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This table aims to show the distribution of welfare of persons in the Netherlands, measured by their income. The figures in this table are broken down to different person characteristics.
The population consists of all persons in private households with income on January 1st of the reporting year. In the population for the subject low-income persons, persons in both student households and households with income only for a part of the year have been excluded. The population for the subject economic independence consists of all persons aged from 15 to the OAP-age in private households with income on January 1st of the reporting year, except for students and pupils.
Data available from: 2011
Status of the figures: The figures for 2011 to 2022 are final. The figures for 2023 are preliminary.
Changes as of November 2024: The preliminary figures for 2023 have been added.
When will new figures be published? New figures will be published in the fall of 2025.
https://www.ontario.ca/page/open-government-licence-ontariohttps://www.ontario.ca/page/open-government-licence-ontario
Data prior to April 1998 includes recipients of:
Data from April 1, 1998 onward includes recipients of:
Introduction
Animal welfare is important because there are so many animals around the world suffering from being used for entertainment, food, medicine, fashion, scientific advancement, and as exotic pets. Every animal deserves to have a good life where they enjoy the benefits of the Five Domains.
About Dataset
We aim to reduce total suffering, society’s ability to reduce this in other animals – which feel pain, too – also matters.
This is especially true when we look at the numbers: every year, humans slaughter more than 80 billion land-based animals for farming alone. Most of these animals are raised in factory farms, often in painful and inhumane conditions.
Estimates for fish are more uncertain, but when we include them, these numbers more than double.
These numbers are large – but this also means that there are large opportunities to alleviate animal suffering by reducing the number of animals we use for food, science, cosmetics, and other industries and improving the living conditions of those we continue to raise.
On this page, you can find all of our data, and writing on animal welfare.
File 1: The estimated number of animal lives that go toward each kilogram of animal product purchased for retail sale, including direct deaths only. For example, the pork numbers include only the deaths of pigs slaughtered for food.
File 2: The estimated number of animal lives that go toward each kilogram of animal product purchased for retail sale, including direct and indirect deaths. For example, the pork numbers include the deaths of pigs slaughtered for food (direct) but also those who die pre-slaughter and feed fish given to those pigs (indirect).
File 3: The estimated quantity of edible meat produced per animal, measured in kilograms.
File 4: Different location on time span = 2013 - 2020
File 5: Share of hens in cages Share of hens housed in a barn or aviary Share of non-organic, free-range hens Share of organic, free-range hens Share of laying hens in unknown housing
File 6: Number of eggs from hens in organic, free-range farms Number of eggs from hens in non-organic, free-range farms Number of eggs from hens in barns Number of eggs from hens in (enriched) cages
File 7: Estimated number of farmed decapod crustaceans Estimated number of farmed decapod crustaceans (upper bound) Estimated number of decapod crustaceans (lower bound)
File 8: Estimated number of farmed fish Estimated number of farmed fish (upper bound) Estimated number of farmed fish (lower bound)
File 9: Share of cage-free eggs Share of all eggs that are produced in cage-free housing systems. This includes barns, pasture and free-range (non-organic and organic) eggs.
Lets diving in dataset and create some excellent notebook for visualization and types of prediction. So, Good luck.
