Data on enterprise births, deaths, active enterprises and survival rates across boroughs.

Data includes:

1. the most recent annual figures for enterprise births and deaths
2. a time series of the number of births and deaths of entrprises together with a percentage of births and deaths to active enterprises in a given year
3. a time series of the number of active enterprises.
4. survival rates of enterprises for up to 5 years after birth

Notes and definitions:

• The starting point for business demography is the concept of a population of active businesses in a reference year (t). These are defined as businesses that had either turnover or employment at any time during the reference period.
• A birth is identified as a business that was present in year t, but did not exist in year t-1 or t-2. Births are identified by making comparison of annual active population files and identifying those present in the latest file, but not the two previous ones.
• A death is defined as a business that was on the active file in year t, but was no longer present in the active file in t+1 and t+2. In order to provide an early estimate of deaths, an adjustment has been made to the 2007 and 2008 deaths to allow for reactivations. These figures are provisional and subject to revision.

Data on size of firms (micro-business, SME, large) for business and employees in London by industry can be found on the ONS website.

More Business Demographics data on the ONS website

Key Table Information.Table Title.Nonemployer Statistics by Demographics series (NES-D): Statistics for Employer and Nonemployer Firms by Industry and Veteran Status for the U.S., States, Metro Areas, Counties, and Places: 2023.Table ID.ABSNESD2023.AB00MYNESD01D.Survey/Program.Economic Surveys.Year.2023.Dataset.ECNSVY Nonemployer Statistics by Demographics Company Summary.Source.U.S. Census Bureau, 2023 Economic Surveys, Nonemployer Statistics by Demographics.Release Date.2025-11-20.Release Schedule.The Nonemployer Statistics by Demographics (NES-D) is released yearly, beginning in 2017..Sponsor.National Center for Science and Engineering Statistics, U.S. National Science Foundation.Table Universe.Data in this table combines estimates from the Annual Business Survey (employer firms) and the Nonemployer Statistics by Demographics (nonemployer firms).Includes U.S. firms with no paid employment or payroll, annual receipts of $1,000 or more ($1 or more in the construction industries) and filing Internal Revenue Service (IRS) tax forms for sole proprietorships (Form 1040, Schedule C), partnerships (Form 1065), or corporations (the Form 1120 series).Includes U.S. employer firms estimates of business ownership by sex, ethnicity, race, and veteran status from the 2024 Annual Business Survey (ABS) collection. The employer business dataset universe consists of employer firms that are in operation for at least some part of the reference year, are located in one of the 50 U.S. states, associated offshore areas, or the District of Columbia, have paid employees and annual receipts of $1,000 or more, and are classified in one of nineteen in-scope sectors defined by the 2022 North American Industry Classification System (NAICS), except for NAICS 111, 112, 482, 491, 521, 525, 813, 814, and 92 which are not covered.Data are also obtained from administrative records, the 2022 Economic Census, and other economic surveys. Note: For employer data only, the collection year is the year in which the data are collected. A reference year is the year that is referenced in the questions on the survey and in which the statistics are tabulated. For example, the 2024 ABS collection year produces statistics for the 2023 reference year. The "Year" column in the table is the reference year..Methodology.Data Items and Other Identifying Records.Total number of employer and nonemployer firmsTotal sales, value of shipments, or revenue of employer and nonemployer firms ($1,000)Number of nonemployer firmsSales, value of shipments, or revenue of nonemployer firms ($1,000)Number of employer firmsSales, value of shipments, or revenue of employer firms ($1,000)Number of employeesAnnual payroll ($1,000)These data are aggregated by the following demographic classifications of firm for:All firms Classifiable (firms classifiable by sex, ethnicity, race, and veteran status) Veteran Status (defined as having served in any branch of the U.S. Armed Forces) Veteran Equally veteran/nonveteran Nonveteran Unclassifiable (firms not classifiable by sex, ethnicity, race, and veteran status) Definitions can be found by clicking on the column header in the table or by accessing the Economic Census Glossary..Unit(s) of Observation.The reporting units for the NES-D and the ABS are companies or firms rather than establishments. A company or firm is comprised of one or more in-scope establishments that operate under the ownership or control of a single organization..Geography Coverage.The 2023 data are shown for the total of all sectors (00) and the 2- to 6-digit NAICS code levels for:United StatesStates and the District of ColumbiaIn addition, the total of all sectors (00) NAICS and the 2-digit NAICS code levels for:Metropolitan Statistical AreasMicropolitan Statistical AreasMetropolitan DivisionsCombined Statistical AreasCountiesEconomic PlacesFor information about geographies, see Geographies..Industry Coverage.The data are shown for the total of all sectors ("00"), and at the 2- through 6-digit NAICS code levels depending on geography. Sector "00" is not an official NAICS sector but is rather a way to indicate a total for multiple sectors. Note: Other programs outside of ABS may use sector 00 to indicate when multiple NAICS sectors are being displayed within the same table and/or dataset.The following are excluded from the total of all sectors:Crop and Animal Production (NAICS 111 and 112)Rail Transportation (NAICS 482)Postal Service (NAICS 491)Monetary Authorities-Central Bank (NAICS 521)Funds, Trusts, and Other Financial Vehicles (NAICS 525)Office of Notaries (NAICS 541120)Religious, Grantmaking, Civic, Professional, and Similar Organizations (NAICS 813)Private Households (NAICS 814)Public Administration (NAICS 92)For information about NAICS, see North American Industry Classification System..Sampling.NES-D nonemployer data are not conducted through sampling. Nonemployer Statistics (NES) data originate from statistical information obtained through business inco...

