This report provides information about the demographics of children and parents at steps in the child welfare system. It is produced in compliance with Local Law 132 of 2022.

A special analysis of the Eurobarometer 2000 opinion poll on behalf of the European Monitoring Centre on Racism and Xenophobia. By SORA, Vienna, Austria, www.sora.at General recommendations and conclusions: These recommendations are based on findings hinted at in the data-analysis which do not permit the development of a complete set of policy recommendations. Policy recommendations should be based on a knowledge of causal relationships and the strength of effects which is beyond the scope of this project. Thus, the recommendations are linked and clearly connected to the evidence within the data. Political leadership: A quarter of all Europeans can be categorised as ‘ambivalent’ – meaning that they harbour positive and negative attitudes towards minorities at the same time. Data show that party affiliation is a part of the causal system producing attitudes towards minorities. Ambivalent people should be considered those who react most political leadership – awareness of this fact can help politicians to make their decisions. Unemployment: Experience with unemployment and the expectation of higher unemployment rates lead to an increase in hostile attitudes towards minorities. Sinking unemployment rates and information about a decrease in unemployment might reduce concerns about migration and minorities. Welfare: Since a large part of xenophobic concerns is about loss of welfare standards, policies which lend large majorities the feeling that they can participate in the increase of wealth within a growing economy will contribute significantly to reducing xenophobic concerns. Demographic developments and their impact have to be considered and researched. Particular attention should be paid to the number of retired people and the increasing number of old people with lower income and with low expectations within that group. An increase in hostility towards minorities might well get stronger in this group. Education: Higher education clearly correlates with positive attitudes towards minorities. More research should be carried out to determine the nature of this effect and establish whether the increase of higher education – which is a stable trend – will result in a more tolerant attitude within Europe in the coming decades. Personal relations: Supporting personal relationships between people of different religions, nations or with different skin colour increases tolerance. In the countries of Southern European, attitudes towards minorities seem to be influenced by other factors than in the rest of Europe. There is not enough evidence about causal relationships within this analysis to confirm that the conclusions mentioned above are meaningful for the southern part of Europe.

The results by volumes are distributed as follows:

• Volume A: Countries
• Volume AA: Groups of countries
• Volume A' (AP): Trends
• Volume AA' (AAP): Trends of groups of countries
• Volume B: EU/socio-demographics
• Volume B' (BP) : Trends of EU/ socio-demographics
• Volume C: Country/socio-demographics ---- Researchers may also contact GESIS - Leibniz Institute for the Social Sciences: https://www.gesis.org/eurobarometer

This dataset includes the race of applicants for Insurance Affordability Programs (IAPs) who reported their race as American Indian and/or Alaska Native, Asian Indian, Black or African American, Chinese, Cambodian, Filipino, Guamanian or Chamorro, Hmong, Japanese, Korean, Laotian, Mixed Race, Native Hawaiian, Other, Other Asian, Other Pacific Islander, Samoan, Vietnamese, or White by reporting period. The race data is from the California Healthcare Eligibility, Enrollment and Retention System (CalHEERS) and includes data from applications submitted directly to CalHEERS, to Covered California, and to County Human Services Agencies through the Statewide Automated Welfare System (SAWS) eHIT interface. Please note the reporting category Other Asian option on the CalHEERS application was removed in September 2017. This dataset is part of public reporting requirements set forth by the California Welfare and Institutions Code 14102.5.

The Child and Caregiver Outcomes Using Linked Data (CCOULD) project developed consistent state datasets containing linked child and caregiver welfare records and Medicaid claims and enrollment records. These datasets contain information from both child welfare and Medicaid information systems on case demographics, medical diagnoses, services, outcomes, and other relevant information. The purpose of the data is to support research on the relationships between Medicaid utilization, behavioral health services, patient-centered outcomes, and child welfare outcomes. Of particular interest are outcomes for families that may have substance use disorders, like opioid use disorder. The CCOULD project worked with Kentucky and Florida (specifically the Florida Department of Children and Families, the Florida Agency for Health Care Administration, and the Kentucky Cabinet for Health and Family Services) to produce linked, state-level data to support research on the relationships among Medicaid utilization, behavioral health services, patient-centered outcomes, and child welfare outcomes. State-level linked data were combined into a de-identified, standardized research use dataset containing data from the two participating states. Documentation for the data includes: Investigators: Tami L. Mark, PhD, RTI International Melissa Dolan, PhD, RTI International Benjamin Allaire, MS, RTI International Keith Smith, BS, RTI International William Parish, PhD, RTI International Christina Bradley, MA, RTI International Claire Strack, BS, RTI International

This dataset includes race/ethnicity of newly Medi-Cal eligible individuals who identified their race/ethnicity as Hispanic, White, Other Asian or Pacific Islander, Black, Chinese, Filipino, Vietnamese, Asian Indian, Korean, Alaskan Native or American Indian, Japanese, Cambodian, Samoan, Laotian, Hawaiian, Guamanian, Amerasian, or Other, by reporting period. The race/ethnicity data is from the Medi-Cal Eligibility Data System (MEDS) and includes eligible individuals without prior Medi-Cal Eligibility. This dataset is part of the public reporting requirements set forth in California Welfare and Institutions Code 14102.5.

