The Gross Domestic Product (GDP) in Australia was worth 1752.19 billion US dollars in 2024, according to official data from the World Bank. The GDP value of Australia represents 1.65 percent of the world economy. This dataset provides - Australia GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The Gross Domestic Product (GDP) in Australia expanded 0.60 percent in the second quarter of 2025 over the previous quarter. This dataset provides - Australia GDP Growth Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The Gross Domestic Product per capita in Australia was last recorded at 61211.90 US dollars in 2024. The GDP per Capita in Australia is equivalent to 485 percent of the world's average. This dataset provides - Australia GDP per capita - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Context

This dataset provides country-level GDP (Gross Domestic Product) in current US dollars from 2000 to 2025, mapped to the seven classic continents (Asia, Africa, Europe, North America, South America, Australia, and Antarctica). It is designed to make global economic data easier to explore, compare, and visualize by combining both geographic and temporal dimensions.

GDP is one of the most widely used indicators to measure the size of an economy, its growth trends, and relative economic performance across regions.

Source

Data Provider: World Bank Open Data

Indicator Used: NY.GDP.MKTP.CD → GDP (current US$)

License: World Bank Dataset Terms of Use (aligned with CC BY 4.0)

Note: 2024–2025 values may be incomplete or missing for some countries, depending on World Bank publication updates.

Dataset Structure

Name of country → Country name

Continent → One of the 7 continents

2000–2025 → GDP values in current US$ (float, may contain missing values NaN)

Format: wide panel data (one row per country, one column per year).

Inspiration & Use Cases

This dataset was prepared to make economic analysis, visualization, and forecasting more accessible. It can be used for:

• Time-series forecasting (predicting GDP growth into the future)
• Cross-country comparisons (e.g., comparing GDP trends of India vs. USA vs. Brazil)
• Continent-level aggregation (summing GDP by continent per year)
• Data visualization (heatmaps, line charts, world choropleths)
• Machine Learning applications (e.g., clustering countries by GDP trajectory)

Citation

If you use this dataset, please cite:

Source: World Bank, World Development Indicators (NY.GDP.MKTP.CD). Licensed under the World Bank Terms of Use.

Monthly and long-term Australia economic indicators data: historical series and analyst forecasts curated by FocusEconomics.

Monthly and long-term Australia GDP Per Capita data: historical series and analyst forecasts curated by FocusEconomics.

The Gross Domestic Product (GDP) in Australia expanded 1.80 percent in the second quarter of 2025 over the same quarter of the previous year. This dataset provides - Australia GDP Annual Growth Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Explore real GDP growth projections dataset, including insights into the impact of COVID-19 on economic trends. This dataset covers countries such as Spain, Australia, France, Italy, Brazil, and more.

growth rate, Real, COVID-19, GDP

Spain, Australia, France, Italy, Brazil, Argentina, United Kingdom, United States, Canada, Russia, Turkiye, World, China, Mexico, Korea, India, Saudi Arabia, South Africa, Germany, Indonesia, JapanFollow data.kapsarc.org for timely data to advance energy economics research..Source: OECD Economic Outlook database.- India projections are based on fiscal years, starting in April. The European Union is a full member of the G20, but the G20 aggregate only includes countries that are also members in their own right. Spain is a permanent invitee to the G20. World and G20 aggregates use moving nominal GDP weights at purchasing power parities. Difference in percentage points, based on rounded figures.

Government spending in Australia was last recorded at 26.5 percent of GDP in 2024 . This dataset provides - Australia Government Spending To Gdp- actual values, historical data, forecast, chart, statistics, economic calendar and news.

TThe ERS International Macroeconomic Data Set provides historical and projected data for 181 countries that account for more than 99 percent of the world economy. These data and projections are assembled explicitly to serve as underlying assumptions for the annual USDA agricultural supply and demand projections, which provide a 10-year outlook on U.S. and global agriculture. The macroeconomic projections describe the long-term, 10-year scenario that is used as a benchmark for analyzing the impacts of alternative scenarios and macroeconomic shocks.

