This dataset summarizes the number of dependent children (less than 18 years old) removed from households due to parental drug abuse. The data indicates if the dependent children were placed in kinship care or not. The total number of children in this data set are provided by the U.S. Census Bureau’s American Community Survey (ACS), which publishes 5 year estimates of the population. The most recent year of entries in this data set may be available before the corresponding ACS population estimates for that year are published. In that case, the data set uses values from the most recently published ACS estimates and notes the year from which those estimates are pulled. These values are updated once the Census Bureau releases the most recent estimates.” *Kinship care refers to the care of children by relatives or, in some jurisdictions, close family friends (often referred to as fictive kin). Relatives are the preferred resource for children who must be removed from their birth parents because it maintains the children's connections with their families. *The Adoption and Foster Care Analysis and Reporting System (AFCARS) definition of parental drug abuse is “Principal caretaker’s compulsive use of drugs that is not of a temporary nature.”

1) Data Introduction • The Income Classification dataset provides data extracted from the U.S. Census Bureau database, aimed at predicting whether an individual's income exceeds $50,000 per year. This dataset is commonly known as the "Adult" dataset and includes features such as age, work class, education, marital status, occupation, race, gender, native-country, and others.

2) Data Utilization (1) Income data has characteristics that: • It includes both continuous and categorical data, enabling various types of analysis to understand the economic demographics of the U.S. • The dataset is often used in predictive modeling to forecast income levels based on demographic and employment information. (2) Income data can be used to: • Economic Research: Analysts use this dataset to study income distribution and the factors affecting economic disparities. • Policy Making: Helps policymakers design more effective social welfare programs targeting low-income families.

USER NOTE: This database no longer contains the most up-to-date information. Some errors and missing data from the previous years have been fixed in the most recent data release in the CCDF Policies Database Series. The most recent release is a cumulative file which includes the most accurate version of this and all past years' data. Please do not use this study's data unless you are attempting to replicate the analysis of someone who specifically used this version of the CCDF Policies Database. For any other type of analysis, please use the most recent release in the CCDF Policies Database Series. The Child Care and Development Fund (CCDF) provides federal money to States and Territories to provide assistance to low-income families receiving or in transition from temporary public assistance, to obtain quality child care so they can work, attend training, or receive education. Within the broad federal parameters, states and territories set the detailed policies. Those details determine whether a particular family will or will not be eligible for subsidies, how much the family will have to pay for the care, how families apply for and retain subsidies, the maximum amounts that child care providers will be reimbursed, and the administrative procedures that providers must follow. Thus, while CCDF is a single program from the perspective of federal law, it is in practice a different program in every state and territory. The CCDF Policies Database project is a comprehensive, up-to-date database of inter-related sources of CCDF policy information that support the needs of a variety of audiences through (1) Analytic Data Files and (2) a Book of Tables. These are made available to researchers, administrators, and policymakers with the goal of addressing important questions concerning the effects of alternative child care subsidy policies and practices on the children and families served, specifically parental employment and self-sufficiency, the availability and quality of care, and children's development. A description of the Data Files and Book of Tables is provided below: 1. Detailed, longitudinal Analytic Data Files of CCDF policy information for all 50 States, the District of Columbia, and United States Territories that capture the policies actually in effect at a point in time, rather than proposals or legislation. They focus on the policies in place at the start of each fiscal year, but also capture changes during that fiscal year. The data are organized into 32 categories with each category of variables separated into its own dataset. The categories span five general areas of policy including: Eligibility Requirements for Families and Children (Datasets 1-5) Family Application, Terms of Authorization, and Redetermination (Datasets 6-13) Family Payments (Datasets 14-18) Policies for Providers, Including Maximum Reimbursement Rates (Datasets 19-27) Overall Administrative and Quality Information Plans (Datasets 28-32) The information in the Data Files is based primarily on the documents that caseworkers use as they work with families and providers (often termed "caseworker manuals"). The caseworker manuals generally provide much more detailed information on eligibility, family payments, and provider-related policies than the documents submitted by states and territories to the federal government. The caseworker manuals also provide ongoing detail for periods in between submission dates. Each dataset contains a series of variables designed to capture the intricacies of the rules covered in the category. The variables include a mix of categorical, numeric, and text variables. Every variable has a corresponding notes field to capture additional details related to that particular variable. In addition, each category has an additional notes field to capture any information regarding the rules that is not already outlined in the category's variables. 2. The Book of Tables is available as seven datasets (Datasets 33-39) and they present key aspects of the differences in CCDF funded programs across all states and territories as of October 1, 2015. The Book of Tables includes variables that are calculated using several variables from the Data Files (Datasets 1-32). The Book of Tables summarizes a subset of the information available in the Data Files, and includes information about eligibility requirements for families; application,

