This report, part of an annual series, presents estimates, by state, of the percentage of eligible persons and working poor individuals who participated in SNAP during an average month in fiscal year (FY 2017) and the two previous fiscal years.

The SNAP participation rate shows how many households in Champaign County receive SNAP benefits, as a percentage of the total number of households in the county. The SNAP participation rate can serve as an indicator of poverty and need in the area, as income-based thresholds establish SNAP eligibility. However, not every household in poverty receives SNAP benefits, as can be determined by comparing the poverty rate between 2005 and 2023 and the percentage of households receiving SNAP benefits between 2005 and 2023.

The number of households and the percentage of households receiving SNAP benefits was higher in 2023 than in 2005, but we cannot establish a trend based on year-to-year changes, as in many years these changes are not statistically significant.

SNAP participation data was sourced from the U.S. Census Bureau’s American Community Survey (ACS) 1-Year Estimates, which are released annually.

As with any datasets that are estimates rather than exact counts, it is important to take into account the margins of error (listed in the column beside each figure) when drawing conclusions from the data.

Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because data is not available for Champaign County, no data for 2020 is included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes a dataset on Receipt of Food Stamps/SNAP in the Past 12 Months by Presence of Children Under 18 Years for Households.

Sources: U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using data.census.gov; (17 October 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using data.census.gov; (26 September 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using data.census.gov; (5 October 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using data.census.gov; (8 June 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using data.census.gov; (8 June 2021).; U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (14 September 2017).; U.S. Census Bureau; American Community Survey, 2015 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (19 September 2016).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2012 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (16 March 2016).

description: This report part of an annual series presents estimates of the percentage of eligible persons, by State, who participated in the U.S. Department of Agriculture s Supplemental Nutrition Assistance Program (SNAP) during an average month in fiscal year (FY) 2014 and in the two previous fiscal years. This report also presents estimates of State participation rates for eligible working poor individuals (persons in households with earnings) over the same period.; abstract: This report part of an annual series presents estimates of the percentage of eligible persons, by State, who participated in the U.S. Department of Agriculture s Supplemental Nutrition Assistance Program (SNAP) during an average month in fiscal year (FY) 2014 and in the two previous fiscal years. This report also presents estimates of State participation rates for eligible working poor individuals (persons in households with earnings) over the same period.

This longitudinal survey was designed to add significantly to the amount of detailed information available on the economic situation of households and persons in the United States. These data examine the level of economic well-being of the population and also provide information on how economic situations relate to the demographic and social characteristics of individuals. There are three basic elements contained in the survey. The first is a control card that records basic social and demographic characteristics for each person in a household, as well as changes in such characteristics over the course of the interviewing period. The second element is the core portion of the questionnaire, with questions repeated at each interview on labor force activity, types and amounts of income, participation in various cash and noncash benefit programs, attendance in postsecondary schools, private health insurance coverage, public or subsidized rental housing, low-income energy assistance, and school breakfast and lunch participation. The third element consists of topical modules which are series of supplemental questions asked during selected household visits. No topical modules were created for the first or second waves. The Wave III Rectangular Core and Topical Module File offers both the core data and additional data on (1) education and work history and (2) health and disability. In the areas of education and work history, data are supplied on the highest level of schooling attained, courses or programs studied in high school and after high school, whether the respondent received job training, and if so, for how long and under what program (e.g., CETA or WIN). Other items pertain to the respondent's general job history and include a description of selected previous jobs, duration of jobs, and reasons for periods spent not working. Health and disability variables present information on the general condition of the respondent's health, functional limitations, work disability, and the need for personal assistance. Data are also provided on hospital stays or periods of illness, health facilities used, and whether health insurance plans (private or Medicare) were available. Respondents whose children had physical, mental, or emotional problems were questioned about the causes of the problems and whether the children attended regular schools. The Wave IV Rectangular Core and Topical Module file contains both the core data and sets of questions exploring the subjects of (1) assets and liabilities, (2) retirement and pension coverage, and (3) housing costs, conditions, and energy usage. Some of the major assets for which data are provided are savings accounts, stocks, mutual funds, bonds, Keogh and IRA accounts, home equity, life insurance, rental property, and motor vehicles. Data on unsecured liabilities such as loans, credit cards, and medical bills also are included. Retirement and pension information covers such items as when respondents expect to stop working, whether they will receive retirement benefits, whether their employers have retirement plans, if so whether they are eligible, and how much they expect to receive per year from these plans. In the category of housing costs, conditions, and energy usage, variables pertain to mortgage payments, real estate taxes, fire insurance, principal owed, when the mortgage was obtained, interest rates, rent, type of fuel used, heating facilities, appliances, and vehicles. The Wave V topical modules explore the subject areas of (1) child care, (2) welfare history and child support, (3) reasons for not working/reservation wage, and (4) support for nonhousehold members/work-related expenses. Data on child care include items on child care arrangements such as who provides the care, the number of hours of care per week, where the care is provided, and the cost. Questions in the areas of welfare history and child support focus on receipt of aid from specific welfare programs and child support agreements and their fulfillment. The reasons for not working/reservation wage module presents data on why persons are not in the labor force and the conditions under which they might join the labor force. Additional variables cover job search activities, pay rate required, and reason for refusal of a job offer. The set of questions dealing with nonhousehold members/work-related expenses contains items on regular support payments for nonhousehold members and expenses associated with a job such as union dues, licenses, permits, special tools, uniforms, or travel expenses. Information is supplied in the Wave VII Topical Module file on (1) assets and liabilities, (2) pension plan coverage, and (3) real estate property and vehicles. Variables pertaining to assets and liabilities are similar to those contained in the topical module for Wave IV. Pension plan coverage items include whether the respondent will receive retirement benefits, whether the employer offers a retirement plan and if the respondent is included in the plan, and contributions by the employer and the employee to the plan. Real estate property and vehicles data include information on mortgages held, amount of principal still owed and current interest rate on mortgages, rental and vacation properties owned, and various items pertaining to vehicles belonging to the household. Wave VIII Topical Module includes questions on support for nonhousehold members, work-related expenses, marital history, migration history, fertility history, and household relationships. Support for nonhousehold members includes data for children and adults not in the household. Weekly and annual work-related expenses are documented. Widowhood, divorce, separation, and marriage dates are part of the marital history. Birth expectations as well as dates of birth for all the householder's children, in the household or elsewhere, are recorded in the fertility history. Migration history data supplies information on birth history of the householder's parents, number of times moved, and moving expenses. Household relationships lists the exact relationships among persons living in the household. Part 49, Wave IX Rectangular Core and Topical Module Research File, includes data on annual income, retirement accounts, taxes, school enrollment, and financing. This topical module research file has not been edited nor imputed, but has been topcoded or bottomcoded and recoded if necessary by the Census Bureau to avoid disclosure of individual respondents' identities. (Source: downloaded from ICPSR 7/13/10)