Explore the International Macroeconomic Data Set 2015 for annual growth rates, consumer price indices, real GDP per capita, exchange rates, and more. Get detailed projections and forecasts for countries worldwide.

Annual growth rates, Consumer price indices (CPI), Real GDP per capita, Real exchange rates, Population, GDP deflator, Real gross domestic product (GDP), Real GDP shares, GDP, projections, Forecast, Real Estate, Per capita, Deflator, share, Exchange Rates, CPI

Afghanistan, Albania, Algeria, Angola, Antigua and Barbuda, Argentina, Armenia, Australia, Austria, Azerbaijan, Bahamas, Bahrain, Bangladesh, Barbados, Belarus, Belgium, Belize, Benin, Bhutan, Bolivia, Bosnia and Herzegovina, Botswana, Brazil, Brunei, Bulgaria, Burkina Faso, Burundi, Côte d'Ivoire, Cabo Verde, Cambodia, Cameroon, Canada, Central African Republic, Chad, Chile, China, Colombia, Congo, Costa Rica, Croatia, Cuba, Cyprus, Denmark, Djibouti, Dominica, Dominican Republic, Ecuador, Egypt, El Salvador, Equatorial Guinea, Eritrea, Estonia, Eswatini, Ethiopia, Fiji, Finland, France, Gabon, Gambia, Georgia, Germany, Ghana, Greece, Grenada, Guatemala, Guinea, Guinea-Bissau, Guyana, Haiti, Honduras, Hungary, Iceland, India, Indonesia, Iran, Iraq, Ireland, Israel, Italy, Jamaica, Japan, Jordan, Kazakhstan, Kenya, Kuwait, Kyrgyzstan, Laos, Latvia, Lebanon, Lesotho, Liberia, Libya, Lithuania, Luxembourg, Madagascar, Malawi, Malaysia, Maldives, Mali, Malta, Mauritania, Mauritius, Mexico, Moldova, Mongolia, Morocco, Mozambique, Myanmar, Namibia, Nepal, Netherlands, New Zealand, Nicaragua, Niger, Nigeria, Norway, Oman, Pakistan, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Poland, Portugal, Qatar, Romania, Russia, Rwanda, Samoa, Saudi Arabia, Senegal, Serbia, Seychelles, Sierra Leone, Singapore, Slovakia, Slovenia, Solomon Islands, South Africa, Spain, Sri Lanka, Sudan, Suriname, Sweden, Switzerland, Syria, Tajikistan, Tanzania, Thailand, Togo, Tonga, Trinidad and Tobago, Tunisia, Turkey, Turkmenistan, Uganda, Ukraine, United Arab Emirates, United Kingdom, Uruguay, Uzbekistan, Vanuatu, Venezuela, Vietnam, Yemen, Zambia, Zimbabwe, WORLD Follow data.kapsarc.org for timely data to advance energy economics research. Notes:

Developed countries/1 Australia, New Zealand, Japan, Other Western Europe, European Union 27, North America

Developed countries less USA/2 Australia, New Zealand, Japan, Other Western Europe, European Union 27, Canada

Developing countries/3 Africa, Middle East, Other Oceania, Asia less Japan, Latin America;

Low-income developing countries/4 Haiti, Afghanistan, Nepal, Benin, Burkina Faso, Burundi, Central African Republic, Chad, Democratic Republic of Congo, Eritrea, Ethiopia, Gambia, Guinea, Guinea-Bissau, Liberia, Madagascar, Malawi, Mali, Mozambique, Niger, Rwanda, Senegal, Sierra Leone, Somalia, Tanzania, Togo, Uganda, Zimbabwe;

