Key information about Australia Household Income per Capita

• Australia Annual Household Income per Capita reached 30,914.027 USD in Jun 2020, compared with the previous value of 34,767.371 USD in Jun 2018.
• Australia Annual Household Income per Capita data is updated yearly, available from Jun 1995 to Jun 2020, with an averaged value of 25,207.153 USD.
• The data reached an all-time high of 43,819.349 USD in Jun 2012 and a record low of 15,753.318 USD in Jun 2001.
• In the latest reports, Retail Sales of Australia grew 4.217 % YoY in May 2023.

CEIC calculates Annual Household Income per Capita from annual Weekly Average Household Income multiplied by 52, annual Number of Household and quarterly Total Population and converts it into USD. The Australian Bureau of Statistics provides Average Household Income in local currency, Number of Household and Total Population. Federal Reserve Board average market exchange rate is used for currency conversions. Household Income per Capita is in annual frequency, ending in June of each year. Household Income per Capita prior to 2008 based on 2017-2018 price.

In the 2018 financial year, the average gross weekly household income in New South Wales, Australia was 2,445 Australian dollars and an equivalized disposable income of 1,232 Australian dollars. The state or territory with the lowest gross income and the only one with an average gross income below 2,000 Australian dollars was Tasmania.

This dataset provides values for DISPOSABLE PERSONAL INCOME reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

In the year ended June 2024, households in the non-metropolitan area of New South Wales spent around ** percent of their household income on rent. In comparison, regional South Australian households spent approximately ** percent of their income on rent.

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

In the year ended June 2024, households in the Greater Perth metropolitan area spent around ** percent of their household income on rent. In comparison, households in the Greater Melbourne metropolitan area spent just ** percent of their income on rent.

A survey conducted in November 2022 among Australian loyalty program users revealed that the largest share of loyalty program members had an annual household income of between 100,000 and ******* Australian dollars, with around one-quarter of active loyalty program members in this income bracket. In comparison, only *** percent of active loyalty program members had an annual household income of **** or more.

In the fourth quarter of 2024, Hobart had the least affordable transport costs among Australia's capital cities, with the weekly average costs per household accounting for **** percent of income. Comparatively, average transport costs in Brisbane accounted for approximately **** percent of the average income per week.

In the fourth quarter of 2024, Launceston had the least affordable transport costs among regional areas across Australia, with the weekly average costs per household accounting for **** percent of income. Comparatively, average transport costs in Wagga Wagga accounted for approximately **** percent of the household income per week.

Key information about Australia Monthly Earnings

• Australia Monthly Earnings stood at 4,026 USD in Nov 2024, compared with the previous figure of 3,903 USD in May 2024
• Australia Monthly Earnings data is updated semiannually, available from May 2012 to Nov 2024, with an average number of 3,740 USD
• The data reached the an all-time high of 4,560 USD in May 2013 and a record low of 3,344 USD in Nov 2015

CEIC calculates Monthly Earnings from Average Weekly Earnings multiplied by 4 and converts it into USD. The Australian Bureau of Statistics provides Average Weekly Earnings in local currency. Federal Reserve Board average market exchange rate is used for currency conversions. Monthly Earnings are in biannual frequency, ending in May and November of each year.


Further information about Australia Monthly Earnings

• In the latest reports, Australia Population reached 27 million people in Dec 2023
• Unemployment Rate of Australia increased to 4 % in Jan 2025
• The country's Labour Force Participation Rate dropped to 67 % in Jan 2025

