About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Au Train Township, Michigan, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 9,401, while the mean income for the highest quintile (20% of households with the highest income) is 158,787. This indicates that the top earners earn 17 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 229,839, which is 144.75% higher compared to the highest quintile, and 2444.84% higher compared to the lowest quintile.

Content

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

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2023 inflation-adjusted dollars for the specific income level.

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 Au Train township median household income. You can refer the same here

This dataset presents data on income (including Government allowances) available from the ABS Data by Region statistics. This release of Data by Region presents various data for 2011-2019 and Census of Population and Housing data for 2011 and 2016 and is based on the Statistical Area 2 (SA2) 2016 boundaries. The dataset includes information in the following specified areas of income: Estimates of Personal Income, Gross Capital Gains, Selected Government Pensions and Allowances, Total Personal Income (Weekly) and Equivalised Total Household Income. Data by Region contains a standard set of data for each region type, depending on the availability of statistics for particular geographies. Data are sourced from a wide variety of collections, both ABS and non-ABS. When analysing these statistics, care needs to be taken as time periods, definitions, methodologies, scope and coverage can differ across collections. Where available, data have been presented as a time series - to enable users to assess changes over time. However, when looked at on a period to period basis, some series may sometimes appear volatile. When analysing the data, users are encouraged to consider the longer term behaviour of the series, where this extra information is available. For more information please visit the Explanatory Notes.

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Au Sable, New York, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 15,950, while the mean income for the highest quintile (20% of households with the highest income) is 222,933. This indicates that the top earners earn 14 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 385,630, which is 172.98% higher compared to the highest quintile, and 2417.74% higher compared to the lowest quintile.

Content

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

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2023 inflation-adjusted dollars for the specific income level.

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 Au Sable town median household income. You can refer the same here

This dataset presents information about total income distribution. The data covers the financial year of 2017-2018, and is based on Statistical Area Level 3 (SA3) according to the 2016 edition of the Australian Statistical Geography Standard (ASGS).

Total Income is the sum of all reported income derived from Employee income, Own unincorporated business, Superannuation, Investments and Other income. Total income does not include the non-lodger population.

Government pensions, benefits or allowances are excluded from the Australian Bureau of Statistics (ABS) income data and do not appear in Other income or Total income. Pension recipients can fall below the income threshold that necessitates them lodging a tax return, or they may only receive tax free pensions or allowances. Hence they will be missing from the personal income tax data set. Recent estimates from the ABS Survey of Income and Housing (which records Government pensions and allowances) suggest that this component can account for between 9% to 11% of Total income.

All monetary values are presented as gross pre-tax dollars, as far as possible. This means they reflect income before deductions and loses, and before any taxation or levies (e.g. the Medicare levy or the temporary budget repair levy) are applied. The amounts shown are nominal, they have not been adjusted for inflation. The income presented in this release has been categorised into income types, these categories have been devised by the ABS to closely align to ABS definitions of income.

The statistics in this release are compiled from the Linked Employer Employee Dataset (LEED), a cross-sectional database based on administrative data from the Australian taxation system. The LEED includes more than 120 million tax records over seven consecutive years between 2011-12 and 2017-18.

Please note:

• All personal income tax statistics included in LEED were provided in de-identified form with no home address or date of birth. Addresses were coded to the ASGS and date of birth was converted to an age at 30 June of the reference year prior to data provision.

• To minimise the risk of identifying individuals in aggregate statistics, perturbation has been applied to the statistics in this release. Perturbation involves small random adjustment of the statistics and is considered the most satisfactory technique for avoiding the release of identifiable statistics, while maximising the range of information that can be released. These adjustments have a negligible impact on the underlying pattern of the statistics. Some cells have also been suppressed due to low counts.

• Totals may not align with the sum of their components due to missing or unpublished information in the underlying data and perturbation.

For further information please visit the Australian Bureau of Statistics.

AURIN has made the following changes to the original data:

• Spatially enabled the original data.

