In 2021, 20.1% of people from the Indian ethnic group were in higher managerial and professional occupations – the highest percentage out of all ethnic groups in this socioeconomic group.

COVID-19 has had worse health, education and labor market effects on groups with low socio-economic status (SES) than on those with high SES. Little is known, however, about whether COVID-19 has also had differential effects on non-cognitive skills that are important for life outcomes. Using panel data from before and during the pandemic, we show that COVID-19 affects one key non-cognitive skill, i.e., prosociality. While prosociality is already lower for low-SES students prior to the pandemic, we show that COVID-19 infections within families amplify the prosociality gap between French high-school students of high- and low-SES by almost tripling its size in comparison to pre-COVID-19 levels.

This dataset contains interview transcriptions of interviews with 13 GPs on their experiences with communication with patients from different cultural backgrounds and/or low socio-economic status

As of July 2024, roughly 40.4 percent of respondents claimed to belong to the middle class, followed by the lower class or poor at nearly 20 percent. Another 14 percent of respondents said they were lower middle class.

This data is Statistical Local Areas (SLA) based Socio-Economic Indexes for Areas (SEIFA) data based on the 2001 census. The data follows the 2001 Australian Standard Geographical Classification (ASGC) boundaries. The Australian Bureau of Statistics (ABS) has developed indexes to allow ranking of regions/areas, providing a method of determining the level of social and economic wellbeing in that region. There are four indexes included in the SEIFA 2001 product. Each index summarises a different aspect of the socio-economic conditions in an area. The indexes have been obtained by a technique called principal components analysis. This technique summarises the information from a variety of social and economic variables, calculating weights that will give the best summary for the underlying variables. For the SEIFA indexes, each index uses a different set of underlying variables. The four indexes are: Index of Disadvantage (IRSD) - focuses on low-income earners, relatively lower educational attainment, high unemployment and variables reflecting disadvantage. Index of Advantage/Disadvantage (IRSAD) - A new index, and is a continuum of advantage to disadvantage. Low values indicate areas of disadvantage; and high values indicate areas of advantage. Index of Economic Resources (IER) - This index includes variables that are associated with economic resources. Variables include rent paid, income by family type, mortgage payments, and rental properties. Index of Education and Occupation (IEO) - This index includes all education and occupation variables only. All the indexes (including the Index of Relative Socio-Economic Disadvantage) have been constructed so that relatively disadvantaged areas (e.g. areas with many low income earners) have low index values. This data is ABS data (catalogue number: 2033.0.55.001) used with permission from the Australian Bureau of Statistics.

The percentage of low socio-economic status youth and adults (15 years and above) who have achieved or exceeded a given level of proficiency in literacy. Functional literacy is defined by UIS as the capacity of a person to engage in all those activities in which literacy is required for effective function of his or her group and community and also for enabling him or her to continue to use reading, writing and calculation for his or her own and the community’s development. This indicator is collected via skills' assessment surveys of the adult population (e.g. the Programme for the International Assessment of Adult Competencies (PIAAC), the Skills Towards Employment and Productivity (STEP) Measurement programme, the Literacy Assessment Measurement Programme (LAMP) and national adult literacy and numeracy surveys.

This dataset provides Census 2021 estimates that classify Household Reference Persons aged 16 years and over in England and Wales by NS-SEC of Household Reference Person and by household composition. The estimates are as at Census Day, 21 March 2021.

As Census 2021 was during a unique period of rapid change, take care when using this data for planning purposes. Read more about this quality notice.

Data about household relationships might not always look consistent with legal partnership status. This is because of complexity of living arrangements and the way people interpreted these questions. Take care when using these two variables together. Read more about this quality notice.

Area type

Census 2021 statistics are published for a number of different geographies. These can be large, for example the whole of England, or small, for example an output area (OA), the lowest level of geography for which statistics are produced.

