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).

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.

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.

The congressional district profiles are part of Advocacy’s state profile series, which provides user-friendly snapshots of national, state, and congressional district small business statistics. The district profiles focus on the impact of small employers. Each profile provides the district’s total number of small employers and their industry breakout, plus the number of people employed and payroll expended. The profiles also show the total number of self-employed workers and a map showing how they are distributed in the district.

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 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 conducted the 2021 Covid-19 Business Needs Survey 12 months after the first survey in 2020. The responses document how organisations, industry sectors and members were impacted by the pandemic immediately before the 2021 four-month lockdown.

This dataset includes anonymized information about all of CSBDF's closed loans that were utilized in the lending economic impact analysis for FY20 (July 1, 2019 through June 30, 2020). 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).

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.

Numbers of enterprises and local units produced from a snapshot of the Inter-Departmental Business Register (IDBR) taken on 8 March 2024.

This table presents experimental counts of businesses that open, close, or continue their operations each month for various levels of geographic and industry detail across Canada going back to January 2015. The data are available as series that are adjusted for seasonality. The level of geographic detail includes national, provincial and territorial, as well as census metropolitan areas (CMA). The data are also broken down by two-digit North American Industry Classification System (NAICS) with some common aggregations, including one for the total business sector for national, provincial and territorial levels of geography.

NFIB Business Optimism Index in the United States decreased to 98.60 points in June from 98.80 points in May of 2025. This dataset provides - United States Nfib Business Optimism Index - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Cyber attacks are a growing concern for small businesses during COVID-19 . Be Protected While You Work. Upgrade Your Small Business's Virus Protection Today! Before going for a Cyber security solutions for small to mid-sized businesses deliver enterprise-level protection.

Download this (Checklist for a Small Firm's Cybersecurity Program 2020-2021) data set to deploy secure functioning of various aspects of your small business including, employee data, website and more.This checklist is provided to
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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.More than 5,200 applications applied for a family grant of $1,000• 64% 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:• 1,265 families were awarded a $1000 grant- 63% were from ACP50 areas- 66% indicated they are renters- 37% cited layoff or furlough as contributing to their economic hardship- 22% cited reduced hours as contributing to their economic hardship- 19% are unbankedThis is a de-identified dataset of the families who applied for the Bridge Fund and includes:• Self-reported survey responses• Award information• Geographic informationAdditional information about the Saint Paul Bridge Fund may be found at stpaul.gov/bridge-fund

The tech industry had a rough start to 2024. Technology companies worldwide saw a significant reduction in their workforce in the first quarter of 2024, with over 57 thousand employees being laid off. By the second quarter, layoffs impacted more than 43 thousand tech employees. In the final quarter of the year around 12 thousand employees were laid off. Layoffs impacting all global tech giants Layoffs in the global market escalated dramatically in the first quarter of 2023, when the sector saw a staggering record high of 167.6 thousand employees losing their jobs. Major tech giants such as Google, Microsoft, Meta, and IBM all contributed to this figure during this quarter. Amazon, in particular, conducted the most rounds of layoffs with the highest number of employees laid off among global tech giants. Industries most affected include the consumer, hardware, food, and healthcare sectors. Notable companies that have laid off a significant number of staff include Flink, Booking.com, Uber, PayPal, LinkedIn, and Peloton, among others. Overhiring led the trend, but will AI keep it going? Layoffs in the technology sector started following an overhiring spree during the COVID-19 pandemic. Initially, companies expanded their workforce to meet increased demand for digital services during lockdowns. However, as lockdowns ended, economic uncertainties persisted and companies reevaluated their strategies, layoffs became inevitable, resulting in a record number of 263 thousand laid off employees in the global tech sector by trhe end of 2022. Moreover, it is still unclear how advancements in artificial intelligence (AI) will impact layoff trends in the tech sector. AI-driven automation can replace manual tasks leading to workforce redundancies. Whether through chatbots handling customer inquiries or predictive algorithms optimizing supply chains, the pursuit of efficiency and cost savings may result in more tech industry layoffs in the future.

The Paycheck Protection Program (PPP) loans provide small businesses with the resources they need to maintain their payroll, hire back employees who may have been laid off, and cover applicable overhead. This data set includes businesses in Connecticut that received PPP funding, how much funding the employer received & how many jobs the employer claims they saved. The NAICS (National Industry Classification) was provided by the loan recipient. This dataset includes loans under $150,000 and loans of $150,000 and above made to Connecticut businesses through August 8, 2020. Please see attached document for more details.