Emerging markets/5 Mexico, Brazil, Chile, Czech Republic, Hungary, Poland, Slovakia, Russia, China, India, Korea, Taiwan, Indonesia, Malaysia, Philippines, Thailand, Vietnam, Singapore

BRIICs/5 Brazil, Russia, India, Indonesia, China; Former Centrally Planned Economies

Former centrally planned economies/7 Cyprus, Malta, Recently acceded countries, Other Central Europe, Former Soviet Union

USMCA/8 Canada, Mexico, United States

Europe and Central Asia/9 Europe, Former Soviet Union

Middle East and North Africa/10 Middle East and North Africa

Other Southeast Asia outlook/11 Malaysia, Philippines, Thailand, Vietnam

Other South America outlook/12 Chile, Colombia, Peru, Bolivia, Paraguay, Uruguay

Indicator Source

Real gross domestic product (GDP) World Bank World Development Indicators, IHS Global Insight, Oxford Economics Forecasting, as well as estimated and projected values developed by the Economic Research Service all converted to a 2015 base year.

Real GDP per capita U.S. Department of Agriculture, Economic Research Service, Macroeconomic Data Set, GDP table and Population table.

GDP deflator World Bank World Development Indicators, IHS Global Insight, Oxford Economics Forecasting, as well as estimated and projected values developed by the Economic Research Service, all converted to a 2015 base year.

Real GDP shares U.S. Department of Agriculture, Economic Research Service, Macroeconomic Data Set, GDP table.

Real exchange rates U.S. Department of Agriculture, Economic Research Service, Macroeconomic Data Set, CPI table, and Nominal XR and Trade Weights tables developed by the Economic Research Service.

Consumer price indices (CPI) International Financial Statistics International Monetary Fund, IHS Global Insight, Oxford Economics Forecasting, as well as estimated and projected values developed by the Economic Research Service, all converted to a 2015 base year.

Population Department of Commerce, Bureau of the Census, U.S. Department of Agriculture, Economic Research Service, International Data Base.

This dataset contains National Regional Profile (NRP) data on Economy at GCCSA level for 2010-2014. The data uses 2011 ABS ASGS boundaries. The NRP is designed for users interested in the socio-economic and environmental characteristics of regions - and in comparisons with similar geographies across Australia. Data are arranged under the broad themes/topics of Economy, Industry, People, and Energy and Environment. Please note some data are not available for all reference years, for a variety of reasons. For example; there may be conceptual breaks in a data series; the collection frequency may be irregular; some series may have revisions pending; or permission to publish in the NRP may have only been granted recently. In addition, some data series are not available for the full range of geographies. The reasons can include: data owner or custodian preferences; industry identification with a few, particular geographies only; confidentiality protection; and the presence of many suppressed data cells (at smaller geographic levels) thus making true aggregations up to larger ASGS regions difficult. This data is ABS data used with permission from the Australian Bureau of Statistics. Please note National Regional Profile (1379.0.55.001) has been discontinued. For the most recent regional data, please see Data By Region (1410.0). For more information please visit the Australian Bureau of Statistics.

GDP from Agriculture in Australia increased to 20128 AUD Million in the second quarter of 2025 from 20070 AUD Million in the first quarter of 2025. This dataset provides - Australia Gdp From Agriculture- actual values, historical data, forecast, chart, statistics, economic calendar and news.

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.

Explore the World Competitiveness Ranking dataset for 2016, including key indicators such as GDP per capita, fixed telephone tariffs, and pension funding. Discover insights on social cohesion, scientific research, and digital transformation in various countries.