For higher levels of geography, more detailed statistics can be produced. When a lower level of geography is used, such as output areas (which have a minimum of 100 persons), the statistics produced have less detail. This is to protect the confidentiality of people and ensure that individuals or their characteristics cannot be identified.

Lower tier local authorities

Lower tier local authorities provide a range of local services. There are 309 lower tier local authorities in England made up of 181 non-metropolitan districts, 59 unitary authorities, 36 metropolitan districts and 33 London boroughs (including City of London). In Wales there are 22 local authorities made up of 22 unitary authorities.

Coverage

Census 2021 statistics are published for the whole of England and Wales. However, you can choose to filter areas by:

• country - for example, Wales
• region - for example, London
• local authority - for example, Cornwall
• health area – for example, Clinical Commissioning Group
• statistical area - for example, MSOA or LSOA

National Statistics Socio-economic Classification (NS-SeC)

The National Statistics Socio-economic Classification (NS-SEC) indicates a person's socio-economic position based on their occupation and other job characteristics.

It is an Office for National Statistics standard classification. NS-SEC categories are assigned based on a person's occupation, whether employed, self-employed, or supervising other employees.

Full-time students are recorded in the "full-time students" category regardless of whether they are economically active.

Household composition

Households according to the relationships between members.

One-family households are classified by:

• the number of dependent children
• family type (married, civil partnership or cohabiting couple family, or lone parent family)

Other households are classified by:

• the number of people
• the number of dependent children
• whether the household consists only of students or only of people aged 66 and over

Summary measures of Socio-economic status (SES) inequality in DBMHL in Bangladesh, Nepal, Myanmar, and Pakistan.

The Ministry of Education (MEQ) annually calculates two deprivation indices for the 69 school service centers and linguistic school boards: • the Socio-economic Environment Index (IMSE), which consists of the proportion of families with children whose mothers do not have a diploma, certificate or degree (which represents two thirds of the weight of the index) and the proportion of households whose parents were not employed during the week of reference of the Canadian census (which represents a third of the weight of the index). • The Low Income Threshold Index (LFS) corresponds to the proportion of families with children whose income is close to or below the low income threshold. The low-income cut-off is defined as the income level at which families are estimated to spend 20% more than the overall average on food, housing, and clothing. It provides information that is used to estimate the proportion of families whose incomes can be considered low, taking into account the size of the family and the environment of residence (rural region, small urban area, large agglomeration, etc.). For the 2023-2024 school year, the socio-economic data used are extracted from the 2016 Canadian census and relate to the situation of Quebec families with at least one child aged 0 to 18. Depending on their geographical position, these families are grouped together in one of the 3,680 settlement units established by the Ministry. The annual school indices are grouped in decimal rank in order to locate the relative position of the school among all public schools, for primary and secondary education. Note that schools may include more than one school building, that no index is calculated for school boards with special status (Cree, Kativik Ilisarniliriniq and Littoral) and that only schools with 30 students or more are selected (without an MEQ-MSSS agreement). For the school year 2023-2024, 695 primary schools and 197 secondary schools are considered disadvantaged (decile ranks 8, 9 or 10) according to the IMSE index. These schools have 15,7109 and 113,781 students respectively, representing 30% of the public network for each of these two levels of education.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Data from applications made to the LMH Foundation Year 2017-2019 showing interaction between individual-level and area-level contextual indicators of disadvantage. This provides a working example of how individual-level contextual indicators could be used to target widening-participation interventions specifically for students from low socioeconomic status (low-SES) groups. It is based on the experience of developing selection criteria to identify disadvantaged students for a Foundation Year at Lady Margaret Hall (LMH), a college of Oxford University. The process built on the body of published academic work looking at the most suitable contextual indicators, and the outcome offers a case study of how suitable individual-level indicators can be effectively collected and verified.