This table provides an overview of the key figures on health and care available on StatLine. All figures are taken from other tables on StatLine, either directly or through a simple conversion. In the original tables, breakdowns by characteristics of individuals or other variables are possible. The period after the year of review before data become available differs between the data series. The number of exam passes/graduates in year t is the number of persons who obtained a diploma in school/study year starting in t-1 and ending in t.

Data available from: 2001

Status of the figures:

2024: Most available figures are definite. Figures are provisional for: - causes of death; - youth care; - persons employed in health and welfare; - persons employed in healthcare; - Mbo health care graduates; - Hbo nursing graduates / medicine graduates (university).

2023: Most available figures are definite. Figures are provisional for: - perinatal mortality at pregnancy duration at least 24 weeks; - diagnoses known to the general practitioner; - hospital admissions by some diagnoses; - average period of hospitalisation; - supplied drugs; - AWBZ/Wlz-funded long term care; - physicians and nurses employed in care; - persons employed in health and welfare; - average distance to facilities; - profitability and operating results at institutions. Figures are revised provisional for: - expenditures on health and welfare.

2022: Most available figures are definite. Figures are revised provisional for: - expenditures on health and welfare.

2021: Most available figures are definite, Figures are revised provisional for: - expenditures on health and welfare.f

2020 and earlier: All available figures are definite.

Changes as of 4 July 2025: More recent figures have been added for: - causes of death; - life expectancy; - life expectancy in perceived good health; - self-perceived health; - hospital admissions by some diagnoses; - sickness absence; - average period of hospitalisation; - contacts with health professionals; - youth care; - smoking, heavy drinkers, physical activity; - overweight; - high blood pressure; - physicians and nurses employed in care; - persons employed in health and welfare; - persons employed in healthcare; - Mbo health care graduates; - Hbo nursing graduates / medicine graduates (university); - expenditures on health and welfare; - profitability and operating results at institutions.

Changes as of 18 december 2024: - Distance to facilities: the figures withdrawn on 5 June have been replaced (unchanged). - Youth care: the previously published final results for 2021 and 2022 have been adjusted due to improvements in the processing. - Due to a revision of the statistics Expenditure on health and welfare 2021, figures for expenditure on health and welfare care have been replaced from 2021 onwards. - Due to the revision of the National Accounts, the figures on persons employed in health and welfare have been replaced for all years. - AWBZ/Wlz-funded long term care: from 2015, the series Wlz residential care including total package at home has been replaced by total Wlz care. This series fits better with the chosen demarcation of indications for Wlz care.

