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.

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.

Net-Income-Applicable-To-Common-Shares Time Series for Rural Funds Group. Rural Funds Group is an agricultural Real Estate Investment Trust (REIT) listed on the ASX under the code RFF. RFF owns a diversified portfolio of Australian agricultural assets which are leased predominantly to corporate agricultural operators. RFF targets distribution growth of 4% per annum by owning and improving farms that are leased to good counterparties. RFF is a stapled security, incorporating Rural Funds Trust (ARSN 112 951 578) and RF Active (ARSN 168 740 805).

Net-Income-Including-Non-Controlling-Interests Time Series for Rural Funds Group. Rural Funds Group is an agricultural Real Estate Investment Trust (REIT) listed on the ASX under the code RFF. RFF owns a diversified portfolio of Australian agricultural assets which are leased predominantly to corporate agricultural operators. RFF targets distribution growth of 4% per annum by owning and improving farms that are leased to good counterparties. RFF is a stapled security, incorporating Rural Funds Trust (ARSN 112 951 578) and RF Active (ARSN 168 740 805).

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

The ATO (Australian Tax Office) made a dataset openly available (see links) showing all the Australian Salary and Wages (2002, 2006, 2010, 2014) by detailed occupation (around 1,000) and over 100 SA4 regions. Sole Trader sales and earnings are also provided. This open data (csv) is now packaged into a database (*.sql) with 45 sample SQL queries (backupSQL[date]_public.txt).See more description at related Figshare #datavis record. Versions:V5: Following #datascience course, I have made main data (individual salary and wages) available as csv and Jupyter Notebook. Checksum matches #dataTotals. In 209,xxx rows.Also provided Jobs, and SA4(Locations) description files as csv. More details at: Where are jobs growing/shrinking? Figshare DOI: 4056282 (linked below). Noted 1% discrepancy ($6B) in 2010 wages total - to follow up.#dataTotals - Salary and WagesYearWorkers (M)Earnings ($B) 20028.528520069.4372201010.2481201410.3584#dataTotal - Sole TradersYearWorkers (M)Sales ($B)Earnings ($B)20020.9611320061.0881920101.11122620141.19630#links See ATO request for data at ideascale link below.See original csv open data set (CC-BY) at data.gov.au link below.This database was used to create maps of change in regional employment - see Figshare link below (m9.figshare.4056282).#packageThis file package contains a database (analysing the open data) in SQL package and sample SQL text, interrogating the DB. DB name: test. There are 20 queries relating to Salary and Wages.#analysisThe database was analysed and outputs provided on Nectar(.org.au) resources at: http://118.138.240.130.(offline)This is only resourced for max 1 year, from July 2016, so will expire in June 2017. Hence the filing here. The sample home page is provided here (and pdf), but not all the supporting files, which may be packaged and added later. Until then all files are available at the Nectar URL. Nectar URL now offline - server files attached as package (html_backup[date].zip), including php scripts, html, csv, jpegs.#installIMPORT: DB SQL dump e.g. test_2016-12-20.sql (14.8Mb)1.Started MAMP on OSX.1.1 Go to PhpMyAdmin2. New Database: 3. Import: Choose file: test_2016-12-20.sql -> Go (about 15-20 seconds on MacBookPro 16Gb, 2.3 Ghz i5)4. four tables appeared: jobTitles 3,208 rows | salaryWages 209,697 rows | soleTrader 97,209 rows | stateNames 9 rowsplus views e.g. deltahair, Industrycodes, states5. Run test query under **#; Sum of Salary by SA4 e.g. 101 $4.7B, 102 $6.9B#sampleSQLselect sa4,(select sum(count) from salaryWageswhere year = '2014' and sa4 = sw.sa4) as thisYr14,(select sum(count) from salaryWageswhere year = '2010' and sa4 = sw.sa4) as thisYr10,(select sum(count) from salaryWageswhere year = '2006' and sa4 = sw.sa4) as thisYr06,(select sum(count) from salaryWageswhere year = '2002' and sa4 = sw.sa4) as thisYr02from salaryWages swgroup by sa4order by sa4

Regional profile tables containing gross regional product and output, employment, household income and expenditure, and trade. The tables are estimates derived as part of the input-output table construction process for South Australia and its regions. They are not taken directly from a census or survey, but are based on a mix of collected data, state shares (if a regional table) and estimates based on “parent” table values.