• Set 'np' (not published to protect the confidentiality of individuals or businesses) values to Null.

This dataset presents a range of data items sourced from a wide variety of collections, both Australian Bureau of Statistics (ABS) and non-ABS. The data is derived from the November 2024 release of Data by region. Individual data items present the latest reference year data available on Data by region. This layer presents data by Local Government Areas (LGA), 2021.

The Income (including government allowances) theme is based on groupings of data within Data by region. Concepts, sources and methods for each dataset can be found on the Data by region methodology page.

The Income (including government allowances) theme includes: Personal income in Australia Selected government pensions and allowances Personal income (Census) Household income (Census)

When analysing these statistics:

Time periods, definitions, methodologies, scope, and coverage can differ across collections.
Some data values have been randomly adjusted or suppressed to avoid the release of confidential data, this means

    some small cells have been randomly set to zero
    care should be taken when interpreting cells with small numbers or zeros.

Data and geography references

Source data publication: Data by region Geographic boundary information: Australian Statistical Geography Standard (ASGS) Edition 3 Further information: Data by region methodology, reference period 2011-24 Source: Australian Bureau of Statistics (ABS)

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Email geography@abs.gov.au if you have any questions or feedback about this web service.
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Privacy at the Australian Bureau of Statistics Read how the ABS manages personal information - ABS privacy policy.

Disposable Personal Income in Australia increased to 424884 AUD Million in the first quarter of 2025 from 415014 AUD Million in the fourth quarter of 2024. This dataset provides - Australia Disposable Personal Income - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Wages in Australia increased to 1510.90 AUD/Week in the fourth quarter of 2024 from 1480.90 AUD/Week in the second quarter of 2024. This dataset provides - Australia Average Weekly Wages - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Abstract

This file contains data on Gini coefficients, cumulative quintile shares, explanations regarding the basis on which the Gini coefficient was computed, and the source of the information. There are two data-sets, one containing the "high quality" sample and the other one including all the information (of lower quality) that had been collected.

The database was constructed for the production of the following paper:

Deininger, Klaus and Lyn Squire, "A New Data Set Measuring Income Inequality", The World Bank Economic Review, 10(3): 565-91, 1996.

This article presents a new data set on inequality in the distribution of income. The authors explain the criteria they applied in selecting data on Gini coefficients and on individual quintile groups’ income shares. Comparison of the new data set with existing compilations reveals that the data assembled here represent an improvement in quality and a significant expansion in coverage, although differences in the definition of the underlying data might still affect intertemporal and international comparability. Based on this new data set, the authors do not find a systematic link between growth and changes in aggregate inequality. They do find a strong positive relationship between growth and reduction of poverty.

Geographic coverage

In what follows, we provide brief descriptions of main features for individual countries that are included in the data-base. Without being comprehensive, these notes are intended to indicate some of the considerations underlying our decision to include or exclude certain observations.

Argentina Various permanent household surveys, all covering urban centers only, have been regularly conducted since 1972 and are quoted in a wide variety of sources and years, e.g., for 1980 (World Bank 1992), 1985 (Altimir 1994), and 1989 (World Bank 1992). Estimates for 1963, 1965, 1969/70, 1970/71, 1974, 1975, 1980, and 1981 (Altimir 1987) are based only on Greater Buenos Aires. Estimates for 1961, 1963, 1970 (Jain 1975) and for 1970 (van Ginneken 1984) have only limited geographic coverage and do not satisfy our minimum criteria.

Despite the many urban surveys, there are no income distribution data that are representative of the population as a whole. References to national income distribution for the years 1953, 1959, and 1961(CEPAL 1968 in Altimir 1986 ) are based on extrapolation from national accounts and have therefore not been included. Data for 1953 and 1961 from Weisskoff (1970) , from Lecaillon (1984) , and from Cromwell (1977) are also excluded.

Australia Household surveys, the result of which is reported in the statistical yearbook, have been conducted in 1968/9, 1975/6, 1978/9, 1981, 1985, 1986, 1989, and 1990.

