About this dataset

Context

The dataset presents the median household income across different racial categories in Au Sable charter 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 Sable charter township population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 94.09% of the total residents in Au Sable charter township. Notably, the median household income for White households is $46,614. Interestingly, White is both the largest group and the one with the highest median household income, which stands at $46,614.

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Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 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 Sable charter township.
• Median household income: Median household income, adjusting for inflation, presented in 2022-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 Sable charter township median household income by race. You can refer the same here

Estimated Resident Population (ERP) is the official measure of the Australian population. ERP for sub-state regions (including SA2s and LGAs) is published annually, with a reference date of 30 June. ERP is the official measure of the Australian population, based on the concept of usual residence. It refers to all people, regardless of nationality, citizenship or legal status, who usually live in Australia, with the exception of foreign diplomatic personnel and their families. Note, years 2012-2016 describe preliminary rebased (PR) data. For more information about PR refer to the dataset's Explanatory Notes. This dataset has been compiled using Census data, mathematical models and a range of indicator data. Current indicators include building approvals, Medicare enrolments (provided by the Department of Human Services) and electoral enrolments (provided by the Australian Electoral Commission). Data is sourced from: ABS.Stat and further information is available at http://stat.data.abs.gov.au/Index.aspx?DataSetCode=ABS_ERP_LGA2016. For additional information about this dataset and other related statistics, contact the National Information and Referral Service on 1300 135 070.

WorldPop produces different types of gridded population count datasets, depending on the methods used and end application. Please make sure you have read our Mapping Populations overview page before choosing and downloading a dataset.

Datasets are available to download in Geotiff and ASCII XYZ format at a resolution of 30 arc-seconds (approximately 1km at the equator)

-Unconstrained individual countries 2000-2020: Population density datasets for all countries of the World for each year 2000-2020 – derived from the corresponding Unconstrained individual countries 2000-2020 population count datasets by dividing the number of people in each pixel by the pixel surface area. These are produced using the unconstrained top-down modelling method.
-Unconstrained individual countries 2000-2020 UN adjusted: Population density datasets for all countries of the World for each year 2000-2020 – derived from the corresponding Unconstrained individual countries 2000-2020 population UN adjusted count datasets by dividing the number of people in each pixel, adjusted to match the country total from the official United Nations population estimates (UN 2019), by the pixel surface area. These are produced using the unconstrained top-down modelling method.

Data for earlier dates is available directly from WorldPop.

WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00674

The City of Port Adelaide Enfield Community Profile provides demographic and economic analysis for the Council area and its suburbs based on results from the 2016, 2011, 2006, 2001, 1996 and 1991 Censuses of Population and Housing. The profile is updated with population estimates when the Australian Bureau of Statistics (ABS) releases new figures. This is an interactive query tool where results can be downloaded in various formats. Three reporting types are available from this resource: 1. Social atlas that delivers the data displayed on a map showing each SA1 area (approx 200 households), 2. Community Profile which delivers data at a District level which contain 2 to 3 suburbs, and 3. Economic Profile which reports statistics of an economic indicators. The general community profile/social atlas themes available for reporting on are: -Age -Education -Ethnicity -Disability -Employment/Income -Household types -Indigenous profile -Migration -Journey to work -Disadvantage -Population Estimates -Building approvals. It also possible to navigate to the Community Profiles of some other Councils as well.

Australia Population: Growth data was reported at 2.371 % in 2023. This records an increase from the previous number of 1.273 % for 2022. Australia Population: Growth data is updated yearly, averaging 1.447 % from Dec 1961 (Median) to 2023, with 63 observations. The data reached an all-time high of 3.380 % in 1971 and a record low of 0.141 % in 2021. Australia Population: Growth 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: Population and Urbanization Statistics. Annual population growth rate for year t is the exponential rate of growth of midyear population from year t-1 to t, expressed as a percentage . Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.;Derived from total population. Population source: (1) United Nations Population Division. World Population Prospects: 2022 Revision, (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme.;Weighted average;

Number of Students: Higher Education: ytd: Other Nationalities: New South Wales data was reported at 20,565.000 Person in Dec 2024. This records an increase from the previous number of 20,499.000 Person for Nov 2024. Number of Students: Higher Education: ytd: Other Nationalities: New South Wales data is updated monthly, averaging 11,761.000 Person from Jan 2002 (Median) to Dec 2024, with 276 observations. The data reached an all-time high of 20,565.000 Person in Dec 2024 and a record low of 3,467.000 Person in Jan 2002. Number of Students: Higher Education: ytd: Other Nationalities: New South Wales data remains active status in CEIC and is reported by Department of Education. The data is categorized under Global Database’s Australia – Table AU.G120: Education Statistics: Number of Enrolments.

