The Research and Development Survey (RANDS) is a platform designed for conducting survey question evaluation and statistical research. RANDS is an ongoing series of surveys from probability-sampled commercial survey panels used for methodological research at the National Center for Health Statistics (NCHS). RANDS estimates are generated using an experimental approach that differs from the survey design approaches generally used by NCHS, including possible biases from different response patterns and sampling frames as well as increased variability from lower sample sizes. Use of the RANDS platform allows NCHS to produce more timely data than would be possible using traditional data collection methods. RANDS is not designed to replace NCHS’ higher quality, core data collections. Below are experimental estimates of loss of work due to illness with coronavirus for three rounds of RANDS during COVID-19. Data collection for the three rounds of RANDS during COVID-19 occurred between June 9, 2020 and July 6, 2020, August 3, 2020 and August 20, 2020, and May 17, 2021 and June 30, 2021. Information needed to interpret these estimates can be found in the Technical Notes. RANDS during COVID-19 included a question about the inability to work due to being sick or having a family member sick with COVID-19. The National Health Interview Survey, conducted by NCHS, is the source for high-quality data to monitor work-loss days and work limitations in the United States. For example, in 2018, 42.7% of adults aged 18 and over missed at least 1 day of work in the previous year due to illness or injury and 9.3% of adults aged 18 to 69 were limited in their ability to work or unable to work due to physical, mental, or emotional problems. The experimental estimates on this page are derived from RANDS during COVID-19 and show the percentage of U.S. adults who did not work for pay at a job or business, at any point, in the previous week because either they or someone in their family was sick with COVID-19. Technical Notes: https://www.cdc.gov/nchs/covid19/rands/work.htm#limitations

This page is no longer updated. It has been superseded by the Business insights and impacts on the UK economy dataset page (see link in Notices). It contains comprehensive weighted datasets for Wave 7 onwards. All future BICS datasets will be available there. The datasets on this page include mainly unweighted responses from the voluntary fortnightly business survey, which captures businesses’ responses on how their turnover, workforce prices, trade and business resilience have been affected in the two-week reference period, up to Wave 17.

US Covid-19 Cases, Deaths and Mobility by State/County

Analyzing the Impact of the Pandemic on Low-Income Populations

By Liz Friedman [source]

About this dataset

Welcome to the Opportunity Insights Economic Tracker! Our goal is to provide a comprehensive, real-time look into how COVID-19 and stabilization policies are affecting the US economy. To do this, we have compiled a wide array of data points on spending and employment, gathered from several sources.

This dataset includes daily/weekly/monthly information at the state/county/city level for eight types of data: Google Mobility; Low-Income Employment and Earnings; UI Claims; Womply Merchants and Revenue; as well as weekly Math Learning from Zearn. Additionally, three files- Accounting for Geoids-State/County/City provide crosswalks between geographic areas that can be merged with other files having shared geographical levels.

Our goal here is to enable data users around the world to follow economic conditions in the US during this tumultuous period with maximum clarity and precision. We make all our datasets freely available so if you use them we kindly ask you attribute our work by linking or citing both our accompanying paper as well as this Economic Tracker at https://tracktherecoveryorg By doing so you are also agreeing to uphold our privacy & integrity standards which commit us both to individual & business confidentiality without compromising on independent nonpartisan research & policy analysis!

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For more datasets, click here.

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How to use the dataset

This dataset provides US COVID-19 case and death data, as well as Google Community Mobility Reports, on the state/county level. Here is how to use this dataset:

• Understand the file structure: This dataset consists of three main files: 1) US Cases & Deaths by State/County, 2) Google Community Mobility Reports, and 3) Data from third-parties providing small business openings & revenue information and unemployment insurance claim data (Low Inc Earnings & Employment, UI Claims and Womply Merchants & Revenue).
• Select your Subset: If you are interested in particular types of data (e.g., mobility or employment), select the corresponding files from within each section based on your geographic area of interest – national, state or county level – as indicated in each filename.
• Review metadata variables: Become familiar with the provided variables so that you can select which ones you need to explore further in your analysis. For example, if analyzing mobility trends at a city level look for columns such as ‘Retailer_and_recreation_percent_change’ or ‘Transit Stations Percent Change’; if focusing on employment decline look for columns such pay or emp figures that align with industries of interest to you such as low-income earners (emp_{inclow},pay_{inclow}).
• Unify dateformatting across row values : Convert date formats into one common unit so that all entries have consistent formatting if necessary; for exampe some entries may display dates using YYYY/MM/DD notation while others may use MM//DD//YY format depending on their source datasets; make sure to review column labels carefully before converting units where needed..

• Merge datasets where applicable : Utilize GeoID crosswalks to combine multiple sets with same geographical coverageregionally covering ; example might be combining low income earnings figures with specific county settings by reference geo codes found in related documents like GeoIDs-County .
  6 . Visualise Data : Now that all the different measures have been reviewed can begin generating charts visualize findings . This process may include cleaning up raw figures normalizing across currency formats , mapping geospatial locations others ; once ready create bar graphs line charts maps other visual according aggregate output desired Insightful representations at this stage will help inform concrete policy decisions during outbreak recovery period..