Data for 1962 (Cromwell, 1977) and 1966/67 (Sawyer 1976) were excluded as they covered only tax payers. Jain's data for 1970 was excluded because it covered income recipients only. Data from Podder (1972) for 1967/68, from Jain (1975) for the same year, from UN (1985) for 78/79, from Sunders and Hobbes (1993) for 1986 and for 1989 were excluded given the availability of the primary sources. Data from Bishop (1991) for 1981/82, from Buhman (1988) for 1981/82, from Kakwani (1986) for 1975/76, and from Sunders and Hobbes (1993) for 1986 were utilized to test for the effect of different definitions. The values for 1967 used by Persson and Tabellini and Alesina and Rodrik (based on Paukert and Jain) are close to the ones reported in the Statistical Yearbook for 1969.

Austria: In addition to data referring to the employed population (Guger 1989), national household surveys for 1987 and 1991 are included in the LIS data base. As these data do not include income from self-employment, we do not report them in our high quality data-set.

Bahamas Data for Ginis and shares are available for 1973, 1977, 1979, 1986, 1988, 1989, 1991, 1992, and 1993 in government reports on population censuses and household budget surveys, and for 1973 and 1975 from UN (1981). Estimates for 1970 (Jain 1975), 1973, 1975, 1977, and 1979 (Fields 1989) have been excluded given the availability of primary sources.

Bangladesh Data from household surveys for 1973/74, 1976/77, 1977/78, 1981/82, and 1985/86 are available from the Statistical Yearbook, complemented by household-survey based information from Chen (1995) and the World Development Report. Household surveys with rural coverage for 1959, 1960, 1963/64, 1965, 1966/67 and 1968/69, and with urban coverage for 1963/64, 1965, 1966/67, and 1968/69 are also available from the Statistical yearbook. Data for 1963/64 ,1964 and 1966/67, (Jain 1975) are not included due to limited geographic coverage, We also excluded secondary sources for 1973/74, 1976/77, 1981/82 (Fields 1989), 1977 (UN 1981), 1983 (Milanovic 1994), and 1985/86 due to availability of the primary source.

Barbados National household surveys have been conducted in 1951/52 and 1978/79 (Downs, 1988). Estimates based on personal tax returns, reported consistently for 1951-1981 (Holder and Prescott, 1989), had to be excluded as they exclude the non-wage earning population. Jain's figure (used by Alesina and Rodrik) is based on the same source.

Belgium Household surveys with national coverage are available for 1978/79 (UN 1985), and for 1985, 1988, and 1992 (LIS 1995). Earlier data for 1969, 1973, 1975, 1976 and 1977 (UN 1981) refer to taxable households only and are not included.

Bolivia The only survey with national coverage is the 1990 LSMS (World Development Report). Surveys for 1986 and 1989 cover the main cities only (Psacharopoulos et al. 1992) and are therefore not included. Data for 1968 (Cromwell 1977) do not refer to a clear definition and is therefore excluded.

Botswana The only survey with national coverage was conducted in 1985-1986 (Chen et al 1993); surveys in 74/75 and 85/86 included rural areas only (UN 1981). We excluded Gini estimates for 1971/72 that refer to the economically active population only (Jain 1975), as well as 1974/75 and 1985/86 (Valentine 1993) due to lack of national coverage or consistency in definition.

Brazil Data from 1960, 1970, 1974/75, 1976, 1977, 1978, 1980, 1982, 1983, 1985, 1987 and 1989 are available from the statistical yearbook, in addition to data for 1978 (Fields 1987) and for 1979 (Psacharopoulos et al. 1992). Other sources have been excluded as they were either not of national coverage, based on wage earners only, or because a more consistent source was available.

Bulgaria: Data from household surveys are available for 1963-69 (in two year intervals), for 1970-90 (on an annual basis) from the Statistical yearbook and for 1991 - 93 from household surveys by the World Bank (Milanovic and Ying).