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.

PROBLEM AND OPPORTUNITY In the United States, voting is largely a private matter. A registered voter is given a randomized ballot form or machine to prevent linkage between their voting choices and their identity. This disconnect supports confidence in the election process, but it provides obstacles to an election's analysis. A common solution is to field exit polls, interviewing voters immediately after leaving their polling location. This method is rife with bias, however, and functionally limited in direct demographics data collected. For the 2020 general election, though, most states published their election results for each voting location. These publications were additionally supported by the geographical areas assigned to each location, the voting precincts. As a result, geographic processing can now be applied to project precinct election results onto Census block groups. While precinct have few demographic traits directly, their geographies have characteristics that make them projectable onto U.S. Census geographies. Both state voting precincts and U.S. Census block groups: are exclusive, and do not overlap are adjacent, fully covering their corresponding state and potentially county have roughly the same size in area, population and voter presence Analytically, a projection of local demographics does not allow conclusions about voters themselves. However, the dataset does allow statements related to the geographies that yield voting behavior. One could say, for example, that an area dominated by a particular voting pattern would have mean traits of age, race, income or household structure. The dataset that results from this programming provides voting results allocated by Census block groups. The block group identifier can be joined to Census Decennial and American Community Survey demographic estimates. DATA SOURCES The state election results and geographies have been compiled by Voting and Election Science team on Harvard's dataverse. State voting precincts lie within state and county boundaries. The Census Bureau, on the other hand, publishes its estimates across a variety of geographic definitions including a hierarchy of states, counties, census tracts and block groups. Their definitions can be found here. The geometric shapefiles for each block group are available here. The lowest level of this geography changes often and can obsolesce before the next census survey (Decennial or American Community Survey programs). The second to lowest census level, block groups, have the benefit of both granularity and stability however. The 2020 Decennial survey details US demographics into 217,740 block groups with between a few hundred and a few thousand people. Dataset Structure The dataset's columns include: Column Definition BLOCKGROUP_GEOID 12 digit primary key. Census GEOID of the block group row. This code concatenates: 2 digit state 3 digit county within state 6 digit Census Tract identifier 1 digit Census Block Group identifier within tract STATE State abbreviation, redundent with 2 digit state FIPS code above REP Votes for Republican party candidate for president DEM Votes for Democratic party candidate for president LIB Votes for Libertarian party candidate for president OTH Votes for presidential candidates other than Republican, Democratic or Libertarian AREA square kilometers of area associated with this block group GAP total area of the block group, net of area attributed to voting precincts PRECINCTS Number of voting precincts that intersect this block group ASSUMPTIONS, NOTES AND CONCERNS: Votes are attributed based upon the proportion of the precinct's area that intersects the corresponding block group. Alternative methods are left to the analyst's initiative. 50 states and the District of Columbia are in scope as those U.S. possessions voting in the general election for the U.S. Presidency. Three states did not report their results at the precinct level: South Dakota, Kentucky and West Virginia. A dummy block group is added for each of these states to maintain national totals. These states represent 2.1% of all votes cast. Counties are commonly coded using FIPS codes. However, each election result file may have the county field named differently. Also, three states do not share county definitions - Delaware, Massachusetts, Alaska and the District of Columbia. Block groups may be used to capture geographies that do not have population like bodies of water. As a result, block groups without intersection voting precincts are not uncommon. In the U.S., elections are administered at a state level with the Federal Elections Commission compiling state totals against the Electoral College weights. The states have liberty, though, to define and change their own voting precincts https://en.wikipedia.org/wiki/Electoral_precinct. The Census Bureau... Visit https://dataone.org/datasets/sha256%3A05707c1dc04a814129f751937a6ea56b08413546b18b351a85bc96da16a7f8b5 for complete metadata about this dataset.