Chile Poverty Statistics: Female: Poor data was reported at 611,896.000 Person in 2017. This records a decrease from the previous number of 775,640.000 Person for 2015. Chile Poverty Statistics: Female: Poor data is updated yearly, averaging 1,123,217.500 Person from Dec 2006 (Median) to 2017, with 6 observations. The data reached an all-time high of 1,383,848.000 Person in 2006 and a record low of 611,896.000 Person in 2017. Chile Poverty Statistics: Female: Poor data remains active status in CEIC and is reported by Ministry of Social Development. The data is categorized under Global Database’s Chile – Table CL.H020: National Socio-Economic Characterization Survey: Poverty Situation.

This dataset provides Census 2021 estimates that classify usual residents aged 16 years and over in England and Wales by NS-SEC and by economic activity status. The estimates are as at Census Day, 21 March 2021.

As Census 2021 was during a unique period of rapid change, take care when using this data for planning purposes. Read more about this quality notice.

As Census 2021 was during a unique period of rapid change, take care when using this data for planning purposes. Read more about this quality notice.

Area type

Census 2021 statistics are published for a number of different geographies. These can be large, for example the whole of England, or small, for example an output area (OA), the lowest level of geography for which statistics are produced.

For higher levels of geography, more detailed statistics can be produced. When a lower level of geography is used, such as output areas (which have a minimum of 100 persons), the statistics produced have less detail. This is to protect the confidentiality of people and ensure that individuals or their characteristics cannot be identified.

Lower tier local authorities

Lower tier local authorities provide a range of local services. There are 309 lower tier local authorities in England made up of 181 non-metropolitan districts, 59 unitary authorities, 36 metropolitan districts and 33 London boroughs (including City of London). In Wales there are 22 local authorities made up of 22 unitary authorities.

Coverage

Census 2021 statistics are published for the whole of England and Wales. However, you can choose to filter areas by:

• country - for example, Wales
• region - for example, London
• local authority - for example, Cornwall
• health area – for example, Clinical Commissioning Group
• statistical area - for example, MSOA or LSOA

National Statistics Socio-economic Classification (NS-SeC)

The National Statistics Socio-economic Classification (NS-SEC) indicates a person's socio-economic position based on their occupation and other job characteristics.

It is an Office for National Statistics standard classification. NS-SEC categories are assigned based on a person's occupation, whether employed, self-employed, or supervising other employees.

Full-time students are recorded in the "full-time students" category regardless of whether they are economically active.

Economic activity status

People aged 16 years and over are economically active if, between 15 March and 21 March 2021, they were:

• in employment (an employee or self-employed)
• unemployed, but looking for work and could start within two weeks
• unemployed, but waiting to start a job that had been offered and accepted

It is a measure of whether or not a person was an active participant in the labour market during this period. Economically inactive are those aged 16 years and over who did not have a job between 15 March to 21 March 2021 and had not looked for work between 22 February to 21 March 2021 or could not start work within two weeks.

The census definition differs from International Labour Organization definition used on the Labour Force Survey, so estimates are not directly comparable.

This classification splits out full-time students from those who are not full-time students when they are employed or unemployed. It is recommended to sum these together to look at all of those in employment or unemployed, or to use the four category labour market classification, if you want to look at all those with a particular labour market status.

This profile is an index of socio-economic vulnerability to COVID-19, highlighting poor populations with limited options to cope with economic shocks.This index is created with a principal component analysis (PCA) with a list of indicators of socio-economic status at the household level (non-farm employment, farmland ownership, livestock ownership, educational attainment), housing type (material for roof, floor, and walls), food security (proxied by child wasting), financial inclusion (household member has a bank account), and domestic violence (household experienced physical, sexual, or emotional abuse of women at last once in a year). This index takes into account urban and rural differences by conducting the PCA for urban and rural settings and combining results for a national index. Units: Categories: 5 (highest risk) to 1 (lowest risk)

