In March 2020, Mayor Carter announced the Saint Paul Bridge Fund to provide emergency relief for families and small businesses most vulnerable to the economic impacts of the COVID-19 pandemic. The program was funded through $3.25 million dollars from the Saint Paul Housing and Redevelopment Authority along with contributions from philanthropic, corporate and individual donors. Through these additional contributions, the fund provided $4.1 million to families and small businesses in Saint Paul.Data previously shared in this space included only the 380 recipients funded through "Phase 1". This dataset includes all three phases that were ultimately rolled out through the Bridge Fund for Small Business program.Nearly 2,000 unique applications applied for a small business grant of $7,50036% were from ACP50 areas (Areas of Concentrated Poverty where 50% or more of the residents are people of color)The applications were reviewed in order of a random number assigned at application close. Of these applications:633 small businesses were awarded a $7,500 grant36% of applications in the city were from ACP50 areas86% of applicants in the city cited they were ordered closed under one of the Governor’s Executive OrdersThis is a dataset of the small businesses that applied for the Bridge Fund and includes:Self-reported survey responsesAward informationGeographic information Additional information about the Saint Paul Bridge Fund may be found at stpaul.gov/bridge-fund.

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...

This dataset includes anonymized information about all of CSBDF's closed loans that were utilized in the lending economic impact analysis for FY21 (July 1, 2020 through June 30, 2021). The data contain anonymized information on all lending transactions during the period, including the socioeconomic characteristics of the recipient small businesses and their owner(s).

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.

The Paycheck Protection Program (PPP) is a $953-billion business loan program established by the United States federal government, led by the Donald Trump administration in 2020 through the Coronavirus Aid, Relief, and Economic Security Act (CARES Act) to help certain businesses, self-employed workers, sole proprietors, certain non-profit organizations, and tribal businesses continue paying their workers.

The Paycheck Protection Program allows entities to apply for low-interest private loans to pay for their payroll and certain other costs. The amount of a PPP loan is approximately equal to 2.5 times the applicant's average monthly payroll costs. In some cases, an applicant may receive a second draw typically equal to the first. The loan proceeds may be used to cover payroll costs, rent, interest, and utilities. The loan may be partially or fully forgiven if the business keeps its employee counts and employee wages stable. The program is implemented by the U.S. Small Business Administration. The deadline to apply for a PPP loan was March 31, 2021.

Some economists have found that the PPP did not save as many jobs as purported and aided too many businesses that were not at risk of going under. They noted that other programs, such as unemployment insurance, food assistance, and aid to state and local governments, would have been more efficient at strengthening the economy. Opponents to this view note that the PPP functioned well to prevent business closures and cannot be measured on the number of jobs saved alone.

According to a 2022 study, the PPP: cumulatively preserved between 2 and 3 million job-years of employment over 14 months at a cost of $169K to $258K per job-year retained. These numbers imply that only 23 to 34 percent of PPP dollars went directly to workers who would otherwise have lost jobs; the balance flowed to business owners and shareholders, including creditors and suppliers of PPP-receiving firms. Program incidence was ultimately highly regressive, with about three-quarters of PPP funds accruing to the top quintile of households. PPP's breakneck scale-up, its high cost per job saved, and its regressive incidence have a common origin: PPP was essentially untargeted because the United States lacked the administrative infrastructure to do otherwise. Harnessing modern administrative systems, other high-income countries were able to better target pandemic business aid to firms in financial distress. Building similar capacity in the U.S. would enable improved targeting when the next pandemic or other large-scale economic emergency inevitably arises.

Additional Information Field: Value Created: April 5, 2022 Format: CSV License: Other (Public Domain) Size: 428.6 MiB

The Paycheck Protection Program (PPP) is a nearly $1 trillion business loan program started in 2020 under the Trump administration to provide relief to businesses struggling due to the Coronavirus epidemic.

This program that was managed by the Small Business Administration (SBA) offers loans to companies based on current payroll expense. The exact amount a business qualifies for depends on a number of factors including corporate structure, but generally follows the guidelines below:

Average monthly payroll (using a maximum annual salary per employee of $100,000) * 2.5

in 2020, a judge ordered the SBA to release all data on PPP loans, even those loans made for less than $150,000.

This dataset represents only businesses who received loans of more than $150,000, and presents an interesting opportunity for researchers in the data science community. Some potential projects are listed below: - Exploring loan amounts industries and business types - Using this data as features to predict business metrics such as company size, revenue, risk of bankruptcy. - Tracking important demographic statistics related to loan amounts and any potential bias in the program.

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.

Context

The Paycheck Protection Program (PPP) was passed by congress to give relief to businesses negatively impacted by Covid-19 in order to maintain pay for employees. The program gives forgivable loans to businesses that continue paying employees during the pandemic. The PPP quickly ran out of funds, limiting which businesses received relief. Some controversy around the program arose from large, publicly traded companies applying for and receiving PPP funds while many small businesses were unable to access relief.