Burkina Faso A priority survey has been undertaken in 1995.

Central African Republic: Except for a household survey conducted in 1992, no information was available.

Cameroon The only data are from a 1983/4 household budget survey (World Bank Poverty Assessment).

Canada Gini- and share data for the 1950-61 (in irregular intervals), 1961-81 (biennially), and 1981-91 (annually) are available from official sources (Statistical Yearbook for years before 1971 and Income Distributions by Size in Canada for years since 1973, various issues). All other references seem to be based on these primary sources.

Chad: An estimate for 1958 is available in the literature, and used by Alesina and Rodrik and Persson and Tabellini but was not included due to lack of primary sources.

Chile The first nation-wide survey that included not only employment income was carried out in 1968 (UN 1981). This is complemented by household survey-based data for 1971 (Fields 1989), 1989, and 1994. Other data that refer either only to part of the population or -as in the case of a long series available from World Bank country operations- are not clearly based on primary sources, are excluded.

China Annual household surveys from 1980 to 1992, conducted separately in rural and urban areas, were consolidated by Ying (1995), based on the statistical yearbook. Data from other secondary sources are excluded due to limited geographic and population coverage and data from Chen et al (1993) for 1985 and 1990 have not been included, to maintain consistency of sources..

Colombia The first household survey with national coverage was conducted in 1970 (DANE 1970). In addition, there are data for 1971, 1972, 1974 CEPAL (1986), and for 1978, 1988/89, and 1991 (World Bank Poverty Assessment 1992 and Chen et al. 1995). Data referring to years before 1970 -including the 1964 estimate used in Persson and Tabellini were excluded, as were estimates for the wage earning population only.

Costa Rica Data on Gini coefficients and quintile shares are available for 1961, 1971 (Cespedes 1973),1977 (OPNPE 1982), 1979 (Fields 1989), 1981 (Chen et al 1993), 1983 (Bourguignon and Morrison 1989), 1986 (Sauma-Fiatt 1990), and 1989 (Chen et al 1993). Gini coefficients for 1971 (Gonzalez-Vega and Cespedes in Rottenberg 1993), 1973 and 1985 (Bourguignon and Morrison 1989) cover urban areas only and were excluded.

Cote d'Ivoire: Data based on national-level household surveys (LSMS) are available for 1985, 1986, 1987, 1988, and 1995. Information for the 1970s (Schneider 1991) is based on national accounting information and therefore excluded

Cuba Official information on income distribution is limited. Data from secondary sources are available for 1953, 1962, 1973, and 1978, relying on personal wage income, i.e. excluding the population that is not economically active (Brundenius 1984).

Czech Republic Household surveys for 1993 and 1994 were obtained from Milanovic and Ying. While it is in principle possible to go back further, splitting national level surveys for the former Czechoslovakia into their independent parts, we decided not to do so as the same argument could be used to

Percentage of Households: One Family: Couple: Source of Income: Zero or Negative Income data was reported at 0.700 % in 2020. This records an increase from the previous number of 0.500 % for 2018. Percentage of Households: One Family: Couple: Source of Income: Zero or Negative Income data is updated yearly, averaging 0.450 % from Jun 2003 (Median) to 2020, with 10 observations. The data reached an all-time high of 0.800 % in 2003 and a record low of 0.400 % in 2016. Percentage of Households: One Family: Couple: Source of Income: Zero or Negative Income data remains active status in CEIC and is reported by Australian Bureau of Statistics. The data is categorized under Global Database’s Australia – Table AU.H040: Survey of Income and Housing: Percentage of Households: by Source of Income.