By Hannah Ritchie, Pablo Rosado and Max Roser (Our world in data)
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This table provides information about the number of young people aged 0 to 25 living in a family on social assistance. For the demarcation of a family on social assistance, we looked at social assistance benefits under the Participation Act or social assistance-related benefits under the Income Provisions for Older and Partially Disabled Unemployed Employees Act (IOAW), the Income Provisions for Older and Partially Disabled Former Self-employed Persons Act (IOAZ), the Decree on assistance for the self-employed (Bbz) and the Work and Income Artists Act (WWIK). Payments under the Temporary Bridging Scheme for Self-Employed Entrepreneurs (Tozo) are not included in this table. The Tozo provides the self-employed with an additional living allowance and a loan for working capital to deal with liquidity problems as a result of the corona crisis. This table shows status figures (December 31 of the reference year) for young people growing up in a social assistance family or families, which can be broken down by region (e.g. municipality level), type of household and the age of the youngest child in the household. In order to show how young people in the Netherlands are doing, the National Youth Monitor describes more than 70 topics in addition to this topic. The subjects are called indicators. Data available from 2007 to 2020. Status of the figures The figures for all years are final. Changes as of August 18, 2022: None, this table has been discontinued. Changes as of 19 January 2022: The provisional figures for 2020 have been replaced by definitive figures. . The method for demarcating welfare families will be revised in 2021. Previously, only persons with general assistance and BBZ were taken. The new demarcation also includes the assistance-related benefits IOAZ, IOAW and WWIk. In addition, the new definition specifies more precisely that it concerns the parent of the child who receives a social assistance benefit. Previously, a person was determined to live in a particular household, regardless of position in the household. If someone in that household received a social assistance benefit, the children in the same household belong to a social assistance family. In the new demarcation, it was examined whether there is a parent-child relationship within a household and on that basis it was determined whether a child belongs to a family on social assistance. The figures have been retroactively calculated for previous years using this new demarcation. When will new numbers come out? Not applicable anymore.
The conventional wisdom maintains that whites’ racial predispositions are exogenous to their views of welfare. Against this position, scattered studies report that prejudice moves in response to new information about policies and groups. Likewise, theories of mediated intergroup contact propose that when individuals encounter messages about racial outgroups their levels of prejudice may wax or wane. In conjunction, these lines of work suggest that whites update their global views of blacks based on how they feel about people on welfare. The current paper tests this “prejudice revision” hypothesis with data from “welfare mother” vignettes embedded on national surveys administered in 1991, 2014, and 2015 and ANES panel data from the 1990s. The results indicate that views of welfare recipients systematically affect racial stereotypes, racial resentment, individualistic explanations for racial inequality, and structural explanations for racial inequality. Prejudice, in short, is endogenous to welfare attitudes.
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Analysis of ‘Unemployment and mental illness survey’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/michaelacorley/unemployment-and-mental-illness-survey on 28 January 2022.
--- Dataset description provided by original source is as follows ---
This is a paid research survey to explore the linkage between mental illness and unemployment. NAMI has conducted multiple surveys verifying the high unemployment rate among those with mental illness, but this is the only survey to date which targets causation (why they are unemployed). Statistical significance of the variance has long since been proven by previous, larger samples.
You are free to visualize and publish results, please just credit me by name.
I received several messages about methodology of collection because various people would like to use this data for papers.
I paid respondents on Survey Monkey in a general population sampling. I did not target any specific demographic as not to get skewed results. Survey Monkey stratifies the sample according to certain characteristics like income and location.
I know that the general population sampling went well because the number of people self identifying as having a mental illness is consistent with larger samples.
Although we disqualified people without a mental illness, they were still given the complete survey. That means that the data contains sampling of people with and without mental illness and a yes/no indicator.
***Sample size:** n = 334; 80 w/ mental illness - this proportion is approximately equal to estimates of the general population diagnosed with mental illness (typically estimated at 20-25% according to various studies).*
Questions:
I identify as having a mental illness Response
Education Response
I have my own computer separate from a smart phone Response
I have been hospitalized before for my mental illness Response
How many days were you hospitalized for your mental illness Open-Ended Response
I am currently employed at least part-time Response
I am legally disabled Response
I have my regular access to the internet Response
I live with my parents Response
I have a gap in my resume Response
Total length of any gaps in my resume in months. Open-Ended Response
Annual income (including any social welfare programs) in USD Open-Ended Response
I am unemployed Response
I read outside of work and school Response
Annual income from social welfare programs Open-Ended Response
I receive food stamps Response
I am on section 8 housing Response
How many times were you hospitalized for your mental illness Open-Ended Response
I have one of the following issues in addition to my illness:
Lack of concentration
Anxiety
Depression
Obsessive thinking
Mood swings
Panic attacks
Compulsive behavior
Tiredness
Age Response
Gender Response
Household Income Response
Region Response
Device Type Response
When comparing the actual rate to government statistics, it is important to take into account the labor force participation rate (the % of people who are legally considered to be in the workforce). People not included in the unemployment statistic, like discouraged workers (for example the mentally ill) will be "not participating" in the workforce.
