About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Low Moor. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Low Moor, the median income for all workers aged 15 years and older, regardless of work hours, was $49,018 for males and $31,250 for females.

These income figures highlight a substantial gender-based income gap in Low Moor. Women, regardless of work hours, earn 64 cents for each dollar earned by men. This significant gender pay gap, approximately 36%, underscores concerning gender-based income inequality in the city of Low Moor.

- Full-time workers, aged 15 years and older: In Low Moor, among full-time, year-round workers aged 15 years and older, males earned a median income of $56,250, while females earned $44,375, leading to a 21% gender pay gap among full-time workers. This illustrates that women earn 79 cents for each dollar earned by men in full-time roles. This analysis indicates a widening gender pay gap, showing a substantial income disparity where women, despite working full-time, face a more significant wage discrepancy compared to men in the same roles.

Surprisingly, the gender pay gap percentage was higher across all roles, including non-full-time employment, for women compared to men. This suggests that full-time employment offers a more equitable income scenario for women compared to other employment patterns in Low Moor.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

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 Low Moor median household income by race. You can refer the same here

How does your organization use this dataset? What other NYSERDA or energy-related datasets would you like to see on Open NY? Let us know by emailing OpenNY@nyserda.ny.gov. The Low- to Moderate-Income (LMI) New York State (NYS) Census Population Analysis dataset is resultant from the LMI market database designed by APPRISE as part of the NYSERDA LMI Market Characterization Study (https://www.nyserda.ny.gov/lmi-tool). All data are derived from the U.S. Census Bureau’s American Community Survey (ACS) 1-year Public Use Microdata Sample (PUMS) files for 2013, 2014, and 2015. Each row in the LMI dataset is an individual record for a household that responded to the survey and each column is a variable of interest for analyzing the low- to moderate-income population. The LMI dataset includes: county/county group, households with elderly, households with children, economic development region, income groups, percent of poverty level, low- to moderate-income groups, household type, non-elderly disabled indicator, race/ethnicity, linguistic isolation, housing unit type, owner-renter status, main heating fuel type, home energy payment method, housing vintage, LMI study region, LMI population segment, mortgage indicator, time in home, head of household education level, head of household age, and household weight. The LMI NYS Census Population Analysis dataset is intended for users who want to explore the underlying data that supports the LMI Analysis Tool. The majority of those interested in LMI statistics and generating custom charts should use the interactive LMI Analysis Tool at https://www.nyserda.ny.gov/lmi-tool. This underlying LMI dataset is intended for users with experience working with survey data files and producing weighted survey estimates using statistical software packages (such as SAS, SPSS, or Stata).

The U.S. Census's LEHD Origin-Destination Employment Statistics (LODES) Dataset was used to map job and worker density in throughout the Twin Cities Metropolitan Area, Minnesota. The LODES data is part of the U.S. Census's Longitudinal Employer-Household Dynamics (LEHD) program which records the number of jobs by workplace location and the number of workers by household location at the census block level. LEHD data is derived from data provided by the Minnesota Department of Employment and Economic Development's (MNDEED) Quarterly Census of Employment and Wages (QCEW) and the U.S. Social Security Administration.

The U.S. Cenus Bureau protects the confidentiality of the original data by using a system of multiplicative noise infusion, whereby all released data are "fuzzed." Although the positional accuracy of the data is not as good as the original MNDEED QCEW data, a more robust dataset is produced that allows allows users to not only map a general representation of overall job density (LEHD Job Density), but also map jobs by income level (see LEHD Low-Wage Job Density) and workers' residence (see LEHD Worker Household Density or LEHD Low-Wage Worker Household Density).

The census block level LEHD data was converted to a smoothly tapered surface of calculated census block values. The resulting data surface provides a good representation of job density in the Twin Cities Metropolitan Area, Minnesota.

