Economics of disasters and climate change最新文献

The impact of the global economic crisis caused by the COVID-19 pandemic will not affect all children equally: those in poorer households and children who are disadvantaged face the most serious consequences. As parents lose their jobs and incomes, the impact on children living in impoverished households must be measured. In this article, we assess the economic consequences of the pandemic on these children. Given that poorer families have a larger number of children than other families, the analysis first establishes the proportion of children living in monetary poor households, as defined by national standards, across developing countries. Then, using historical changes and trends of income distribution per country, the latest projections about economic decline due to the pandemic, and demographic information about the distribution of children by deciles, we estimate the expected increase in the number of children in monetary poor households in developing countries as of end of 2020 to be an additional 122-144 million and, at best, a moderate decline in these numbers by end of 2021.

In light of the COVID-19 pandemic, we scrutinize what has been established in the literature on whether entrepreneurship can cause and resolve extreme events, the immediate and long-run impacts of extreme events on entrepreneurship, and whether extreme events can positively impact (some) entrepreneurship and innovation. Based on this, we utilize a partial equilibrium model to provide several conjectures on the impact of COVID-19 on entrepreneurship, and to derive policy recommendations for recovery. We illustrate that while entrepreneurship recovery will benefit from measures such as direct subsidies for start-ups, firms' revenue losses, and loan liabilities, it will also benefit from aggregate demand-side support and income redistribution measures, as well as from measures that facilitate the innovation-response to the Keynesian supply-shock caused by the pandemic, such as access to online retail and well-functioning global transportation and logistics.

Natural disaster risk is escalating around the globe and in the United States. A large body of research has found that lower-income households disproportionally suffer from disasters and are less likely to recover. Poorer households often lack the financial resources for rebuilding, endangering other aspects of wellbeing. Parametric microinsurance has been used in many developing countries to improve the financial resilience of low-income households. This paper presents a review of the evidence for implementing parametric microinsurance in the U.S., with spillover lessons for other highly developed countries. We discuss the benefits and the challenges of microinsurance in a US context and explore 4 possible distribution models that could help overcome difficulties, with policies being provided: (1) by an aggregator, (2) through a mobile-based technology, (3) by linking to other products or retailers, or (4) through a public sector insurer.

The COVID19 pandemic has created a massive shock, unexpectedly increasing mortality levels and generating economic recessions all around the world. In recent years, several efforts have been made to develop models that link the environment, population and the economy which may be used to estimate potential longer term effects of the pandemic. Unfortunately, many of the parameters used in these models lack appropriate empirical identification. In this study, first I estimate the parameters of "Wonderland", a system dynamics model of the population-economy-environment nexus, and posteriorly, add external GDP and mortality shocks to the model. The estimated parameters are able to closely match world data, while future simulations point, on average and regardless of the COVID19 pandemic, to a world reaching dangerous environmental levels in the following decades, in line with consensus forecasts. On the other hand, the effects of the pandemic on the economy are highly uncertain and may last for several decades.

We present a formal analysis of the macroeconomic impacts of the COVID-19 pandemic in the U.S., China and the rest of the world. Given the uncertainty regarding the severity and time-path of the infections and related conditions, we examine three scenarios, ranging from a relatively moderate event to a disaster. The study considers a comprehensive list of causal factors affecting the impacts, including: mandatory closures and the gradual re-opening process; decline in workforce due to morbidity, mortality and avoidance behavior; increased demand for health care; decreased demand for public transportation and leisure activities; potential resilience through telework; increased demand for communication services; and increased pent-up demand. We apply a computable general equilibrium (CGE) model, a state-of-the-art economy-wide modeling technique. It traces the broader economic ramifications of individual responses of producers and consumers through supply chains both within and across countries. We project that the net U.S. GDP losses from COVID-19 would range from $3.2 trillion (14.8%) to $4.8 trillion (23.0%) in a 2-year period for the three scenarios. U.S. impacts are estimated to be higher than those for China and the ROW in percentage terms. The major factor affecting the results in all three scenarios is the combination of Mandatory Closures and Partial Reopenings of businesses. These alone would have resulted in a 22.3% to 60.6% decrease in U.S. GDP across the scenarios. Pent-up Demand, generated from the inability to spend during the Closures/Reopenings, is the second most influential factor, significantly offsetting the overall negative impacts.

At the onset of the COVID-19 pandemic a large number of countries introduced a range of non-pharmaceutical interventions. Whereas the policies are similar across countries, country characteristics vary substantially. We examine the effectiveness of such policies using a cross-country variation in socio-economic, environmental and geographic, and health system dimensions. The effectiveness of policies that prescribe closures of schools and workplaces is declining with population density, country surface area, employment rate and proportion of elderly in the population; and increasing with GDP per capita and health expenditure. Cross-country human mobility data reinforce some of these results. We argue that the findings can be explained by behavioural response to risk perceptions and resource constraints. Voluntary practice of social distancing might be less prevalent in communities with lower perceived risk, associated with better access to health care and smaller proportion of elderly population. Higher population density, larger geographical area, and higher employment rate may require more resources to ensure compliance with lockdown policies.

The Covid-19 pandemic led to an unprecedented decline of economic activity at the globe scale. To slow down the spread of the virus, most governments reacted with various measures of social distancing, such as mobility controls, business and school closures, etc. We investigate the short-term impact of social distancing measures on the US labour market, using a panel threshold model with high frequency (weekly) data on unemployment across US states allowing for heteroscedasticity. Labour is a key input in production, and thus a good proxy for the state of the economy. We find that changes in the restrictiveness of mandated social distancing, as measured by the Oxford Stringency Index, exert a strong impact on unemployment. The bulk of the reaction of unemployment to a change in the social distancing restrictions does not arise immediately, but with a delay of 2-4 weeks. In addition, the impact is asymmetric. If the policies switch to tighter regulations, the increase in unemployment is quicker and higher in absolute value than a decrease after relaxation. The state of the pandemic, proxied by the number of new infections and fatalities, constitutes only a marginal factor.