United Kingdom E Commerce: Business: Over a Website: Manufacturing data was reported at 18.200 % in 2016. This records an increase from the previous number of 18.000 % for 2015. United Kingdom E Commerce: Business: Over a Website: Manufacturing data is updated yearly, averaging 17.000 % from Dec 2008 (Median) to 2016, with 9 observations. The data reached an all-time high of 18.200 % in 2016 and a record low of 9.100 % in 2008. United Kingdom E Commerce: Business: Over a Website: Manufacturing data remains active status in CEIC and is reported by Office for National Statistics. The data is categorized under Global Database’s UK – Table UK.S031: E Commerce: Proportion of Businesses Making E Commerce Sales.

This data provides general information about businesses registered with the local government in Busanjin-gu, Busan Metropolitan City, operating in the manufacturing industry (Category C) according to the Standard Industrial Classification Table of Statistics Korea. This data covers businesses operating in the manufacturing industry (Category C) according to the Standard Industrial Classification Table of Statistics Korea. It includes information such as factory management number, industrial complex name, company name, street address, representative name, competent administrative agency, contact information, number of foreign workers, number of employees, factory registration date, industry name, main product, and website address. The data will be updated annually to reflect the current status of factory registrations in Busanjin-gu. This data can be used as a reference not only by administrative agencies and companies, but also in various fields such as manufacturing-related research, policy development, and regional industry analysis. It provides practical data that helps understand the current status of manufacturing in Busanjin-gu and its industrial base. Furthermore, this data can be utilized to understand the distribution and characteristics of manufacturers within the region. For example, analyzing the number of companies in each industrial complex and the employment volume by industry can help us assess Busanjin-gu's industrial concentration and growth potential. This can aid in establishing business attraction strategies and infrastructure improvement plans, and can serve as practical foundational data for revitalizing the local economy. It can also be used as a reference for designing SME support policies and systems related to foreign workers, making it highly practical and applicable across a wide range of fields.

Westchester County is home to a diverse advanced manufacturing industry that produces everything from package inspection equipment and plastic fluid handling products to circuit boards and aluminum parts. Our 28 higher education institutions and award-winning public schools allow us to deliver highly skilled talent to meet the needs of this growing sector.In addition to top talent, Westchester County offers manufacturers access to a wide-range of real estate opportunities. More digitized, specialized and cleaner manufacturing processes are allowing companies to do business within a smaller footprint and we are able to meet that need, as well as provide opportunities to scale. And, our convenient location, just minutes from New York City, allows for easy access to the Port of New York and New Jersey for shipping, as well as multiple airports and freight rail service.Westchester’s advanced manufacturing industry offers many well-paying jobs throughout the County and makes a tremendous impact on our economy.For more information on the Advanced Manufacturing sector in the County, visit the Economic Development Catalyst website The Catalyst.