  Remember to cite

Research Ideas

• Estimating the Impact of the COVID-19 Pandemic on Small Businesses - By comparing county-level Womply revenue and employment data with pre-COVID data, policymakers can gain an understanding of the economic impact that COVID has had on local small businesses.
• Analyzing Effects of Mobility Restrictions - The Google Mobility data provides insight into geographic areas where...

Context

Different states in United States have different lockdown policy. I found this nice summary of the state action from https://www.ncsl.org that might be useful to those who like to investigate how different lockdown policies can help in flattening the curve. I cleaned up the dataset (like fill the null values, etc) without alternating any information and thus all the 'No' in the state can also be interpreted as NaN.

Content

Specifically, this dataset summarizes if each state (and US territories) perform the following actions (column of the dataset), as of April 10, 2020: 1. Emergency Declaration 2. Major Disaster Declaration 3. National Guard State Activation 4. State Employee Travel Restrictions 5. Statewide Limits on Gatherings and Stay at Home Orders 6. Statewide School Closures
7. Statewide Closure of Non-Essential Businesses
8. Statewide Closure of Some or All Non-Essential Businesses
9. Essential Business Designations Issued
10. Statewide Curfew
11. 1135 Waiver Status
12. Extension of Individual Income Tax Deadlines
13. Primary Election
14. Domestic Travel Limitations 15. Statewide Mask Policy
16. Ventilator Sharing

Acknowledgements

Again, this dataset was found from National Conference of State Legislatures website.

P.S.: Hope that this dataset is useful/helpful in understanding the impact of different lockdown policy.

To create the dataset, the top 10 countries leading in the incidence of COVID-19 in the world were selected as of October 22, 2020 (on the eve of the second full of pandemics), which are presented in the Global 500 ranking for 2020: USA, India, Brazil, Russia, Spain, France and Mexico. For each of these countries, no more than 10 of the largest transnational corporations included in the Global 500 rating for 2020 and 2019 were selected separately. The arithmetic averages were calculated and the change (increase) in indicators such as profitability and profitability of enterprises, their ranking position (competitiveness), asset value and number of employees. The arithmetic mean values of these indicators for all countries of the sample were found, characterizing the situation in international entrepreneurship as a whole in the context of the COVID-19 crisis in 2020 on the eve of the second wave of the pandemic. The data is collected in a general Microsoft Excel table. Dataset is a unique database that combines COVID-19 statistics and entrepreneurship statistics. The dataset is flexible data that can be supplemented with data from other countries and newer statistics on the COVID-19 pandemic. Due to the fact that the data in the dataset are not ready-made numbers, but formulas, when adding and / or changing the values in the original table at the beginning of the dataset, most of the subsequent tables will be automatically recalculated and the graphs will be updated. This allows the dataset to be used not just as an array of data, but as an analytical tool for automating scientific research on the impact of the COVID-19 pandemic and crisis on international entrepreneurship. The dataset includes not only tabular data, but also charts that provide data visualization. The dataset contains not only actual, but also forecast data on morbidity and mortality from COVID-19 for the period of the second wave of the pandemic in 2020. The forecasts are presented in the form of a normal distribution of predicted values and the probability of their occurrence in practice. This allows for a broad scenario analysis of the impact of the COVID-19 pandemic and crisis on international entrepreneurship, substituting various predicted morbidity and mortality rates in risk assessment tables and obtaining automatically calculated consequences (changes) on the characteristics of international entrepreneurship. It is also possible to substitute the actual values identified in the process and following the results of the second wave of the pandemic to check the reliability of pre-made forecasts and conduct a plan-fact analysis. The dataset contains not only the numerical values of the initial and predicted values of the set of studied indicators, but also their qualitative interpretation, reflecting the presence and level of risks of a pandemic and COVID-19 crisis for international entrepreneurship.

Updated weekly Public Health — Seattle & King County is monitoring changes in key economic, social, and other health indicators resulting from strategies to slow the spread of COVID-19. The metrics below were selected based on studies from previous outbreaks, which have linked strategies such as social distancing, school closures, and business closures to specific outcomes. Individual indicators in the grid below are updated daily, weekly, or monthly, depending on the source of data. Additional data will be added over time.

In March, 2020 U.S. state governors in 44 states issued "do not leave home" orders and assigned "essential" designations to specific industries. We developed a catalog of closure policies (open, open with restrictions, closed) by state for industries whose designation was publicly questioned. The database which accompanies the article identifies restrictions imposed by each state.