--- Original source retains full ownership of the source dataset ---
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This table aims to show the distribution of welfare of private households, measured by their income, expenditures and wealth. The figures in this table are broken down to different household characteristics.
The population consists of all private households with income on January 1st of the reporting year. In the population for the subject low-income households, both student households and households with income only for a part of the year have been excluded.
Data available from: 2011
Status of the figures: The figures for 2011 to 2022 are final. The figures for 2023 are preliminary.
Changes as of 1 November 2024: Figures for 2022 are finalized. Preliminary figures for 2023 are added.
Changes as of 9 February 2022: The preliminary figures for 2020 concerning ‘Mean expenditures’ have been added. The topic 'Mean expenditures' only contains 5-annual data, for 2015 and 2020. The data for 2015 for this topic were still preliminary and are now final.
When will new figures be published? New figures will be published in the fall of 2025.
Attribution 3.0 (CC BY 3.0)https://creativecommons.org/licenses/by/3.0/
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The DSS Payment Demographic data set is made up of:
Selected DSS payment data by
Geography: state/territory, electorate, postcode, LGA and SA2 (for 2015 onwards)
Demographic: age, sex and Indigenous/non-Indigenous
Duration on Payment (Working Age & Pensions)
Duration on Income Support (Working Age, Carer payment & Disability Support Pension)
Rate (Working Age & Pensions)
Earnings (Working Age & Pensions)
Age Pension assets data
JobSeeker Payment and Youth Allowance (other) Principal Carers
Activity Tested Recipients by Partial Capacity to Work (NSA,PPS & YAO)
Exits within 3, 6 and 12 months (Newstart Allowance/JobSeeker Payment, Parenting Payment, Sickness Allowance & Youth Allowance)
Disability Support Pension by medical condition
Care Receiver by medical conditions
Commonwealth Rent Assistance by Payment type and Income Unit type have been added from March 2017. For further information about Commonwealth Rent Assistance and Income Units see the Data Descriptions and Glossary included in the dataset.
From December 2022, the "DSS Expanded Benefit and Payment Recipient Demographics – quarterly data" publication has introduced expanded reporting populations for income support recipients. As a result, the reporting population for Jobseeker Payment and Special Benefit has changed to include recipients who are current but on zero rate of payment and those who are suspended from payment. The reporting population for ABSTUDY, Austudy, Parenting Payment and Youth Allowance has changed to include those who are suspended from payment. The expanded report will replace the standard report after June 2023.
Additional data for DSS Expanded Benefit and Payment Recipient Demographics – quarterly data includes:
• A new contents page to assist users locate the information within the spreadsheet
• Additional data for the ‘Suspended’ population in the ‘Payment by Rate’ tab to enable users to calculate the old reporting rules.
• Additional information on the Employment Earning by ‘Income Free Area’ tab.
From December 2022, Services Australia have implemented a change in the Centrelink payment system to recognise gender other than the sex assigned at birth or during infancy, or as a gender which is not exclusively male or female. To protect the privacy of individuals and comply with confidentialisation policy, persons identifying as ‘non-binary’ will initially be grouped with ‘females’ in the period immediately following implementation of this change. The Department will monitor the implications of this change and will publish the ‘non-binary’ gender category as soon as privacy and confidentialisation considerations allow.
Local Government Area has been updated to reflect the Australian Statistical Geography Standard (ASGS) 2022 boundaries from June 2023.
