The Market Research and Statistical Services industry has performed poorly because of mixed demand across years for market research and related services. Industry revenue is anticipated to shrink at an annualised 1.3% over the five years through 2024-25, totalling $3.6 billion, with revenue falling by 1.5% in the current year. The overall revenue decrease can be attributed to mixed growth in prior years because of uncertainty and demand changes in response to the COVID-19 pandemic and ABS funding volatility. Industry revenue displays significant volatility from year to year, mainly because of fluctuations in ABS funding by the Federal Government. As the next census is set to occur in 2026, ABS revenue over the past two years has been constrained. Some companies that previously used industry businesses have been increasingly performing market research and statistical analysis in-house. Many external companies have improved their technology and data collection capabilities, which has made it more cost-effective to perform these activities internally. While the introduction of artificial intelligence has provided cost-cutting opportunities for market research businesses, it has also encouraged clients to bring industry services in-house, reducing demand. Profitability has also waned because of heightened price competition and wage costs increasing as a share of revenue. Ongoing growth in online media and big data presents both challenges and opportunities for market research businesses. Mounting demand for research and statistics relating to new media audience numbers and advertising effectiveness represents a potential opportunity. Even so, market research businesses will face challenges in developing effective measurement systems, and competition from information technology specialists that are developing similar systems will intensify. Despite these challenges, industry revenue is forecast to increase at an annualised 2.0% through 2029-30 to reach $3.9 billion.

The Bureau of the Census has released Census 2000 Summary File 1 (SF1) 100-Percent data. The file includes the following population items: sex, age, race, Hispanic or Latino origin, household relationship, and household and family characteristics. Housing items include occupancy status and tenure (whether the unit is owner or renter occupied). SF1 does not include information on incomes, poverty status, overcrowded housing or age of housing. These topics will be covered in Summary File 3. Data are available for states, counties, county subdivisions, places, census tracts, block groups, and, where applicable, American Indian and Alaskan Native Areas and Hawaiian Home Lands. The SF1 data are available on the Bureau's web site and may be retrieved from American FactFinder as tables, lists, or maps. Users may also download a set of compressed ASCII files for each state via the Bureau's FTP server. There are over 8000 data items available for each geographic area. The full listing of these data items is available here as a downloadable compressed data base file named TABLES.ZIP. The uncompressed is in FoxPro data base file (dbf) format and may be imported to ACCESS, EXCEL, and other software formats. While all of this information is useful, the Office of Community Planning and Development has downloaded selected information for all states and areas and is making this information available on the CPD web pages. The tables and data items selected are those items used in the CDBG and HOME allocation formulas plus topics most pertinent to the Comprehensive Housing Affordability Strategy (CHAS), the Consolidated Plan, and similar overall economic and community development plans. The information is contained in five compressed (zipped) dbf tables for each state. When uncompressed the tables are ready for use with FoxPro and they can be imported into ACCESS, EXCEL, and other spreadsheet, GIS and database software. The data are at the block group summary level. The first two characters of the file name are the state abbreviation. The next two letters are BG for block group. Each record is labeled with the code and name of the city and county in which it is located so that the data can be summarized to higher-level geography. The last part of the file name describes the contents . The GEO file contains standard Census Bureau geographic identifiers for each block group, such as the metropolitan area code and congressional district code. The only data included in this table is total population and total housing units. POP1 and POP2 contain selected population variables and selected housing items are in the HU file. The MA05 table data is only for use by State CDBG grantees for the reporting of the racial composition of beneficiaries of Area Benefit activities. The complete package for a state consists of the dictionary file named TABLES, and the five data files for the state. The logical record number (LOGRECNO) links the records across tables.

About this dataset

Context

The dataset tabulates the population of Bad Axe by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Bad Axe across both sexes and to determine which sex constitutes the majority.

Key observations

There is a majority of female population, with 57.68% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Bad Axe is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Bad Axe total population. 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 Bad Axe Population by Gender. You can refer the same here

Key Table Information.Table Title.Manufacturing: E-Commerce Statistics for the U.S.: 2022.Table ID.ECNECOMM2022.EC2231ECOMM.Survey/Program.Economic Census.Year.2022.Dataset.ECN Core Statistics Manufacturing: E-Commerce Statistics for the U.S.: 2022.Release Date.2025-01-23.Release Schedule.The Economic Census occurs every five years, in years ending in 2 and 7.The data in this file come from the 2022 Economic Census data files released on a flow basis starting in January 2024 with First Look Statistics. Preliminary U.S. totals released in January 2024 are superseded with final data shown in the releases of later economic census statistics through March 2026.For more information about economic census planned data product releases, see 2022 Economic Census Release Schedule..Dataset Universe.The dataset universe consists of all establishments that are in operation for at least some part of 2022, are located in one of the 50 U.S. states, associated offshore areas, or the District of Columbia, have paid employees, and are classified in one of nineteen in-scope sectors defined by the 2022 North American Industry Classification System (NAICS)..Methodology.Data Items and Other Identifying Records.Sales, value of shipments, or revenue ($1,000)E-Shipments value ($1,000) E-Shipments as percent of total sales, value of shipments, or revenue (%) Range indicating imputed percentage of total sales, value of shipments, or revenueDefinitions 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 economic census are employer establishments. An establishment is generally a single physical location where business is conducted or where services or industrial operations are performed. A company or firm is comprised of one or more in-scope establishments that operate under the ownership or control of a single organization. For some industries, the reporting units are instead groups of all establishments in the same industry belonging to the same firm..Geography Coverage.The data are shown for the U.S. level only. For information about economic census geographies, including changes for 2022, see Geographies..Industry Coverage.The data are shown at the 2- through 3-digit 2022 NAICS code levels for the U.S. For information about NAICS, see Economic Census Code Lists..Sampling.The 2022 Economic Census sample includes all active operating establishments of multi-establishment firms and approximately 1.7 million single-establishment firms, stratified by industry and state. Establishments selected to the sample receive a questionnaire. For all data on this table, establishments not selected into the sample are represented with administrative data. For more information about the sample design, see 2022 Economic Census Methodology..Confidentiality.The Census Bureau has reviewed this data product to ensure appropriate access, use, and disclosure avoidance protection of the confidential source data (Project No. 7504609, Disclosure Review Board (DRB) approval number: CBDRB-FY23-099).To protect confidentiality, the U.S. Census Bureau suppresses cell values to minimize the risk of identifying a particular business’ data or identity.To comply with disclosure avoidance guidelines, data rows with fewer than three contributing firms or three contributing establishments are not presented. Additionally, establishment counts are suppressed when other select statistics in the same row are suppressed. More information on disclosure avoidance is available in the 2022 Economic Census Methodology..Technical Documentation/Methodology.For detailed information about the methods used to collect data and produce statistics, survey questionnaires, Primary Business Activity/NAICS codes, NAPCS codes, and more, see Economic Census Technical Documentation..Weights.No weighting applied as establishments not sampled are represented with administrative data..Table Information.FTP Download.https://www2.census.gov/programs-surveys/economic-census/data/2022/sector31/.API Information.Economic census data are housed in the Census Bureau Application Programming Interface (API)..Symbols.D - Withheld to avoid disclosing data for individual companies; data are included in higher level totalsN - Not available or not comparableS - Estimate does not meet publication standards because of high sampling variability, poor response quality, or other concerns about the estimate quality. Unpublished estimates derived from this table by subtraction are subject to these same limitations and should not be attributed to the U.S. Census Bureau. For a description of publication standards and the total quantity response rate, see link to program methodology page.X - Not applicableA - Relative standard error of 100% or morer - Reviseds - Relative standard error exceeds 40%For a complete list of symbols, see Economic Census Data Dictionary..Data-Specific Notes.Data users who create their own es...