Context

Coronavirus infection is currently the most important health topic. It surely tested and continues to test to the fullest extent the healthcare systems around the world. Although big progress is made in handling this pandemic, a tremendous number of questions are needed to be answered. I hereby present to you the local Bulgarian COVID-19 dataset with some context. It could be used as a comparator because it stands out compared to other countries and deserves analysis.

Context for Bulgarian population: Population - 6 948 445 Median age - 44.7 years Aged >65 - 20.801 % Aged >70 - 13.272%

Summary of the results: - first pandemic wave was weak, probably because of the early state of emergency (5 days after the first confirmed case). Whether this was a good decision or it was too early and just postpone the inevitable is debatable. -healthcare system collapses (probably due to delayed measures) in the second and third waves which resulted in Bulgaria gaining the top ranks for mortality and morbidity tables worldwide and in the EU. - low percentage of vaccinated people results in a prolonged epidemic and delaying the lifting of the preventive measures.

Some of the important moments that should be considered when interpreting the data: 08.03.2020 - Bulgaria confirmed its first two cases. The government issued a nationwide ban on closed-door public events (first lockdown); 13.03.2020- after 16 reported cases in one day, Bulgaria declared a state of emergency for one month until 13.04.2020. Schools, shopping centres, cinemas, restaurants, and other places of business were closed. All sports events were suspended. Only supermarkets, food markets, pharmacies, banks, and gas stations remain open. 03.04.2020 - The National Assembly approved the government's proposal to extend the state of emergency by one month until 13.05.2020; 14.05.2020 - the national emergency was lifted, and in its place was declared a state of an emergency epidemic situation. Schools and daycares remain closed, as well as shopping centers and indoor restaurants; 18.05.2020 - Shopping malls and fitness centers opened; 01.06.2020 - Restaurants and gaming halls opened; 10.07.2020 - discos and bars are closed, the sports events are without an audience; 29.10.2020 - High school and college students are transitioning to online learning; 27.11.2020 - the whole education is online, restaurants, nightclubs, bars, and discos are closed (second lockdown 27.11 - 21.12); 05.12.2020 - the 14-day mortality rate is the highest in the world; 16.01.2021 - some of the students went back to school; 01.03.2021 - restaurants and casinos opened; 22.03.2021 - restaurants, shopping malls, fitness centers, and schools are closed (third lockdown for 10 days - 22.03 - 31.03); 19.04.2021 - children daycare facilities, fitness centers, and nightclubs are opened;

Content

This dataset consists of 447 rows with 29 columns and covers the period 08.03.2020 - 28.05.2021. In the beginning, there are some missing values until the proper statistical report was established.

Inspiration

A publication proposal is sent to anyone who wishes to collaborate. Based on the results and the value of the findings and the relevance of the topic it is expected to publish: - in a local journal (guaranteed); - in a SCOPUS journal (highly probable); - in an IF journal (if the results are really insightful).

The topics could be, but not limited to: - descriptive analysis of the pandemic outbreak in the country; - prediction of the pandemic or the vaccination rate; - discussion about the numbers compared to other countries/world; - discussion about the government decisions; - estimating cut-off values for step-down or step-up of the restrictions.

Error or query reporting

If you find an error, have a question, or wish to make a suggestion, I encourage you to reach me.

This is a list of locations of which the following conditions apply:ACTIVITY TYPE ID 4 SURVEY – Surveillance was conducted on the business and no violations were found. Reviews conducted during routine inspections of permitted establishments from 1/21/21 on.LMPHW Narrative: Louisville Metro Public Health and Wellness (LMPHW) investigates and responds to reports of alleged violations related to COVID-19. LMPHW has provided an open dataset of businesses that were observed to not be following the covid requirements as prescribed by the Governor’s Office. The data does not distinguish between the type of enforcement action taken with the exception of the closure of a facility for operating when they were to be closed. The data shows that an order or citation was issued with or without a fine assessed. A minimum of one violation or multiple violations were observed on this day. Violations include but are not limited to failure to wear a face covering, lack of social distancing, failure to properly isolate or quarantine personnel, failure to conduct health checks, and other violations of the Governor’s Orders. Closure orders documented in the data portal where issued by either LMPHW, Shively Police or the Kentucky Labor Cabinet. Detail the Enforcement Process: The Environmental Division receives complaints of non-compliance on local businesses. Complaints are received from several sources including: Metro Call, Louisville Metro Public Health and Wellness’ Environmental call line, Facebook, email, and other sources. Complaints are investigated by inspectors in addition to surveillance of businesses to ensure compliance. Violations observed result in both compliance guidance being given to the business along with an enforcement notice which consists of either a Face Covering Citation and/or a Public Health Notice and Order depending on the type of violation. Citations result in fines being assessed. Violations are to be addressed immediately.Community members can report a complaint via Metro Call by calling 574-5000. For COVID 19 Guidance please visit Louisville Metro’s Covid Resource Center at https://louisvilleky.gov/government/louisville-covid-19-resource-center or calling the Covid Helpline at (502)912-8598.ACTIVITY TYPE ID 12 indicates an Enforcement Action has been taken against the establishment which include Notice to Correct, Citation which include financial penalties and/or Cease Operation. LMPHW Narrative Example: Louisville Metro Public Health and Wellness (LMPHW) investigates and responds to reports of alleged violations related to COVID-19. They also conduct surveillance of businesses to determine compliance. LMPHW has provided an open dataset of businesses that were observed to be following the covid requirements as prescribed by the Governor’s Office. ACTIVITY TYPE ID 4 SURVEY – Surveillance was conducted on the business and no violations were found. ACTIVITY TYPE ID 7 FIELD – A complaint was investigated on the business and no violations were found.ACTIVITY TYPE ID 12 Enforcement Action – Action has been taken against the establishment which could include Notice to Correct, Citation which include financial penalties and/or Cease Operation. ACTIVITY TYPE ID 12 Enforcement Action – Action Code Z – The establishment has been issued an order to cease operation.Data Set Explanation:Activity Type ID 4 Survey has two separate files: COVID_4_Surveillance_Open_Data – Surveillance conducted prior to 1/21/2021 in which were conducted as part of random survey of businessesCOVID_4_Compliance_Reviews_Open_Data – Reviews conducted during routine inspections of permitted establishments from 1/21/21 on. Data Dictionary: REQ ID-ID of RequestRequest Date-Date of Requestperson premiseaddress1zipActivity Date-Date Activity OccurredACTIVITY TYPE IDActivity Type Desc-Description of ActivityContact:Gerald Kaforskigerald.kaforski@louisvilleky.gov