This data is Census Collection Districts (CD) based Socio-Economic Indexes for Areas (SEIFA) Index of Disadvantage (IRSD) - focuses on low-income earners, relatively lower educational attainment, high unemployment and variables reflecting disadvantage. This data is based on the 2006 census and follows the 2006 Australian Standard Geographical Classification (ASGC) boundaries. The Australian Bureau of Statistics (ABS) has developed indexes to allow ranking of regions/areas, providing a method of determining the level of social and economic wellbeing in that region. There are four indexes included in the SEIFA 2006 product. They relate to socio-economic aspects of geographic areas. Each index summarises a different aspect of the socio-economic conditions in an area. The indexes have been obtained by a technique called principal components analysis. This technique summarises the information from a variety of social and economic variables, calculating weights that will give the best summary for the underlying variables. For the SEIFA indexes, each index uses a different set of underlying variables. All the indexes (including the Index of Relative Socio-Economic Disadvantage) have been constructed so that relatively disadvantaged areas (e.g. areas with many low income earners) have low index values. This data is ABS data (catalogue number: 2033.0.55.001) used with permission from the Australian Bureau of Statistics. For more information on this data please visit the Australian Bureau of Statistics.Please note:

AURIN has spatially enabled the original data following the 2006 ASGC.

Prominent social psychologists and major media outlets have put forward the notion that people of high socioeconomic status (SES) are more selfish and behave more unethically than people of low SES. In contrast, other research in economics and sociology has hypothesized and found a positive relationship between SES and prosocial and ethical behavior. We review the empirical evidence for these contradictory findings and conduct two direct, well-powered, and preregistered replications of the field studies by Piff and colleagues (2012) to test the relationship between SES and unethical/selfish behavior. Unlike the original findings, we find no evidence of a positive relationship between SES and unethical/selfish behavior in the two field replication studies. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Abstract

In order to develop an effective poverty reduction policies and programs, Iraqi policy makers need to know how large the poverty problem is, what kind of people are poor, and what are the causes and consequences of poverty. Until recently, they had neither the data nor an official poverty line. (The last national income and expenditure survey was in 1988.) In response to this situation, the Iraqi Ministry of Planning and Development Cooperation established the Household Survey and Policies for Poverty Reduction Project in 2006, with financial and technical support of the World Bank. The project has been led by the Iraqi Poverty Reduction Strategy High Committee, a group which includes representatives from Parliament, the prime minister’s office, the Kurdistan Regional Government, and the ministries of Planning and Development Cooperation, Finance, Trade, Labor and Social Affairs, Education, Health, Women’s Affairs, and Baghdad University. The Project has consisted of three components: - Collection of data which can provide a measurable indicator of welfare, i.e.the Iraq Household Socio Economic Survey (IHSES). - Establishment of an official poverty line (i.e. a cut off point below which people are considered poor) and analysis of poverty (how large the poverty problem is, what kind of people are poor and what are the causes and consequences of poverty). - Development of a Poverty Reduction Strategy, based on a solid understanding of poverty in the Republic of Iraq.

Geographic coverage

National coverage Domains: Urban/rural/metropolitan; governorates

Analysis unit

• Household - Individual

Kind of data

Sample survey data [ssd]

Sampling procedure

Total sample size and stratification

The total effective sample size of the IHSES 2007 is 17,822 households. The survey was nominally designed to visit 18,144 households - 324 in each of 56 major strata. The strata are the rural, urban and metropolitan sections of each of the Republic of Iraq's 18 governorates, with the exception of Baghdad, which has three metropolitan strata. The IHSES 2007 and the MICS 2006 survey intended to visit the same nominal sample. Variable q0040 indicates whether this was indeed the case.

