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.

This dataset is no longer being updated as of 7/1/2021.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

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

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.

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.

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.

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.

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.

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.

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.

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.

Abstract

As part of the efforts of the World Bank Group to understand the impact of COVID-19 on the private sector, the Enterprise Analysis unit is conducting follow-up surveys on recently completed Enterprise Surveys (ES) in several countries. These short surveys follow the baseline ES and are designed to provide quick information on the impact and adjustments that COVID-19 has brought about in the private sector. The Zimbabwe Informal Businesses COVID-19 Impact Survey is different from the standard follow-up survey conducted by the unit in other countries, the major difference veing that this is not a follow-up survey.

Geographic coverage

National

Analysis unit

Enterprise

Universe

The universe of inference is all registered establishments with five or more employees that are engaged in one of the following activities defined using ISIC Rev. 3.1: manufacturing (groupd D), construction (group F), services sector (groups G and H), transport, storage, and communcations sector (group I) and information technology (division 72 of group K)

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for the survey was selected using stratified random sampling, broadly following similar methodology explained in the ES Sampling Note. However, unlike ES that uses three levels of stratification (size, location, and sector), this survey uses two levels of stratification, namely location/region of the informal busines and the gender of the main business owner.

Stratifies random sampling was preferred over simple random sampling for several reasons: a. To obtain unbiased estimates for different subdivisions of the population with some known level of precision b. To obtain unbiased estimates for the whole population. The whole population, or universe of the study, is informal sector businesses operating in Zimbabwe. Informality is defined as any business that doesn't have registration from Zimbabwe Registrar of Companies. c. To make sure that the final total sample includes establishments from different regions and from businesses owned by male and femal. d. To exploit the benefits of stratifies sampling where population estimates, in most cases, will be more precise than using a simple random sampling method (i.e. lower standard errors, other things being equal.) e. Stratification may produce a smaller bound on the error of estimation than would be produced by a simple random sample of the same size. This result is particularly true if measurements within strata are homogeneous. f. The cost per observation in the survey may be reduced by stratification of the population elements into convenient groupings.

Total sample target: 1020

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The questionnaire contains the following modules: - Control information and introduction - General information - Sales and operations - Production - Labor force - Finance - Policies and prospects - Registration - Information on permanently closed establishments - Interview protocol

Response rate

37.8%

The Paycheck Protection Program (PPP) established by the CARES Act, is implemented by the Small Business Administration (SBA) with support from the Department of the Treasury. The program provided small businesses with funds to pay up to 8 weeks of payroll costs including benefits. Funds could also be used to pay interest on mortgages, rent, and utilities This dataset details New York State recipients of PPP funds.

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

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).