About this dataset

Context

The dataset presents median household incomes for various household sizes in Bad Axe, MI, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, Bad Axe did not include 6, or 7-person households. Across the different household sizes in Bad Axe the mean income is $72,260, and the standard deviation is $32,915. The coefficient of variation (CV) is 45.55%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2023, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $22,200. It then further increased to $68,333 for 5-person households, the largest household size for which the bureau reported a median household income.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific household size.

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 Bad Axe median household income. You can refer the same here

Key Table Information.Table Title.Information: Summary Statistics for the U.S., States, and Selected Geographies: 2022.Table ID.ECNBASIC2022.EC2251BASIC.Survey/Program.Economic Census.Year.2022.Dataset.ECN Core Statistics Summary Statistics for the U.S., States, and Selected Geographies: 2022.Source.U.S. Census Bureau, 2022 Economic Census, Core Statistics.Release Date.2024-12-05.Dataset Universe.The dataset universe consists of all establishments that are in operation for at least some part of 2022, are located in one of the 50 U.S. states, associated offshore areas, or the District of Columbia, have paid employees, and are classified in one of nineteen in-scope sectors defined by the 2022 North American Industry Classification System (NAICS)..Methodology.Data Items and Other Identifying Records.Number of firmsNumber of establishmentsSales, value of shipments, or revenue ($1,000)Annual payroll ($1,000)First-quarter payroll ($1,000)Number of employeesRange indicating imputed percentage of total sales, value of shipments, or revenueRange indicating imputed percentage of total annual payrollRange indicating imputed percentage of total employeesDefinitions 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 economic census are employer establishments. An establishment is generally a single physical location where business is conducted or where services or industrial operations are performed. A company or firm is comprised of one or more in-scope establishments that operate under the ownership or control of a single organization. For some industries, the reporting units are instead groups of all establishments in the same industry belonging to the same firm..Geography Coverage.The data are shown for the U.S., State, Combined Statistical Area, Metropolitan and Micropolitan Statistical Area, Metropolitan Division, Consolidated City, County (and equivalent), and Economic Place (and equivalent; incorporated and unincorporated) levels that vary by industry. For information about economic census geographies, including changes for 2022, see Geographies..Industry Coverage.The data are shown at the 2- through 6-digit 2022 NAICS code levels and selected 7-digit 2022 NAICS-based code levels. For information about NAICS, see Economic Census Code Lists..Sampling.The 2022 Economic Census sample includes all active operating establishments of multi-establishment firms and approximately 1.7 million single-establishment firms, stratified by industry and state. Establishments selected to the sample receive a questionnaire. For all data on this table, establishments not selected into the sample are represented with administrative data. For more information about the sample design, see 2022 Economic Census Methodology..Confidentiality.The Census Bureau has reviewed this data product to ensure appropriate access, use, and disclosure avoidance protection of the confidential source data (Project No. 7504609, Disclosure Review Board (DRB) approval number: CBDRB-FY23-099).To protect confidentiality, the U.S. Census Bureau suppresses cell values to minimize the risk of identifying a particular business’ data or identity.To comply with disclosure avoidance guidelines, data rows with fewer than three contributing firms or three contributing establishments are not presented. Additionally, establishment counts are suppressed when other select statistics in the same row are suppressed. More information on disclosure avoidance is available in the 2022 Economic Census Methodology..Technical Documentation/Methodology.For detailed information about the methods used to collect data and produce statistics, survey questionnaires, Primary Business Activity/NAICS codes, NAPCS codes, and more, see Economic Census Technical Documentation..Weights.No weighting applied as establishments not sampled are represented with administrative data..Table Information.FTP Download.https://www2.census.gov/programs-surveys/economic-census/data/2022/.API Information.Economic census data are housed in the Census Bureau Application Programming Interface (API)..Symbols.D - Withheld to avoid disclosing data for individual companies; data are included in higher level totalsN - Not available or not comparableS - Estimate does not meet publication standards because of high sampling variability, poor response quality, or other concerns about the estimate quality. Unpublished estimates derived from this table by subtraction are subject to these same limitations and should not be attributed to the U.S. Census Bureau. For a description of publication standards and the total quantity response rate, see link to program methodology page.X - Not applicableA - Relative standard error of 100% or morer - Reviseds - Relative standard error exceeds 40%For a complete list of symbols, see Economic Census Data Dictionary..Data-Specific Notes.Data users who create their own estimates us...

Key Table Information.Table Title.Mining: Location of Mining Establishments by Employment Size for the U.S., States, and Offshore Areas: 2022.Table ID.ECNLOCMINE2022.EC2221LOCMINE.Survey/Program.Economic Census.Year.2022.Dataset.ECN Sector Statistics Economic Census: Mining: Location of Mines by Employment Size for Subsectors and Industries for the U.S., States, and Offshore Areas.Source.U.S. Census Bureau, 2022 Economic Census, Sector Statistics.Release Date.2025-05-15.Release Schedule.The Economic Census occurs every five years, in years ending in 2 and 7.The data in this file come from the 2022 Economic Census data files released on a flow basis starting in January 2024 with First Look Statistics. Preliminary U.S. totals released in January 2024 are superseded with final data shown in the releases of later economic census statistics through March 2026.For more information about economic census planned data product releases, see 2022 Economic Census Release Schedule..Dataset Universe.The dataset universe consists of all establishments that are in operation for at least some part of 2022, are located in one of the 50 U.S. states, associated offshore areas, or the District of Columbia, have paid employees, and are classified in one of nineteen in-scope sectors defined by the 2022 North American Industry Classification System (NAICS)..Methodology.Data Items and Other Identifying Records.Employment size of establishmentsNumber of establishmentsDefinitions 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 economic census are employer establishments. An establishment is generally a single physical location where business is conducted or where services or industrial operations are performed. A company or firm is comprised of one or more in-scope establishments that operate under the ownership or control of a single organization. For some industries, the reporting units are instead groups of all establishments in the same industry belonging to the same firm..Geography Coverage.The data are shown for the U.S., State, and Offshore Area levels that vary by industry. For information about economic census geographies, including changes for 2022, see Geographies..Industry Coverage.The data are shown at the 2- through 6-digit 2022 NAICS code levels for U.S., States, and Offshore Area. For information about NAICS, see Economic Census Code Lists..Sampling.The 2022 Economic Census sample includes all active operating establishments of multi-establishment firms and approximately 1.7 million single-establishment firms, stratified by industry and state. Establishments selected to the sample receive a questionnaire. For all data on this table, establishments not selected into the sample are represented with administrative data. For more information about the sample design, see 2022 Economic Census Methodology..Confidentiality.The Census Bureau has reviewed this data product to ensure appropriate access, use, and disclosure avoidance protection of the confidential source data (Project No. 7504609, Disclosure Review Board (DRB) approval number: CBDRB-FY23-099).To protect confidentiality, the U.S. Census Bureau suppresses cell values to minimize the risk of identifying a particular business’ data or identity.To comply with disclosure avoidance guidelines, data rows with fewer than three contributing firms or three contributing establishments are not presented. Additionally, establishment counts are suppressed when other select statistics in the same row are suppressed. More information on disclosure avoidance is available in the 2022 Economic Census Methodology..Technical Documentation/Methodology.For detailed information about the methods used to collect data and produce statistics, survey questionnaires, Primary Business Activity/NAICS codes, NAPCS codes, and more, see Economic Census Technical Documentation..Weights.No weighting applied as establishments not sampled are represented with administrative data..Table Information.FTP Download.https://www2.census.gov/programs-surveys/economic-census/data/2022/sector21/.API Information.Economic census data are housed in the Census Bureau Application Programming Interface (API)..Symbols.D - Withheld to avoid disclosing data for individual companies; data are included in higher level totalsN - Not available or not comparableS - Estimate does not meet publication standards because of high sampling variability, poor response quality, or other concerns about the estimate quality. Unpublished estimates derived from this table by subtraction are subject to these same limitations and should not be attributed to the U.S. Census Bureau. For a description of publication standards and the total quantity response rate, see link to program methodology page.X - Not applicableA - Relative standard error of 100% or morer - Reviseds - Relative standard error exceeds 40%For a complete list of sy...