The 1991 Census Expanded Community Profiles present 44 tables comprising more detailed information than that of the basic community profiles which provide characteristics of persons and/or dwellings for Local Government Areas (LGA) in Australia. This table contains data relating to birthplace (countries) by age. Counts are of all persons, based on place of enumeration on census night which; includes overseas visitors; excludes Australians overseas; and excludes adjustment for under-enumeration. The data is by LGA 1991 boundaries. Periodicity: 5-Yearly. This data is ABS data (cat. no. 2101.0 & original geographic boundary cat. no. 1261.0.30.001) used with permission from the Australian Bureau of Statistics. The tabular data was processed and supplied to AURIN by the Australian Data Archives. The cleaned, high resolution 1991 geographic boundaries are available from data.gov.au. For more information please refer to the 1991 Census Dictionary.

Abstract

This dataset, a product of the Trade Team - Development Research Group, is part of a larger effort in the group to measure the extent of the brain drain as part of the International Migration and Development Program. It measures international skilled migration for the years 1975-2000.

The methodology is explained in: "Tendance de long terme des migrations internationals. Analyse à partir des 6 principaux pays recerveurs", Cécily Defoort.

This data set uses the same methodology as used in the Docquier-Marfouk data set on international migration by educational attainment. The authors use data from 6 key receiving countries in the OECD: Australia, Canada, France, Germany, the UK and the US.

It is estimated that the data represent approximately 77 percent of the world’s migrant population.

Bilateral brain drain rates are estimated based observations for every five years, during the period 1975-2000.

Geographic coverage

Australia, Canada, France, Germany, UK and US

Kind of data

Aggregate data [agg]

Mode of data collection

Other [oth]

Estimated resident population (ERP) is the official measure of the Australian population.

This dataset presents estimated resident population for 30 June 2001 to 30 June 2023 by Local Government Areas (LGAs), 2023. Estimates are final for 2001 to 2021, revised for 2022, and preliminary for 2023.

What is ERP? ERP links people to a place of usual residence within Australia. Usual residence is the address at which a person considers themselves to currently live. ERP includes all people who usually live in Australia (regardless of nationality, citizenship or legal status), with the exception of foreign diplomatic personnel and their families. It includes usual residents who are overseas for less than 12 months out of a continuous 16-month period. It excludes those who are in Australia for less than 12 months out of a continuous 16-month period. ERP is prepared by adding births, subtracting deaths and adding the net of overseas and internal migration to a base population derived from the latest Census of Population and Housing.

The LGA estimates in this product are subject to some error. Some caution should be exercised when using the estimates, especially for areas with very small populations. Estimates of under three people should be regarded as synthetic due to confidentiality procedures. For further information about the data see: Regional Population Methodology.

Made possible by the Digital Atlas of Australia The Digital Atlas of Australia is an Australian Government initiative being led by Geoscience Australia. It will bring together trusted datasets from across government in an interactive, secure, and easy-to-use geospatial platform. The Australian Bureau of Statistics (ABS) is working in partnership with Geoscience Australia to establish a set of web services to make ABS data available in the Digital Atlas.

Contact the Australian Bureau of Statistics (ABS) If you have questions, feedback or would like to receive updates about this web service, please email geography@abs.gov.au. For information about how the ABS manages any personal information you provide view the ABS privacy policy.

