SAP2022T6T8LEA22 - Occupancy Status of Permanent Dwellings on Census Night. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Occupancy Status of Permanent Dwellings on Census Night...

Occupancy status of permanent dwellings on Census night by Small Area. (Census 2022 Theme 6 Table 8 )Census 2022 table 6.8 is occupancy status of permanent dwellings on Census night. Details include the occupancy status of dwellings. Census 2022 theme 6 is Housing.Census Small Areas are the lowest level of geography for the dissemination of Census data and typically contain between 50 and 200 dwellings. They are generally comprised of complete neighbourhoods or townlands and they nest within CSO Electoral Divisions.Census 2022 Small Areas have been redrawn to ensure they remain consistent with the principle of data protection and are relatively comparable in size. This redraw was necessary following changes in population size and distribution between 2016 and 2022 and was done by the CSO with support from Tailte Éireann.Small Areas were first published for Census 2011 following work undertaken by the National Institute of Regional and Spatial Analysis (NIRSA) on behalf of Tailte Éireann and in consultation with the CSO.Coordinate reference system: Irish Transverse Mercator (EPSG 2157). These boundaries are based on 20m generalised boundaries sourced from Tailte Éireann Open Data Portal. CSO Small Areas 2022.This Census 2022 table is available at other levels of geography from Ireland Census Data Hub.

Occupancy Status of Permanent Dwellings on Census Night

There is a requirement, as per Commission Implementing Regulation (EU) 2018/1799, to deliver Census data for the reference year 2021 to Eurostat. In September 2020, the Irish Government decided to postpone the scheduled April 2021 Census to April 2022 following a recommendation from CSO related to the impact of the Covid-19 pandemic. The CSO however has agreed that the office will still meet its legal requirement. It will base the Eurostat requirements on Census 2022 data, using administrative and other sources to appropriately adjust the data to reference year 2021. A (preliminary) headcount of usual residents at the 1 km² grid level (there are approximately 73,000 such square kilometres in Ireland) is required by Eurostat by 31^st December 2022. The data was produced in the following manner:

Initial preliminary Census estimate for April 2022 As part of the field operation for the 2022 Census, the CSO introduced a new smartphone-based application that allowed field staff to capture information about every dwelling in the country. This application facilitated the production of a preliminary population publication less than 12 weeks (June 23^rd) after census night (April 3^rd). The information includes data on the number of de facto occupants. This information is provisional, and the final file will not be completed until all collected paper forms are fully processed, which is expected to be around the end of January 2023. The provisional data should however be a very strong indicator of the final results.

The preliminary Census de facto population estimate was 5,123,536 persons, available at the 1 km² grid level. As we need the population on a usual resident basis, it was decided to adjust this estimated de facto population at the 1 km² grid level by applying the arithmetic differences between the 2016 usual resident and de facto population counts at that level to the de facto population for 2022. A ratio model, where rates of change of de facto to usual resident counts are applied instead of differences, was also considered but this led to more extreme adjustments, mainly where there was a large change in the population count of a cell between 2016 and 2022. This reduced the usual resident population to 5,101,268 for April 2022, a fall of 22,268 persons.

Temporary Absent Dwellings Census also provided data on the temporarily absent dwellings dataset (at 1 km² grid level), containing a count of persons usually resident in the State but whose entire household were abroad on census night and therefore not included in the de facto population count. This covers 33,365 temporarily absent dwellings with 50,749 temporarily absent persons across 9,138 grid cells. This category was not present in the 2016 figures so it was decided to include these absent persons as they meet the definition of usual residents and will be present in the final transmission, due March 2024. The resulting usually resident population count for 3^rd April 2022 was estimated as 5,152,671 persons.

Note that in a small number cases (80 grid cells), adjustments resulted in a negative cell value, but these were set to zero.

Final preliminary estimate

The CSO then adjusted this figure of estimated usual residents for 3^rd April 2022 back to the 3^rd December 2021 reference point by performing a reverse cohort-survival model.

Firstly, there are an estimated 21,528 births, some 12,405 deaths and approximately 63,595 inward and 25,730 outward migrants for the four-month period December 2021 to March 2022. This affects a total of approximately 123,000 persons, or about 2.4% in a total population of around 5.15 million persons. These population changes were ‘reversed’, as indicated below. Secondly, we also ‘reversed’ those persons who moved from their address within Ireland after December 3^rd 2021 to their Census April 3^rd 2022 address. Based on the selection method approximately 85,000 persons were moved to their previous address, representing about 1.7% of the population.