The Paycheck Protection Program (PPP) established by the CARES Act, is implemented by the Small Business Administration (SBA) with support from the Department of the Treasury. The program provided small businesses with funds to pay up to 8 weeks of payroll costs including benefits. Funds could also be used to pay interest on mortgages, rent, and utilities This dataset details New York State recipients of PPP funds.

This is a list of locations of which the following conditions apply:ACTIVITY TYPE ID 12 Enforcement Action – Action Code Z – The establishment has been issued an order to cease operation.These are infrequent. File will only be renewed if there is a new order.LMPHW Narrative: Louisville Metro Public Health and Wellness (LMPHW) investigates and responds to reports of alleged violations related to COVID-19. LMPHW has provided an open dataset of businesses that were observed to not be following the covid requirements as prescribed by the Governor’s Office. The data does not distinguish between the type of enforcement action taken with the exception of the closure of a facility for operating when they were to be closed. The data shows that an order or citation was issued with or without a fine assessed. A minimum of one violation or multiple violations were observed on this day. Violations include but are not limited to failure to wear a face covering, lack of social distancing, failure to properly isolate or quarantine personnel, failure to conduct health checks, and other violations of the Governor’s Orders. Closure orders documented in the data portal where issued by either LMPHW, Shively Police or the Kentucky Labor Cabinet. Detail the Enforcement Process: The Environmental Division receives complaints of non-compliance on local businesses. Complaints are received from several sources including: Metro Call, Louisville Metro Public Health and Wellness’ Environmental call line, Facebook, email, and other sources. Complaints are investigated by inspectors in addition to surveillance of businesses to ensure compliance. Violations observed result in both compliance guidance being given to the business along with an enforcement notice which consists of either a Face Covering Citation and/or a Public Health Notice and Order depending on the type of violation. Citations result in fines being assessed. Violations are to be addressed immediately.Community members can report a complaint via Metro Call by calling 574-5000. For COVID 19 Guidance please visit Louisville Metro’s Covid Resource Center at https://louisvilleky.gov/government/louisville-covid-19-resource-center or calling the Covid Helpline at (502)912-8598.ACTIVITY TYPE ID 12 indicates an Enforcement Action has been taken against the establishment which include Notice to Correct, Citation which include financial penalties and/or Cease Operation. LMPHW Narrative Example: Louisville Metro Public Health and Wellness (LMPHW) investigates and responds to reports of alleged violations related to COVID-19. They also conduct surveillance of businesses to determine compliance. LMPHW has provided an open dataset of businesses that were observed to be following the covid requirements as prescribed by the Governor’s Office. ACTIVITY TYPE ID 4 SURVEY – Surveillance was conducted on the business and no violations were found. ACTIVITY TYPE ID 7 FIELD – A complaint was investigated on the business and no violations were found.ACTIVITY TYPE ID 12 Enforcement Action – Action has been taken against the establishment which could include Notice to Correct, Citation which include financial penalties and/or Cease Operation. ACTIVITY TYPE ID 12 Enforcement Action – Action Code Z – The establishment has been issued an order to cease operation.Data Set Explanation:Activity Type ID 4 Survey has two separate files: COVID_4_Surveillance_Open_Data – Surveillance conducted prior to 1/21/2021 in which were conducted as part of random survey of businessesCOVID_4_Compliance_Reviews_Open_Data – Reviews conducted during routine inspections of permitted establishments from 1/21/21 on. Data Dictionary: REQ ID-ID of RequestRequest Date-Date of Requestperson premiseaddress1zipActivity Date-Date Activity OccurredACTIVITY TYPE IDActivity Type Desc-Description of ActivityContact:Gerald Kaforskigerald.kaforski@louisvilleky.gov