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

This study contains data files and documentation for the survey data from all four sites of the Project on Devolution and Urban Change (Urban Change, for short). This study examines the implementation and effects of Temporary Assistance for Needy Families (TANF) in four urban counties: Cuyahoga (Cleveland), Philadelphia, Miami-Dade, and Los Angeles. The study's focal period of the late 1990s through the early 2000s was one of prolonged economic expansion and unprecedented decline in unemployment. The study thus captures the most promising context for welfare reform: one of high labor market demand and ample resources to support families in the process of moving from welfare to work. The included data set is a concatenated version of the longitudinal client survey data used in the following MDRC publications: Welfare Reform in Cleveland: Implementation, Effects, and Experiences of Poor Families and Neighborhoods. (September 2002) Welfare Reform in Philadelphia: Implementation, Effects, and Experiences of Poor Families and Neighborhoods. (September 2003) Welfare Reform in Miami: Implementation, Effects, and Experiences of Poor Families and Neighborhoods. (June 2004) Welfare Reform in Los Angeles: Implementation, Effects, and Experiences of Poor Families and Neighborhoods. (August 2005) The files consist of one SAS data set containing responses to two waves of interviews on education, training, employment, family and household composition, housing, income, material hardship, welfare, health and health care, fertility and childbearing, parenting, child care, domestic violence, substance use, and demographic background. These data are a Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped for release, but not checked or processed.

The assessment of dairy cow welfare has become increasingly important in recent years. Welfare assessments that use animal-based indicators, which are considered the most direct indicators, are time consuming and therefore not feasible for assessments on a large number of farms. One approach to reducing this effort is the use of data-based indicators (DBIs) calculated from routine herd data. The aim of this study was to explore the relationship between common DBIs and the welfare of 35 dairy herds to evaluate the feasibility of a data-based welfare prediction method. For this purpose, the WelfareQuality® (WQ) protocol was used to assess the welfare of dairy cows on 35 Swiss farms, for each of which 10 commonly used DBIs were calculated from herd data. Spearman's rank correlations were used to investigate the relationship between DBIs and WQ criteria and measurements. The study found only a few statistically weak associations between DBIs and animal welfare, with no associations for measurements or criteria of resting comfort and appropriate behavior. Thus, the multidimensional welfare definition is insufficiently covered, and the present publication does not support the approach of a purely data-based prediction of dairy welfare status at the farm level. Instead, the regular calculation of DBIs that are indicative of isolated animal welfare problems or metrics of animal health could allow monitoring of these specific areas of animal welfare.

The Australian Priority Investment Approach to Welfare (PIA) policy initiative was established as part of the 2015-16 Budget, following a comprehensive review of Australia’s welfare system. The initiative uses data analysis to identify groups at risk of long-term welfare dependence. These analyses provide insights into how the system is working and uses those insights to find innovative ways of helping more Australians live independently of welfare. As part of the PIA, in September 2016, the Minister for Social Services announced a plan to allow limited public access to PIA data. A synthetic version of the PIA data has been created for use by researchers and teachers. The synthetic data relates to individuals who have made a claim for, are receiving or have received payments or services administered under social security law and family assistance law. This includes benefit types such as Aged Pension, Youth Allowance, Newstart and Disability Support Pension. The synthetic data contains a limited number of variables suitable for research, while maintaining the privacy and confidentiality of individuals. The synthetic dataset has been created by applying a privacy-preserving algorithm on the original PIA data. This process results in each person’s true data being modified such that the overall group data very closely represents that of the original dataset, yet no one individual’s data can be identified in the synthetic dataset. That is, each line of data that would normally represent an individual no longer does. The dataset is a combination of synthetic records that, when combined, reflect the shape of the original dataset. The synthetic PIA data contains a series of point-in-time quarterly snapshots dated from July 2001 to June 2015. This results in 56 separate quarters of administrative data. Each quarter includes 31 variables (available in the ‘PIA Data Dictionary – Variable and Codes’ file) that are consistent across all quarters. There are approximately 5 million individual records in each quarter.

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.

Locations of Welfare Offices and Programs in Los Angeles CountyThis dataset is maintained through the County of Los Angeles Location Management System. The Location Management System is used by the County of Los Angeles GIS Program to maintain a single, comprehensive geographic database of locations countywide. For more information on the Location Management System, visit http://egis3.lacounty.gov/lms/.