The steps in the process were:

Births We took the actual November 2015 to April 2016 births from Census 2016 with the variables grid reference, gender and NUTS3 as the sampling frame for the selection of births. Then, using data from table 19 in the Q1 2022 Vital Stats quarterly release (Table VSQ19 on Statbank), we derived the number of Q1 2022 births at NUTS3 by gender level. We also included a proportion of Q4 2021 births, taking one-third to represent December 2021. There are 21,528 births in total for the four-month period we are interested in (16,121 for Q1 2022 plus a third of the value of Q4 2021 which is 5,407), see table 2. Then, using the SAS procedure surveyselect, we selected, at random, the required number of births per strata from the frame and totalled up per grid reference. The resulting figure is the number of people removed from the Census 2021 grid totals, as these figures represent those born during December 2021 to March 2022.

We took the entire Census 2016 data with the variables grid reference, gender, NUTS3 and broad age group (0-14, 15-29, 30-49, 50-64, 65-84 and 85+) as the sampling frame for the selection of people to add back in who died between December 2020 and March 2022. This stratification results in 96 cells. This frame serves as a proxy for the distribution of deaths across the 1km grid square strata. Next, we obtained the Q4 2021 and Q1 2022 mortality data stratified by gender, NUTS3 and age group, provided by the Vital Stats statistician. The total number is 12,405 deaths for the four-month period of interest (9,535 for Q1 2022 plus one third of the value for Q4 2021 which is 8,626), see tables 3 and 4.

Then using the SAS procedure surveyselect, we selected, at random, the required number of deaths per strata from the frame and total up per grid reference. The resulting figure is simply the number of people added to the Census 2021 grid figures as summarised at the grid level, as they represent those who died during December 2021 to March 2022.

Inward and outward migrants

The processing of the inward and outward migrants essentially follows the same methodology in that we used Census 2016 as a sampling frame for the inclusion of those who emigrated in December 2021 and March 2022 and the exclusion of those who immigrated in the same period.

We took the Census 2016 with the variables grid reference, gender, NUTS3, broad nationality (Irish, UK, EU14 excl. IE, EU15 to 27 and Rest of the World) and broad age group (0-14, 15-29, 30-49, 50-64, 65-84 and 85+) as the sampling frame for the selection of migrants. Using the Q4 2021 and Q1 2022 migration data, we got the required inward and outward movers. The Population and Migration statistician provided the data at an individual level for our purposes. There are 63,780 inward migrators (53,403 in Q1 2022 and 10,377 taking one-third of the Q4 2021 values) and 25,730 outward migrators (19,779 in Q1 2022 and 5,951 taking one-third of the Q4 2021 values), see tables 5 to 7.

Then, using SAS procedure surveyselect, we selected, at random, the required number of inward and outward migrants per strata from the frame and sum over grid reference. Given that there will be more inward than outward migrants, the resulting figures will generally be negative i.e., the population will fall.

Ukrainian refugees There are no official statistics, but it was estimated that there were more than 23,000 Ukrainian refugees present in the State in April 3 2022. It is difficult to know the exact numbers captured by the Census until the full final dataset is available. Ukrainian refugees were to be counted as immigrants and usual residents (UR) on the census form unless an individual classed themselves as a visitor, in which case they were de facto (DF) residents. From the point of view of the procedure being described here, Ukrainians who are classified

Analysis of ‘CD970 - Population Aged 3 Years and Over Usually Resident and Present in the State on Census Night’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/82b7b2da-7d17-4962-b03d-8952c43287cc on 19 January 2022.

--- Dataset description provided by original source is as follows ---

Population Aged 3 Years and Over Usually Resident and Present in the State on Census Night

--- Original source retains full ownership of the source dataset ---

Occupancy status of permanent dwellings on Census night by Province. (Census 2022 Theme 6 Table 8 )Census 2022 table 6.8 is occupancy status of permanent dwellings on Census night. Details include the occupancy status of dwellings. Census 2022 theme 6 is Housing. Ireland is divided into four provinces - Leinster, Ulster, Munster and Connacht. They do not have any administrative functions and they are relevant for a number of historical, cultural and sporting reasons. The borders of the provinces coincide with the boundaries of counties. Three of the nine counties in Ulster are within the jurisdiction of the State.Coordinate reference system: Irish Transverse Mercator (EPSG 2157). These boundaries are based on 20m generalised boundaries sourced from Tailte Éireann Open Data Portal. Provinces - National Statutory Boundaries - 2019This dataset is provided by Tailte Éireann

Abstract

Censuses are principal means of collecting basic population and housing statistics required for social and economic development, policy interventions, their implementation and evaluation. The Post-Apartheid South African government has conducted four Censuses, in 1996, 2001, 2011 and 2022.

Geographic coverage

The South African Census 2022 has national coverage.

Analysis unit

Households and individuals

Universe

The South African Census 2022 covered every person present in South Africa on the Census reference night, midnight of 2-3 February 2022 including all de jure household members and residents of institutions.