The U.S. Census's 2010 LEHD Origin-Destination Employment Statistcs (LODES) Dataset was used to map job and worker density in throughout the Twin Cities Metropolitan Area, Minnesota. The LODES data is part of the U.S. Census's Longitudinal Employer-Household Dynamics program which records the number of jobs by workplace location and the number of workers by household location at the census block level. LEHD data is derived from data provided by the Minnesota Department of Employment and Economic Development's (MNDEED) Quarterly Census of Employment and Wages (QCEW) and the U.S. Social Security Administration.

The U.S. Cenus Bureau protects the confidentiality of the original data by using a system of multiplicative noise infusion, whereby all released data are "fuzzed." Although the positional accuracy of the data is not as good as the original MNDEED QCEW data, a more robust dataset is produced that allows allows users to not only map a general representation of job density (see LEHD Job Density), but also map jobs by income level (see LEHD Low-Wage Job Density) and workers' residence (LEHD Worker Household Density or LEHD Low-Wage Worker Household Density).

Jobs and workers are classifies in three earning categories at the U.S. Census 2010 Block level: earnings of $1,250 per month or less, earnings $1,251 per month to $3,333 per month, and earnings greater than $3,333 per month. Earnings of $3,333 per month or less ($40,000 or less, annually) are consider low-wage jobs and workers.

The census block level data was converted to a smoothly tapered surface of calculated census block value. The resulting data surface provides a general representation of overall density of low-wage worker households in the Twin Cities Metropolitan Area, Minnesota.

The series is intended to provide tabulations of two or three interrelated variables for small geographic areas. There about 75 tables in the series, covering the following geographic units; federal electoral districts; census divisions/subdivisions; census metropolitan area/census agglomerations; census tracts; forward sortation areas; and enumeration areas. Census variables are grouped into the following categories: counts and demographic data, ethnic origin, population group, place of birth, citizenship and immigration, language, Aboriginal peoples, schooling, household activities, labour force, income, families and households, housing, institutions and other collectives, as well as disability. The aggregate data tables are presented in Beyond 20/20 Format (.ivt).

Please be advised that there are issues with the Small Area boundary dataset generalised to 20m which affect Small Area 268014010 in Ballygall D, Dublin City. The Small Area boundary dataset generalised to 20m is in the process of being revised and the updated datasets will be available as soon as the boundaries are amended.This feature layer was created using Census 2016 data produced by the Central Statistics Office (CSO) and Small Areas national boundary data (generalised to 20m) produced by Tailte Éireann. The layer represents Census 2016 theme 14.1, persons at work by industry and sex. Attributes include a breakdown of population occupation by industry and sex (e.g. agriculture, forestry and fishing - males, commerce and trade - females). Census 2016 theme 14 represents Industries. The Census is carried out every five years by the CSO to determine an account of every person in Ireland. The results provide information on a range of themes, such as, population, housing and education. The data were sourced from the CSO.The Small Area Boundaries were created with the following credentials. National boundary dataset. Consistent sub-divisions of an ED. Created not to cross some natural features. Defined area with a minimum number of GeoDirectory building address points. Defined area initially created with minimum of 65 – approx. average of around 90 residential address points. Generated using two bespoke algorithms which incorporated the ED and Townland boundaries, ortho-photography, large scale vector data and GeoDirectory data. Before the 2011 census they were split in relation to motorways and dual carriageways. After the census some boundaries were merged and other divided to maintain privacy of the residential area occupants. They are available as generalised and non generalised boundary sets.