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 table contains data from the December release of Canadian Business Counts for 2007 until the latest complete year. The data includes the year, 2-digit North American Industry Classification System (NAICS) code, and a count of the number of businesses by number of employees. The table data shows the number of businesses categorized by the number of employees they have. Please ensure you read the notes provided below, as there is very important information on classification and comparability. NotesStatistics Canada advises users not to use these data as a time series. Further, the counts may reflect some of the business openings and closures caused by the COVID-19 pandemic, although they will not be fully represented as the evolving resumption or permanent closure of businesses may not yet be fully processed and confirmed by Statistics Canada's Business Register (The Daily — Canadian business counts, December 2021 (statcan.gc.ca)).Changes in methodology or in business industrial classification strategies used by Statistics Canada's Business Register can create increases or decreases in the number of active businesses reported in the data on Canadian business patterns. As a result, these data do not represent changes in the business population over time. Statistics Canada recommends users not to use these data as a time series. Beginning in December 2014, there were several important changes that were made:

The data appear in two separate series, one covering locations with employees, the other covering locations without employees. The second series corresponds to locations previously coded to the employment category called "indeterminate." A new North American Industrial Classification System (NAICS) category has been added to include locations that have not yet received a NAICS code: unclassified. It represents an additional 78,718 locations with employees and 313,107 locations without employees. The second series, locations without employees, also includes locations that were not previously included in tables but that meet the criteria used to define the Business Register coverage. The impact of the change will be the inclusion of approximately 600,000 additional locations.

Before 2014, the following notes apply:

The establishments in the "Indeterminate" category do not maintain an employee payroll, but may have a workforce which consists of contracted workers, family members or business owners. However, the Business Register does not have this information available, and has therefore assigned the establishments to an "Indeterminate" category. This category also includes employers who did not have employees in the last 12 months. Please note that the employment size ranges are based on data derived from payroll remittances. As such, it should be viewed solely as a business stratification variable. Its primary purpose is to improve the efficiency of samples selected to conduct statistical surveys. It should not be used in any manner to compile industry employment estimates. Employment, grouped in employment size ranges, is more often than not an estimation of the annual maximum number of employees. For example, a measure of "10 employees" could represent "10 full-time employees", "20 part-time employees" or any other combination.For more information refer to Statistics Canada's Definitions and Concepts used in Business Register.

Time series data for the statistic Business/establishment survey (Availability score over 10 years) and country Luxembourg. Indicator Definition:The business/establishment survey provides information on employment, hours, and earnings of employees from a sample of business establishments including private and public, entities that are classified based on an establishment's principal activity from the business or establishment census. Establishment surveys include surveys of businesses, farms, and institutions. They may ask for information about the establishment itself and/or employee characteristics and demographics.

As of July 2nd, 2024 the COVID-19 Deaths by Population Characteristics Over Time dataset has been retired. This dataset is archived and will no longer update. We will be publishing a cumulative deaths by population characteristics dataset that will update moving forward.

A. SUMMARY This dataset shows San Francisco COVID-19 deaths by population characteristics and by date. This data may not be immediately available for recently reported deaths. Data updates as more information becomes available. Because of this, death totals for previous days may increase or decrease. More recent data is less reliable.

Population characteristics are subgroups, or demographic cross-sections, like age, race, or gender. The City tracks how deaths have been distributed among different subgroups. This information can reveal trends and disparities among groups.

B. HOW THE DATASET IS CREATED As of January 1, 2023, COVID-19 deaths are defined as persons who had COVID-19 listed as a cause of death or a significant condition contributing to their death on their death certificate. This definition is in alignment with the California Department of Public Health and the national https://preparedness.cste.org/wp-content/uploads/2022/12/CSTE-Revised-Classification-of-COVID-19-associated-Deaths.Final_.11.22.22.pdf">Council of State and Territorial Epidemiologists. Death certificates are maintained by the California Department of Public Health.

Data on the population characteristics of COVID-19 deaths are from: *Case reports *Medical records *Electronic lab reports *Death certificates

Data are continually updated to maximize completeness of information and reporting on San Francisco COVID-19 deaths.

To protect resident privacy, we summarize COVID-19 data by only one characteristic at a time. Data are not shown until cumulative citywide deaths reach five or more.

Data notes on each population characteristic type is listed below.

Race/ethnicity * We include all race/ethnicity categories that are collected for COVID-19 cases.

Gender * The City collects information on gender identity using these guidelines.

C. UPDATE PROCESS Updates automatically at 06:30 and 07:30 AM Pacific Time on Wednesday each week.

Dataset will not update on the business day following any federal holiday.

D. HOW TO USE THIS DATASET Population estimates are only available for age groups and race/ethnicity categories. San Francisco population estimates for race/ethnicity and age groups can be found in a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2016-2020 5-year American Community Survey (ACS).

This dataset includes many different types of characteristics. Filter the “Characteristic Type” column to explore a topic area. Then, the “Characteristic Group” column shows each group or category within that topic area and the number of deaths on each date.