Australia GDP: USD: Gross National Income per Capita: Atlas Method data was reported at 63,150.000 USD in 2023. This records an increase from the previous number of 60,710.000 USD for 2022. Australia GDP: USD: Gross National Income per Capita: Atlas Method data is updated yearly, averaging 18,750.000 USD from Dec 1962 (Median) to 2023, with 62 observations. The data reached an all-time high of 66,080.000 USD in 2013 and a record low of 1,870.000 USD in 1962. Australia GDP: USD: Gross National Income per Capita: Atlas Method data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Australia – Table AU.World Bank.WDI: Gross Domestic Product: Nominal. GNI per capita (formerly GNP per capita) is the gross national income, converted to U.S. dollars using the World Bank Atlas method, divided by the midyear population. GNI is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad. GNI, calculated in national currency, is usually converted to U.S. dollars at official exchange rates for comparisons across economies, although an alternative rate is used when the official exchange rate is judged to diverge by an exceptionally large margin from the rate actually applied in international transactions. To smooth fluctuations in prices and exchange rates, a special Atlas method of conversion is used by the World Bank. This applies a conversion factor that averages the exchange rate for a given year and the two preceding years, adjusted for differences in rates of inflation between the country, and through 2000, the G-5 countries (France, Germany, Japan, the United Kingdom, and the United States). From 2001, these countries include the Euro area, Japan, the United Kingdom, and the United States.;World Bank national accounts data, and OECD National Accounts data files.;Weighted average;

Salaries and Job Postings by Company in Australia

Uncovering Industry Trends and Analyzing Companies’ Salary Structures

By PromptCloud [source]

About this dataset

This dataset from JobsPikr offers an invaluable insight into the Australian job market. With over 30,000 job postings from SEEK Australia, it contains extensive information such as job categories, cities and states of postings, companies looking to hire and their respective salaries offered, dates when they were posted and descriptions of what these jobs include.

Analyzing this data can help us identify the top paying companies in Australia, visualize locations with highest job opening count and more such insights. We can also draw definitive conclusions on salary distribution by state or specializations that are better suited to different areas. This dataset thus serves as an amazing source for those curious about Australia’s ever-changing employment landscape

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How to use the dataset

How to Use This Dataset

This dataset contains over 30,000 job postings from SEEK Australia, giving comprehensive information about the roles, salaries and locations within the field. The dataset includes columns such as category, company name, job title and job description all of which can be used to gain insights into how much different roles pay in various regions and industries.

Here are some ways you could use this dataset:

Analyze salary distributions: Explore which states have higher paying jobs or use the provided categories (such as Accounting) to investigate differences in salary across company types.

Uncover trends in specific areas or industries: Look for changes in hiring trends over time or analyze areas with higher than average advertised salaries for particular positions.

Benchmark roles against competitors: Compare posted salaries for similar positions across companies to get an idea of what your organization should be offering when recruiting for a particular role or level of experience.

Assess employee mobility by region or sector: Identify popular markets among certain job seekers by exploring migration between different cities/states alond with any career shifts earyrly on in an individual's career journey,.

By understanding these patterns you can develop insights that may impact business decisions ranging from budget management to talent acquisition strategies!

Research Ideas

• Analyzing salary differences between different states in Australia to see which ones offer the highest salaries for various job types.
• Finding out which cities have the most job opportunities and researching what kind of jobs they are offering.
• Identifying which companies are paying the highest salary and investigating what kinds of jobs they tend to hire for and where the company is located

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original.

Columns

File: seek_australia.csv | Column name | Description | |:--------------------|:------------------------------------------------------------| | category | The category of the job posting. (String) | | city | The city in which the job is located. (String) | | company_name | The name of the company offering the job. (String) | | geo | The geographic coordinates of the job location. (String) | | job_board | The job board on which the job was posted. (String) | | job_description | The description of the job. (String) | | job_title | The title of the job. (String) | | job_type | The type of job (e.g. full-time, part-time, etc.). (String) | | post_date | The date on which...

Australia: Bank non-interest income to total income, in percent: The latest value from 2021 is 24.53 percent, a decline from 27.39 percent in 2020. In comparison, the world average is 38.13 percent, based on data from 133 countries. Historically, the average for Australia from 2000 to 2021 is 29.33 percent. The minimum value, 20.5 percent, was reached in 2011 while the maximum of 41.52 percent was recorded in 2001.