Abstract

The Core Welfare Indicators Questionnaire (CWIQ) currently constitutes one of the largest socio-economic household survey databases on Tanzania. Since 2003 EDI has interviewed roughly 20,000 households in 35 different districts. For 9 districts repeat surveys were organised to track changes over time.

Rationale: Absence of district level survey data does not rhyme with the devolution of power to districts. Tanzania is undergoing a decentralisation process whereby each of its roughly 128 districts is becoming an increasingly important policy actor. A district taking on this challenge needs accurate information to monitor and develop its own policies. Much relevant information is currently not available as national statistics are not representative at district level and many of the routine data collection mechanisms are still under development. CWIQ then provides an attractive, one-stop survey-based method to collect basic development indicators. Furthermore, the survey results can be disseminated - through Swahili briefs and posters - to a district's population; thus increasing the extent to which people are able to hold their local governments accountable. Exciting new ground is being broken on such population-wide dissemination by the Prime Minister's Office.

Methodology: The data are collected through a small 10-page questionnaire, called the Core Welfare Indicators Questionnaire (CWIQ). The questionnaire and data software constitute an off-the-shelf survey package developed by the World Bank to produce standardised monitoring indicators of welfare. The questionnaire is purposively concise and is designed to collect information on household demographics, employment, education, health and nutrition as well as utilisation and satisfaction with social services. Questionnaires are scannable, with interviewers shading bubbles and writing numbers later recognised by the scanning software. The data system is fully automated allowing the results to roll out within weeks of the fieldwork.

Funding: projects are typically funded by organisations that care about making decentralisation work in Tanzania. CWIQ is a method to promote evidence-based policy formulation and debate in the district and a tool for the population to hold their local governments accountable. With funding from the RNE (Royal Netherlands Embassy) and SNV (Stichting Nederlands Vrijwilligers), CWIQ surveys were implemented between 2003-2005 in 16 districts. In 2006/07 PMO-RALG (Prime Minister's Office - Regional Administration and Local Government) commissioned EDI to cover a further 28 districts. In 9 of these districts this constituted a repeat survey and thus a unique opportunity arises to monitor changes that occurred in the district over this time period.

Dissemination: EDI disseminated the results of CWIQ on posters and briefs to district level stakeholders (councillors, district officials, NGOs, CBOs, Advocacy Groups, MPs, 'interested citizens', etc.), with the aim at district level, to: (i) promote evidence-based policy debate, (ii) promote evidence-based policy formulation, (iii) provide tools for district level M&E and (iv) increase accountability of LGA to citizens.

Geographic coverage

Subnational

Analysis unit

• Households
• Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

The CWIQ surveys were sampled to be representative at district level. Data from the 2002 Census was used to put together a list of all villages in each district. In the first stage of the sampling process villages were chosen proportional to their population size. In a second stage the subvillage (kitongoji) was chosen within the village through simple random sampling. In the selected sub-village (also referred to as cluster or enumeration area), all households were listed and 15 households were randomly selected. In total 450 households in 30 clusters were visited. All households were given statistical weights reflecting the number of households that they represent.

Mode of data collection

Face-to-face [f2f]

Research instrument

CWIQ is an off-the-shelf survey package developed by the World Bank to produce standardised monitoring indicators of welfare. The questionnaire is purposively concise and is designed to collect information on household demographics, employment, education, health and nutrition, as well as utilisation of and satisfaction with social services. An extra section on governance and satisfaction with people in public office was added specifically for this survey.

The standardised nature of the questionnaire allows comparison between districts and regions within and across countries, as well as monitoring change in a district or region over time.

The 2006/7 questionnaire is in Swahili, but it closely follows the 2000 generic CWIQ questionnaire, which is included in external resources, and all variables and values are labeled in English.

Cleaning operations

The data entry was done by scanning the questionnaires, to minimise data entry errors and thus ensure high quality in the final dataset.

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Context

The gender pay gap or gender wage gap is the average difference between the remuneration for men and women who are working. Women are generally considered to be paid less than men. There are two distinct numbers regarding the pay gap: non-adjusted versus adjusted pay gap. The latter typically takes into account differences in hours worked, occupations were chosen, education, and job experience. In the United States, for example, the non-adjusted average female's annual salary is 79% of the average male salary, compared to 95% for the adjusted average salary.