Data and geography references Source data publication: Regional population, 2022-23 Geographic boundary information: Australian Statistical Geography Standard (ASGS) Edition 3 Further information: Regional population methodology Source: Australian Bureau of Statistics (ABS)

This report provides a comprehensive analysis of Pasifika communities in Australia based on the 2021 Australian Bureau of Statistics (ABS) Census. The Australian Pasifika Educators Network (APEN) define 'Pasifika' as peoples and communities, who are genealogically, spiritually, and culturally connected to the lands, the skies and seas of the Pasifika region (including Aotearoa, New Zealand), and who have chosen to settle in and call Australia home. This analysis seeks to track key trends that have emerged since the 2015 Pacific Communities report published by Professor Jioji Ravulo based on the 2011 Census. In accordance with the intent of the original report, this current version seeks to provide an understanding of the current demographic, and socioeconomic experiences of Pasifika communities with a particular focus on education. This analysis covers population, education levels, employment patterns, as well as family and household characteristics to not only shed light on the unique circumstances faced by Pasifika communities, but also track key trends over the last decade. In addition, an examination of the Western Sydney region has been included, highlighting the significance of place-based insights on Pasifika communities towards informing policy responses and initiatives. Dataset: OVERVIEW This report compiles data from the 2021 Census of Population and Housing conducted by the Australian Bureau of Statistics (ABS). This document provides the second iteration on the human geography of Pasifika peoples within an Australian context,10 aimed at gathering greater insight into Pasifika communities, as well as comparing and tracing key trends through various datapoints. In addition, this report provides a spotlight on Pasifika communities in Western Sydney, where nearly 20% of the nation’s Pasifika population currently resides.

This document describes the background and methodology of four surveys under the general study title Issues in Multicultural Australia. The four surveys are: a general sample of the population; non-English speaking born immigrants in general (the NESB sample); persons born in Australia whose father or mother was born in a non-English speaking country (the second generation sample); and persons who migrated to Australia since July 1981 from non-English speaking countries (the new arrivals sample). The general of this study are: to examine multiculturalism as a policy, through the experience of Australians; as a set of beliefs, through their attitudes; and as an aspect of cultural maintenance, through their perceptions. The study concentrates on three broad themes. First, it examines the attitudes of the Australian and overseas born towards multiculturalism, focussing in particular on views about the maintenance of customs, ways of life and patterns of behaviour among immigrants. Second, the barriers which exist to providing full access and equity to overseas born groups are analysed, principally in the fields of education, jobs and in the provision of general health and welfare programmes and services. Third, the study looks at levels of participation in the social and political spheres in community, culture and work related organisations, and in the use of the political process to remedy problems and grievances. Separate sections of the questionnaire deal with the respondent's background - country of birth and parents' country of birth, father's occupation and educational level; language - English language ability, languages spoken, use of own language, ethnicity - identification with ethnic groups, government aid to such groups, religious observance; education - school leaving age, qualifications obtained, recognition of overseas qualifications, transition to employment; current job - job status, occupation , industry, working conditions, trade union membership, gross income, problems looking for work; spouse - country of birth, education and qualifications, occupation and industry, income and income sources; immigration - attitudes to immigration policy, opportunities for immigrants, social distance from various ethnic groups, and attitudes to authority; family and social networks - numbers of children, siblings in Australia, numbers of close friends in Australia, neighbours; citizenship - citizenship status, participation in political matters and interest in politics, trust in government; and multiculturalism - views on what multiculturalism means, and its importance to Australian society.

The 1991 Census Basic Community profiles present 57 tables containing summary characteristics of persons and/or dwellings for Census Collection Districts (CD) in Australia. This table contains data relating to countries of birth by sex. Counts are of all persons, based on place of enumeration on census night which; includes overseas visitors; excludes Australians overseas; and excludes adjustment for under-enumeration. The data is by CD 1991 boundaries. Periodicity: 5-Yearly. This data is ABS data (cat. no. 2101.0 & original geographic boundary cat. no. 1261.0.30.001) used with permission from the Australian Bureau of Statistics. The tabular data was extracted from CDATA91 which was supplied to AURIN by the University of Melbourne. The cleaned, high resolution 1991 geographic boundaries are available from data.gov.au For more information please refer to the 1991 Census Dictionary

This Australian and New Zealand food category cost dataset was created to inform diet and economic modelling for low and medium socioeconomic households in Australia and New Zealand. The dataset was created according to the INFORMAS protocol, which details the methods to systematically and consistently collect and analyse information on the price of foods, meals and affordability of diets in different countries globally. Food categories were informed by the Food Standards Australian New Zealand (FSANZ) AUSNUT (AUStralian Food and NUTrient Database) 2011-13 database, with additional food categories created to account for frequently consumed and culturally important foods.

