The indicators and analysis presented in this bulletin are based on responses from the new 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. These data relate to the period 6 April 2020 to 19 April 2020.

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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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...

NOTE: This program is no longer active. This dataset is only for historical reference. This directory contains businesses that have joined VAX CHI NATION, meaning they committed to ensuring that their staff and patrons are vaccinated. These businesses completed the City of Chicago self-certification.

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.

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.

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

Business Activity Trends During COVID-19 uses the rate that businesses post on Facebook compared to pre-crisis levels to measure how crisis events are affecting different economic sectors each day.

Learn more details here: https://dataforgood.facebook.com/dfg/tools/business-activity-trends and https://dataforgood.facebook.com/dfg/resources/business-activity-trends-methodology-paper

https://data.humdata.org/dataset/facebook-business-activity-trends-during-covid19?force_layout=desktop

The City conducted the 2020 Business Needs Survey following the first lockdown initiated in response to Covid-19. The survey aimed to provide insight into the needs of small business operators to determine the best approach in supporting them to remain economically viable. The City conducted the 2021 Covid-19 Business Needs Survey 12 months after the first survey in 2020. The responses document how organisations, industry sectors and members were impacted by the pandemic immediately before the 2021 four-month lockdown.

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.

Dataset

This dataset was created by Ashish Sharma

Contents

Business Needs Survey 2022 – Impact of the Covid-19 pandemic on the needs of businesses in the City.The City conducted the 2020 Business Needs Survey following the first lockdown initiated in response to Covid-19. The survey aimed to provide insight into the needs of small business operators to determine the best approach in supporting them to remain economically viable.The City has conducted 2021 and 2022 Covid-19 Business Needs Surveys. The responses document how organisations, industry sectors and members were impacted by the pandemic immediately before the 2021 four-month lockdown.See previous surveys

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.

Files used to create the tables in the paper Business Restrictions and COVID-19 Fatalities. Sample datasets are included. Programs are written in either Stata or Python.

This dataset includes small business loans or grants issued for emergency COVID-19 financial assistance. Underlying data is provided by the Department of Small Business Services (SBS). Dollar amounts are in actual dollars.

NOTE: THIS LAYER HAS BEEN DEPRECATED (last updated 5/31/2022). This was formerly a weekly update. Summary The number of cases interviewed who had a completed answer to the question asking if they had physically gone to work in the last 14 days during their covidLINK interviews. Description MD COVID-19 - Contact Tracing Cases Reported Employment layer reflects the number of cases interviewed who had a completed answer to the question asking if they had physically gone to work in the last 14 days during their covidLINK interviews. Respondents may indicate more than one category of employment if they have multiple jobs. For a variety of reasons, some individuals choose not to answer particular questions during the course of their interview. Information about how to prevent and reduce COVID-19 transmission in businesses and workplaces — including for both employers and employees — is available from the Centers for Disease Control and Prevention. Note the following regarding select employment categories: Childcare/Education: Includes teachers, babysitters, school administrators, etc. Commercial Construction and Manufacturing: Includes poultry/meat processors, electricians, carpenters, HVAC workers, welders, contractors, painters Healthcare: Includes home healthcare and administrative positions in a healthcare setting Restaurant/Food Service: Includes cooks, waitstaff, food delivery personnel, alcohol delivery services, etc. Retail, Essential Worker: Includes grocery and pharmacy workers Retail, Other: Includes all retail establishments that do not sell food or medicine Transportation: Includes positions related to transport of people or goods Other, Non-Public-Facing: Includes workers that do not have direct interactions with the public, including warehouse workers, some office workers, some car mechanics, etc. Other, Public-Facing: Includes workers who have direct interactions with the public such as, but not limited to, administrative/front desk workers, home repair workers, lawncare workers, security guards, etc. Unknown: Indicates that the interviewer was unable to ascertain the employment category based on the information provided. Answers to interview questions do not provide strong evidence of cause and effect. Due to the nature of COVID-19 and the wide range of scenarios in which a person can become infected, most of the time it will not be possible to pinpoint exactly how and when a case became infected. Though a person may report employment at a particular location, that does not necessarily imply that transmission happened at that location. The covidLINK interview questionnaire is updated as necessary to capture relevant information related to case exposure and potential onward transmission. These revisions should be taken into consideration when evaluating trends in case responses over time. Terms of Use The Spatial Data, and the information therein, (collectively the "Data") is provided "as is" without warranty of any kind, either expressed, implied, or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted, nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct, indirect, incidental, consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data, nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

The Shuttered Venue Operators Grant (SVOG) program was established by the Economic Aid to Hard-Hit Small Businesses, Nonprofits, and Venues Act, and amended by the American Rescue Plan Act.

The City conducted the 2020 Business Needs Survey following the first lockdown initiated in response to Covid-19. The survey aimed to provide insight into the needs of small business operators to determine the best approach in supporting them to remain economically viable. The City conducted the 2021 Covid-19 Business Needs Survey 12 months after the first survey in 2020. The responses document how organisations, industry sectors and members were impacted by the pandemic immediately before the 2021 four-month lockdown.

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.

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.