Text source: https://www.huduser.gov/portal/publications/hsgfin/addi.html In recognition of the fact that a lack of savings is the most significant barrier to homeownership for most low-income families1, Congress passed the American Dream Downpayment Act of 2003, which established the American Dream Downpayment Initiative (ADDI). The ADDI program was designed to provide assistance with downpayments, closing costs, and, if necessary, rehabilitation work done in conjunction with a home purchase. This formula-based program disburses assistance through a network of Participating Jurisdictions (PJs) in all 50 states and affords them significant flexibility in designing homebuyer programs to meet the needs of their communities. Established as part of the HOME program,2 ADDI is a prime example of direct federal assistance to promote low-income homeownership. In recent years there have been growing concerns that many new low-income homeowners have had difficulty maintaining homeownership.3 To address these concerns in the context of the ADDI program, the Fiscal Year 2006 U.S. Senate Report on the Transportation, Treasury and HUD Appropriations Bill directed the U.S. Department of Housing and Urban Development (HUD) to report on the foreclosure and delinquency rate of households who received downpayment assistance through ADDI.4 This report has been developed in response to this congressional mandate. Due to the limited program history of ADDI, and since HOME-assisted homebuyers are quite similar to those assisted by the ADDI, this study jointly estimates annual foreclosure and delinquency rates for both HOME- and ADDI-assisted borrowers who purchased homes during the period from 2001 through 2005.5 While all HOME/ADDI-assisted borrowers were included in the analysis, in order to have the results be representative of the ADDI program, the sample of PJs was limited to those that were eligible for an allocation of ADDI funds in 2004, the year in which the largest number of PJs were eligible. The primary objective of the study, which addresses the congressional inquiry, is to provide an estimate of the foreclosure and delinquency rates among HOME/ADDI-assisted homebuyers. HUD was also interested in an analysis of the reasons behind these outcomes. Thus, a secondary objective of this study is to analyze the factors associated with variations in delinquency and default rates. 1 See, for example, U. S. Department of Housing and Urban Development, Barriers to Minority Homeownership, July 17, 2002, and Herbert et al., Homeownership Gaps Among Low-Income and Minority Borrowers and Neighborhoods, U.S. Department of Housing and Urban Development, March 2005. 2 Created under Title II of the National Affordable Housing Act of 1990, the HOME program is designed to provide affordable housing to low-income households, expand the capacity of nonprofit housing providers, and strengthen the ability of state and local governments to develop and implement affordable housing strate-gies tailored to local needs and priorities. 3 See, for example, Dean Baker, "Who's Dreaming?: Homeownership Among Low-Income Families," Center for Eco-nomic and Policy Research, Washington, DC, January 2005. 4 Throughout our discussion the terms "default" and "foreclosure" are used to refer to the same outcome where homeowners lose their home in foreclosure. 5 Foreclosure and delinquency rates for 2000 are not included here as the data was not consistent enough to produce valid estimations. This report is based in part on surveys of participating jurisdictions.

IntroductionWe characterized the challenges and innovations of states’ Ryan White HIV/AIDS Program (RWHAP) Part B programs, including AIDS Drug Assistance Programs (ADAPs), during the COVID-19 pandemic. In the United States, these are important safety net programs for HIV healthcare, providing essential medical and support services, and medications, to people with HIV with low incomes who are uninsured/underinsured.MethodsData were collected via the 2021-2022 NASTAD National RWHAP Part B and ADAP Monitoring Project Report, a cross-sectional survey of state, district, and territorial programs through a mixed method study design. For quantitative data, we used descriptive statistics. Qualitative responses were coded and analyzed using content analysis.ResultsForty-seven RWHAP Part B and ADAPs responded (92% response rate). The majority of respondents reported that maintaining client eligibility (78%) and working remotely (70%) were the most challenging aspects of the pandemic, particularly in regards to implementing new telehealth and e-certification platforms. In response to COVID-19, programs introduced enrollment “grace periods” (19%), bolstered client outreach (11%), allowed more than a 30 day supply of medications (79%), and supported medication home delivery for clients (80%).DiscussionDespite the challenges of the COVID-19 pandemic, RWHAP Part B and ADAPs implemented several operational innovations in order to continue providing essential medicines and services. Other public health programs may adopt similar innovations, including digital innovations, for greater public health benefit. Future studies should assess the retention of policy innovations over time, their impact on the individual client level satisfaction or health outcomes, and what factors may improve the acceptability of telehealth and e-certification platforms.

Abstract

The benefits of the water and sanitation sub activity will be measured using a rigorous quasi-experimental impact evaluation methodology. An impact evaluation is a study that measures the changes in outcomes that measure aspects of wellbeing which can be attributed to a specific intervention. Impact evaluations require a credible and rigorously defined counterfactual, which estimates what would have happened to the beneficiaries absent the project. Estimated impacts, when contrasted with total related costs, provide an assessment of the intervention's cost-effectiveness.

The evaluator divided the primary evaluation questions in different categories: welfare indicators, coping cost in cash and time, health, education, reliability and quality of service, spillover effects. In addition, we allow for differential impacts in gender and social groups for these main outcomes.

Household welfare o Do water and sanitation infrastructure investments increase household expenditure or income? What factors might explain the impact (or lack of impact) in this area? o What are the consequences of water and sanitation investments for expenditure patterns?

Coping costs and cash expenditure on water o Do water and sanitation interventions reduce coping costs? What factors might explain the impact (or lack of impact) in this area? o Do they reduce cash expenditures on water and on sanitation services? What factors might explain the impact (or lack of impact) in this area?

Health o Do water and sanitation interventions reduce incidence of diarrheal illness? o What factors (hygiene behavior, source and household-level water quality, household source choice) might explain the impact (or lack of impact) in this area?

Education o Do water and sanitation interventions increase school enrollment among children aged 7 to 12? And children age 6 to 18? What factors might explain the impact (or lack of impact) in this area? o Do water and sanitation interventions increase school attendance among children aged 7 to 12? And children age 6 to 18? What factors might explain the impact (or lack of impact) in this area?

Service, use, and sustainability o Were the water and sanitation projects implemented according to plan? o Are the results from the activity expected to be sustained over time? o Did the MCC investment reach intended/unintended beneficiaries?

Gender and social exclusion o Do the effect on health, education and access of water and sanitation interventions differ by gender or by expenditure levels (initial conditions)? o What factors (hygiene behavior, source and household-level water quality, household source choice) might explain the impact (or lack of impact) in a specific subpopulation?

The key to measuring the impacts caused by the water and sanitation interventions is to compare conditions with the interventions to conditions that would have prevailed without them. The counterfactual state is not naturally observable - we can never know what change would have occurred in program participants (the treatment group) if the program were not implemented. As it was not possible to apply randomization in the selection of water and sanitation projects in this case, the benefits of the water and sanitation projects will be measured with a rigorous quasi-experimental design that incorporates matching, pre- and post-implementation data collection, difference-in-difference estimation, and econometric analysis to estimate the counterfactual and address selection and other biases. This requires selecting a comparison group-households that are observationally similar to beneficiary households but do not participate in the program-and observing both sets of households before and after the program is implemented.