Methods The dataset was created according to the INFORMAS protocol [1], which detailed the methods to collect and analyse information systematically and consistently on the price of foods, meals, and affordability of diets in different countries globally.

Cost data were collected from four supermarkets in each country: Australia and New Zealand. In Australia, two (Coles Merrylands and Woolworths Auburn) were located in a low and two (Coles Zetland and Woolworths Burwood) were located in a medium metropolitan socioeconomic area in New South Wales from 7-11th December 2020. In New Zealand, two (Countdown Hamilton Central and Pak ‘n Save Hamilton Lake) were located in a low and two (Countdown Rototuna North and Pak ‘n Save Rosa Birch Park) in a medium socioeconomic area in the North Island, from 16-18th December 2020.

Locations in Australia were selected based on the Australian Bureau of Statistics Index of Relative Socio-Economic Advantage and Disadvantage (IRSAD) [2]. The index ranks areas from most disadvantaged to most advantaged using a scale of 1 to 10. IRSAD quintile 1 was chosen to represent low socio-economic status and quintile 3 for medium SES socio-economic status. Locations in New Zealand were chosen using the 2018 NZ Index of Deprivation and statistical area 2 boundaries [3]. Low socio-economic areas were defined by deciles 8-10 and medium socio-economic areas by deciles 4-6. The supermarket locations were chosen according to accessibility to researchers. Data were collected by five trained researchers with qualifications in nutrition and dietetics and/or nutrition science.

All foods were aggregated into a reduced number of food categories informed by the Food Standards Australian New Zealand (FSANZ) AUSNUT (AUStralian Food and NUTrient Database) 2011-13 database, with additional food categories created to account for frequently consumed and culturally important foods. Nutrient data for each food category can therefore be linked to the Australian Food and Nutrient (AUSNUT) 2011-13 database [4] and NZ Food Composition Database (NZFCDB) [5] using the 8-digit codes provided for Australia and New Zealand, respectively.

Data were collected for three representative foods within each food category, based on criteria used in the INFORMAS protocol: (i) the lowest non-discounted price was chosen from the most commonly available product size, (ii) the produce was available nationally, (iii) fresh produce of poor quality was omitted. One sample was collected per representative food product per store, leading to a total of 12 food price samples for each food category. The exception was for the ‘breakfast cereal, unfortified, sugars ≤15g/100g’ food category in the NZ dataset, which included only four food price samples because only one representative product per supermarket was identified.

Variables in this dataset include: (i) food category and description, (ii) brand and name of representative food, (iii) product size, (iv) cost per product, and (v) 8-digit code to link product to nutrient composition data (AUSNUT and NZFCDB).

References

Vandevijvere, S.; Mackay, S.; Waterlander, W. INFORMAS Protocol: Food Prices Module [Internet]. Available online: https://auckland.figshare.com/articles/journal_contribution/INFORMAS_Protocol_Food_Prices_Module/5627440/1 (accessed on 25 October).
2071.0 - Census of Population and Housing: Reflecting Australia - Stories from the Census, 2016 Available online: https://www.abs.gov.au/ausstats/abs@.nsf/Lookup/by Subject/2071.0~2016~Main Features~Socio-Economic Advantage and Disadvantage~123 (accessed on 10 December).
Socioeconomic Deprivation Indexes: NZDep and NZiDep, Department of Public Health. Available online: https://www.otago.ac.nz/wellington/departments/publichealth/research/hirp/otago020194.html#2018 (accessed on 10 December)
AUSNUT 2011-2013 food nutrient database. Available online: https://www.foodstandards.gov.au/science/monitoringnutrients/ausnut/ausnutdatafiles/Pages/foodnutrient.aspx (accessed on 15 November).
NZ Food Composition Data. Available online: https://www.foodcomposition.co.nz/ (accessed on 10 December)

Usage Notes The uploaded data includes an Excel spreadsheet where a separate worksheet is provided for the Australian food price database and New Zealand food price database, respectively. All cost data are presented to two decimal points, and the mean and standard deviation of each food category is presented. For some representative foods in NZ, the only NFCDB food code available was for a cooked product, whereas the product is purchased raw and cooked prior to eating, undergoing a change in weight between the raw and cooked versions. In these cases, a conversion factor was used to account for the weight difference between the raw and cooked versions, to ensure that nutrient information (on accessing from the NZFCDB) was accurate. This conversion factor was developed based on the weight differences between the cooked and raw versions, and checked for accuracy by comparing quantities of key nutrients in the cooked vs raw versions of the product.