Abstract

The Project for Statistics on Living standards and Development was a countrywide World Bank Living Standards Measurement Survey. It covered approximately 9000 households, drawn from a representative sample of South African households. The fieldwork was undertaken during the nine months leading up to the country's first democratic elections at the end of April 1994. The purpose of the survey was to collect statistical information about the conditions under which South Africans live in order to provide policymakers with the data necessary for planning strategies. This data would aid the implementation of goals such as those outlined in the Government of National Unity's Reconstruction and Development Programme.

Geographic coverage

National

Analysis unit

Households

Universe

All Household members. Individuals in hospitals, old age homes, hotels and hostels of educational institutions were not included in the sample. Migrant labour hostels were included. In addition to those that turned up in the selected ESDs, a sample of three hostels was chosen from a national list provided by the Human Sciences Research Council and within each of these hostels a representative sample was drawn on a similar basis as described above for the households in ESDs.

Kind of data

Sample survey data [ssd]

Sampling procedure

(a) SAMPLING DESIGN

Sample size is 9,000 households. The sample design adopted for the study was a two-stage self-weighting design in which the first stage units were Census Enumerator Subdistricts (ESDs, or their equivalent) and the second stage were households. The advantage of using such a design is that it provides a representative sample that need not be based on accurate census population distribution in the case of South Africa, the sample will automatically include many poor people, without the need to go beyond this and oversample the poor. Proportionate sampling as in such a self-weighting sample design offers the simplest possible data files for further analysis, as weights do not have to be added. However, in the end this advantage could not be retained, and weights had to be added.

(b) SAMPLE FRAME

The sampling frame was drawn up on the basis of small, clearly demarcated area units, each with a population estimate. The nature of the self-weighting procedure adopted ensured that this population estimate was not important for determining the final sample, however. For most of the country, census ESDs were used. Where some ESDs comprised relatively large populations as for instance in some black townships such as Soweto, aerial photographs were used to divide the areas into blocks of approximately equal population size. In other instances, particularly in some of the former homelands, the area units were not ESDs but villages or village groups. In the sample design chosen, the area stage units (generally ESDs) were selected with probability proportional to size, based on the census population. Systematic sampling was used throughout that is, sampling at fixed interval in a list of ESDs, starting at a randomly selected starting point. Given that sampling was self-weighting, the impact of stratification was expected to be modest. The main objective was to ensure that the racial and geographic breakdown approximated the national population distribution. This was done by listing the area stage units (ESDs) by statistical region and then within the statistical region by urban or rural. Within these sub-statistical regions, the ESDs were then listed in order of percentage African. The sampling interval for the selection of the ESDs was obtained by dividing the 1991 census population of 38,120,853 by the 300 clusters to be selected. This yielded 105,800. Starting at a randomly selected point, every 105,800th person down the cluster list was selected. This ensured both geographic and racial diversity (ESDs were ordered by statistical sub-region and proportion of the population African). In three or four instances, the ESD chosen was judged inaccessible and replaced with a similar one. In the second sampling stage the unit of analysis was the household. In each selected ESD a listing or enumeration of households was carried out by means of a field operation. From the households listed in an ESD a sample of households was selected by systematic sampling. Even though the ultimate enumeration unit was the household, in most cases "stands" were used as enumeration units. However, when a stand was chosen as the enumeration unit all households on that stand had to be interviewed.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

All the questionnaires were checked when received. Where information was incomplete or appeared contradictory, the questionnaire was sent back to the relevant survey organization. As soon as the data was available, it was captured using local development platform ADE. This was completed in February 1994. Following this, a series of exploratory programs were written to highlight inconsistencies and outlier. For example, all person level files were linked together to ensure that the same person code reported in different sections of the questionnaire corresponded to the same person. The error reports from these programs were compared to the questionnaires and the necessary alterations made. This was a lengthy process, as several files were checked more than once, and completed at the beginning of August 1994. In some cases, questionnaires would contain missing values, or comments that the respondent did not know, or refused to answer a question.

These responses are coded in the data files with the following values: VALUE MEANING -1 : The data was not available on the questionnaire or form -2 : The field is not applicable -3 : Respondent refused to answer -4 : Respondent did not know answer to question

Data appraisal

The data collected in clusters 217 and 218 should be viewed as highly unreliable and therefore removed from the data set. The data currently available on the web site has been revised to remove the data from these clusters. Researchers who have downloaded the data in the past should revise their data sets. For information on the data in those clusters, contact SALDRU http://www.saldru.uct.ac.za/.

Abstract

The main objective of the HEIS survey is to obtain detailed data on household expenditure and income, linked to various demographic and socio-economic variables, to enable computation of poverty indices and determine the characteristics of the poor and prepare poverty maps. Therefore, to achieve these goals, the sample had to be representative on the sub-district level. The raw survey data provided by the Statistical Office was cleaned and harmonized by the Economic Research Forum, in the context of a major research project to develop and expand knowledge on equity and inequality in the Arab region. The main focus of the project is to measure the magnitude and direction of change in inequality and to understand the complex contributing social, political and economic forces influencing its levels. However, the measurement and analysis of the magnitude and direction of change in this inequality cannot be consistently carried out without harmonized and comparable micro-level data on income and expenditures. Therefore, one important component of this research project is securing and harmonizing household surveys from as many countries in the region as possible, adhering to international statistics on household living standards distribution. Once the dataset has been compiled, the Economic Research Forum makes it available, subject to confidentiality agreements, to all researchers and institutions concerned with data collection and issues of inequality.

Data collected through the survey helped in achieving the following objectives: 1. Provide data weights that reflect the relative importance of consumer expenditure items used in the preparation of the consumer price index 2. Study the consumer expenditure pattern prevailing in the society and the impact of demographic and socio-economic variables on those patterns 3. Calculate the average annual income of the household and the individual, and assess the relationship between income and different economic and social factors, such as profession and educational level of the head of the household and other indicators 4. Study the distribution of individuals and households by income and expenditure categories and analyze the factors associated with it 5. Provide the necessary data for the national accounts related to overall consumption and income of the household sector 6. Provide the necessary income data to serve in calculating poverty indices and identifying the poor characteristics as well as drawing poverty maps 7. Provide the data necessary for the formulation, follow-up and evaluation of economic and social development programs, including those addressed to eradicate poverty

Geographic coverage

National

Analysis unit

• Households
• Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

The Household Expenditure and Income survey sample for 2010, was designed to serve the basic objectives of the survey through providing a relatively large sample in each sub-district to enable drawing a poverty map in Jordan. The General Census of Population and Housing in 2004 provided a detailed framework for housing and households for different administrative levels in the country. Jordan is administratively divided into 12 governorates, each governorate is composed of a number of districts, each district (Liwa) includes one or more sub-district (Qada). In each sub-district, there are a number of communities (cities and villages). Each community was divided into a number of blocks. Where in each block, the number of houses ranged between 60 and 100 houses. Nomads, persons living in collective dwellings such as hotels, hospitals and prison were excluded from the survey framework.

A two stage stratified cluster sampling technique was used. In the first stage, a cluster sample proportional to the size was uniformly selected, where the number of households in each cluster was considered the weight of the cluster. At the second stage, a sample of 8 households was selected from each cluster, in addition to another 4 households selected as a backup for the basic sample, using a systematic sampling technique. Those 4 households were sampled to be used during the first visit to the block in case the visit to the original household selected is not possible for any reason. For the purposes of this survey, each sub-district was considered a separate stratum to ensure the possibility of producing results on the sub-district level. In this respect, the survey framework adopted that provided by the General Census of Population and Housing Census in dividing the sample strata. To estimate the sample size, the coefficient of variation and the design effect of the expenditure variable provided in the Household Expenditure and Income Survey for the year 2008 was calculated for each sub-district. These results were used to estimate the sample size on the sub-district level so that the coefficient of variation for the expenditure variable in each sub-district is less than 10%, at a minimum, of the number of clusters in the same sub-district (6 clusters). This is to ensure adequate presentation of clusters in different administrative areas to enable drawing an indicative poverty map.

