Notes

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More than a quarter of working-age households in the United States do not have sufficient savings to cover their expenditures after a month of unemployment. Recent proposals suggest giving workers early access to a small portion of their future Social Security benefits to finance their consumption during the COVID-19 pandemic. We empirically analyze their impact. https://www.selleckchem.com/products/milademetan.html Relying on data from the Survey of Consumer Finances, we build a measure of households' expected time to cash shortfall based on the incidence of COVID-induced unemployment. We show that access to 1% of future benefits allows 75% of households to maintain their current consumption for three months in case of unemployment. We then compare the efficacy of access to Social Security benefits to already legislated approaches, including early access to retirement accounts, stimulus relief checks, and expanded unemployment insurance.The COVID-19 pandemic has led to an economic slowdown as more people practice social distancing and shelter at home. The increase in family isolation, unemployment, and economic stress has the potential to increase domestic violence. We document the pandemic's impact on police calls for service for domestic violence. The pandemic increased domestic violence calls by 7.5% during March through May of 2020, with effects concentrated during the first five weeks after social distancing began. The increase in reported domestic violence incidents began before official stay-at-home orders were mandated. It is not driven by any particular demographic group but does appear to be driven by households without a previous history of domestic violence.We use job vacancy data collected in real time by Burning Glass Technologies, as well as unemployment insurance (UI) initial claims and the more traditional Bureau of Labor Statistics (BLS) employment data to study the impact of COVID-19 on the labor market. Our job vacancy data allow us to track the economy at disaggregated geography and by detailed occupation and industry. We find that job vacancies collapsed in the second half of March. By late April, they had fallen by over 40%. To a first approximation, this collapse was broad based, hitting all U.S. states, regardless of the timing of stay-at-home policies. UI claims and BLS employment data also largely match these patterns. Nearly all industries and occupations saw contraction in postings and spikes in UI claims, with little difference depending on whether they are deemed essential and whether they have work-from-home capability. Essential retail, the "front line" job most in-demand during the current crisis, took a much smaller hit, while leisure and hospitality services and non-essential retail saw the biggest collapses. This set of facts suggests the economic collapse was not caused solely by the stay-at-home orders, and is therefore unlikely to be undone simply by lifting them.Evaluating the economic impact of "social distancing" measures taken to arrest the spread of COVID-19 raises a fundamental question about the modern economy how many jobs can be performed at home? We classify the feasibility of working at home for all occupations and merge this classification with occupational employment counts. We find that 37% of jobs in the United States can be performed entirely at home, with significant variation across cities and industries. These jobs typically pay more than jobs that cannot be done at home and account for 46% of all US wages. Applying our occupational classification to 85 other countries reveals that lower-income economies have a lower share of jobs that can be done at home.
To investigate the effect of the COVID-19 pandemic on the frequency of various crime types (property, violent, and mischief) in Vancouver, Canada.

Crime data representing residential burglary, commercial burglary, theft of vehicle, theft from vehicle, theft, violence, and mischief are analysed at the city level using interrupted time series techniques.

While COVID-19 has not had an impact on all crime types, statistically significant change has been identified in a number of cases. Depending on the crime type, the magnitude and direction of the change in frequency varies. It is argued that (mandated) social restrictions, shifted activity patterns and opportunity structures which are responsible for these findings.

We find support for changes in the frequency of particular crime types during the COVID-19 pandemic. This is important for criminal justice and social service practitioners when operating within an extraordinary event.
We find support for changes in the frequency of particular crime types during the COVID-19 pandemic. This is important for criminal justice and social service practitioners when operating within an extraordinary event.Deep learning applications with robotics contribute to massive challenges that are not addressed in machine learning. The present world is currently suffering from the COVID-19 pandemic, and millions of lives are getting affected every day with extremely high death counts. Early detection of the disease would provide an opportunity for proactive treatment to save lives, which is the primary research objective of this study. The proposed prediction model caters to this objective following a stepwise approach through cleaning, feature extraction, and classification. The cleaning process constitutes the cleaning of missing values ,which is proceeded by outlier detection using the interpolation of splines and entropy-correlation. The cleaned data is then subjected to a feature extraction process using Principle Component Analysis. A Fitness Oriented Dragon Fly algorithm is introduced to select optimal features, and the resultant feature vector is fed into the Deep Belief Network. The overall accuracy of the proposed scheme experimentally evaluated with the traditional state of the art models. The results highlighted the superiority of the proposed model wherein it was observed to be 6.96% better than Firefly, 6.7% better than Particle Swarm Optimization, 6.96% better than Gray Wolf Optimization ad 7.22% better than Dragonfly Algorithm.
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