Social cohesion, The image abroad of your country encourages business development, Scientific articles published by origin of author, International Telecommunication Union, World Telecommunication/ICT Indicators database, Data reproduced with the kind permission of ITU, National sources, Fixed telephone tariffs, GDP (PPP) per capita, Overall, Exports of goods - growth, Pension funding is adequately addressed for the future, Companies are very good at using big data and analytics to support decision-making, Gross fixed capital formation - real growth, Economic Performance, Scientific research legislation, Percentage of GDP, Health infrastructure meets the needs of society, Estimates based on preliminary data for the most recent year., Singapore: including re-exports., Value, Laws relating to scientific research do encourage innovation, % of GDP, Gross Domestic Product (GDP), Health Infrastructure, Digital transformation in companies is generally well understood, Industrial disputes, EE, Female / male ratio, State ownership of enterprises, Total expenditure on R&D (%), Score, Colombia, Estimates for the most recent year., Percentage change, based on US$ values, Number of listed domestic companies, Tax evasion is not a threat to your economy, Scientific articles, Tax evasion, % change, Use of big data and analytics, National sources, Disposable Income, Equal opportunity, Listed domestic companies, Government budget surplus/deficit (%), Pension funding, US$ per capita at purchasing power parity, Estimates; US$ per capita at purchasing power parity, Image abroad or branding, Equal opportunity legislation in your economy encourages economic development, Number, Article counts are from a selection of journals, books, and conference proceedings in S&E from Scopus. Articles are classified by their year of publication and are assigned to a region/country/economy on the basis of the institutional address(es) listed in the article. Articles are credited on a fractional-count basis. The sum of the countries/economies may not add to the world total because of rounding. Some publications have incomplete address information for coauthored publications in the Scopus database. The unassigned category count is the sum of fractional counts for publications that cannot be assigned to a country or economy. Hong Kong: research output items by the higher education institutions funded by the University Grants Committee only., State ownership of enterprises is not a threat to business activities, Protectionism does not impair the conduct of your business, Digital transformation in companies, Total final energy consumption per capita, Social cohesion is high, Rank, MTOE per capita, Percentage change, based on constant prices, US$ billions, National sources, World Trade Organization Statistics database, Rank, Score, Value, World Rankings

Argentina, Australia, Austria, Belgium, Brazil, Bulgaria, Canada, Chile, China, Colombia, Croatia, Cyprus, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, India, Indonesia, Ireland, Israel, Italy, Japan, Jordan, Kazakhstan, Latvia, Lithuania, Luxembourg, Malaysia, Mexico, Mongolia, Netherlands, New Zealand, Norway, Oman, Peru, Philippines, Poland, Portugal, Qatar, Romania, Russia, Saudi Arabia, Singapore, Slovenia, South Africa, Spain, Sweden, Switzerland, Thailand, Turkey, Ukraine, United Kingdom, Venezuela

Follow data.kapsarc.org for timely data to advance energy economics research.

Abstract

Financial inclusion is critical in reducing poverty and achieving inclusive economic growth. When people can participate in the financial system, they are better able to start and expand businesses, invest in their children’s education, and absorb financial shocks. Yet prior to 2011, little was known about the extent of financial inclusion and the degree to which such groups as the poor, women, and rural residents were excluded from formal financial systems.

By collecting detailed indicators about how adults around the world manage their day-to-day finances, the Global Findex allows policy makers, researchers, businesses, and development practitioners to track how the use of financial services has changed over time. The database can also be used to identify gaps in access to the formal financial system and design policies to expand financial inclusion.

Geographic coverage

National coverage

Universe

The target population is the civilian, non-institutionalized population 15 years and above.

Kind of data

Observation data/ratings [obs]

Sampling procedure

The indicators in the 2017 Global Findex database are drawn from survey data covering almost 150,000 people in 144 economies-representing more than 97 percent of the world's population (see Table A.1 of the Global Findex Database 2017 Report for a list of the economies included). The survey was carried out over the 2017 calendar year by Gallup, Inc., as part of its Gallup World Poll, which since 2005 has annually conducted surveys of approximately 1,000 people in each of more than 160 economies and in over 150 languages, using randomly selected, nationally representative samples. The target population is the entire civilian, noninstitutionalized population age 15 and above. Interview procedure Surveys are conducted face to face in economies where telephone coverage represents less than 80 percent of the population or where this is the customary methodology. In most economies the fieldwork is completed in two to four weeks.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used.