It should be noted that in addition to the standard non response rate assumed, higher rates were expected in areas where poor households are concentrated in major cities. Therefore, those were taken into consideration during the sampling design phase, and a higher number of households were selected from those areas, aiming at well covering all regions where poverty spreads.

Mode of data collection

Face-to-face [f2f]

Research instrument

• General form
• Expenditure on food commodities form
• Expenditure on non-food commodities form

Cleaning operations

Raw Data: - Organizing forms/questionnaires: A compatible archive system was used to classify the forms according to different rounds throughout the year. A registry was prepared to indicate different stages of the process of data checking, coding and entry till forms were back to the archive system. - Data office checking: This phase was achieved concurrently with the data collection phase in the field where questionnaires completed in the field were immediately sent to data office checking phase. - Data coding: A team was trained to work on the data coding phase, which in this survey is only limited to education specialization, profession and economic activity. In this respect, international classifications were used, while for the rest of the questions, coding was predefined during the design phase. - Data entry/validation: A team consisting of system analysts, programmers and data entry personnel were working on the data at this stage. System analysts and programmers started by identifying the survey framework and questionnaire fields to help build computerized data entry forms. A set of validation rules were added to the entry form to ensure accuracy of data entered. A team was then trained to complete the data entry process. Forms prepared for data entry were provided by the archive department to ensure forms are correctly extracted and put back in the archive system. A data validation process was run on the data to ensure the data entered is free of errors. - Results tabulation and dissemination: After the completion of all data processing operations, ORACLE was used to tabulate the survey final results. Those results were further checked using similar outputs from SPSS to ensure that tabulations produced were correct. A check was also run on each table to guarantee consistency of figures presented, together with required editing for tables' titles and report formatting.

Harmonized Data: - The Statistical Package for Social Science (SPSS) was used to clean and harmonize the datasets. - The harmonization process started with cleaning all raw data files received from the Statistical Office. - Cleaned data files were then merged to produce one data file on the individual level containing all variables subject to harmonization. - A country-specific program was generated for each dataset to generate/compute/recode/rename/format/label harmonized variables. - A post-harmonization cleaning process was run on the data. - Harmonized data was saved on the household as well as the individual level, in SPSS and converted to STATA format.

Key Table Information.Table Title.Manufacturing: Materials Consumed by Kind of Industry for the U.S.: 2022.Table ID.ECNMATFUEL2022.EC2231MATFUEL.Survey/Program.Economic Census.Year.2022.Dataset.ECN Sector Statistics Combined version: Manufacturing and Mining: Materials Consumed and Selected Supplies, Minerals Received for Preparation, Purchased Machinery and Fuels Consumed by Type of Industry for the U.S..Source.U.S. Census Bureau, 2022 Economic Census, Sector Statistics.Release Date.2025-05-08.Release Schedule.The Economic Census occurs every five years, in years ending in 2 and 7.The data in this file come from the 2022 Economic Census data files released on a flow basis starting in January 2024 with First Look Statistics. Preliminary U.S. totals released in January 2024 are superseded with final data shown in the releases of later economic census statistics through March 2026.For more information about economic census planned data product releases, see 2022 Economic Census Release Schedule..Dataset Universe.The dataset universe consists of all establishments that are in operation for at least some part of 2022, are located in one of the 50 U.S. states, associated offshore areas, or the District of Columbia, have paid employees, and are classified in one of nineteen in-scope sectors defined by the 2022 North American Industry Classification System (NAICS)..Methodology.Data Items and Other Identifying Records.Material or fuel codeDelivered cost ($1,000)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 economic census are employer establishments. An establishment is generally a single physical location where business is conducted or where services or industrial operations are performed. A company or firm is comprised of one or more in-scope establishments that operate under the ownership or control of a single organization. For some industries, the reporting units are instead groups of all establishments in the same industry belonging to the same firm..Geography Coverage.The data are shown for the U.S. level only. For information about economic census geographies, including changes for 2022, see Geographies..Industry Coverage.The data are shown at the 6-digit 2022 NAICS code level. For information about NAICS, see Economic Census Code Lists..Sampling.The 2022 Economic Census sample includes all active operating establishments of multi-establishment firms and approximately 1.7 million single-establishment firms, stratified by industry and state. Establishments selected to the sample receive a questionnaire. For all data on this table, establishments not selected into the sample are represented with administrative data. For more information about the sample design, see 2022 Economic Census Methodology..Confidentiality.The Census Bureau has reviewed this data product to ensure appropriate access, use, and disclosure avoidance protection of the confidential source data (Project No. 7504609, Disclosure Review Board (DRB) approval number: CBDRB-FY23-099).To protect confidentiality, the U.S. Census Bureau suppresses cell values to minimize the risk of identifying a particular business’ data or identity.To comply with disclosure avoidance guidelines, data rows with fewer than three contributing firms or three contributing establishments are not presented. Additionally, establishment counts are suppressed when other select statistics in the same row are suppressed. More information on disclosure avoidance is available in the 2022 Economic Census Methodology..Technical Documentation/Methodology.For detailed information about the methods used to collect data and produce statistics, survey questionnaires, Primary Business Activity/NAICS codes, NAPCS codes, and more, see Economic Census Technical Documentation..Weights.No weighting applied as establishments not sampled are represented with administrative data..Table Information.FTP Download.https://www2.census.gov/programs-surveys/economic-census/data/2022/API_Datasets/.API Information.Economic census data are housed in the Census Bureau Application Programming Interface (API)..Symbols.D - Withheld to avoid disclosing data for individual companies; data are included in higher level totalsN - Not available or not comparableS - Estimate does not meet publication standards because of high sampling variability, poor response quality, or other concerns about the estimate quality. Unpublished estimates derived from this table by subtraction are subject to these same limitations and should not be attributed to the U.S. Census Bureau. For a description of publication standards and the total quantity response rate, see link to program methodology page.X - Not applicableA - Relative standard error of 100% or morer - Reviseds - Relative standard error exceeds 40%For a complete list of symbols, see Economic Census Data Dictionary..Data-Specific Notes.Data users w...

Abstract

Mauritania, like many countries in the Sahel, regularly face recurrent plagues such as droughts, floods, bird invasions, off-season rains, as well as, regional security issues. Drought, for example, is a common phenomenon in the south of Mauritania, which favors food insecurity and malnutrition, and significantly reduces household resilience while increasing their vulnerability to future shocks. Apart from the fact that only 0.5 percent of the land is suitable for agriculture, Mauritania consists of reliefs and very large, fragile agroecological complexes which are also faced with the effects of climate change.

In recent years, food crises and nutritional factors have been regularly observed due to structural causes which has poverty as its common denominator. These crises, as well as, climatic factors have a negative consequence on natural resources and reduce the resilience of livelihoods, thereby generating a loss in productivity and poor governance of natural resources. The concept of resilience generally defines the capacity of individuals, households, communities and countries to absorb shocks and adapt to a changing environment, while transforming the institutional environment in the long term. Thus, it is necessary to set up interventions that will have an impact on adaptability and risk management over time, in order to strengthen the resilience of vulnerable households.

For more than 10 years, FAO has measured the household resilience index in different countries, using a tool developed for this purpose; Resilience Index Measure and Analysis (RIMA). RIMA analysis requires household data, covering the different aspects of livelihood; activities (productive and non-productive), social safety nets, income, access to basic services (such as schools, markets, transportation etc.) and adaptive capacity. Following the two RIMA surveys carried out in 2015 and 2016 during the lean season and the post-harvest period, this survey was carried out in 2017 to determine the resilience index in all regions of the country.