Displays a representation of where all the surveyed businesses across York Region are located. This data is collected through the Region’s annual comprehensive employment survey and each record contains employment and business contact information about each business with the exception of home and farm-based businesses. Home-based businesses are not included as they are distributed throughout residential communities within the Region and are difficult to survey. Employment data for farm-based businesses are collected through the Census of Agriculture conducted by Statistics Canada, and are not included in the York Region Employment Survey dataset.Update Frequency: Not PlannedDate Created: 17/03/2023Date Modified: 17/03/2023Metadata Date: 17/03/2023Citation Contacts: York Region, Long Range Planning Attribute Definitions BUSINESSID: Unique key to identify a business.NAME: The common business name used in everyday transactions. FULL_ADDRESS: Full street address of the physical address. (This field concatenates the following fields: Street Number, Street Name, Street Type, Street Direction)STREET_NUM: Street number of the physical addressSTREET_NAME: Street name of the physical addressSTREET_TYPE: Street type of the physical addressSTREET_DIR: Street direction of the physical addressUNIT_NUM: Unit number of the physical addressCOMMUNITY: Community name where the business is physically locatedMUNICIPALITY: Municipality where the business is physically locatedPOST_CODE: Postal code corresponding to the physical street addressEMPLOYEE_RANGE: The numerical range of employees working in a given firm. PRIM_NAICS, PRIM_NAICS_DESC: The Primary 5-digit NAIC code defines the main business activity that occurs at that particular physical business location.SEC_NAICS, SEC_NAICS_DESC: If there is more than one business activity occurring at a particular business location (that is substantially different from the primary business activity), then a secondary NAIC is assigned.PRIM_BUS_CLUSTER, SEC_BUS_CLUSTER: A business cluster is defined as a geographic concentration of interconnected businesses and institutions in a common industry that both compete and cooperate. As defined by York Region, this field indicates the primary business cluster that this business belongs to.BUS_ACTIVITY_DESC: This is a comment box with a detailed text description of the business activity. TRAFFIC_ZONE: Specifies the traffic zone in which the business is located. MANUFACTURER: Indicates whether or not the business manufactures at the physical business location. CAN_HEADOFFICE: The business at this location is considered the Canadian head office.HEADOFFICEPROVSTATE: Indicates which state or province the head office is located if the head office is located in Canada (outside of Ontario) or in the United StatesHEADOFFICECOUNTRY: Indicates which country the head office is locatedYR_CURRENTLOC: Indicates the year that the business moved into its current address.MAIL_FULL_ADDRESS: The mailing address is the address through which a business receives postal service. This may or may not be the same as the physical street address.MAIL_STREET_NUM, MAIL_STREET_NAME, MAIL_STREET_TYPE, MAIL_STREET_DIR, MAIL_UNIT_NUM, MAIL_COMMUNITY, MAIL_MUNICIPALITY, MAIL_PROVINCE, MAIL_COUNTRY, MAIL_POST_CODE, MAIL_POBOX: Mailing address fields are similar to street address fields and in most cases will be the same as the Street Address. Some examples where the two addresses might not be the same include, multiple location businesses, home-based businesses, or when a business receives mail through a P.O. Box.WEBSITE: The General/Main business website.GEN_BUS_EMAIL: The general/main business e-mail address for that location.PHONE_NO: The general/main phone number for the business location.PHONE_EXT: The extension (if any) for the general/main business phone number.LAST_SURVEYED: The date the record was last surveyedLAST_UPDATED: The date the record was last updatedUPDATEMETHOD: Displays how the business was last updated, based on a predetermined list.X_COORD, Y_COORD: The x,y coordinates of the surveyed business location Frequently Asked QuestionsHow many businesses are included in the 2022 York Region Business Directory? The 2022 York Region Business Directory contains just over 34,000 business listings. In the past, businesses were annually surveyed, either in person or by telephone to improve the accuracy of the directory. Due to the COVID-19 Pandemic, a survey was not complete in 2020 and 2021. The Region may return to annual surveying in future years, however the next employment survey will be in 2024. This listing also includes home-based businesses that participated in the 2022 employment survey. What is a NAIC code?The North American Industrial Classification (NAIC) coding system is a hierarchical classification system developed in Canada, Mexico and the United States. It was developed to allow for the comparison of business and employment information across a variety of industry categories. The NAICS has a hierarchical structure, designed as follows: Two-digits = sector (e.g., 31-33 contain the Manufacturing sectors) Three-digits = subsector (e.g., 336 = Transportation Equipment Manufacturing) Four-digits = industry group (e.g., 3361 = Motor Vehicle Manufacturing) Five-digits = industry (e.g., 33611 = Automobile and Light Duty Motor Vehicle Manufacturing) For more information on the NAIC coding system click here How do I add or update my business information in the York Region Business Directory? To add or update your business information, please select one of the following methods: • Email: Please email businessdirectory@york.ca to request to be added to the Business Directory.• Online: Go to www.york.ca/employmentsurvey and participate in the employment survey - note, this will only be active in 2024 when the Region performs its next employment surveyThere is no charge for obtaining a basic listing of your business in the York Region Business Directory. How up-to-date is the information?This directory is based on the 2022 York Region Employment Survey, a survey of businesses which attempts to gather information from all businesses across York Region. In instances where we were unable to gather information, the most recent data was used. Farm-based businesses have not been included in the survey and home-based businesses that participated in the 2022 survey are included in the dataset. The date that the business listing was last updated is located in the LastUpdate column in the attached spreadsheet. Are different versions of the York Region Business Directory available?Yes, the directory is available in two online formats:• An interactive, map-based directory searchable by company name, street address, municipality and industry sector.• The entire dataset in downloadable Microsoft Excel format via York Region's Open Data Portal. This version of the York Region Business Directory 2022 is offered free of charge. The Directory allows for the detailed analysis of business and employment trends, as well as the construction of targeted contact lists. To view the map-based directory and dataset, go to:2022 Business Directory - Map Is there any analysis of business and employment trends in York Region?Yes. The "2022 Employment and Industry Report" contains information on employment trends in York Region and is based on results from the employment survey. please visit www.york.ca/york-region/plans-reports-and-strategies/employment-and-industry-report to view the report. What other resources are available for York Region businesses?York Region offers an export advisory service and a number of other business development programs and seminars for interested individuals.For details, consult the York Region Economic Strategy Branch. Who do I contact to obtain more information about the Directory?For more information on the York Region Business Directory, contact the Planning and Economic Development Branch at:businessdirectory@york.ca.

