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.

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.

This dataset is no longer being updated as of 7/1/2021.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.

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).

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.

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

During a ********** survey, **** percent of surveyed small businesses in the United States claimed that the COVID-19 pandemic had a large negative effect on business. In comparison, only *** percent of respondents said that the pandemic had a large positive effect on their business.

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.

Up to 23 percent of businesses were completely closed in January 2021, of which only 1.4 percent remained shut down since the outbreak of the coronavirus (COVID-19). Up to 13 percent of those also closed for at least one month between June and December of the previous year, while the rest opened in December but closed again in January.

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.

We use high-frequency Google search data, combined with data on the announcement dates of non-pharmaceutical interventions (NPIs) during the COVID-19 pandemic in U.S. states, to isolate the direct impact of state-level NPIs on own-state unemployment in an event study framework. Exploiting the differential timing of the introduction of restaurant and bar limitations, non-essential business closures, stay-at-home orders, large-gatherings bans, school closures, and emergency declarations, we analyze how Google searches for claiming unemployment insurance varied from day to day and across states. We describe a set of assumptions under which proxy outcomes can be used to estimate a causal parameter of interest when data on the outcome of interest are limited. Using this method, we quantify the share of overall growth in unemployment during the COVID-19 pandemic that was directly due to each of these state-level NPIs. We find that between March 14 and 28, restaurant and bar limitations and non-essential business closures can explain 6.0% and 6.4% of UI claims respectively, while the other NPIs did not directly increase own-state UI claims.

Decisions to shutdown economic activities to control the spread of COVID-19 early in the pandemic remain controversial, with negative impacts including high rates of unemployment. Here we present a counterfactual scenario for the state of California in which the economy remained open and active during the pandemic’s first year. The exercise provides a baseline against which to compare actual levels of job losses. We developed an economic-epidemiological mathematical model to simulate outbreaks of COVID-19 in ten large Californian socio-economic areas. Results show that job losses are an unavoidable consequence of the pandemic, because even in an open economy, debilitating illness and death among workers drive economic downturns. Although job losses in the counterfactual scenario were predicted to be less than those actually experienced, the cost would have been the additional death or disablement of tens of thousands of workers. Furthermore, whereas an open economy would have favoured populous, services-oriented coastal areas in terms of employment, the opposite would have been true of smaller inland areas and those with relatively larger agricultural sectors. Thus, in addition to the greater cost in lives, the benefits of maintaining economic activity would have been unequally distributed, exacerbating other realized social inequities of the disease’s impact.

Extract from raw data from “It remains open”: list of places open or closed during the confinement period.

To edit the data, you can contribute directly to OpenStreetMap or It remains open, all contribution info is here.

The file is in CSV format (separated by commas), with UTF-8 encoding. It is updated every hour.

The data structure is as follows: * osm_id: OpenStreetMap identifier of the place * name: name of the place * cat: category (office tags, shop, craft, amenity from OpenStreetMap) * brand: name of the sign/network * Wikidata: Wikidata ID associated with the sign * url_hours: URL link to which the business schedules of the associated sign are entered * info: free text to give more details on access conditions * status: state of opening or closure. Values: open = as usual, open_adapted = hours likely to have changed, partial = potentially closed place, closed = closed place. * opening_hours: opening hours during containment (see OSM wiki) * lon: longitude (WGS84, decimal degrees) * Lat: latitude (WGS84, decimal degrees)

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.

Since 2020, the outbreak of the COVID-19 crisis has caused a great deal of social and economic damages to micro and small-scale enterprises (MSEs). This research examined the most common damages of this crisis in active and inactive rural MSEs and also assessed different kind of responses the managers and owners of theses MSEs have received dealing with these damages. The sample population of this study consisted of all managers of 72 active and 38 closed rural MSEs in the Dastjerd village, Hamedan, Iran. These MSEs were mainly garment small factories. This research utilized a mixed approach (quantitative-qualitative) to study the research objectives in depth. First, in qualitative part, semi-constructed interviews and field visits were done. Then, using quantitative, results of the qualitative section, previous studies and the existing literature, a researcher-made questionnaire was created. Based on qualitative part information through interviews, damages of rural MSEs during COVID-19 pandemic were categorized into three classes, including damages related to production, and financial and marketing issues. Also, two categories of managers' responses that could be labeled as passive and adaptive behavior were identified. Findings showed that active rural MSEs have taken more adaptive measures and tried to find appropriate ways to reduce or overcome damages. Active MSEs were mainly owned and managed collaboratively by more literate and experienced managers. Also results revealed that rural MSEs' managers reacted to different kinds of damages based on their ability, knowledge, and experience. Based on research results, managers' knowledge and skills can help them find more adaptive solutions to keep the firms stable and overcome damages. It can be concluded that COVID-19 pandemic has a great impact on rural MSEs and they need more financial support and managerial advice to overcome this kind of crisis situation.

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

These small business statistics will tell you everything you need to know about the growth of business and where it’s going in the future.

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 ** days in France.

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)

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.