Geographic coverage

National coverage

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling size used in the household survey was determined by the FAO - ESA statistical team based on the results of the General Census of Population and Housing (RGPH) 2013, Permanent Survey on Household Living Conditions (EPCV) 2014 and the results of Resilience Index Measurement and Analysis (RIMA) surveys conducted in 2015 and 2016. A total sample of 3,560 households was selected. A 2 stage, simple random sampling method was employed to select the sampled households, distributed among the different rural and urban areas of the country.

The first stage sampling frame consists of an exhaustive list of Census Districts (CD) from the mapping of the RGPH carried out in 2013. An average CD has a population of about 1,000 people (approximately 200 households). The frame has been reorganized into 25 strata, corresponding to the total number of districts in the country, each subdivided into two environments, except Nouakchott which constitutes the 25th stratum. Drawing units called primary units are made up of census districts in the sampling frame at the level of each stratum.

The second stage sampling frame consists of the list of households in each CD sampled. This database was updated after a preliminary count which takes place shortly before the actual data collection in order to reduce the risks linked to the mobility of households. A total of 20 households were drawn from each CD counted.

Out of the 3,560 sampled households, 2,826 were interviewed.

Sampling deviation

Some teams encountered several difficulties related mainly to access, due to the collection period (winter). Also, the methodology used i.e carrying out census of districts before drawing sample households caused a delay in data collection and therefore the time provided was not sufficient to ensure collection at all level of the sampled census districts.

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

The data collection operation was performed using tablets. The program entered, designed by the statistical office has been tested and all constraints/controls necessary to ensure data quality have been integrated into the program.This program has been shared and tested before training. Also, consistency procedures have been incorporated into the program to minimize collection errors and ensure harmonization and consistency between different sections of the questionnaire.

In addition to regular checks carried out by supervisors, a mission to supervise progress and quality of data collected as part of the RIMA-National project was organized during the period from 11 to 22 August 2017. This 10-day mission allowed to visit all the deployed teams in the field. It was organized just after the departure of the teams, on August 8, 2017, in order to better supervise the start of the data collection phase in the field. This mission had several objectives: 1. Identify problems and provide solutions 2. Examine the quality of work by verifying the data collected 3. Recover all the data already collected and corrected in the field to serve as a backup.

Response rate

The response rate was 79.4%.

Data appraisal

A 5-day training was provided by the FAO team in collaboration with the team from the national statistical office on the RIMA-national questionnaire. This training was done to examine the questionnaire and explain to the different participants the meaning of all the questions asked. During this training, a practical session on the tablets was provided by the statistical team in order to allow the data collection agents understand the handling and testing of the questionnaire. At the end of this training, a pilot survey was organized in some districts of Nouakchott. This survey revealed errors in the collection program which were corrected before field teams were deployed for data collection.

The data collection in the field lasted 1 month and 10 days. In addition to regular checks carried out by supervisors, a mission to supervise progress and quality of data collected as part of the RIMA-National project was organized during the period from 11 to 22 August 2017. This 10-day mission allowed to visit all the deployed teams in the field. It was organized just after the departure of the teams, on August 8, 2017, in order to better supervise the start of the data collection phase in the field. This mission had several objectives: 1. Identify problems and provide solutions 2. Examine the quality of work by verifying the data collected 3. Recover all the data already collected and corrected in the field to serve as a backup.

Release Date: 2021-12-09.Release Schedule:.The data in this file come from the 2020 Annual Survey of Manufactures data files released in December 2021. For more information about the Annual Survey of Manufactures data, see About: Annual Survey of Manufactures...Key Table Information:.Includes only establishments of firms with payroll..Data may be subject to employment- and/or sales-size minimums that vary by industry...The average days closed for an industry are estimated based on those establishments in the industry reporting days closed. Simple weighted estimates of the days closed are formed by applying the establishment's sample weight to its respective values and adding these weighted values across the reporting establishments. The average is formed as the ratio of the days closed weighted sum to the sum of the weights for the reporting establishments...Data Items and Other Identifying Records: .First-quarter payroll ($1,000).Standard error for estimate of first-quarter payroll .Number of employees.Standard error for estimate of number of employees .First-quarter Production workers wages ($1,000) .Standard error for estimate of first-quarter Production workers wages .Production workers for pay period including March 12.Standard error for estimate of production workers for pay period including March 12 .Second-quarter Production workers wages ($1,000) .Standard error for estimate of second-quarter Production workers wages .Production workers for pay period including June 12.Standard error for estimate of production workers for pay period including June 12 .Third-quarter Production workers wages ($1,000) .Standard error for estimate of third-quarter Production workers wages .Production workers for pay period including September 12.Standard error for estimate of production workers for pay period including September 12 .Fourth-quarter Production workers wages ($1,000) .Standard error for estimate of fourth-quarter Production workers wages .Production workers for pay period including December 12.Standard error for estimate of production workers for pay period including December 12 ..Geography Coverage:.The data are shown for employer establishments and firms for the U.S. and State levels that vary by industry..For information about 2020 Annual Survey of Manufactures, see About: Annual Survey of Manufactures...Industry Coverage:.The data are shown at the 2-through 6-digit 2017 NAICS code levels for the U.S. and at the 2-digit 2017 NAICS code level for States. For information about NAICS, see Annual Survey of Manufactures (ASM): Technical Documentation: ASM Product Class Codes and Descriptions...Footnotes:.Not applicable...FTP Download:.Download the entire table at: https://www2.census.gov/programs-surveys/asm/data/2020/AM1831BASIC04.zip..API Information:.Annual Survey of Manufactures API data are housed in the Census Bureau API. For more information, see ASM API..Methodology:.To maintain confidentiality, the U.S. Census Bureau suppresses data to protect the identity of any business or individual. The census results in this file contain sampling and/or nonsampling error. Data users who create their own estimates using data from this file should cite the U.S. Census Bureau as the source of the original data only..To comply with disclosure avoidance guidelines, data rows with fewer than three contributing establishments are not presented. Additionally, establishment counts are suppressed when other select statistics in the same row are suppressed. For detailed information about the methods used to collect and produce statistics, including sampling, eligibility, questions, data collection and processing, data quality, review, weighting, estimation, coding operations, confidentiality protection, sampling error, nonsampling error, and more, see Annual Survey of Manufactures (ASM): Technical Documentation: Annual Survey of Manufactures Methodology...Symbols:.D - Withheld to avoid disclosing data of individual companies; data are included in higher level totals.N - Not available or not comparable.S - Estimate does not meet publication standards because of high sampling variability, poor response quality, or other concerns about the estimate quality. Unpublished estimates derived from this table by subtraction are subject to these same limitations and should not be attributed to the U.S. Census Bureau. For a description of publication standards and the total quantity response rate, see link to program methodology page..X - Not applicable.A - Relative standard error of 100% or more.r - Revised (represented as a superscript).s - Relative standard error is 40 percent or more and less than 100 percent (data variable displayed as a superscript).For a complete list of all economic programs symbols, see the Economic Census: Technical Documentation: Data Dictionary...Source:.U.S. Census Bureau, 2020 Annual Survey of Manufactures (ASM).For information about the Annual Survey of Manufactures (ASM), see Business and...

This data set contains zip code tabulation areas for 5-digit zip codes in New Mexico. It was obtained from the U.S. Census Bureau web page http://www.census.gov/geo/www/cob/zt.html. The metadata at this site is so poor that this metadata record has been created. Technical details are available from the technical documentation at http://www.census.gov/geo/ZCTA/zctatddr.pdf. A ZIP Code tabulation area (ZCTA ) is a statistical geographic entity that approximates the delivery area for a U.S. Postal Service five-digit or three-digit ZIP Code. ZCTAs are aggregations of census blocks that have the same predominant ZIP Code associated with the addresses in the U.S. Census Bureau's Master Address File. Three -digit ZCTA codes are applied to large contiguous areas for which the U.S. Census Bureau does not have five-digit ZIP Code information in its Master Address File. ZCTAs do not precisely depict ZIP Code delivery areas, and do not include all ZIP Codes used for mail delivery. The U.S. Census Bureau has established ZCTAs as a new geographic entity similar to, but replacing, data tabulations for ZIP Codes undertaken in conjunction with the 1990 and earlier censuses.

