Marketing organizations in the United Kingdom (UK) are making the most of different schemes and coping strategies to help deal with the impact of the coronavirus pandemic. According to a survey conducted in September 2020, 52 percent of respondents said they had already taken advantage of the Coronavirus Job Retention Scheme. On top of that, 22 percent of respondents had deferred VAT payments.

The impact of the coronavirus (COVID-19) pandemic on the retirement plans of households in the United States varied greatly across the generations considered. As of 2020, approximately one-quarter of the millennial surveyed (24 to 38 years of age) declared to have withdrawn from their emergency fund or savings account because of the coronavirus pandemic. On the other hand, this impact was reported by only ten percent of the respondents aged between 55 to 73 years (boomers).

Due to the extensive economic disruption caused by the COVID-19 pandemic, the United Kingdom's Government created a range of measures to help support businesses survive the loss in revenues and cashflow. To help smaller businesses (SMEs), the Coronavirus Business Interruption Loan Scheme (CBILS) was set up. The scheme operates through the British Business Bank via more than 40 accredited lenders including high street banks, challenger banks, asset based lenders and smaller specialist local lenders. These lenders can then provide up to five million British pounds (GBP) in the form of term loans, overdraft, invoice finance and asset finance.

Between the 10th of May, 2020 and the 31st of May, 2021, the cumulative number of approved facilities through the Coronavirus Business Interruption Loan Scheme (CBILS) in the United Kingdom (UK) has reached more than 110,000 at a combined value of approximately 26 billion British pounds.

This statistics bank shows how business has made use of ordinary and extraordinary support schemes throughout the corona crisis.

A number of measures were initiated to increase activity in Norwegian business, prevent unnecessary closures and to get as many people as possible into work during the corona crisis. Several actors in the industry-oriented instrument apparatus were given additional tasks and new extraordinary measures were created, such as the compensation scheme through the Tax Agency.

In order to be able to monitor the use of the measures, the Ministry of Trade and Fisheries has commissioned Innovation Norway to expand its reporting to include regularly updated data on how the measures affect business. Innovation Norway has, with assistance from Societal Economic Analysis, also obtained information on schemes other than its own in order to get a more complete picture of the use of measures.

The statistics bank contains statistics on allocations per week from the business-oriented policy apparatus. The statistics bank is updated every month and contains data from week 1 of 2020.

This data package includes the underlying data and files to replicate the calculations, charts, and tables presented in COVID-19 credit support programs in Europe’s five largest economies, PIIE Working Paper 21-6.

If you use the data, please cite as: Anderson, Julia, Francesco Papadia, and Nicolas Véron. (2021). COVID-19 credit support programs in Europe’s five largest economies. PIIE Working Paper 21-6. Peterson Institute for International Economics.

Data on the number and value of grants to small and medium sized businesses (SMEs) in response to the coronavirus pandemic. The spreadsheet shows the total amount of money that each local authority and parliamentary constituency in England has: received from central government distributed to SMEs as at 5 July 2020 31 July 2021: coronavirus grant schemes Local Restrictions Support Grant (LRSG): (Open) Local Restrictions Support Grant (LRSG): (Closed) Additional Restrictions Grant (ARG) - scheme open until 31 March 2022. A final update will be released afterwards Christmas Support Payment (CSP) Restart 5 July 2020: coronavirus grant schemes: Small Business Grants Fund (SBGF) scheme Retail, Hospitality and Leisure Business Grants Fund (RHLGF) Local Authority Discretionary Grant Fund (LADGF)

Note: This dataset is no longer being updated due to the end of the COVID-19 Public Health Emergency. The California Department of Public Health (CDPH) is identifying ​the prevalence of circulating SARS-CoV-2 variants by analyzing ​CDPH Genomic Surveillance Data and ​CalREDIE, CDPH's communicable disease reporting and surveillance system. Viruses mutate into new strains or variants over time. Some variants emerge and then disappear. Other variants become common and circulate for a long time. Several specialized laboratories statewide sequence the genomes of a fraction of all positive COVID-19 tests to determine which variants are circulating. Sequencing and reporting of variant results takes several days after a test is identified as a positive for COVID-19. Not all ​viruses from positive COVID-19 tests are ​sequenced. Knowing what variants are circulating in California informs public health and clinical action. Note: There is a natural reporting lag in these data due to the time commitment to complete whole genome sequencing; therefore, a 14 day lag is applied to these datasets to allow for data completeness. Please note that more recent data should be used with caution. For more information, please see: https://www.cdph.ca.gov/Programs/CID/DCDC/Pages/COVID-19/COVID-Variants.aspx