Matching represents a credible non-experimental option for identifying comparison groups. The evaluator uses propensity score matching (PSM) using data from the 2007 census to match the treatment communities to comparable communities before program implementation. PSM identifies comparison communities that have a similar probability of receiving the treatment and are similar to the treatment communities in terms of observable characteristics. Accordingly, they provide measures of indicators in communities that are similar except for the treatment; thus addressing selection on observables.

Geographic coverage

The survey was administered in the El Salvador Northern Zone departments of Cabañas, Chalatenango, Cuscatlán, La Unión, Morazán, San Miguel and Santa Ana.

Analysis unit

Census segments, households, individuals

Universe

Sixty-two municipalities in the Northern Zone, classified as either “Extrema Pobreza Moderada” or “Extrema Pobreza Alta” (extreme moderate poverty or extreme high poverty, respectively) by the national poverty map, were invited to submit proposals for water and sanitation projects. To be considered eligible for the program, the proposals had to meet four criteria: (1) the municipality had to be eligible to participate, meaning there were classified as high or moderate extreme poverty; (2) both the community and municipality had to be willing to make a financial/labor commitment to the project, (3) the community had to be organized and willing to work with the municipality, and (4) the estimated cost of the project could not exceed $850 per beneficiary. After projects that did not meet the eligibility criteria were excluded, a list of 68 projects remained. These were cleared to enter the feasibility stage. Comparisons segments were selected from non-beneficiary segments that where eligible to participate taking into account the poverty map, an proxies for financial capacity of the municipality and community involvement where included in the propensity score estimation.

Kind of data

Sample survey data [ssd]

Sampling procedure

In 2009, the evaluator recommended 18 observations per cluster and 164 communities, while adding an additional contingency -- 6 extra treatment segments and 6 extra control segments, for a total of 216 additional households in order to provide extra cushion for the loss of projects during implementation or inaccuracies in the sample frame. The final sample size for recommended for the study was 3,168, with 88 comparison and 88 treatment segments, each with 18 households.

However, in 2011, given changes to program design and required revisions to power calculations, the evaluator ultimately collected baseline data on 3,284 households, with 65 segments in both treatment and control and an average of 24-27 households per segment.

Research instrument

The household level survey is administered in the departments of Cabañas, Chalatenango, Cuscatlán, La Unión, Morazán, San Miguel and Santa Ana. The survey is composed of a set of sections to characterize the water access situation of households, household demographics, consumption, income/productive activities and time allocation of women and children.

The community level survey includes 130 census segments representing 196 caseríos. The information is obtained from interviews of key informants from the communities. Key informants include health workers/promoters, members of the water boards and other community leaders.

Response rate

94.5% for 2012 survey 96% for 2013 survey

Release Date: 2021-05-20.Release Schedule:.The data in this file come from the 2017 Economic Census. For information about economic census planned data product releases, see Economic Census: About: 2017 Release Schedules...Key Table Information:.Includes only establishments of firms with payroll...Data Items and Other Identifying Records:.Number of establishments.Sales, value of shipments, or revenue ($1,000).Type of payer revenue ($1,000).Response coverage of type of payer inquiry (%)..Each record includes a code which represents a specific type of payer category...Geography Coverage:.The data are shown for employer establishments at the U.S. and state levels. For information about economic census geographies, including changes for 2017, see Economic Census: Economic Geographies...Industry Coverage:.The data are shown at the 4-digit code level for 2017 NAICS codes beginning with 621, 622, and 623. For information about NAICS, see Economic Census: Technical Documentation: Economic Census Code Lists...Footnotes:.Not applicable...FTP Download:.Download the entire table at: https://www2.census.gov/programs-surveys/economic-census/data/2017/sector62/EC1762TYPEPAYER.zip..API Information:.Economic census data are housed in the Census Bureau API. For more information, see Explore Data: Developers: Available APIs: Economic Census..Methodology:.To maintain confidentiality, the U.S. Census Bureau suppresses data to protect the identity of any business or individual. The census results in this file contain sampling and/or nonsampling error. Data users who create their own estimates using data from this file should cite the U.S. Census Bureau as the source of the original data only...To comply with disclosure avoidance guidelines, data rows with fewer than three contributing establishments are not presented. Additionally, establishment counts are suppressed when other select statistics in the same row are suppressed. For detailed information about the methods used to collect and produce statistics, including sampling, eligibility, questions, data collection and processing, data quality, review, weighting, estimation, coding operations, confidentiality protection, sampling error, nonsampling error, and more, see Economic Census: Technical Documentation: Methodology...Symbols:.D - Withheld to avoid disclosing data for individual companies; data are included in higher level totals.N - Not available or not comparable.S - Estimate does not meet publication standards because of high sampling variability, poor response quality, or other concerns about the estimate quality. Unpublished estimates derived from this table by subtraction are subject to these same limitations and should not be attributed to the U.S. Census Bureau. For a description of publication standards and the total quantity response rate, see link to program methodology page..X - Not applicable.A - Relative standard error of 100% or more.r - Revised.s - Relative standard error exceeds 40%.For a complete list of symbols, see Economic Census: Technical Documentation: Data Dictionary.. .Source:.U.S. Census Bureau, 2017 Economic Census.For information about the economic census, see Business and Economy: Economic Census...Contact Information:.U.S. Census Bureau.For general inquiries:. (800) 242-2184/ (301) 763-5154. ewd.outreach@census.gov.For specific data questions:. (800) 541-8345.For additional contacts, see Economic Census: About: Contact Us.

