Key Table Information.Table Title.Nonemployer Statistics by Demographics series (NES-D): Statistics for Employer and Nonemployer Firms by Industry and Sex for the U.S., States, and Metro Areas: 2020.Table ID.ABSNESD2020.AB00MYNESD01A.Survey/Program.Economic Surveys.Year.2020.Dataset.ECNSVY Nonemployer Statistics by Demographics Company Summary.Source.U.S. Census Bureau, 2020 Economic Surveys, Nonemployer Statistics by Demographics.Release Date.2024-02-08.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 2021 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 2017 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 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 2021 ABS collection year produces statistics for the 2020 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) Sex Female Male Equally male-owned and female-owned 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 data are shown for the total of all sectors (00) and the 2-digit NAICS code levels for:United StatesStates and the District of ColumbiaMetropolitan Statistical AreasData are also shown for the 3- and 4-digit NAICS code for:United StatesStates and the District of ColumbiaFor information about geographies, see Geographies..Industry Coverage.The data are shown for the total of all sectors ("00"), and at the 2- through 4-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)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 income tax records that the Internal Revenue Service (IRS) provides to the Census Bureau. The NES-D adds demographic characteristics to the NES data and produces the total firm counts and the total receipts by those demographic characteristics. The NES-D utilizes various administrative records (AR) and the Census Bureau data sources that inc...

50 Million Rows MSSQL Backup File with Clustered Columnstore Index.

This dataset contains -27K categorized Turkish supermarket items. -81 stores (Every city of Turkey has a store) -100K real Turkish names customer, address -10M rows sales data generated randomly. -All data has a near real price with influation factor by the time.

All the data generated randomly. So the usernames have been generated with real Turkish names and surnames but they are not real people. The sale data generated randomly. But it has some rules. For example, every order can contains 1-9 kind of item. Every orderline amount can be 1-9 pieces. The randomise function works according to population of the city. So the number of orders for Istanbul (the biggest city of Turkey) is about 20% of all data and another city for example orders for the Gaziantep (the population is 2.5% of Turkey population) is about 2.5% off all data. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1611072%2F9442f2a1dbae7f05ead4fde9e1033ac6%2Finbox_1611072_135236e39b79d6fae8830dec3fca4961_1.png?generation=1693509562300174&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1611072%2F1c39195270db87250e59d9f2917ccea1%2Finbox_1611072_b73d9ca432dae956564cfa5bfe42268c_3.png?generation=1693509575061587&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1611072%2Fa908389f33ae5c983e383d17f0d9a763%2Finbox_1611072_c5d349aa1f33c0fc4fc74b79b7167d3a_F3za81TXkAA1Il4.png?generation=1693509586158658&alt=media" alt="">

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.

This is analytical proofs and raw data for research article, “The Influence of Dependability in Adopting Cloud Computing: Focus on Similarities and Differences by IT Intensity and Service Type”. The original study explored the role of a multi-faceted dependability in cloud computing adotion, focusing on clarifying the similarities and differences by IT intensity (by industry) and service type. This article is consists of analytical proofs, measurement items, analytic tables, and raw data. Files included are as follows.

○ File 1 - Title: Details of prior studies on cloud computing adoption - Description: This file presents a review of seventy-nine studies (2009 to 2019) that focused on the adoption of cloud computing adoption at both individual and organizational level.

○ File 2 - Title: Measurement items - Description: This file reports details of the measurement items used in the original research article.

○ File 3 - Title: Samples profiles - Description: This file shows the demographic profiles of the samples.

○ File 4 - Title: Results of measurement invariance test - Description: This file shows the results of measurement invariance test.

○ File 5 - Title: Results of multicollinearity - Description: This file shows the results of variance inflation factor (VIF).

○ File 6 - Title: Hypothesis test results (table format) - Description: This file shows the results of hypothesis test on both full data set and subgroups.

○ File 7 - Title: results of mediation test for full data set - Description: This file shows the results of hypothesis test on both full data set and subgroups.

○ File 8 - Title: results of mediation test for IT intensity by industry - Description: This file shows the results of hypothesis test on IT intensity by industry

○ File 9 - Title: results of mediation test for for service type - Description: This file shows the results of hypothesis test on on service type

○ File 10 - Title: data - Description: This file contains raw data for the original study: 230 samples were used for its analysis.