Kind of data

Census/enumeration data

Sampling procedure

Census 2022 micro-data were sampled from the latest census data based on prescribed business requirements to ensure the generated estimates match the census population counts at local municipal level by age, sex, and population group. The Census 2022 Household record file and Person record file were used as a basis for the creation of the household and person sampling frame respectively. Households and household members in the in-scope households (records of the household questionnaire) formed part of the 10% sample. The municipal sample sizes for households were determined by taking 10% of the respective municipal measure of sizes. The household frame was implicitly stratified within each local municipality using household characteristics. Systematic samples of households were selected in each local municipality from the implicitly sorted household frame. The procedure used the allocated sample within each local municipality to produce the sample of households as well as the sampling weights, which are the inverse of the inclusion probabilities.

A Post-enumeration Survey (PES) is an independent sample survey that is conducted immediately after the completion of census enumeration to evaluate the coverage and content errors of the census. A sample of 840 sub-enumeration areas was selected across South Africa's nine provinces for the PES sampling frame. A mixed-mode data collection methodology was implemented to counteract the effects of the COVID19 pandemic. This was made possible by having integrated, digitally enabled survey processes with a geo-spatial information frame as a base.

Mode of data collection

Face-to-Face and Computer Assisted Personal and Telephone Interview

Research instrument

One questionnaire was used to capture the Census 2022 which included information on: 1. Households - particulars of the household (cover page of the household questionnaire) and household information sections of the household questionnaire. 2. Persons - particulars of the household member and person information sections of the household questionnaire. 3. Geography - geographic information associated with households and persons

A Post-Enumeration Survey was carried out after the census, which used a PES questionnaire.

Sampling error estimates

Coverage errors are a measure of how many persons or households were missed or counted more than once in the census. The final net coverage error rate relative to the final true population of 61,4 million persons is thus 31,1%. The final net coverage error rate relative to the final true population of 19,3 million households is 30,5%.

Content errors indicate the quality of key characteristics in the census. With respect to content errors, six variables were tested for consistency in terms of the responses that were recorded in the Census and the PES. The aggregated index of inconsistency was 7,5% for population group, 8,2% for sex, and 13,6% for age group, indicating a high level of agreement. The aggregated index of inconsistency for marital status was 23,0%, relationship to head of household was 34,8%, and country of birth was 42,3%, indicating moderate rates of agreement.

Dataset contains counts for territorial authority local board area (TALB) of usual residence by TALB of usual residence address one year ago and five years ago, and by life cycle age group, for the census usually resident population count, 2023 Census.

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This dataset compares usual residence at the 2023 Census with usual residence one and five years earlier to show population mobility and internal migration patterns of people within New Zealand.

‘Usual residence address’ is the address of the dwelling where a person considers that they usually live.

‘Usual residence one year ago address’ identifies an individual’s usual residence on 7 March 2022, which may be different to their current usual residence on census night 2023 (7 March 2023).

‘Usual residence five years ago address’ identifies an individual’s usual residence on 6 March 2018, which may be different to their current usual residence on census night 2023 (7 March 2023).

Note: This dataset only includes usual residence address information for individuals whose usual residence address one year ago and five years ago is available at TALB.

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Life cycle age groups are categorised as:

• under 15 years
• 15–29 years
• 30–64 years
• 65 years and over.

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

• Territorial authority local board (generalised)

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Footnotes

Geographical boundaries

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

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Subnational census usually resident population

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

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Population counts

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

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Rows excluded from the dataset

Rows show TALB of usual residence by TALB of usual residence one year ago and five years ago, by life cycle age group. Cells with a number less than six have been confidentialised. Responses to categories unable to be mapped, such as response unidentifiable, not stated, and Auckland (not further defined), have also been excluded from this dataset.

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About the 2023 Census dataset

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

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Data quality

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

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Quality rating of a variable

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

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Age quality rating

Age is rated as very high quality.

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

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Census usually resident population quality rating

The census usually resident population count is rated as very high quality.

Census usually resident population count – 2023 Census: Information by concept has more information, for example, definitions and data quality.

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Usual residence address quality rating

Usual residence address is rated as high quality.

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

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Usual residence one year ago quality rating

Usual residence one year ago area is rated as high quality.

Usual residence one year ago – 2023 Census: Information by concept has more information, for example, definitions and data quality.

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Usual residence five years ago quality rating

Usual residence five years ago area is rated as high quality.

Usual residence five years ago – 2023 Census: Information by concept has more information, for example, definitions and data quality.

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Using data for good

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

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Confidentiality

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

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Symbol

-999 Confidential

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Inconsistencies in definitions

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

Analysis of ‘C1215 - Population At Work Aged 15 Years and Over Usually Resident in the State and Present in their Usual Residence on Census Night’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/8430a14e-1481-4ec4-9a96-e14b5c78d85f on 17 January 2022.

--- Dataset description provided by original source is as follows ---

Population At Work Aged 15 Years and Over Usually Resident in the State and Present in their Usual Residence on Census Night

--- Original source retains full ownership of the source dataset ---

Analysis of ‘C1216 - Population Usually Resident in the State and Present in their Usual Residence on Census Night’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/acf04dac-495a-42a9-b353-6c268904fcd1 on 19 January 2022.