BackgroundA lack of evidence exists on the association between area-level income inequality and oral health within Australia. This study examined associations between area-level income inequality and oral health outcomes (inadequate dentition (<21 teeth) and poor self-rated oral health) among Australian adults. Variations in the association between area-level income inequality and oral health outcomes according to area-level mean income were also assessed. Finally, household-income gradients in oral health outcomes according to area-level income inequality were compared.MethodsFor the analyses, data on Australian dentate adults (n = 5,165 nested in 435 Local Government Areas (LGAs)) was obtained from the National Dental Telephone Interview Survey-2013. Multilevel multivariable logistic regression models with random intercept and fixed slopes were fitted to test associations between area-level income inequality and oral health outcomes, examine variations in associations according to area-level mean income, and examine variations in household-income gradients in outcomes according to area-level income inequality. Covariates included age, sex, LGA-level mean weekly household income, geographic remoteness and household income.ResultsLGA-level income inequality was not associated with poor self-rated oral health and inversely associated with inadequate dentition (OR: 0.64; 95% CI: 0.48, 0.87) after adjusting for covariates. Inverse association between income inequality and inadequate dentition at the individual level was limited to LGAs within the highest tertile of mean weekly household income. Household income gradients in both outcomes showed poorer oral health at lower levels of household income. The household income gradients for inadequate dentition varied according to the LGA-level income inequality.ConclusionFindings suggest that income inequality at the LGA-level in Australia is not positively associated with poorer oral health outcomes. Inverse association between income inequality and inadequate dentition is likely due to the contextual differences between Australia and other high-income countries.

AbstractTaxation Statistics 2020-21 is a continental dataset providing an overview of the income and tax status of Australian individuals, companies, partnerships, trusts and funds for the 2020-21 financial year.The dataset was compiled for the annual publication, Taxation Statistics, the ATO’s key statistical report. It provides a comprehensive statistical summary of information taxpayers report to the ATO. It includes information sourced from:The income tax returns of individuals, companies, super funds, partnerships and trusts.Annual returns for fringe benefits tax (FBT) and goods and services tax (GST).Business activity statements (BAS) and instalment activity statements (IAS).Schedules for rental properties, capital gains tax (CGT) and international dealings.Superannuation member contribution statements (MCS).Other information reported to the ATO in relation to excise, the pay as you go (PAYG) system, and charitable institutions.Previous versions of this dataset are available on the Australian Government open government data portal data.gov.auCurrencyDate Published: 07 March 2023Date Updated: 13 September 2024Modification Frequency: As neededData ExtentGeocentric Datum of Australia 1994 (GDA94)Spatial ExtentNorth: -9.1°South: -43.6°East: 159.1°West: 96.8°Source InformationData and Metadata are available from Taxation Statistics 2020-21 - Dataset - data.gov.auThe data was obtained from the Australian Taxation Office.Catalog Entry: Taxation Statistics 2020-21 | Datasets | data.gov.auLineage StatementThis layer was put together using two datasets. Australian taxation and income data provided by the Australian Taxation Office (ATO), was joined to the 2016 Postal Areas shapefile provided by the Australian Bureau of Statistics (ABS).Postal AreasPostal Areas (POA) are an ABS Mesh Block approximation of a general definition of postcodes. They enable comparison of ABS data with other data collected using postcodes as the geographic reference. ABS approximations of administrative boundaries do not match official legal boundaries exactly and should only be used for statistical purposes.Data and geography referencesSource data publication: Australian Statistical Geography Standard (ASGS) Edition 2 - Postal AreasFurther information: Australian Statistical Geography Standard (ASGS) Edition 2 - Non ABS StructuresSource: Australian Bureau of Statistics (ABS)Data PreparationThe CSV was joined to the POA geographies using the 4 digit postcode. For the CSV, it was exported as a file geodatabase and a new field had to be generated where the postcodes were entered as text data to maintain the leading zeroes. The new text postcode field was then joined to the ABS POA_Name field.All data manipulations, joins, and spatial operations were performed using ArcGIS Pro 3.4.3.Data dictionaryAttribute nameDescriptionPostcodesThe postcode affiliated with that areaAREA_SQKMThe area in square kilometres of the postcodeAverage taxable income or lossThe average taxable income or loss of the postcodeNumber of individuals lodging an income tax returnThe number of individuals lodging a tax return in that postcodeCount taxable income or lossThe number of individuals reporting taxable income or loss in the postcodeMedian taxable income or lossThe median taxable income or loss of the postcodeProportion with salary or wagesThe proportion of individuals reporting salary or wages in the postcodeCount salary or wagesThe number of individuals reporting salary or wages in the postcodeAverage salary or wagesThe average salary or wages of the postcodeMedian salary or wagesThe median salary or wages of the postcodeProportion with net rentThe proportion of individuals reporting net rent in the postcodeCount net rentThe number of individuals reporting net rent in the postcodeAverage net rentThe average cost of net rent in the postcodeMedian net rentThe median net rent in the postcodeCount total income or lossThe number of individuals reporting total income or loss in the postcodeAverage total income or lossThe average total income or loss of the postcodeMedian total income or lossThe median total income or loss of the postcodeCount total deductionsCount of individuals reporting total deductions in the postcodeAverage total deductionsThe average total deductions of the postcodeMedian total deductionsThe median total deductions of the postcodeProportion with total business incomeThe proportion of individuals reporting business income in the postcodeCount total business incomeThe number of individuals reporting business income in the postcodeAverage total business incomeThe average total business income in the postcodeMedian total business incomeThe median total business income in the postcodeCount total business expensesThe number of individuals reporting business expenses in the postcodeAverage total business expensesThe average business expenses in the postcodeMedian total business expensesThe median business expenses in the postcodeProportion with net taxThe proportion of individuals with net tax in the postcodeCount net taxThe number of individuals with net tax in the postcodeAverage net taxThe average net tax in the postcodeMedian net taxThe median net tax in the postcodeCount super total accounts balanceThe total number of super accounts in the postcodeAverage super total accounts balanceThe average balance of super accounts in the postcodeMedian super total accounts balanceThe median balance of super accounts in the postcodeProportion with total business expensesThe proportion of individuals reporting business expenses in the postcodeSHAPE_LengthLength of polygon outlineSHAPE_AreaArea of the polygonContactAustralian Taxation Office, taxstats@ato.gov.au