The COVID-19 pandemic has brought about massive declines in well-being around the world. This paper seeks to quantify and compare two important components of those losses—increased mortality and higher poverty—using years of human life as a common metric. The paper estimates that almost 20 million life-years were lost to COVID-19 by December 2020. Over the same period and by the most conservative definition, more than 120 million additional years were spent in poverty because of the pandemic. The mortality burden, whether estimated in lives or years of life lost, increases sharply with gross domestic product per capita. By contrast, the poverty burden declines with per capita national income when a constant absolute poverty line is used, or is uncorrelated with national income when a more relative approach is taken to poverty lines. In both cases, the poverty burden of the pandemic, relative to the mortality burden, is much higher for poor countries. The distribution of aggregate welfare losses—combining mortality and poverty and expressed in terms of life-years —depends on the choice of poverty line(s) and the relative weights placed on mortality and poverty. With a constant absolute poverty line and a relatively low welfare weight on mortality, poorer countries are found to bear a greater welfare loss from the pandemic. When poverty lines are set differently for poor, middle-income, and high-income countries and/or a greater welfare weight is placed on mortality, upper-middle-income and rich countries suffer the most.

This database was created as preparatory work for the Scientific Opinion on the assessment of dairy cow welfare in small-scale farming systems (EFSA, 2015) to collect data for the description and the categorisation of European Small-Scale Dairy Farms (SSDF) based on size, farming system and husbandry practices and (ii) to analyse the feasibility in SSDF of animal-based measures usually used for intensive farming. The Scientific Opinion was necessary to address specific expectations of consumers on locally produced food and acceptable animal welfare conditions in the context of the EU Strategy for the protection and welfare of animals 2012-2015.

The on-farm survey was run to collect data for welfare assessment covering Austria, France, Italy and Spain. A total of 124 farms with up to 75 cows were selected based on three criteria reflecting use of local resources or enrolment in a certification scheme: (1) the type of enterprise (ownership and workers), (2) the use of inputs in the production process, including the use of local feed and local breeds, and (3) the production type (certification schemes). From 124 dairy farms visited 119 were considered as SSDF. Among the 119 farms included in the survey as non-conventional, some of them had a very small herd size (44 had less than 25 cows and one had only 10 cows) and some of them had more animals (19 farms had between 51 and 75 dairy cows).

The database includes 53 continuous and categorical farm descriptor variables, 23 continuous and categorical risk-factor variables and 47 animal-based measures in small-scale farms. The final data model used was based on data collection at farm/herd level, pen level and animal level.

Welfare and Services in Finland is a survey that combines telephone and face-to-face interviews, postal surveys and register data. The aim of the study is to offer up-to-date, reliable and extensive research data on Finnish welfare and the use of welfare services. This dataset contains face-to-face survey aimed at the elderly. Main topics included housing, economic circumstances, health and health services, need for care and assistance, informal care, social networks, and quality of life. Relating to housing, questions charted housing tenure, number of rooms, floor area, plans of moving to some other housing, and the best housing alternative for elderly people who require care and assistance. Some questions studied the respondents' economic circumstances, for example, savings and ability to pay for food, medicine etc. They were also asked whether different services were close enough to their home (e.g. grocery shop, bank). Relating to health and health services, questions were asked about health status, limiting long-term illnesses or disabilities and their impact on daily life, exercise habits, alcohol consumption, and visits to a doctor, nurse or hospital in the previous 12 months. Further questions probed where the respondents would primarily try to get a doctor's appointment during daytime, whether they had been in hospital as an inpatient in the previous 12 months, and whether they had received sufficient care for health problems. Perceptions of the quality of public and private health services were surveyed. Need for care and assistance was charted by asking about managing with daily activities without help, help received for different activities, person or organisation that helped the respondents the most, services used in the previous 12 months and sufficiency of the services, financial problems caused by service fees, and person or organisation from whom the respondents would ask information regarding health and available services. Further questions studied whether the respondents had been evaluated in terms of service needs (by municipal authorities), whether informal care agreement had been made on caring for the respondents, whether the respondents had not received the assistance they had needed in the previous 12 months, and whether they trusted they would receive assistance, support and services should they need them. Views on the quality of public and private social services were surveyed. Questions concerning informal care studied whether the respondents assisted an aged, disabled or sick friend or relative, whether they were the primary caregiver of the person they cared for, how often they helped this person, how satisfied they were with public and private services the person they helped had received, and whether they had made an informal care agreement with the municipality. Questions related to social networks investigated contact with relatives, feelings on loneliness, loss of interest towards things that were previously pleasing, and financial and physical abuse suffered in the previous 12 months. Finally, perceptions of quality of life were charted as well as satisfaction with own health, experiences of physical pain, enjoyment of life, sense of significance, ability to focus on things, sense of security or insecurity in daily life, healthiness of physical environment, ability to do things (in terms of, for instance, money and energy), ability to move, satisfaction with various things in life (e.g. quality of sleep), and negative feelings. Background variables included, among others, the household size, type of municipality of residence, region of residence, hospital district, and disposable income of the household as well as the respondent's year of retirement, latest occupation, gender, marital status, age group, and level of education (3-level ISCED classification).