Dataset Summary About this data: MWBE is a federal program administered through each state. Each state individually establishes its own certification program and requirements. In 2018, the City of Rochester set new goals for the use of minority and women owned businesses (MWBEs) on City contracts. The City of Rochester is committed to providing opportunities for MWBE businesses to participate in and become an integral part of the City's procurement process. This table has information on agreements between primary contractors and consultants (primes) and the City of Rochester, as well as the subcontractors used by those primes. This report pulls information on contracts with payments only. Recently entered agreements may be excluded if there have not been any payments to contractors yet. Data Dictionary:ContractNumber: Unique number assigned to the prime contract in the City of Rochester's financial system. ContractTitle: Title of the agreement between the prime contractor and the City of Rochester. ContractValue: Total value of the agreement between the prime contractor and the City of Rochester. DiversityGoal: This is the percentage of the total value of the agreement that the prime contractor intends to award to minority-owned, women-owned or disadvantaged business entities. Whether or not an MWBE or DBE sub-contractor will count towards this calculation is determined by the prime contractors’ selection when entering sub-contractors into the B2GNow system. AssignedDepartment: The City of Rochester department or bureau responsible for managing the project. ContractType: Agreements are grouped into types depending on what the City is purchasing through the contract. Terms are agreements between the City of Rochester and a contractor to provide a product or service for a set amount of time, or term. Construction is for a set project to build, renovate or update City buildings, properties and infrastructure. Professional Services are agreements for services which require special skills, knowledge, training, expertise, or a high degree of creativity. TierSortOrder: B2GNow generated number assigned to sub-contractors on a project. The numbers are assigned starting at 1 in the order the sub-contractors are entered into the system by the prime contractor. VendorType: This indicates if the business is the prime or sub-contractor. Prime: The business who has made an agreement directly with the City of Rochester to complete a project or provide goods and services. Sub-Contractor: Business hired by the prime contractor or consultant to help complete the agreement with the City of Rochester. BusinessName: Name of the company. GoalType: Indicates if the business is certified as a minority or woman owned business or a certified disadvantaged business entity. Businesses may be certified as both minority and women owned businesses. If businesses have dual certification, their participation is counted to either MBE or WBE goals, based on the selection made by the prime contractor. Blank – This business is not certified. DBE – This business is certified as a disadvantaged business entity (DBE) and their agreement will count toward DBE participation goals. The disadvantaged business enterprise program is administered by the federal Department of Transportation. MBE – This business is New York State certified minority-owned business. WBE – This business is New York State certified woman-owned business. ForCredit: Yes or Blank, indicating whether a certified firm will count toward the project’s participation goals. Ethnicity: Indicates ethnicity or race of MWBE and DBE business owners. Gender: Indicates gender of MWBE and DBE business owners. TotalAward: Total value of agreement between either the prime and City of Rochester or the sub-contractor and the prime. AwardShare: This is an adjustment to show the amount of the contract that will be performed by the business less any sub-contracting agreements. It is calculated differently for Primes and Sub-Contractors. For Primes: SubcontractValue = Total Award – Sum of Sub-Contractor Agreement Values. For Sub-contractors: Award Share = Total Award TotalPayment: The total amount paid to date for the agreement. City: City of the primary business address. State: State of the primary business address. ZIP: ZIP code of the primary business address. Source: This information is pulled from B2GNow, the City of Rochester’s platform for tracking prime contractor and prime consultants’ payments to sub-contractors and their use of MWBEs and DBEs on City contracts. The City began using B2GNow for new contracts in 2019. All agreements with MWBE and DBE goals were entered into B2GNow beginning in 2020. All public works consulting contracting with MWBE goals began being entered in 2021. Data from 2019-2020 may not capture the full use of MWBE and DBE contractors. Last Update: June 30, 2022

Context

The dataset has scraped from Defence News.

Defense News is a global website and magazine about politics, business and technology of defense. Defense News serves an audience of senior military, government and industry decision-makers throughout the world.

Dataset includes defence companies which are in top 100 and There are same feature about the companies.

Content

>defence_companies_from_2005.csv - The dataset which scraped from Defence News thanks to Python Script >defence_companies_from_2005_cleaned.csv - The dataset which cleaned

Includes 11 columns and 1604 rows.