🖨️ Additive manufacturing (3D Printing) data is a crucial driver of smart production and a foundational element of Industry 4.0. ISTARI.AI provides verified, scalable Additive Manufacturing data by analyzing how prominently Additive Manufacturing know-how is communicated on company websites. This enables both quantitative benchmarking and qualitative insight into how central Additive Manufacturing is to a company’s offerings - ensuring consistently high data quality and reliability.

📊 The dataset includes: - additive_manufacturing_intensity: Numerical indicator reflecting the prominence of additive manufacturing adoption - additive_manufacturing_intensity_level: Categorized engagement level (from very low to very high) - additive_manufacturing_keywords: Relevant Additive Manufacturing-related keywords found on the company’s website

📊 The Additive Manufacturing Score in Detail: The Additive Manufacturing Score reflects how central the topic of additive manufacturing is communicated by the company on its own website and presented as essential for its own business model. It specifically captures evidence of: - Products and services in additive manufacturing - Personnel with skills in additive manufacturing - Strategic positioning of additive manufacturing in the company’s communication

Rather than simple binary classification ("AI: yes/no"), ISTARI’s WebAI delivers a continuous, nuanced score that distinguishes between marginal mentions of Additive Manufacturing and core Additive Manufacturing-focused business models.

🔍 How do we measure? The webAI AI Agent, developed by ISTARI.AI, reads and analyzes company websites to: - Identify Additive Manufacturing-related keywords - Detect and validate text segments (“paragraphs”) containing Additive Manufacturing-related content - Classify whether a paragraph reflects genuine Additive Manufacturing know-how or simply general information - Calculate a ratio of Additive Manufacturing-know-how paragraphs to total website content, resulting in a numeric Additive Manufacturing Score

This approach ensures a deep contextual analysis of how central the Additive Manufacturing Score is to each company’s external communication and positioning.

🔍 How can the data be interpreted? - 0.0 = No communication of Additive Manufacturing-related know-how - 0.25 = Limited communication; e.g., a consulting firm mentioning "Additive Manufacturing services" among other topics - 2.5+ = High intensity; e.g., a startup exclusively focused on Additive Manufacturing solutions - 3.5+ = Exceptional additive manufacturing focus; typically, AM-first companies or specialized industrial technology providers. An additional categorical interpretation is provided as a helper column, ranging from "very low" to "very high" intensity.

✅ Ensuring Data Quality - The webAI AI Agent was developed in close collaboration with academic experts to guarantee expert-level accuracy. - Developed together with researchers at the University of Mannheim - Validated in the award-winning academic study: "When is AI Adoption Contagious? Epidemic Effects and Relational Embeddedness in the Inter-Firm Diffusion of Artificial Intelligence" - Co-authored by scholars from University of Mannheim, University of Giessen, University of Hohenheim, and ETH Zurich - Winner of the Best Paper Award at the R&D Management Conference 2022 - Currently under peer review in a leading international journal

United Kingdom E Commerce Sales: Over a Website: Manufacturing data was reported at 14.700 GBP bn in 2016. This records an increase from the previous number of 12.900 GBP bn for 2015. United Kingdom E Commerce Sales: Over a Website: Manufacturing data is updated yearly, averaging 12.800 GBP bn from Dec 2008 (Median) to 2016, with 9 observations. The data reached an all-time high of 14.700 GBP bn in 2016 and a record low of 4.300 GBP bn in 2010. United Kingdom E Commerce Sales: Over a Website: Manufacturing data remains active status in CEIC and is reported by Office for National Statistics. The data is categorized under Global Database’s UK – Table UK.S028: E Commerce: Sales: By Industry.

The Gyeonggi-do Gunpo City Factory Registration Status provides data on companies that have registered factories in Gunpo City. The data consists of company name, company type, industry, products, phone number, fax number, factory address, factory road address, and data reference date. The data is updated monthly and can be found on the Gunpo City Enterprise Portal website (https://www.gunpo.go.kr/biz/index.do). *Website address: https://www.gunpo.go.kr/biz/selectBbsNttList.do?bbsNo=2285&key=2266 *Directions: City Website☞Information by Sector☞Companies/Jobs☞Go to Enterprise Portal☞Company Status☞Monthly Factory Registration Status

This dataset is about stocks. It has 2 rows and is filtered where the company is Simpson Manufacturing. It features 8 columns including stock name, company, exchange, and exchange symbol.

Clearco is a financial technology company that specializes in providing payment processing solutions to businesses. The company's website contains a large amount of data related to financial transactions, payment gateways, and merchant services, which can be beneficial for businesses looking to understand the payment landscape better. The company's solutions cater to a wide range of industries, from e-commerce to brick-and-mortar establishments.