The recognition of issues as public problems and the ways governments prioritize them constitute focal points in the study of policy change and policy dynamics. In Brazil and other Latin American countries, social welfare systems and related policies have undergone transformations throughout the recent democratic period. This article aims to understand changes in the Brazilian social welfare agenda by means of an analysis of the attention given to social welfare policies at the federal level. The main analytical and methodological contribution of this article is its use of the research strategy developed under the Comparative Agendas Project (CAP) to analyze the Brazilian situation. We drew on a set of unpublished datasets on the attention given by governments to social welfare policies from 1988 to 2018 that involves more than one thousand observations across six different datasets. The analyses are made at two different levels: first, we seek to understand macro trends and moments of continuity and inflection in social welfare policy by federal government administration. Second, we analyze the composition of the attention given to social welfare policies, thereby identifying the themes given the highest priority.

SHWALITA, short for ‘Survey of Household Welfare and Labour in Tanzania’, is a 4,000 household survey that randomly assigns different survey modules to its respondents. The survey consists of 3 separate experiments, carefully bundled into one survey: (i) A consumption experiment in which we developed eight alternative consumption questionnaires which were randomly distributed across 4,000 households. These eight designs vary by method (3 diaries and 5 recall modules), length of reference period in recall modules, and the number of items in the recall modules. (ii) labour module experiments in which we assess the effect of different ways of collecting labour statistics. It uses two different modules, a long module and a short module, and administers each to either the person him/herself or to someone else in the household answering on their behalf (a proxy respondent). Both proxy respondents and self-reporting respondents are sampled randomly from the roster of household members. (iii) subjective welfare experiments in which we use an innovative approach to enhance comparability of subjective welfare questions. The technique, developed in political sciences by Gary King, involves the respondent to provide scaled answers on qualitative questions (on a scale of 1 to 5, how do you feel about….). In order to ‘anchor’ the response the respondent is given a ‘vignette’ a short, but powerful story about a fictitious person and is then asked to place this person on the same scale. The placing of the vignette on the same scale allows answers to become more comparable across households, communities and countries.

