Economics of disasters and climate change最新文献

The precipitous spread of COVID-19 has created a conflict between human health and economic well-being. To contain the spread of its contagious effect, India imposed a stringent lockdown, and then the stringency was relaxed to some extent in its succeeding phases. We measure social benefits of the lockdown in terms of improved air quality in Indian cities by quantifying the effects with city-specific slope coefficients. We find that the containment measures have resulted in improvement in air quality, but it is not uniform across cities and across pollutants. The level of PM_2.5 decreases from about 6 to 25% in many cities. Moreover, we observe that partial relaxations do not help in resuming economic and social activities. It should also be noted that counter-virus measures could not bring levels of the emissions to WHO standards; it highlights the importance of role of green production and consumption activities.

The COVID-19 pandemic has caused a massive economic shock across the world due to business interruptions and shutdowns from social-distancing measures. To evaluate the socio-economic impact of COVID-19 on individuals, a micro-economic model is developed to estimate the direct impact of distancing on household income, savings, consumption, and poverty. The model assumes two periods: a crisis period during which some individuals experience a drop in income and can use their savings to maintain consumption; and a recovery period, when households save to replenish their depleted savings to pre-crisis level. The San Francisco Bay Area is used as a case study, and the impacts of a lockdown are quantified, accounting for the effects of unemployment insurance (UI) and the CARES Act federal stimulus. Assuming a shelter-in-place period of three months, the poverty rate would temporarily increase from 17.1% to 25.9% in the Bay Area in the absence of social protection, and the lowest income earners would suffer the most in relative terms. If fully implemented, the combination of UI and CARES could keep the increase in poverty close to zero, and reduce the average recovery time, for individuals who suffer an income loss, from 11.8 to 6.7 months. However, the severity of the economic impact is spatially heterogeneous, and certain communities are more affected than the average and could take more than a year to recover. Overall, this model is a first step in quantifying the household-level impacts of COVID-19 at a regional scale. This study can be extended to explore the impact of indirect macroeconomic effects, the role of uncertainty in households' decision-making and the potential effect of simultaneous exogenous shocks (e.g., natural disasters).

Key factors in modeling a pandemic and guiding policy-making include mortality rates associated with infections; the ability of government policies, medical systems, and society to adapt to the changing dynamics of a pandemic; and institutional and demographic characteristics affecting citizens' perceptions and behavioral responses to stringent policies. This paper traces the cross-country associations between COVID-19 mortality, policy interventions aimed at limiting social contact, and their interactions with institutional and demographic characteristics. We document that, with a lag, more stringent pandemic policies were associated with lower mortality growth rates. The association between stricter pandemic policies and lower future mortality growth is more pronounced in countries with a greater proportion of the elderly population and urban population, greater democratic freedoms, and larger international travel flows. Countries with greater policy stringency in place prior to the first death realized lower peak mortality rates and exhibited lower durations to the first mortality peak. In contrast, countries with higher initial mobility saw higher peak mortality rates in the first phase of the pandemic, and countries with a larger elderly population, a greater share of employees in vulnerable occupations, and a higher level of democracy took longer to reach their peak mortalities. Our results suggest that policy interventions are effective at slowing the geometric pattern of mortality growth, reducing the peak mortality, and shortening the duration to the first peak. We also shed light on the importance of institutional and demographic characteristics in guiding policy-making for future waves of the pandemic.

Coronavirus disease of 2019 (COVID-19) started in December 2019 in Wuhan, China. In a few months, it has become a pandemic with devastating consequences for the global economy. By the end of June, with almost 2.6 million confirmed COVID-19 cases, United States is above other countries in the rankings. Furthermore, New York with more than 416 thousand cases is the epicenter of outbreak in the US and had more cases than any other countries in the world until first half of June. In this paper, we use a two-step Vector Auto Regressive (VAR) model to forecast the effect of the virus outbreak on the economic output of the New York state. In our model, we forecast the effect of the shutdown on New York's Gross Domestic Product (GDP) working with Unemployment Insurance Claim series representing a workforce factor, as well as the Metropolitan Transportation Authority (MTA) ridership data indicating the economic activity. We predict annualized quarterly growth rate of real GDP to be between -3.99 to -4.299% for the first quarter and between -19.79 to -21.67% for the second quarter of 2020.

What factors affected whether or not a U.S. state governor issued a state-wide stay-at-home order in response to the COVID-19 pandemic of early 2020? Once issued, what factors affected the length of this stay-at-home order? Using duration analysis, we test a number of epidemiological, economic, and political factors for their impact on a state governor's decision to ultimately issue, and then terminate, blanket stay-at-home orders across the 50 U.S. states. Results indicate that while epidemiologic and economic variables had some impact on the delay to initiation and length of the stay-at-home orders, political factors dominated both the initiation and ultimate duration of stay-at-home orders across the United States.