Governments worldwide have implemented countless policies in response to the COVID-19 pandemic. We present an initial public release of a large hand-coded dataset of over 13,000 such policy announcements across more than 195 countries. The dataset is updated daily, with a 5-day lag for validity checking. We document policies across numerous dimensions, including the type of policy, national versus subnational enforcement, the specific human group and geographical region targeted by the policy, and the time frame within which each policy is implemented. We further analyse the dataset using a Bayesian measurement model, which shows the quick acceleration of the adoption of costly policies across countries beginning in mid-March 2020 through 24 May 2020. We believe that these data will be instrumental for helping policymakers and researchers assess, among other objectives, how effective different policies are in addressing the spread and health outcomes of COVID-19.

Data harmonization is an important method for combining or transforming data. To date however, articles about data harmonization are field-specific and highly technical, making it difficult for researchers to derive general principles for how to engage in and contextualize data harmonization efforts. This commentary provides a primer on the tradeoffs inherent in data harmonization for researchers who are considering undertaking such efforts or seek to evaluate the quality of existing ones. We derive this guidance from the extant literature and our own experience in harmonizing data for the emergent and important new field of COVID-19 public health and safety measures (PHSM).

What explains the great variation in the adoption, timing, and duration of government policies made in response to the COVID-19 pandemic? In this article, we explore whether government incentives to repress domestic dissidents influence their responses to the COVID-19 pandemic. We argue that COVID-19 containment policies are observationally equivalent to those that abusive governments use to limit domestic dissent – that is, policies that restrict citizens’ freedom of movement. This creates an opportunity for abusive governments to engage in repressive behavior without countervailing pressure from citizens and the international community. Following this logic, we expect abusive governments to be more likely to adopt restrictive policies, adopt them earlier in the course of the pandemic, and take longer to relax restrictions. Empirically, we find that governments that have recently engaged in state violence against civilians or abused citizens’ human rights were about 10% more likely to enact lockdown and curfew policies. Compared to less repressive countries, these policies were implemented approximately 48 days earlier in the pandemic and kept in place for approximately 23 days longer. Overall, our results advance our understanding of how the repressiveness of state institutions can shape policy responses to a global health crisis.

If foreign aid is provided primarily for strategic reasons, as much of the field finds, how can donor generosity following natural disasters be explained? This article addresses this puzzle by building on the literature in three ways. First, it differentiates between three major types of aid: humanitarian, civil society and development. Second, it demonstrates that natural disasters act as an exogenous shock to the strategic calculus that donor countries undertake when making foreign aid allocation decisions. Specifically, the authors argue that donor countries use natural disasters as opportunities to exert influence on strategic opponents through the allocation of humanitarian and civil society aid. However, donors still reserve development aid for strategic allies irrespective of the incidence of natural disasters. Third, the findings are substantiated using a new measure of strategic interest that accounts for the indirect ties states share and the multiple dimensions upon which they interact.

Since the start of the 21st century, the world has not confronted a more serious threat to global public health than the COVID-19 pandemic. While governments initially took radical actions in response to the pandemic to avoid catastrophic collapse of their health care systems, government policies have also had numerous knock-on socioeconomic, political, behavioral and economic effects. Researchers, thus, have a unique opportunity to forward our collective understanding of the modern world and to respond to the emergency situation in a way that optimizes resources and maximizes results. The PERISCOPE project, funded by the European Commission, brings together a large number of research institutions to collect data and carry out research to understand all the impacts of the pandemic, and create predictive models that can be used to optimize intervention strategies and better face possible future health emergencies. One of the main tangible outcomes of this project is the PERISCOPE Atlas: an interactive tool that allows to visualize and analyze COVID-19-related health, economic and sociopolitical data, featuring a WebGIS and several dashboards. This paper describes the first release of the Atlas, listing the data sources used, the main functionalities and the future development.

Public health and safety measures (PHSM) made in response to the COVID-19 pandemic have been singular, rapid, and profuse compared to the content, speed, and volume of normal policy-making. Not only can they have a profound effect on the spread of the disease, but they may also have multitudinous secondary effects, in both the social and natural worlds. Unfortunately, despite the best efforts by numerous research groups, existing data on COVID-19 PHSM only partially captures their full geographical scale and policy scope for any significant duration of time. This paper introduces our effort to harmonize data from the eight largest such efforts for policies made before September 21, 2021 into the taxonomy developed by the CoronaNet Research Project in order to respond to the need for comprehensive, high quality COVID-19 data. In doing so, we present a comprehensive comparative analysis of existing data from different COVID-19 PHSM datasets, introduce our novel methodology for harmonizing COVID-19 PHSM data, and provide a clear-eyed assessment of the pros and cons of our efforts.