This table presents income shares, thresholds, tax shares, and total counts of individual Canadian tax filers, with a focus on high income individuals (95% income threshold, 99% threshold, etc.). Income thresholds are based on national threshold values, regardless of selected geography; for example, the number of Nova Scotians in the top 1% will be calculated as the number of taxfiling Nova Scotians whose total income exceeded the 99% national income threshold. Different definitions of income are available in the table namely market, total, and after-tax income, both with and without capital gains.

About this dataset

Context

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

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Stoy, while the Census reported a median income of $30,990 for all male workers aged 15 years and older, data for females in the same category was unavailable due to an insufficient number of sample observations.

Given the absence of income data for females from the Census Bureau, conducting a thorough analysis of gender-based pay disparity in the village of Stoy was not possible.

- Full-time workers, aged 15 years and older: In Stoy, among full-time, year-round workers aged 15 years and older, males earned a median income of $31,702, while females earned $31,500, resulting in a 1% gender pay gap among full-time workers. This illustrates that women earn 99 cents for each dollar earned by men in full-time positions. While this gap shows a trend where women are inching closer to wage parity with men, it also exhibits a noticeable income difference for women working full-time in the village of Stoy.

Content

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

Gender classifications include:

• Male
• Female

Employment type classifications include:

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

Variables / Data Columns

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

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

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

Income of individuals by age group, sex and income source, Canada, provinces and selected census metropolitan areas, annual.

U.S. citizens with a professional degree had the highest median household income in 2023, at 172,100 U.S. dollars. In comparison, those with less than a 9th grade education made significantly less money, at 35,690 U.S. dollars. Household income The median household income in the United States has fluctuated since 1990, but rose to around 70,000 U.S. dollars in 2021. Maryland had the highest median household income in the United States in 2021. Maryland’s high levels of wealth is due to several reasons, and includes the state's proximity to the nation's capital. Household income and ethnicity The median income of white non-Hispanic households in the United States had been on the rise since 1990, but declining since 2019. While income has also been on the rise, the median income of Hispanic households was much lower than those of white, non-Hispanic private households. However, the median income of Black households is even lower than Hispanic households. Income inequality is a problem without an easy solution in the United States, especially since ethnicity is a contributing factor. Systemic racism contributes to the non-White population suffering from income inequality, which causes the opportunity for growth to stagnate.

Context

There is a lack of public available datasets on financial services and specially in the emerging mobile money transactions domain. Financial datasets are important to many researchers and in particular to us performing research in the domain of fraud detection. Part of the problem is the intrinsically private nature of financial transactions, that leads to no publicly available datasets.

We present a synthetic dataset generated using the simulator called PaySim as an approach to such a problem. PaySim uses aggregated data from the private dataset to generate a synthetic dataset that resembles the normal operation of transactions and injects malicious behaviour to later evaluate the performance of fraud detection methods.

Content

PaySim simulates mobile money transactions based on a sample of real transactions extracted from one month of financial logs from a mobile money service implemented in an African country. The original logs were provided by a multinational company, who is the provider of the mobile financial service which is currently running in more than 14 countries all around the world.

This synthetic dataset is scaled down 1/4 of the original dataset and it is created just for Kaggle.

Headers

This is a sample of 1 row with headers explanation:

1,PAYMENT,1060.31,C429214117,1089.0,28.69,M1591654462,0.0,0.0,0,0

step - maps a unit of time in the real world. In this case 1 step is 1 hour of time. Total steps 744 (30 days simulation).

type - CASH-IN, CASH-OUT, DEBIT, PAYMENT and TRANSFER.

amount - amount of the transaction in local currency.

nameOrig - customer who started the transaction

oldbalanceOrg - initial balance before the transaction

newbalanceOrig - new balance after the transaction

nameDest - customer who is the recipient of the transaction

oldbalanceDest - initial balance recipient before the transaction. Note that there is not information for customers that start with M (Merchants).

newbalanceDest - new balance recipient after the transaction. Note that there is not information for customers that start with M (Merchants).

isFraud - This is the transactions made by the fraudulent agents inside the simulation. In this specific dataset the fraudulent behavior of the agents aims to profit by taking control or customers accounts and try to empty the funds by transferring to another account and then cashing out of the system.

isFlaggedFraud - The business model aims to control massive transfers from one account to another and flags illegal attempts. An illegal attempt in this dataset is an attempt to transfer more than 200.000 in a single transaction.

Past Research

There are 5 similar files that contain the run of 5 different scenarios. These files are better explained at my PhD thesis chapter 7 (PhD Thesis Available here http://urn.kb.se/resolve?urn=urn:nbn:se:bth-12932).

We ran PaySim several times using random seeds for 744 steps, representing each hour of one month of real time, which matches the original logs. Each run took around 45 minutes on an i7 intel processor with 16GB of RAM. The final result of a run contains approximately 24 million of financial records divided into the 5 types of categories: CASH-IN, CASH-OUT, DEBIT, PAYMENT and TRANSFER.

Acknowledgements

This work is part of the research project ”Scalable resource-efficient systems for big data analytics” funded by the Knowledge Foundation (grant: 20140032) in Sweden.

Please refer to this dataset using the following citations:

PaySim first paper of the simulator:

E. A. Lopez-Rojas , A. Elmir, and S. Axelsson. "PaySim: A financial mobile money simulator for fraud detection". In: The 28th European Modeling and Simulation Symposium-EMSS, Larnaca, Cyprus. 2016

This table contains statistics regarding income and capital of self-employed persons in the Netherlands. A distinction is made between, on the one hand, persons for whom self-employment provides for the main source of income, and on the other hand all persons with income from self-employed work. The figures in this table are broken down by type of self-employed person, sector, gender, age, migration background, position in the household, and by income and wealth decile groups.

All statistics in this table are at the individual level, this includes capital; (corporate) assets are summed per household and then assigned to all household members, thus serving as a measure of personal prosperity. The sample date for both population and capital is the first of January of the reporting year. For the older years 2007 up to and including 2010, capital is sampled on the first of January of the year following the reporting year.

The General Business Register (ABR) is used to determine the sector (SBI) of self-employed persons. The ABR has been subject to various trend breaks in the period 2007-2011. This leads to a sharp decrease in the number of self-employed persons in the financial services (sector K) in 2010. Therefore caution is advised when consulting sector trends or comparing numbers across sectors.

Data available from: 2007.

Status of the figures: The figures for 2006 to 2022 are final. The figures for 2023 are preliminary.

Changes as of November 1 2024: Figures for 2022 have been finalized. Figures for 2023 have been added.

Changes as of March 2022: Figures on the wealth of the self-employed in 2010 were incorrect, and have been removed. For this year the wealth of 2011 applies, as 2011 marks a shift in sample date from December 31 to January 1. Missing wealth figures for 2013 have been supplemented.

Changes as of July 2021: Revised data for 2006 to 2019 have been added. Due to the availability of new sources and improvements in the methodology, wealth figures have changed. Additionally everyone with personnel is now classified as self-employed with employee (formerly this distinction was based solely on the enterprise constituting the main source of income).

When will new figures be published? New figures for 2024 will be published in December 2025.

A Home for Everyone is the City of Boise’s (city) initiative to address needs in the community by supporting the development and preservation of housing affordable to residents on Boise budgets. A Home for Everyone has three core goals: produce new homes affordable at 60% of area median income, create permanent supportive housing for households experiencing homelessness, and preserve home affordable at 80% of area median income. This dataset includes information about all homes that count toward the city’s Home for Everyone goals.