Census data reveals that population density varies noticeably from area to area. Small area census data do a better job depicting where the crowded neighborhoods are. In this map, the yellow areas of highest density range from 30,000 to 150,000 persons per square kilometer. In those areas, if the people were spread out evenly across the area, there would be just 4 to 9 meters between them. Very high density areas exceed 7,000 persons per square kilometer. High density areas exceed 5,200 persons per square kilometer. The last categories break at 3,330 persons per square kilometer, and 1,500 persons per square kilometer.This dataset is comprised of multiple sources. All of the demographic data are from Michael Bauer Research with the exception of the following countries:Australia: Esri Australia and MapData ServicesCanada: Esri Canada and EnvironicsFrance: Esri FranceGermany: Esri Germany and NexigaIndia: Esri India and IndicusJapan: Esri JapanSouth Korea: Esri Korea and OPENmateSpain: Esri España and AISUnited States: Esri Demographics

Four datasets are presented here. The original dataset is a collection of the COVID-19 data maintained by Our World in Data. It includes data on confirmed cases, and deaths, as well as other variables of potential interest for ten countries such as Australia, Brazil, Canada, China, Denmark, France, Israel, Italy, the United Kingdom, and the United States. The original dataset includes the data from the date of 31st December in 2019 to 31st May in 2020 with a total of 1.530 instances and 19 features. This dataset is collected from a variety of sources (the European Centre for Disease Prevention and Control, United Nations, World Bank, Global Burden of Disease, Blavatnik School of Government, etc.). After the original dataset is pre-processed by cleaning and removing some data including unnecessary and blank. Then, all strings are converted numeric values, and some new features such as continent, hemisphere, year, month, and day are added by extracting the original features. After that, the processed original dataset is organized for prediction of the number of new cases of COVID-19 for 1 day, 3 days, and 10 days ago and three datasets (Dataset-1, 2, 3) are created for that.

Techsalerator’s Import/Export Trade Data for Oceania

Techsalerator’s Import/Export Trade Data for Oceania provides a thorough and detailed examination of trade activities across the Oceania region. This expansive dataset offers deep insights into import and export transactions involving companies throughout Oceania, covering a diverse range of countries and territories.

Coverage Across All Oceania Countries

The dataset encompasses all key countries and territories within Oceania, including:

Australia and New Zealand:

Australia

Detailed trade data for Australia, including extensive records on import and export transactions, key trading partners, product categories, and economic sectors. New Zealand

Comprehensive data for New Zealand covering its trade activities, including detailed records on exports and imports, major product classifications, and trade relationships. Pacific Island Nations:

Fiji

Trade data for Fiji includes information on its export and import activities, key sectors, and trade dynamics with both regional and global partners. Papua New Guinea

Detailed records on trade transactions for Papua New Guinea, including product descriptions, quantities, values, and trade relationships with major partners. Solomon Islands

Comprehensive trade data covering the Solomon Islands, with insights into its import and export activities and key trading partners. Vanuatu

Data on Vanuatu’s trade flows, including detailed information on its import and export transactions and trade dynamics. Other Pacific Island Nations:

Samoa

Trade data for Samoa includes details on import and export transactions, product categories, and trade relationships. Tonga

Comprehensive data on Tonga’s trade activities, including detailed transaction records and sector-specific trade information. Tuvalu

Detailed trade data for Tuvalu, covering import and export activities, major products, and trade dynamics. Nauru

Trade records for Nauru include detailed insights into import and export transactions and key trading relationships. Kiribati

Data on Kiribati’s trade activities, including import and export details, product classifications, and trading partners. Marshall Islands

Trade data for the Marshall Islands, covering import and export transactions and sector-specific insights. Palau

Comprehensive records on trade for Palau, including detailed import and export information and trade relationships. Federated States of Micronesia

Data on trade activities for the Federated States of Micronesia, including import and export details and major trade partners. Comprehensive Data Features

Transaction Details: The dataset provides granular information on each trade transaction, such as product descriptions, quantities, values, and transaction dates, allowing for precise tracking and analysis of trade flows.

