Hodgehurst3474

In this article we apply a large-scale collective action framework on the spread of the COVID-19 virus. We compare the pandemic with other large-scale collective action problems - such as climate change, antimicrobial resistance and biodiversity loss - which are identified by the number of actors involved (the more actors, the larger the scale); the problem's complexity; and the spatial and temporal distance between the actors causing and being affected by the problem. #link# The greater the extent of these characteristics, the larger the scale of the collective action problem and the smaller the probability of spontaneous collective action. We argue that by unpacking the social dilemma logic underlying the spread of the COVID-19 virus, we can better understand the great variation in policy responses worldwide, e.g., why some countries are adopting harsher policies and enforcing them, while others tend to rely more on recommendations. We claim that one key factor is trust and, more precisely, reciprocal trust, both horizontally among people and also vertically between people and their governments - and vice versa. Citizens must trust that the recommendations they receive from the public authorities are correct, that these are in their (or the collective's) best interest, and that most others will follow the recommendations. Simultaneously, government authorities must trust that their citizens will transform the recommendations into collective action. When this situation is present, we argue that governments enjoy a large degree of collective action capital, which potentially open up for a wider palette of policy options.The spread of COVID-19 has raised difficult questions that interrogate the pandemic as a public health emergency, an economic crisis and a disruptor of consolidated governance forms. While addressing the public health emergency must be the main priority, we also need to track the ways in which the crisis is reconfiguring economic and political ordering and diverse actors are renegotiating relations in the exceptional circumstances the pandemic has created. These dimensions can have far-reaching implications in wide-ranging policy areas, both as the crisis unfolds and in the longer term. Based on a review of developments concerning land governance, this Viewpoint discusses continuities with longstanding patterns as well as ruptures and distinctive features that outline the initial contours of an agenda for research and action.COVID-19 is proving to be the long awaited 'big one' a pandemic capable of bringing societies and economies to their knees. There is an urgent need to examine how COVID-19 - as a health and development crisis - unfolded the way it did it and to consider possibilities for post-pandemic transformations and for rethinking development more broadly. Drawing on over a decade of research on epidemics, we argue that the origins, unfolding and effects of the COVID-19 pandemic require analysis that addresses both structural political-economic conditions alongside far less ordered, 'unruly' processes reflecting complexity, uncertainty, contingency and context-specificity. This structural-unruly duality in the conditions and processes of pandemic emergence, progression and impact provides a lens to view three key challenge areas. The first is how scientific advice and evidence are used in policy, when conditions are rigidly 'locked in' to established power relations and yet so uncertain. Second is how economies function, with the COVID-19 crisis having revealed the limits of a conventional model of economic growth. The third concerns how new forms of politics can become the basis of reshaped citizen-state relations in confronting a pandemic, such as those around mutual solidarity and care. COVID-19 demonstrates that we face an uncertain future, where anticipation of and resilience to major shocks must become the core problematic of development studies and practice. Where mainstream approaches to development have been top down, rigid and orientated towards narrowly-defined economic goals, post-COVID-19 development must have a radically transformative, egalitarian and inclusive knowledge and politics at its core.We propose a partial identification method for estimating disease prevalence from serology studies. Our data are results from antibody tests in some population sample, where the test parameters, such as the true/false positive rates, are unknown. GW441756 in vitro scans the entire parameter space, and rejects parameter values using the joint data density as the test statistic. The proposed method is conservative for marginal inference, in general, but its key advantage over more standard approaches is that it is valid in finite samples even when the underlying model is not point identified. Moreover, our method requires only independence of serology test results, and does not rely on asymptotic arguments, normality assumptions, or other approximations. We use recent Covid-19 serology studies in the US, and show that the parameter confidence set is generally wide, and cannot support definite conclusions. Specifically, recent serology studies from California suggest a prevalence anywhere in the range 0%-2% (at the time of study), and are therefore inconclusive. However, this range could be narrowed down to 0.7%-1.5% if the actual false positive rate of the antibody test was indeed near its empirical estimate ( ∼ 0.5%). In another study from New York state, Covid-19 prevalence is confidently estimated in the range 13%-17% in mid-April of 2020, which also suggests significant geographic variation in Covid-19 exposure across the US. Combining all datasets yields a 5%-8% prevalence range. Our results overall suggest that serology testing on a massive scale can give crucial information for future policy design, even when such tests are imperfect and their parameters unknown.The paper evaluates the dynamic impact of various policies adopted by US states on the growth rates of confirmed Covid-19 cases and deaths as well as social distancing behavior measured by Google Mobility Reports, where we take into consideration people's voluntarily behavioral response to new information of transmission risks in a causal structural model framework. Our analysis finds that both policies and information on transmission risks are important determinants of Covid-19 cases and deaths and shows that a change in policies explains a large fraction of observed changes in social distancing behavior. Our main counterfactual experiments suggest that nationally mandating face masks for employees early in the pandemic could have reduced the weekly growth rate of cases and deaths by more than 10 percentage points in late April and could have led to as much as 19 to 47 percent less deaths nationally by the end of May, which roughly translates into 19 to 47 thousand saved lives. We also find that, without stay-at-home orders, cases would have been larger by 6 to 63 percent and without business closures, cases would have been larger by 17 to 78 percent.