While the “produce affordable housing” and “create permanent supportive housing” goals are focused on supporting the development of new housing, the preservation goal is focused on maintaining existing housing affordable. As a result, many of the data fields related to new development are not relevant to preservation projects. For example, zoning incentives are only applicable to new construction projects.

Data may be unavailable for some projects and details are subject to change until construction is complete. Addresses are excluded for projects with fewer than five homes for privacy reasons.

The dataset includes details on the number of “homes”. We use the word "home" to refer to any single unit of housing regardless of size, type, or whether it is rented or owned. For example, a building with 40 apartments counts as 40 homes, and a single detached house counts as one home.

The dataset includes details about the phase of each project when a project involves constructing new housing. The process for building a new development is as follows: First, one must receive approval from the city’s Planning Division, which is also known as being “entitled.” Next, one must apply for and receive a permit from the city’s Building Division before beginning construction. Finally, once construction is complete and all city inspections have been passed, the building can be occupied.

To contribute to a city goal, homes must meet affordability requirements based on a standard called area median income. The city considers housing affordable if is targeted to households earning at or below 80% of the area median income. For a three-person household in Boise, that equates to an annual income of $60,650 and monthly housing cost of $1,516. Deeply affordable housing sets the income limit at 60% of area median income, or even 30% of area median income. See Boise Income Guidelines for more details.Project Name – The name of each project. If a row is related to the Home Improvement Loan program, that row aggregates data for all homes that received a loan in that quarter or year. Primary Address – The primary address for the development. Some developments encompass multiple addresses.Project Address(es) – Includes all addresses that are included as part of the development project.Parcel Number(s) – The identification code for all parcels of land included in the development.Acreage – The number of acres for the parcel(s) included in the project.Planning Permit Number – The identification code for all permits the development has received from the Planning Division for the City of Boise. The number and types of permits required vary based on the location and type of development.Date Entitled – The date a development was approved by the City’s Planning Division.Building Permit Number – The identification code for all permits the development has received from the city’s Building Division.Date Building Permit Issued – Building permits are required to begin construction on a development.Date Final Certificate of Occupancy Issued – A certificate of occupancy is the final approval by the city for a development, once construction is complete. Not all developments require a certificate of occupancy.Studio – The number of homes in the development that are classified as a studio. A studio is typically defined as a home in which there is no separate bedroom. A single room serves as both a bedroom and a living room.1-Bedroom – The number of homes in a development that have exactly one bedroom.2-Bedroom – The number of homes in a development that have exactly two bedrooms.3-Bedroom – The number of homes in a development that have exactly three bedrooms.4+ Bedroom – The number of homes in a development that have four or more bedrooms.# of Total Project Units – The total number of homes in the development.# of units toward goals – The number of homes in a development that contribute to either the city’s goal to produce housing affordable at or under 60% of area median income, or the city’s goal to create permanent supportive housing for households experiencing homelessness. Rent at or under 60% AMI - The number of homes in a development that are required to be rented at or below 60% of area median income. See the description of the dataset above for an explanation of area median income or see Boise Income Guidelines for more details. Boise defines a home as “affordable” if it is rented or sold at or below 80% of area median income.Rent 61-80% AMI – The number of homes in a development that are required to be rented at between 61% and 80% of area median income. See the description of the dataset above for an explanation of area median income or see Boise Income Guidelines for more details. Boise defines a home as “affordable” if it is rented or sold at or below 80% of area median income.Rent 81-120% AMI - The number of homes in a development that are required to be rented at between 81% and 120% of area median income. See the description of the dataset above for an explanation of area median income or see Boise Income Guidelines for more details.Own at or under 60% AMI - The number of homes in a development that are required to be sold at or below 60% of area median income. See the description of the dataset above for an explanation of area median income or see Boise Income Guidelines for more details. Boise defines a home as “affordable” if it is rented or sold at or below 80% of area median income.

Household income statistics by household type (couple family, one-parent family, non-census family households) and household size for Canada, provinces and territories, census divisions and census subdivisions.

About this dataset

Context

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

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Pepperton township, while the Census reported a median income of $44,844 for all male workers aged 15 years and older, data for females in the same category was unavailable due to an insufficient number of sample observations.

Given the absence of income data for females from the Census Bureau, conducting a thorough analysis of gender-based pay disparity in the township of Pepperton township was not possible.

- Full-time workers, aged 15 years and older: In Pepperton township, among full-time, year-round workers aged 15 years and older, males earned a median income of $51,250, while females earned $50,625, resulting in a 1% gender pay gap among full-time workers. This illustrates that women earn 99 cents for each dollar earned by men in full-time positions. While this gap shows a trend where women are inching closer to wage parity with men, it also exhibits a noticeable income difference for women working full-time in the township of Pepperton township.

Content

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

Gender classifications include:

• Male
• Female

Employment type classifications include:

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

Variables / Data Columns

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

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

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

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

This Gloucester dataset contains v8.2 of the Asset database (GLO_asset_database_20160212.mdb), a Geodatabase version for GIS mapping purposes (GLO_asset_database_20160212_GISOnly.gdb), the draft Water Dependent Asset Register spreadsheet (BA-NSB-GLO-130-WaterDependentAssetRegister-AssetList-v20160212.xlsx), a data dictionary (GLO_asset_database_doc_20160212.doc), a folder (Indigenous_doc) containing documentation associated with Indigenous water asset project, a folder (NRM_DOC) and a folder (NRM_DOC) containing documentation associated with the Water Asset Information Tool (WAIT) process as outlined below.

The Gloucester Asset database v8.2 supersedes the previous version of the GLO Asset database in asset relevant tables/ feature class only (i.e. AssetDecisions, AssetList, Element_to_Asset, ElementList, tbl_Indigenous_water_asset, tbl_GAL_Species_TEC_decisions_review_23112015 in GLO_asset_database_20160212.mdb and GM_GLO_AssetList_pt, GM_GLO_ElementList_pt in GLO_asset_database_20160212_GISOnly.gdb). This version of GLO asset database has been updated to:

(1) Total number of registered water assets was increased by 18 due to:

(a) The 3 assets changed M2 test to "Yes" from the review done by Ecologist group.

(b) 15 indigenous water assets from OWS were added.

The Asset database is registered to the BA repository as an ESRI personal goedatabase (.mdb - doubling as a MS Access database) that can store, query, and manage non-spatial data while the spatial data is in a separated file geodatabase joined by AID/Element ID/BARID. Under the BA program, a spatial assets database is developed for each defined bioregional assessment project. The spatial elements that underpin the identification of water dependent assets are identified in the first instance by regional NRM organisations (via the WAIT tool) and supplemented with additional elements from national and state/territory government datasets. All reports received associated with the WAIT process for Gloucester are included in the zip file as part of this dataset. Elements are initially included in the preliminary assets database if they are partly or wholly within the subregion's preliminary assessment extent (Materiality Test 1, M1). Elements are then grouped into assets which are evaluated by project teams to determine whether they meet the second Materiality Test (M2). Assets meeting both Materiality Tests comprise the water dependent asset list. Descriptions of the assets identified in the Gloucester subregion are found in the "AssetList" table of the database. In this version of the database only M1 has been assessed. Assets are the spatial features used by project teams to model scenarios under the BA program. Detailed attribution does not exist at the asset level. Asset attribution includes only the core set of BA-derived attributes reflecting the BA classification hierarchy, as described in Appendix A of "GLO_asset_database_doc_20160212.doc", located in the zip file as part of this dataset. The "Element_to_Asset" table contains the relationships and identifies the elements that were grouped to create each asset. Detailed information describing the database structure and content can be found in the document "GLO_asset_database_doc_20160212.doc" located in the zip file. The public version of this asset database can be accessed via the following dataset: Asset database for the Gloucester subregion on 12 February 2016 Public v02 (https://data.gov.au/dataset/5def411c-dbc4-4b75-b509-4230964ce0fa).

Purpose

Used for Gloucester subregion for bioregional assessments

The public version of this asset database can be accessed via the following dataset: Asset database for the Gloucester subregion on 12 February 2016 Public v02 (https://data.gov.au/dataset/5def411c-dbc4-4b75-b509-4230964ce0fa).

Dataset History

VersionID Date Notes

1.0 17/03/2014 Initial database

1.01 19/03/2014 Update classification using latest one

2.0 23/05/2014 Update asset area for some assets

3.0 9/07/2014 updated to include new assets and elements identified by community.

4.0 29/08/2014 updated assets and elements from WSP

5.0 4/09/2014 Table AssetDecisions is added to record decision making process and decisions about M2 are also added in table

                                    asset list

6.0 8/04/2015 195/9 Groundwater economic point elements/assets were added in while 81/7 Groundwater economic point

                                    elements/assets were turned off

7.0 27/05/2015 The receptor data ( tables: ReceptorList, tbl_Receptors_GDE, tbl_Receptors_GW, tbl_Receptors_SW and

                                   tbl_Receptors_SW_Catchment_Ref_Only; and spatial data: GM_GLO_ReceptorList_pt) is added

7.1 21/08/2015 "(1) Delete (a) line 26 from tab "Description" and (b) column E from tab "Receptor register" about "Depth" parameters

                                   in BA-NSB-GLO-140-ReceptorRegister-v20150821.xlsx

                                 (2) Delete field of "Depth" from table "ReceptorList" in GLO_asset_database_20150821.mdb

                                 (3) Add two fields of "InRegister" and "Registered Date" to table "ReceptorList" in GLO_asset_database_20150821.mdb 

                                   for the consistency with other subregions in the future"

8 16/09/2015 "(1) (a) Update Latitude, Longitude, LandscapeClass using the latest data from GLO project team and update the values

                                    for RegisteredDate, and Group using "GDE", "SW" and "GW" in table ReceptorList in 

                                    GLO_asset_database_20150916.mdb; (b) Create draft BA-NSB-GLO-140-ReceptorRegister-v20150916.xlsx

                                 (2) Update tbl_Receptors_GDE, tbl_Receptors_GW and tbl_Receptors_SW in GLO_asset_database_20150916.mdb, 

                                    using the latest data from GLO project team.

                                 (3) Update GM_GLO_ReceptorList_pt in GLO_asset_database_20150916_GISOnly.gdb, using the latest data from GLO 

                                   project team"

8.1 29/10/2015 (a) Update LandscapeClass field in table ReceptorList for all 222 economic Receptors to match the latest decision about

                                   this parameter (b) Create draft BA-NSB-GLO-140-ReceptorRegister-v20151029.xlsx

8.2 12/02/2016 "(1) Total number of registered water assets was increased by 18 due to:

                                  (a) The 3 assets changed M2 test to "Yes" from the review done by Ecologist group. The original data is included the 

                                      database as the table tbl_GLO_Species_TEC_decisions_review_23112015

                                  (b) 15 indigenous water assets from OWS were added. The data and documents from OWS are included in

                                     subdirectory Indigenous_doc

                                 (c)The draft new Water Dependent Asset Register file (BA-NSB-GLO-130-WaterDependentAssetRegister-AssetList-

                                   v20160212.xlsx) was created"

The source metadata was updated to meet the purpose of the Bioregional Assessment Programme

Dataset Citation

Bioregional Assessment Programme (2014) Asset database for the Gloucester subregion on 12 February 2016. Bioregional Assessment Derived Dataset. Viewed 18 July 2018, http://data.bioregionalassessments.gov.au/dataset/72a47bec-1393-49d6-b379-0e48551d26a9.

Dataset Ancestors

• Derived From Standard Instrument Local Environmental Plan (LEP) - Heritage (HER) (NSW)

• Derived From NSW Office of Water GW licence extract linked to spatial locations - GLO v5 UID elements 27032014

• Derived From Asset database for the Gloucester subregion on 21 August 2015

• Derived From Gloucester digitised coal mine boundaries

• Derived From Groundwater Dependent Ecosystems supplied by the NSW Office of Water on 13/05/2014

• Derived From [NSW Office of Water GW licence extract linked to spatial locations GLOv4 UID

Abstract The dataset was derived by the Bioregional Assessment Programme. This dataset was derived from multiple datasets. You can find a link to the parent datasets in the Lineage Field in this …Show full descriptionAbstract The dataset was derived by the Bioregional Assessment Programme. This dataset was derived from multiple datasets. 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. Asset database for the Hunter subregion on 24 February 2016 (V2.5) supersedes the previous version of the HUN Asset database V2.4 (Asset database for the Hunter subregion on 20 November 2015, GUID: 0bbcd7f6-2d09-418c-9549-8cbd9520ce18). It contains the Asset database (HUN_asset_database_20160224.mdb), a Geodatabase version for GIS mapping purposes (HUN_asset_database_20160224_GISOnly.gdb), the draft Water Dependent Asset Register spreadsheet (BA-NSB-HUN-130-WaterDependentAssetRegister-AssetList-V20160224.xlsx), a data dictionary (HUN_asset_database_doc_20160224.doc), and a folder (NRM_DOC) containing documentation associated with the Water Asset Information Tool (WAIT) process as outlined below. This version should be used for Materiality Test (M2) test. The Asset database is registered to the BA repository as an ESRI personal goedatabase (.mdb - doubling as a MS Access database) that can store, query, and manage non-spatial data while the spatial data is in a separate file geodatabase joined by AID/ElementID. Under the BA program, a spatial assets database is developed for each defined bioregional assessment project. The spatial elements that underpin the identification of water dependent assets are identified in the first instance by regional NRM organisations (via the WAIT tool) and supplemented with additional elements from national and state/territory government datasets. A report on the WAIT process for the Hunter is included in the zip file as part of this dataset. Elements are initially included in the preliminary assets database if they are partly or wholly within the subregion's preliminary assessment extent (Materiality Test 1, M1). Elements are then grouped into assets which are evaluated by project teams to determine whether they meet the second Materiality Test (M2). Assets meeting both Materiality Tests comprise the water dependent asset list. Descriptions of the assets identified in the Hunter subregion are found in the "AssetList" table of the database. Assets are the spatial features used by project teams to model scenarios under the BA program. Detailed attribution does not exist at the asset level. Asset attribution includes only the core set of BA-derived attributes reflecting the BA classification hierarchy, as described in Appendix A of "HUN_asset_database_doc_20160224.doc ", located in this filet. The "Element_to_Asset" table contains the relationships and identifies the elements that were grouped to create each asset. Detailed information describing the database structure and content can be found in the document "HUN_asset_database_doc_20160224.doc" located in this file. Some of the source data used in the compilation of this dataset is restricted. The public version of this asset database can be accessed via the following dataset: Asset database for the Hunter subregion on 24 February 2016 Public 20170112 v02 (https://data.gov.au/data/dataset/9d16592c-543b-42d9-a1f4-0f6d70b9ffe7) Dataset History OBJECTID VersionID Notes Date_ 1 1 Initial database. 29/08/2014 3 1.1 Update the classification for seven identical assets from Gloucester subregion 16/09/2014 4 1.2 Added in NSW GDEs from Hunter - Central Rivers GDE mapping from NSW DPI (50 635 polygons). 28/01/2015 5 1.3 New AIDs assiged to NSW GDE assets (Existing AID + 20000) to avoid duplication of AIDs assigned in other databases. 12/02/2015 6 1.4 "(1) Add 20 additional datasets required by HUN assessment project team after HUN community workshop (2) Turn off previous GW point assets (AIDs from 7717-7810 inclusive) (3) Turn off new GW point asset (AID: 0) (4) Assets (AIDs: 8023-8026) are duplicated to 4 assets (AID: 4747,4745,4744,4743 respectively) in NAM subregion . Their AID, Asset Name, Group, SubGroup, Depth, Source, ListDate and Geometry are using values from that NAM assets. (5) Asset (AID 8595) is duplicated to 1 asset ( AID 57) in GLO subregion . Its AID, Asset Name, Group, SubGroup, Depth, Source, ListDate and Geometry are using values from that GLO assets. (6) 39 assets (AID from 2969 to 5040) are from NAM Asset database and their attributes were updated to use the latest attributes from NAM asset database (7)The databases, especially spatial database, were changed such as duplicated attributes fields in spatial data were removed and only ID field is kept. The user needs to join the Table Assetlist or Elementlist to the spatial data" 16/06/2015 7 2 "(1) Updated 131 new GW point assets with previous AID and some of them may include different element number due to the change of 77 FTypes requested by Hunter assessment project team (2) Added 104 EPBC assets, which were assessed and excluded by ERIN (3) Merged 30 Darling Hardyhead assets to one (asset AID 60140) and deleted another 29 (4) Turned off 5 assets from community workshop (60358 - 60362) as they are duplicated to 5 assets from 104 EPBC excluded assets (5) Updated M2 test results (6) Asset Names (AID: 4743 and 4747) were changed as requested by Hunter assessment project team (4 lower cases to 4 upper case only). Those two assets are from Namoi asset database and their asset names may not match with original names in Namoi asset database. (7)One NSW WSP asset (AID: 60814) was added in as requested by Hunter assessment project team. The process method (without considering 1:M relation) for this asset is not robust and is different to other NSW WSP assets. It should NOT use for other subregions. (8) Queries of Find_All_Used_Assets and Find_All_WD_Assets in the asset database can be used to extract all used assts and all water dependant assts" 20/07/2015 8 2.1 "(1) There are following six assets (in Hun subregion), which is same as 6 assets in GIP subregion. Their AID, Asset Name, Group, SubGroup, Depth, Source and ListDate are using values from GIP assets. You will not see AIDs from AID_from_HUN in whole HUN asset datable and spreadsheet anymore and you only can see AIDs from AID_from_GIP ( Actually (a) AID 11636 is GIP got from MBC (B) only AID, Asset Name and ListDate are different and changed) (2) For BA-NSB-HUN-130-WaterDependentAssetRegister-AssetList-V20150827.xlsx, (a) Extracted long ( >255 characters) WD rationale for 19 assets (AIDs: 8682,9065,9073,9087,9088,9100,9102,9103,60000,60001,60792,60793,60801,60713,60739,60751,60764,60774,60812 ) in tab "Water-dependent asset register" and 37 assets (AIDs: 5040,8651,8677,8682,8650,8686,8687,8718,8762,9094,9065,9067,9073,9077,9081,9086,9087,9088,9100,9102,9103,60000,60001,60739,60742,60751,60713,60764,60771, 60774,60792,60793,60798,60801,60809,60811,60812) in tab "Asset list" in 1.30 Excel file (b) recreated draft BA-NSB-HUN-130-WaterDependentAssetRegister-AssetList-V20150827.xlsx (3) Modified queries (Find_All_Asset_List and Find_Waterdependent_asset_register) for (2)(a)" 27/08/2015 9 2.2 "(1) Updated M2 results from the internal review for 386 Sociocultural assets (2)Updated the class to Ecological/Vegetation/Habitat (potential species distribution) for assets/elements from sources of WAIT_ALA_ERIN, NSW_TSEC, NSW_DPI_Fisheries_DarlingHardyhead" 8/09/2015 10 2.3 "(1) Updated M2 results from the internal review * Changed "Assessment team do not say No" to "All economic assets are by definition water dependent" * Changed "Assessment team say No" : to "These are water dependent, but excluded by the project team based on intersection with the PAE is negligible" * Changed "Rivertyles" to "RiverStyles"" 22/09/2015 11 2.4 "(1) Updated M2 test results for 86 assets from the external review (2) Updated asset names for two assets (AID: 8642 and 8643) required from the external review (3) Created Draft Water Dependent Asset Register file using the template V5" 20/11/2015 12 2.5 "Total number of registered water assets was increased by 1 (= +2-1) due to: Two assets changed M2 test from "No" to "Yes" , but one asset assets changed M2 test from "Yes" to "No" from the review done by Ecologist group." 24/02/2016 Dataset Citation Bioregional Assessment Programme (2015) Asset database for the Hunter subregion on 24 February 2016. Bioregional Assessment Derived Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/a39290ac-3925-4abc-9ecb-b91e911f008f. Dataset Ancestors Derived From GW Element Bores with Unknown FTYPE Hunter NSW Office of Water 20150514 Derived From Travelling Stock Route Conservation Values Derived From Spatial Threatened Species and Communities (TESC) NSW 20131129 Derived From NSW Wetlands Derived From Climate Change Corridors Coastal North East NSW Derived From Communities of National Environmental Significance Database - RESTRICTED - Metadata only Derived From Climate Change Corridors for Nandewar and New England Tablelands Derived From National Groundwater Dependent Ecosystems (GDE) Atlas Derived From Asset database for the Hunter subregion on 27 August 2015 Derived From Birds Australia - Important Bird Areas (IBA) 2009 Derived From Estuarine Macrophytes of Hunter Subregion NSW DPI Hunter 2004 Derived From Hunter CMA GDEs (DRAFT DPI pre-release) Derived From Camerons Gorge Grassy White Box Endangered Ecological Community (EEC) 2008 Derived From NSW Office of Water Surface Water Licences Processed for Hunter v1 20140516 Derived From Fauna Corridors for North East NSW Derived From Asset database for the Hunter subregion on 12 February 2015 Derived From New South Wales NSW Regional CMA Water Asset Information WAIT tool databases,

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This table contains information on the balance sheet of the general government sector. The balance sheet shows stock levels of assets and financial liabilities, as well as net worth of the general government sector. Assets are either financial (e.g. loans) or non-financial (e.g. non-residential buildings). The stock of assets equals the sum of the stock of financial liabilities and net worth. Stocks of assets and liabilities in this table are mostly valued at market value. This is the value of the asset or liability as if it were being acquired or sold on the date to which the balance sheet relates. When there are no observable market prices, estimates are made for the market value. Financial assets and liabilities that are not commonly traded on a market, such as cash, deposits, loans and other accounts receivable/payable are valued at nominal value.

The figures in this table are consolidated at the general government level. This means that stocks between units that both belong to the general government sector are eliminated.

The terms and definitions used are in accordance with the framework of the Dutch national accounts. National accounts are based on the international definitions of the European System of Accounts (ESA 2010). Small temporary differences with publications of the National Accounts may occur due to the fact that the government finance statistics are sometimes more up to date.

Data available from: 1995

Status of the figures: The figures for the period 1995-2022 are final. The figures for 2023 are provisional.

Changes as of 23 September 2024: Annual figures for 2023 are available. The financial assets and liabilities and the net saving and capital transfers of general government for 2022 have been revised due to updated information. In the context of the revision policy of the National accounts the annual figures from 1995 of the financial accounts of general government have been revised. The annual figures for 2022 are final.

When will new figures be published? New provisional data are published in July or August after the end of the reporting year. The previous provisional figures will become final and previous final figures can be revised at the same time. More information on the revision policy of National Accounts can be found under 'relevant articles' under paragraph 3.

The study included four separate surveys: 1. The LSMS survey of general population of Serbia in 2002 2. The survey of Family Income Support (MOP in Serbian) recipients in 2002 These two datasets are published together. 3. The LSMS survey of general population of Serbia in 2003 (panel survey) 4. The survey of Roma from Roma settlements in 2003 These two datasets are published together separately from the 2002 datasets. Objectives LSMS represents multi-topical study of household living standard and is based on international experience in designing and conducting this type of research. The basic survey was carried out in 2002 on a representative sample of households in Serbia (without Kosovo and Metohija). Its goal was to establish a poverty profile according to the comprehensive data on welfare of households and to identify vulnerable groups. Also its aim was to assess the targeting of safety net programs by collecting detailed information from individuals on participation in specific government social programs. This study was used as the basic document in developing Poverty Reduction Strategy (PRS) in Serbia which was adopted by the Government of the Republic of Serbia in October 2003. The survey was repeated in 2003 on a panel sample (the households which participated in 2002 survey were re-interviewed). Analysis of the take-up and profile of the population in 2003 was the first step towards formulating the system of monitoring in the Poverty Reduction Strategy (PRS). The survey was conducted in accordance with the same methodological principles used in 2002 survey, with necessary changes referring only to the content of certain modules and the reduction in sample size. The aim of the repeated survey was to obtain panel data to enable monitoring of the change in the living standard within a period of one year, thus indicating whether there had been a decrease or increase in poverty in Serbia in the course of 2003. [Note: Panel data are the data obtained on the sample of households which participated in the both surveys. These data made possible tracking of living standard of the same persons in the period of one year.] Along with these two comprehensive surveys, conducted on national and regional representative samples which were to give a picture of the general population, there were also two surveys with particular emphasis on vulnerable groups. In 2002, it was the survey of living standard of Family Income Support recipients with an aim to validate this state supported program of social welfare. In 2003 the survey of Roma from Roma settlements was conducted. Since all present experiences indicated that this was one of the most vulnerable groups on the territory of Serbia and Montenegro, but with no ample research of poverty of Roma population made, the aim of the survey was to compare poverty of this group with poverty of basic population and to establish which categories of Roma population were at the greatest risk of poverty in 2003. However, it is necessary to stress that the LSMS of the Roma population comprised potentially most imperilled Roma, while the Roma integrated in the main population were not included in this study.

Abstract The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The …Show full descriptionAbstract The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. This Namoi (NAM) dataset contains v5 of the Asset database (NAM_asset_database_20160218.mdb), a Geodatabase version for GIS mapping purposes (NAM_asset_database_20160218_GISOnly.gdb), the draft Water Dependent Asset Register spreadsheet (BA-NIC-NAM-130-WaterDependentAssetRegister-AssetList-v20160218.xlsx), a data dictionary (NAM_asset_database_doc_20160218.doc), a folder (Indigenous_doc) containing documentation associated with Indigenous water asset project, and a folder (NRM_DOC) containing documentation associated with the Water Asset Information Tool (WAIT) process as outlined below. The Asset database for the Namoi subregion on 18 February 2016 supersedes the previous version of the Asset database v4 "Asset database for the Namoi subregion on 15 January 2015" GUID: c32e70ad-9357-4297-a5dd-e1f1e1f5255f. Updates in this v5 database include: (1) Total number of registered water assets was increased by 7 due to: (a) The 6 assets changed M2 test to "Yes" and 1 assets changed reason from the review done by Ecologist group. The original data is included the database as the table tbl_NAM _Species_TEC_decisions_reveiw_23112015 (b) One indigenous water asset from OWS were added. The data and documents from OWS are included in subdirectory Indigenous_doc (c)The draft new Water Dependent Asset Register file (BA-NIC-NAM-130-WaterDependentAssetRegister-AssetList-v20160218.xlsx) was created (2) The databases, especially spatial database, were changed such as (a) spatial data are saved in a separated file geodatabase, (b) duplicated attributes fields in spatial data were removed and only ID field is kept in the spatial data. The user can use AID or ElementID to join the table in personal geodatabase with relevant spatial data The user can join the Table Assetlist (in NAM_asset_database_20160218.mdb) to the spatial data (GM_NAM_AssetList_ln, GM_NAM_AssetList_poly and GM_NAM_AssetList_pt in NAM_asset_database_20160218_GISOnly.gdb) from ArcMap by AID to get those attributes for assets. The user can join the Table Elementlist (in NAM_asset_database_20160218.mdb) to the spatial data (GM_NAM_ElementsList_ln, GM_NAM_ElementsList_poly and GM_NAM_ElementsList_pt in NAM_asset_database_20160218_GISOnly.gdb) from ArcMap by ElementID to get those attributes for elements . Element_to_asset (in NAM_asset_database_20160218.mdb) can join to above the spatial data or above two joined results for more information. Detailed attribution does not exist at the asset level. Asset attribution includes only the core set of BA-derived attributes reflecting the BA classification hierarchy, as described in Appendix A of " NAM_asset_database_doc_20160218.doc", located in the zip file as part of this dataset. The "Element_to_Asset" table contains the relationships and identifies the elements that were grouped to create each asset. Detailed information describing the database structure and content can be found in the document "NAM_asset_database_doc_20160218.doc doc" located in the zip file. The public version of this asset database can be accessed via the following dataset: Asset database for the Namoi subregion on 18 February 2016 Public (https://data.gov.au/data/dataset/3134fa6b-f876-46dd-b26b-88d46d424185). Purpose The Asset List Database was developed to spatially identify water dependent assets found within the Namoi subregion. The public version of this asset database can be accessed via the following dataset: Asset database for the Namoi subregion on 18 February 2016 Public (https://data.gov.au/data/dataset/3134fa6b-f876-46dd-b26b-88d46d424185). Dataset History On 20 April 2015 the title of this database was changed from "Namoi_AssetList_Database_v4_20150115". This dataset replicates the spatial and tabular content and structure of the previous version of the Namoi asset list ("Asset list for Namoi - CURRENT"; ID: 538c717c-c04a-4720-8bcd-96fbdf7f0d80) with the exception that decisions made by the Namoi Project Team concerning Materiality Test 2 (water dependency) have been incorporated into the AssetList table, which are used to define the water dependent asset register. Date Notes 22/07/2014 Initial database for asset related tables and feature classes, and imported element data from element list database 5/09/2014 Updated database with updated WSP/GWMP/RegRiv assets/elements; additional WSP plus point water volume data and additional RegRiv plus point water volume data 18/11/2014 Merge some assets with non standard classification to standard classification 18/11/2014 add additional point groundwater economic data ( 121 new elements) 18/11/2014 add additional point surface water economic data (49 new elements) 15/01/2015 Incorporate materiality decisions (M2) from project team into AssetList table 18/02/2016 "(1)Total number of registered water assets was increased by 7 due to: (a) The 6 assets changed M2 test to "Yes" and 1 assets changed reason from the review done by Ecologist group. The original data is included the database as the table tbl_NAM _Species_TEC_decisions_reveiw_23112015 (b) One indigenous water asset from OWS were added. The data and documents from OWS are included in subdirectory Indigenous_doc (c)The draft new Water Dependent Asset Register file (BA-NIC-NAM-130-WaterDependentAssetRegister- AssetList-v20160218.xlsx) was created (2) The databases, especially spatial database, were changed such as (a) spatial data are saved in a separated file geodatabase, (b) duplicated attributes fields in spatial data were removed and only ID field is kept in the spatial data." Dataset Citation Bioregional Assessment Programme (2016) Asset database for the Namoi subregion on 18 February 2016. Bioregional Assessment Derived Dataset. Viewed 11 December 2018, http://data.bioregionalassessments.gov.au/dataset/22061f2c-e86d-4ca8-9860-c349c2513fd8. Dataset Ancestors Derived From Asset list for Namoi - CURRENT Derived From NSW Office of Water GW licence extract linked to spatial locations NIC v2 (28 February 2014) Derived From NSW Office of Water Surface Water Entitlements Locations v1_Oct2013 Derived From Communities of National Environmental Significance Database - RESTRICTED - Metadata only Derived From NSW Office of Water Surface Water Licences in NIC linked to locations v1 (22 April 2014) Derived From Birds Australia - Important Bird Areas (IBA) 2009 Derived From Asset database for the Namoi subregion on 15 January 2015 Derived From Missing SW Licensing Data in the Namoi PAE 20140711 Derived From Environmental Asset Database - Commonwealth Environmental Water Office Derived From NSW Office of Water Surface Water Offtakes - NIC v1 20131024 Derived From Collaborative Australian Protected Areas Database (CAPAD) 2010 - External Restricted Derived From National Groundwater Dependent Ecosystems (GDE) Atlas (including WA) Derived From Species Profile and Threats Database (SPRAT) - Australia - Species of National Environmental Significance Database (BA subset - RESTRICTED - Metadata only) Derived From Key Environmental Assets - KEA - of the Murray Darling Basin Derived From National Heritage List Spatial Database (NHL) (v2.1) Derived From Great Artesian Basin and Laura Basin groundwater recharge areas Derived From NSW Office of Water combined geodatabase of regulated rivers and water sharing plan regions Derived From New South Wales NSW Regional CMA Water Asset Information WAIT tool databases, RESTRICTED Includes ALL Reports Derived From New South Wales NSW - Regional - CMA - Water Asset Information Tool - WAIT - databases Derived From NSW Office of Water Groundwater licences extract linked to spatial locations NIC v3 (13 March 2014) Derived From Australia - Species of National Environmental Significance Database Derived From NSW Office of Water Groundwater Licence Extract NIC- Oct 2013 Derived From Australia, Register of the National Estate (RNE) - Spatial Database (RNESDB) Internal Derived From NSW Office of Water Groundwater Entitlements Spatial Locations Derived From National Groundwater Dependent Ecosystems (GDE) Atlas Derived From Directory of Important Wetlands in Australia (DIWA) Spatial Database (Public) Derived From Collaborative Australian Protected Areas Database (CAPAD) 2010 (Not current release)

Abstract

This data set holds the publicly-available version of the database of water-dependent assets that was compiled for the bioregional assessment (BA) of the Cooper subregion as part of the Bioregional Assessment Technical Programme. Though all life is dependent on water, for the purposes of a bioregional assessment, a water-dependent asset is an asset potentially impacted by changes in the groundwater and/or surface water regime due to coal resource development. The water must be other than local rainfall. Examples include wetlands, rivers, bores and groundwater dependent ecosystems.

The dataset was derived by the Bioregional Assessment Programme. This dataset was derived from multiple datasets including Natural Resource Management regions, and Australian and state and territory government databases. 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. A single asset is represented spatially in the asset database by single or multiple spatial features (point, line or polygon). Individual points, lines or polygons are termed elements.

This dataset contains the unrestricted publicly-available components of spatial and non-spatial (attribute) data of the (restricted) Asset database for the Cooper subregion on 12 May 2016 (90230311-b2e7-4d4d-a69a-03daab0d03cc). The database is provided primarily as an ESRI File geodatabase (.gdb), which is able to be opened in readily available open source software such as QGIS. Other formats include the Microsoft Access database (.mdb in ESRI Personal Geodatabase format), industry-standard ESRI Shapefiles and tab-delimited text files of all the attribute tables.

The restricted version of the Cooper Asset database has a total count of 63910 Elements and 1 611 Assets. In the public version of the Asset Cooper database 6209 spatial Element features (~10%) have been removed from the Element List and Element Layer(s) and 47 spatial Assets (~3%) have been removed from the spatial Asset Layer(s)

The elements/assets removed from the restricted Asset Database are from the following data sources:

1) Species Profile and Threats Database (SPRAT) - Australia - Species of National Environmental Significance Database (BA subset - RESTRICTED - Metadata only) (7276dd93-cc8c-4c01-8df0-cef743c72112)

2) Australia, Register of the National Estate (RNE) - Spatial Database (RNESDB) (Internal 878f6780-be97-469b-8517-54bd12a407d0)

3) Lake Eyre Basin (LEB) Aquatic Ecosystems Mapping and Classification (9be10819-0e71-4d8d-aae5-f179012b6906)

4) Communities of National Environmental Significance Database - RESTRICTED - Metadata only (c01c4693-0a51-4dbc-bbbd-7a07952aa5f6)

These important assets are included in the bioregional assessment, but are unable to be publicly distributed by the Bioregional Assessment Programme due to restrictions in their licensing conditions. Please note that many of these data sets are available directly from their custodian. For more precise details please see the associated explanatory Data Dictionary document enclosed with this dataset

Dataset History

The public version of the asset database retains all of the unrestricted components of the Asset database for the Cooper subregion on 12 May 2016 - any material that is unable to be published or redistributed to a third party by the BA Programme has been removed from the database. The data presented corresponds to the assets published Cooper subregion product 1.3: Description of the water-dependent asset register and asset list for the Cooper subregion on 12 May 2016, and the associated Water-dependent asset register and asset list for the Cooper subregion on 12 May 2016.

Individual spatial features or elements are initially included in database if they are partly or wholly within the subregion's preliminary assessment extent (Materiality Test 1, M1). In accordance to BA submethodology M02: Compiling water-dependent assets, individual spatial elements are then grouped into assets which are evaluated by project teams to determine whether they meet materiality test 2 (M2), which are assets that are considered to be water dependent.

Following delivery of the first pass asset list, project teams make a determination as to whether an asset (comprised of one or more elements) is water dependent, as assessed against the materiality tests detailed in the BA Methodology. These decisions are provided to ERIN by the assessment team and incorporated into the AssetList table in the Asset database.

Development of the Asset Register from the Asset database:

Decisions for M0 (fit for BA purpose), M1 (PAE) and M2 (water dependent) determine which assets are included in the "asset list" and "water-dependent asset register" which are published as Product 1.3.

The rule sets are applied as follows:

M0 M1 M2 Result

No n/a n/a Asset is not included in the asset list or the water-dependent asset register

(≠ No) No n/a Asset is not included in the asset list or the water-dependent asset register

(≠ No) Yes No Asset included in published asset list but not in water dependent asset register

(≠ No) Yes Yes Asset included in both asset list and water-dependent asset register

Assessment teams are then able to use the database to assign receptors and impact variables to water-dependent assets and the development of a receptor register as detailed in BA submethodology M03: Assigning receptors to water-dependent assets and the receptor register is then incorporated into the asset database.

At this stage of its development, the Asset database for the Cooper subregion on 12 May 2016, which this document describes, does not contain receptor information.

Dataset Citation

Bioregional Assessment Programme (2014) Asset database for the Cooper subregion on 12 May 2016 Public. Bioregional Assessment Derived Dataset. Viewed 07 February 2017, http://data.bioregionalassessments.gov.au/dataset/bffa0c44-c86f-4f81-8070-2f0b13e0b774.

Dataset Ancestors

• Derived From QLD Dept of Natural Resources and Mines, Groundwater Entitlements 20131204

• Derived From Queensland QLD - Regional - NRM - Water Asset Information Tool - WAIT - databases

• Derived From Matters of State environmental significance (version 4.1), Queensland

• Derived From Geofabric Surface Network - V2.1

• Derived From Communities of National Environmental Significance Database - RESTRICTED - Metadata only

• Derived From South Australia SA - Regional - NRM Board - Water Asset Information Tool - WAIT - databases

• Derived From National Groundwater Dependent Ecosystems (GDE) Atlas

• Derived From National Groundwater Information System (NGIS) v1.1

• Derived From Birds Australia - Important Bird Areas (IBA) 2009

• Derived From Queensland QLD Regional CMA Water Asset Information WAIT tool databases RESTRICTED Includes ALL Reports

• Derived From Queensland wetland data version 3 - wetland areas.

• Derived From SA Department of Environment, Water and Natural Resources (DEWNR) Water Management Areas 141007

• Derived From South Australian Wetlands - Groundwater Dependent Ecosystems (GDE) Classification

• Derived From National Groundwater Dependent Ecosystems (GDE) Atlas (including WA)

• Derived From Asset database for the Cooper subregion on 14 August 2015

• Derived From QLD Dept of Natural Resources and Mines, Groundwater Entitlements linked to bores v3 03122014

• Derived From Species Profile and Threats Database (SPRAT) - Australia - Species of National Environmental Significance Database (BA subset - RESTRICTED - Metadata only)

• Derived From Ramsar Wetlands of Australia

• Derived From Permanent and Semi-Permanent Waterbodies of the Lake Eyre Basin (Queensland and South Australia) (DRAFT)

• Derived From SA EconomicElements v1 20141201

• Derived From QLD Dept of Natural Resources and Mines, Groundwater Entitlements linked to bores and NGIS v4 28072014

• Derived From National Heritage List Spatial Database (NHL) (v2.1)

• Derived From Great Artesian Basin and Laura Basin groundwater recharge areas

• Derived From [Asset database

Abstract

A strong evidence base is needed to understand the socioeconomic implications of the COVID-19 pandemic for the Solomon Islands. High Frequency Phone Surveys (HFPS) are designed to collect data on the evolving implications of the COVID-19 pandemic over several years. This data is the second of at least five planned rounds of mobile surveys. The first round of survey was already completed in late June 2020. Round 2 interviewed 2,882 households across the country in December 2020 and early January 2021, on topics including awareness of COVID-19, employment, and income, coping strategies, and public trust and security.

Geographic coverage

Urban and rural areas of Solomon Islands.

Analysis unit

Households, Individuals

Universe

Respondents aged 18 and over.

Kind of data

Sample survey data [ssd]

Sampling procedure

As the objective of the survey was to measure changes as the pandemic progresses, Round Two data collection sought to re-contact all 2,665 households contacted in Round One. The protocols for re-contact were a maximum of 3 attempts per caller shift, spaced between 1.5 and 2.5 hours apart depending on whether the phone was busy or there was no answer, and 15 attempts in total. Of the Round One households, 1,048 were successfully re-contacted. In Round One, Honiara was over-represented in the World Bank HFPS (constituting 32.8 percent of the survey sample). All other provinces were deemed under-represented, with the largest differences being for Makira-Ulawa, which represented 3.9 percent of the survey sample compared to 7.2 percent of the population in the census, and Guadalcanal, which represented 14.3 percent of the survey sample compared to 21.4 percent of the population in the census. Urban areas constituted almost half (49.2 percent) of the survey sample, compared to a quarter (25.6 percent) of the census. To reach the target sample size of at least 2500 households, 1,833 replacement households were added to the World Bank survey. The target geographic distribution for the survey was based on the population distribution across provinces from the preliminary 2019 census results. According to the population census, Honiara constituted almost one quarter (18.0 percent) of the total population. Compensating factors for these differences were developed and included in the re-weighting calculations.

The majority of these were replaced through Random Digit Dialing, but the project did attempt to leverage contact information from ward-level focal points for the Rural Development Project (RDP) in provinces underrepresented in Round One. Of the 145 RDP contacts provided to the call center, 41 were reached, who in turn provided 379 numbers which were attempted as part of regular call schedule. Overall, the sample size achieved for the second round of the HFPS was 2,882 households.

Due to the limited sample sizes outside of Honiara, most results are disaggregated into only three geographic regions: Honiara, other urban areas, and rural areas. For more information on sampling, please refer to the report provided in the External Resources.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Cleaning operations

At the end of data collection, the dataset was cleaned by the World Bank team. This included formatting, and correcting results based on monitoring issues, enumerator feedback and survey changes. Data was edited using STATA. The data is presented in two data sets: household data set and individual data set. The total number of observations in the household data set is 2,882 and is 4,279 in the individual data set. The individual data set contains employment information for some household members. The household data set contains information about public services, income, coping strategies, and awareness of COVID-19.

Response rate

Re-contact was attempted with all households from the World Bank Round Two HFPS sample, by phone, for follow up interviews for the UNICEF SIAS. Up to 5 re-contact call attempts were made per house, resulting in 1530 households being interviewed successfully including households without children. Of these households, a total of 1197 had at least one child (aged 0 to 14 years of age). While the goal was to recontact at least 1500 households with at least one child in the household, this was not possible due to lower than hoped for response rate. Given the time elapsed between the Round Two HFPS and the UNICEF SIAS, the response rate may have suffered because of some households changing phone numbers.

Response rate for returning households: 39.32%

Comparing the 130 selected regions regarding the gini index , South Africa is leading the ranking (0.63 points) and is followed by Namibia with 0.58 points. At the other end of the spectrum is Slovakia with 0.23 points, indicating a difference of 0.4 points to South Africa. The Gini coefficient here measures the degree of income inequality on a scale from 0 (=total equality of incomes) to one (=total inequality).The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).

Abstract

The basic goal of this survey is to provide the necessary database for formulating national policies at various levels. It represents the contribution of the household sector to the Gross National Product (GNP). Household Surveys help as well in determining the incidence of poverty, and providing weighted data which reflects the relative importance of the consumption items to be employed in determining the benchmark for rates and prices of items and services. Generally, the Household Expenditure and Consumption Survey is a fundamental cornerstone in the process of studying the nutritional status in the Palestinian territory.

The raw survey data provided by the Statistical Office was cleaned and harmonized by the Economic Research Forum, in the context of a major research project to develop and expand knowledge on equity and inequality in the Arab region. The main focus of the project is to measure the magnitude and direction of change in inequality and to understand the complex contributing social, political and economic forces influencing its levels. However, the measurement and analysis of the magnitude and direction of change in this inequality cannot be consistently carried out without harmonized and comparable micro-level data on income and expenditures. Therefore, one important component of this research project is securing and harmonizing household surveys from as many countries in the region as possible, adhering to international statistics on household living standards distribution. Once the dataset has been compiled, the Economic Research Forum makes it available, subject to confidentiality agreements, to all researchers and institutions concerned with data collection and issues of inequality. Data is a public good, in the interest of the region, and it is consistent with the Economic Research Forum's mandate to make micro data available, aiding regional research on this important topic.

Geographic coverage

The survey data covers urban, rural and camp areas in West Bank and Gaza Strip.

Analysis unit

1- Household/families. 2- Individuals.

Universe

The survey covered all the Palestinian households who are a usual residence in the Palestinian Territory.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample and Frame:

The sampling frame consists of all enumeration areas which enumerated in 1997 and the numeration area consists of buildings and housing units and has in average about 150 households in it. We use the enumeration areas as primary sampling units PSUs in the first stage of the sampling selection. The enumeration areas of the master sample were updated in 2003.

Sample Design:

The sample is stratified cluster systematic random sample with two stages: First stage: selection a systematic random sample of 120 enumeration areas. Second stage: selection a systematic random sample of 12-18 households from each enumeration area selected in the first stage.

Sample strata:

The population is divided by: 1-Region (North West Bank, Middle West Bank, South West Bank, Gaza Strip) 2-Type of Locality (urban, rural, refugee camps)

Target cluster size:

The target cluster size or "sample-take" is the average number of households to be selected per PSU. In this survey, the sample take is around 12 households.

Sample Size:

The calculated sample size is 1,714 households, the completed households were 1,231 (812 in the west bank and 419 in the Gaza strip).

Mode of data collection

Face-to-face [f2f]

Research instrument

The PECS questionnaire consists of two main sections:

First section: Certain articles / provisions of the form filled at the beginning of the month, and the remainder filled out at the end of the month. The questionnaire includes the following provisions:

Cover sheet: It contains detailed and particulars of the family, date of visit, particular of the field/office work team, number/sex of the family members.

Statement of the family members: Contains social, economic and demographic particulars of the selected family.

Statement of the long-lasting commodities and income generation activities: Includes a number of basic and indispensable items (i.e., Livestock, or agricultural lands).

Housing Characteristics: Includes information and data pertaining to the housing conditions, including type of shelter, number of rooms, ownership, rent, water, electricity supply, connection to the sewer system, source of cooking and heating fuel, and remoteness/proximity of the house to education and health facilities.

Monthly and Annual Income: Data pertaining to the income of the family is collected from different sources at the end of the registration / recording period.

Assistance and poverty: includes questions about household conditions and assistances that got through the the past month.

Second section: The second section of the questionnaire includes a list of 55 consumption and expenditure groups itemized and serially numbered according to its importance to the family. Each of these groups contains important commodities. The number of commodities items in each for all groups stood at 667 commodities and services items. Groups 1-21 include food, drink, and cigarettes. Group 22 includes homemade commodities. Groups 23-45 include all items except for food, drink and cigarettes. Groups 50-55 include all of the long-lasting commodities. Data on each of these groups was collected over different intervals of time so as to reflect expenditure over a period of one full year, except the cars group the data of which was collected for three previous years. These data was abotained from the recording book which is covered a period of month for each household.

Cleaning operations

Raw Data

Data editing took place through a number of stages, including: 1. Office editing and coding 2. Data entry 3. Structure checking and completeness 4. Structural checking of SPSS data files

Harmonized Data

• The Statistical Package for Social Science (SPSS) is used to clean and harmonize the datasets.
• The harmonization process starts with cleaning all raw data files received from the Statistical Office.
• Cleaned data files are then all merged to produce one data file on the individual level containing all variables subject to harmonization.
• A country-specific program is generated for each dataset to generate/compute/recode/rename/format/label harmonized variables.
• A post-harmonization cleaning process is run on the data.
• Harmonized data is saved on the household as well as the individual level, in SPSS and converted to STATA format.

Response rate

The survey sample consists of about 1,714 households interviewed over a twelve months period between (January 2007-January 2008).1,231 households completed the interview, of which 812 were from the West Bank and 419 households in Gaza Strip; the response rate was 71.8% in the Palestinian Territory.

Sampling error estimates

The calculations of standard errors for the main survey estimates enable the user to identify the accuracy of estimates and the survey reliability. Total errors of the survey can be divided into two kinds: statistical errors, and non-statistical errors. Non-statistical errors are related to the procedures of statistical work at different stages, such as the failure to explain questions in the questionnaire, unwillingness or inability to provide correct responses, bad statistical coverage, etc. These errors depend on the nature of the work, training, supervision, and conducting of all the various related activities. The work team spared no effort at the different stages to minimize non-statistical errors; however, it is difficult to estimate numerically such errors due to absence of technical computation methods based on theoretical principles to tackle them. On the other hand, statistical errors can be measured. Frequently they are measured by the standard error, which is the positive square root of the variance. The variance of this survey has been computed by using the "programming package" CENVAR

Data appraisal

The impact of errors on the data quality was reduced to the minimal due to the high efficiency and outstanding selection, training, and performance of the fieldworkers. Procedures adopted during the fieldwork of the survey were considered a necessity to ensure the collection of accurate data, notably: 1) Develop schedules to conduct field visits to households during survey fieldwork. The objectives of the visits and the data that is collected on each visit were predetermined. 2) Fieldwork editing rules were applied during the data collection to ensure corrections were implemented before the end of fieldwork activities 3) Fieldworker were instructed to provide details in case of extreme expenditure or consumption of the household. 4) Postpone the questions on income to the last visit at the end of the month 5) Validation rules were embedded in the data processing systems along with procedures to verify data entry and data editing.

GCCSA based data for Total Family Income (Weekly) by Labour Force Status of Parent for One Parent Families, in General Community Profile (GCP), 2016 Census. Count of one parent families. 'Employed, worked full-time' is defined as having worked 35 hours or more in all jobs during the week prior to Census Night. Employed, away from work' comprises employed persons who did not work any hours in the week prior to Census Night or who did not state their number of hours worked. G56 is broken up into two sections (G56a-G56b) this section contains 'All incomes not stated Employed Away from work' - 'Total Total'. The data is by GCCSA 2016 boundaries. Periodicity: 5-Yearly. Note: There are small random adjustments made to all cell values to protect the confidentiality of data. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals. For more information visit the data source: http://www.abs.gov.au/census.