Company Information: Includes details about the companies involved in trade, such as company names, locations, and industry sectors, facilitating targeted market research and business intelligence.

Categorization: Transactions are categorized by industry sectors, product types, and trade partners, offering insights into market dynamics and sector-specific trends within Oceania.

Trade Trends: Historical data allows users to analyze trade trends, identify emerging markets, and understand the impact of economic or political events on trade patterns in the region.

Geographical Insights: Provides insights into regional trade flows and cross-border dynamics between Oceania’s countries and their global trade partners, including significant international trade relationships.

Regulatory and Compliance Data: Includes information on trade regulations, tariffs, and compliance requirements, helping businesses navigate the complex regulatory environments within Oceania.

Applications and Benefits

Market Research: Businesses can use the data to discover new market opportunities, analyze competitive landscapes, and understand consumer demand across various Oceania countries and territories.

Strategic Planning: Insights from the data enable companies to develop more effective trade strategies, optimize supply chains, and manage risks associated with international trade in Oceania.

Economic Analysis: Analysts and policymakers can monitor economic performance, evaluate trade balances, and make informed decisions on trade policies and economic development initiatives.

Investment Decisions: Investors can assess trade trends and market potentials to make informed decisions about investments in Oceania’s diverse economies.

Techsalerator’s Import/Export Trade Data for Oceania is an essential resource for organizations involved in international trade, offering a detailed, reliable, and expansive view of trade activities across the Oceania region.

This dataset presents information on historical central government revenues for 31 countries in Europe and the Americas for the period from 1800 (or independence) to 2012. The countries included are: Argentina, Australia, Austria, Belgium, Bolivia, Brazil, Canada, Chile, Colombia, Denmark, Ecuador, Finland, France, Germany (West Germany between 1949 and 1990), Ireland, Italy, Japan, Mexico, New Zealand, Norway, Paraguay, Peru, Portugal, Spain, Sweden, Switzerland, the Netherlands, the United Kingdom, the United States, Uruguay, and Venezuela. In other words, the dataset includes all South American, North American, and Western European countries with a population of more than one million, plus Australia, New Zealand, Japan, and Mexico. The dataset contains information on the public finances of central governments. To make such information comparable cross-nationally we have chosen to normalize nominal revenue figures in two ways: (i) as a share of the total budget, and (ii) as a share of total gross domestic product. The total tax revenue of the central state is disaggregated guided by the Government Finance Statistics Manual 2001 of the International Monetary Fund (IMF) which provides a classification of types of revenue, and describes in detail the contents of each classification category. Given the paucity of detailed historical data and the needs of our project, we combined some subcategories. First, we are interested in total tax revenue (centaxtot), as well as the shares of total revenue coming from direct (centaxdirectsh) and indirect (centaxindirectsh) taxes. Further, we measure two sub-categories of direct taxation, namely taxes on property (centaxpropertysh) and income (centaxincomesh). For indirect taxes, we separate excises (centaxexcisesh), consumption (centaxconssh), and customs(centaxcustomssh).

For a more detailed description of the dataset and the coding process, see the codebook available in the .zip-file.