In a September 2020 survey among adults in the United States, around 20 percent were more interested in buying a home after the outbreak of the coronavirus (COVID-19) pandemic. For 56 percent of respondents, however, their interest had not changed due to the arrival of the pandemic. Interestingly enough, there were less women whose interest had not changed (52 percent) than that there were men (61 percent).In the United States, the 2020 homeownership rate reached 65.8 percent.

Local authorities have received and distributed funding to support small and medium businesses in England during coronavirus. The datasets cover schemes managed by local authorities: Additional Restrictions Support Grant (ARG) Restart Grant - closed June 2021 Local Restrictions Support Grants (LRSG) and Christmas support payments - closed 2021 Small Business Grants Fund (SBGF) - closed August 2020 Retail, Hospitality and Leisure Business Grants Fund (RHLGF) - closed August 2020 Local Authority Discretionary Grants Fund (LADGF) - closed August 2020 The spreadsheets show the total amount of money that each local authority in England: received from central government distributed to SMEs 20 December 2021 update We have published the latest estimates by local authorities for payments made under this grant programme: Additional Restrictions Grants (up to and including 28 November 2021) The number of grants paid out is not necessarily the same as the number of businesses paid. The data has not received full verification.

In response to the COVID-19 pandemic, the federal government of Canada implemented various support and recovery measures to support the Canadian economy. This table presents the major programs (e.g. Canada Emergency Wage Subsidy, Canada Emergency Response Benefit, etc.) within the System of Macroeconomic Accounts. The data are at quarterly rates and are not seasonally adjusted.

Data on distance learning and supplemental feeding programs were collected from a stratified sample of 600 school districts. School districts were divided into quartiles based on the percentage of students eligible for free/reduced-price lunch, an indicator of family economic status, as reported by the National Center for Education Statistics (https://nces.ed.gov/ccd/). A simple random sample was taken in each stratum, and sample size per stratum was calculated using 95% confidence interval of 50% ± 10%. Data on the availability and method of delivery of both distance learning and supplemental feeding programs were collected from publicly available announcements on school district websites and their official social media pages (Facebook, Twitter). Google searches were performed for news resources when information was not available from online district sources.

United Kingdom Coronavirus Business Interruption Loan Scheme (CBILS): Applications data was reported at 122,885.000 Unit in 16 Aug 2020. This records an increase from the previous number of 121,669.000 Unit for 09 Aug 2020. United Kingdom Coronavirus Business Interruption Loan Scheme (CBILS): Applications data is updated weekly, averaging 104,569.000 Unit from May 2020 (Median) to 16 Aug 2020, with 15 observations. The data reached an all-time high of 122,885.000 Unit in 16 Aug 2020 and a record low of 64,531.000 Unit in 10 May 2020. United Kingdom Coronavirus Business Interruption Loan Scheme (CBILS): Applications data remains active status in CEIC and is reported by HM Treasury. The data is categorized under Global Database’s United Kingdom – Table UK.KB043: Coronavirus Business Interruption Loan Scheme.

List of coronavirus (COVID-19) global appeals and plans.

The PREDICT Consortium strengthened global preparedness for emerging threats, in particular to detect viruses that may have the potential to spillover from wild animal hosts to people. As part of USAID’s Emerging Pandemic Threats program, PREDICT was implemented from October 2009 through September 2020 by the PREDICT Consortium, as a multi-institutional cross-disciplinary team with numerous global, implementing and government partners in 30 countries (see https://ohi.vetmed.ucdavis.edu/programs-projects/predict-project/authorship for a list of contributors). This project pioneered a One Health approach to emerging infectious virus surveillance and risk communication at high-risk human-wildlife interfaces. This data asset and related datasets contain the test results of the PREDICT Coronavirus Extension Project, implemented from March 2020 through September 2020 to perform additional coronavirus PCR testing on archived animal specimens collected from 2009 to 2019.