A. SUMMARY This dataset includes COVID-19 tests by resident neighborhood and specimen collection date (the day the test was collected). Specifically, this dataset includes tests of San Francisco residents who listed a San Francisco home address at the time of testing. These resident addresses were then geo-located and mapped to neighborhoods. The resident address associated with each test is hand-entered and susceptible to errors, therefore neighborhood data should be interpreted as an approximation, not a precise nor comprehensive total.

In recent months, about 5% of tests are missing addresses and therefore cannot be included in any neighborhood totals. In earlier months, more tests were missing address data. Because of this high percentage of tests missing resident address data, this neighborhood testing data for March, April, and May should be interpreted with caution (see below)

Percentage of tests missing address information, by month in 2020 Mar - 33.6% Apr - 25.9% May - 11.1% Jun - 7.2% Jul - 5.8% Aug - 5.4% Sep - 5.1% Oct (Oct 1-12) - 5.1%

To protect the privacy of residents, the City does not disclose the number of tests in neighborhoods with resident populations of fewer than 1,000 people. These neighborhoods are omitted from the data (they include Golden Gate Park, John McLaren Park, and Lands End).

Tests for residents that listed a Skilled Nursing Facility as their home address are not included in this neighborhood-level testing data. Skilled Nursing Facilities have required and repeated testing of residents, which would change neighborhood trends and not reflect the broader neighborhood's testing data.

This data was de-duplicated by individual and date, so if a person gets tested multiple times on different dates, all tests will be included in this dataset (on the day each test was collected).

The total number of positive test results is not equal to the total number of COVID-19 cases in San Francisco. During this investigation, some test results are found to be for persons living outside of San Francisco and some people in San Francisco may be tested multiple times (which is common). To see the number of new confirmed cases by neighborhood, reference this map: https://sf.gov/data/covid-19-case-maps#new-cases-maps

B. HOW THE DATASET IS CREATED COVID-19 laboratory test data is based on electronic laboratory test reports. Deduplication, quality assurance measures and other data verification processes maximize accuracy of laboratory test information. All testing data is then geo-coded by resident address. Then data is aggregated by analysis neighborhood and specimen collection date.

Data are prepared by close of business Monday through Saturday for public display.

C. UPDATE PROCESS Updates automatically at 05:00 Pacific Time each day. Redundant runs are scheduled at 07:00 and 09:00 in case of pipeline failure.

D. HOW TO USE THIS DATASET San Francisco population estimates for geographic regions can be found in a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2016-2020 5-year American Community Survey (ACS).

Due to the high degree of variation in the time needed to complete tests by different labs there is a delay in this reporting. On March 24 the Health Officer ordered all labs in the City to report complete COVID-19 testing information to the local and state health departments.

In order to track trends over time, a data user can analyze this data by "specimen_collection_date".

Calculating Percent Positivity: The positivity rate is the percentage of tests that return a positive result for COVID-19 (positive tests divided by the sum of positive and negative tests). Indeterminate results, which could not conclusively determine whether COVID-19 virus was present, are not included in the calculation of pe

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.

Abstract

To better understand the impact of the shock induced by the COVID-19 pandemic on micro and small enterprises in Tunisia and assess the policy responses in a rapidly changing context, reliable data is imperative, and the need to resort to a dynamic data collection tool at a time when countries in the region are in a state of flux cannot be overstated. The COVID-19 MENA Monitor Survey was led by the Economic Research Forum (ERF) to provide data for researchers and policy makers on the economic and labor market impact of the global COVID-19 pandemic on enterprises.

The ERF COVID-19 MENA Monitor Survey is constructed using a series of short panel phone surveys, that are conducted approximately every two months, and it will cover business closure (temporary/permanent) due to lockdowns, ability to telework/deliver the service, disruptions to supply chains (for inputs and outputs), loss of product markets, increased cost of supplies, worker layoffs, salary adjustments, access to lines of credit and delays in transportation. Understanding the strategies of enterprises (particularly micro and small enterprises) to cope with the crisis is one of the main objectives of this survey. Specific constraints such as weak access to the internet in some areas or laws constraining goods' delivery will be analyzed. Enterprise owners will also be asked about prospects for the future, including ability to stay open, and whether they benefited from any measures to support their businesses. The ERF COVID-19 MENA Monitor Survey is a wide-ranging, nationally representative panel survey. The wave 3 of this dataset was collected from August to September 2021 and harmonized by the Economic Research Forum (ERF) and is featured as data for enterprise data.

