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.

Australia: Gini income inequality index: The latest value from 2018 is 34.3 index points, an increase from 33.7 index points in 2016. In comparison, the world average is 35.68 index points, based on data from 91 countries. Historically, the average for Australia from 1981 to 2018 is 33.52 index points. The minimum value, 31.3 index points, was reached in 1981 while the maximum of 35.4 index points was recorded in 2008.

In Australia, 44 percent of all national wealth was owned by the wealthiest ten percent of the population in 2023. Within the age group of 65 years or older, the wealthiest ten percent also own 44 percent of the wealth, while the lowest 60 percent own just 20 percent.

In 2022, the wealthiest top one percent of Australians held 9.9 percent of the national income. The bottom 50 percent of Australians had 17.2 percent of the national income.

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

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 …Show full descriptionThese 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. The metadata and files (if any) are available to the public.

In Australia, 44 percent of all net wealth was owned by the wealthiest ten percent of the population in 2023. 66 percent of wealth generated by real estate was held by the wealthiest ten percent, with only five percent of the wealth being held by the lowest 60 percent of the population.

According to a survey conducted by Ipsos on predictions for global issues in 2020, 54 percent of Australians believed it was unlikely that women would be paid the same amount as men for the same work in 2020. Australian respondents held an overall pessimistic view on closing the gender pay gap in 2020.

AU: Proportion of Population Pushed Below the $1.90: Poverty Line by Out-of-Pocket Health Care Expenditure: 2011 PPP: % data was reported at 0.000 % in 2015. This stayed constant from the previous number of 0.000 % for 2009. AU: Proportion of Population Pushed Below the $1.90: Poverty Line by Out-of-Pocket Health Care Expenditure: 2011 PPP: % data is updated yearly, averaging 0.000 % from Dec 2009 (Median) to 2015, with 2 observations. The data reached an all-time high of 0.000 % in 2015 and a record low of 0.000 % in 2015. AU: Proportion of Population Pushed Below the $1.90: Poverty Line by Out-of-Pocket Health Care Expenditure: 2011 PPP: % 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: Social: Poverty and Inequality. Proportion of population pushed below the $1.90 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure. This indicator shows the fraction of a country’s population experiencing out-of-pocket health impoverishing expenditures, defined as expenditures without which the household they live in would have been above the $ 1.90 poverty line, but because of the expenditures is below the poverty line. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home).; ; World Health Organization and World Bank. 2021. Global Monitoring Report on Financial Protection in Health 2021.; Weighted Average; This indicator is related to Sustainable Development Goal 3.8.2 [https://unstats.un.org/sdgs/metadata/].

In financial year 2020, over 460 thousand households in Australia had a gross weekly household income of 6,000 Australian dollars or more. On the other end of the spectrum, over 30,000 households had a negative income and around over 32,000 had no income.

Educational resources and lesson plans based on Income Inequality (Gini Coefficients) for Australian regions data collection Lineage: Fleming, David; Measham, Tom (2015): Income Inequality (Gini Coefficients) for Australian regions. v1. CSIRO. Data Collection. https://doi.org/10.4225/08/55093772960E4

About this dataset

Context

The dataset presents the median household income across different racial categories in Au Gres township. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to gain insights into economic disparities and trends and explore the variations in median houshold income for diverse racial categories.

Key observations

Based on our analysis of the distribution of Au Gres township population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 99.03% of the total residents in Au Gres township. Notably, the median household income for White households is $59,712. Interestingly, White is both the largest group and the one with the highest median household income, which stands at $59,712.

Content

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

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in Au Gres township.
• Median household income: Median household income, adjusting for inflation, presented in 2023-inflation-adjusted dollars

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 Gres township median household income by race. You can refer the same here

Using taxation statistics, we estimate the income share held by top income groups in Australia over the period 1921-2002. We find that the income share of the richest fell from the 1920s until the mid-1940s, rose briefly in the post-war decade, and then declined until the early-1980s . During the 1980s and 1990s, top income shares rose rapidly. At the start of the twenty-first century, the income share of the richest was higher than it had been at any point in the previous fifty years. Among top income groups, recent decades have also seen a rise in the share of top income accruing to the super-rich. Trends in top income shares are similar to those observed among other elite groups, such as judges, politicians, top bureaucrats and CEOs. We speculate that changes in top income shares may have been affected by top marginal tax rates, skill-biased technological change, social norms about inequality, and the internationalisation of the market for English-speaking CEOs.