This dataset presents aggregated values of Income Distribution as a category of the estimates of Personal Income for Small Areas ABS release. The data spans over the financial years of 2010-11 and is aggregated to the 2016 Local Government Area (LGA) boundaries. This release presents regional data on the number of income earners, amounts they receive, and the distribution of income for the 2010-11 to 2014-15 financial years. An improved geocoding process has been introduced for this release. As such, previously released estimates for the 2010-11 and 2012-13 financial year have been superseded. The following personal income categories are provided in this census release: Employee Income Own Unincorporated Business Income

This dataset presents aggregated values of Income Distribution as a category of the estimates of Personal Income for Small Areas ABS release. The data spans over the financial years of 2013-14 and is aggregated to the 2016 Statistical Area Level 2 (SA2) boundaries. This release presents regional data on the number of income earners, amounts they receive, and the distribution of income for the 2010-11 to 2014-15 financial years. An improved geocoding process has been introduced for this …Show full descriptionThis dataset presents aggregated values of Income Distribution as a category of the estimates of Personal Income for Small Areas ABS release. The data spans over the financial years of 2013-14 and is aggregated to the 2016 Statistical Area Level 2 (SA2) boundaries. This release presents regional data on the number of income earners, amounts they receive, and the distribution of income for the 2010-11 to 2014-15 financial years. An improved geocoding process has been introduced for this release. As such, previously released estimates for the 2010-11 and 2012-13 financial year have been superseded. The following personal income categories are provided in this census release: Employee Income Own Unincorporated Business Income Investment Income Superannuation Income Other Income (Income not allocatable to any other categories) Total Income (Sum of previous categories) These statistics provide insights into the nature of regional economies and the economic well-being of the people who live there. The data has been sourced from the Australian Taxation Office (ATO) and is presented with the updated 2016 editions of the Australian Statistical Geography Standards (ASGS): Statistical Area Level 2 (SA2); Statistical Area Level 3 (SA3); Statistical Area Level 4 (SA4); Greater Capital City Statistical Area (GCCSA) and Local Government Area (LGA). For more information on the release please visit the Australian Bureau of Statistics. Please note: When interpreting these results, it should be noted that some low income earners, for example those receiving Government pensions and allowances, or those who earned below the tax free threshold, may not be present in the data, as they may not be required to lodge personal tax forms. Other individuals may not lodge a tax return even if required, therefore care should be taken in interpreting the data as well as comparing the data in this publication with other income data produced by the ABS. To minimise the risk of identifying individuals in aggregate statistics, a confidentialisation process called perturbation has been applied to the data. Perturbation involves small random adjustment of the statistics and is considered the most satisfactory technique for avoiding the release of identifiable statistics while maximising the range of information that can be released. Where data is not available or not for publication, the record has been set to a null value. Copyright attribution: Government of the Commonwealth of Australia - Australian Bureau of Statistics, (2017): ; accessed from AURIN on 12/3/2020. Licence type: Creative Commons Attribution 2.5 Australia (CC BY 2.5 AU)

This dataset presents aggregated values of Income Distribution as a category of the estimates of Personal Income for Small Areas ABS release. The data spans over the financial years of 2010-11 and is aggregated to the 2016 Statistical Area Level 4 (SA4) boundaries. This release presents regional data on the number of income earners, amounts they receive, and the distribution of income for the 2010-11 to 2014-15 financial years. An improved geocoding process has been introduced for this release. As such, previously released estimates for the 2010-11 and 2012-13 financial year have been superseded. The following personal income categories are provided in this census release: Employee Income Own Unincorporated Business Income

Housing Affordability Supply and Demand Data. Number of households in the very low, low and median income brackets This dataset relates to section 4, Housing Stress, of the Affordability master reports produced by the SA Housing Authority. Each master report covers one Local Government Area and is entitled ‘Housing Affordability – Demand and Supply by Local Government Area’. The Demand for Supply for LGA reports are available online at: https://data.sa.gov.au/data/dataset/housing-affordability-demand-and-supply-by-local-government-area Explanatory Notes: Data sourced from the Australian Bureau of Statistics (ABS), Census for Population and Housing and it is updated every 5 years in line with the ABS Census. The nature of the income imputation means that the reported proportion may significantly overstate the true proportion. Census housing stress data is best used in comparing results over Censuses (ie did it increase or decrease in an area) rather than using it to ascertain what proportion of households were in rental stress. Income bands are based on household income. High income households can also experience rental stress. These households are included in the total but not identified separately. Data is representative of households in very low, low and moderate income brackets. Please note that there are small random adjustments made to all cell values to protect the confidentiality of data. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals.