Hotel customer dataset with 31 variables describing a total of 83,590 instances (customers). It comprehends three full years of customer behavioral data. In addition to personal and behavioral information, the dataset also contains demographic and geographical information. This dataset contributes to reducing the lack of real-world business data that can be used for educational and research purposes. The dataset can be used in data mining, machine learning, and other analytical field problems in the scope of data science. Due to its unit of analysis, it is a dataset especially suitable for building customer segmentation models, including clustering and RFM (Recency, Frequency, and Monetary value) models, but also be used in classification and regression problems.

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, and Metro Areas: 2019.Table ID.ABSNESD2019.AB00MYNESD01D.Survey/Program.Economic Surveys.Year.2019.Dataset.ECNSVY Nonemployer Statistics by Demographics Company Summary.Source.U.S. Census Bureau, 2019 Economic Surveys, Nonemployer Statistics by Demographics.Release Date.2023-05-11.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 2020 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 2017 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 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 2020 ABS collection year produces statistics for the 2019 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 data are shown for the total of all sectors (00) and the 2-digit NAICS code levels for:United StatesStates and the District of ColumbiaMetropolitan Statistical AreasData are also shown for the 3-digit NAICS code for:United StatesStates and the District of ColumbiaFor information about geographies, see Geographies..Industry Coverage.The data are shown for the total of all sectors ("00"), and at the 2- through 3-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)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 income tax records that the Internal Revenue Service (IRS) provides to the Census Bureau. The NES-D adds demographic characteristics to the NES data and produces the total firm counts and the total receipts by those demographic characteristics. The NES-D utilizes ...

About this dataset

Context

The dataset tabulates the Plano population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Plano across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Plano was 290,190, a 0.15% increase year-by-year from 2022. Previously, in 2022, Plano population was 289,750, an increase of 0.33% compared to a population of 288,792 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Plano increased by 66,105. In this period, the peak population was 290,190 in the year 2023. The numbers suggest that the population has not reached its peak yet and is showing a trend of further growth. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Plano is shown in this column.
• Year on Year Change: This column displays the change in Plano population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Plano Population by Year. You can refer the same here

This dataset is designed to advance labelled financial sentiment analysis research. It combines two notable datasets, FiQA and Financial PhraseBank, into a single, easy-to-use CSV file. The primary purpose is to provide financial sentences accompanied by their corresponding sentiment labels, which can be positive, negative, or neutral. This resource is valuable for understanding market and corporate sentiment expressed in textual data.

Columns

The dataset is structured with at least two key columns: * Sentence: This column contains the textual financial statement or phrase. * Sentiment Label: This column provides the associated sentiment of the sentence, categorised as 'positive', 'negative', or 'neutral'.

Distribution

The dataset is provided in a CSV file format. It organises financial sentences with their assigned sentiment labels. Specific details regarding the exact number of rows or records are not available in the provided information.

Usage

This dataset is ideal for various applications and use cases, including: * Developing and testing Natural Language Processing (NLP) models for sentiment detection in financial texts. * Conducting data science and analytics projects focused on market dynamics and corporate communications. * Building tools for business intelligence to gauge sentiment from financial news and reports. * Academic research into the nuances of economic language and its emotional tone.

Coverage

The dataset's regional scope is global. The financial sentences included refer to various companies and market events, with examples from periods such as 2008 and 2010. While a precise time range for all data points is not specified, the content is relevant to corporate financial and market sentiment over several years. There are no specific notes on demographic scope; the focus is on business and financial entities.

License

CCO

Who Can Use It

This dataset is particularly suited for: * Researchers keen on exploring financial sentiment analysis techniques and models. * Data Scientists working on machine learning applications for textual data in the finance domain. * Financial Analysts looking to integrate sentiment indicators into their market assessments. * Developers creating applications that require understanding the emotional tone of financial statements.

Dataset Name Suggestions

• Financial Sentence Sentiment Corpus
• Global Financial Sentiment Labeled Data
• Market Sentiment Analysis Dataset
• Corporate Financial Text Sentiment