Clearco's focus on payment processing and financial transactions generates a significant amount of data related to payment processing fees, transaction volumes, and industry trends. This data can be used by businesses to optimize their payment strategies, reduce costs, and improve customer satisfaction. With a robust portfolio of financial solutions, Clearco has established itself as a prominent player in the payment processing industry, making its website a valuable resource for businesses looking to streamline their financial operations.

Description

This dataset contains contact details of battery manufacturers in China, including company names, websites, emails, LinkedIn profiles, and phone numbers. It has been compiled to support research, networking, and outreach activities in the energy storage and battery industry.

The dataset provides a starting point for anyone looking to:

• Explore the Chinese battery manufacturing landscape
• Identify potential partners, suppliers, or clients
• Conduct market research on renewable energy and storage solutions
• Build outreach pipelines for sales or collaborations

Data Fields

• id → Unique identifier for each entry
• contact_number → Company phone numbers (varied formats)
• linkedin → LinkedIn company or personal profile
• company_name → Name of the company
• website → Official company website
• emails → One or multiple email addresses (in some cases missing or unstructured)

Data Preprocessing & Cleaning

The dataset is raw and may require preprocessing before use:

• Standardize phone numbers into a consistent format
• Extract multiple emails into separate rows or structured lists
• Clean missing values (some companies may not have all fields filled)
• Normalize company names (some in English, some in Chinese)
• Verify websites/LinkedIn links for accessibility and accuracy

Potential Uses

• Business development & lead generation
• Market mapping of Chinese battery manufacturers
• Supplier discovery for EV, solar, and renewable energy projects
• Academic research on China’s role in the global battery supply chain

This dataset is a useful resource but will benefit from data cleaning and enrichment for maximum accuracy and usability.

The intention is to collect data for the calendar year 2009 (or the nearest year for which each business keeps its accounts. The survey is considered a one-off survey, although for accurate NAs, such a survey should be conducted at least every five years to enable regular updating of the ratios, etc., needed to adjust the ongoing indicator data (mainly VAGST) to NA concepts. The questionnaire will be drafted by FSD, largely following the previous BAS, updated to current accounting terminology where necessary. The questionnaire will be pilot tested, using some accountants who are likely to complete a number of the forms on behalf of their business clients, and a small sample of businesses. Consultations will also include Ministry of Finance, Ministry of Commerce, Industry and Labour, Central Bank of Samoa (CBS), Samoa Tourism Authority, Chamber of Commerce, and other business associations (hotels, retail, etc.).

The questionnaire will collect a number of items of information about the business ownership, locations at which it operates and each establishment for which detailed data can be provided (in the case of complex businesses), contact information, and other general information needed to clearly identify each unique business. The main body of the questionnaire will collect data on income and expenses, to enable value added to be derived accurately. The questionnaire will also collect data on capital formation, and will contain supplementary pages for relevant industries to collect volume of production data for selected commodities and to collect information to enable an estimate of value added generated by key tourism activities.

The principal user of the data will be FSD which will incorporate the survey data into benchmarks for the NA, mainly on the current published production measure of GDP. The information on capital formation and other relevant data will also be incorporated into the experimental estimates of expenditure on GDP. The supplementary data on volumes of production will be used by FSD to redevelop the industrial production index which has recently been transferred under the SBS from the CBS. The general information about the business ownership, etc., will be used to update the Business Register.

Outputs will be produced in a number of formats, including a printed report containing descriptive information of the survey design, data tables, and analysis of the results. The report will also be made available on the SBS website in “.pdf” format, and the tables will be available on the SBS website in excel tables. Data by region may also be produced, although at a higher level of aggregation than the national data. All data will be fully confidentialised, to protect the anonymity of all respondents. Consideration may also be made to provide, for selected analytical users, confidentialised unit record files (CURFs).

A high level of accuracy is needed because the principal purpose of the survey is to develop revised benchmarks for the NA. The initial plan was that the survey will be conducted as a stratified sample survey, with full enumeration of large establishments and a sample of the remainder.

v01: This is the first version of the documentation. Basic raw data, obtained from data entry.

The scope of the 2009 BAS is all employing businesses in the private sector other than those involved in agricultural activities.