Collection Methodology

If you wonder how to i collected the dataset. Here is my repo: https://github.com/onur-duman/DefenceCompanies-WebScraping-And-Cleaning

Context

There are two commonly understood ways in which a company is considered public: first, the company’s securities trade on public markets; and second, the company discloses certain business and financial information regularly to the public.

With the COVID-19 pandemic affecting many aspects of the business, a large portion of companies have proceeded with executive compensation adjustments as a response. Corporate boards running America’s largest public firms are giving top executives outsize compensation packages that have grown much faster than the stock market and the pay of typical workers, college graduates, and even the top 0.1%.
Excessive CEO pay is a major contributor to rising inequality that we could safely do away with. CEOs are getting more because of their power to set pay and because so much of their pay (more than 80%) is stock-related, not because they are increasing their productivity or possess specific, high-demand skills. This escalation of CEO compensation, and of executive compensation more generally, has fueled the growth of top 1.0% and top 0.1% incomes, leaving less of the fruits of economic growth for ordinary workers and widening the gap between very high earners and the bottom 90%. The economy would suffer no harm if CEOs were paid less (or were taxed more).

Content

CEO_compensation_top50_2020.csv contains data about the top 50 paid CEOs in 2020. Parameters include: - Total Granted Compensation (TGC) - Total Realized Compensation (TRC) - Total Shareholder Return (TSR) in % - TSR 1YR growth in % - TGC 1YR growth in % - TRC 1YR growth in %

CEO_largestrevenue_highestpaid_2020-21.csv contains data about the CEO & Employee pay at the largest companies by revenue in 2020/2021, as well as the New York Times published 200 highest-paid CEOs in 2020. Parameters include: - CEO Total Compensation - Median Employee Pay - Pay change over previous year - Fiscal Year Revenue - Revenue change over previous year - CEO to Employee Pay Ratio

Acknowledgements

Source: CGLytics and Equilar

If you enjoyed this dataset, here's some similar datasets you may like 😎

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching *** zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than *** zettabytes. In 2020, the amount of data created and replicated reached a new high. The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic, as more people worked and learned from home and used home entertainment options more often. Storage capacity also growing Only a small percentage of this newly created data is kept though, as just * percent of the data produced and consumed in 2020 was saved and retained into 2021. In line with the strong growth of the data volume, the installed base of storage capacity is forecast to increase, growing at a compound annual growth rate of **** percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached *** zettabytes.

Analysis of ‘Fortune 1000’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/winston56/fortune-500-data-2021 on 13 November 2021.

--- Dataset description provided by original source is as follows ---

Context

Every year Fortune, an American Business Magazine, publishes the Fortune 500, which ranks the top 500 corporations by revenue. This dataset includes the entire Fortune 1000, as opposed to just the top 500.

Content

The Fortune 1000 dataset is from the Fortune website, collected by the processes outlined in this notebook. It contains U.S. company data for the year 2021. The dataset is 1000 rows and 18 columns.

Features

• Company - values are the name of the company
• Rank - The 2021 rank established by Fortune (1-1000)
• Rank Change - The change in the rank from 2020 to 2021. There is only a rank change listed if the company is currently in the top 500 and was previously in the top 500.
• Revenue - Revenue of each company in millions. This is the criteria used to rank each company.
• Profit - Profit of each company in millions.
• Num. of Employees - The number of employees each company employs.
• Sector - The sector of the market the company operates in.
• City - The city where the company's headquarters is located.
• State - The state where the company's headquarters is located
• Newcomer - Indicates whether or not the company is new to the top Fortune 500 ("yes" or "no"). No value will be listed for companies outside of the top 500.
• CEO Founder - Indicates whether the CEO of the company is also the founder ("yes" or "no").
• CEO Woman - Indicates whether the CEO of the company is a woman ("yes" or "no").
• Profitable - Indicates whether the company is profitable or not ("yes" or "no").
• Prev. Rank - The 2020 rank of the company, as established by Fortune. There will only be previous rank data for the top 500 companies.
• CEO - The name of the CEO of the company
• Website - The url of the company website
• Ticker - The stock ticker symbol of public companies. Some rows will have empty values because the company is a private corporation.
• Market Cap - The market cap (or value) of the company in millions. Some rows will have empty values because the company is private. Market valuations were determined on January 20, 2021.

Inspiration

This dataset is made to explore the top corporations in the U.S. Answer questions such as: What percentage of companies have women ceo's? How many companies are newcomers? What percentage of companies have ceos who were also founders? What role does profitability play in ranking?

--- Original source retains full ownership of the source dataset ---