Youth unemployment rose sharply as a result of the Covid-19 pandemic and subsequent sector lockdowns in the UK and across the world with 18.5% of young people aged 15-24, unemployed across EU, 40% in Spain (European Parliament Study, 2021), and 14.9% in the UK (House of Commons Library, 2023). Although, the employment rates are showing some recovery, research shows that youth unemployment has delayed long-term negative impacts on future well-being, health and job satisfaction of individuals. It increases young people’s chances of being unemployed in later years and carry a wage penalty (Bell and Blanchflower, 2011). Young people (15-24 year olds) are also more likely to work part time, often not out of choice (Pay Rise Campaign 2015), are at higher risk of ‘in-work poverty’ (Hick and Lanau 2018), more likely to be employed in low-paid and insecure jobs (across OECD countries). In the UK, labour market disadvantage is coupled with the rising cost of higher education and crucially the tightening of social security conditionality through Welfare Reform (since 2012) which could be linked to a drop in eligible young people claiming welfare support (Wells 2018). A vast body of literature has emerged in the West on youth policies and the nature of welfare state (Esping-Andersen 1990; Taylor-Gooby 2004; Wallace and Bendit 2009; Pierson 2011). It, however, remains silent on the crucial question of devolution. This ESRC funded research examines the impact of devolution on welfare provision and the sub-state welfare regimes in the UK in the focused context of youth unemployment. The project is progressing in three phases (Wave 1: 2020-21 / Wave 2: 2022-23). Wave1 identified, categorised and compared scales and types of civil society involvement in youth unemployment policy between the three devolved nations of the UK: England, Scotland and Wales. In doing so examined the implications of these differences for both youth unemployment provision and devolved policy arrangements. It has provided an internationally salient analysis located in the global phenomenon of state reconfiguration, the emergence of sub-state welfare regimes and the adoption of welfare pluralism. The research found that devolved social policy in Scotland and, to a lesser extent, Wales goes some way to mitigating the work first policy approach emanating from Westminster. Crucial to this are the key points of convergence and contention between devolved (education) and non-devolved (welfare) areas of youth employment policy on the ground (Pearce and Lagana 2023). The way in which these key points of policy tension play-out in key institutional areas like Jobcentre Plus, is the focus of the second phase of project. Wave 2 focused on ground level sites of service delivery (2022-2023). Research shows that the policy structures and the perceptions of frontline staff about the policy provisions and people claiming them, shape the nature, attitudes and processes of service delivery, and have implications for service claimants and unemployment addressal (Cagliesi and Hawkes 2015; Fletcher 2011; Fletcher and Redman 2022; Rosenthal and Peccei 2006). This phase of project was a more in-depth, critical and comparative examination of the way policy plays out on the ground through a systematic investigation of the perspectives of frontline staff interacting with the young people, in the specific context of devolution. We interviewed frontline staff in England, Scotland and Wales to study how policy is perceived and translated on ground level at the sites of service delivery in these three devolved nations from the following five categories: 1). Work Coaches (Jobcentre Plus- All ages) 2). Youth Employability Coaches (Jobcentre Plus- Young People) 3). Additional Work Coaches (Youth Hubs) 4). Careers Wales / Fair Start / National Careers Service Advisers 5). Civil Society job advisers (CWVYS/Skills Development Scotland /Youth Employment UK) This research will continue to take advantage of the UK’s unique, asymmetrical devolved arrangements to address the identified gap in research examining youth (un)employment under devolved systems of governance. The broader aim is to critique the notion of 'one UK welfare state' and, in doing so, progress our understanding of the impact of decentralisation, devolution and territorial rescaling on welfare state formation across Western Europe.