New deaths are the count of deaths within that characteristic group on that specific date. Cumulative deaths are the running total of all San Francisco COVID-19 deaths in that characteristic group up to the date listed.

This data may not be immediately available for more recent deaths. Data updates as more information becomes available.

To explore data on the total number of deaths, use the COVID-19 Deaths Over Time dataset.

E. CHANGE LOG

• 9/11/2023 - on this date, we began using an updated definition of a COVID-19 death to align with the California Department of Public Health. This change was applied to COVID-19 deaths retrospectively beginning on 1/1/2023. More information about the recommendation by the Council of State and Territorial Epidemiologists that motivated this change can be found https://preparedness.cste.org/wp-content/uploads/2022/12/CSTE-Revised-Classification-of-COVID-19-associated-Deaths.Final_.11.22.22.pdf">here.
• 6/6/2023 - data on deaths by transmission type have been removed. See section ARCHIVED DATA for more detail.
• 5/16/2023 - data on deaths by sexual orientation, comorbidities, homelessness, and single room occupancy have been removed. See section ARCHIVED DATA for more detail.
• 4/6/2023 - the State implemented system updates to improve the integrity of historical data.
• 1/31/2023 - column “population_estimate” added.
• 3/23/2022 - ‘Native American’ changed to ‘American Indian or Alaska Native’ to align with the census.
• 1/22/2022 - system updates to improve timeliness and accuracy of cases and deaths data were implemented.

A. SUMMARY This dataset shows San Francisco COVID-19 deaths by population characteristics. This data may not be immediately available for recently reported deaths. Data updates as more information becomes available. Because of this, death totals may increase or decrease. Population characteristics are subgroups, or demographic cross-sections, like age, race, or gender. The City tracks how deaths have been distributed among different subgroups. This information can reveal trends and disparities among groups. B. HOW THE DATASET IS CREATED As of January 1, 2023, COVID-19 deaths are defined as persons who had COVID-19 listed as a cause of death or a significant condition contributing to their death on their death certificate. This definition is in alignment with the California Department of Public Health and the national Council of State and Territorial Epidemiologists. Death certificates are maintained by the California Department of Public Health. Data on the population characteristics of COVID-19 deaths are from: Case reports Medical records Electronic lab reports Death certificates Data are continually updated to maximize completeness of information and reporting on San Francisco COVID-19 deaths. To protect resident privacy, we summarize COVID-19 data by only one population characteristic at a time. Data are not shown until cumulative citywide deaths reach five or more. Data notes on select population characteristic types are listed below. Race/ethnicity * We include all race/ethnicity categories that are collected for COVID-19 cases. Gender * The City collects information on gender identity using these guidelines. C. UPDATE PROCESS Updates automatically at 06:30 and 07:30 AM Pacific Time on Wednesday each week. Dataset will not update on the business day following any federal holiday. D. HOW TO USE THIS DATASET Population estimates are only available for age groups and race/ethnicity categories. San Francisco population estimates for race/ethnicity and age groups can be found in a dataset based on the San Francisco Population and Demographic Census dataset.These population estimates are from the 2018-2022 5-year American Community Survey (ACS). This dataset includes several characteristic types. Filter the “Characteristic Type” column to explore a topic area. Then, the “Characteristic Group” column shows each group or category within that topic area and the number of cumulative deaths. Cumulative deaths are the running total of all San Francisco COVID-19 deaths in that characteristic group up to the date listed. To explore data on the total number of deaths, use the COVID-19 Deaths Over Time dataset. E. CHANGE LOG

Time series data for the statistic Business/establishment survey (Availability score over 10 years) and country Nauru. Indicator Definition:The business/establishment survey provides information on employment, hours, and earnings of employees from a sample of business establishments including private and public, entities that are classified based on an establishment's principal activity from the business or establishment census. Establishment surveys include surveys of businesses, farms, and institutions. They may ask for information about the establishment itself and/or employee characteristics and demographics.

Time series data for the statistic Business/establishment survey (Availability score over 10 years) and country Maldives. Indicator Definition:The business/establishment survey provides information on employment, hours, and earnings of employees from a sample of business establishments including private and public, entities that are classified based on an establishment's principal activity from the business or establishment census. Establishment surveys include surveys of businesses, farms, and institutions. They may ask for information about the establishment itself and/or employee characteristics and demographics.

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.

Time series data for the statistic Business/establishment survey (Availability score over 10 years) and country Rwanda. Indicator Definition:The business/establishment survey provides information on employment, hours, and earnings of employees from a sample of business establishments including private and public, entities that are classified based on an establishment's principal activity from the business or establishment census. Establishment surveys include surveys of businesses, farms, and institutions. They may ask for information about the establishment itself and/or employee characteristics and demographics.