Respondents are randomly selected within the selected households. Each eligible household member is listed and the handheld survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer's gender.

In economies where telephone interviewing is employed, random digit dialing or a nationally representative list of phone numbers is used. In most economies where cell phone penetration is high, a dual sampling frame is used. Random selection of respondents is achieved by using either the latest birthday or household enumeration method. At least three attempts are made to reach a person in each household, spread over different days and times of day. Sample size was 1008.

Mode of data collection

Other [oth]

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup Inc. also provided valuable input. The questionnaire was piloted in multiple countries, using focus groups, cognitive interviews, and field testing. The questionnaire is available in more than 140 languages upon request.

Questions on cash on delivery, saving using an informal savings club or person outside the family, domestic remittances, and agricultural payments are only asked in developing economies and few other selected countries. The question on mobile money accounts was only asked in economies that were part of the Mobile Money for the Unbanked (MMU) database of the GSMA at the time the interviews were being held.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar, and Jake Hess. 2018. The Global Findex Database 2017: Measuring Financial Inclusion and the Fintech Revolution. Washington, DC: World Bank

Australia Tariff Rate: Most Favored Nation: Weighted Mean: All Products data was reported at 2.840 % in 2022. This records a decrease from the previous number of 2.860 % for 2021. Australia Tariff Rate: Most Favored Nation: Weighted Mean: All Products data is updated yearly, averaging 3.880 % from Dec 1991 (Median) to 2022, with 29 observations. The data reached an all-time high of 19.870 % in 1991 and a record low of 2.470 % in 2011. Australia Tariff Rate: Most Favored Nation: Weighted Mean: All Products 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: Trade Tariffs. Weighted mean most favored nations tariff is the average of most favored nation rates weighted by the product import shares corresponding to each partner country. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups and import weights. Import weights were calculated using the United Nations Statistics Division's Commodity Trade (Comtrade) database.;World Bank staff estimates using the World Integrated Trade Solution system, based on tariff data from the United Nations Conference on Trade and Development's Trade and Development's Trade Analysis and Information System (TRAINS) database and global imports data from the United Nations Statistics Division's Comtrade database.;;The tariff data for the European Union (EU) apply to EU Member States in alignment with the EU membership for the respective countries/economies and years. In the context of the tariff data, the EU membership for a given country/economy and year is defined for the entire year during which the country/economy was a member of the EU (irrespective of the date of accession to or withdrawal from the EU within a given year). The tariff data for the EU are, thus, applicable to Belgium, France, Germany, Italy, Luxembourg, and the Netherlands (EU Member State(s) since 1958), Denmark and Ireland (EU Member State(s) since 1973), the United Kingdom (EU Member State(s) from 1973 until 2020), Greece (EU Member State(s) since 1981), Spain and Portugal (EU Member State(s) since 1986), Austria, Finland, and Sweden (EU Member State(s) since 1995), Czech Republic, Estonia, Cyprus, Latvia, Lithuania, Hungary, Malta, Poland, Slovakia, and Slovenia (EU Member State(s) since 2004), Romania and Bulgaria (EU Member State(s) since 2007), Croatia (EU Member State(s) since 2013). For more information, please revisit the technical note on bilateral applied tariff (https://wits.worldbank.org/Bilateral-Tariff-Technical-Note.html).

