According to a survey fielded in Mexico in ***********, ** percent of respondents stated having to close their businesses as a result of the COVID-19 pandemic. This represents a noticeable decrease compared to April, when ** percent of participants said they had to shut down their businesses.

During an online survey, *** percent of surveyed small businesses in the United States said they had temporarily closed a location due to the COVID-19 pandemic during the week ending April 17, 2022. Another *** percent of respondents said that they had opened a previously closed location during the same week.

This dataset is no longer being updated as of 7/1/2021. It is being retained on the Open Data Portal for its potential historical interest.

A list of retail stores, restaurants, personal services and other businesses open and closed during the COVID-19 pandemic. Also indicates if business is offering delivery, pick up or on-line sales.

Updated at least biweekly during Covid-19 Pandemic.

Almost one quarter of all businesses have temporarily closed or paused trading due to the Coronavirus (COVID-19) pandemic in the United Kingdom as of April 2020. The sector with the highest share of business closures were those in the arts, entertainment, and recreation sector, with over ** percent of them currently closed, compared with just *** percent of human health, and social work businesses.

According to a survey conducted in February 2022, around *** percent of small to medium-sized companies in Japan foresaw a likelihood of discontinuation of their business activities due to the impact of the coronavirus (COVID-19) outbreak. By comparison, about *** percent of large business enterprises reported the potential closing down.

The number of small and medium-sized enterprises in the United States was forecast to continuously decrease between 2024 and 2029 by in total 6.7 thousand enterprises (-2.24 percent). After the fourteenth consecutive decreasing year, the number is estimated to reach 291.94 thousand enterprises and therefore a new minimum in 2029. According to the OECD an enterprise is defined as the smallest combination of legal units, which is an organisational unit producing services or goods, that benefits from a degree of autonomy with regards to the allocation of resources and decision making. Shown here are small and medium-sized enterprises, which are defined as companies with 1-249 employees.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).

A list of businesses deemed essential and non-essential during the coronavirus (COVID-19) outbreak in Delaware. Non-essential businesses were closed on March 24, 2020 at 8am by order from the Governor. Certain business categories are allowed to re-open by Governor's announcements. Data from Division of Small Business.

Changes businesses have made to adapt to the COVID-19 pandemic, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership.

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.

27% of the entire small business workforce had to be laid off or furloughed in 2020 due to the COVID-19 pandemic.

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.

Companies in the arts, entertainment and recreation sector have had to shut down their activity the most, since the beginning of the coronavirus crisis (COVID-19). Companies in this sector have been closed for an average of almost 100 days. The hotel industry was the second sector most affected by closures. The pharmaceutical industry was the least affected by closures. On average, French companies were closed for 57 days in France.

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.

The dataset provided contains records related to the impact of various economic and operational factors on businesses in three major cities in the UAE: Dubai, Abu Dhabi, and Sharjah. Each record represents a business's condition during a specific period, capturing attributes related to profitability, operational status, government support, and recovery. Below is an analysis of the dataset: Attributes in the Dataset: 1. Geographic Location: Represents the city where the business operates: Dubai, Abu Dhabi, or Sharjah. This attribute allows for a regional analysis of how economic and operational disruptions vary across different urban areas. 2. Profitability Change: Indicates whether the business experienced a change in profitability: Increase, Decrease, or No Change. Provides insight into the economic performance of businesses under varying conditions. 3. Operational Disruptions: Describes the severity of operational challenges faced by businesses: None, Mild, Moderate, or Severe. Reflects the operational resilience or vulnerability of businesses. 4. Business Closure: Specifies whether the business remained operational or had to shut down: Open or Closed. This critical indicator highlights the impact of disruptions on business continuity. 5. Government Support: Indicates whether the business received any form of support: None, Loan, or Grant. Offers insights into the role of government interventions in aiding businesses during difficult periods. 6. Sector Type: Identifies the industry to which the business belongs, such as Retail, Hospitality, Tourism, Technology, or Manufacturing. Useful for understanding sector-specific challenges and opportunities. 7. Size of Business: Categorizes businesses as Small or Medium. This attribute helps analyze how business size impacts operational resilience and revenue loss. 8. Revenue Loss (%): Quantifies the percentage of revenue lost by the business due to disruptions. Provides a measure of economic impact and vulnerability. 9. Recovery Time (Months): Indicates the estimated number of months required for the business to recover. Reflects the time needed for businesses to return to pre-crisis levels, giving insights into recovery dynamics.

Acknowledgment

Special thanks to the framework provided in the original example from Data.World, which inspired the structured analysis of this dataset. This approach aids in generating actionable insights and a detailed understanding of the underlying trends.

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

NOTE: This 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.

Textual analysis of responses from the Business Impacts of Coronavirus (COVID-19) Survey (BICS), providing further insights into the experiences of individual businesses.

Listing of recipients of several City of Cambridge COVID-19 grant and loan award programs. Data covers MDRF, CDBG-CARES Act, and ARPA funding.

As of June 9, 2025, there are no future updates planned for this dataset.

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.

The American Rescue Plan Act established the Restaurant Revitalization Fund (RRF) to provide funding to help restaurants and other eligible businesses keep their doors open. The American Rescue Plan Act established the Restaurant Revitalization Fund (RRF) to provide funding to help restaurants and other eligible businesses keep their doors open. This program provided restaurants with funding equal to their pandemic-related revenue loss up to $10 million per business and no more than $5 million per physical location. Recipients are not required to repay the funding as long as funds are used for eligible uses no later than March 11, 2023. This dataset details New York State recipients of RRF funds.