--- Dataset description provided by original source is as follows ---

Population Usually Resident in the State and Present in their Usual Residence on Census Night

--- Original source retains full ownership of the source dataset ---

Abstract

The population and housing census (PHC) is the unique source of reliable and comprehensive data about the size of population and also on major socio-economic & socio-demographic characteristics of the country. It provides data on geographic and administrative distribution of population and household in addition to the demographic and socio-economic characteristics of all the people in the country. Generally, it provides for comparing and projecting demographic data, social and economic characteristics, as well as household and housing conditions at all levels of the country’s administrative units and dimensions: national, regional, districts and localities. The data from the census is classified, tabulated and disseminated so that researchers, administrators, policy makers and development partners can use the information in formulating and implementing various multi-sectorial development programs at the national and community levels. Data on all key variables namely area, household, population, economic activity, literacy and education, fertility and child survival, housing conditions and sanitation are collected and available in the census data. The 2021 PHC in Ghana had an overarching goal of generating updated demographic, social and economic data, housing characteristics and dwelling conditions to support national development planning activities.

Geographic coverage

National Coverage , Region , District

Analysis unit

• Individuals
• Households
• Emigrants
• Absentee population
• Mortality
• Type of residence (households and non household)

Universe

All persons who spent census night (midnight of 27th June 2021) in Ghana

Kind of data

Census/enumeration data [cen]

Sampling procedure

This 10% sample data for the 2021 PHC is representative at the district/subdistrict level and also by the urban rural classification.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

GSS developed two categories of instruments for the 2021 PHC: the listing form and the enumeration instruments. The listing form was only one, while the enumeration instruments comprised six questionnaires, designated as PHC 1A, PHC 1B, PHC 1C, PHC 1D, PHC 1E and PHC 1F. The PHC 1A was the most comprehensive with the others being its subsets.

1. Listing Form: The listing form was developed to collect data on type of structures, level of completion, whether occupied or vacant and use(s) of the structures. It was also used to collect information about the availability, number and types of toilet facilities in the structures. It was also used to capture the number of households in a structure, number of persons in households and the sex of the persons residing in the households if occupied. Finally, the listing form was used to capture data on non-household populations such as the population in institutions, floating population and sex of the non-household populations.

2. PHC 1A: The PHC 1A questionnaire was used to collect data from all households in the country. Primarily, it was used to capture household members and visitors who spent the Census Night in the dwelling of the household, and their relationship with the head of the household. It was also used to collect data on homeless households. Members of the households who were absent were enumerated at the place where they had spent the Census Night. The questionnaire was also used to collect the following household information: emigration; socio-demographic characteristics (sex, age, place of birth and enumeration, survival status of parents, literacy and education; economic activities; difficulty in performing activities; ownership and usage of information, technology and communication facilities; fertility; mortality; housing characteristics and conditions and sanitation.

3. PHC 1B: The PHC 1B questionnaire was used to collect data from persons in stable institutions comprising boarding houses, hostels and prisons who were present on Census Night. Other information that was captured with this instrument are socio-demographic characteristics, literacy and education, economic activities, difficulty in performing activities; ownership and usage of information, technology and communication facilities; fertility; mortality; housing characteristics and conditions and sanitation.

4. PHC 1C: The PHC 1C questionnaire was used to collect data from persons in “unstable” institutions such as hospitals and prayer camps who were present at these places on Census Night. The instrument was used to capture only the socio-demographic characteristics of individuals.

5. PHC 1D: The PHC 1D questionnaire was used to collect data from the floating population. This constitutes persons who were found at airports, seaports, lorry stations and similar locations waiting for or embarking on long-distance travel, as well as outdoor sleepers on Census Night. The instrument captured the socio-demographic information of individuals.

6. PHC 1E: All persons who spent the Census Night at hotels, motels and guest houses were enumerated using the PHC 1E. The content of the questionnaire was similar to that of the PHC 1D.

7. PHC 1F: The PHC 1F questionnaire was administered to diplomats in the country.

Cleaning operations

The Census data editing was implemented at three levels: 1. data editing by enumerators and supervisors during data collection 2. data editing was done at the regional level by the regional data quality monitors during data collection 3. Final data editing was done at the national level using the batch edits in CSPro and STATA Data editing and cleaning was mainly digital.

Response rate

100 percent

Data appraisal

A post Enumeration Survey (PES) was conducted to assess the extent of coverage and content error.