This dataset contains National Regional Profile (NRP) data on Economy at SA3 level for 2010-2014. The data uses 2011 ABS ASGS boundaries. The NRP is designed for users interested in the …Show full descriptionThis dataset contains National Regional Profile (NRP) data on Economy at SA3 level for 2010-2014. The data uses 2011 ABS ASGS boundaries. The NRP is designed for users interested in the socio-economic and environmental characteristics of regions - and in comparisons with similar geographies across Australia. Data are arranged under the broad themes/topics of Economy, Industry, People, and Energy and Environment. Please note some data are not available for all reference years, for a variety of reasons. For example; there may be conceptual breaks in a data series; the collection frequency may be irregular; some series may have revisions pending; or permission to publish in the NRP may have only been granted recently. In addition, some data series are not available for the full range of geographies. The reasons can include: data owner or custodian preferences; industry identification with a few, particular geographies only; confidentiality protection; and the presence of many suppressed data cells (at smaller geographic levels) thus making true aggregations up to larger ASGS regions difficult. This data is ABS data used with permission from the Australian Bureau of Statistics. Please note National Regional Profile (1379.0.55.001) has been discontinued. For the most recent regional data, please see Data By Region (1410.0). For more information please visit the Australian Bureau of Statistics. Copyright attribution: Government of the Commonwealth of Australia - Australian Bureau of Statistics, (2016): ; accessed from AURIN on 12/16/2021. Licence type: Creative Commons Attribution 2.5 Australia (CC BY 2.5 AU)

This data collection presents the results of a continental scale assessment of the economic feasibility of blue carbon projects in Australia. A land use trade-offs approach is employed to estimate the economic profitability of current agriculture and blue carbon projects at locations identified as having the potential to support either mangrove, saltmarsh or salt flat natural systems. Where blue carbon economic returns are greater than estimated current agricultural economic returns then a project is considered economically feasible. The specific projects in scope are those that re-introduce tidal flow to coastal ecosystems and lead to net abatement of carbon emissions, that is, projects that are relevant to the Tidal Restoration of Blue Carbon Ecosystems method of the Australian Government Clean Energy Regulator. At each identified location estimates of abatement were calculated using the BlueCAM model. Carbon abatement was calculated using the BlueCAM method (Lovelock et al. 2022) as described in the BlueCAM technical report (Lovelock et al. 2021), the carbon credits determination (Australian Government Clean Energy Regulator 2022) and the BlueCAM spreadsheet tool (Australian Government Clean Energy Regulator 2021). Our implementation was simplified according to our objective of estimating abatement per se rather than formally calculating carbon credits. For example, abatement was calculated on a one-year time step rather than reporting periods and project-level emissions (such as fuel use) were not included. Total abatement is calculated over 25 years following tidal introduction. The net present value of blue carbon projects was estimated for combinations of carbon price, costs, and discount rates. Scenario settings: • cp -> carbon price [$35, $50, $65, $80] • ec -> establishment costs per hectare [$1000, $2000, $4000, $6000, $8000 and $10,000] • ac -> 5-yearly costs per hectare [$100, $200, $300] • dr -> discount rate for net present value [4%, 7%] Modelled economically feasible abatement and area for carbon estimation areas under four carbon prices are presented aggregated by Statistical Area 2 (2011) and Local Government Areas (2023), which were clipped to 100km from the coast, by primary sediment compartments (McPherson et al., 2015) and by state/territory. Results are grouped by combinations of scenario settings and includes modelled maximum potential abatement and area.