Included are:
· Non-governmental organizations (NGOs, not-for profit organizations, etc.);
· Government Public Bodies

Excluded are:
· Non-employing units (e.g., market sellers);
· Government ministries, constitutional offices and those public bodies involved in public administration and included in the Central Government Budget Sector;
· Agricultural units (unless large scale/commercial - if the Agriculture census only covers household activities);
· “Non-resident” bodies such as international agencies, diplomatic missions (e.g., high commissions and embassies, UNDP, FAO, WHO);

The survey coverage is of all businesses in scope as defined above. Statistical units relevant to the survey are the enterprise and the establishment. The enterprise is an institutional unit and generally corresponds to legal entities such as a company, cooperative, partnership or sole proprietorship. The establishment is an institutional unit or part of an institutional unit, which engages in one, or predominantly one, type of economic activity. Sufficient data must be available to derive or meaningfully estimate value added in order to recognize an establishment. The main statistical unit from which data will be collected in the survey is the establishment. For most businesses there will be a one-to-one relationship between the enterprise and the establishment, i.e., simple enterprises will comprise only one establishment. The purpose of collecting data from establishments (rather than from enterprises) is to enable the most accurate industry estimates of value added possible.

• Collection start: 2009
• Collection end: 2009

Structural business statistics (SBS) describes the structure, conduct and performance of economic activities, down to the most detailed activity level (several hundred economic sectors).

SBS are transmitted annually by the EU Member States on the basis of a legal obligation from 1995 onwards.

SBS covers all activities of the business economy with the exception of agricultural activities and personal services and the data are provided by all EU Member States, Iceland, Norway and Switzerland, some candidate and potential candidate countries. The data are collected by domain of activity (annex) :

• Annex I - Services,
• Annex II - Industry,
• Annex III - Trade, and
• Annex IV- Constructions and by datasets. Each annex contains several datasets as indicated in the SBS Regulation.

The majority of the data is collected by National Statistical Institutes (NSIs) by means of statistical surveys, business registers or from various administrative sources. Regulatory or controlling national offices for financial institutions or central banks often provide the information required for the financial sector (NACE Rev 2 Section K / NACE Rev 1.1 Section J).

Member States apply various statistical methods, according to the data source, such as grossing up, model based estimation or different forms of imputation, to ensure the quality of SBSs produced.

Main characteristics (variables) of the SBS data category:

• Business Demographic variables (e.g. Number of enterprises),
• "Output related" variables (e.g. Turnover, Value added),
• "Input related" variables: labour input (e.g. Employment, Hours worked); goods and services input (e.g. Total of purchases); capital input (e.g. Material investments).

All SBS characteristics are published on Eurostat’s website by tables and an example of the existent tables is presented below:

• Annual enterprise statistics: Characteristics collected are published by country and detailed on NACE Rev 2 and NACE Rev 1.1 class level (4-digits). Some classes or groups in 'services' section have been aggregated.
• Annual enterprise statistics broken down by size classes: Characteristics are published by country and detailed down to NACE Rev 2 and NACE Rev 1.1 group level (3-digits) and employment size class. For trade (NACE Rev 2 and NACE Rev 1.1 Section G) a supplementary breakdown by turnover size class is available.
• Annual regional statistics: Four characteristics are published by NUTS-2 country region and detailed on NACE Rev 2 and NACE Rev 1.1 division level (2-digits) (but to group level (3-digits) for the trade section).

More information on the contents of different tables: the detail level and breakdowns required starting with the reference year 2008 is defined in Commission Regulation N° 251/2009. For previous reference years it is included in Commission Regulations (EC) N° 2701/98 and amended by Commission Regulation N°1614/2002 and Commission Regulation N°1669/2003.

Several important derived indicators are generated in the form of ratios of certain monetary characteristics or per head values. A list with the available derived indicators is available below in the Annex.

This is the current status of factory registrations in Yeongdo-gu, Busan Metropolitan City (July 10, 2025) based on the Industrial Complex Activation and Factory Establishment Act, including company name, address, industry, number of employees, products, phone number, number of foreign employees, etc. This data is publicly available and registered on the district office website, and is used as data for startups, businesses, and research, and the number of registrations is constantly changing. The number of employees is the number of employees at the time of registration, and may differ from the current number of actual employees. Please also note that factory registration is not always subject to mandatory registration. If changes occur, the latest information will be reflected and continuously managed.

Structural business statistics (SBS) describes the structure, conduct and performance of economic activities, down to the most detailed activity level (several hundred economic sectors).

SBS are transmitted annually by the EU Member States on the basis of a legal obligation from 1995 onwards.

SBS covers all activities of the business economy with the exception of agricultural activities and personal services and the data are provided by all EU Member States, Iceland, Norway and Switzerland, some candidate and potential candidate countries. The data are collected by domain of activity (annex) :

• Annex I - Services,
• Annex II - Industry,
• Annex III - Trade, and
• Annex IV- Constructions and by datasets. Each annex contains several datasets as indicated in the SBS Regulation.