Abstract copyright UK Data Service and data collection copyright owner.BackgroundThe British Social Attitudes (BSA) survey series began in 1983. The series is designed to produce annual measures of attitudinal movements to complement large-scale government surveys that deal largely with facts and behaviour patterns, and the data on party political attitudes produced by opinion polls. One of the BSA's main purposes is to allow the monitoring of patterns of continuity and change, and the examination of the relative rates at which attitudes, in respect of a range of social issues, change over time. Some questions are asked regularly, others less often. Funding for BSA comes from a number of sources (including government departments, the Economic and Social Research Council and other research foundations), but the final responsibility for the coverage and wording of the annual questionnaires rests with NatCen Social Research (formerly Social and Community Planning Research). The BSA has been conducted every year since 1983, except in 1988 and 1992 when core funding was devoted to the British Election Study (BES).Further information about the series and links to publications may be found on the NatCen Social Research British Social Attitudes webpage. Main Topics:Each year, the BSA interview questionnaire contains a number of 'core' questions, which are repeated in most years. In addition, a wide range of background and classificatory questions is included. The remainder of the questionnaire is devoted to a series of questions (modules) on a range of social, economic, political and moral issues - some are asked regularly, others less often. Cross-indexes of those questions asked more than once appear in the reports. In the 1993 survey, as well as the standard demographic and classificatory items, the following topics were covered: government spending and the National Health Service; labour market participation, the workplace, redundancy and employee decision-making; AIDS; primary and secondary school education; Northern Ireland; charitable giving; illegal drugs; sexual relations; the countryside; transport and the environment; European Community; economic issues and policies (including income and taxation); social security benefits and child maintenance; the environment (ISSP module); environmental consumerism. Multi-stage stratified random sample See documentation for each BSA year for full details. 1993 ACADEMIC ACHIEVEMENT ACCESS TO EDUCATION ACCESS TO INFORMATION ADMINISTRATION ADOPTION AGE AGRICULTURAL LAND AGRICULTURAL POLICY AGRICULTURAL PRODUC... AGRICULTURAL SUBSIDIES AID AIDS DISEASE AIR POLLUTION AMBULANCE SERVICES ANIMAL RIGHTS ANIMAL TESTING ARMED FORCES ATTITUDES BLOOD TRANSFUSIONS BONUS PAYMENTS BUSES BUSINESSES CANNABIS CAR PARKING AREAS CAR SHARING CAREERS GUIDANCE CATHOLICISM CENSORSHIP CHARITABLE ORGANIZA... CHEMICALS CHILD BEHAVIOUR CHILD BENEFITS CHILD DEVELOPMENT CHILD PROTECTION CHILDREN CLINICAL TESTS AND ... COLLECTIVE BARGAINING COMMUNITY IDENTIFIC... COMMUTING CONDITIONS OF EMPLO... CONSERVATION OF NATURE COST OF LIVING COUNTRYSIDE COUNTRYSIDE CONSERV... CRIME AND SECURITY CULTURAL EXPENDITURE CULTURAL IDENTITY CURRENCIES CURRICULUM DEATH PENALTY DECISION MAKING DEMOCRACY DENTISTS DISABLED PERSONS DOMESTIC RESPONSIBI... DRIVING DRUG ABUSE DRUG CONTROL ECONOMIC ACTIVITY ECONOMIC CONDITIONS ECONOMIC GROWTH ECONOMIC POLICY EDUCATIONAL ADMINIS... EDUCATIONAL BACKGROUND EDUCATIONAL CHOICE EDUCATIONAL ENVIRON... EDUCATIONAL EXPENDI... EDUCATIONAL FEES EDUCATIONAL OPPORTU... EDUCATIONAL POLICY EDUCATIONAL STANDARDS EDUCATIONAL TESTS ELDERLY EMPLOYEES EMPLOYERS EMPLOYMENT EMPLOYMENT HISTORY EMPLOYMENT OPPORTUN... EMPLOYMENT PROGRAMMES ENERGY CONSUMPTION ENERGY EFFICIENCY ENERGY SECURITY ENVIRONMENT POLICY ENVIRONMENTAL CHANGES ENVIRONMENTAL CONSE... ENVIRONMENTAL DEGRA... ENVIRONMENTAL LAW ENVIRONMENTAL MANAG... ENVIRONMENTAL MOVEM... ENVIRONMENTAL PLANN... ENVIRONMENTAL QUALITY ETHNIC GROUPS EUROPEAN UNION EVALUATION OF EDUCA... EXAMINATIONS EXPECTATION FAITH SCHOOLS FAMILY MEMBERS FATHERS FIELDS OF STUDY FINANCIAL COMMITMENTS FINANCIAL EXPECTATIONS FINANCIAL RESOURCES FINANCIAL SUPPORT FINANCING FOOD AID FREEDOM OF SPEECH FRINGE BENEFITS FULL TIME EMPLOYMENT FUND RAISING GENDER GENERAL PRACTITIONERS GIFTS GOVERNMENT GOVERNMENT POLICY HEADS OF HOUSEHOLD HEALTH FOODS HEALTH PROFESSIONALS HEALTH RELATED BIOT... HEALTH SERVICES HEROIN HIGHER EDUCATION HOME OWNERSHIP HOMEWORK HOMOSEXUALITY HOSPITAL OUTPATIENT... HOSPITAL SERVICES HOSPITALIZATION HOURS OF WORK HOUSEHOLDS HOUSING HOUSING TENURE HUMAN SETTLEMENT HUNTING INCOME INCOME DISTRIBUTION INDIVIDUAL DEVELOPMENT INDUSTRIAL POLLUTION INDUSTRIES INFIDELITY INFLATION INFORMATION INTEREST COGNITIVE ... INTERNATIONAL COMPE... INTERNATIONAL COOPE... INTERNATIONAL RELAT... INTERNATIONAL ROLE JOB CHANGING JOB EVALUATION JOB HUNTING JOB LOSSES JOB REQUIREMENTS JOB SATISFACTION JOB SECURITY LABOUR MIGRATION LABOUR RELATIONS LAND USE LANDLORDS LAW LAW ENFORCEMENT LAWFUL OPPOSITION LEGISLATION LIFE SUPPORT SYSTEMS MANAGEMENT MANAGERS MARITAL STATUS MEDICAL CARE MEDICAL EXAMINATIONS MEDICAL INSURANCE MEMBERSHIP MILITARY EXPENDITURE MORAL EDUCATION MORAL VALUES MOTHERS MOTOR VEHICLES NATIONAL BACKGROUND NATIONAL CHARACTER NATURAL ENERGY RESO... NATURAL ENVIRONMENT NATURAL SELECTION NEWSPAPER READERSHIP NEWSPAPERS NUCLEAR POWER STATIONS NUCLEAR REACTOR SAFETY NURSING CARE OCCUPATIONAL PENSIONS OCCUPATIONS ORGANIC FARMING PACKAGING PARENT PARTICIPATION PARENT RESPONSIBILITY PARENT SCHOOL RELAT... PARENT TEACHER RELA... PART TIME EMPLOYMENT PARTNERSHIPS BUSINESS PEDESTRIAN FACILITIES PERSONAL EFFICACY PESTICIDES PETROL CONSUMPTION POLICY MAKING POLITICAL ALLEGIANCE POLITICAL ATTITUDES POLITICAL AWARENESS POLITICAL REPRESENT... POLITICAL UNIFICATION POLLUTION POLLUTION CONTROL POVERTY PRE PRIMARY EDUCATION PRE PRIMARY SCHOOLS PREMARITAL SEX PRICE POLICY PRICES PRIMARY EDUCATION PRIMARY SCHOOLS PRIVATE EDUCATION PRIVATE SCHOOLS PRIVATE SECTOR PRODUCTIVITY PROFESSIONAL CONSUL... PROFESSIONAL OCCUPA... PROFIT SHARING PROGRESS PROTESTANTISM PUBLIC EXPENDITURE PUBLIC INFORMATION PUBLIC SECTOR PUBLIC TRANSPORT PURCHASING QUALIFICATIONS QUALITY OF EDUCATION RADIATION HAZARDS RADIOACTIVE WASTES RADIOACTIVITY RAILWAY TRANSPORT RATES OF PAY REDUNDANCY RELIGIOUS AFFILIATION RELIGIOUS ATTENDANCE RELIGIOUS CONFLICT RELIGIOUS DISCRIMIN... RELIGIOUS DOCTRINES RELIGIOUS SEGREGATION RENTED ACCOMMODATION REPRESENTATIVE DEMO... RESOURCES CONSERVATION RESPONSIBILITY RETIREMENT RETRAINING RIGHT TO NON DISCRI... ROAD SAFETY ROAD TOLL CHARGES ROAD TRAFFIC ROAD TRAFFIC NOISE ROADS RURAL AREAS RURAL DEVELOPMENT RURAL TRANSPORT SATISFACTION SCHOOL DISCIPLINE SCHOOL LEAVING AGE SCHOOLS SCHOOLTEACHERS SCIENCE SCIENTIFIC PROGRESS SCIENTISTS SEA RESCUE SECONDARY EDUCATION SECONDARY SCHOOL CU... SECONDARY SCHOOL TE... SECONDARY SCHOOLS SELF EMPLOYED SELF GOVERNMENT SET ASIDE LAND SEXUAL BEHAVIOUR SHARES SHOPPING SICK PERSONS SOCIAL ATTITUDES SOCIAL HOUSING SOCIAL INEQUALITY SOCIAL POLICY SOCIAL RESPONSIBILITY SOCIAL SECURITY SOCIAL SECURITY BEN... SOCIAL SECURITY CON... SOCIAL SUCCESS SOCIAL SUPPORT SOCIAL WELFARE SOCIAL WELFARE FINANCE SOCIAL WELFARE PHIL... SOCIAL WORK SOCIAL WORKERS SOCIALIZATION SPECIAL NEEDS EDUCA... SPOUSE S ECONOMIC A... SPOUSE S EMPLOYMENT SPOUSE S OCCUPATION SPOUSES STANDARD OF LIVING STATE AID STATE CONTROL STATE EDUCATION STATE RESPONSIBILITY STATE RETIREMENT PE... STUDENT BEHAVIOUR STUDENT SELECTION STUDENTS SUPERVISORS Social behaviour an... Social conditions a... TAXATION TEACHER SALARIES TEACHER STUDENT REL... TEACHING TELEPHONES TERMINATION OF SERVICE TRADE UNION MEMBERSHIP TRADE UNIONS TRANSITION FROM SCH... TRANSMISSION OF DIS... TRANSPORT TRANSPORT PLANNING TRANSPORT POLICY TRAVEL TRAVELLING TIME UNEMPLOYED UNEMPLOYMENT UNEMPLOYMENT BENEFITS URBAN AREAS URBAN CENTRES URBAN TRANSPORT VEGETARIANISM VOLUNTARY WORK VOTING INTENTION WAGE DETERMINATION WAGE INCREASES WAGES WALKING WASTE COLLECTION WASTE DISPOSAL AND ... WASTES WATER POLLUTION WATER SUPPLY WELFARE POLICY WILDLIFE PROTECTION WORKING CONDITIONS YOUTH

Over ** million Americans were estimated to be enrolled in the Medicaid program as of 2023. That is a significant increase from around ** million ten years earlier. Medicaid is basically a joint federal and state health program that provides medical coverage to low-income individuals and families. Currently, Medicaid is responsible for ** percent of the nation’s health care bill, making it the third-largest payer behind private insurances and Medicare. From the beginning to ObamacareMedicaid was implemented in 1965 and since then has become the largest source of medical services for Americans with low income and limited resources. The program has become particularly prominent since the introduction of President Obama’s health reform – the Patient Protection and Affordable Care Act - in 2010. Medicaid was largely impacted by this reform, for states now had the opportunity to expand Medicaid eligibility to larger parts of the uninsured population. Thus, the percentage of uninsured in the United States decreased from over ** percent in 2010 to *** percent in 2022. Who is enrolled in Medicaid?Medicaid enrollment is divided mainly into four groups of beneficiaries: children, adults under 65 years of age, seniors aged 65 years or older, and disabled people. Children are the largest group, with a share of approximately ** percent of enrollees. However, their share of Medicaid expenditures is relatively small, with around ** percent. Compared to that, disabled people, accounting for **** percent of total enrollment, were responsible for **** percent of total expenditures. Around half of total Medicaid spending goes to managed care and health plans.