The harmonization was designed to create comparable data that can facilitate cross-country and comparative research between other Arab countries (Morocco, Egypt, and Jordan). All the COVID-19 MENA Monitor surveys incorporate similar survey designs, with data on enterprises within Arab countries (Egypt, Jordan, Tunisia, and Morocco).

Geographic coverage

National

Analysis unit

Enterprises

Universe

The sample universe for the enterprise survey was enterprises that had 6-199 workers pre-COVID-19

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample universe for the firm survey was firms that had 6-199 workers pre-COVID-19. Stratified random samples were used to ensure adequate sample size in key strata. A target of 500 firms was set as a sample. Up to Five attempts were made to ensure response if a phone number was not picked up/answered, was disconnected or busy, or picked up but could not complete the interview at that time. After the fifth failed attempt, a firm was treated as a non-response and a random firm from the same stratum was used as an alternate.

Use the National Institute of Statistics (INS) and Agency for the Promotion of Industry and Innovation (APII) databases as follow: o Tunisia did not have a Yellow Pages or similar database, so administrative/statistics data sources had to be used o The sample started with the INS frame with 1,238 enterprises with 6-200 wage employees § Enterprises were stratified into: (1) Agriculture (2) Industry (3) Construction (4) Trade (5) Accommodation (6) Service § Enterprises were also stratified by size in terms of 6-49 versus 50-200 employees § A random stratified sample (order) was selected § Further restricted to enterprises with 6-199 workers in February 2020 based on an eligibility question during the phone interview § This sample frame was eventually exhausted o After the INS sample was exhausted, the APII sample was used § APII only covered enterprises with 10+ workers § APII only covered (1) services & transport, and (2) industry o Weights are based on the underlying data on all enterprises from INS, specifically: Entreprises privées selon l'activité principale et la tranche de salariés (RNE 2019). § We ultimately stratify the Tunisia weights by industry and enterprises sized: 6-9 employees (since APII only covered 10+), 10-49, and 50-199.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The enterprise questionnaire is carried out to understand the strategies of enterprises -particularly micro and small enterprises- to cope with the crisis as well as related constraints and prospects for the future. It includes questions on business closure (temporary/permanent) due to lockdowns, ability to telework/deliver the service, disruptions to supply chains (for inputs and outputs), loss of product markets, increased cost of supplies, worker layoffs, salary adjustments, access to lines of credit and delays in transportation.

Note: The questionnaire can be seen in the documentation materials tab.

Introduction

Since the outbreak of the COVID-19 pandemic, there has been a massive pursuit by the research community to find drugs to treat this disease as well as discover vaccines against the disease. A large number of research papers have been published to this end, peer-reviewed as well as those posted in preprint repositories such as bioRxiv (www.bioRxiv.org) and medRxiv (www.medRxiv.org). In addition, a large number of peer-reviewed papers on earlier coronavirus-related diseases such as SARS and MERS are also available.

The COVID-19 Open Research Dataset (CORD-19 corpus) consists of abstracts and full-text articles on COVID-19, SARS-CoV-2, and related coronaviruses. This freely available dataset is provided to the global research community via this Kaggle challenge to apply recent advances in natural language processing (NLP) and other related techniques to generate insights in support of the ongoing fight against this infectious disease.

At a specific level, it means we have to help uncover "*unknown known*" entities such as drugs and vaccines that are maybe unknown to the larger set of researchers but mentioned in specific scientific article(s) part of the CORD-19 dataset. Our goal is to help the medical research community uncover these "unknown known" entities through a combination of text-mining and network analyses.

Approach

Association Network Creation

We had earlier built a framework for NLP called TPX, a web-based text-mining tool that supports real-time entity assisted search and navigation of the MEDLINE repository whilst continuing to use PubMed as the underlying search engine (1). TPX is a modular and versatile biomedical text-mining framework. For instance, we recently built PRIORI-T (2), a pipeline for phenotype-driven rare disease gene prioritization, by re-purposing specific modules of TPX. The modules include: 1. Dictionary Curation module 2. Annotator for entity annotations 3. MEDLINE Processor 4. Network Creation module, to build a network of the correlations extracted by the Correlation Extraction module

We re-purposed TPX for the COVID-19 Open Research Dataset Challenge (CORD-19) as follows: 1. We took the provided CORD19 dataset corpus (Corpus Date: 2020-03-27 consisting of 45,774 rows) and filter these for unique articles (titles and abstracts only). We used the Corpus Processor module of TPX to process this corpus. 2. The Annotator module of TPX performed the annotation based on the following dictionaries: HUMAN_GENE, GENE_SARS, GENE_MERS, GENE_COVID, PHENOTYPE, CHEMICALS, DRUGS, DISEASE, SYMPTOM, GOPROC, GOFUNC, GOLOC, CELLTYPE, TISSUE, ANATOMY, ORGANISM, COUNTRIES, ETHICS TERMS (general terms related to human ethics), NON-PHARMA INTERVENTION (terms related to non-pharmaceutical intervention), SURVEILLANCE TERMS (general terms related to disease surveillance), VACCINE TERMS, VIROLOGY TERMS (general terms used in virology studies) and EARTH SCIENCE TERMS. 3. We then used the Correlation Extraction module to extract out correlations amongst these entity types and 4. These correlations extracted by the Correlation Extraction module are then used by the Network Creation module to build a network called TCS_COVID_NETWORK (cord19_pc_assocs_v1.tsv). This network can is queried to obtain information and pointers from the corpus.