Commonwealth Electorate Division has been updated to reflect the Australian Statistical Geography Standard (ASGS) 2021 boundaries from June 2023.
SA2 has been updated to reflect the Australian Statistical Geography Standard (ASGS) 2021 boundaries from June 2023.
From December 2021, the following are included in the report:
selected payments by work capacity, by various demographic breakdowns
rental type and homeownership
Family Tax Benefit recipients and children by payment type
Commonwealth Rent Assistance by proportion eligible for the maximum rate
an age breakdown for Age Pension recipients
For further information, please see the Glossary.
From June 2021, data on the Paid Parental Leave Scheme is included yearly in June releases. This includes both Parental Leave Pay and Dad and Partner Pay, across multiple breakdowns. Please see Glossary for further information.
From March 2017 the DSS demographic dataset will include top 25 countries of birth. For further information see the glossary.
From March 2016 machine readable files containing the three geographic breakdowns have also been published for use in National Map, links to these datasets are below:
Pre June 2014 Quarter Data contains:
Selected DSS payment data by
Geography: state/territory; electorate; postcode and LGA
Demographic: age, sex and Indigenous/non-Indigenous
Note: JobSeeker Payment replaced Newstart Allowance and other working age payments from 20 March 2020, for further details see: https://www.dss.gov.au/benefits-payments/jobseeker-payment
For data on DSS payment demographics as at June 2013 or earlier, the department has published data which was produced annually. Data is provided by payment type containing timeseries’, state, gender, age range, and various other demographics. Links to these publications are below:
Concession card data in the March and June 2020 quarters have been re-stated to address an over-count in reported cardholder numbers.
28/06/2024 – The March 2024 and December 2023 reports were republished with updated data in the ‘Carer Receivers by Med Condition’ section, updates are exclusive to the ‘Care Receivers of Carer Payment recipients’ table, under ‘Intellectual / Learning’ and ‘Circulatory System’ conditions only.
In Brussels as in the rest of Belgium, many people do not have sufficient means of subsistence. In some circumstances, these people may qualify for welfare benefits. These are aimed at ensuring that all the population has a minimum income. Visit also the Monitoring des Quartiers website, which offers a range of indicators for the 145 districts within the Brussels-Capital Region ➜ https://monitoringdesquartiers.brussels/
The Russia Longitudinal Monitoring Survey (RLMS) is a series of nationally representative surveys designed to monitor the effects of Russian reforms on the health and economic welfare of households and individuals in the Russian Federation. These effects are measured by a variety of means: detailed monitoring of individuals' health status and dietary intake, precise measurement of household-level expenditures and service utilization, and collection of relevant community-level data, including region-specific prices and community infrastructure data. Phase II data have been collected annually (with two exceptions) since 1994. The project has been run jointly by the Carolina Population Center at the University of North Carolina at Chapel Hill, headed by Barry M. Popkin, and the Demoscope team in Russia, headed by Polina Kozyreva and Mikhail Kosolapov. Please note The sample size in 2014 was cut by about 20%, because the cost of the project increased due to inflation, but financial support remained the same. The original 1994 sample remained the same, and all cuts applied only to the part of the sample which was added in 2010. It should be stated that the implemented procedure of cutting the sample size guarantees that the smaller sample is still representative at the national level. To lower the cost it was also decided to dro p the Educational Expenses section from the HH questionnaire, which was added back in 2010. Household Data For the household interview, a single member of the household was asked questions that pertained to the entire family. The respondent was usually the oldest living woman in the home since she was available to be interviewed during the daytime. Any attempt to identify one person as the "household head" is as problematic in Russia as it is in the United States. Thus, the interviewer was instructed to speak with "the person who knows the most about this family's shop ping and health." Individual Data In theory, the individual questionnaire is administered to every person living in the household. In practice, however, some individuals, such as very young children and elderly people, did not receive an individual interview. Individual-level information is the primary source of information pertaining to a person's health, employment status, demographic characteristics, and anthropometry. It can also be used to supplement household-level income an d expenditure information. To safeguard the confidentiality of RLMS respondents, individual-level data sets omit text variables (designated char on questionnaires). Please note that almost all text variables exist in Russian only. English translations exist for only a few of these variables. Please contact us to check on the availability of English translations of specific variables of interest.