** Inputs related to Analysis for additional reference:** 1. Why do we need customer Segmentation? As every customer is unique and can be targeted in different ways. The Customer segmentation plays an important role in this case. The segmentation helps to understand profiles of customers and can be helpful in defining cross sell/upsell/activation/acquisition strategies. 2. What is RFM Segmentation? RFM Segmentation is an acronym of recency, frequency and monetary based segmentation. Recency is about when the last order of a customer. It means the number of days since a customer made the last purchase. If it’s a case for a website or an app, this could be interpreted as the last visit day or the last login time. Frequency is about the number of purchases in a given period. It could be 3 months, 6 months or 1 year. So we can understand this value as for how often or how many customers used the product of a company. The bigger the value is, the more engaged the customers are. Alternatively We can define, average duration between two transactions Monetary is the total amount of money a customer spent in that given period. Therefore big spenders will be differentiated with other customers such as MVP or VIP. 3. What is LTV and How to define it? In the current world, almost every retailer promotes its subscription and this is further used to understand the customer lifetime. Retailer can manage these customers in better manner if they know which customer is high life time value. Customer lifetime value (LTV) can also be defined as the monetary value of a customer relationship, based on the present value of the projected future cash flows from the customer relationship. Customer lifetime value is an important concept in that it encourages firms to shift their focus from quarterly profits to the long-term health of their customer relationships. Customer lifetime value is an important metric because it represents an upper limit on spending to acquire new customers. For this reason it is an important element in calculating payback of advertising spent in marketing mix modelling. 4. Why do need to predict Customer Lifetime Value? The LTV is an important building block in campaign design and marketing mix management. Although targeting models can help to identify the right customers to be targeted, LTV analysis can help to quantify the expected outcome of targeting in terms of revenues and profits. The LTV is also important because other major metrics and decision thresholds can be derived from it. For example, the LTV is naturally an upper limit on the spending to acquire a customer, and the sum of the LTVs for all of the customers of a brand, known as the customer equity, is a major metric forbusiness valuations. Similarly to many other problems of marketing analytics and algorithmic marketing, LTV modelling can be approached from descriptive, predictive, and prescriptive perspectives. 5. How Next Purchase Day helps to Retailers? Our objective is to analyse when our customer will purchase products in the future so for such customers we can build strategy and can come up with strategies and marketing campaigns accordingly. a. Group-1: Customers who will purchase in more than 60 days b. Group-2: Customers who will purchase in 30-60 days c. Group-3: Customers who will purchase in 0-30 days 6. What is Cohort Analysis? How it will be helpful? A cohort is a group of users who share a common characteristic that is identified in this report by an Analytics dimension. For example, all users with the same Acquisition Date belong to the same cohort. The Cohort Analysis report lets you isolate and analyze cohort behaviour. Cohort analysis in e-commerce means to monitor your customers’ behaviour based on common traits they share – the first product they bought, when they became customers, etc. - - to find patterns and tailor marketing activities for the group.

Transaction data has been provided for the period of 1st Jan 2019 to 31st Dec 2019. The below data sets have been provided. Online_Sales.csv: This file contains actual orders data (point of Sales data) at transaction level with below variables. CustomerID: Customer unique ID Transaction_ID: Transaction Unique ID Transaction_Date: Date of Transaction Product_SKU: SKU ID – Unique Id for product Product_Description: Product Description Product_Cateogry: Product Category Quantity: Number of items ordered Avg_Price: Price per one quantity Delivery_Charges: Charges for delivery Coupon_Status: Any discount coupon applied Customers_Data.csv: This file contains customer’s demographics. CustomerID: Customer Unique ID Gender: Gender of customer Location: Location of Customer Tenure_Months: Tenure in Months Discount_Coupon.csv: Discount coupons have been given for different categories in different months Month: Discount coupon applied in that month Product_Category: Product categor...

The 2014 London Business Survey (LBS) is an innovative survey designed by the Office for National Statistics, on behalf of the London Enterprise Panel and the GLA. The survey collected information from a representative sample of private sector businesses in London in May-July 2014. This dataset contains information on the profile of London businesses corresponding with Section 1 of the London Business Survey 2014: Main Findings report. Information is provided on: The country or region of business ownership of London businesses UK versus foreign ownership of London businesses What London businesses provide: goods, services and intellectual property The types of customers of London businesses The age of London businesses, including the numbers of start-ups As with any survey, the 2014 LBS is based on a sample and as such is subject to variability in the results. Care should therefore be taken in interpreting the survey findings. For all estimates, lower and upper limits of 95% confidence intervals are provided in the data files to assist with interpretation. The LBS results represent the population of business units in London. A business unit is defined as a site/workplace, which may also be a head office if the head office is in London. It will be the whole business in the case of businesses which only have one site, or part of the business in the case of multi-site firms. The results are presented by enterprise size band and industry sector.