This information was complied from the Australian Bureau of Statistics in Partial fullfilment of Coursework for the Master of Data Science taught at UNSW

Household income and wealth Australia, Building Activity Australia, Affordable Housing Database, National and Regional House Price Indices, Population Projections, Lending Indicators

Household income and wealth Australia ->https://www.abs.gov.au/statistics/economy/finance/household-income-and-wealth-australia/latest-release, Affordable Housing Database ->http://www.oecd.org/social/affordable-housing-database.htm, National and Regional House Price Indices ->https://stats.oecd.org/Index.aspx?DataSetCode=RHPI_TARGET, Population Projections ->https://stats.oecd.org/Index.aspx?DataSetCode=POPPROJ, Lending Indicators ->https://www.abs.gov.au/statistics/economy/finance/lending-indicators/apr-2021

Regional profile tables containing gross regional product and output, employment, household income and expenditure, and trade. The tables are estimates derived as part of the input-output table construction process for South Australia and its regions. They are not taken directly from a census or survey, but are based on a mix of collected data, state shares (if a regional table) and estimates based on “parent” table values.

These data contain Gini coefficient estimates (2001 and 2011), for different regions in Australia.

When referencing this material, please cite: Fleming, D. and Measham, T. (2015) 'Income inequality across Australian Regions during the mining boom: 2011-11'. Australian Geographer 46(2), 201-214.

Background: Northern New South Wales in Australia is a “hotspot” for natural disaster declarations with recent extensive flooding in early 2017. With limited knowledge about how climate change affects mental health and resilience, robust local assessments are required to better understand long-term impact, particularly in communities prone to extreme weather events.Methods: Six months post-flood, a cross-sectional survey of adults living in the region during the flood was conducted to quantify associations between flood impact and psychological morbidity (post-traumatic stress (PTSD), anxiety, depression, suicidal ideation) for different exposure scenarios, and respondent groups. We adopted a community-academic partnership approach and purposive recruitment to increase participation from marginalized groups.Results: Of 2,180 respondents, almost all (91%) were affected by some degree of flood-related exposure at an individual and community level (ranging from suburb damage to home or business inundated). Socio-economically marginalized respondents were more likely to have their homes inundated and to be displaced. Mental health risk was significantly elevated for respondents: whose home/business/farm was inundated [e.g., home inundation: PTSD adjusted odds ratio (AOR) 13.72 (99% CI 4.53–41.56)]; who reported multiple exposures [e.g., three exposures: PTSD AOR 6.43 (99% CI 2.11–19.60)]; and who were still displaced after 6 months [e.g., PTSD AOR 24.43 (99% CI 7.05–84.69)].Conclusion: The 2017 flood had profound impact, particularly for respondents still displaced and for socio-economically marginalized groups. Our community-academic partnership approach builds community cohesion, informs targeted mental health disaster preparedness and response policies for different sectors of the community and longer-term interventions aimed at improving community adaptability to climate change.

Regional profile tables containing gross regional product and output, employment, household income and expenditure, and trade. The tables are estimates derived as part of the input-output table construction process for South Australia and its regions. They are not taken directly from a census or survey, but are based on a mix of collected data, state shares (if a regional table) and estimates based on “parent” table values.

Regional profile tables containing gross regional product and output, employment, household income and expenditure, and trade. The tables are estimates derived as part of the input-output table construction process for South Australia and its regions. They are not taken directly from a census or survey, but are based on a mix of collected data, state shares (if a regional table) and estimates based on “parent” table values.

B & W Rural is a Proprietary Company that generates the majority of its income from the Professional Services industry.

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)

Regional profile tables containing gross regional product and output, employment, household income and expenditure, and trade. The tables are estimates derived as part of the input-output table construction process for South Australia and its regions. They are not taken directly from a census or survey, but are based on a mix of collected data, state shares (if a regional table) and estimates based on “parent” table values.