The Child Care and Development Fund (CCDF) provides federal money to states and territories to provide assistance to low-income families, to obtain quality child care so they can work, attend training, or receive education. Within the broad federal parameters, States and Territories set the detailed policies. Those details determine whether a particular family will or will not be eligible for subsidies, how much the family will have to pay for the care, how families apply for and retain subsidies, the maximum amounts that child care providers will be reimbursed, and the administrative procedures that providers must follow. Thus, while CCDF is a single program from the perspective of federal law, it is in practice a different program in every state and territory. The CCDF Policies Database project is a comprehensive, up-to-date database of CCDF policy information that supports the needs of a variety of audiences through (1) analytic data files, (2) a project website and search tool, and (3) an annual report (Book of Tables). These resources are made available to researchers, administrators, and policymakers with the goal of addressing important questions concerning the effects of child care subsidy policies and practices on the children and families served. A description of the data files, project website and search tool, and Book of Tables is provided below: 1. Detailed, longitudinal analytic data files provide CCDF policy information for all 50 states, the District of Columbia, and the United States territories and outlying areas that capture the policies actually in effect at a point in time, rather than proposals or legislation. They capture changes throughout each year, allowing users to access the policies in place at any point in time between October 2009 and the most recent data release. The data are organized into 32 categories with each category of variables separated into its own dataset. The categories span five general areas of policy including: Eligibility Requirements for Families and Children (Datasets 1-5) Family Application, Terms of Authorization, and Redetermination (Datasets 6-13) Family Payments (Datasets 14-18) Policies for Providers, Including Maximum Reimbursement Rates (Datasets 19-27) Overall Administrative and Quality Information Plans (Datasets 28-32) The information in the data files is based primarily on the documents that caseworkers use as they work with families and providers (often termed "caseworker manuals"). The caseworker manuals generally provide much more detailed information on eligibility, family payments, and provider-related policies than the CCDF Plans submitted by states and territories to the federal government. The caseworker manuals also provide ongoing detail for periods in between CCDF Plan dates. Each dataset contains a series of variables designed to capture the intricacies of the rules covered in the category. The variables include a mix of categorical, numeric, and text variables. Most variables have a corresponding notes field to capture additional details related to that particular variable. In addition, each category has an additional notes field to capture any information regarding the rules that is not already outlined in the category's variables. Beginning with the 2020 files, the analytic data files are supplemented by four additional data files containing select policy information featured in the annual reports (prior to 2020, the full detail of the annual reports was reproduced as data files). The supplemental data files are available as 4 datasets (Datasets 33-36) and present key aspects of the differences in CCDF-funded programs across all states and territories as of October 1 of each year (2009-2022). The files include variables that are calculated using several variables from the analytic data files (Datasets 1-32) (such as copayment amounts for example family situations) and information that is part of the annual project reports (the annual Book of Tables) but not stored in the full database (such as summary market rate survey information from the CCDF plans). 2. The project website and search tool provide access to a point-and-click user interface. Users can select from the full set of public data to create custom tables. The website also provides access to the full range of reports and products released under the CCDF Policies Data