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This table provides information about the number of young people aged 0 to 18 living in a family on social assistance. This table shows status figures (December 31 of the reference year) for 'children growing up in a family on social assistance', with a breakdown by region (including municipal level). For the regional breakdown by municipality, for the years 2009 up to and including 2011, and from 2015, the municipal breakdown as of 1 January of the year following the year under review is used. For the years 2012 to 2014, this concerns the municipal division as of 1 January 2014. To show how young people in the Netherlands are doing, the National Youth Monitor describes more than 70 subjects in addition to this subject. The subjects are called indicators. Data available for 2004 up to and including 2019. Status of the figures These are final figures. Changes as of October 8, 2021: None, this table has been discontinued. When will new numbers come out? Not applicable anymore.
These data are monthly listings of households, recipients and expenditures for the Supplemental Nutrition Assistance Program.
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This survey inquiring the Swedish people´s attitudes towards their taxes was financed by the ministry of finance through the ´Kommittén för utvärdering av skattereformen´ (KUSK). For a number of statements occurring in the current Swedish debate the respondents had to state if they agreed or not. Other questions dealt with their opinion on public expenses and social service; if they wanted the amount of tax money spent on a number of different areas to increase, decrease or to be kept unchanged; how to finance education, medical service, child care and care of the elderly; and best suited to take care of education, medical service, child care, care of elderly, and social welfare. Furthermore, the respondents had to state how to distribute the responsibility for financing social insurances between the individual and the public sector; how common they believe it to be that social security benefits and social care services are misused. Respondents also had to report their own experiences of social services during the last three years. A number of questions dealt with opinions on the Swedish taxes; the pressure of taxation in general and the respondents´ judgement on total taxation for recipients of high, middle and low incomes; and opinion on the tax reform introduced in 1991. Socio-economic background information include occupation, trade union affiliation, education, housing, income, marital status, spouse´s occupation and income, number of children, citizenship of parents, and political preferences.
Abstract copyright UK Data Service and data collection copyright owner.
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Although there is evidence for the generosity of high‑status individuals, there seems to be a strong perception that the elites are selfish and contribute little to others’ welfare, and even less so than poorer people. We argue that this perception may derive from a gap between normative and empirical expectations regarding the behavior of the elites. Using large‑scale survey experiments, we show that high‑status individuals are held to higher ethical standards in both the US and China, and that there is a strong income gradient in normatively expected generosity. We also present evidence for a gap between people’s normative expectations of how the rich should behave, and their empirical expectations of how they actually do: empirical expectations are generally lower than both normative expectations and actual giving.
https://www.ontario.ca/page/open-government-licence-ontariohttps://www.ontario.ca/page/open-government-licence-ontario
This data includes the characteristics of Ontario Works and Ontario Disability Support Program cases, by census metropolitan area, and by the province including:
A census metropolitan area (CMA) is formed by one or more adjacent municipalities centred on a population centre (known as the core). A CMA must have a total population of at least 100,000 of which 50,000 or more must live in the core.
*[CMA]: census metropolitan area
http://opendatacommons.org/licenses/dbcl/1.0/http://opendatacommons.org/licenses/dbcl/1.0/
In Japan, there are more and more elderly people who need to get a care service. Japanese government's budget related to the social care have been increasing and more complicated. This dataset shows the details about a long term care system in Japan.
This dataset is provided by Ministry of Health, Labour and Welfare.
This dataset contributes to understand the system of long term care insurance and our next generation.
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License information was derived automatically
Analysis of ‘COVID-19 dataset in Japan’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/lisphilar/covid19-dataset-in-japan on 28 January 2022.