This data - count of employees by 100 regions within Australia (over 12 years; 2002 - 2014) - 8.5M 2002 -> 10.3M 2014.Parent data - Employee $ DATA by detailed occupation, by location (SA4), by year; This dataset is an aggregation of all Australian Salaries and Wages by location and over 12 years in four year snapshots (2002, 2006, 2010, 2014). Some data excluded which was not allocated to a SA4 location. Source Data from ATO; Australian Tax Office.HeadcountRaw.csv provides total data (employee count). Includes total counts per SA4 location, and percent change between each of the years; 2002 - 6; 2006 - 10; 2010 - 14 eg 101 means 1% increase.HeadcountRaw_display.csv provides data (employee count) to visualise at (1) National Map.gov.au or (2) Aurin.org.au. This only includes the data for SA4 regions which can be visualised.Method; CSV files loaded into MariaDB on Nectar Infrastructure (refer NCRIS). Access through http://areff2000.net16.net.Data request at: data.gov.au IdeascaleOriginal data (parent data) at: data.gov.auParent data description: "Individuals data for 2001-02, 2005-06, 2009-10 and 2013-14 income years. Table 1: Salary and Wages income, by Occupation and SA4 location Table 2: Sole trader business income, by Industry and SA4 location." Sole trader data not included in this sub-collection.See analysis in progress for:=> Individual income by occupation / location at: http://areff2000.net16.netTo recreate #datavis - How To To view on National Map (data.gov.au mapping tool). 1. Save data as csv. Data (loaded here), currently at: http://118.138.240.130/sa4_deltaHeadcountRaw_display.csv2. Open http://nationalmap.gov.au. 3. Click 'Add Data'. 4. Drag csv file onto map. 5. Click Done. 6. Select Year in control panel (lower left of screen). Raw shows count of jobs. Year shows % change from four years earlier. 7. Click on region (SA4) to see data for that region.Data format: Year | Occupation | Location (SA4) | Count of Workers | $ of Workers * Year: [2002, 2006, 2010, 2014]* Salary and Wages; 200,000 lines (summary only included here)* Sole Proprietors; 100,000 lines (not included here)* Occupation: Description at Australian Bureau of Statistics. (3,216 lines) (link below)* SA4 Location descriptions at: http://stat.abs.gov.au/itt/r.jsp?databyregion#/. #dataTotals - Salary and WagesYearWorkers (M)Earnings ($B)GDP USD($B)20028.528540020069.4372746201010.24811142201410.35841450Table 1: Aust. Salary and Wages 2002 - 2014.GDP info from: Trading Economics (link below).#datavis1. Three Chloro images made at aurin.org.au (AU researcher login required). eg Chloro12_2014 is 12 colour chloropeth, for 2010 - 2014, Chloro12_2010 is 2006 - 2010, Chloro12_2006 is 2002 - 2006.Please cite images as: Ferrers, R., ATO - User uploaded data (2016) visualised in AURIN portal (map visualisation chloropeth) on 25.8.2016. Viewed online at: https://dx.doi.org/10.6084/m9.figshare.4056282.v22. Red/Orange (year.tiff) images made at nationalmap.org.au, where 2014.tiff is percent difference 2010 - 2014, 2010.tiff is 2006 - 2010, 2006.tiff is 2002 - 2006.#usageThis #datavis was used in a University of Melbourne Library Hackathon - Hack for Good (25.8.16) - https://twitter.com/ValueMgmt/status/769041449862168577Slides attached below: (see Canva link; Ferrers, Li, Kreunen and Lindsay (2016). L^2 Local Livability Index. Online at: https://www.canva.com/design/DAB8-48tlEw/view)https://twitter.com/ValueMgmt/status/770144651953135616

Net-Income-Applicable-To-Common-Shares Time Series for QUBE Holdings Ltd. Qube Holdings Limited, together with its subsidiaries, provides import and export logistics services in Australia, New Zealand, and Southeast Asia. It operates through two segments, Operating Division and Patrick. The company offers logistics and infrastructure solutions, including containerised cargo and grain trading, as well as outsourced industrial logistics across the heavy transport, mobile crane, and renewable energy industries. It also provides import/export supply chain services, such as road and rail transport of containers to and from ports, operation of container parks, customs and quarantine services, warehousing, international freight forwarding, lifting services or equipment, bulk rail and containerised haulage storage, and handling of rural commodities, as well as operation of automotive and break-bulk, grain, intermodal, and regional rail terminals. In addition, the company offers port logistics, such as processing and delivery of energy and forestry products, project, and general cargo; bulk logistics comprising mine-to-ship transport, stockpile management, and storage facilities; container sale, hire, modification, and stevedoring services. Further, it operates Australian Amalgamated terminals. The company was formerly known as Qube Logistics Holdings Limited and changed its name to Qube Holdings Limited in November 2012. Qube Holdings Limited was incorporated in 2011 and is headquartered in Sydney, Australia.

Regional profile tables containing gross regional product and output, employment, household income and expenditure, and trade. The tables are estimates derived as part of the input-output table construction process for South Australia and its regions. They are not taken directly from a census or survey, but are based on a mix of collected data, state shares (if a regional table) and estimates based on “parent” table values.

Regional profile tables containing gross regional product and output, employment, household income and expenditure, and trade. The tables are estimates derived as part of the input-output table construction process for South Australia and its regions. They are not taken directly from a census or survey, but are based on a mix of collected data, state shares (if a regional table) and estimates based on “parent” table values.