The majority of the data is collected by National Statistical Institutes (NSIs) by means of statistical surveys, business registers or from various administrative sources. Regulatory or controlling national offices for financial institutions or central banks often provide the information required for the financial sector (NACE Rev 2 Section K / NACE Rev 1.1 Section J).

Member States apply various statistical methods, according to the data source, such as grossing up, model based estimation or different forms of imputation, to ensure the quality of SBSs produced.

Main characteristics (variables) of the SBS data category:

• Business Demographic variables (e.g. Number of enterprises),
• "Output related" variables (e.g. Turnover, Value added),
• "Input related" variables: labour input (e.g. Employment, Hours worked); goods and services input (e.g. Total of purchases); capital input (e.g. Material investments).

All SBS characteristics are published on Eurostat’s website by tables and an example of the existent tables is presented below:

• Annual enterprise statistics: Characteristics collected are published by country and detailed on NACE Rev 2 and NACE Rev 1.1 class level (4-digits). Some classes or groups in 'services' section have been aggregated.
• Annual enterprise statistics broken down by size classes: Characteristics are published by country and detailed down to NACE Rev 2 and NACE Rev 1.1 group level (3-digits) and employment size class. For trade (NACE Rev 2 and NACE Rev 1.1 Section G) a supplementary breakdown by turnover size class is available.
• Annual regional statistics: Four characteristics are published by NUTS-2 country region and detailed on NACE Rev 2 and NACE Rev 1.1 division level (2-digits) (but to group level (3-digits) for the trade section).

More information on the contents of different tables: the detail level and breakdowns required starting with the reference year 2008 is defined in Commission Regulation N° 251/2009. For previous reference years it is included in Commission Regulations (EC) N° 2701/98 and amended by Commission Regulation N°1614/2002 and Commission Regulation N°1669/2003.

Several important derived indicators are generated in the form of ratios of certain monetary characteristics or per head values. A list with the available derived indicators is available below in the Annex.

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from the U.S. Census Bureau’s American Community Survey 5-year estimates for 2013-2017, to show populations by industries by city in the Atlanta region. The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2013-2017). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website. Naming conventions: Prefixes:NoneCountpPercentrRatemMedianaMean (average)tAggregate (total)chChange in absolute terms (value in t2 - value in t1)pchPercent change ((value in t2 - value in t1) / value in t1)chpChange in percent (percent in t2 - percent in t1)Suffixes:NoneChange over two periods_eEstimate from most recent ACS_mMargin of Error from most recent ACS_00Decennial 2000 Attributes: SumLevelSummary level of geographic unit (e.g., County, Tract, NSA, NPU, DSNI, SuperDistrict, etc)GEOIDCensus tract Federal Information Processing Series (FIPS) code NAMEName of geographic unitPlanning_RegionPlanning region designation for ARC purposesAcresTotal area within the tract (in acres)SqMiTotal area within the tract (in square miles)CountyCounty identifier (combination of Federal Information Processing Series (FIPS) codes for state and county)CountyNameCounty NameCivEmployed_e# Civilian employed, 2017CivEmployed_m# Civilian employed, 2017 (MOE)AgForInd_e# Agriculture, forestry, fishing and hunting, and mining industries, 2017AgForInd_m# Agriculture, forestry, fishing and hunting, and mining industries, 2017 (MOE)pAgForInd_e% Agriculture, forestry, fishing and hunting, and mining industries, 2017pAgForInd_m% Agriculture, forestry, fishing and hunting, and mining industries, 2017 (MOE)ConstInd_e# Construction industry, 2017ConstInd_m# Construction industry, 2017 (MOE)pConstInd_e% Construction industry, 2017pConstInd_m% Construction industry, 2017 (MOE)ManufInd_e# Manufacturing industry, 2017ManufInd_m# Manufacturing industry, 2017 (MOE)pManufInd_e% Manufacturing industry, 2017pManufInd_m% Manufacturing industry, 2017 (MOE)WholesaleInd_e# Wholesale trade industry, 2017WholesaleInd_m# Wholesale trade industry, 2017 (MOE)pWholesaleInd_e% Wholesale trade industry, 2017pWholesaleInd_m% Wholesale trade industry, 2017 (MOE)RetailInd_e# Retail trade industry, 2017RetailInd_m# Retail trade industry, 2017 (MOE)pRetailInd_e% Retail trade industry, 2017pRetailInd_m% Retail trade industry, 2017 (MOE)TransportInd_e# Transportation and warehousing, and utilities industries, 2017TransportInd_m# Transportation and warehousing, and utilities industries, 2017 (MOE)pTransportInd_e% Transportation and warehousing, and utilities industries, 2017pTransportInd_m% Transportation and warehousing, and utilities industries, 2017 (MOE)InfoInd_e# Information industry, 2017InfoInd_m# Information industry, 2017 (MOE)pInfoInd_e% Information industry, 2017pInfoInd_m% Information industry, 2017 (MOE)FIREInd_e# Finance and insurance, and real estate and rental and leasing industries, 2017FIREInd_m# Finance and insurance, and real estate and rental and leasing industries, 2017 (MOE)pFIREInd_e% Finance and insurance, and real estate and rental and leasing industries, 2017pFIREInd_m% Finance and insurance, and real estate and rental and leasing industries, 2017 (MOE)ProfSciInd_e# Professional, scientific, and management, and administrative and waste management services industries, 2017ProfSciInd_m# Professional, scientific, and management, and administrative and waste management services industries, 2017 (MOE)pProfSciInd_e% Professional, scientific, and management, and administrative and waste management services industries, 2017pProfSciInd_m% Professional, scientific, and management, and administrative and waste management services industries, 2017 (MOE)EdHealthInd_e# Educational services, health care and social assistance industries, 2017EdHealthInd_m# Educational services, health care and social assistance industries, 2017 (MOE)pEdHealthInd_e% Educational services, health care and social assistance industries, 2017pEdHealthInd_m% Educational services, health care and social assistance industries, 2017 (MOE)ArtEntInd_e# Arts, entertainment, and recreation, and accommodation and food services industries, 2017ArtEntInd_m# Arts, entertainment, and recreation, and accommodation and food services industries, 2017 (MOE)pArtEntInd_e% Arts, entertainment, and recreation, and accommodation and food services industries, 2017pArtEntInd_m% Arts, entertainment, and recreation, and accommodation and food services industries, 2017 (MOE)OthServiceInd_e# Other service industries, except public administration, 2017OthServiceInd_m# Other service industries, except public administration, 2017 (MOE)pOthServiceInd_e% Other service industries, except public administration, 2017pOthServiceInd_m% Other service industries, except public administration, 2017 (MOE)PubAdminInd_e# Public administration industry, 2017PubAdminInd_m# Public administration industry, 2017 (MOE)pPubAdminInd_e% Public administration industry, 2017pPubAdminInd_m% Public administration industry, 2017 (MOE)last_edited_dateLast date the feature was edited by ARC Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2013-2017 For additional information, please visit the Census ACS website.