Australia Income Share Held by Third 20% data was reported at 16.400 % in 2018. This records a decrease from the previous number of 16.500 % for 2016. Australia Income Share Held by Third 20% data is updated yearly, averaging 16.500 % from Dec 1981 (Median) to 2018, with 12 observations. The data reached an all-time high of 17.000 % in 1981 and a record low of 15.900 % in 2008. Australia Income Share Held by Third 20% 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: Social: Poverty and Inequality. Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles. Percentage shares by quintile may not sum to 100 because of rounding.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

About this dataset

Context

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

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Au Train township, the median income for all workers aged 15 years and older, regardless of work hours, was $45,650 for males and $21,181 for females.

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

- Full-time workers, aged 15 years and older: In Au Train township, among full-time, year-round workers aged 15 years and older, males earned a median income of $62,500, while females earned $42,350, leading to a 32% gender pay gap among full-time workers. This illustrates that women earn 68 cents for each dollar earned by men in full-time roles. This level of income gap emphasizes the urgency to address and rectify this ongoing disparity, where women, despite working full-time, face a more significant wage discrepancy compared to men in the same employment roles.

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

Content

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

Gender classifications include:

• Male
• Female

Employment type classifications include:

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

Variables / Data Columns

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

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Au Train township median household income by race. You can refer the same here

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 table contains estimates of Incomes (Median Equivalised, Median Disposable), Poverty (using the proportion of people below a half median equivalised disposable household income poverty line), Inequality (using the Gini coefficient) and financial stress (Had no access to emergency money, Can't afford a night out once a fortnight and Leaving low income from benefit). Leaving low income from benefit is the gross earning (expressed as a percentage of average full time earnings) required for a family to reach a 60% of median household income threshold from benefits of last resort (State welfare payments or income support). All estimates were derived using a spatial microsimulation model which used the Survey of Income and Housing and the 2011 Census data as base datasets, so they are synthetic estimates. This table forms part of the AURIN Social Indicators project.

60 percent of Australians were in the wealth range between 100,000 and one million U.S. dollars in 2020. Just 9.4 percent of Australian adults had wealth of over one million U.S. dollars, which was slightly less than the share of people who had under 10,000 U.S. dollars in wealth.

Wealth distribution in the Asia-Pacific

In 2020, China had the highest number of millionaires, followed by Japan and Australia. The number of millionaires in Australia was forecasted to increase from 1.8 million to three million by 2025. According to a source, among the Asia-Pacific countries, Australia ranked second in the share of wealth per adult. The source had revealed the wealth per adult in Australia was more than 483 thousand U.S. dollars in 2020.

LGBTQ community of Australia

In 2020, a survey of working adults in Australia revealed that LGBTQ adults were employed in public services and the law enforcement across the country. On the one hand, more than 38 percent of LGBTQ individuals had a role as as a team member, above 12 percent of respondents answered that they were either team leader or supervisor.

Australia Income Share Held by Highest 10% data was reported at 26.200 % in 2018. This records an increase from the previous number of 25.500 % for 2016. Australia Income Share Held by Highest 10% data is updated yearly, averaging 24.800 % from Dec 1981 (Median) to 2018, with 12 observations. The data reached an all-time high of 27.400 % in 2008 and a record low of 22.900 % in 1981. Australia Income Share Held by Highest 10% 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: Social: Poverty and Inequality. Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

A multi-millionaire is defined as someone owning 10 million U.S. dollars or more. It was forecasted that there would be almost 18 thousand individuals in Australia defined as multi-millionaires by 2026. This is in line with the country’s growing economy over the years as well as the growing wealth inequality that was becoming a cause for concern in the island nation.

Distribution of the wealthy

As a rich country with plenty of natural resources and a high Human Development Index, Australia had always had a large number of high net-worth individuals or HNWIs. There were over ten thousand millionaires including a couple dozen of billionaires, with these figures expected to grow significantly over the next few years.

Income inequality

Despite the increase of wealth and economic growth, there was a concern at the level of poverty and homelessness due to the rising wealth inequality nationally. The number of homeless people living in Australia had only been increasing with more than a hundred thousand people currently without shelter. Furthermore, most of the wealth was being pushed from the country to the cities, affecting the livelihood of those living in the countryside or outback.