(1) Human well-being on the Qinghai Tibet Plateau based on the human development index: the human well-being on the Qinghai Tibet Plateau (Qinghai and Xizang provinces) is measured quantitatively using the comprehensive human development index, an objective well-being assessment indicator. Referring to the functional structure framework of human welfare in China in the new era, the functional structure of human groups is divided into basic functions, harmonious functions, development functions and sustainable functions. On the basis of the four functions, functional indicators and specific indicator systems are further designed, that is, health, education, integration of urban and rural areas, living standards and coping with climate change account for 1/5 of the five functional indicators, and the secondary indicators are also set with equal rights. This data can reflect the comprehensive development level of human beings in Qinghai and Xizang to a certain extent, and has certain reference significance for the future development planning of the Qinghai Tibet Plateau. (2) Regional Social Relations Comprehensive Index: Based on data collected from the 2010-2019 China Regional Economic Statistical Yearbook, China Urban Statistical Yearbook, China Civil Affairs Statistical Yearbook, Provincial (Autonomous Region) Statistical Yearbook and Statistical Bulletin, relevant City Statistical Bulletin, etc., a regional social relations evaluation index system was constructed on the basis of regional social relations analysis in provincial-level areas of the Qinghai Tibet Plateau. The weights of various indicators were determined, and the regional social relations comprehensive index of 37 prefecture level cities on the Qinghai Tibet Plateau was calculated. Based on this data, obtain a spatiotemporal distribution map of regional social relations at the prefecture level on the Qinghai Tibet Plateau. (3) Human economic well-being related data: Based on data from the China Statistical Yearbook of six provinces in the Qinghai Tibet Plateau region from 2000 to 2017, and considering the complexity of human well-being, 18 indicators were selected to construct a human economic well-being indicator system suitable for evaluating the Qinghai Tibet Plateau region from four aspects: income and consumption, production materials, living materials, and resource acquisition capacity; Based on data from 17 prefecture level cities in the Qinghai Tibet Plateau region from 2007 to 2018, including the China Urban Statistical Yearbook, provincial (autonomous region) statistical yearbooks and bulletins, and relevant urban statistical bulletins, and considering the actual situation of typical cities in the Qinghai Tibet Plateau region, 22 indicators were selected to construct a human welfare index system from five aspects: income and consumption, means of production, means of livelihood, resource acquisition ability, and physical health. This indicator helps to better understand the actual conditions of basic living conditions such as economy, material resources, and means of production of residents in various regions of the Qinghai Tibet Plateau. (4) Habitat quality of the Qinghai Tibet Plateau: This dataset is based on the InVEST model and uses land use data, road data, and terrain data to calculate the habitat quality of the Qinghai Tibet Plateau from 2000 to 2020. The data span is 20 years, with data provided every 5 years and a resolution of 1000m. Among them, the land use data is sourced from the global 30 meter land cover fine classification product（ http://data.casearth.cn/sdo/list ）. The DEM data is sourced from the National Qinghai Tibet Plateau Science Data Center（ http://data.tpdc.ac.cn ）. The road data is sourced from the OpenStreetMap website（ http://openstreetmap.org/ ）. (5) Educational welfare: Based on the education statistical data of various provinces from 2013 to 2021 released on the official website of the Ministry of Education of the People's Republic of China, the compilation of science and technology statistical data of higher education institutions, the Statistical Yearbook of China's Disability Affairs, the Statistical Yearbook of China's Education Funds, relevant research reports, and other publicly available data, the entropy weight method is selected to objectively determine the weights of each evaluation indicator. The natural breakpoint method is used to grade the various educational welfare evaluation data obtained in 2013 and 2021, and to draw educational welfare evaluation maps and comprehensive educational welfare evaluation maps of various levels and types of schools. This provides a more accurate understanding of the spatiotemporal pattern of various educational welfare and comprehensive educational welfare on the Qinghai Tibet Plateau, and provides scientific basis and decision-making reference for relevant departments. (6) Human welfare in the Dadu River Basin: Based on meteorological data from

BR: Survey Mean Consumption or Income per Capita: Total Population: 2011 PPP per day data was reported at 20.390 Intl $/Day in 2019. This records an increase from the previous number of 20.250 Intl $/Day for 2014. BR: Survey Mean Consumption or Income per Capita: Total Population: 2011 PPP per day data is updated yearly, averaging 20.320 Intl $/Day from Dec 2014 (Median) to 2019, with 2 observations. The data reached an all-time high of 20.390 Intl $/Day in 2019 and a record low of 20.250 Intl $/Day in 2014. BR: Survey Mean Consumption or Income per Capita: Total Population: 2011 PPP per day data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Brazil – Table BR.World Bank.WDI: Social: Poverty and Inequality. Mean consumption or income per capita (2011 PPP $ per day) used in calculating the growth rate in the welfare aggregate of total population.; ; World Bank, Global Database of Shared Prosperity (GDSP) (http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity).; ; The choice of consumption or income for a country is made according to which welfare aggregate is used to estimate extreme poverty in the Poverty and Inequality Platform (PIP). The practice adopted by the World Bank for estimating global and regional poverty is, in principle, to use per capita consumption expenditure as the welfare measure wherever available; and to use income as the welfare measure for countries for which consumption is unavailable. However, in some cases data on consumption may be available but are outdated or not shared with the World Bank for recent survey years. In these cases, if data on income are available, income is used. Whether data are for consumption or income per capita is noted in the footnotes. Because household surveys are infrequent in most countries and are not aligned across countries, comparisons across countries or over time should be made with a high degree of caution.