Primark is one of the first brands that come to mind in fast fashion. An Ireland-based retailer of value clothing, Primark operates in 14 countries and owns around 400 stores worldwide, concentrated mostly in the United Kingdom (UK). Supplier transparency in fast fashion Primark’s global supply network became prominent in 2013 with the collapse of the Rana Plaza building, a garment supplier factory for Primark. Since then, more attention has been given to the health and safety and labour conditions of third-party suppliers for fast fashion companies, and Primark is one of the many other retailers that have become more transparent about their global sourcing practices. According to data published on the retailer’s website, Primark worked with 407 factories in China as of October 2024. In Bangladesh, there were 132 factories supplying clothing and accessories for the company. Producing and selling more According to a study in 2023 looking at the leading European apparel retailers worldwide in 2021 by retail sales, Primark was one of the top performers. The company ranked ninth, behind sportswear giant Adidas and ahead of German footwear retailer, Deichmann.

Change-To-Inventory Time Series for MSC Industrial Direct Company Inc. MSC Industrial Direct Co., Inc., together with its subsidiaries, engages in the distribution of metalworking and maintenance, repair, and operations (MRO) products and services in the United States, Canada, Mexico, the United Kingdom, and internationally. The company's metalworking and MRO products include cutting tools, abrasives, machining fluids, measuring instruments, metalworking products, machinery and accessories, tooling components, fasteners, flat stock products, raw materials, machinery hand and power tools, safety and janitorial supplies, plumbing supplies, materials handling products, power transmission components, and electrical supplies. It also offers stock-keeping units through its catalogs and brochures; e-commerce channels, including its website; inventory management solutions; and customer care centers, customer fulfillment centers, regional inventory centers and warehouses. In addition, the company serves individual machine shops, manufacturing companies, and government agencies. MSC Industrial Direct Co., Inc. was founded in 1941 and is headquartered in Melville, New York.

Short-term business statistics (STS) give information on a wide range of economic activities. All STS data are index data. Additionally, annual absolute values are released for building permits indicators. Percentage changes are also available for each indicator: Infra-annual percentage changes - changes between two consecutive months or quarters - are calculated on the basis of non-adjusted data (prices) or calendar and seasonally adjusted data (volume and value indicators) and year-on-year changes - comparing a period to the same period one year ago - are calculated on the basis of non-adjusted data (prices and employment) or calendar adjusted data (volume and value indicators).