Web map containing various layers to be used as reference in Experience Builder. It will serve as a one-stop tool for waste hauler contractors working with Los Angeles County Department of Public Works, Environmental Programs Division, to identify customers that are eligible for fee waivers due to their property falling within areas deemed to be too low in population or too high in elevation; these are conditions used to identify areas that may be too prohibitively costly to provide organics recovery programs due to them being in rural or remote areas.The Experience Builder page, https://experience.arcgis.com/experience/df8689f7d5964f48a5390f6f937533d2 (that references this web map), was created to cross-reference qualifying low-population/high elevation census tracts with various residential franchise, garbage disposal district, and commercial franchise waste collection service areas in Los Angeles County and to assist haulers in providing Public Works with the number of waste generators that are located on each census tract. This information will assist Public Works with applying for SB1383 low population and/or high elevation waivers for these census tracts. More information regarding SB1383 can be found at California Legislative Information (https://leginfo.legislature.ca.gov/faces/billNavClient.xhtml?bill_id=201520160SB1383)For inquiries about how SB 1383 impacts Los Angeles County, please contact Kawsar Vazifdar, (626) 458-3514.

Please be advised that there are issues with the Small Area boundary dataset generalised to 20m which affect Small Area 268014010 in Ballygall D, Dublin City. The Small Area boundary dataset generalised to 20m is in the process of being revised and the updated datasets will be available as soon as the boundaries are amended.This feature layer was created using Census 2016 data produced by the Central Statistics Office (CSO) and Small Areas national boundary data (generalised to 20m) produced by Tailte Éireann. The layer represents Census 2016 theme 13.1, persons at work or unemployed by occupation and sex. Attributes include a breakdown of population by occupation and sex (e.g. sales and customer service occupations - males, professional occupations - females, managers, directors and senior officials - total). Census 2016 theme 13 represents Occupation. The Census is carried out every five years by the CSO to determine an account of every person in Ireland. The results provide information on a range of themes, such as, population, housing and education. The data were sourced from the CSO. The Small Area Boundaries were created with the following credentials. National boundary dataset. Consistent sub-divisions of an ED. Created not to cross some natural features. Defined area with a minimum number of GeoDirectory building address points. Defined area initially created with minimum of 65 – approx. average of around 90 residential address points. Generated using two bespoke algorithms which incorporated the ED and Townland boundaries, ortho-photography, large scale vector data and GeoDirectory data. Before the 2011 census they were split in relation to motorways and dual carriageways. After the census some boundaries were merged and other divided to maintain privacy of the residential area occupants. They are available as generalised and non generalised boundary sets.

Description: Census 2021 - Employed working population according to: Place of residence (Province), Gender, Age (M), Place of work (In/outside Belgium) and Country of citizenship (L)

The level of detail of the variables is expressed via the letters in parentheses, (L) for low = low, (M) medium = medium and (H) high = high.

Period: 2021

Metadata: Variables, European Implementing Regulation (EU) No 2017/543, Regulation (EC) No 763/2008

You can find more information, data and publications on Census 2021

This dataset contains the digitised censuses which were created in the NWO-funded Replication Study '(Re)counting the Uncounted. Replication and Contextualisation of Dutch and Belgian Premodern Population Estimates (1350-1800)'.

In total, close to 2,000 premodern censuses (of hearths, houses, communicants, individuals, etc.) in the Low Countries were identified and catalogued. Around 750 of these were used by one or more of the four studies that were replicated in the study. The first batch of completed censuses can be found in this dataset. More data will be added incrementally.

All files are plain text files that contain tab-separated-values (TSV). A period (.) is used as decimal separator (where applicable). The file names of the censuses refer to the census identifier which is defined in our catalogue. That catalogue also contains definitions of the units that are being counted (up to 15). For contextual information on the census, we refer to the typology, which is under development and will be made available here in due course. Links between the census observations and GIS polygons of pseudo-territories in the Historical Atlas of the Low Countries, 1350-1800 will be available here.

A codebook for the census files, definitions of the bibliographical references and the two-letter territorial codes, and an empty data entry form, we refer to the project's documentation. Note that some of the censuses have space to include fifteen unit variables, whereas older ones have only ten. Other than this, the older censuses are fully compatible with the newer census files.

