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.

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.

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).

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

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.

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.

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.

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

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.

This dataset represents a single index value representing the percent change in the number of open small businesses in North Carolina counties. It is calculated as a seven-day moving average, seasonally adjusted and indexed to January 4-31 2020. Temporal and spatial resolution The data obtained from the Opportunity Insights data repository in Github reports daily percentage change in the number of open small businesses compared to January levels. The data is then aggregated as a rolling seven-day average. Twenty-two of North Carolina’s 100 counties are represented in this dataset, none of which are non-CBSA (outside of both metropolitan and micropolitan areas). Additionally, only one county is identified as having high pre-existing unemployment, and two as having lower median income. As a result, the dataset disproportionately represents relatively prosperous metropolitan centers and does not represent other regions of the state.

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

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.

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.

Percentage of businesses with layoffs since the start of the COVID-19 pandemic, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership.

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.

Contagion channels.

🦠 COVID-19 Global Data Analysis Project This dataset supports a complete end-to-end analysis of the COVID-19 pandemic, covering case numbers, deaths, vaccinations, and testing across countries.

The accompanying Jupyter notebook uses Python, Pandas, and Matplotlib to explore key questions, such as: 1. How did COVID-19 spread globally over time? 2. Which countries saw the highest recovery rates? 3. How effective were vaccination rollouts? 4. How does testing frequency correlate with case detection?

📊 The dataset is cleaned and preprocessed, making it suitable for immediate use in data analysis, visualization, or teaching purposes.