Abstract

Tonga Household Income and Expenditure Survey 2009 (HIES), undertaken by the Tonga Statistics Department during the period from 1 January 2009 to 31 December 2009. This is the second survey of its kind in Tonga. The last one was carried out in 2000/01, and the results were used in November 2002 to rebase the Consumer Price Index (CPI). A report from that survey was produced in December 2002, and where possible, results from this report will be made to be comparable to the previous report.

• To provide updated information for the expenditure item weights for the CPI; • To provide some data for the components of National Accounts; and • To provide information on the nature and distribution of household income and expenditure for planners, policy makers, and the general public.

Geographic coverage

National Coverage and Island Division

Analysis unit

Private Household, individual, income and expenditure item

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample design was done in such a way that promoted estimates primarily at the national level, but also at the island division level. For that reason a higher sample fraction was selected in the smaller island divisions.

Rural Tongatapu received the smallest sample fraction (8.3%) as it had the highest population. On the other hand the Ongo Niua received the largest sample fraction (21.5%) as their population was the smallest. Overall a sample of roughly 10 per cent was selected for Tonga.

The sample was selected independently within each of the 6 target areas. Firstly, extremely remote areas were removed from the frame (and thus not given a chance of selection) as it was considered too expensive to cover these areas. These areas only represented about 3.5 per cent of the total population for Tonga, so the impact of their removal was considered very minimal.

The sampling in each area was then undertaken using a two-stage process. The first stage involved the selection of census blocks using Probability Proportional to Size (PPS) sampling, where the size measure was the expected number of households in that block. For the second stage, a fixed number (twelve) of households were selected from each selected census block using systematic sampling. The household lists for all selected blocks were updated just prior to the second stage of selection.

Given the sample was spread out over four quarters during the 2009 calendar year, every 4th selected census block was allocated to a respective quarter. To ensure an equally distribution of sample to each quarter, the number of census blocks selected for each of the six target group was made divisible by four. This therefore meant the sample size for each target group was adjusted so that it was divisible by (4*12)=48, as can be seen in Table 1 of Section 1 of the survey report.

Mode of data collection

Face-to-face [f2f]

Research instrument

There were 4 main survey schedules used to collect the information for the survey: 1) Household Questionnaire 2) Individual Questionnaire - Part 1 3) Individual Questionnaire - Part 2 4) Individual Diary (x2)

Household Questionnaire This questionnaire is primarily used to collect information on large expenditure items, but also collects information about the dwelling characteristics. In total there are 14 sections to this questionnaire which cover: 1 Dwelling Characteristics 2 Household Possessions 3 Dwelling Tenure 4 Construction of Dwellings 5 Household Bills 6 Transport Expenses 7 Major Consumer Durables 8 Education/Recreation 9 Medical & Health 10 Overseas Travel 11 Special Events 12 Subsistence Activity Sales 13 Remittances 14 Contributions to Church/Village/School As stated above, the first section is devoted to collecting information about key dwelling characteristics, whereas the second section collects information on household possessions. Sections 3-11, and Section 14, focus on expenses the household incurs, whereas Section 13 focuses on remittances both paid by and received by the household. Finally, Section 12 collects information from households about the income they generate from subsistence activities. This section is the main question collecting income from the household questionnaire, as was included here as it was considered more appropriate to collect this data at the household level. The front page of this Questionnaire is also used for collecting the Roster of Household Members.

Individual Questionnaire - Part 1 This questionnaire collects basic demographic information about each individual in the household, including: • Relationship to Household Head • Sex • Age • Ethnicity • Marital Status

Also collected in this form is information about health problems each individual may have encountered in the last 3 months, followed by education information. For the education section, if a person is currently attending an education institution, then current level is asked, whereas if the person attended an education institution but no longer attends, then the highest level completed is collected. The last main section of this form collects information about labour force and is only asked of individuals aged 10 years and above. These questions aim to classify each person in scope for this section as either: • In the Labour Force - Employed • In the Labour Force - Unemployed • Not in the Labour Force

Individual Questionnaire - Part 2 This questionnaire is focused on collecting information from individuals regarding their income. There are eight sections to this questionnaire of which six are devoted to income. They include: 1 Wages and Salary 2 Self-Employment
3 Previous Jobs
4 Ad-hoc Jobs 5 Pensions/Welfare Benefits 6 Other Income 7 Loan Information 8 Contributions to Benefit Schemes

As stated above, the first six sections of this questionnaire focus on income. Section 7 collects information pertaining to loans for i) households, ii) cars, iii) special events and iv) other, and finally the last question is an expense related question covering contributions to benefit schemes which was considered best covered at an individual level.

Individual Diary The last form used for the survey was the Individual Diary which each individual aged 10 years and over was required to fill in for two weeks (two one-week diaries).

Each diary had 4 sections covering the following: 1) Items Purchased: This section had a separate page for each day and was for recording all items bought in a store, street vendors, market or any other place (including credit) 2) Home Grown/Produced Items: This section was for recording home grown/produced items consisting of items such as food grown at home or at the family plantation, self caught or gathered fish and homemade handicrafts and other goods grown and produced at home. Information is recorded for these items consumed by the household which they produced themselves, these items they gave away as a gift, and these items they received as a gift. 3) Gifts Given and Received: This section of the diary is for recording gifts given and received including both cash and purchased goods (but not home produced). If any member of the household receives a gift that meets this criteria during the diary keeping period from someone who is not a member of their household it is recorded here. 4) Winnings from Gambling: The last section of the Diary is for recording all winnings from gambling during the diary keeping period.

Cleaning operations

Batch edits in CSPro were performed on the data after data entry was completed. The batch edits were aimed at identifying any values falling outside acceptable ranges, as well as other inconsistencies in the data. As this process was done at the batch level, questionnaires were often referred to and manual changes to the data were performed to amend identified errors.

One significant problem which was identified during this process was the incorrect coding of phone card purchase to the purchase of actual phones. As there were many such cases, an automatic code change was applied to any purchase of phones which was less than $40 - recoding them to purchase of phone cards.

Response rate

The final Response Rates for the survey was high, which will assist in yielding statistically significant estimates. Across all six target groups the response rate was in excess of 95 per cent, with the exception of Ongo Niua who only reported 50 per cent. The reason the number was so low in the Ongo Niua was because this target area was only visited in the 2nd quarter, where half the total sample were enumerated (to make up for the sample loss in the first quarter), and was not visited again in quarter 3 and 4.

The reason behind the high response rates in other areas was due to the updated lists for selected census blocks excluding vacant dwellings. As such, it was mostly refusals that impacted on the final response rates.

Sampling error estimates

Sampling errors refer to those errors that are implicit in any sample survey, where only a portion of the population is covered. Non-sampling errors refer to all other types of error. These can arise at any stage of the survey process. Examples of activities that are likely to increase the level of non-sampling error are: failing to select a proper sample, poor questionnaire design, weak field supervision, inaccurate data entry, insufficient data editing, or failure to analyze or report on the data correctly. If a census of all the households in Tonga were carried out, there would be no sampling error (but probably increased non-sampling

