In a survey conducted by the Australian Institute of Family Studies between May and June 2021, ** percent of the respondents reported that they asked for financial help from friends or family in the previous six months. More than ** percent reported experiencing ***** or more of the financial stresses in this time period.

This dataset presents the synthetically modeled indicators relating to the financials of the population of small regions of Australia based on the 2016 Census and aggregated following the 2016 edition of the Australian Statistical Geography Standard (ASGS). The synthetic indicators are produced by the spatial micro-simulation model (SpatialMSM). The data has been provided by The National Centre for Social and Economic Modelling (NATSEM). NATSEM’s spatial microsimulation model uses a technique that takes a survey and reweights it to small area Census data. SpatialMSM18 is the application of the NATSEM Spatial Microsimulation model using the Household, Income and Labour Dynamics in Australia (HILDA) dataset, the ABS Housing Expenditure Survey (for financial stress) and the 2016 Census of Population and Housing at the SA2 level (Tanton et al. 2011). All the indicators from the SpatialMSM model are synthetic, so there is some model error as well as other error from the survey. Therefore, they are not as accurate as the Census data used. Data in this dataset comes from NATSEM's spatial microsimulation model. Estimates are for financial stress, trust and life satisfaction. A description of the model, and validation, can be found in the accompanying NATSEM Technical Report. Please note: AURIN has spatially enabled the original data provided directly from NATSEM.

This dataset, released April 2017, contains an Estimated number of people aged 18 years and over whose household could raise $2,000 within a week (modelled estimates), 2014; Estimated number of people aged 18 years and over who had government support as their main source of income in the last 2 years (modelled estimates), 2014; Estimated number of people aged 18 years and over who had government support as their main source of income, for 13 months or more, within the past 24 months (modelled estimates), 2014. The data is by Local Government Area (LGA) 2016 geographic boundaries. For more information please see the data source notes on the data. Source: Modelled by PHIDU based on the ABS General Social Survey, 2014. AURIN has spatially enabled the original data. Data that was not shown/not applicable/not published/not available for the specific area ('#', '..', '^', 'np, 'n.a.', 'n.y.a.' in original PHIDU data) was removed.It has been replaced by by Blank cells. For other keys and abbreviations refer to PHIDU Keys.

People aged 18 years and over who experienced financial stress in 2014 according to the following categories: household could raise $2,000 within a week, government support as their main source of …Show full descriptionPeople aged 18 years and over who experienced financial stress in 2014 according to the following categories: household could raise $2,000 within a week, government support as their main source of income in the last 24 months, and government support as their main source of income for 13 months or more in the last 24 months (all entries that were classified as not shown, not published or not applicable were assigned a null value; no data was provided for Maralinga Tjarutja LGA, in South Australia). The data is by LGA 2015 profile (based on the LGA 2011 geographic boundaries). For more information on statistics used please refer to the PHIDU website, available from: http://phidu.torrens.edu.au/. Source: Estimates for Population Health Areas (PHAs) are modelled estimates and were produced by the ABS; estimates at the LGA and PHN level were derived from the PHA estimates. Copyright attribution: Torrens University Australia - Public Health Information Development Unit, (2017): ; accessed from AURIN on 12/3/2020. Licence type: Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Australia (CC BY-NC-SA 3.0 AU)

The number of households in the bottom 40 percent of the income distribution, spending more than 30 percent of their income on mortgage repayments or rent and those households as a proportion of all low income households, 2011 (all entries that were classified as not shown, not published or not applicable were assigned a null value; no data was provided for Maralinga Tjarutja LGA, in South Australia). The data is by LGA 2015 profile (based on the LGA 2011 geographic boundaries). Source: Compiled by PHIDU based on the ABS Census 2011 (unpublished) data.

Part of a series of factsheets on COVID-19 changes. This factsheet is about financial assistance for separating famliies.

People aged 18 years and over who experienced financial stress in 2014 according to the following categories: household could raise $2,000 within a week, government support as their main source of income in the last 24 months, and government support as their main source of income for 13 months or more in the last 24 months (all entries that were classified as not shown, not published or not applicable were assigned a null value; no data was provided for Maralinga Tjarutja LGA, in South Australia). The data is by LGA 2015 profile (based on the LGA 2011 geographic boundaries). For more information on statistics used please refer to the PHIDU website, available from: http://phidu.torrens.edu.au/. Source: Estimates for Population Health Areas (PHAs) are modelled estimates and were produced by the ABS; estimates at the LGA and PHN level were derived from the PHA estimates.

The Emergency Electricity Payment Scheme (EEPS) is to provide assistance to households in a financial crisis who are unable to pay their electricity debt. As at Friday 9 June 2016 the Emergency Electricity Payment Scheme (EEPS) approved 1,020 applications for the 2016 - 2017 financial year. Note: a new data system was introduced this financial year, when data were transitioned from the old to the new system, some of the “reasons” and “regions” data were not transferred and resulted in the “Null” response category. Dataset contains: Number and % of approved applications by reason for applying (e.g. illness, financial stress, etc.) Number and % approve applications by Regions where approved applicants live Number and % of ATSI identifier

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.

Health justice partnerships (HJPs) are early intervention programs. Recognising the complexity of disadvantage, they occupy the space where health and social justice issues overlap. Providing integrated, person-centred services, HJPs work with individuals to overcome structural barriers that negatively impact on a person’s quality of life and to advocate for systemic change. Although there isn’t a clear causality, the relationship between unmet legal need and adverse health outcomes is thought to be mutually reinforcing. On the one hand, chronic illness or disability can cause barriers to accessing legal assistance and can compound social disadvantage. On the other hand, unaddressed legal issues can cause stress and anxiety contributing to mental and physical ill health1. By taking a holistic approach, HJPs remove the need to draw a distinction between legal and health issues. In its evaluation report, Redfern Legal Centre describe the interrelated nature of health and legal issues for one of their clients: “Aron was in the MERIT program. He told his counsellor that he was having difficulty in paying back his loan under a Financial Management Order. He told his counsellor that his drug use was increasing due to the stress and anxiety about the debt, which was also increasing due to default fees and other charges. He was referred to the solicitor who found that as Aron was under a Financial Management Order, legally he was unable to deal with his own finances. The loan should never have been given and as such was voided through our advocacy. He reported saying that his stress had decreased significantly, and therefore his drug use.”2 By working together through an HJP the health and legal services are able to improve the negative physical and psychosocial factors which jointly impact the health of the individual, and alleviate the strain on the healthcare and legal systems