The reasons link to legal, social, and economic factors, and extend beyond "equal pay for equal work".

The gender pay gap can be a problem from a public policy perspective because it reduces economic output and means that women are more likely to be dependent upon welfare payments, especially in old age.

This dataset aims to replicate the data used in the famous paper "The Gender Wage Gap: Extent, Trends, and Explanations", which provides new empirical evidence on the extent of and trends in the gender wage gap, which declined considerably during the 1980–2010 period.

Citation

fedesoriano. (January 2022). Gender Pay Gap Dataset. Retrieved [Date Retrieved] from https://www.kaggle.com/fedesoriano/gender-pay-gap-dataset.

Content

There are 2 files in this dataset: a) the Panel Study of Income Dynamics (PSID) microdata over the 1980-2010 period, and b) the Current Population Survey (CPS) to provide some additional US national data on the gender pay gap.

PSID variables:

NOTES: THE VARIABLES WITH fz ADDED TO THEIR NAME REFER TO EXPERIENCE WHERE WE HAVE FILLED IN SOME ZEROS IN THE MISSING PSID YEARS WITH DATA FROM THE RESPONDENTS’ ANSWERS TO QUESTIONS ABOUT JOBS WORKED ON DURING THESE MISSING YEARS. THE fz variables WERE USED IN THE REGRESSION ANALYSES THE VARIABLES WITH A predict PREFIX REFER TO THE COMPUTATION OF ACTUAL EXPERIENCE ACCUMULATED DURING THE YEARS IN WHICH THE PSID DID NOT SURVEY THE RESPONDENTS. THERE ARE MORE PREDICTED EXPERIENCE LEVELS THAT ARE NEEDED TO IMPUTE EXPERIENCE IN THE MISSING YEARS IN SOME CASES. NOTE THAT THE VARIABLES yrsexpf, yrsexpfsz, etc., INCLUDE THESE COMPUTATIONS, SO THAT IF YOU WANT TO USE FULL TIME OR PART TIME EXPERIENCE, YOU DON’T NEED TO ADD THESE PREDICT VARIABLES IN. THEY ARE INCLUDED IN THE DATA SET TO ILLUSTRATE THE RESULTS OF THE COMPUTATION PROCESS. THE VARIABLES WITH AN orig PREFIX ARE THE ORIGINAL PSID VARIABLES. THESE HAVE BEEN PROCESSED AND IN SOME CASES RENAMED FOR CONVENIENCE. THE hd SUFFIX MEANS THAT THE VARIABLE REFERS TO THE HEAD OF THE FAMILY, AND THE wf SUFFIX MEANS THAT IT REFERS TO THE WIFE OR FEMALE COHABITOR IF THERE IS ONE. AS SHOWN IN THE ACCOMPANYING REGRESSION PROGRAM, THESE orig VARIABLES AREN’T USED DIRECTLY IN THE REGRESSIONS. THERE ARE MORE OF THE ORIGINAL PSID VARIABLES, WHICH WERE USED TO CONSTRUCT THE VARIABLES USED IN THE REGRESSIONS. HD MEANS HEAD AND WF MEANS WIFE OR FEMALE COHABITOR.

1. intnum68: 1968 INTERVIEW NUMBER
2. pernum68: PERSON NUMBER 68
3. wave: Current Wave of the PSID
4. sex: gender SEX OF INDIVIDUAL (1=male, 2=female)
5. intnum: Wave-specific Interview Number
6. farminc: Farm Income
7. region: regLab Region of Current Interview
8. famwgt: this is the PSID’s family weight, which is used in all analyses
9. relhead: ER34103L this is the relation to the head of household (10=head; 20=legally married wife; 22=cohabiting partner)
10. age: Age
11. employed: ER34116L Whether or not employed or on temp leave (everyone gets a 1 for this variable, since our wage analyses use only the currently employed)
12. sch: schLbl Highest Year of Schooling
13. annhrs: Annual Hours Worked
14. annlabinc: Annual Labor Income
15. occ: 3 Digit Occupation 2000 codes
16. ind: 3 Digit Industry 2000 codes
17. white: White, nonhispanic dummy variable
18. black: Black, nonhispanic dummy variable
19. hisp: Hispanic dummy variable
20. othrace: Other Race dummy variable
21. degree: degreeLbl Agent's Degree Status (0=no college degree; 1=bachelor’s without advanced degree; 2=advanced degree)
22. degupd: degreeLbl Agent's Degree Status (Updated with 2009 values)
23. schupd: schLbl Schooling (updated years of schooling)
24. annwks: Annual Weeks Worked
25. unjob: unJobLbl Union Coverage dummy variable
26. usualhrwk: Usual Hrs Worked Per Week
27. labincbus: Labor Income from...