The SME data warehouse is based on existing administrative data sources from Statistics Canada and Canada Revenue Agency. Data covers tax year 2001 to tax year 2006. The SME Data Warehouse contains a complete, up to date and unduplicated list of all businesses in Canada based on Statistics Canada's Business Register for tax years 2001-2006. This product currently produces data for Small and Medium Sized Enterprises (SMEs). SMEs are defined as enterprises with less than 250 employees and less than $50 million in total revenue.

Dataset shows an individual’s statistical area 3 (SA3) of usual residence and the SA3 of their workplace address, for the employed census usually resident population count aged 15 years and over, by main means of travel to work from the 2018 and 2023 Censuses.

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The main means of travel to work categories are:

• Work at home
• Drive a private car, truck, or van
• Drive a company car, truck, or van
• Passenger in a car, truck, van, or company bus
• Public bus
• Train
• Bicycle
• Walk or jog
• Ferry
• Other.

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Main means of travel to work is the usual method which an employed person aged 15 years and over used to travel the longest distance to their place of work.

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Workplace address refers to where someone usually works in their main job, that is the job in which they worked the most hours. For people who work at home, this is the same address as their usual residence address. For people who do not work at home, this could be the address of the business they work for or another address, such as a building site.

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Workplace address is coded to the most detailed geography possible from the available information. This dataset only includes travel to work information for individuals whose workplace address is available at SA3 level. The sum of the counts for each region in this dataset may not equal the total employed census usually resident population count aged 15 years and over for that region. Workplace address – 2023 Census: Information by concept has more information.

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This dataset can be used in conjunction with the following spatial files by joining on the SA3 code values:

• Statistical area 3 2023 (generalised)

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Download data table using the instructions in the Koordinates help guide.

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Footnotes

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Geographical boundaries

Statistical standard for geographic areas 2023 (updated December 2023) has information about geographic boundaries as of 1 January 2023. Address data from 2013 and 2018 Censuses was updated to be consistent with the 2023 areas. Due to the changes in area boundaries and coding methodologies, 2013 and 2018 counts published in 2023 may be slightly different to those published in 2013 or 2018.

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Subnational census usually resident population

The census usually resident population count of an area (subnational count) is a count of all people who usually live in that area and were present in New Zealand on census night. It excludes visitors from overseas, visitors from elsewhere in New Zealand, and residents temporarily overseas on census night. For example, a person who usually lives in Christchurch city and is visiting Wellington city on census night will be included in the census usually resident population count of Christchurch city. 

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Population counts

Stats NZ publishes a number of different population counts, each using a different definition and methodology. Population statistics – user guide has more information about different counts. 

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Caution using time series

Time series data should be interpreted with care due to changes in census methodology and differences in response rates between censuses. The 2023 and 2018 Censuses used a combined census methodology (using census responses and administrative data).

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Workplace address time series

Workplace address time series data should be interpreted with care at lower geographic levels, such as statistical area 2 (SA2). Methodological improvements in 2023 Census resulted in greater data accuracy, including a greater proportion of people being counted at lower geographic areas compared to the 2018 Census. Workplace address – 2023 Census: Information by concept has more information.

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Working at home

In the census, working at home captures both remote work, and people whose business is at their home address (e.g. farmers or small business owners operating from their home). The census asks respondents whether they ‘mostly’ work at home or away from home. It does not capture whether someone does both, or how frequently they do one or the other.

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Rows excluded from the dataset

Rows show SA3 of usual residence by SA3 of workplace address. Rows with a total population count of less than six have been removed to reduce the size of the dataset, given only a small proportion of SA3-SA3 combinations have commuter flows.

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About the 2023 Census dataset

For information on the 2023 dataset see Using a combined census model for the 2023 Census. We combined data from the census forms with administrative data to create the 2023 Census dataset, which meets Stats NZ's quality criteria for population structure information. We added real data about real people to the dataset where we were confident the people who hadn’t completed a census form (which is known as admin enumeration) will be counted. We also used data from the 2018 and 2013 Censuses, administrative data sources, and statistical imputation methods to fill in some missing characteristics of people and dwellings.

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Data quality

The quality of data in the 2023 Census is assessed using the quality rating scale and the quality assurance framework to determine whether data is fit for purpose and suitable for release. Data quality assurance in the 2023 Census has more information.

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Quality rating of a variable

The quality rating of a variable provides an overall evaluation of data quality for that variable, usually at the highest levels of classification. The quality ratings shown are for the 2023 Census unless stated. There is variability in the quality of data at smaller geographies. Data quality may also vary between censuses, for subpopulations, or when cross tabulated with other variables or at lower levels of the classification. Data quality ratings for 2023 Census variables has more information on quality ratings by variable.

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Main means of travel to work quality rating

Main means of travel to work is rated as moderate quality.

Main means of travel to work – 2023 Census: Information by concept has more information, for example, definitions and data quality.