The index data are generally presented in the following forms:

• unadjusted
• calendar adjusted
• calendar and seasonally adjusted.

Depending on the EBS Regulation data are accessible as monthly, quarterly and annual data.

The STS indicators are listed below in five different sectors, reflecting the dissemination of these data in Eurostat’s online database “Eurobase”.

Based on the national data, Eurostat compiles short-term indicators for the EU and euro area. Among these, a list of indicators, called Principal European Economic Indicators (PEEIs) has been identified by key users as being of primary importance for the conduct of monetary and economic policy of the euro area. The PEEIs contributed by STS are marked with * in the text below.

The euro indicators are released through Eurostat's website.

INDUSTRY

• production (volume)*
• turnover: Total, Domestic market and Non-domestic market. A further breakdown of the non-domestic turnover into euro area and non-euro area is available for the euro area countries.
• producer prices (output prices)*: Total, Domestic market and Non-domestic market. A further breakdown of the non-domestic producer prices into euro area and non-euro area is available for the euro area countries.
• import prices*: Total, euro area market, Non euro area market (euro area countries only)
• labour input indicators: Number of employees and self-employed persons, Hours worked by employees, Gross wages and salaries

CONSTRUCTION

• production (volume)*
• building permits indicators*: number of dwellings, square meters of useful floor
• producer (output) prices in construction (if not available, they can be approximated by the construction costs variable)
• labour input indicators: number of employees and self-employed persons, hours worked by employees, gross wages and salaries

TRADE

• volume of sales (deflated turnover)*
• turnover (value)
• labour input indicators: number of employees and self-employed persons, hours worked by employees, gross wages and salaries

SERVICES

• production (volume)*
• turnover (in value)
• labour input indicators: number of employees and self-employed persons, hours worked by employees, gross wages and salaries
• producer prices (output prices)*

MARKET ECONOMY

• total market production (volume)
• registrations
• bankruptcies

National reference metadata of the reporting countries are available in the Annexes to this metadata file.

The Industrial Development Bureau of the Ministry of Economic Affairs has opened the dataset of "List of Companies that Have Passed the Evaluation for Creative Life Business" from now on. It welcomes everyone to make good use of it. In order to encourage businesses to unleash creativity from their daily lives, integrate innovative thinking and cultural connotations into their business models, and shape their unique business styles, the Industrial Development Bureau of the Ministry of Economic Affairs has been promoting the creative life industry since 2003. So far, it has evaluated 170 creative life businesses to drive the diffusion of industry innovation and promote industry exchanges, learning, and cooperation.Creative life is based on the demand for a good quality of life that pursues delicacy, creativity, comfort, and taste. With experience as the core, it integrates entertainment, education, aesthetics, interaction, and other aspects, combines core knowledge, high-quality aesthetics, and deep experiences to shape its characteristics, strengthens the cultural depth of enterprise operations, and promotes enterprise transformation, upgrades, and innovation. It provides consumers with experiential connotations of lifestyle, conveys deep emotional memories, cultivates high-quality taste, and thereby creates positive and qualitative influences on industrial economic activities.In line with the government's measures to promote open data, the Industrial Development Bureau of the Ministry of Economic Affairs has opened the website for querying the list of companies that have passed the evaluation for creative life business (http://www.creativelife.org.tw/index.php) from now on, and welcomes everyone to make good use of it.

The Business Structure Database is managed by the Secure Data Service (SDS) and can only be accessed through secure conditions. The ‘domestic use’ input-output matrix, contains domestic trade flows describing intermediate demand between Standard-Industrial-Classification (SIC) coded sectors. This was obtained from the ONS.

GRIT (‘Geospatial Restructuring of Industrial Trade’) is an ESRC-funded project in the School of Geography at the University of Leeds. An energy revolution must take place if the worst effects of climate change are to be avoided. Even without the impact this may have (eg through carbon pricing), fuel costs have a very uncertain future. GRIT has two aims:

create a fine-grained picture of the current spatial structure of the UK economy

consider how changing fuel prices could alter that structure over the long term. GRIT examines the web of connections between businesses in the UK to identify sectors and locations facing the greatest changes.

GRIT will work with a unique dataset: the Business Structure Database contains information for nearly every UK business, including location and sector classification. This will be linked to sectoral trade flow data. These two sources offer an opportunity to map the current spatial distribution of economic activity in the UK and to think about how that distribution may change in the future. GRIT combines this data-driven approach with a plan to engage with organisations directly affected. GRIT will work closely with a small number of organisations and engage others through the project website.