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.

United States US: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data was reported at 1.310 % in 2016. United States US: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data is updated yearly, averaging 1.310 % from Dec 2016 (Median) to 2016, with 1 observations. United States US: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Poverty. The growth rate in the welfare aggregate of the bottom 40% is computed as the annualized average growth rate in per capita real consumption or income of the bottom 40% of the population in the income distribution in a country from household surveys over a roughly 5-year period. Mean per capita real consumption or income is measured at 2011 Purchasing Power Parity (PPP) using the PovcalNet (http://iresearch.worldbank.org/PovcalNet). For some countries means are not reported due to grouped and/or confidential data. The annualized growth rate is computed as (Mean in final year/Mean in initial year)^(1/(Final year - Initial year)) - 1. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported. The initial year refers to the nearest survey collected 5 years before the most recent survey available, only surveys collected between 3 and 7 years before the most recent survey are considered. The final year refers to the most recent survey available between 2011 and 2015. Growth rates for Iraq are based on survey means of 2005 PPP$. The coverage and quality of the 2011 PPP price data for Iraq and most other North African and Middle Eastern countries were hindered by the exceptional period of instability they faced at the time of the 2011 exercise of the International Comparison Program. See PovcalNet for detailed explanations.; ; World Bank, Global Database of Shared Prosperity (GDSP) circa 2010-2015 (http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity).; ; The comparability of welfare aggregates (consumption or income) for the chosen years T0 and T1 is assessed for every country. If comparability across the two surveys is a major concern for a country, the selection criteria are re-applied to select the next best survey year(s). Annualized growth rates are calculated between the survey years, using a compound growth formula. The survey years defining the period for which growth rates are calculated and the type of welfare aggregate used to calculate the growth rates are noted in the footnotes.

This data explores the U.S. Department of Health and Human Services (DHHS) Administration for Children and Families Administration on Children, Youth and Families Children's Bureau Adoption of Children with Public Child Welfare Agency Involvement by State for Fiscal Years 1995 - 2006. For Fiscal Years 1995 - 1997, The data for FY 1995-FY 1997 were reported by States to set baselines for the Adoption Incentive Program. They came from a variety of sources including the Adoption and Foster Care Analysis and Reporting System (AFCARS), court records, file reviews and legacy information systems. For Fiscal Years 1998 - 2006, Unless otherwise noted, the data come from the AFCARS adoption database. Because AFCARS adoption data are being continuously updated and cleaned, the numbers reported here may differ from data reported elsewhere. In addition, data reported for the Adoption Incentive Program will differ from these data because adoptions reported for that program are identified through a different AFCARS data element and must qualify in other ways to be counted toward the award of incentive funds. Counts include adoptions reported as of 6/1/2005. Where appropriate, AFCARS data have been adjusted for duplication.

This dataset explores Foster Care FY2000 - FY2005 Entries, Exits, and Numbers of Children In Care on the Last Day of Each Federal Fiscal Year. NOTE: This table reflects State data submitted to the Children's Bureau as of March 2007. The table does not include any estimates for individual States. Jurisdictions with insufficient data ("NA") are not included in the total for that year. Pre-2003 Nevada data were generated from various sources, rather than from a statewide child welfare system. NOTE: Ideally, if the number of children in the "in care" count declines, as it did during this period, the number of exits should consistently be greater than the number of entries in that year. However, this does not occur with these data. Underreporting of foster care exits by some States is the major reason for this data quality issue.

Latin American pension reforms during the 1990s dramatically increased the number of Latin Americans with a direct stake in the returns to financial capital. This paper asks: How, if at all, has this expansion affected Latin American politics? I focus particularly on popular attitudes towards neoliberalism. I argue that government-induced expansions of capital ownership do not affect public preferences about neoliberalism directly, but indirectly by shaping the information that people use to judge whether neoliberalism is welfare enhancing. In this view, participation in a reformed Latin American pension system should lead to acceptance of neoliberalism when pensions returns are high, but have the opposite effect when pension returns are low. I find support for this theory in analyses of multiple datasets of Latin American survey data.

This comparative project (UK, Japan, Germany, US & New Zealand) examined how governments prepare citizens for collapse in the Critical National Infrastructure (CNI); how they model collapse and population response; case studies of CNI collapse (with particular reference to health and education) and the globalisation of CNI policy. It was funded by the Economic and Social Research Council under grant reference ES/K000233/1. It considered:- 1. How is the critical infrastructure defined and operationalised in different national contexts? How is population response defined, modelled and refined in the light of crisis? 2. What are the most important comparative differences between countries with regard to differences in mass population response to critical infrastructure collapse? 3. To what degree are factors such as differences in national levels of trust, degrees of educational or income inequality, social capital or welfare system differences particularly in the education and health systems significant in understanding differential population response to critical infrastructure collapse? 4. How can a comparative understanding of mass population response to critical infrastructure collapse help us to prepare for future crisis? Research design and methodology Methodologically the study was focused on national systems in developed countries. The focus was on different 'welfare regimes' being broadly liberal market economies (the UK, US and New Zealand) and broadly centralised market economies (Germany and Japan). The data arising from the project was of various types including interviews, focus groups, archival data and documentary evidence. The 'National Infrastructure' is seldom out of the news. Although the infrastructure is not always easy to define it includes things such as utilities (water, energy, gas), transportation systems and communications. We often hear about real or perceived threats to the infrastructure. In this research we will construct 'timelines' of infrastructure protection policy and mass population response to see exactly how and why policy changes in countries over time. We will select a range of countries to represent different political and social factors (US, UK, New Zealand, Japan and Germany). The analysis of these timelines will suggest why national infrastructure policy changes over time. We will then test our results using case studies of actual disasters and expert groups of policy makers across countries. Ultimately this will help us to understand national infrastructure protection changes over time, what drives such changes and the different ways in which countries prepare themselves for infrastructure threats. In addition, through a series of 'leadership activities' the research will bring together researchers in different academic disciplines and people from the public, private and third sectors. The methodology used was to enable an understanding of how countries had developed strategies of mass population response to critical infrastructure failure. The methods were:- 1. Archival research using data in country archives from 1945 to the present day on population response (planned and actual to disasters) 2. Focus groups and interviews with selected experts to enable us to further understand the data in (1). 3. Case studies of actual infrastructure failures - summary notes were prepared from documentary evidence on disasters.