Infectious disease experts have predicted a pandemic, saying it was not a question of if but when. Drawing on experiences with severe acute respiratory syndrome (SARS), avian influenza (H5N1), and novel influenza A (H1N1), the World Health Organization (WHO) and other health authorities, such as the Centers for Disease Control and Prevention (CDC), urged nations and local governments to prepare pandemic response plans. Many ministries of health and subnational departments of health around the world have activated those plans in response to coronavirus and are sharing data as required by the updated International Health Regulations.Esri's work with health organizations and government leaders has proven location intelligence from geographic information system (GIS) technology and data to be critical for the following:Assessing risk and evaluating threatsMonitoring and tracking outbreaksMaintaining situational awarenessEnsuring resource allocationNotifying agencies and communitiesThe current coronavirus disease pandemic presents an opportunity to build on the experience and readiness of Esri's existing global user community in health and human services. Through real-time maps, apps, and dashboards, GIS will also facilitate a seamless flow of relevant data as a component of the response from local to global levels. A compelling case exists for building on top of the public health GIS foundation that is already in place both in the United States and around the world.After reading this paper, leadership and senior staff should understand the following:The necessity to apply location intelligence to public health processes in coronavirus responseHow GIS can support immediate and long-term actionWhat resources Esri provides its customers

Abstract

An effective policy response to the economic impacts of the COVID-19 pandemic requires an enormous range of data to inform the design and response of programs. Public health measures require data on the spread of the disease, beliefs in the population, and capacity of the health system. Relief efforts depend on an understanding of hardships being faced by various segments of the population. Food policy requires measurement of agricultural production and hunger. In such a rapidly evolving pandemic, these data must be collected at a high frequency. Given the unexpected nature of the shock and urgency with which a response was required, Indian policymakers needed to formulate policies affecting India’s 1.4 billion people, without the detailed evidence required to construct effective programs. To help overcome this evidence gap, the World Bank, IDinsight, and the Development Data Lab sought to produce rigorous and responsive data for policymakers across six states in India: Jharkhand, Rajasthan, Uttar Pradesh, Andhra Pradesh, Bihar, and Madhya Pradesh.

Geographic coverage

Andhra Pradesh, Bihar, Jharkhand, Madhya Pradesh, Rajasthan, and Uttar Pradesh

Analysis unit

Household

Kind of data

Sample survey data [ssd]

Sampling procedure

This dataset includes observations covering six states (Andhra Pradesh, Bihar, Jharkhand, Madhya Pradesh, Rajasthan, Uttar Pradesh) and three survey rounds. The survey did not have a single, unified frame from which to sample phone numbers. The final sample was assembled from several different sample frames, and the choice of frame sample frames varied across states and survey rounds.

These frames comprise four prior IDinsight projects and from an impact evaluation of the National Rural Livelihoods project conducted by the Ministry of Rural Development. Each of these surveys sought to represent distinct populations, and employed idiosyncratic sample designs and weighting schemes.

A detailed note covering key features of each sample frame is available for download.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The survey questionnaires covered the following subjects:

1. Agriculture: COVID-19-related changes in price realisation, acreage decisions, input expenditure, access to credit, access to fertilisers, etc.

2. Income and consumption: Changes in wage rates, employment duration, consumption expenditure, prices of essential commodities, status of food security etc.

3. Migration: Rates of in-migration, migrant income and employment status, return migration plans etc.

4. Access to relief: Access to in-kind, cash and workfare relief, quantities of relief received, and constraints on the access to relief.

5. Health: Access to health facilities and rates of foregone healthcare, knowledge of COVID-19 related symptoms and protective behaviours.

While a number of indicators were consistent across all three rounds, questions were added and removed as and when necessary to account for seasonal changes (i.e: in the agricultural cycle).

Response rate

Round 1: ~55% Round 2: ~46% Round 3: ~55%

Dataset of the work consists of 3 excel workbooks. They are briefly described below :(1) 1_million_primary_transactionsThis file contains 1 million data (transactions) of participants that is used in our blockchain experimentation. These transactions (aka primary data) are stored in local chain of our architecture.(2) 1_million_secondary_transactionsThis file contains 1 million data (transactions) of participants that is used in our blockchain experimentation. These transactions (aka secondary data) are stored in off-chain of our architecture.