Sampling strategy and sampling stages

The sample was selected in two stages, with groups of majals (Census Enumeration Areas) as Primary Sampling Units (PSUs) and households as Secondary Sampling Units. In the first stage, 54 PSUs were selected with probability proportional to size (pps) within each stratum, using the number of households recorded by the 1997 Census as a measure of size. In the second stage, six households were selected by systematic equal probability sampling (seps) within each PSU. To these effects, a cartographic updating and household listing operation was conducted in 2006 in all 3,024 PSUs, without resorting to the segmentation of any large PSUs. The total sample is thus nominally composed of 6 households in each of 3,024 PSUs.

Trios, teams and survey waves

The PSUs selected in each governorate (270 in Baghdad and 162 in each of the other governorates) were sorted into groups of three neighboring PSUs called trios -- 90 trios in Baghdad and 54 per governorate elsewhere. The three PSUs in each trio do not necessarily belong to the same stratum. The 12 months of the data collection period were divided into 18 periods of 20 or 21 days called survey waves. Fieldworkers were organized into teams of three interviewers, each team being responsible for interviewing one trio during a survey wave. The survey used 56 teams in total - 5 in Baghdad and 3 per governorate elsewhere. The 18 trios assigned to each team were allocated into survey waves at random. The 'time use' module was administered to two of the six households selected in each PSU: nominally the second and fifth households selected by the seps procedure in the PSU.

(For a formatted version of this field, see "IHSES sampling design and sampling weights.pdf" in "External Resources".) (For a map of Iraq's governorates and districts, see "Iraq governorates and districts.pdf" in "External Resources".)

Sampling deviation

The design did not consider the replacement of any of the randomly selected units (PSUs or households.) However, certain emergency procedures were defined to deal with security situations: If a survey team was unable to visit a trio of PSUs in the originally allocated wave, that trio was to be swapped with the trio from a randomly selected future wave that was secure at the time. If none of the still unvisited trios was secure, one of the secure trios already visited was randomly selected instead, and the team visited in each of its PSUs a new seps sample of six households - different from those interviewed when the trio was visited the first time.

This explains why the survey datasets only contain data from 2,876 of the 3,024 originally selected PSUs, whereas 55 of the PSUs contain more that the six households nominally dictated by the design.

The wave number in the survey datasets is always the nominal wave number, corresponding to the random allocation considered by the design. The effective interview dates can be found in questions 35 to 39 of the survey questionnaires.

Practice deviated from the designed procedures in two cases: In one of the governorates (Suleimaniya,) the survey was fielded for an additional two waves (waves 19 and 20,) in order to visit an extra 18 PSUs, selected from certain metropolitan areas that were not included in the original sample frame. These areas are to be analyzed jointly with the rest of metropolitan Suleimaniya, but from a sampling standpoint they constitute a de facto fourth stratum in the governorate. In another governorate (Kirkuk,) local managers used their judgment rather than the established procedures to select 12 replacement PSUs. To identify the 30 PSUs resulting from these deviations in the survey datasets, their original 'cluster numbers' (ranging from 0001 to 3024) were increased by 5000.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was designed by COSIT in continuous consultation with the WB consultants. It is composed of 18 sections covering household characteristics, government ration, housing, education, health, recreation facilities, employment, expenditure and income, transfers and risks along with the diary and time use. A pre-test of the questionnaire was conducted at an early stage of the project in a small number of households with different characteristics in some governorates. To facilitate its administration, the questionnaire was divided into 5 physical booklets called "forms". Form 1 gathers socio economic information on household members and housing; Form 2 is to record non food expenditures, Form 3 is for employment, transfers and others; Form 4 is the diary used to record household's food purchases during 10 days and finally Form 5 with the time use sheet administered to one third of the households in the sample. All forms where produced in three languages: Arabic, Kurdish and English (all available in "External Resources").

Cleaning operations

Data editing took place at a number of stages throughout the processing, including: 1. Office editing by local supervisors. 2. Based on the validation rules incorporated in the data entry program (CSPro), rejection reports were produced, based on which data are corrected. 3. Structural checking of SPSS data files. 4. Automatic fixing programme at the analysis phase. Detailed documentation of the editing of data can be found in the "Data processing guidelines" document provided as an external resource.