Content

This dataset released by the treasury department on July 6 shows all loans above $150K given through the Paycheck Protection Program.

Acknowledgements

This dataset was posted by GovTrades, a mission-oriented organization working to increase transparency and accountability in policymaking. Check out GovTrades.org for data on stocks that elected officials buy and sell.

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. This dataset will be refreshed on a quarterly basis.

This dataset consolidates public U.S. Small Business Administration (SBA) Economic Injury Disaster Loan (EIDL) data released during the COVID-19 pandemic.

It contains loan-level records issued between April and November 2020, documenting the scale and timing of emergency relief for small businesses across all U.S. states and territories.

The files were retrieved from the SBA’s open-data portal and standardized for analysis in Python (Pandas) and visualization in Tableau.

Fields include: • Loan amount (face value or obligation) • Approval date • State of recipient • Recipient identifier

Analytical use: This data supports exploration of post-COVID economic recovery patterns, showing how federal loan programs helped stabilize small businesses by region and time period.

Source: U.S. Small Business Administration Open Data

Last updated: November 2020

Prepared by: Christopher White (@cwhiteprofessional)

License: U.S. Government Works — This dataset is derived from public SBA data and is not subject to copyright protection under 17 U.S.C. §105. It may be freely reused and shared.

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.

RRF funding aims to fill gaps in immediate working capital for small businesses and nonprofits (including cultural organizations) until they can resume more normal operations. Recipients may or may not have applied for additional funding through Small Business Administration (SBA) loans and other federal disaster relief funding sources. Using $2 million of the City’s share of Food and Beverage Tax funds that the Bloomington Common Council approved for expenditure April 7, plus $500,000 of additional support approved by the Bloomington Urban Enterprise Association on April 8, the City is providing these immediate loans of up to $50,000 each to sustain area businesses in the short term and foster the regional economy. An advisory committee was appointed by the City of Bloomington to review applications and make recommendations for funding. This committee includes representatives from banking, financial services, and community organizations. Additionally, applicants may seek support on their application by contacting the City of Bloomington at economicvitality@bloomington.in.gov. For more information about this and other Recover Forward efforts, see: https://bloomington.in.gov/recoverforward

This dataset from Dimensions.ai contains all published articles, preprints, clinical trials, grants and research datasets that are related to COVID-19. This growing collection of research information now amounts to hundreds of thousands of items, and it is the only dataset of its kind. You can find an overview of the content in this interactive Data Studio dashboard: https://reports.dimensions.ai/covid-19/ The full metadata includes the researchers and organizations involved in the research, as well as abstracts, open access status, research categories and much more. You may wish to use the Dimensions web application to explore the dataset: https://covid-19.dimensions.ai/. This dataset is for researchers, universities, pharmaceutical & biotech companies, politicians, clinicians, journalists, and anyone else who wishes to explore the impact of the current COVID-19 pandemic. It is updated daily, and free for anyone to access. Please share this information with anyone you think would benefit from it. If you have any suggestions as to how we can improve our search terms to maximise the volume of research related to COVID-19, please contact us at support@dimensions.ai. About Dimensions: Dimensions is the largest database of research insight in the world. It contains a comprehensive collection of linked data related to the global research and innovation ecosystem, all in a single platform. This includes hundreds of millions of publications, preprints, grants, patents, clinical trials, datasets, researchers and organizations. Because Dimensions maps the entire research lifecycle, you can follow academic and industry research from early stage funding, through to output and on to social and economic impact. This Covid-19 dataset is a subset of the full database. The full Dimensions database is also available on BigQuery, via subscription. Please visit www.dimensions.ai/bigquery to gain access.Más información

Content

Stock data of the following companies from Oct 2019 is included in this dataset. (BioNTech , Moderna , Johnson & Johnson , Inovio Pharmaceuticals, Sinovac , Sinopharm , Novavax ,Astrazeneca(Oxford)) (The date 2019 was chosen because few companies got IPO just in 2019)

Inspiration

To do more analysis on the performance of the companies with the influence of covid vaccine.

End note

Please let me know if any more companies are to be included or any changes have to be made to improve the quality of the dataset in the discussion section.

Context

CERF (Central Emergency Response Fund) and CBPF ( Country Based Pooled Funds) pooled funds have allocated a combined total of US$340 million to respond to the COVID-19 pandemic. To date, the funds have supported a broad range of humanitarian partners to launch time-critical projects in over 50 countries. Additional opportunities to support partners responding to the Global COVID-19 HRP continue to be identified.

https://data.humdata.org/dataset/cerf-covid-19-allocations

Content

CERF (Central Emergency Response Fund) and CBPF (Country Based Pooled Funds) pooled funds have allocated a combined total of US$222 million to COVID-19 pandemic responses.