This dataset presents the footprint of participation statistics in the National Bowel Cancer Screening Program (NBCSP) for people aged 50 to 74. The NBCSP began in 2006. It aims to reduce morbidity and mortality from bowel cancer by actively recruiting and screening the eligible target population for early detection or prevention of the disease. The data spans the years of 2015-2017 and is aggregated to Statistical Area Level 2 (SA2) geographic areas from the 2011 Australian Statistical Geography Standard (ASGS).

Cancer is one of the leading causes of illness and death in Australia. Cancer screening programs aim to reduce the impact of selected cancers by facilitating early detection, intervention and treatment. Australia has three cancer screening programs:

• BreastScreen Australia

• National Cervical Screening Program (NCSP)

• National Bowel Cancer Screening Program (NBCSP)

The National cancer screening programs participation data presents the latest cancer screening participation rates and trends for Australia's 3 national cancer screening programs. The data has been sourced from the Australian Institute of Health and Welfare (AIHW) analysis of National Bowel Cancer Screening Program register data, state and territory BreastScreen Australia register data and state and territory cervical screening register data.

For further information about this dataset, visit the data source:Australian Institute of Health and Welfare - National Cancer Screening Programs Participation Data Tables.

Please note:

• AURIN has spatially enabled the original data.

• Participation rates represent the percentage of people invited to screen through the NBCSP during the relevant 2-year period, who returned a completed screening test within that period or by 30 June of the following year. The number of individuals invited to screen excludes those who deferred or opted out without completing their screening test.

• Values assigned to n.p. in the original data have been set to null.

• SA2 areas were assigned to NBCSP invitees using an SA1 to SA2 correspondence. Those invitees without reliable SA1 details were mapped with a postcode to SA2 correspondences instead, which may lead to some minor inaccuracies in results.

• Some invitee SA1 codes and postcodes cannot be attributed to an SA2. These invitees were included in an 'Unknown' group where applicable.

• Some postcodes cross SA2 boundaries, leading to slight inaccuracies.

• Biennial screening for those aged 50-74 is not fully rolled out. During the time period reported, the specific ages invited within the 50-74 age range included 50, 54, 55, 58, 60, 64, 65, 68, 70, 72 and 74.

• These results calculate participation rates using the new NBCSP performance indicator specifications. This indicator now measures a 2-year invitation period and also excludes those who opted off or suspended participation. Therefore, these results cannot be compared to rates reported prior to 2014.

• NBCSP participation rates per area are not related to bowel cancer incidence rates.

• SA2 areas with a numerator less than 20 or a denominator less than 100 have been suppressed. SA2 data for the Blue Mountains - South, Christmas Island, Cocos (Keeling) Islands, Illawarra Catchment Reserve, Jervis Bay and Lord Howe Island were suppressed due to reliability concerns from low numbers in these regions.

• The 2015-2016 period covers 1 January 2015 to 31 December 2016, and the 2016-2017 period covers 1 January 2016 to 31 December 2017.

• Participation by SA2 is not available for the period 2014-2015.

• The number of people in different SA2s may not sum to 'Australia' total due to rounding.

Abstract

The National Family Health Survey 2019-21 (NFHS-5), the fifth in the NFHS series, provides information on population, health, and nutrition for India, each state/union territory (UT), and for 707 districts.

The primary objective of the 2019-21 round of National Family Health Surveys is to provide essential data on health and family welfare, as well as data on emerging issues in these areas, such as levels of fertility, infant and child mortality, maternal and child health, and other health and family welfare indicators by background characteristics at the national and state levels. Similar to NFHS-4, NFHS-5 also provides information on several emerging issues including perinatal mortality, high-risk sexual behaviour, safe injections, tuberculosis, noncommunicable diseases, and the use of emergency contraception.

The information collected through NFHS-5 is intended to assist policymakers and programme managers in setting benchmarks and examining progress over time in India’s health sector. Besides providing evidence on the effectiveness of ongoing programmes, NFHS-5 data will help to identify the need for new programmes in specific health areas.

The clinical, anthropometric, and biochemical (CAB) component of NFHS-5 is designed to provide vital estimates of the prevalence of malnutrition, anaemia, hypertension, high blood glucose levels, and waist and hip circumference, Vitamin D3, HbA1c, and malaria parasites through a series of biomarker tests and measurements.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15 to 54

Universe

The survey covered all de jure household members (usual residents), all women aged 15-49, all men age 15-54, and all children aged 0-5 resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

A uniform sample design, which is representative at the national, state/union territory, and district level, was adopted in each round of the survey. Each district is stratified into urban and rural areas. Each rural stratum is sub-stratified into smaller substrata which are created considering the village population and the percentage of the population belonging to scheduled castes and scheduled tribes (SC/ST). Within each explicit rural sampling stratum, a sample of villages was selected as Primary Sampling Units (PSUs); before the PSU selection, PSUs were sorted according to the literacy rate of women age 6+ years. Within each urban sampling stratum, a sample of Census Enumeration Blocks (CEBs) was selected as PSUs. Before the PSU selection, PSUs were sorted according to the percentage of SC/ST population. In the second stage of selection, a fixed number of 22 households per cluster was selected with an equal probability systematic selection from a newly created list of households in the selected PSUs. The list of households was created as a result of the mapping and household listing operation conducted in each selected PSU before the household selection in the second stage. In all, 30,456 Primary Sampling Units (PSUs) were selected across the country in NFHS-5 drawn from 707 districts as on March 31st 2017, of which fieldwork was completed in 30,198 PSUs.

For further details on sample design, see Section 1.2 of the final report.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Four survey schedules/questionnaires: Household, Woman, Man, and Biomarker were canvassed in 18 local languages using Computer Assisted Personal Interviewing (CAPI).

Cleaning operations

Electronic data collected in the 2019-21 National Family Health Survey were received on a daily basis via the SyncCloud system at the International Institute for Population Sciences, where the data were stored on a password-protected computer. Secondary editing of the data, which required resolution of computer-identified inconsistencies and coding of open-ended questions, was conducted in the field by the Field Agencies and at the Field Agencies central office, and IIPS checked the secondary edits before the dataset was finalized.

Field-check tables were produced by IIPS and the Field Agencies on a regular basis to identify certain types of errors that might have occurred in eliciting information and recording question responses. Information from the field-check tables on the performance of each fieldwork team and individual investigator was promptly shared with the Field Agencies during the fieldwork so that the performance of the teams could be improved, if required.

Response rate

A total of 664,972 households were selected for the sample, of which 653,144 were occupied. Among the occupied households, 636,699 were successfully interviewed, for a response rate of 98 percent.

In the interviewed households, 747,176 eligible women age 15-49 were identified for individual women’s interviews. Interviews were completed with 724,115 women, for a response rate of 97 percent. In all, there were 111,179 eligible men age 15-54 in households selected for the state module. Interviews were completed with 101,839 men, for a response rate of 92 percent.