Occupancy status of permanent dwellings on Census night by NUTS3. (Census 2022 Theme 6 Table 8 )Census 2022 table 6.8 is occupancy status of permanent dwellings on Census night. Details include the occupancy status of dwellings. Census 2022 theme 6 is Housing. The Nomenclature of Territorial Units for Statistics (NUTS) were created by Eurostat in order to define territorial units for the production of regional statistics across the European Union. In 2003 the NUTS classification was established within a legal framework (Regulation (EC) No 1059/2003).Changes made under the 2014 Local Government Act prompted a revision of the Irish NUTS 2 and NUTS 3 Regions. The main changes at NUTS 3 level were the transfer of South Tipperary from the South-East into the Mid-West NUTS 3 region and the movement of Louth from the Border to the Mid-East NUTS 3 Region. NUTS 3 Regions are grouped into three NUTS 2 Regions (Northern and Western, Southern, Eastern and Midland) which correspond to the Regional Assemblies established in the 2014 Local Government Act. The revisions made to the NUTS boundaries have been given legal status under Commission Regulation (EU) 2016/2066.Coordinate reference system: Irish Transverse Mercator (EPSG 2157). These boundaries are based on 20m generalised boundaries sourced from Tailte Éireann Open Data Portal. NUTS3 Regions 2015This dataset is provided by Tailte Éireann

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Knight town. Based on the latest 2018-2022 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Knight town. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

Key observations: Insights from 2022

In terms of income distribution across age cohorts, in Knight town, where there exist only two delineated age groups, the median household income is $57,269 for householders within the 45 to 64 years age group, compared to $57,269 for the 65 years and over age group.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Age groups classifications include:

• Under 25 years
• 25 to 44 years
• 45 to 64 years
• 65 years and over

Variables / Data Columns

• Age Of The Head Of Household: This column presents the age of the head of household
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific age group

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 Knight town median household income by age. You can refer the same here

About this dataset

Context

The dataset tabulates the Knight town population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Knight town. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 to 64 years with a poulation of 112 (57.73% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Age cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the Knight town population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in Knight town is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the Knight town is shown in the following column.

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 Knight town Population by Age. You can refer the same here

Analysis of ‘CDD20 - Females Usually Resident and Present in the State on Census Night’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/32f518fa-a8f8-446c-a696-8e72493e7846 on 19 January 2022.

--- Dataset description provided by original source is as follows ---

Females Usually Resident and Present in the State on Census Night

--- Original source retains full ownership of the source dataset ---

Occupancy status of permanent dwellings on Census night by Local Electoral Areas. (Census 2022 Theme 6 Table 8 )Census 2022 table 6.8 is occupancy status of permanent dwellings on Census night. Details include the occupancy status of dwellings. Census 2022 theme 6 is Housing. For the purposes of Local Authority elections, each county and city is divided into Local Electoral Areas (LEAs) which are constituted on the basis of Orders made under the Local Government Act, 1941. Statutory Instruments 610-638 of 2018 and 6-8, 27-28, 156-157 of 2019 state the current composition of LEAs.In general, LEAs are formed by aggregating Electoral Divisions. However, in a number of cases, Electoral Divisions are split between LEAs and in order to render them suitable for the production of statistics, the CSO has amended some LEA boundaries to ensure that statistical disclosure does not occur. As a result of these amendments, Census 2022 LEAs are comprised of whole Census 2022 Electoral Divisions.Coordinate reference system: Irish Transverse Mercator (EPSG 2157). These boundaries are based on 20m generalised boundaries sourced from Tailte Éireann Open Data Portal. CSO Local Electoral Areas 2022

Analysis of ‘C1214 - Population Usually Resident in the State and Present in their Usual Residence on Census Night’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/6f5b1580-c1b9-4882-bf78-d21a1ebba525 on 19 January 2022.

--- Dataset description provided by original source is as follows ---

Population Usually Resident in the State and Present in their Usual Residence on Census Night

--- Original source retains full ownership of the source dataset ---

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 Knight town. 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 2021

Based on our analysis ACS 2017-2021 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Knight town, the median income for all workers aged 15 years and older, regardless of work hours, was $35,468 for males and $16,214 for females.

These income figures highlight a substantial gender-based income gap in Knight town. Women, regardless of work hours, earn 46 cents for each dollar earned by men. This significant gender pay gap, approximately 54%, underscores concerning gender-based income inequality in the town of Knight town.

- Full-time workers, aged 15 years and older: In Knight town, among full-time, year-round workers aged 15 years and older, males earned a median income of $54,890, while females earned $33,779, leading to a 38% gender pay gap among full-time workers. This illustrates that women earn 62 cents for each dollar earned by men in full-time roles. This level of income gap emphasizes the urgency to address and rectify this ongoing disparity, where women, despite working full-time, face a more significant wage discrepancy compared to men in the same employment roles.

Remarkably, across all roles, including non-full-time employment, women displayed a similar gender pay gap percentage. This indicates a consistent gender pay gap scenario across various employment types in Knight town, showcasing a consistent income pattern irrespective of employment status.

https://i.neilsberg.com/ch/knight-wi-income-by-gender.jpeg" alt="Knight, Wisconsin gender based income disparity">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2022-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 2022
• 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 Knight town median household income by gender. You can refer the same here

These population projections were prepared by the Australian Bureau of Statistics (ABS) for Geoscience Australia. The projections are not official ABS data and are owned by Geoscience Australia. These projections are for Statistical Areas Level 2 (SA2s) and Local Government Areas (LGAs), and are projected out from a base population as at 30 June 2022, by age and sex. Projections are for 30 June 2023 to 2032, with results disaggregated by age and sex.