Acknowledgements

https://data.humdata.org/dataset/cerf-covid-19-allocations Nafissatou Pouye for changing the extra "dataset_date" of the dataset CERF and CBPF COVID-19 Allocations.

Photo by Nathan Dumlao on Unsplash In fact, I tried to upload a picture "Small business fighting for Survival" (by Gene Gallin) but it had an issue, and didn't upload.

Inspiration

Covid-19 Pandemic.

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

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)

Summary of this dataset

This dataset has been designed and obtained for discussing control measures during the COVID-19 pandemic. In this study, 1,260 people living in Tokyo and Kanagawa prefectures in Japan participated in the survey. This survey was used to collect participants’ behaviors and the objects that they touched on the days that they went out at 15 types of locations and vehicles.

This dataset is expected to improve our understanding of actual human behavior and contact with objects that could. Although it is impossible to disinfect all objects and spaces, this dataset is expected to contribute to the prioritization of disinfection during periods of widespread infection.

Datasets

1. Study and Dataset Design

The participants living in Tokyo and Kanagawa prefectures in Japan were asked to respond, in detail, to a survey regarding the locations they stayed at for an extended period between December 3 (Thursday) and December 7 (Monday), 2020, and all the items that they touched during this time. Using the locations where clusters of infections were found during April 2020, 12 locations were selected (e.g., medical facilities, including hospitals; restaurants; stores whose main objective was to sell alcohol, such as bars; companies, including the participants’ own companies and the offices of others; and sports facilities such as gyms) and investigated. Similarly, three means of transport, namely trains, buses, and taxis, were selected as spaces where people often crowd together.

The main survey was conducted with 1,536 subjects during December 3–8. Data from 1,260 subjects who gave valid responses were used for the dataset. To ensure that the respondents could respond while their memories were still fresh, the survey was distributed to each subject on the day of their corresponding behavior. Participants were asked to respond about the locations where they spent most of their time during the corresponding period. They were also asked to detail all the objects they touched (excluding personal objects) during this time. The objects in this study were evaluated using a free-writing description. Typographical errors and differences in expressions were frequently observed (e.g., water closet, toilet, and bathroom). A categorization rule was thus developed to better ascertain the actual status of locations and object contact. The participants’ expressions were modified through visual inspection.

2. Patient and Public Involvement

This survey was conducted after appropriate review by the Ethics Committee of the Graduate School of Engineering, University of Tokyo (examination number: 20-61, approval number: KE20-72).

Reference

Teruaki Hayashi, Daisuke Hase, Hikaru Suenaga, Yukio Ohsawa, "The Actual Conditions of Person-to-Object Contact and a Proposal for Prevention Measures During the COVID-19 Pandemic," medRxiv, 2021. DOI: https://doi.org/10.1101/2021.04.11.21255290

Acknowledgements

This research project was supported by the “Startup Research Program for Post-Corona Society” of the Academic Strategy Office, School of Engineering, the University of Tokyo, and the “COVID-19 AI and Simulation Project” run by Mitsubishi Research Institute commissioned by the Office for Novel Coronavirus Disease Control, Cabinet Secretariat, Government of Japan. The authors would like to thank PLUG-Inc. for survey design and implementation.

The datasets come from two surveys of Jamaican businesses conducted between May and June 2020. Two sets of self-administered surveys were conducted using Survey Monkey. A very small sample of financial institutions was surveyed to gain perspective on the challenges facing financiers as a result of the pandemic, and their efforts to respond to such challenges. Nine financial institutions completed this survey, and the results were used to complement the information derived from the second and major survey. The second survey targeted non-financial businesses operating in Jamaica. The sample of firms was selected from a list of all registered Jamaican firms, obtained from the Companies Office of Jamaica. A stratified random sample was used based on firm type, region, and sector. Some firms may have also participated in the study through contact made by their respective affiliations, which were approached to endorse the study and encourage their members to engage. A total of 390 firms completed the second survey. A significant degree of representation was achieved across size, type and age of business, sector and location of operation. Good gender representation was also achieved.

Frequently Asked Questions for Business in the COVID-19 Context

This dataset contains the articles published on the Covid-19 FAQ for companies published by the Directorate-General for Enterprises at https://info-entreprises-covid19.economie.fr

The data are presented in the JSON format as follows: JSON [ { “title”: “Example article for documentation”, “content”: [ this is the first page of the article. here the second, “‘div’these articles incorporate some HTML formatting‘/div’” ], “path”: [ “File to visit in the FAQ”, “to join the article”] }, ... ] “'” The update is done every day at 6:00 UTC. This data is extracted directly from the site, the source code of the script used to extract the data is available here: https://github.com/chrnin/docCovidDGE