SA1 based data for Total Family Income (Weekly) by Family Composition, in General Community Profile (GCP), 2016 Census. Count of families in family households. Total Family Income (Weekly) is calculated by summing the personal incomes reported by all family members aged 15 years and over. Families includes same-sex couple families. Household excludes 'Lone person', 'Group', ‘Visitors only' and 'Other non-classifiable' households. Excludes overseas visitors. The data is by SA1 2016 boundaries. Periodicity: 5-Yearly. Note: There are small random adjustments made to all cell values to protect the confidentiality of data. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals. For more information visit the data source: http://www.abs.gov.au/census.

The Household, Income and Labour Dynamics in Australia (HILDA) Survey is a nationally representative longitudinal study of Australian households which commenced in 2001. Funded by the Australian Government Department of Social Services (DSS), the HILDA Survey is managed by the Melbourne Institute of Applied Economic and Social Research at the University of Melbourne. The HILDA Survey provides longitudinal data on the lives of Australian residents. Its primary objective is to support research questions falling within three broad and inter-related areas of income, labour market and family dynamics. The HILDA Survey is a household-based panel study of Australian households and, as such, it interviews all household members (15 years and over) of the selected households and then re-interviews the same people in subsequent years. This dataset is the 21st release of the HILDA data, incorporating data collected from 2001 through 2021 (Waves 1-21). The special topic module in Wave 21 is health, and includes questions on health care utilisation, physical and mental health, diet, lifestyle, quantity and quality of sleep, and children’s health. Please note that this release of the HILDA Restricted Release is now superseded, and is available by email request only to ada@ada.edu.au. For the current release, please visit https://ada.edu.au/hilda_rr_current

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Au Sable Township, Michigan, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 8,509, while the mean income for the highest quintile (20% of households with the highest income) is 131,533. This indicates that the top earners earn 15 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 197,858, which is 150.42% higher compared to the highest quintile, and 2325.28% higher compared to the lowest quintile.

Content

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

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2023 inflation-adjusted dollars for the specific income level.

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 Au Sable township median household income. You can refer the same here

This dataset presents aggregated values of Income Distribution as a category of the estimates of Personal Income for Small Areas ABS release. The data spans over the financial years of 2014-15 and …Show full descriptionThis dataset presents aggregated values of Income Distribution as a category of the estimates of Personal Income for Small Areas ABS release. The data spans over the financial years of 2014-15 and is aggregated to the 2016 Greater Capital City Statistical Area (GCCSA) boundaries. This release presents regional data on the number of income earners, amounts they receive, and the distribution of income for the 2010-11 to 2014-15 financial years. An improved geocoding process has been introduced for this release. As such, previously released estimates for the 2010-11 and 2012-13 financial year have been superseded. The following personal income categories are provided in this census release: Employee Income Own Unincorporated Business Income Investment Income Superannuation Income Other Income (Income not allocatable to any other categories) Total Income (Sum of previous categories) These statistics provide insights into the nature of regional economies and the economic well-being of the people who live there. The data has been sourced from the Australian Taxation Office (ATO) and is presented with the updated 2016 editions of the Australian Statistical Geography Standards (ASGS): Statistical Area Level 2 (SA2); Statistical Area Level 3 (SA3); Statistical Area Level 4 (SA4); Greater Capital City Statistical Area (GCCSA) and Local Government Area (LGA). For more information on the release please visit the Australian Bureau of Statistics. Please note: When interpreting these results, it should be noted that some low income earners, for example those receiving Government pensions and allowances, or those who earned below the tax free threshold, may not be present in the data, as they may not be required to lodge personal tax forms. Other individuals may not lodge a tax return even if required, therefore care should be taken in interpreting the data as well as comparing the data in this publication with other income data produced by the ABS. To minimise the risk of identifying individuals in aggregate statistics, a confidentialisation process called perturbation has been applied to the data. Perturbation involves small random adjustment of the statistics and is considered the most satisfactory technique for avoiding the release of identifiable statistics while maximising the range of information that can be released. Where data is not available or not for publication, the record has been set to a null value. Copyright attribution: Government of the Commonwealth of Australia - Australian Bureau of Statistics, (2017): ; accessed from AURIN on 12/3/2020. Licence type: Creative Commons Attribution 2.5 Australia (CC BY 2.5 AU)