Method The cohort-component method was used for these projections. In this method, the base population is projected forward annually by calculating the effect of births, deaths and migration (the components) within each age-sex cohort according to the specified fertility, mortality and overseas and internal migration assumptions. The projected usual resident population by single year of age and sex was produced in four successive stages – national, state/territory, capital city/rest of state, and finally SA2s. Assumptions were made for each level and the resulting projected components and population are constrained to the geographic level above for each year.
These projections were derived from a combination of assumptions published in Population Projections, Australia, 2022 (base) to 2071 on 23 November 2023, and historical patterns observed within each state/territory.

Projections – capital city/rest of state regions The base population is 30 June 2022 Estimated Resident Population (ERP) as published in National, state and territory population, June 2022. For fertility, the total fertility rate (at the national level) is based on the medium assumption used in Population Projections, Australia, 2022 (base) to 2071, of 1.6 babies per woman being phased in from 2022 levels over five years to 2027, before remaining steady for the remainder of the projection span. Observed state/territory, and greater capital city level fertility differentials were applied to the national data so that established trends in the state and capital city/rest of state relativities were preserved. Mortality rates are based on the medium assumption used in Population Projections, Australia, 2022 (base) to 2071, and assume that mortality rates will continue to decline across Australia with state/territory differentials persisting. State/territory and capital city/rest of state differentials were used to ensure projected deaths are consistent with the historical trend. Annual net overseas migration (NOM) is based on the medium assumption used in Population Projections, Australia, 2022 (base) to 2071, with an assumed gain (at the national level) of 400,000 in 2022-23, increasing to 315,000 in 2023-24, then declining to 225,000 in 2026-27, after which NOM is assumed to remain constant. State and capital city/rest of state shares are based on a weighted average of NOM data from 2010 to 2019 at the state and territory level to account for the impact of COVID-19. For internal migration, net gains and losses from states and territories and capital city/rest of state regions are based on the medium assumption used in Population Projections, Australia, 2022 (base) to 2071, and assume that net interstate migration will trend towards long-term historic average flows.

Projections – Statistical Areas Level 2 The base population for each SA2 is the estimated resident population in each area by single year of age and sex, at 30 June 2022, as published in Regional population by age and sex, 2022 on 28 September 2023. The SA2-level fertility and mortality assumptions were derived by combining the medium scenario state/territory assumptions from Population Projections, Australia, 2022 (base) to 2071, with recent fertility and mortality trends in each SA2 based on annual births (by sex) and deaths (by age and sex) published in Regional Population, 2021-22 and Regional Population by Age and Sex, 2022. Assumed overseas and internal migration for each SA2 is based on SA2-specific annual overseas and internal arrivals and departures estimates published in Regional Population, 2021-22 and Regional Population by Age and Sex, 2022. The internal migration data was strengthened with SA2-specific data from the 2021 Census, based on the usual residence one year before Census night question. Assumptions were applied by SA2, age and sex. Assumptions were adjusted for some SA2s, to provide more plausible future population levels, and age and sex distribution changes, including areas where populations may not age over time, for example due to significant resident student and defence force populations. Most assumption adjustments were made via the internal migration component. For some SA2s with zero or a very small population base, but where significant population growth is expected, replacement migration age/sex profiles were applied. All SA2-level components and projected projections are constrained to the medium series of capital city/rest of state data in Population Projections, Australia, 2022 (base) to 2071.

Projections – Local Government Areas The base population for each LGA is the estimated resident population in each area by single year of age and sex, at 30 June 2022, as published in Regional population by age and sex, 2022 on 28 September 2023. Projections for 30 June 2023 to 2032 were created by converting from the SA2-level population projections to LGAs by age and sex. This was done using an age-specific population correspondence, where the data for each year of the projection span were converted based on 2021 population shares across SA2s. The LGA and SA2 projections are congruous in aggregation as well as in isolation. Unlike the projections prepared at SA2 level, no LGA-specific projection assumptions were used.