Sampling error estimates

The estimation of standard errors must account for the design features explained in the "Sampling" field. (See also "IHSES sampling design and sample weights" in "External Resources.")

The following variables, included in all datasets, are needed for the estimation of standard errors:

xweight : sampling weight

xstrat: sampling stratum

xcluster: primary sampling unit

Warning: Variable 'xbeea', also present in all datasets, identifies rural, urban and metropolitan environments for tabulation purposes; it is sometimes wrongly referred to as 'stratum', but it should not be used for the estimation of sampling errors. The variable that needs to be used for these purposes is 'xstrat', which identifies the 57 sampling strata, defined as the rural, urban and metropolitan sectors of each of each of the 18 governorates, with the exception of Baghdad (which has three metropolitan sectors,) and Suleimaniya (which has two.)

Series Name: Low to high socio-economic parity status index for achievement (ratio)Series Code: SE_TOT_SESPIRelease Version: 2020.Q2.G.03 This dataset is the part of the Global SDG Indicator Database compiled through the UN System in preparation for the Secretary-General's annual report on Progress towards the Sustainable Development Goals.Indicator 4.5.1: Parity indices (female/male, rural/urban, bottom/top wealth quintile and others such as disability status, indigenous peoples and conflict-affected, as data become available) for all education indicators on this list that can be disaggregatedTarget 4.5: By 2030, eliminate gender disparities in education and ensure equal access to all levels of education and vocational training for the vulnerable, including persons with disabilities, indigenous peoples and children in vulnerable situationsGoal 4: Ensure inclusive and equitable quality education and promote lifelong learning opportunities for allFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/

Abstract

In 2005, BPS do the Social Economic Colletion (PSE05), which aims to get the data in the form of micro poverty households directory that deserves a direct cash assistance (BLT) in 2005-2006. Given the data turns PSE05 considered better results compared to a database available in local government. Nevertheless, it is recognized that the data from PSE05 not perfect. The study of 56 universities found the data from PSE05 still contains 8 percent and 22.36 percent error inclusion exclusion error (Coordinating Minister for People's Welfare 2005). In addition to BLT program, data PSE05 also used in targeting households on several national programs, such as the Health Insurance of the Poor (HIP) and cheap rice program for the poor (Raskin). These programs succeeded in reducing poverty levels, poor households proved as much 17.8 percent in 2006 down to 15.4 percent in 2008. But be aware that the collection PSE05 an activity that is great for BPS, BPS so many other activities are pending at this time. After PSE05 activities, in 2007 the BPS also conducted the data collection for the household conditional direct cash assistance program (Family Hope Program / PKH) in 2007. The collection of data to support this program called Basic Health Care Survey and Education 2007 (SPDKP07). Results from SPDKP07 considered much better than the data from PSE05 because only less inclusion and exclusion errors of his. This is because SPDKP07 implemented only in 953 districts / cities were selected and a much larger budget.

Geographic coverage

Coverage provincial representative to the level of the village / district.

Analysis unit

The unit of analysis is the individual in the household, from each selected household collected information about the general state of each member of the household including name, relationship to head of household, sex, and age.

Universe

This survey covers all household members.

Kind of data

Sample survey data

Sampling procedure

In measuring poverty, BPS uses the concept of the ability to meet basic needs (basic needs). For macro data and information poverty, the data source is the National Socio-Economic Survey (NSES) BPS conducted every year. For micro poverty data, in 2005 the BPS has conducted Social Economic Colletion (PSE05), which aims to get a database of poor households who deserve direct cash assistance (BLT) in 2005-2006. In addition to BLT program, data PSE05 also used in targeting households on several national programs, such as the Health Insurance of the Poor (HIP) and cheap rice program for the poor (Raskin). After PSE05 activities, in 2007 the BPS also organize poverty micro data collection for household database program recipients of Direct Conditional Cash Transfer (Family Hope Program) in 2007 and 2008 through a survey of Primary Health Care and Education 2007 (SPDKP07).