Thus, the TCS_COVID_NETWORK serves as a knowledge base that could help the COVID-19 research community obtain pointers in this regard through a combination of text-mining and network analyses. It is available from Kaggle for use by anyone to possibly try and solve some of text mining related questions that are posed in this Kaggle challenge

PyVis Visualization

We then Integration with PyVis for user-friendly and intuitive graphical exploration of the network. The Jupyter notebook provides details on this. For instructive purpose, we have included a set of use cases for exploring the network using PyVis and NetworkX library. These cases are by no means exhaustive. However, the PyVis and NetworkX functionalities can be easily used to provide richer exploration features.

Discussion

Our network captures associations between different entities in the provided Kaggle corpus. The network nodes correspond to the biological entities and the edges correspond to the extracted associations. The edges are weighted where the edge weight denote the strength of association (correlation strength in this network) between the entity pair. The entities span a comprehensive set of entity types, namely HUMAN_GENE, GENE_SARS, GENE_MERS, GENE_COVID, PHENOTYPE, CHEMICALS, DRUGS, DISEASE, SYMPTOM, GOPROC, GOFUNC, GOLOC, CELLTYPE, TISSUE, ANATOMY, ORGANISM, COUNTRIES, ETHICS TERMS, NON-PHARMA INTERVENTION, SURVEILLANCE TERMS, VACCINE TERMS, VIROLOGY TERMS and EARTH SCIENCE TERMS. The current network is built from only the title...

In 2020, global gross domestic product declined by 6.7 percent as a result of the coronavirus (COVID-19) pandemic outbreak. In Latin America, overall GDP loss amounted to 8.5 percent.

COVID-19 Reopening Data from Associated Press and Kantar Media

Access regularly updated data from The Associated Press and Kantar Media containing information on events at the global, national and state levels as economies reopen following the coronavirus pandemic via AP Planner.

AP Planner is a paid service from The Associated Press & Kantar Media.

The four data files below feature the following event types:

• Environmental, Social and Governance (ESG): Events related the factors that measure the sustainability and societal impact of an investment in a company or business.
• Healthcare, Pharmaceuticals and Bio Tech: Health care providers and services, health care equipment and supplies, and health care technology companies. Drug and vaccine production, as well as biological substances for the purposes of drug discovery and diagnostic development.
• Politics: U.S. political news and events.
• Diversity and Discrimination: News and events related to race, religion, gender, sexuality, disability and age.

All data is compiled by a dedicated staff with over 15 years of forward planning research experience, employing data verification and processes designed to provide reliable and up-to-date information.

The data can be used to help:

• Provide signals to investors on how to act and at what speed based on the types of events returning across industries.
• Analyze risk associated with companies based on when they're reopening.
• Retain your own customer base based on reopening dates for vendors and competitors.
• Track COVID regulations to prepare inventory and guest policies.

The following data files are samples - if you are interested in licensing the full, regularly updated database, please contact Opal Barclay (obarclay@ap.org) at The Associated Press or Click on Request Access Button above.

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FAQs

Why does AP and Kantar compile this data?_ The data is sourced from AP Planner, a product offered by The Associated Press and Kantar Media. AP Planner is a searchable database of future events that is updated daily and intended for research, not publication.

What information does AP Planner contain?_ AP Planner is global in scope and contains more than 100,000 U.S. and international events from the world of news, current affairs, politics, business, lifestyle and more - all searchable up to 12 months ahead.

Where does the information come from?_ AP Planner aggregates listings from tens of thousands of organizations worldwide. Our research staff monitors over 350,000 websites and uses a verity of secondary sources including press releases, corporate announcements and other outlets to ensure accuracy.

How can I be confident of the data's quality and accuracy?_ We have a dedicated research staff with over 15 years of forward planning research experience. They employ data verification and updating processes designed to provide our customers with completely reliable and up-to-date information.

Can I export data into other applications?_ Yes, AP Planner data can be exported as an Excel file or an Outlook calendar file. The data is also accessible via API.

Who can I contact to learn more about AP Planner?_ Opal Barclay, obarclay@ap.org.

Context

Due to covid measures, the dutch government has arranged the NOW-policy which created an opportunity for companies to be funded by the government (to be able to pay employees). This dataset contains company names, locations and the amount applied for. This dataset is still in progress as it will include further company info in the future.

Content

This dataset contains company info, location info and the amount of money they have applied for.