--- Dataset description provided by original source is as follows ---
This is a COVID-19 dataset in Japan. This does not include the cases in Diamond Princess cruise ship (Yokohama city, Kanagawa prefecture) and Costa Atlantica cruise ship (Nagasaki city, Nagasaki prefecture). - Total number of cases in Japan - The number of vaccinated people (New/experimental) - The number of cases at prefecture level - Metadata of each prefecture
Note: Lisphilar (author) uploads the same files to https://github.com/lisphilar/covid19-sir/tree/master/data
This dataset can be retrieved with CovsirPhy (Python library).
pip install covsirphy --upgrade
import covsirphy as cs
data_loader = cs.DataLoader()
japan_data = data_loader.japan()
# The number of cases (Total/each province)
clean_df = japan_data.cleaned()
# Metadata
meta_df = japan_data.meta()
Please refer to CovsirPhy Documentation: Japan-specific dataset.
Note: Before analysing the data, please refer to Kaggle notebook: EDA of Japan dataset and COVID-19: Government/JHU data in Japan. The detailed explanation of the build process is discussed in Steps to build the dataset in Japan. If you find errors or have any questions, feel free to create a discussion topic.
covid_jpn_total.csv
Cumulative number of cases:
- PCR-tested / PCR-tested and positive
- with symptoms (to 08May2020) / without symptoms (to 08May2020) / unknown (to 08May2020)
- discharged
- fatal
The number of cases: - requiring hospitalization (from 09May2020) - hospitalized with mild symptoms (to 08May2020) / severe symptoms / unknown (to 08May2020) - requiring hospitalization, but waiting in hotels or at home (to 08May2020)
In primary source, some variables were removed on 09May2020. Values are NA in this dataset from 09May2020.
Manually collected the data from Ministry of Health, Labour and Welfare HP:
厚生労働省 HP (in Japanese)
Ministry of Health, Labour and Welfare HP (in English)
The number of vaccinated people:
- Vaccinated_1st
: the number of vaccinated persons for the first time on the date
- Vaccinated_2nd
: the number of vaccinated persons with the second dose on the date
- Vaccinated_3rd
: the number of vaccinated persons with the third dose on the date
Data sources for vaccination: - To 09Apr2021: 厚生労働省 HP 新型コロナワクチンの接種実績(in Japanese) - 首相官邸 新型コロナワクチンについて - From 10APr2021: Twitter: 首相官邸(新型コロナワクチン情報)
covid_jpn_prefecture.csv
Cumulative number of cases:
- PCR-tested / PCR-tested and positive
- discharged
- fatal
The number of cases: - requiring hospitalization (from 09May2020) - hospitalized with severe symptoms (from 09May2020)
Using pdf-excel converter, manually collected the data from Ministry of Health, Labour and Welfare HP:
厚生労働省 HP (in Japanese)
Ministry of Health, Labour and Welfare HP (in English)
Note:
covid_jpn_prefecture.groupby("Date").sum()
does not match covid_jpn_total
.
When you analyse total data in Japan, please use covid_jpn_total
data.
covid_jpn_metadata.csv
- Population (Total, Male, Female): 厚生労働省 厚生統計要覧(2017年度)第1-5表
- Area (Total, Habitable): Wikipedia 都道府県の面積一覧 (2015)
Hospital_bed: With the primary data of 厚生労働省 感染症指定医療機関の指定状況(平成31年4月1日現在), 厚生労働省 第二種感染症指定医療機関の指定状況(平成31年4月1日現在), 厚生労働省 医療施設動態調査(令和2年1月末概数), 厚生労働省 感染症指定医療機関について and secondary data of COVID-19 Japan 都道府県別 感染症病床数,
Clinic_bed: With the primary data of 医療施設動態調査(令和2年1月末概数) ,
Location: Data is from LinkData 都道府県庁所在地 (Public Domain) (secondary data).