In 2018, over nine billion chickens were slaughtered in the United States. As the demand for chickens increases, so too have concerns regarding the welfare of the chickens in these systems and the damage such practices cause to the surrounding ecosystems. To address welfare concerns, there is large-scale interest in raising chickens on pasture and switching to slower-growing, higher-welfare breeds as soon as 2024. We created a box model of US chicken demographics to characterize aggregate broiler chicken welfare and land use consequences at the country scale for US shifts to slower-growing chickens, housing with outdoor access, and pasture management. The US produces roughly 20 million metric tons of chicken meat annually. Maintaining this level of consumption entirely with a slower-growing breed would require a 44.6%–86.8% larger population of chickens and a 19.2% – 27.2% higher annual slaughter rate, relative to the current demographics of primarily “Ross 308” chickens that are slaugh...

Poverty and low-income statistics by visible minority group, Indigenous group and immigration status, Canada and provinces.

This dataset presents the footprint of the number of emergency department presentations in public hospitals by patient demographics and location. Mental health-related emergency department (ED) presentations are defined as presentations to public hospital EDs that have a principal diagnosis of mental and behavioural disorders. However, the definition does not fully capture all potential mental health-related presentations to EDs such as intentional self-harm, as intent can be difficult to identify in an ED environment and can also be difficult to code. The data spans the financial years of 2014-2018 and is aggregated to Statistical Area Level 3 (SA3) geographic areas from the 2016 Australian Statistical Geography Standard (ASGS).

State and territory health authorities collect a core set of nationally comparable information on most public hospital ED presentations in their jurisdiction, which is compiled annually into the National Non-Admitted Patient Emergency Department Care Database (NNAPEDCD). The data reported for 2014–15 to 2017–18 is sourced from the NNAPEDCD. Information about mental health-related services provided in EDs prior to 2014–15 was supplied directly to the Australian Institute of Health and Welfare (AIHW) by states and territories.

Mental health services in Australia (MHSA) provides a picture of the national response of the health and welfare service system to the mental health care needs of Australians. MHSA is updated progressively throughout each year as data becomes available. The data accompanies the Mental Health Services - In Brief 2018 Web Report.

For further information about this dataset, visit the data source:Australian Institute of Health and Welfare - Mental health services in Australia Data Tables.

Please note:

• AURIN has spatially enabled the original data.

• Caution is required when conducting time-series analyses. The data source changed in 2014–15 from data provided by state and territory health authorities (2004–05 to 2013–14) to the NNAPEDCD. Additionally, due to the methodology applied for mapping the data over time, years prior to 2017–18 may be an undercount or data may not be displayed where SA3s have changed over time.

• Mental health-related emergency department presentations included in this report are those that had a principal diagnosis that fell within the Mental and behavioural disorders chapter (Chapter 5) of ICD-10-AM (codes F00–F99) or the equivalent ICD-9-CM or SNOMED codes. It does not include codes for self-harm or poisoning.

• From 2014–15 onwards, diagnosis information was not reported using a uniform classification. The mapping of SNOMED codes (used by NSW) to ICD-10AM may lead to an under-estimation of mental health-related presentations.

• Changes in the volume of patients over time for NSW may be attributed, in part, to the increased number of hospitals included in the data for this jurisdiction.

This dataset includes fifteen biographical interviews, which were conducted within the PARTISPACE Project. The initial stimulus varies from Country to Country, but is mainly “tell me your life story from the beginning until now”. The transcripts are partly transcribed, the whole document is about 269 pages. Intervieews are Young People from Bulgaria, Italy, Turkey, Germany, Switzerland, Sweden and United Kingdom. The PARTISPACE project receives funding from the European Union's Horizon 2020 research and innovation Programme and provides empirical knowledge on youth participation across formal, non-formal and informal Settings. More Information: partispace.eu

Annual profiles of Alberta's health care and social assistance industry. Information and statistics on demographics, wages and employment trends and outlook are included.

Social and demographic characteristics of 1174 individuals accessing Macmillan welfare rights advice service (April 2009–March 2010).

The questions contained in the core modules of the two SASAS questionnaires for 2006 (demographics and core thematic issues) were asked of 7000 respondents, while the remaining rotating modules were asked of a half sample of approximately 3500 respondents each. The data set contains 2939 records and 388 variables. Topics included in the questionnaires are: democracy, identity, public services, moral issues, crime, voting, demographics and other classificatory variables. Rotating modules are: communication, International Social Surveys Programme (ISSP) module, national identity, social exclusion, poverty, tourism and leisure, media and communication, work and welfare.