Lineage: The approach we take relies on inferences derived from modelling inundation at Highest Astronomical Tide and comparing the inundation with current land uses. If land use within an area where inundation is predicted is classified as agricultural land, we infer that there might be a barrier to tide. Stronger inferences would be facilitated by nationally consistent maps of the locations of barriers to tide, but these do not currently exist for Australia. The process of generating estimates of abatement followed a sequence of steps. First, a digital elevation model was constructed. Inundation over this was then predicted from modelled predictions of Highest Astronomical Tide (HAT). The land uses classes within areas that the model predicted would be inundated were then identified, and transitions to alternate natural systems were predicted based on elevation. The approach relies on national datasets, which might not be accurate in all places. Data are therefore not appropriate for uses that encompass small spatial extents (e.g. tens of hectares project). Data are instead provided in aggregated spatial units that give an approximation of the potential abatement that might be possible. To create consistent digital elevation (topography and bathymetry) data for Australia we analysed each jurisdiction (i.e. state and territory) separately but with the same method. After a few trials we determined that a 10 m × 10 m grid was an appropriate resolution. For ease of computation, the entire country was tiled into a 100 km × 100 km grid as defined by Digital Earth Australia (DEA). We used the Australia Albers Projection (AusAlb) with the GDA2020 datum and AVWS vertical datum. A baseline dataset was created using a CSIRO modified version of Geoscience Australia 250 m × 250 m bathymetric data and the Multi-Error-Removed Improved-Terrain DEM 30 m × 30 m terrestrial data. Datasets from national, state and regional agencies were acquired, aiming for open and public data as primary sources. Each dataset was assessed for quality and appropriateness. Elevation data were then projected into the AusAlb projection, tiled into the DEA grid and then gridded into 10 m × 10 m cells. Once all data for a jurisdiction was processed into the same coordinate system, spatial resolution and ranking, the data were sequentially combined onto the baseline to create the best data for each cell. To model HAT, the Australian Hydrographic Office (AHO) provided to CSIRO a tidal plane dataset that is derived from measured data from all historically available water level gauges. Accuracy of the AHO HAT tidal plane was assessed against the recently released Global Extreme Sea Level Analysis v3 (GESLA3) which includes a public release license for 125 tidal gauge data from the Australian Bureau of Meteorology (Haigh et al., 2023). The agreement between the AHO HAT and the HAT calculated from GESLA3 is very good, which is to be expected as the same tidal gauge data is used in both. The AHO HAT tidal plane was utilised in this study. The HAT model does not account for the effects of estuaries attenuating or amplifying tide. The baseline land subtype was estimated from land use mapping using an approach that combined land use, topographic feature and forest cover attributes from land use datasets. Land use was obtained from the Catchment scale land use of Australia 2020 map (CLUM, resolution 50 m, ABARES 2021). Topographic feature and forest cover were obtained from the Land use of Australia 2015–16 map (National Land Use Map, NLUM, resolution 250 m, ABARES 2022). More specifically, topographic feature and forest cover were obtained from the input layers that accompany the NLUM map. The land use classes within areas that the model predicted would be inundated were then identified, and transitions to alternate natural systems were predicted based on elevation. See Vanderklift et al. (2024) for further details.

Abstract

Financial inclusion is critical in reducing poverty and achieving inclusive economic growth. When people can participate in the financial system, they are better able to start and expand businesses, invest in their children’s education, and absorb financial shocks. Yet prior to 2011, little was known about the extent of financial inclusion and the degree to which such groups as the poor, women, and rural residents were excluded from formal financial systems.

By collecting detailed indicators about how adults around the world manage their day-to-day finances, the Global Findex allows policy makers, researchers, businesses, and development practitioners to track how the use of financial services has changed over time. The database can also be used to identify gaps in access to the formal financial system and design policies to expand financial inclusion.

Geographic coverage

National Coverage

Analysis unit

Individual

Universe

The target population is the civilian, non-institutionalized population 15 years and above.

Kind of data

Sample survey data [ssd]

Frequency of data collection

Triennial

Sampling procedure

As in the first edition, the indicators in the 2014 Global Findex are drawn from survey data covering almost 150,000 people in more than 140 economies-representing more than 97 percent of the world's population. The survey was carried out over the 2014 calendar year by Gallup, Inc. as part of its Gallup World Poll, which since 2005 has continually conducted surveys of approximately 1,000 people in each of more than 160 economies and in over 140 languages, using randomly selected, nationally representative samples. The target population is the entire civilian, noninstitutionalized population age 15 and above. The set of indicators will be collected again in 2017.