Admin
To create this dataset, edited and transformed data of the following sites was used.
厚生労働省 Ministry of Health, Labour and Welfare, Japan:
厚生労働省 HP (in Japanese)
Ministry of Health, Labour and Welfare HP (in English)
厚生労働省 HP 利用規約・リンク・著作権等 CC BY 4.0 (in Japanese)
国土交通省 Ministry of Land, Infrastructure, Transport and Tourism, Japan: 国土交通省 HP (in Japanese) 国土交通省 HP (in English) 国土交通省 HP 利用規約・リンク・著作権等 CC BY 4.0 (in Japanese)
Code for Japan / COVID-19 Japan: Code for Japan COVID-19 Japan Dashboard (CC BY 4.0) COVID-19 Japan 都道府県別 感染症病床数 (CC BY)
Wikipedia: Wikipedia
LinkData: LinkData (Public Domain)
Kindly cite this dataset under CC BY-4.0 license as follows. - Hirokazu Takaya (2020-2022), COVID-19 dataset in Japan, GitHub repository, https://github.com/lisphilar/covid19-sir/data/japan, or - Hirokazu Takaya (2020-2022), COVID-19 dataset in Japan, Kaggle Dataset, https://www.kaggle.com/lisphilar/covid19-dataset-in-japan
--- Original source retains full ownership of the source dataset ---
Occupation describes the kind of work a person does on the job. Occupation data were derived from answers to questions 45 and 46 in the 2015 American Community Survey (ACS). Question 45 asks: “What kind of work was this person doing?” Question 46 asks: “What were this person’s most important activities or duties?”
These questions were asked of all people 15 years old and over who had worked in the past 5 years. For employed people, the data refer to the person’s job during the previous week. For those who worked two or more jobs, the data refer to the job where the person worked the greatest number of hours. For unemployed people and people who are not currently employed but report having a job within the last five years, the data refer to their last job.
These questions describe the work activity and occupational experience of the American labor force. Data are used to formulate policy and programs for employment, career development, and training; to provide information on the occupational skills of the labor force in a given area to analyze career trends; and to measure compliance with antidiscrimination policies. Companies use these data to decide where to locate new plants, stores, or offices.
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Poverty is not a simple matter. He is not only related to income. Poverty is associated as well to the lack of fundamental rights to develop and maintain a more dignified life. One of the basic rights of poor people in inherent rights is to have the human value, to be audible voice. Even when defining the "poor", they must be given space to define their poverty with their own perspective and mind. On the other hand, some of the various poverty reduction programs that have been done in Indonesia, were not exactly targeted, so that often raise conflicts among people, and between communities with the government. Incomplete data and wrong targeting people suspected as some of the causes of these problems. So they who should be the target and get the help do not receive it, and vice versa, they who had not been feasible receive the donation. Targeting becomes priority for programs of social assistance for poor families. In order to provide better targeting results, it needs to search a better indicator or the effective method to improve the identification of the target households who are feasible for various assistance programs that will be implemented in the future period. This activity is called then the Determination of the Household Welfare Ranking 2008 (P2K08). This method namely the Determination of the households Welfare Ranking 2008 (P2K08) from the most insecure to the most secure combines participatory approaches and statistical test. District, city and village and village elected in the application of this method are determined randomly.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This table aims to show the distribution of welfare of persons in the Netherlands, measured by their income. The figures in this table are broken down to different person characteristics.
The population consists of all persons in private households with income on January 1st of the reporting year. In the population for the subject low-income persons, persons in both student households and households with income only for a part of the year have been excluded. The population for the subject economic independence consists of all persons aged from 15 to the OAP-age in private households with income on January 1st of the reporting year, except for students and pupils.
Data available from: 2011
Status of the figures: The figures for 2011 to 2022 are final. The figures for 2023 are preliminary.
Changes as of November 2024: The preliminary figures for 2023 have been added.
When will new figures be published? New figures will be published in the fall of 2025.