As of October 2024, there were 133.89 million full-time employees in the United States. This is a slight decrease from the previous month, when there were 134.15 million full-time employees. The impact COVID-19 on employment In December 2019, the COVID-19 virus began its spread across the globe. Since being classified as a pandemic, the virus caused a global health crisis that has taken the lives of millions of people worldwide. The COVID-19 pandemic changed many facets of society, most significantly, the economy. In the first years, many businesses across all industries were forced to shut down, with large numbers of employees being laid off. The economy continued its recovery in 2022 with the nationwide unemployment rate returning to a more normal 3.4 percent as of April 2023. Unemployment benefits Because so many people in the United States lost their jobs, record numbers of individuals applied for unemployment insurance for the first time. As an early response to this nation-wide upheaval, the government issued relief checks and extended the benefits paid by unemployment insurance. In May 2020, the amount of unemployment insurance benefits paid rose to 23.73 billion U.S. dollars. As of December 2022, this value had declined to 2.24 billion U.S. dollars.

Release Date: 2020-06-09.Release Schedule:.The data in this file come from the 2017 Economic Census data files released on a flow basis starting in September 2019. As such, preliminary U.S. totals released in September 2019 will be superseded with final totals, by sector, once data for all states have been released. Users should be aware that during the release of this consolidated file, data at more detailed North American Industry Classification System (NAICS) and geographic levels may not add to higher-level totals. However, at the completion of the economic census (once all the component files have been released), the detailed data in this file will add to the totals. For more information about economic census planned data product releases, see Economic Census: About: 2017 Release Schedules...Key Table Information:.U.S. totals released in September 2019 will be superseded with final totals, by sector, once data for all states have been released. .Includes only establishments and firms with payroll..Data may be subject to employment- and/or sales-size minimums that vary by industry...Data Items and Other Identifying Records:.Number of firms.Number of establishments.Sales, value of shipments, or revenue ($1,000).Annual payroll ($1,000).First-quarter payroll ($1,000).Number of employees.Operating expenses ($1,000).Range indicating percent of total sales, value of shipments, or revenue imputed.Range indicating percent of total annual payroll imputed.Range indicating percent of total employees imputed..Data are published by Tax Status (All establishments, Establishments subject to federal income tax, and Establishments exempt from federal income tax)...Geography Coverage:.The data are shown for employer establishments and firms at the U.S., State, Combined Statistical Area, Metropolitan and Micropolitan Statistical Area, Metropolitan Division, Consolidated City, County (and equivalent), and Economic Place (and equivalent; incorporated and unincorporated) levels that vary by industry. For information about economic census geographies, including changes for 2017, see Economic Census: Economic Geographies...Industry Coverage:.The data are shown at the 2- through 6-digit, and selected 7-digit 2017 NAICS code levels. For information about NAICS, see Economic Census: Technical Documentation: Code Lists...Footnotes:.Not applicable...FTP Download:.Download the entire table at: https://www2.census.gov/programs-surveys/economic-census/data/2017/sector62/EC1762BASIC.zip..API Information:.Economic census data are housed in the Census Bureau API. For more information, see Explore Data: Developers: Available APIs: Economic Census..Methodology:.To maintain confidentiality, the U.S. Census Bureau suppresses data to protect the identity of any business or individual. The census results in this file contain sampling and/or nonsampling error. Data users who create their own estimates using data from this file should cite the U.S. Census Bureau as the source of the original data only...To comply with disclosure avoidance guidelines, data rows with fewer than three contributing establishments are not presented. Additionally, establishment counts are suppressed when other select statistics in the same row are suppressed. For detailed information about the methods used to collect and produce statistics, including sampling, eligibility, questions, data collection and processing, data quality, review, weighting, estimation, coding operations, confidentiality protection, sampling error, nonsampling error, and more, see Economic Census: Technical Documentation: Methodology...Symbols:.D - Withheld to avoid disclosing data for individual companies; data are included in higher level totals.N - Not available or not comparable.S - Estimate does not meet publication standards because of high sampling variability, poor response quality, or other concerns about the estimate quality. Unpublished estimates derived from this table by subtraction are subject to these same limitations and should not be attributed to the U.S. Census Bureau. For a description of publication standards and the total quantity response rate, see link to program methodology page..X - Not applicable.A - Relative standard error of 100% or more.r - Revised.s - Relative standard error exceeds 40%.For a complete list of symbols, see Economic Census: Technical Documentation: Data Dictionary.. .Source:.U.S. Census Bureau, 2017 Economic Census.For information about the economic census, see Business and Economy: Economic Census...Contact Information:.U.S. Census Bureau.For general inquiries:. (800) 242-2184/ (301) 763-5154. ewd.outreach@census.gov.For specific data questions:. (800) 541-8345.For additional contacts, see Economic Census: About: Contact Us.

Abstract

This data is from the first round of a unique, cross-country panel survey conducted in Uganda by the Secure Livelihoods Research Consortium (SLRC). The Overseas Development Institute (ODI) is the lead organisation of SLRC. SLRC partners who participated in the survey were: the Centre for Poverty Analysis (CEPA) in Sri Lanka, Feinstein International Center (FIC, Tufts University), the Sustainable Development Policy Institute(SDPI) in Pakistan, Humanitarian Aid and Reconstruction, based at Wageningen University (WUR) in the Netherlands, the Nepal Centre for Contemporary Research (NCCR), and the Food and Agriculture Organization (FAO).

This survey generated the first round of data on people's livelihoods, their access to and experience of basic services, and their views of governance actors. SLRC will attempt to re-interview the same respondents in 2015 to find out how the livelihoods and governance perceptions of people shift (or not) over time, and which factors may have contributed towards that change.

Geographic coverage

Regional

Analysis unit

Households

Universe

Randomly selected households in purposely sampled sites (sampling procedure varied slightly by country). Within a selected household, only one household members was interviewed about the household. Respondents were adults and we aimed to interview a fairly even share of men/ women. In some countries this was achieved, but in other countries the share of male respondents is substantially higher (e.g. Pakistan).

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling strategy was designed to select households that are relevant to the main research questions and as well as being of national relevance, while also being able to produce statistically significant conclusions at the study and village level. To meet these objectives, purposive and random sampling were combined at different stages of the sampling strategy. The first stages of the sampling process involved purposive sampling, with random sampling only utilized in the last stage of the process. Sampling locations were selected purposely (including districts and locations within districts), and then randomly households were selected within these locations. A rigorous sample is geared towards meeting the objectives of the research. The samples are not representative for the case study countries and cannot be used to represent the case study countries as a whole, nor for the districts. The samples are representative at the village level, with the exception of Uganda.