Attributes

Original Data Source:Financial Sentiment Analysis

A. SUMMARY This dataset contains sales tax collected in San Francisco for calendar years 2018 through 2023 (CY 2018 to 2023). Sales tax is aggregated, or summed, at the census block level. However, some census blocks have been combined to maintain the anonymity of businesses based on Taxation Code Section 7056. See “How to use this dataset” below for more details on how the data has been aggregated. Sales tax is collected by businesses on many types of transactions and regulated by the California Department of Tax and Fee Administration. B. HOW THE DATASET IS CREATED Data is collected by HDL. The data is then aggregated based on the criteria outlined in the "How to use this dataset" section. C. UPDATE PROCESS This dataset will be updated annually. D. HOW TO USE THIS DATASET This dataset can be used to analyze sales tax data over time across census blocks in San Francisco. Due to data privacy protection regulations for businesses, sales tax data is not available for all census blocks. Census blocks where there are less than 4 businesses paying sales tax or a single business that pays 80% or more of the total sales tax have been combined with neighboring Census Blocks to protect the confidentiality of affected businesses. Because of this aggregation, some Census Block groups in this dataset may change in future years as the number of businesses in a particular Census Block changes. The historical data changes based on audit findings and amended returns. If census block groupings change, it will happen when the dataset is updated - on an annual basis. These new blocks will be backfilled to previous years. Additionally, business payers with multiple locations (for example chain stores) are excluded because sales tax cannot be tied back to the _location where it was collected. Finally, census blocks in the area field are from 2010 (GEOID10) and not from 2020. A map of this dataset can be viewed here.

BRA16 - Business Demography NACE Rev 2. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Business Demography NACE Rev 2...

The Global Entrepreneurship Monitor [GEM] research program was developed to provide comparisons among countries related to participation of adults in the firm creation process. The initial data was assembled as a pretest of five countries in 1998 and by 2012 over 100 countries had been involved in the program. The initial design for the GEM initiative was based on the first US Panel Study of Entrepreneurial Dynamics, and by 2012 data from 1,827,513 individuals had been gathered in 563 national samples and 6 specialized regional samples. This dataset is a harmonized file capturing results from all of the surveys. The procedure has been to harmonize the basic items across all surveys in all years, followed by implementing a standardized transform to identify those active as nascent entrepreneurs in the start-up process, as owner-managers of new firms, or as owner-managers of established firms. Those identified as nascent entrepreneurs or new business owners are the basis for the Total Entrepreneurial Activity [TEA] or Total Early-Stage index. This harmonized, consolidated assessment not only facilitates comparisons across countries, but provides a basis for temporal comparisons for individual countries. Respondents were queried on the following main topics: general entrepreneurship, start-up activities, ownership and management of the firm, and business angels (angel investors). Respondents were initially screened by way of a series of general questions pertaining to starting a business, such as whether they were currently trying to start a new business, whether they knew anyone who had started a new business, whether they thought it was a good time to start a new business, as well as their perceptions of the income potential and the prestige associated with starting a new business. Demographic variables include respondent age, sex, and employment status.

BRA11 - Business Demography NACE Rev 2. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Business Demography NACE Rev 2...

This GIS layer contains the geographical boundaries of the 2020 census block groups for Loudoun County, Virginia. The 2020 Census block group boundaries are used for Census Bureau statistical data tabulation purposes, including the 2020 Decennial Census and American Community Surveys. Census block groups are part of the sub-county census geography hierarchy of tracts, block groups, and blocks. The three census geographies nest to each other, forming a hierarchy of census tract, followed by block groups, and then blocks, with blocks being the smallest. A census block group is a cluster of census blocks within the same census tract that have the same first digit of their four-digit census block numbers within a census tract. For example, block group 3 within census tract 610700 is a cluster of all the blocks numbered from 3000 to 3999 in that census tract. Block groups are uniquely numbered within census tracts, with the block group's valid range being 0 to 9. Block Groups are designed to be relatively homogeneous units with respect to population characteristics, economic status, and living conditions, census tracts and generally contain between 600 and 3,000 people or 240 and 1,200 housing units. This 2010 Census block group GIS layer's boundaries are based on the U.S. Census Bureau Census 2020 TIGER/Line files. The boundaries are an extract of aerial photography and cartographic information, such as roads and streams, from the Loudoun County GIS system. Census block groups are bounded on all sides by visible features, such as roads, streams, lakes, power lines, and railroad tracks, and/or by non-visible boundaries such as town and county boundaries, and short line-of-sight extensions of streets and roads.

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.

Medical Insurance Expenses & Premium Dataset

This dataset captures demographic and financial information related to medical insurance policyholders. It includes key features such as age, gender, BMI, number of children, discount eligibility status, and the geographic region of the insured. The dataset also provides the actual medical expenses incurred (expenses) and the insurance premium charged (premium).

The purpose of this dataset is to support research and development of machine learning models for predicting healthcare costs, optimizing pricing strategies, and understanding factors that influence insurance expenses and premiums.

Columns

age: Age of the policyholder

gender: Gender (male/female)

bmi: Body Mass Index

children: Number of children covered by the insurance

discount_eligibility: Whether the policyholder is eligible for a discount (yes/no)

region: Geographic region (e.g., southeast, northwest)

expenses: Actual medical costs incurred by the policyholder (Target number 1)

premium: Insurance premium charged (Target number 2)

Example Use Cases

Predicting insurance expenses for new applicants

Analyzing which demographic factors contribute most to higher premiums

Exploring correlations between BMI, age, and healthcare costs

Developing regression and classification models for pricing optimization

This dataset aims to explain the increasing demand for online food delivery services within metropolitan areas, specifically focusing on Bangalore, India. It compiles consumer survey data to shed light on the underlying reasons for this trend. The data is useful for building classification models, such as predicting customer repurchase behaviour, conducting text analysis on consumer reviews, and performing geo-spatial analysis related to consumer locations. This dataset originated from a master's thesis research project.

Columns

The dataset contains nearly 55 variables covering various aspects of consumer demographics and purchase decisions. Key columns include: * Age: The age of the consumer. * Gender: The gender of the consumer (e.g., Male, Female). * Marital Status: The marital status of the consumer (e.g., Single, Married, Other). * Occupation: The job or occupation status of the consumer (e.g., Student, Employee, Other). * Monthly Income: The income bracket of the consumer (e.g., No Income, 25001 to 50000, Other). * Educational Qualifications: The education level of the consumer (e.g., Graduate, Post Graduate, Other). * Family size: The number of family members or friends living with the consumer. * latitude: The latitude of the consumer's residence. * longitude: The longitude of the consumer's residence. * Pin code: The pincode of the consumer's residence within Bangalore. * Overall/general purchase decision: Information related to the consumer's general purchase choices. * Time of delivery influencing the purchase decision: Data on how delivery time affects purchase decisions. * Rating of Restaurant influencing the purchase decision: Data on how restaurant ratings influence purchase decisions.

Distribution

The dataset is structured with nearly 55 variables and is typically provided in a CSV file format. While specific total record counts are not available, value counts for various attributes offer insight into the data distribution. For example, 57% of consumers are Male and 43% are Female. Regarding marital status, 69% are Single, 28% are Married, and 3% fall into other categories. Students represent the largest occupation group at 53%, followed by Employees at 30%. Income distribution shows 48% with No Income. Educational qualifications are split almost evenly between Graduates (46%) and Post Graduates (45%). The data includes ranges and counts for attributes like age, family size, latitude, longitude, and Bangalore pincodes, indicating a diverse set of consumer responses.

Usage

This dataset is ideal for: * Classification modelling: Predicting consumer behaviour, such as whether a consumer will make a repeat purchase. * Text analysis: Analysing consumer reviews to extract insights. * Geo-spatial analysis: Understanding purchasing patterns based on consumer location (latitude and longitude). * Market research: Gaining insights into consumer preferences and demographics impacting online food delivery.

Coverage

The dataset focuses geographically on the Bangalore region in India. It covers a diverse demographic scope of consumers residing in Bangalore, with detailed attributes including age, gender, marital status, occupation, income bracket, educational qualifications, and family size. Distributions for these demographic groups are available within the dataset. A specific time range for the data collection is not explicitly stated.

License

CCO

Who Can Use It

• Market Researchers: To analyse consumer demographics, purchase decisions, and the factors influencing online food delivery in Bangalore.
• Food Delivery Companies: For strategic planning, customer segmentation, predicting repeat purchases, and optimising delivery services and restaurant partnerships.
• Data Scientists and Analysts: For building predictive models, performing geo-spatial analysis, and conducting text mining on consumer feedback.
• Academic Researchers and Students: Particularly those studying consumer behaviour, urban consumption trends, or e-commerce in India.

Dataset Name Suggestions

• Online Food Delivery Preferences - Bangalore Region
• Bangalore Consumer Food Delivery Survey
• Indian Metropolitan Food Delivery Habits
• Consumer Demographics in Online Food Ordering
• Food Delivery Behaviour - Bangalore

Attributes

Original Data Source: Online Food Delivery Preferences-Bangalore region

Self-Employment by Occupation by Town reports the total population of employed civilian workers aged 16 years and older by occupation. In the Business Type column, individual Business Types are presented as percentages of the Total employed population for the corresponding occupation in the Occupation column. For example, for 2012-2016, there were approximately 320,110 Service employees in Connecticut, 14.6% of those employees worked in Government. 'Self-Employed, Incorporated' includes workers in their own incorporated businesses, 'Self-Employed, Not Incorporated' includes workers in their own non-incorporated businesses and unpaid family workers, 'Government' includes local, state, and federal government workers, 'Private, Profit' includes employees of for-profit private companies, 'Private, Not-for-profit' includes both wage and salary employees of not-for-profit private companies. These data originate from the American Community Survey (ACS) 5-Year estimates, table S2406.

Each year, the Forecasting and Trends Office (FTO) publishes population estimates and future year projections. The population estimates can be used for a variety of planning studies including statewide and regional transportation plan updates, subarea and corridor studies, and funding allocations for various planning agencies.The 2020 population estimates reported are based on the US Census Bureau 2020 Decennial Census. The 2021 population estimates are based on the population estimates developed by the Bureau of Economic and Business Research (BEBR) at the University of Florida. BEBR uses the decennial census count for April 1, 2020, as the starting point for state-level projections. More information is available from BEBR here.This dataset contains boundaries for all 2010 Census Urbanized Areas (UAs) in the State of Florida with 2020 census population and 2021 population estimates. It reports population by both UA and county. For example, Pensacola, FL--AL Urbanized Area is located in three counties: Escambia County, FL, Santa Rosa County, FL, and Baldwin County, AL. This dataset contains three records that report Pensacola, FL—AL UA’s population that live in each county separately. All legal boundaries and names in this dataset are from the US Census Bureau’s TIGER/Line Files (2021).BEBR provides 2021 population estimates for counties in Florida. However, UA boundaries may not coincide with the jurisdictional boundaries of counties and UAs often spread into several counties. To estimate the population for an UA, first the ratio of the subject UA that is contained within a county (or sub-area) to the area of the entire county was determined. That ratio was multiplied by the estimated county population to obtain the population for that sub-area. The population for the entire UA is the sum of all sub-area populations estimated from the counties they are located within.For the 2010 Census, urban areas comprised a “densely settled core of census tracts and/or census blocks that meet minimum population density requirements, along with adjacent territory containing non-residential urban land uses as well as territory with low population density included to link outlying densely settled territory with the densely settled core.” In 2010, the US Census Bureau identified two types of urban areas—UAs and Urban Clusters (UCs). UAs have a population of 50,000 or more people. Note: Pensacola, FL--AL Urbanized Area is the only Urbanized Area in Florida that crosses the state border. 2021 population of Baldwin County, AL used for this estimation is from the US Census annual population estimates (2020-2021). Please see the Data Dictionary for more information on data fields. Data Sources:US Census Bureau 2020 Decennial CensusUS Census Bureau’s TIGER/Line Files (2021)Bureau of Economic and Business Research (BEBR) – Florida Estimates of Population 2021 Data Coverage: StatewideData Time Period: 2020 – 2021 Date of Publication: July 2022 Point of Contact:Dana Reiding, ManagerForecasting and Trends OfficeFlorida Department of TransportationDana.Reiding@dot.state.fl.us605 Suwannee Street, Tallahassee, Florida 32399850-414-4719

Key Table Information.Table Title.Nonemployer Statistics by Demographics series (NES-D): Statistics for Employer and Nonemployer Firms by Industry and Race for the U.S., States, and Metro Areas: 2018.Table ID.ABSNESD2018.AB00MYNESD01C.Survey/Program.Economic Surveys.Year.2018.Dataset.ECNSVY Nonemployer Statistics by Demographics Company Summary.Source.U.S. Census Bureau, 2018 Economic Surveys, Nonemployer Statistics by Demographics.Release Date.2021-12-16.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 2019 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 2017 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 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 2019 ABS collection year produces statistics for the 2018 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) Race White Black or African American American Indian and Alaska Native Asian Native Hawaiian and Other Pacific Islander Minority (Firms classified as any race and ethnicity combination other than non-Hispanic and White) Equally minority/nonminority Nonminority (Firms classified as non-Hispanic and White) 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.Data are shown for the total for all sectors (00) and the 2-digit NAICS levels for the U.S., states and District of Columbia, and metro areas.For information about geographies, see Geographies..Industry Coverage.The data are shown for the total of all sectors ("00"), and at the 2-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)Management of Companies and Enterprises (NAICS 55)Private Households (NAICS 814)Public Administration (NAICS 92)Industries Not Classified (NAICS 99)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 income tax records that the Internal Revenue Service (IRS) provides to the Census Bureau. The NES-D adds demographic characteristics to the NES da...

Abstract

The documented dataset covers Enterprise Survey (ES) panel data collected in Sierra Leone in 2009 and 2017, as part of the Enterprise Survey initiative of the World Bank. An Indicator Survey is similar to an Enterprise Survey; it is implemented for smaller economies where the sampling strategies inherent in an Enterprise Survey are often not applicable due to the limited universe of firms.

The objective of the 2009-2017 survey is to obtain feedback from enterprises in client countries on the state of the private sector as well as to build a panel of enterprise data that will make it possible to track changes in the business environment over time and allow, for example, impact assessments of reforms. Through interviews with firms in the manufacturing and services sectors, the Indicator Survey data provides information on the constraints to private sector growth and is used to create statistically significant business environment indicators that are comparable across countries. As part of its strategic goal of building a climate for investment, job creation, and sustainable growth, the World Bank has promoted improving the business environment as a key strategy for development, which has led to a systematic effort in collecting enterprise data across countries. The Enterprise Surveys (ES) are an ongoing World Bank project in collecting both objective data based on firms' experiences and enterprises' perception of the environment in which they operate.

Questionnaire topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, land and permits, taxation, business-government relations, performance measures, AIDS and sickness. The mode of data collection is face-to-face interviews.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is the 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 the universe, covered in the Enterprise Surveys is the non-agricultural economy. It comprises: all manufacturing sectors according to the ISIC Revision 3.1 group classification (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this population 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 for registered establishments in Sierra Leone was selected using stratified random sampling, following the methodology explained in the Sampling Note.

Stratified random sampling was preferred over simple random sampling for several reasons: a. To obtain unbiased estimates for different subdivisions of the population with some known level of precision. b. To obtain unbiased estimates for the whole population. The whole population, or universe of the study, is 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. c. To make sure that the final total sample includes establishments from all different sectors and that it is not concentrated in one or two of industries/sizes/regions. d. To exploit the benefits of stratified sampling where population estimates, in most cases, will be more precise than using a simple random sampling method (i.e., lower standard errors, other things being equal.) e. Stratification may produce a smaller bound on the error of estimation than would be produced by a simple random sample of the same size. This result is particularly true if measurements within strata are homogeneous. f. The cost per observation in the survey may be reduced by stratification of the population elements into convenient groupings.

Three levels of stratification were used in the Sierra Leone sample: firm sector, firm size, and geographic region.

Industry stratification was designed as follows: the universe was stratified into one manufacturing industry and one services industry (retail).

Size stratification was defined following the standardized definition used for the Indicator Surveys: 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.

Regional stratification was defined in terms of the geographic regions with the largest commercial presence in the country: Kenema and W/A Urban. In 2017, regional stratification was done across four regions: Bo, Western Urban, Kenema, and Bombali.

Given the stratified design, sample frames containing a complete and updated list of establishments as well as information on all stratification variables (number of employees, industry, and region) are required to draw the sample. Great efforts were made to obtain the best source for these listings.

The sample frame consisted of listings of firms from two sources: For panel firms the list of 150 firms from the Sierra Leone 2009 ES was used and for fresh firms (i.e., firms not covered in 2009) firm data from 2016 Business Establishment Census and Dun & Bradstreet Global database (June 2017), was used.

Necessary measures were taken to ensure the quality of the frame; however, the sample frame was not immune to the typical problems found in establishment surveys: positive rates of non-eligibility, repetition, non-existent units, etc.

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 8.9% (18 out of 202 establishments).

Mode of data collection

Face-to-face [f2f]

Research instrument

The current survey instruments are available: - Services and Manufacturing Questionnaire - Screener Questionnaire.

The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90% of the questions objectively ascertain characteristics of a country's business environment. The remaining questions assess the survey respondents' opinions on what are the obstacles to firm growth and performance.

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

There was a high response rate especially as a result of positive attitude towards the international community in collaboration with the government in their reconstruction efforts after a period of civil strife. It is period in which a lot of statistics is being collected by the Sierra Leone Statistics for reconstruction thus most respondents were enlightened on research benefits.