Acknowledgements

This dataset is due to the work of UWV: https://www.uwv.nl/overuwv/pers/documenten/2020/gegevens-ontvangers-now-1-0-regeling.aspx .

This 6MB download is a zip file containing 5 pdf documents and 2 xlsx spreadsheets. Presentation on COVID-19 and the potential impacts on employment

May 2020Waka Kotahi wants to better understand the potential implications of the COVID-19 downturn on the land transport system, particularly the potential impacts on regional economies and communities.

To do this, in May 2020 Waka Kotahi commissioned Martin Jenkins and Infometrics to consider the potential impacts of COVID-19 on New Zealand’s economy and demographics, as these are two key drivers of transport demand. In addition to providing a scan of national and international COVID-19 trends, the research involved modelling the economic impacts of three of the Treasury’s COVID-19 scenarios, to a regional scale, to help us understand where the impacts might be greatest.

Waka Kotahi studied this modelling by comparing the percentage difference in employment forecasts from the Treasury’s three COVID-19 scenarios compared to the business as usual scenario.

The source tables from the modelling (Tables 1-40), and the percentage difference in employment forecasts (Tables 41-43), are available as spreadsheets.

Arataki - potential impacts of COVID-19 Final Report

Employment modelling - interactive dashboard

The modelling produced employment forecasts for each region and district over three time periods – 2021, 2025 and 2031. In May 2020, the forecasts for 2021 carried greater certainty as they reflected the impacts of current events, such as border restrictions, reduction in international visitors and students etc. The 2025 and 2031 forecasts were less certain because of the potential for significant shifts in the socio-economic situation over the intervening years. While these later forecasts were useful in helping to understand the relative scale and duration of potential COVID-19 related impacts around the country, they needed to be treated with care recognising the higher levels of uncertainty.

The May 2020 research suggested that the ‘slow recovery scenario’ (Treasury’s scenario 5) was the most likely due to continuing high levels of uncertainty regarding global efforts to manage the pandemic (and the duration and scale of the resulting economic downturn).

The updates to Arataki V2 were framed around the ‘Slower Recovery Scenario’, as that scenario remained the most closely aligned with the unfolding impacts of COVID-19 in New Zealand and globally at that time.

Find out more about Arataki, our 10-year plan for the land transport system

May 2021The May 2021 update to employment modelling used to inform Arataki Version 2 is now available. Employment modelling dashboard - updated 2021Arataki used the May 2020 information to compare how various regions and industries might be impacted by COVID-19. Almost a year later, it is clear that New Zealand fared better than forecast in May 2020.Waka Kotahi therefore commissioned an update to the projections through a high-level review of:the original projections for 2020/21 against performancethe implications of the most recent global (eg International monetary fund world economic Outlook) and national economic forecasts (eg Treasury half year economic and fiscal update)The treasury updated its scenarios in its December half year fiscal and economic update (HYEFU) and these new scenarios have been used for the revised projections.Considerable uncertainty remains about the potential scale and duration of the COVID-19 downturn, for example with regards to the duration of border restrictions, update of immunisation programmes. The updated analysis provides us with additional information regarding which sectors and parts of the country are likely to be most impacted. We continue to monitor the situation and keep up to date with other cross-Government scenario development and COVID-19 related work. The updated modelling has produced employment forecasts for each region and district over three time periods - 2022, 2025, 2031.The 2022 forecasts carry greater certainty as they reflect the impacts of current events. The 2025 and 2031 forecasts are less certain because of the potential for significant shifts over that time.

Data reuse caveats: as per license.

Additionally, please read / use this data in conjunction with the Infometrics and Martin Jenkins reports, to understand the uncertainties and assumptions involved in modelling the potential impacts of COVID-19.

COVID-19’s effect on industry and regional economic outcomes for NZ Transport Agency [PDF 620 KB]

Data quality statement: while the modelling undertaken is high quality, it represents two point-in-time analyses undertaken during a period of considerable uncertainty. This uncertainty comes from several factors relating to the COVID-19 pandemic, including:

a lack of clarity about the size of the global downturn and how quickly the international economy might recover differing views about the ability of the New Zealand economy to bounce back from the significant job losses that are occurring and how much of a structural change in the economy is required the possibility of a further wave of COVID-19 cases within New Zealand that might require a return to Alert Levels 3 or 4.

While high levels of uncertainty remain around the scale of impacts from the pandemic, particularly in coming years, the modelling is useful in indicating the direction of travel and the relative scale of impacts in different parts of the country.

Data quality caveats: as noted above, there is considerable uncertainty about the potential scale and duration of the COVID-19 downturn. Please treat the specific results of the modelling carefully, particularly in the forecasts to later years (2025, 2031), given the potential for significant shifts in New Zealand's socio-economic situation before then.

As such, please use the modelling results as a guide to the potential scale of the impacts of the downturn in different locations, rather than as a precise assessment of impacts over the coming decade.