Sampling locations (sub-regions or districts, sub-districts and villages) were purposively selected, using criteria, such as levels of service provision or levels of conflict, in order to locate the specific groups of interest and to select geographical locations that are relevant to the broader SLRC research areas and of policy relevance at the national level. For instance, locations experienced high/ low levels of conflict and locations with high/ low provision of services were selected and locations that accounted for all possible combinations of selection criteria were included. Survey locations with different characteristics were chose, so that we could explore the relevance of conflict affectedness, access to services and variations in geography and livelihoods on our outcome variables. Depending on the administrative structure of the country, this process involved selecting a succession of sampling locations (at increasingly lower administrative units).

The survey did not attempt to achieve representativeness at the country /or district level, but it aimed for representativeness at the sub-district /or village level through random sampling (Households were randomly selected within villages so that the results are representative and statistically significant at the village level and so that a varied sample was captured. Households were randomly selected using a number of different tools, depending on data availability, such as random selection from vote registers (Nepal), construction of household listings (DRC) and a quasi-random household process that involved walking in a random direction for a random number of minutes (Uganda).

The samples are statistically significant at the survey level and village level (in all countries) and at the district level in Sri Lanka and sub-region level in Uganda. The sample size was calculated with the aim to achieve statistical significance at the study and village level, and to accommodate the available budget, logistical limitations, and to account for possible attrition between 2012-2015. In a number of countries estimated population data had to be used, as recent population data were not available.

The minimum overall sample size required to achieve significance at the study level, given population and average household size across districts, was calculated using a basic sample size calculator at a 95% confidence level and confidence interval of 5. The sample size at the village level was again calculated at the using a 95% confidence level and confidence interval of 5. Finally, the sample was increased by 20% to account for possible attrition between 2012 and 2015, so that the sample size in 2015 is likely to be still statistically significant. The overall sample required to achieve the sampling objectives in selected districts in each country ranged from 1,259 to 3,175 households.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

CSPro was used for data entries in most countries.

Data editing took place at a number of stages throughout the processing, including: • Office editing and coding • During data entry • Structure checking and completeness • Extensive secondary editing conducted by ODI

Response rate

The required sample sizes were achieved in all countries. Response rates were extremely high, ranging from 99%-100%.

Sampling error estimates

No further estimations of sampling error was conducted beyond the sampling design stage.

Data appraisal

Done on an ad hoc basis for some countries, but not consistently across all surveys and domains.

Abstract

This data is from the first round of a unique, cross-country panel survey conducted in Pakistan by the Secure Livelihoods Research Consortium (SLRC). The Overseas Development Institute (ODI) is the lead organisation of SLRC. SLRC partners who participated in the survey were: the Centre for Poverty Analysis (CEPA) in Sri Lanka, Feinstein International Center (FIC, Tufts University), the Sustainable Development Policy Institute(SDPI) in Pakistan, Humanitarian Aid and Reconstruction, based at Wageningen University (WUR) in the Netherlands, the Nepal Centre for Contemporary Research (NCCR), and the Food and Agriculture Organization (FAO).

This survey generated the first round of data on people's livelihoods, their access to and experience of basic services, and their views of governance actors. SLRC will attempt to re-interview the same respondents in 2015 to find out how the livelihoods and governance perceptions of people shift (or not) over time, and which factors may have contributed towards that change.

Geographic coverage

Pakistan: Swat and Lower Dir districts of Khyber Pakhtunkhwa (KP) Rural and urban

Analysis unit

Some questions are at the level of individuals in household (e.g. livelihood activities, education levels); other questions are at the household level (e.g. assets). A sizeable share of the questionnaire is devoted to perceptions based questions, which are at the individual (respondent) level.

Universe

Randomly selected households in purposely sampled sites (sampling procedure varied slightly by country).

Within a selected household, only one household members was interviewed about the household. Respondents were adults and we aimed to interview a fairly even share of men/ women. In some countries this was achieved, but in other countries the share of male respondents is substantially higher (e.g. Pakistan).

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling strategy was designed to select households that are relevant to the main research questions and as well as being of national relevance, while also being able to produce statistically significant conclusions at the study and village level. To meet these objectives, purposive and random sampling were combined at different stages of the sampling strategy. The first stages of the sampling process involved purposive sampling, with random sampling only utilized in the last stage of the process. Sampling locations were selected purposely (including districts and locations within districts), and then randomly households were selected within these locations. A rigorous sample is geared towards meeting the objectives of the research. The samples are not representative for the case study countries and cannot be used to represent the case study countries as a whole, nor for the districts. The samples are representative at the village level, with the exception of Uganda.

Sampling locations (sub-regions or districts, sub-districts and villages) were purposively selected, using criteria, such as levels of service provision or levels of conflict, in order to locate the specific groups of interest and to select geographical locations that are relevant to the broader SLRC research areas and of policy relevance at the national level. For instance, locations experienced high/ low levels of conflict and locations with high/ low provision of services were selected and locations that accounted for all possible combinations of selection criteria were included. Survey locations with different characteristics were chose, so that we could explore the relevance of conflict affectedness, access to services and variations in geography and livelihoods on our outcome variables. Depending on the administrative structure of the country, this process involved selecting a succession of sampling locations (at increasingly lower administrative units).

The survey did not attempt to achieve representativeness at the country /or district level, but it aimed for representativeness at the sub-district /or village level through random sampling (Households were randomly selected within villages so that the results are representative and statistically significant at the village level and so that a varied sample was captured. Households were randomly selected using a number of different tools, depending on data availability, such as random selection from vote registers (Nepal), construction of household listings (DRC) and a quasi-random household process that involved walking in a random direction for a random number of minutes (Uganda).

The samples are statistically significant at the survey level and village level (in all countries) and at the district level in Sri Lanka and sub-region level in Uganda. The sample size was calculated with the aim to achieve statistical significance at the study and village level, and to accommodate the available budget, logistical limitations, and to account for possible attrition between 2012-2015. In a number of countries estimated population data had to be used, as recent population data were not available.

The minimum overall sample size required to achieve significance at the study level, given population and average household size across districts, was calculated using a basic sample size calculator at a 95% confidence level and confidence interval of 5. The sample size at the village level was again calculated at the using a 95% confidence level and confidence interval of 5. . Finally, the sample was increased by 20% to account for possible attrition between 2012 and 2015, so that the sample size in 2015 is likely to be still statistically significant.

The overall sample required to achieve the sampling objectives in selected districts in each country ranged from 1,259 to 3,175 households.

Mode of data collection

Face-to-face [f2f]

Research instrument

One questionnaire per country that includes household level, individual level and respondent level perceptions based questions.

The general structure and content of the questionnaire is similar across all five countries, with about 80% of questions similar, but tailored to the country-specific process. Country-specific surveys were tailored on the basis of a generic survey instrument that was developed by ODI specifically for this survey.

The questionnaires are published in English.

Cleaning operations

CSPro was used for data entries in most countries.

Data editing took place at a number of stages throughout the processing, including: • Office editing and coding • During data entry • Structure checking and completeness • Extensive secondary editing conducted by ODI

Response rate

The required sample sizes were achieved in all countries. Response rates were extremely high, ranging from 99%-100%.

Sampling error estimates

No further estimations of sampling error was conducted beyond the sampling design stage.

Data appraisal

Done on an ad hoc basis for some countries, but not consistently across all surveys and domains.