Mode of data collection

Face-to-face [f2f]

This dataset provides Census 2021 estimates that classify usual residents aged 16 years and over in England and Wales by NS-SEC and by age. The estimates are as at Census Day, 21 March 2021.

As Census 2021 was during a unique period of rapid change, take care when using this data for planning purposes. Read more about this quality notice.

Estimates for single year of age between ages 90 and 100+ are less reliable than other ages. Estimation and adjustment at these ages was based on the age range 90+ rather than five-year age bands. Read more about this quality notice.

Area type

Census 2021 statistics are published for a number of different geographies. These can be large, for example the whole of England, or small, for example an output area (OA), the lowest level of geography for which statistics are produced.

For higher levels of geography, more detailed statistics can be produced. When a lower level of geography is used, such as output areas (which have a minimum of 100 persons), the statistics produced have less detail. This is to protect the confidentiality of people and ensure that individuals or their characteristics cannot be identified.

Lower tier local authorities

Lower tier local authorities provide a range of local services. There are 309 lower tier local authorities in England made up of 181 non-metropolitan districts, 59 unitary authorities, 36 metropolitan districts and 33 London boroughs (including City of London). In Wales there are 22 local authorities made up of 22 unitary authorities.

Coverage

Census 2021 statistics are published for the whole of England and Wales. However, you can choose to filter areas by:

• country - for example, Wales
• region - for example, London
• local authority - for example, Cornwall
• health area – for example, Clinical Commissioning Group
• statistical area - for example, MSOA or LSOA

National Statistics Socio-economic Classification (NS-SeC)

The National Statistics Socio-economic Classification (NS-SEC) indicates a person's socio-economic position based on their occupation and other job characteristics.

It is an Office for National Statistics standard classification. NS-SEC categories are assigned based on a person's occupation, whether employed, self-employed, or supervising other employees.

Full-time students are recorded in the "full-time students" category regardless of whether they are economically active.

Age

A person’s age on Census Day, 21 March 2021 in England and Wales. Infants aged under 1 year are classified as 0 years of age.

This data is Local Government Areas (LGA) based Socio-Economic Indexes for Areas (SEIFA) Index of Disadvantage (IRSD) - focuses on low-income earners, relatively lower educational attainment, high …Show full descriptionThis data is Local Government Areas (LGA) based Socio-Economic Indexes for Areas (SEIFA) Index of Disadvantage (IRSD) - focuses on low-income earners, relatively lower educational attainment, high unemployment and variables reflecting disadvantage. This data is based on the 2006 census and follows the 2006 Australian Standard Geographical Classification (ASGC) boundaries. The Australian Bureau of Statistics (ABS) has developed indexes to allow ranking of regions/areas, providing a method of determining the level of social and economic wellbeing in that region. There are four indexes included in the SEIFA 2006 product. They relate to socio-economic aspects of geographic areas. Each index summarises a different aspect of the socio-economic conditions in an area. The indexes have been obtained by a technique called principal components analysis. This technique summarises the information from a variety of social and economic variables, calculating weights that will give the best summary for the underlying variables. For the SEIFA indexes, each index uses a different set of underlying variables. All the indexes (including the Index of Relative Socio-Economic Disadvantage) have been constructed so that relatively disadvantaged areas (e.g. areas with many low income earners) have low index values. This data is ABS data (catalogue number: 2033.0.55.001) used with permission from the Australian Bureau of Statistics. For more information on this data please visit the Australian Bureau of Statistics. Please note: AURIN has spatially enabled the original data following the 2006 ASGC. Copyright attribution: Government of the Commonwealth of Australia - Australian Bureau of Statistics, (2008): ; accessed from AURIN on 12/16/2021. Licence type: Creative Commons Attribution 4.0 International (CC BY 4.0)