Nature of projections and considerations for usage The nature of the projection method and inherent fluctuations in population dynamics mean that care should be taken when using and interpreting the projection results. The projections are not forecasts, but rather illustrate future changes which would occur if the stated assumptions were to apply over the projection period. These projections do not attempt to allow for non-demographic factors such as major government policy decisions, economic factors, catastrophes, wars and pandemics, which may affect future demographic behaviour. To illustrate a range of possible outcomes, alternative projection series for national, state/territory and capital city/rest of state areas, using different combinations of fertility, mortality, overseas and internal migration assumptions, are prepared. Alternative series are published in Population Projections, Australia, 2022 (base) to 2071. Only one series of SA2-level projections was prepared for this product. Population projections can take account of planning and other decisions by governments known at the time the projections were derived, including sub-state projections published by each state and territory government. The ABS generally does not have access to the policies or decisions of commonwealth, state and local governments and businesses that assist in accurately forecasting small area populations. Migration, especially internal migration, accounts for the majority of projected population change for most SA2s. Volatile and unpredictable small area migration trends, especially in the short-term, can have a significant effect on longer-term projection results. Care therefore should be taken with SA2s with small total populations and very small age-sex cells, especially at older ages. While these projections are calculated at the single year of age level, small numbers, and fluctuations across individual ages in the base population and projection assumptions limit the reliability of SA2-level projections at single year of age level. These fluctuations reduce and reliability improves when the projection results are aggregated to broader age groups such as the five-year age bands in this product. For areas with small elderly populations, results aggregated to 65 and over are more reliable than for the individual age groups above 65. With the exception of areas with high planned population growth, SA2s with a base total population of less than 500 have generally been held constant for the projection period in this product as their populations are too small to be reliably projected at all, however their (small) age/sex distributions may change slightly. These SA2s are listed in the appendix. The base (2022) SA2 population estimates and post-2022 projections by age and sex include small artificial cells, including 1s and 2s. These are the result of a confidentialisation process and forced additivity, to control SA2 and capital city/rest of state age/sex totals, being applied to their original values. SA2s and LGAs in this product are based on the Australian Statistical Geography Standard (ASGS) boundaries as at the 2021 Census (ASGS Edition 3). For further information, see Australian Statistical Geography Standard (ASGS) Edition 3.

Made possible by the Digital Atlas of Australia The Digital Atlas of Australia is a key Australian Government initiative being led by Geoscience Australia, highlighted in the Data and Digital Government Strategy. It brings together trusted datasets from across government in an interactive, secure, and easy-to-use geospatial platform. The Australian Bureau of Statistics (ABS) is working in partnership with Geoscience Australia to establish a set of web services to make ABS data available in the Digital Atlas of Australia.

Contact the Australian Bureau of Statistics If you have questions or feedback about this web service, please email geography@abs.gov.au. To subscribe to updates about ABS web services and geospatial products, please complete this form. For information about how the ABS manages any personal information you provide view the ABS privacy policy.

Data and geography references Source data publication: Population Projections, Australia, 2022 (base)

These population projections were prepared by the Australian Bureau of Statistics (ABS) for Geoscience Australia. The projections are not official ABS data and are owned by Geoscience Australia. These projections are for Statistical Areas Level 2 (SA2s) and Local Government Areas (LGAs), and are projected out from a base population as at 30 June 2022, by age and sex. Projections are for 30 June 2023 to 2032, with results disaggregated by age and sex.

Method The cohort-component method was used for these projections. In this method, the base population is projected forward annually by calculating the effect of births, deaths and migration (the components) within each age-sex cohort according to the specified fertility, mortality and overseas and internal migration assumptions. The projected usual resident population by single year of age and sex was produced in four successive stages – national, state/territory, capital city/rest of state, and finally SA2s. Assumptions were made for each level and the resulting projected components and population are constrained to the geographic level above for each year.
These projections were derived from a combination of assumptions published in Population Projections, Australia, 2022 (base) to 2071 on 23 November 2023, and historical patterns observed within each state/territory.

Projections – capital city/rest of state regions The base population is 30 June 2022 Estimated Resident Population (ERP) as published in National, state and territory population, June 2022. For fertility, the total fertility rate (at the national level) is based on the medium assumption used in Population Projections, Australia, 2022 (base) to 2071, of 1.6 babies per woman being phased in from 2022 levels over five years to 2027, before remaining steady for the remainder of the projection span. Observed state/territory, and greater capital city level fertility differentials were applied to the national data so that established trends in the state and capital city/rest of state relativities were preserved. Mortality rates are based on the medium assumption used in Population Projections, Australia, 2022 (base) to 2071, and assume that mortality rates will continue to decline across Australia with state/territory differentials persisting. State/territory and capital city/rest of state differentials were used to ensure projected deaths are consistent with the historical trend. Annual net overseas migration (NOM) is based on the medium assumption used in Population Projections, Australia, 2022 (base) to 2071, with an assumed gain (at the national level) of 400,000 in 2022-23, increasing to 315,000 in 2023-24, then declining to 225,000 in 2026-27, after which NOM is assumed to remain constant. State and capital city/rest of state shares are based on a weighted average of NOM data from 2010 to 2019 at the state and territory level to account for the impact of COVID-19. For internal migration, net gains and losses from states and territories and capital city/rest of state regions are based on the medium assumption used in Population Projections, Australia, 2022 (base) to 2071, and assume that net interstate migration will trend towards long-term historic average flows.