Searching for specific census files works best by using an asterisk: *HO for all censuses pertaining to Holland, or *1469 for all censuses associated with the year 1469. However, be aware that the census identifiers (a two-letter territorial code and the year) carry no meaning of their own. Observations within a census can be linked to more than one year and to localities that are located in different sovereign territories.

Last but not least, especially when you use specific digitised censuses from this dataset, it is considerate and generally good practice to also cite the original works that published the census data. Hundreds of authors have worked hard to unlock and sometimes analyse these censuses and it is important to continuously give credit to their efforts.

This data download gives a spreadsheet gives the occupation classification described in R.J. Bennett, H. Smith, C. van Lieshout and G. Newton, ‘Business sectors, occupations and aggregations of census data, 1851–1911’ (2017), Working Paper 5, ESRC project ES/M0010953L, ‘Drivers of entrepreneurship and small businesses’, https://doi.org/10. 17863/CAM.9874. See working paper for definitions and discussion of the classifications for PST (2017 version) and SIC (2007 version).

VITAL SIGNS INDICATOR Jobs by Wage Level (EQ1)

FULL MEASURE NAME Distribution of jobs by low-, middle-, and high-wage occupations

LAST UPDATED January 2019

DESCRIPTION Jobs by wage level refers to the distribution of jobs by low-, middle- and high-wage occupations. In the San Francisco Bay Area, low-wage occupations have a median hourly wage of less than 80% of the regional median wage; median wages for middle-wage occupations range from 80% to 120% of the regional median wage, and high-wage occupations have a median hourly wage above 120% of the regional median wage.

DATA SOURCE California Employment Development Department OES (2001-2017) http://www.labormarketinfo.edd.ca.gov/data/oes-employment-and-wages.html

American Community Survey (2001-2017) http://api.census.gov

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) Jobs are determined to be low-, middle-, or high-wage based on the median hourly wage of their occupational classification in the most recent year. Low-wage jobs are those that pay below 80% of the regional median wage. Middle-wage jobs are those that pay between 80% and 120% of the regional median wage. High-wage jobs are those that pay above 120% of the regional median wage. Regional median hourly wages are estimated from the American Community Survey and are published on the Vital Signs Income indicator page. For the national context analysis, occupation wage classifications are unique to each metro area. A low-wage job in New York, for instance, may be a middle-wage job in Miami. For the Bay Area in 2017, the median hourly wage for low-wage occupations was less than $20.86 per hour. For middle-wage jobs, the median ranged from $20.86 to $31.30 per hour; and for high-wage jobs, the median wage was above $31.30 per hour.

Occupational employment and wage information comes from the Occupational Employment Statistics (OES) program. Regional and subregional data is published by the California Employment Development Department. Metro data is published by the Bureau of Labor Statistics. The OES program collects data on wage and salary workers in nonfarm establishments to produce employment and wage estimates for some 800 occupations. Data from non-incorporated self-employed persons are not collected, and are not included in these estimates. Wage estimates represent a three-year rolling average.

Due to changes in reporting during the analysis period, subregion data from the EDD OES have been aggregated to produce geographies that can be compared over time. West Bay is San Mateo, San Francisco, and Marin counties. North Bay is Sonoma, Solano and Napa counties. East Bay is Alameda and Contra Costa counties. South Bay is Santa Clara County from 2001-2004 and Santa Clara and San Benito counties from 2005-2017.

Due to changes in occupation classifications during the analysis period, all occupations have been reassigned to 2010 SOC codes. For pre-2009 reporting years, all employment in occupations that were split into two or more 2010 SOC occupations are assigned to the first 2010 SOC occupation listed in the crosswalk table provided by the Census Bureau. This method assumes these occupations always fall in the same wage category, and sensitivity analysis of this reassignment method shows this is true in most cases.