Purpose:

This dataset presents information on historical central government revenues for 31 countries in Europe and the Americas for the period from 1800 (or independence) to 2012. The countries included are: Argentina, Australia, Austria, Belgium, Bolivia, Brazil, Canada, Chile, Colombia, Denmark, Ecuador, Finland, France, Germany (West Germany between 1949 and 1990), Ireland, Italy, Japan, Mexico, New Zealand, Norway, Paraguay, Peru, Portugal, Spain, Sweden, Switzerland, the Netherlands, the United Kingdom, the United States, Uruguay, and Venezuela. In other words, the dataset includes all South American, North American, and Western European countries with a population of more than one million, plus Australia, New Zealand, Japan, and Mexico. The dataset contains information on the public finances of central governments. To make such information comparable cross-nationally we have chosen to normalize nominal revenue figures in two ways: (i) as a share of the total budget, and (ii) as a share of total gross domestic product. The total tax revenue of the central state is disaggregated guided by the Government Finance Statistics Manual 2001 of the International Monetary Fund (IMF) which provides a classification of types of revenue, and describes in detail the contents of each classification category. Given the paucity of detailed historical data and the needs of our project, we combined some subcategories. First, we are interested in total tax revenue (centaxtot), as well as the shares of total revenue coming from direct (centaxdirectsh) and indirect (centaxindirectsh) taxes. Further, we measure two sub-categories of direct taxation, namely taxes on property (centaxpropertysh) and income (centaxincomesh). For indirect taxes, we separate excises (centaxexcisesh), consumption (centaxconssh), and customs(centaxcustomssh).

A cross-national data archive located in Luxembourg that contains two primary databases: the Luxembourg Income Study Database (LIS Database) includes income microdata from a large number of countries at multiple points in time. The newer Luxembourg Wealth Study Database(LWS Database) includes wealth microdata from a smaller selection of countries. Both databases include labor market and demographic data as well. Our mission is to enable, facilitate, promote, and conduct cross-national comparative research on socio-economic outcomes and on the institutional factors that shape those outcomes. Since its beginning in 1983, the LIS has grown into a cooperative research project with a membership that includes countries in Europe, North America, and Australia. The database now contains information for more than 30 countries with datasets that span up to three decades. The LIS databank has a total of over 140 datasets covering the period 1968 to 2005. The primary objectives of the LIS are as follows: * Test the feasibility for creating a database containing social and economic data collected in household surveys from different countries; * Provide a method which allows researchers to use the data under restrictions required by the countries providing the data; * Create a system that allows research requests to be received from and returned to users at remote locations; and * Promote comparative research on the social and economic status of various populations and subgroups in different countries. Data Availability: The dataset is accessed globally via electronic mail networks. Extensive documentation concerning technical aspects of the survey data, variables list, and the social institutions of income provision in member countries are also available to users through the project Website. * Dates of Study: 1968-present * Study Features: International * Sample Size: 30+ Countries Link: * ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/00150

The 1991 Census Usual Residents Community Profiles present 25 tables containing summary characteristics of usual residents for Local Government Areas (LGA) in Australia. This table contains data relating to birthplace (countries) by age. Counts are of all persons, based on their usual place of residence; excludes Australians overseas; and excludes adjustment for under-enumeration. The data is by LGA 1991 boundaries. Periodicity: 5-Yearly. This data is ABS data (cat. no. 2101.0 & original geographic boundary cat. no. 1261.0.30.001) used with permission from the Australian Bureau of Statistics. The tabular data was processed and supplied to AURIN by the Australian Data Archives. The cleaned, high resolution 1991 geographic boundaries are available from data.gov.au. For more information please refer to the 1991 Census Dictionary. Please note: (a) Includes England, Scotland, Wales and Northern Ireland.

The Australian Survey of Social Attitudes (AuSSA) is Australia’s main source of data for the scientific study of the social attitudes, beliefs and opinions of Australians, how they change over time, and how they compare with other societies. The survey is used to help researchers better understand how Australians think and feel about their lives. It produces important information about the changing views and attitudes of Australians as we move through the 21st century. Similar surveys are run in other countries, so data from the AuSSA also allows us to compare Australia with countries all over the world. The aims of the survey are to discover: the range of Australians’ views on topics that are important to all of us; how these views differ for people in different circumstances; how they have changed over the past quarter century; and how they compare with people in other countries. AuSSA is also the Australian component of the International Social Survey Project (ISSP). The ISSP is a cross-national collaboration on surveys covering important topics. Each year, survey researchers in some 40 countries each do a national survey using the same questions. The ISSP focuses on a special topic each year, repeating that topic from time to time. The topic for the 2019 survey is "Social Inequality". This is the fifth time this has been the topic of the survey, having previously been the theme for the survey in 1987, 1992, 1999 and 2009.