Key Table Information.Table Title.Island Areas: Summary Statistics for Domestic and Nondomestic Corporations for Puerto Rico: 2022.Table ID.ISLANDAREASIND2022.IA2200SIZE05.Survey/Program.Economic Census of Island Areas.Year.2022.Dataset.ECNIA Economic Census of Island Areas.Source.U.S. Census Bureau, 2022 Economic Census of Island Areas, Core Statistics.Release Date.2024-12-19.Release Schedule.The Economic Census occurs every five years, in years ending in 2 and 7.2022 Economic Census of Island Areas tables are released on a flow basis from June through December 2024.For more information about economic census planned data product releases, see 2022 Economic Census Release Schedule..Dataset Universe. The dataset universe consists of all establishments that are in operation for at least some part of 2022, are located in Puerto Rico, have paid employees, and are classified in one of eighteen in-scope sectors defined by the 2022 NAICS..Sponsor.U.S. Department of Commerce.Methodology.Data Items and Other Identifying Records.Number of establishmentsSales, value of shipments, or revenue ($1,000)Annual payroll ($1,000)First-quarter payroll ($1,000)Number of employeesOperating expenses ($1,000)Total inventories, beginning of year ($1,000)Total inventories, end of year ($1,000)Range indicating imputed percentage of total sales, value of shipments, or revenueRange indicating imputed percentage of total annual payrollRange indicating imputed percentage of total employeesEach record includes a LFO code, which represents a specific legal form of organization category.The data are shown for legal form of organization.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 Economic Census of Island Areas are employer establishments. An establishment is generally a single physical location where business is conducted or where services or industrial operations are performed..Geography Coverage.The data are shown for employer establishments and firms that vary by industry:At the Territory level for Puerto RicoFor information about economic census geographies, including changes for 2022, see Geographies..Industry Coverage.The data are shown at the 2-digit 2022 NAICS code levels for selected economic census sectors and geographies.For information about NAICS, see Economic Census Code Lists..Sampling.The Economic Census of Island Areas is a complete enumeration of establishments located in the islands (i.e., all establishments on the sampling frame are included in the sample). Therefore, the accuracy of tabulations is not affected by sampling error..Confidentiality.The Census Bureau has reviewed this data product to ensure appropriate access, use, and disclosure avoidance protection of the confidential source data (Project No. 7504609, Disclosure Review Board (DRB) approval number: CBDRB-FY24-0044).The primary method of disclosure avoidance protection is noise infusion. Under this method, the quantitative data values such as sales or payroll for each establishment are perturbed prior to tabulation by applying a random noise multiplier (i.e., factor). Each establishment is assigned a single noise factor, which is applied to all its quantitative data value. Using this method, most published cell totals are perturbed by at most a few percentage points.To comply with disclosure avoidance guidelines, data rows with fewer than three contributing establishments are not presented. For more information on disclosure avoidance, see Methodology for the 2022 Economic Census- Island Areas..Technical Documentation/Methodology.For detailed information about the methods used to collect data and produce statistics, see Methodology for the 2022 Economic Census- Island Areas.For more information about survey questionnaires, Primary Business Activity/NAICS codes, and NAPCS codes, see Economic Census Technical Documentation..Weights.Because the Economic Census of Island Areas is a complete enumeration, there is no sample weighting..Table Information.FTP Download.https://www2.census.gov/programs-surveys/economic-census/data/2022/sector00.API Information.Economic census data are housed in the Census Bureau Application Programming Interface (API)..Symbols.D - Withheld to avoid disclosing data for individual companies; data are included in higher level totalsN - Not available or not comparableS - Estimate does not meet publication standards because of high sampling variability, poor response quality, or other concerns about the estimate quality. Unpublished estimates derived from this table by subtraction are subject to these same limitations and should not be attributed to the U.S. Census Bureau. For a description of publication standards and the total quantity response rate, see link to program methodology page.X - Not applicableA - Relative standard error of 100% or morer - Reviseds - Relative standard error exceeds 40%For a complete lis...

This layer contains the latest 14 months of unemployment statistics from the U.S. Bureau of Labor Statistics (BLS). The data is offered at the nationwide, state, and county geography levels. Puerto Rico is included. These are not seasonally adjusted values. The layer is updated monthly with the newest unemployment statistics available from BLS. There are attributes in the layer that specify which month is associated to each statistic. Most current month: August 2025 (preliminary values at the state and county level) The attributes included for each month are:Unemployment rate (%)Count of unemployed populationCount of employed population in the labor forceCount of people in the labor force Data obtained from the U.S. Bureau of Labor Statistics. Data downloaded: October 1, 2025Local Area Unemployment Statistics table download: https://www.bls.gov/lau/#tablesLocal Area Unemployment FTP downloads:State and CountyNation Data Notes:This layer is updated automatically when the BLS releases their most current monthly statistics. The layer always contains the most recent estimates. It is updated within days of the BLS"s county release schedule. BLS releases their county statistics roughly 2 months after-the-fact. The data is joined to 2023 TIGER boundaries from the U.S. Census Bureau.Monthly values are subject to revision over time.For national values, employed plus unemployed may not sum to total labor force due to rounding.As of the January 2022 estimates released on March 18th, 2022, BLS is reporting new data for the two new census areas in Alaska - Copper River and Chugach - and historical data for the previous census area - Valdez Cordova.As of the March 17th, 2025 release, BLS now reports data for 9 planning regions in Connecticut rather than the 8 previous counties. To better understand the different labor force statistics included in this map, see the diagram below from BLS:

This dataset provides Census 2022 estimates for General health by ethnic group by age for all people in Scotland.

General health

General health is a self-assessment of a person's general state of health. People were asked to assess whether their health was very good, good, fair, bad or very bad. This assessment is not based on a person's health based over any specified period of time.

Details of classification can be found here

The quality assurance report can be found here

Ethnic group

Ethnic group classifies people according to their own perceived ethnic group and cultural background. Whilst the main ethnic group categories have not changed from the question asked in Census 2011, some of the detailed response options and write-in prompts for Scotland's Census 2022 were changed based on stakeholder engagement and subsequent question testing.

Details of classification can be found here

The quality assurance report can be found here

Age

A person's age on Census Day, 20 March 2022. Infants aged under 1 year are classified as 0 years of age.

The quality assurance report can be found here

Abstract

OBJECTIVES

For making current administrative decisions and prepare longer term socio-economic development policies governments and private organisations need reliable up-to-date knowledge about available natural and human resources. In a country like Solomon Islands one of the most important statistical systems for obtaining the required socio-economic information is the population census. This does not only provide a numerical description of the population at a given census date - through comparison with previous census results - but also of the ongoing trends in a sustained and sustainable development of certain population characteristics such as changes in population growth, age composition, direction of mobility and levels of urbanisation, economic activities and educational status. Such knowledge may allow the development planner to devise policies that will stem the flow of trends considered not in line with development aims. Alternatively, trends considered fitting can be identified and fostered by the introduction of appropriate policies. The success thereof can then be assessed when a next census is held some ten years later.

By the end of the project it is expected: 1. To have provided basic information on population development indicators at a particularly point in time namely November 2009. 2. To have ensured the continuity of collection of demographic and socio-economic data so that comparison with the previous census is possible and population projections can be made. 3. To have strengthened the technical and managerial capability at national and regional level, for efficient data collection, processing, analysis and dissemination.

The results of the 2009 census will be required to:

a. help produce high-quality information for planning, decision-making, and monitoring of development progress in Solomon Islands. This implies very heavy data requirements and these requirements are continuously increasing, particularly towards development planning, implementation monitoring and evaluation of Government policies outlined in NERDEP and the current Medium Term Development Strategies.

b. The data from the Census will also be used for monitoring the achievement of the Millennium Development Goals (MDG's) and other goals included in the International Conference for Population & Development (ICPD).

c. check whether the population policies, which were put in place after the 1986 census on the basis of 1976-86 population trends and then as reviewed in the early 2000s in respect of the 1999 population trends, proved effective, and

d. Establish a new benchmark and a new set of post-1999 population trends on which to base a reconsideration of existing (population) policies in the framework of sustained and sustainable development.

e. Also, the results of this census will help facilitate updating of constituencies in preparation to the 2010 national election of Solomon Islands.

f. Further to these, the results of the census will provide a sample Frame from which further household capability surveys which include a household income expenditure in 2010/2011, a second demographic and health survey (DHS) 2011/2012 and a Labour Force Survey before the next census can be undertaken.

g. The 2009 census will also provide the much needed village level data on population, resources and infrastructure for government's bottom-up approach development policy initiative.