Between March 2024 and March 2025, wages in Australia declined by around 0.6 percent. Wage growth in recent years has been relatively low in comparison to previous years, in particular in December 2020, which only saw a wage growth of 1.3%. Inflation and CPI outstripping wages While wages have increased in Australia, they have still not matched the rate of inflation, which was sitting at 2.4 percent at the end of 2024, down from a high of 7.8 percent at the end of 2022. The high cost of goods has also put pressure on the public, with the Consumer Price Index standing at around 139.4 points, compared to a base year of 2011-12. Rent is on the rise As with many around the world, Australians are also feeling the costs of rent increases. The majority of people in Australia perceive that the cost of rent has risen significantly in their local area. This in turn has seen the government expenditure on rental assistance continue to be high, with around 4.7 billion Australian dollars spent to assist the Australian public in maintaining their housing needs.

This dataset provides values for P...INCOME TAX RATE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

This dataset provides values for PERSONAL INCOME TAX RATECONTINENT=EUROPE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

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

• 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.

This dataset presents the Rental Affordability Index (RAI) for all dwellings. The data uses a single median income value for all of Australia (enabling comparisons across regions), and spans the quarters Q1 2011 to Q2 2021. The RAI covers all states with available data, the Northern Territory does not form part of this dataset. National Shelter, Bendigo Bank, The Brotherhood of St Laurence, and SGS Economics and Planning have released the RentalAffordability Index (RAI) on a biannual basis since 2015. Since 2019, the RAI has been released annually. It is generally accepted that if housing costs exceed 30% of a low-income household's gross income, the household is experiencing housing stress (30/40 rule). That is, housing is unaffordable and housing costs consume a disproportionately high amount of household income. The RAI uses the 30 per cent of income rule. Rental affordability is calculated using the following equation, where 'qualifying income' refers to the household income required to pay rent where rent is equal to 30% of income: RAI = (Median income ∕ Qualifying Income) x 100 In the RAI, households who are paying 30% of income on rent have a score of 100, indicating that these households are at the critical threshold for housing stress. A score of 100 or less indicates that households would pay more than 30% of income to access a rental dwelling, meaning they are at risk of experiencing housing stress. For more information on the Rental Affordability Index please refer to SGS Economics and Planning. The RAI is a price index for housing rental markets. It is a clear and concise indicator of rental affordability relative to household incomes, applied to geographic areas across Australia. AURIN has spatially enabled the original data using geometries provided by SGS Economics and Planning. Values of 'NA' in the original data have been set to NULL.