Projections – Statistical Areas Level 2 The base population for each SA2 is the estimated resident population in each area by single year of age and sex, at 30 June 2022, as published in Regional population by age and sex, 2022 on 28 September 2023. The SA2-level fertility and mortality assumptions were derived by combining the medium scenario state/territory assumptions from Population Projections, Australia, 2022 (base) to 2071, with recent fertility and mortality trends in each SA2 based on annual births (by sex) and deaths (by age and sex) published in Regional Population, 2021-22 and Regional Population by Age and Sex, 2022. Assumed overseas and internal migration for each SA2 is based on SA2-specific annual overseas and internal arrivals and departures estimates published in Regional Population, 2021-22 and Regional Population by Age and Sex, 2022. The internal migration data was strengthened with SA2-specific data from the 2021 Census, based on the usual residence one year before Census night question. Assumptions were applied by SA2, age and sex. Assumptions were adjusted for some SA2s, to provide more plausible future population levels, and age and sex distribution changes, including areas where populations may not age over time, for example due to significant resident student and defence force populations. Most assumption adjustments were made via the internal migration component. For some SA2s with zero or a very small population base, but where significant population growth is expected, replacement migration age/sex profiles were applied. All SA2-level components and projected projections are constrained to the medium series of capital city/rest of state data in Population Projections, Australia, 2022 (base) to 2071.

Projections – Local Government Areas The base population for each LGA is the estimated resident population in each area by single year of age and sex, at 30 June 2022, as published in Regional population by age and sex, 2022 on 28 September 2023. Projections for 30 June 2023 to 2032 were created by converting from the SA2-level population projections to LGAs by age and sex. This was done using an age-specific population correspondence, where the data for each year of the projection span were converted based on 2021 population shares across SA2s. The LGA and SA2 projections are congruous in aggregation as well as in isolation. Unlike the projections prepared at SA2 level, no LGA-specific projection assumptions were used.

Nature of projections and considerations for usage The nature of the projection method and inherent fluctuations in population dynamics mean that care should be taken when using and interpreting the projection results. The projections are not forecasts, but rather illustrate future changes which would occur if the stated assumptions were to apply over the projection period. These projections do not attempt to allow for non-demographic factors such as major government policy decisions, economic factors, catastrophes, wars and pandemics, which may affect future demographic behaviour. To illustrate a range of possible outcomes, alternative projection series for national, state/territory and capital city/rest of state areas, using different combinations of fertility, mortality, overseas and internal migration assumptions, are prepared. Alternative series are published in Population Projections, Australia, 2022 (base) to 2071. Only one series of SA2-level projections was prepared for this product. Population projections can take account of planning and other decisions by governments known at the time the projections were derived, including sub-state projections published by each state and territory government. The ABS generally does not have access to the policies or decisions of commonwealth, state and local governments and businesses that assist in accurately forecasting small area populations. Migration, especially internal migration, accounts for the majority of projected population change for most SA2s. Volatile and unpredictable small area migration trends, especially in the short-term, can have a significant effect on longer-term projection results. Care therefore should be taken with SA2s with small total populations and very small age-sex cells, especially at older ages. While these projections are calculated at the single year of age level, small numbers, and fluctuations across individual ages in the base population and projection assumptions limit the reliability of SA2-level projections at single year of age level. These fluctuations reduce and reliability improves when the projection results are aggregated to broader age groups such as the five-year age bands in this product. For areas with small elderly populations, results aggregated to 65 and over are more reliable than for the individual age groups above 65. With the exception of areas with high planned population growth, SA2s with a base total population of less than 500 have generally been held constant for the projection period in this product as their populations are too small to be reliably projected at all, however their (small) age/sex distributions may change slightly. These SA2s are listed in the appendix. The base (2022) SA2 population estimates and post-2022 projections by age and sex include small artificial cells, including 1s and 2s. These are the result of a confidentialisation process and forced additivity, to control SA2 and capital city/rest of state age/sex totals, being applied to their original values. SA2s and LGAs in this product are based on the Australian Statistical Geography Standard (ASGS) boundaries as at the 2021 Census (ASGS Edition 3). For further information, see Australian Statistical Geography Standard (ASGS) Edition 3.

Made possible by the Digital Atlas of Australia The Digital Atlas of Australia is a key Australian Government initiative being led by Geoscience Australia, highlighted in the Data and Digital Government Strategy. It brings together trusted datasets from across government in an interactive, secure, and easy-to-use geospatial platform. The Australian Bureau of Statistics (ABS) is working in partnership with Geoscience Australia to establish a set of web services to make ABS data available in the Digital Atlas of Australia.

Contact the Australian Bureau of Statistics If you have questions or feedback about this web service, please email geography@abs.gov.au. To subscribe to updates about ABS web services and geospatial products, please complete this form. For information about how the ABS manages any personal information you provide view the ABS privacy policy.

Data and geography references Source data publication: Population Projections, Australia, 2022 (base)