Housing Affordability Supply and Demand Data. Number of South Australian households paying more than 30% of their household income on housing (rent or mortgage) broken down by very low, low and moderate income brackets. This dataset relates to section 4, Housing Stress, of the Affordability master reports produced by the SA Housing Authority. Each master report covers one Local Government Area and is entitled ‘Housing Affordability – Demand and Supply by Local Government Area’. The Demand for Supply for LGA reports are available online at: https://data.sa.gov.au/data/dataset/housing-affordability-demand-and-supply-by-local-government-area Explanatory Notes: Data sourced from the Australian Bureau of Statistics (ABS), Census for Population and Housing and it is updated every 5 years in line with the ABS Census. The nature of the income imputation means that the reported proportion may significantly overstate the true proportion. Census housing stress data is best used in comparing results over Censuses (ie did it increase or decrease in an area) rather than using it to ascertain what proportion of households were in rental stress. Income bands are based on household income. High income households can also experience rental stress. These households are included in the total but not identified separately. Data is representative of households in very low, low and moderate income brackets. Please note that there are small random adjustments made to all cell values to protect the confidentiality of data. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals.

Australia Standardised Price-Income Ratio: sa data was reported at 149.268 Ratio in Dec 2024. This records a decrease from the previous number of 152.371 Ratio for Sep 2024. Australia Standardised Price-Income Ratio: sa data is updated quarterly, averaging 82.643 Ratio from Mar 1970 (Median) to Dec 2024, with 220 observations. The data reached an all-time high of 153.422 Ratio in Jun 2024 and a record low of 62.554 Ratio in Sep 1983. Australia Standardised Price-Income Ratio: sa data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Australia – Table AU.OECD.AHPI: House Price Index: Seasonally Adjusted: OECD Member: Quarterly. Nominal house prices divided by nominal disposable income per head. Net household disposable income is used. The population data come from the OECD national accounts database. The long-term average is calculated over the whole period available when the indicator begins after 1980 or after 1980 if the indicator is longer. This value is used as a reference value. The ratio is calculated by dividing the indicator source on this long-term average, and indexed to a reference value equal to 100.

Housing Affordability Supply and Demand Data. Number of South Australian households paying more than 25% of their household income on housing (rent or mortgage) broken down by very low, low and moderate income brackets. This dataset relates to section 4, Housing Stress, of the Affordability master reports produced by the SA Housing Authority. Each master report covers one Local Government Area and is entitled ‘Housing Affordability – Demand and Supply by Local Government Area’. The Demand for Supply for LGA reports are available online at: https://data.sa.gov.au/data/dataset/housing-affordability-demand-and-supply-by-local-government-area Explanatory Notes: Data sourced from the Australian Bureau of Statistics (ABS), Census for Population and Housing and it is updated every 5 years in line with the ABS Census. The nature of the income imputation means that the reported proportion may significantly overstate the true proportion. Census housing stress data is best used in comparing results over Censuses (ie did it increase or decrease in an area) rather than using it to ascertain what proportion of households were in rental stress. Income bands are based on household income. High income households can also experience rental stress. These households are included in the total but not identified separately. Data is representative of households in very low, low and moderate income brackets. Please note that there are small random adjustments made to all cell values to protect the confidentiality of data. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals.