The survey interviewed 254 retailer shops in 10 sub-cities of Addis Ababa. 30 supermarkets, 20 mini-markets, 100 regular shops, 80 dairy shops and 24 open market shops selling dairy products were interviewed. Details of the sampling strategy is found in the attachment. The survey collected information on the characteristics of the shop, details of dairy products sold, prices and quality. Policy makers, research, and other stakeholders can use this data to analyses dairy value chain in Ethiopia and dairy retailing practices in Ethiopia. This data set was collected through research of the project “Improving the evidence and policies for better performing livestock systems in Ethiopia” lead by the International Food Policy Research Institution as part of the Feed the Future Innovation Lab for Livestock Systems.
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This table describes transactions of incomes, uses, savings and assets of the household sector in the national accounts to different households groups. Households are distinguished by the main source of income, housing situation, household composition, age class of the main cost earner, 20 % income groups and 20 % equity groups.
Data available from: 2015.
Status of the figures: Data for 2015, 2016, 2017, 2018 and 2019 are provisional.
Changes as of 10 September 2021: The figures for 2015-2018 have been adjusted as a result of the CBS update policy. In addition, distribution information was used for the 2018 figures, which was not yet available at the time of the previous publication. The results for 2019 have been added.
When are new figures coming?
The new figures will come in September 2022.
Dataset shows the Percentage of Households by Ownership of Household Assets as per the 2009 census findings
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Graph and download economic data for Households; Net Worth, Level (BOGZ1FL192090005Q) from Q4 1987 to Q1 2025 about net worth, Net, households, and USA.
The Distributional Financial Accounts (DFAs) provide a quarterly measure of the distribution of U.S. household wealth since 1989, based on a comprehensive integration of disaggregated household-level wealth data with official aggregate wealth measures. The data set contains the level and share of each balance sheet item on the Financial Accounts' household wealth table (Table B.101.h), for various sub-populations in the United States. In our core data set, aggregate household wealth is allocated to each of four percentile groups of wealth: the top 1 percent, the next 9 percent (i.e., 90th to 99th percentile), the next 40 percent (50th to 90th percentile), and the bottom half (below the 50th percentile). Additionally, the data set contains the level and share of aggregate household wealth by income, age, generation, education, and race. The quarterly frequency makes the data useful for studying the business cycle dynamics of wealth concentration--which are typically difficult to observe in lower-frequency data because peaks and troughs often fall between times of measurement. These data will be updated about 10 or 11 weeks after the end of each quarter, making them a timely measure of the distribution of wealth.
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2009 Census: Percentage Households by Ownership of Household Assets and District
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HFC14 - Household Financial Assets. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Household Financial Assets...
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This table describes the wealth distribution of the sector households in the national accounts over different household groups. Households are identified by main source of income, living situation, household composition, age classes of the head of the household, income class by 20 % groups, and net worth class by 20 % groups.
Data available from: 2015.
Status of the figures: Dates of 2015, 2016, 2017, 2018, and 2019 are provisional.
Changes as of September 10th 2021: The figures of 2015-2018 are revised, because national accounts figures are changed due to the revision policy of Statistics Netherlands. For 2018 also microdata are added that were not available before. Results for 2019 are added to the table.
When will new figures be published? New figures will be released in September 2022.
The survey interviewed 254 retailer shops in 10 sub-cities of Addis Ababa. 30 supermarkets, 20 mini-markets, 100 regular shops, 80 dairy shops and 24 open market shops selling dairy products were interviewed. Details of the sampling strategy is found in the attachment. The survey collected information on the characteristics of the shop, details of dairy products sold, prices and quality. Policy makers, research, and other stakeholders can use this data to analyses dairy value chain in Ethiopia and dairy retailing practices in Ethiopia. This data set was collected through research of the project “Improving the evidence and policies for better performing livestock systems in Ethiopia” lead by the International Food Policy Research Institution as part of the Feed the Future Innovation Lab for Livestock Systems.
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Analysis of ‘HFC13 - Household Financial Assets’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/cd02fffb-ac8f-4b31-ac06-fe66a18473a0 on 19 January 2022.
--- Dataset description provided by original source is as follows ---
Household Financial Assets
--- Original source retains full ownership of the source dataset ---
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Analysis of ‘HFC08 - Household Financial Assets’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/5f2ba0b4-fe74-4ba8-9ce2-e9086cda4622 on 19 January 2022.
--- Dataset description provided by original source is as follows ---
Household Financial Assets
--- Original source retains full ownership of the source dataset ---
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This table provides data on household assets by characteristics such as household composition, age and migration background of the main cost-winner, main source of income, housing situation, income group, wealth group and asset class. The data is available in various regional breakdowns based on the municipal classification as of 1 January 2019.
Data available from 2011 to 2018. The data relate to the state of power as of 1 January.
Status of the figures: The figures are for now.
Changes as of 13 October 2020: None, this table has been discontinued.
When are new figures coming? No longer applicable. This table is followed up by the table 'Power of households; region (classification 2020)’. See paragraph 3.
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Household Assets: NR: HLB: For Business data was reported at 2,105,334.000 THB mn in 2015. This records an increase from the previous number of 1,591,497.000 THB mn for 2013. Household Assets: NR: HLB: For Business data is updated yearly, averaging 1,104,325.175 THB mn from Dec 2006 (Median) to 2015, with 6 observations. The data reached an all-time high of 2,105,334.000 THB mn in 2015 and a record low of 720,617.000 THB mn in 2006. Household Assets: NR: HLB: For Business data remains active status in CEIC and is reported by National Statistical Office. The data is categorized under Global Database’s Thailand – Table TH.G042: Household Income & Assets Statistics.
This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada); Statistics (3 items: Value; Distribution of value; Value per household); Characteristics (13 items: All households; Lowest income quintile; Second income quintile; Third income quintile; ...); Wealth (11 items: Total assets; Financial assets; Life insurance and pensions; Other financial assets; ...).
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Households that have liquidity problems and solvency problems only
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Wealth indicators and distributions, by household characteristics such as income quintile, age, housing tenure and composition, Canada, annual 2010 to 2019 and quarterly starting 2020.
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Thailand Household Assets: NE: Financial: Investment data was reported at 17,218.164 THB mn in 2015. This records a decrease from the previous number of 17,305.000 THB mn for 2013. Thailand Household Assets: NE: Financial: Investment data is updated yearly, averaging 17,261.582 THB mn from Dec 2013 (Median) to 2015, with 2 observations. The data reached an all-time high of 17,305.000 THB mn in 2013 and a record low of 17,218.164 THB mn in 2015. Thailand Household Assets: NE: Financial: Investment data remains active status in CEIC and is reported by National Statistical Office. The data is categorized under Global Database’s Thailand – Table TH.G042: Household Income & Assets Statistics.
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Thailand Household Assets: NR: Financial: Saving data was reported at 372,902.000 THB mn in 2015. This records an increase from the previous number of 365,090.000 THB mn for 2013. Thailand Household Assets: NR: Financial: Saving data is updated yearly, averaging 368,996.000 THB mn from Dec 2013 (Median) to 2015, with 2 observations. The data reached an all-time high of 372,902.000 THB mn in 2015 and a record low of 365,090.000 THB mn in 2013. Thailand Household Assets: NR: Financial: Saving data remains active status in CEIC and is reported by National Statistical Office. The data is categorized under Global Database’s Thailand – Table TH.G042: Household Income & Assets Statistics.
001 -- Assets, liabilities and income of households by age of reference person in 1987 - 2016
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This dataset contains the all-India, quarter wise flow of financial assets and liabilities and net financial assets of the households.
The survey interviewed 254 retailer shops in 10 sub-cities of Addis Ababa. 30 supermarkets, 20 mini-markets, 100 regular shops, 80 dairy shops and 24 open market shops selling dairy products were interviewed. Details of the sampling strategy is found in the attachment. The survey collected information on the characteristics of the shop, details of dairy products sold, prices and quality. Policy makers, research, and other stakeholders can use this data to analyses dairy value chain in Ethiopia and dairy retailing practices in Ethiopia. This data set was collected through research of the project “Improving the evidence and policies for better performing livestock systems in Ethiopia” lead by the International Food Policy Research Institution as part of the Feed the Future Innovation Lab for Livestock Systems.