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Workplace address quality rating

Workplace address is rated as moderate quality.

Workplace address – 2023 Census: Information by concept has more information, for example, definitions and data quality.

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Using data for good

Stats NZ expects that, when working with census data, it is done so with a positive purpose, as outlined in the Māori Data Governance Model (Data Iwi Leaders Group, 2023). This model states that "data should support transformative outcomes and should uplift and strengthen our relationships with each other and with our environments. The avoidance of harm is the minimum expectation for data use. Māori data should also contribute to iwi and hapū tino rangatiratanga”.

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Confidentiality

The 2023 Census confidentiality rules have been applied to 2013, 2018, and 2023 data. These rules protect the confidentiality of individuals, families, households, dwellings, and undertakings in 2023 Census data. Counts are calculated using fixed random rounding to base 3 (FRR3) and suppression of ‘sensitive’ counts less than six, where tables report multiple geographic variables and/or small populations. Individual figures may not always sum to stated totals. Applying confidentiality rules to 2023 Census data and summary of changes since 2018 and 2013 Censuses has more information about 2023 Census confidentiality rules.

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Percentages

To calculate percentages, divide the figure for the category of interest by the figure for ‘Total stated’ where this applies.

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Symbol

-999 Confidential

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Inconsistencies in definitions

Please note that there may be differences in definitions between census classifications and those used for other data collections.

Key Table Information.Table Title.Nonemployer Statistics by Demographics series (NES-D): Statistics for Employer and Nonemployer Firms by Industry and Veteran Status for the U.S., States, Metro Areas, Counties, and Places: 2022.Table ID.ABSNESD2022.AB00MYNESD01D.Survey/Program.Economic Surveys.Year.2022.Dataset.ECNSVY Nonemployer Statistics by Demographics Company Summary.Source.U.S. Census Bureau, 2022 Economic Surveys, Nonemployer Statistics by Demographics.Release Date.2025-05-08.Release Schedule.The Nonemployer Statistics by Demographics (NES-D) is released yearly, beginning in 2017..Sponsor.National Center for Science and Engineering Statistics, U.S. National Science Foundation.Table Universe.Data in this table combines estimates from the Annual Business Survey (employer firms) and the Nonemployer Statistics by Demographics (nonemployer firms).Includes U.S. firms with no paid employment or payroll, annual receipts of $1,000 or more ($1 or more in the construction industries) and filing Internal Revenue Service (IRS) tax forms for sole proprietorships (Form 1040, Schedule C), partnerships (Form 1065), or corporations (the Form 1120 series).Includes U.S. employer firms estimates of business ownership by sex, ethnicity, race, and veteran status from the 2023 Annual Business Survey (ABS) collection. The employer business dataset universe consists of employer firms that are in operation for at least some part of the reference year, are located in one of the 50 U.S. states, associated offshore areas, or the District of Columbia, have paid employees and annual receipts of $1,000 or more, and are classified in one of nineteen in-scope sectors defined by the 2022 North American Industry Classification System (NAICS), except for NAICS 111, 112, 482, 491, 521, 525, 813, 814, and 92 which are not covered.Data are also obtained from administrative records, the 2022 Economic Census, and other economic surveys. Note: For employer data only, the collection year is the year in which the data are collected. A reference year is the year that is referenced in the questions on the survey and in which the statistics are tabulated. For example, the 2023 ABS collection year produces statistics for the 2022 reference year. The "Year" column in the table is the reference year..Methodology.Data Items and Other Identifying Records.Total number of employer and nonemployer firmsTotal sales, value of shipments, or revenue of employer and nonemployer firms ($1,000)Number of nonemployer firmsSales, value of shipments, or revenue of nonemployer firms ($1,000)Number of employer firmsSales, value of shipments, or revenue of employer firms ($1,000)Number of employeesAnnual payroll ($1,000)These data are aggregated by the following demographic classifications of firm for:All firms Classifiable (firms classifiable by sex, ethnicity, race, and veteran status) Veteran Status (defined as having served in any branch of the U.S. Armed Forces) Veteran Equally veteran/nonveteran Nonveteran Unclassifiable (firms not classifiable by sex, ethnicity, race, and veteran status) Definitions can be found by clicking on the column header in the table or by accessing the Economic Census Glossary..Unit(s) of Observation.The reporting units for the NES-D and the ABS are companies or firms rather than establishments. A company or firm is comprised of one or more in-scope establishments that operate under the ownership or control of a single organization..Geography Coverage.The 2022 data are shown for the total of all sectors (00) and the 2- to 6-digit NAICS code levels for:United StatesStates and the District of ColumbiaIn addition, the total of all sectors (00) NAICS and the 2-digit NAICS code levels for:Metropolitan Statistical AreasMicropolitan Statistical AreasMetropolitan DivisionsCombined Statistical AreasCountiesEconomic PlacesFor information about geographies, see Geographies..Industry Coverage.The data are shown for the total of all sectors ("00"), and at the 2- through 6-digit NAICS code levels depending on geography. Sector "00" is not an official NAICS sector but is rather a way to indicate a total for multiple sectors. Note: Other programs outside of ABS may use sector 00 to indicate when multiple NAICS sectors are being displayed within the same table and/or dataset.The following are excluded from the total of all sectors:Crop and Animal Production (NAICS 111 and 112)Rail Transportation (NAICS 482)Postal Service (NAICS 491)Monetary Authorities-Central Bank (NAICS 521)Funds, Trusts, and Other Financial Vehicles (NAICS 525)Office of Notaries (NAICS 541120)Religious, Grantmaking, Civic, Professional, and Similar Organizations (NAICS 813)Private Households (NAICS 814)Public Administration (NAICS 92)For information about NAICS, see North American Industry Classification System..Sampling.NES-D nonemployer data are not conducted through sampling. Nonemployer Statistics (NES) data originate from statistical information obtained through business inco...