Surveys are conducted face to face in economies where telephone coverage represents less than 80 percent of the population or is the customary methodology. In most economies the fieldwork is completed in two to four weeks. In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households by means of the Kish grid. In economies where cultural restrictions dictate gender matching, respondents are randomly selected through the Kish grid from among all eligible adults of the interviewer's gender.

In economies where telephone interviewing is employed, random digit dialing or a nationally representative list of phone numbers is used. In most economies where cell phone penetration is high, a dual sampling frame is used. Random selection of respondents is achieved by using either the latest birthday or Kish grid method. At least three attempts are made to reach a person in each household, spread over different days and times of day.

The sample size in Australia was 1,002 individuals.

Mode of data collection

Other [oth]

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup Inc. also provided valuable input. The questionnaire was piloted in multiple countries, using focus groups, cognitive interviews, and field testing. The questionnaire is available in 142 languages upon request.

Questions on cash withdrawals, saving using an informal savings club or person outside the family, domestic remittances, school fees, and agricultural payments are only asked in developing economies and few other selected countries. The question on mobile money accounts was only asked in economies that were part of the Mobile Money for the Unbanked (MMU) database of the GSMA at the time the interviews were being held.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Asli Demirguc-Kunt, Leora Klapper, Dorothe Singer, and Peter Van Oudheusden, “The Global Findex Database 2014: Measuring Financial Inclusion around the World.” Policy Research Working Paper 7255, World Bank, Washington, D.C.

Abstract

The dataset is derived from the Australian Coal Basins dataset supplied by Geoscience Australia. You can find a link to the parent datasets in the Lineage Field in this metadata statement. The History Field in this metadata statement describes how this dataset was derived.

This is the eastern discontiguous part of what the GA Coal Basins dataset has included as "Gunnedah (coal) Basin". However other sources refer to the coal deposit east of the Hunter-Mooki thrust fault as the "Werrie Basin". This area has been extracted from the GA Coal Basins dataset and re-attributed from "Gunnedah Basin" to "Werrie Basin". It is intended for use in report map images only.

Source dataset abstract follows:

The Coal Basin Outlines dataset is a combination of data from various sources displaying the best available (7 March 2013) coal basin extents for all of Australia. These extents were taken predominantly from the Australian Geological Provinces Database(extract from 4 November 2012 - see metadata statement below) with additional data coming from the Australian Sedimentary Basins Database(27 August 2012 update), State (South Australia) basin databases and hydrogeological basin databases.

The Australian Geological Provinces Database contains descriptions and spatial extents of the fundamental geological elements of the Australian continent and immediate surrounds. Captured province types include sedimentary basins, tectonic provinces such as cratons and orogens, igneous provinces, and metallogenic provinces. Spatial data has been captured largely at approximately 1:1M scale for best use at between 1:2M to 1:5M scale.

Where possible, provinces have been attributed with their age, contained lithostratigraphic units, relationships to other provinces, and geological history. The geological definition of some provinces, in particular certain sedimentary basins and orogens, is contentious. While every effort has been made to achieve a consensus interpretation of each province, scientific debate may still occur about the nature and extent of some provinces.

The total 2D spatial extent of most provinces in the database has been captured (ie, the full extent of a province under any overlying cover). The extent of outcrop of some provinces has also been captured. Where possible, the full extent outlines of provinces have been attributed with information about the source, accuracy, and observation method of those lines.

Dataset History

The section of the Gunnedah Basin in the Australian Coal Basins dataset (see lineage) lying east of the Hunter-Mooki fault was extracted and the attibution in the 'Name' field changed from "Gunnedah Basin" to "Werrie Basin"

Dataset Citation

Bioregional Assessment Programme (2014) Werrie Basin component of the Hunter subregion extent. Bioregional Assessment Derived Dataset. Viewed 14 June 2018, http://data.bioregionalassessments.gov.au/dataset/f299857f-602f-4f8a-89de-a25e1994f3ee.

Dataset Ancestors

• Derived From Australian Coal Basins