ID: Survey Mean Consumption or Income per Capita: Total Population: 2017 PPP per day data was reported at 7.650 Intl $/Day in 2023. This records an increase from the previous number of 6.970 Intl $/Day for 2018. ID: Survey Mean Consumption or Income per Capita: Total Population: 2017 PPP per day data is updated yearly, averaging 7.310 Intl $/Day from Dec 2018 (Median) to 2023, with 2 observations. The data reached an all-time high of 7.650 Intl $/Day in 2023 and a record low of 6.970 Intl $/Day in 2018. ID: Survey Mean Consumption or Income per Capita: Total Population: 2017 PPP per day data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Indonesia – Table ID.World Bank.WDI: Social: Poverty and Inequality. Mean consumption or income per capita (2017 PPP $ per day) used in calculating the growth rate in the welfare aggregate of total population.;World Bank, Global Database of Shared Prosperity (GDSP) (http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity).;;The choice of consumption or income for a country is made according to which welfare aggregate is used to estimate extreme poverty in the Poverty and Inequality Platform (PIP). The practice adopted by the World Bank for estimating global and regional poverty is, in principle, to use per capita consumption expenditure as the welfare measure wherever available; and to use income as the welfare measure for countries for which consumption is unavailable. However, in some cases data on consumption may be available but are outdated or not shared with the World Bank for recent survey years. In these cases, if data on income are available, income is used. Whether data are for consumption or income per capita is noted in the footnotes. Because household surveys are infrequent in most countries and are not aligned across countries, comparisons across countries or over time should be made with a high degree of caution.

The National Longitudinal Study of Adolescent to Adult Health (Add Health) is a longitudinal study of a nationally representative sample of adolescents in grades 7-12 in the United States during the 1994-95 school year. The Add Health cohort has been followed into young adulthood with four in-home interviews, the most recent in 2008, when the sample was aged 24-32*. Add Health combines longitudinal survey data on respondents’ social, economic, psychological and physical well-being with contextual data on the family, neighborhood, community, school, friendships, peer groups, and romantic relationships, providing unique opportunities to study how social environments and behaviors in adolescence are linked to health and achievement outcomes in young adulthood. The fourth wave of interviews expanded the collection of biological data in Add Health to understand the social, behavioral, and biological linkages in health trajectories as the Add Health cohort ages through adulthood. Wave III The Wave III public use data are helpful in analyzing the transition between adolescence and young adulthood. A total of 4,882 of the original Wave I public-use respondent sample were re-interviewed between August 2001 and April 2002. Wave III respondents were between 18 and 26 years old. The Wave III public use dataset includes the following data files: Main Respondent File: includes the In-Home Questionnaire data, grand sampling weights, AHP VT scores, and biospecimen data for 4,882 respondents Relationship Table File Pregnancy Table File Relationship Detail File Completed Pregnancies File Current Pregnancies File Live Births File Children and Parenting File Education Data *17 respondents in the Wave IV public use sample were 33 years old at the time of the interview.

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. Constructed Variable Programs Constructed variable data sets for both household economics and health currently exist for rounds 1994 through 2005 only. After 2005 the health variables were no longer constructed. In the absence of detailed documentation, the variable labels in each data set describe the contents of the variables. The number of observations in each data set does not necessarily match the total number of observations in the original data files. For the economics data sets, filter criteria were established so that only families with complete economic information were included. The health data sets used the maximum number of non-missing observations per individual analysis. Thus, the health data sets vary in composition more than do the economics ones. In the economics data sets, nominal ruble values are those figures that appear in the original data. Real ruble amounts are nominal values that have been adjusted to June 1992 rubles.