Abstract

This survey was conducted in Hungary between February 2013 and August 2013 as part of the fifth round of the Business Environment and Enterprise Performance Survey (BEEPS V), a joint initiative of the World Bank Group and the European Bank for Reconstruction and Development. The objective of the survey is to obtain feedback from enterprises on the state of the private sector as well as to help in building a panel of enterprise data that will make it possible to track changes in the business environment over time, thus allowing, for example, impact assessments of reforms. Through interviews with firms in the manufacturing and services sectors, the survey assesses the constraints to private sector growth and creates statistically significant business environment indicators that are comparable across countries.

Data from 310 establishments was analyzed. Stratified random sampling was used to select the surveyed businesses.

The survey topics include firm characteristics, information about sales and suppliers, competition, infrastructure services, judiciary and law enforcement collaboration, security, government policies, laws and regulations, financing, overall business environment, bribery, capacity utilization, performance and investment activities, and workforce composition.

In 2011, the innovation module was added to the standard set of Enterprise Surveys questionnaires to examine in detail how introduction of new products and practices influence firms' performance and management.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is an establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The whole population, or universe of the study, is the non-agricultural economy. It comprises: all manufacturing sectors according to the group classification of ISIC Revision 3.1: (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities-sectors.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample was selected using stratified random sampling. Three levels of stratification were used in this country: industry, establishment size, and region.

Industry stratification was designed in the way that follows: the universe was stratified into one manufacturing industry, and two service industries (retail, and other services).

Size stratification was defined following the standardized definition for the rollout: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees). For stratification purposes, the number of employees was defined on the basis of reported permanent full-time workers. This seems to be an appropriate definition of the labor force since seasonal/casual/part-time employment is not common practice, apart from the construction and agriculture sectors which are not included in the survey.

Regional stratification was defined in 3 regions (city and the surrounding business area) throughout Hungary.

The database from Hungarian Central Statistical Office was used as the frame for the selection of a sample with the aim of obtaining interviews at 270 establishments with five or more employees.

Given the impact that non-eligible units included in the sample universe may have on the results, adjustments may be needed when computing the appropriate weights for individual observations. The percentage of confirmed non-eligible units as a proportion of the total number of sampled establishments contacted for the survey was 9.2% (102 out of 1,106 establishments).

In the dataset, the variables a2 (sampling region), a6a (sampling establishment's size), and a4a (sampling sector) contain the establishment's classification into the strata chosen for each country using information from the sample frame. Variable a4a is coded using ISIC Rev 3.1 codes for the chosen industries for stratification. These codes include most manufacturing industries (15 to 37), retail (52), and (45, 50, 51, 55, 60-64, 72) for other services.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three different versions of the questionnaire were used. The basic questionnaire, the Core Module, includes all common questions asked to all establishments from all sectors. The second expanded variation, the Manufacturing Questionnaire, is built upon the Core Module and adds some specific questions relevant to manufacturing sectors. The third expanded variation, the Retail Questionnaire, is also built upon the Core Module and adds to the core specific questions.

The innovation module was added to the standard set of Enterprise Surveys questionnaires to examine how introduction of new products and practices influence firms' performance and management.

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

Response rate

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether, while the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.

Item non-response was addressed by two strategies: a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect the refusal to respond as a different option from don’t know. b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary.

Survey non-response was addressed by maximizing efforts to contact establishments that were initially selected for interview. Attempts were made to contact the establishment for interview at different times/days of the week before a replacement establishment (with similar strata characteristics) was suggested for interview. Survey non-response did occur but substitutions were made in order to potentially achieve strata-specific goals.

The number of realized interviews per contacted establishment was 0.26. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The number of rejections per contact was 0.28.