.18 It should be noted that because the timing of the T1 declarants is ahead of the survey (see also endnote 8), we cannot exclude the possibility that the effect of the budget and human resources for T1 is overestimated.…”