In order to use OES data for time series analysis, several steps were taken to handle missing wage or employment data. For some occupations, such as airline pilots and flight attendants, no wage information was provided and these were removed from the analysis. Other occupations did not record a median hourly wage (mostly due to irregular work hours) but did record an annual average wage. Nearly all these occupations were in education (i.e. teachers). In this case, a 2080 hour-work year was assumed and [annual average wage/2080] was used as a proxy for median income. Most of these occupations were classified as high-wage, thus dispelling concern of underestimating a median wage for a teaching occupation that requires less than 2080 hours of work a year (equivalent to 12 months fulltime). Finally, the OES has missing employment data for occupations across the time series. To make the employment data comparable between years, gaps in employment data for occupations are ‘filled-in’ using linear interpolation if there are at least two years of employment data found in OES. Occupations with less than two years of employment data were dropped from the analysis. Over 80% of interpolated cells represent missing employment data for just one year in the time series. While this interpolating technique may impact year-over-year comparisons, the long-term trends represented in the analysis generally are accurate.

The U.S. Census Bureau, in collaboration with five federal agencies, launched the Household Pulse Survey to produce data on the social and economic impacts of Covid-19 on American households. The Household Pulse Survey was designed to gauge the impact of the pandemic on employment status, consumer spending, food security, housing, education disruptions, and dimensions of physical and mental wellness. The survey was designed to meet the goal of accurate and timely weekly estimates. It was conducted by an internet questionnaire, with invitations to participate sent by email and text message. The sample frame is the Census Bureau Master Address File Data. Housing units linked to one or more email addresses or cell phone numbers were randomly selected to participate, and one respondent from each housing unit was selected to respond for him or herself. Estimates are weighted to adjust for nonresponse and to match Census Bureau estimates of the population by age, sex, race and ethnicity, and educational attainment. All estimates shown meet the NCHS Data Presentation Standards for Proportions.

Israel Employment: 2008 Census: Male: Human Health & Social Work Activities data was reported at 97.041 Person th in Mar 2018. This records an increase from the previous number of 88.599 Person th for Dec 2017. Israel Employment: 2008 Census: Male: Human Health & Social Work Activities data is updated quarterly, averaging 83.376 Person th from Mar 2012 (Median) to Mar 2018, with 25 observations. The data reached an all-time high of 97.041 Person th in Mar 2018 and a record low of 70.458 Person th in Sep 2013. Israel Employment: 2008 Census: Male: Human Health & Social Work Activities data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Global Database’s Israel – Table IL.G009: Employment: 2008 Census: 2011 Classification: by Industry.

This map uses an archive of Version 1.0 of the CEJST data as a fully functional GIS layer. See an archive of the latest version of the CEJST tool using Version 2.0 of the data released in December 2024 here.This map assesses and identifies communities that are Workforce Disadvantaged according to Justice40 Initiative criteria. "Communities are identified as disadvantaged if they are in census tracts that:ARE at or above the 90th percentile for linguistic isolation OR low median income OR poverty OR unemploymentAND fewer than 10% of people ages 25 or older have a high school education (i.e. graduated with a high school diploma)"Census tracts in the U.S. and its territories that meet the criteria are shaded in blue colors. Suitable for dashboards, apps, stories, and grant applications.Details of the assessment are provided in the popup for every census tract in the United States and its territories American Samoa, Guam, the Northern Mariana Islands, Puerto Rico, and the U.S. Virgin Islands. This map uses 2010 census tracts from Version 1.0 of the source data downloaded November 22, 2022.Use this map to help plan for grant applications, to perform spatial analysis, and to create informative dashboards and web applications.From the source:This data "highlights disadvantaged census tracts across all 50 states, the District of Columbia, and the U.S. territories. Communities are considered disadvantaged:If they are in census tracts that meet the thresholds for at least one of the tool’s categories of burden, orIf they are on land within the boundaries of Federally Recognized TribesCategories of BurdensThe tool uses datasets as indicators of burdens. The burdens are organized into categories. A community is highlighted as disadvantaged on the CEJST map if it is in a census tract that is (1) at or above the threshold for one or more environmental, climate, or other burdens, and (2) at or above the threshold for an associated socioeconomic burden.In addition, a census tract that is completely surrounded by disadvantaged communities and is at or above the 50% percentile for low income is also considered disadvantaged.Census tracts are small units of geography. Census tract boundaries for statistical areas are determined by the U.S. Census Bureau once every ten years. The tool utilizes the census tract boundaries from 2010. This was chosen because many of the data sources in the tool currently use the 2010 census boundaries."PurposeThe goal of the Justice40 Initiative is to provide 40 percent of the overall benefits of certain Federal investments in [eight] key areas to disadvantaged communities. These [eight] key areas are: climate change, clean energy and energy efficiency, clean transit, affordable and sustainable housing, training and workforce development, the remediation and reduction of legacy pollution, [health burdens] and the development of critical clean water infrastructure." Source: Climate and Economic Justice Screening tool"Sec. 219. Policy. To secure an equitable economic future, the United States must ensure that environmental and economic justice are key considerations in how we govern. That means investing and building a clean energy economy that creates well‑paying union jobs, turning disadvantaged communities — historically marginalized and overburdened — into healthy, thriving communities, and undertaking robust actions to mitigate climate change while preparing for the impacts of climate change across rural, urban, and Tribal areas. Agencies shall make achieving environmental justice part of their missions by developing programs, policies, and activities to address the disproportionately high and adverse human health, environmental, climate-related and other cumulative impacts on disadvantaged communities, as well as the accompanying economic challenges of such impacts. It is therefore the policy of my Administration to secure environmental justice and spur economic opportunity for disadvantaged communities that have been historically marginalized and overburdened by pollution and underinvestment in housing, transportation, water and wastewater infrastructure, and health care." Source: Executive Order on Tackling the Climate Crisis at Home and AbroadUse of this Data"The pilot identifies 21 priority programs to immediately begin enhancing benefits for disadvantaged communities. These priority programs will provide a blueprint for other agencies to help inform their work to implement the Justice40 Initiative across government." Source: The Path to Achieving Justice 40

The Survey of Consumer Finances (SCF) is conducted annually to obtain work experience and income information from Canadian households. The Survey provides up-to-date information on the distribution and sources of income, before and after taxes, for families and individuals. With this file, users may identify specific family types, such as two-parent and lone-parent families. Information is also provided on earnings, transfers, and total income for the head and the spouse of the census family unit, as well as personal and labour-related characteristics. This reference year for this file is 1994. Commencing with the 1998 microdata files, annual cross-sectional income data will be sourced from the Survey of Labour and Income Dynamics (SLID).

Individual low-income status by low-income measure (before and after tax), age and gender for Canada, provinces and territories, census divisions and census subdivisions.

Israel Employment: 2008 Census: Extent of Work: Female: Temporarily Absent from Work: sa data was reported at 166.848 Person th in May 2018. This records a decrease from the previous number of 167.780 Person th for Apr 2018. Israel Employment: 2008 Census: Extent of Work: Female: Temporarily Absent from Work: sa data is updated monthly, averaging 162.339 Person th from Jan 2012 (Median) to May 2018, with 77 observations. The data reached an all-time high of 203.822 Person th in Apr 2015 and a record low of 126.257 Person th in Jan 2012. Israel Employment: 2008 Census: Extent of Work: Female: Temporarily Absent from Work: sa data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Global Database’s Israel – Table IL.G007: Employment: 2008 Census: by Extent of Work.

Abstract copyright UK Data Service and data collection copyright owner.

The UK censuses took place on 29th April 2001. They were run by the Northern Ireland Statistics & Research Agency (NISRA), General Register Office for Scotland (GROS), and the Office for National Statistics (ONS) for both England and Wales. The UK comprises the countries of England, Wales, Scotland and Northern Ireland.

Statistics from the UK censuses help paint a picture of the nation and how we live. They provide a detailed snapshot of the population and its characteristics, and underpin funding allocation to provide public services.

The Census 2001: Small Area Microdata for Imputation Analysis (SAM) is a 5% sample of individuals for all countries of the UK, with 2.96 million cases. Local Authority is the lowest level of geography for England and Wales, Council Areas for Scotland and Parliamentary Constituencies for Northern Ireland. The Scilly Isles have been merged with Penwith and the City of London with Westminster. Orkney and Shetland are merged into one area. All other areas are identified. The median sample size for an authority is 5,600 records and nearly eighty authorities have more than 10,000 records. The amount of individual detail in the SAM is less than in the 2001 Individual Licenced Sample of Anonymised Records (I-SAR)(see under SNs 7205 and 7206) because of the greater geographical detail in the SAM.

Caveat - Students:
As with the Individual SAR, the SAM includes those enumerated in a communal establishment and also full-time students who were enumerated at an address that was not their usual term-time residence. For the latter there is only individual-level information on age, sex, marital status and full-time student status. It is recommended that these students are not included in any analyses as they do not form part of the usual residents population base.
This dataset contains 155 variables, including 67 imputation flag variables. The standard version, containing 88 SAM variables, is available under SN 7207.


Main Topics:
Accommodation type (brief)Accommodation type (detailed)
Adults, Number Employed in Household
Adults, Number in Household
Age
Age of Family Reference Person (FRP)
Age of Household Reference Person (HRP)
Age of Students and Schoolchildren
Amenities
Armed Forces
Bath/Shower and Toilet, use of
Care (unpaid), Provision of
Care, Provision of
Carers and their Economic Activity, Number of
Cars and vans
Central heating
Children
Children, dependent
Communal Establishment Residents
Communal establishment, combined type and management
Concealed families
Country of birth
Country of Birth (additional categories)
Daytime Population
Dwelling Type
Economic Activity
Economic Activity of Associated People Resident in Households
Economic Activity of Full-time students
Economic Activity of Household Reference Person (HRP)
Ethnic group (England and Wales)
Ethnic group (England and Wales) of Household Reference Person
Family composition
Family status
Family type
Health, General
Hours worked
Household composition
Household composition (alternative classification)
Household dependent children
Household deprivation
Household Reference Person indicator
Household size
Household Space Type
Household Type
Households with students away during term-time
Industry
Industry, former
Limiting long-term illness
Limiting Long-Term Illness (LLTI), Household residents with
Limiting long-Term Illness, number of people with in household
Living arrangements
Living arrangements of Household Reference Person (HRP)
Lowest floor level
Marital status
Migration (armed forces)
Migration (Communal establishment)
Migration (People)
Multiple ethnic identifier
Occupancy Rating
Occupation (brief)
Occupation (detailed)
Occupation, former
Pensioner household
People aged 17 or over in household, Number of
Population Type
Public transport users in households
Qualifications (England and Wales)
Qualifications, highest level of (England and Wales)
Qualifications, professional
Religion (England and Wales)
Religion (England and Wales) of Household Reference Person
Resident Basis
Resident Type
Rooms in a dwelling, number of
Rooms, Number of
Rooms, Persons per
Sex
Sex of Household Reference Person (HRP)
Single Adult Households
Social Grade of Household Reference Person (HRP), approximated
Social Grade, approximated
Socio-economic Classification (NS-SeC)
Socio-economic Classification (NS-SeC) of Household Reference Person (HRP)Socio-economic Classification (NS-SeC) of Household Reference Person (HRP), Main categories of
Student accommodation (Standard Output)
Student accommodation Type
Student...

Data on individual low-income and poverty status by occupation broad category (1-digit code) from the National Occupational Classification (NOC) 2021, industry sector (2-digit code) from the North American Industry Classification System (NAICS) 2017, and work activity during the reference year for the population aged 15 years and over in private households in Canada, provinces and territories, and census divisions.