Geographic coverage

The 2009 Population and Housing Census Covers 100% of geography as in Urban and Rural Areas for the Entire Country :

The Solomon Islands as a whole by:

• 10 Provinces
• Constituencies
• Wards
• Enumeration Areas
• Household Level

Analysis unit

• Households
• Individuals

Universe

All de facto population of Solomon Islands on census night, in private and institutional households, including expatriates and tourists, but excluding diplomats

Kind of data

Census/enumeration data [cen]

Sampling procedure

Not applicable for complete enumeration survey.

Mode of data collection

Face-to-face [f2f]

Research instrument

1. QUESTIONNAIRE AND SCANNING

The different Government Ministries were consulted in formulating the questionnaire.

The need to set up the questionnaire in terms of suitability for local printing was done, using a software package called in-design, or whatever is most appropriate, which will then allow “optimisation ” for scanning with check boxes, drop-out colours (colours which are then filtered out by the scanner) etc. It is important that the questions are laid out correctly to make sure the results of the scan are possible and legible and eligible or recorded. Prior to the pilot census, the questionnaire needs to be finalised and come up with something everyone is happy with, finalise it and then make sure it works (if questions/formatting needs amendments as a result of the pilot, such changes will of course be done).

The questionnaire was finalised and a reliable printer to print the questionnaires was sought in advance through the tender bidding process. There are a whole series of things the Census office need to check here to make sure that the job gets done to a sufficient standard and that the scanning works well (good quality machines, paper, ink, air conditioned operating environment etc). There was no printing company in Honiara who can do this thus the printing done in Australia

In addition the questionnaire develop and were all in English language as people normally understand the English reading than the Solomons pidgin.The quetionnaire was designed in Adobe Illustrator as to make sure the lines and writtings all well linned and parallel to what had written.Hence the census form have to have the right color which the scannning has to read and can easily collect the characters and values.

As such the census forms had been well protected while in field and properly manage in a way which the forms will not destroyed easily by rain or sea. Hence,the census questionnaire covers Households and Housing.

Cleaning operations

Data editing took place at a number of stages throughout the processing, including:

a) After Scanning data exported to CSPro4.0 edited done by data proccessing officer. b) Secondly the Data proccessing officer pass the data to Data verifiers c) Structure checking and completeness by verifiers in terms of wrong written numbers and spellings

d) Batch editing: - Variables out of range - Fertility Questions - Coding and Value sets - Editing of Variables..eg.age,date of birth and etc.

Detailed documentation of the editing of data can be found in the "Data processing guidelines" document provided as an external resource.

Sampling error estimates

Not apply for Census

Data appraisal

The 2009 Census data was involved people from SPC and SINSO for checking and assisting in terms of cleaning,and verifying.After Census dataset cleaned on 19/09/2011,Census dataset has checked my running tabulation on Male and female by villages,and checking Villages were all coded and no village coded with zero "0".mean makesure all villages has values and makesure the villages with same name coded with unique code where they located by their on provinces.

This dataset provides Census 2022 estimates for ethnic group by unpaid care by general health in UK in Scotland.

Ethnic group

Ethnic group classifies people according to their own perceived ethnic group and cultural background. Whilst the main ethnic group categories have not changed from the question asked in Census 2011, some of the detailed response options and write-in prompts for Scotland's Census 2022 were changed based on stakeholder engagement and subsequent question testing.

Details of classification can be found here

The quality assurance report can be found here

Provision of unpaid care

A person is a provider of unpaid care if they look after or give help or support to family members, friends, neighbours because of long-term physical or mental ill health or disability, or problems related to old age. This does not include any activities as part of paid employment. No distinction is made about whether any care that a person provides is within their own household or outside the household, so no explicit link can be made about whether the care provided is for a person within the household who has poor general health or a long-term health problem or disability.

Details of classification can be found here

The quality assurance report can be found here

General health

General health is a self-assessment of a person's general state of health. People were asked to assess whether their health was very good, good, fair, bad or very bad. This assessment is not based on a person's health based over any specified period of time.

Details of classification can be found here

The quality assurance report can be found here

Abstract

The Population and Housing Census (PHC) 2006 provides a population count of all people that resided in Samoa on the 6th of November, 2006. It collected a range of socio-economic and demographic information pertaining to household members and their associated housing facilities and household status. The information were used to develop statistical indicators to support national plannning and policy-making and also to monitor MDG indicators and all other related conventions. This included population growth rates, educational attainment, employment rates, fertility rates, mortality rates, internal movements, household access to water supply, electricity, sanitation, and many other information. The full report is available at SBS website: http://www.sbs.gov.ws under the section on Publications and Reports.

Geographic coverage

National coverage

Analysis unit

Private households Institution households Individuals Women 15-49 Housing facilities

Universe

The Population and Housing Census (PHC) covered all de facto household members, institutional households such as boarding schools, hospitals, prison inmates, expatriats residing in Samoa for more than 3 months and also all women 15-49 years .The PHC excluded tourists visiting Samoa and Samoans living overseas.

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face [f2f]

Research instrument

The Population and Housing Census (PHC) 2006 questionnaire was developed on the basis of the PHC 2001 with some modifications and additions. The Questionnaire has separate A-3 page for the Population questionnaire and a separate A4 page for the Housing questionnaire.

A Population questionnaire was administered in each household, which collected various information on household members including age, sex, citizenship, ethnicity, orphanhood, marital status, matai status, disability, language of communication, residence (birth, usual, previous), religion, education and employment.

In the Population questionnaire, a special section was administered in each household for women age 15-49, which also asked information on their children ever born still living, died or living somewhere else. Mothers of children under one year were also asked whether they have immunized their babies for measles and rubella.

The Housing questionnaire was also administered in each household which collected information on the types of building the household lived, floor materials, wall materials, roof materials, land tenure, house tenure, water supply, drinking water, lighting, cooking fuel, waste disposal, toilet facility, telephone, computer, internet, cell phones, homezone phone, refrigerator, radio, television, play-station or kidz video games, vehicle, and also the household three main sources of income.

In the Housing questionnaire, a special section was designed to capture household deaths and maternal deaths between November 2004-2006 including the deceased's sex, age at death, and ,the main cause of death.

Cleaning operations

How to edit on field and in the office to data processing: Data editing took place at a number of stages throughout the processing, including: a) Office editing and coding b) During data entry c) Structure checking and completeness d) Secondary editing e) Structural checking of SPSS data files Detailed documentation of the editing of data can be found in the "Data processing guidelines" document provided as an external resource.

At SBS, a team of Office editors was responsible for reviewing each completed questionnaire that came into the office and checking for missed questions, skip errors, fields incorrectly completed, and checking for inconsistencies in the data. In problematic EA, the Office editors liased with the ACEO:Census-Survey and recommended re-enumeration in areas where coverage was not good or quality of the questionnaire was poor. In 2006, the re-enumeration was carried out in some of the villages in the Apia urban region and some areas of Vaitele mainly due to the unavailability of household members during the allocated enumeration period, and, also due to poor quality of data collection.

On the other hand, the good completed questionnaires were passed on by the Office editors to the Office coders who then performed their coding processes of all the questionnaires in a sequential order. After each questionnaire is coded, the Office coders recorded their dates of completion and then passed on the coded questionnaires to the Data processing team for their controls and data entry processes.

The Data processing team is lead by the Computer Manager and Programmer who also works closely with the ACEO Census-Surveys in monitoring the flow of work. The Computer Manager/Programmer designed the data entry and editing programs, conducted the data entry training and then monitored the data entry and made progress reports. Any problems relating to coding at the data entry will be reported to the ACEO Census-Surveys for improvement.

The Computer Manager/Programmer ran data structural checkings and monitored completeness of data entries. Data verfication had also been closely monitored and double data entry was made at 50%. The ACEO Census-Surveys produced the Tabulation plan in which the Computer Programmer also used to monitor structural checks and data quality.

Any detalied information can be asked directly to the Computer Progammer/Manager of SBS or check into our website at http://www.sbs.gov.ws