This dataset contains school-level expenditures reported by major functional spending category starting with fiscal year 2019. It also includes school-level enrollment, demographic, and performance indicators as well as teacher salary and staffing data.

The dataset shows school-level per pupil expenditures by major functional expenditure categories and funding sources, including state and local funds (general fund and state grants) and federal funds.

School districts only report instructional expenditures by school. This report attributes other costs to each school on a per pupil basis to show a full resource picture. The three cost centers are:

1. Non-instructional per pupil spending reported at the school district level;
2. Instructional per pupil spending reported at the district level; and
3. Instructional per pupil spending reported at the school level

Economically Disadvantaged was used 2015-2021. Low Income was used prior to 2015, and a different version of Low Income has been used since 2022. Please see the DESE Researcher's Guide for more information.

This dataset is one of three containing the same data that is also published in the School Finance Dashboard: District Expenditures by Spending Category District Expenditures by Function Code School Expenditures by Spending Category

List of Indicators by Category

Student Enrollment

• In-District FTE Pupils
• Out-of-District FTE Pupils
• Total FTE Pupils

Student Demographics

• Student Headcount
• Low-Income % Headcount
• English Learner % Headcount
• Students with Disabilities % Headcount

Teacher Salaries

• Teacher FTE
• Teachers per 100 FTE Students
• Average Teacher Salary

Other Staff

• Instructional Coach FTE
• Instructional Support FTE
• Special Education Instructional Support FTE
• Paraprofessional FTE

MCAS Performance

• ELA Grades 3-8 % Meets Exceeds
• Math Grades 3-8 % Meets Exceeds
• ELA Grade 10 % Meets Exceeds
• Math Grade 10 % Meets Exceeds

District-Level FTE Pupils

District-Level State and Local Non-Instructional Expenditures Per Pupil

• Administration
• Benefits and Fixed Costs
• Operations and Maintenance
• Pupil Services

District-Level Federal Non-Instructional Expenditures Per Pupil

• Administration
• Benefits and Fixed Costs
• Operations and Maintenance
• Pupil Services

Sub-total A

District-Level State and Local Instructional Expenditures Per Pupil

• Guidance and Psychological Services
• Instructional Leaders
• Instructional Materials
• Other Teaching Services
• Professional Development
• Teachers

District-Level Federal Instructional Expenditures Per Pupil

• Guidance and Psychological Services
• Instructional Leaders
• Instructional Materials
• Other Teaching Services
• Professional Development
• Teachers

Sub-total B

School-Level State and Local Instructional Expenditures Per Pupil

• Guidance and Psychological Services
• Instructional Leaders
• Instructional Materials
• Other Teaching Services
• Professional Development
• Teachers

School-Level Federal Instructional Expenditures Per Pupil

• Guidance and Psychological Services
• Instructional Leaders
• Instructional Materials
• Other Teaching Services
• Professional Development
• Teachers

Sub-total C

Total A+B+C

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2023 American Community Survey 1-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Respondents could report more than one major for their bachelor's degree. This table only counts the first major that was reported and does not necessarily reflect the first degree earned..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

In 2021, about 37.7 percent of the U.S. population who were aged 25 and above had graduated from college or another higher education institution, a slight decline from 37.9 the previous year. However, this is a significant increase from 1960, when only 7.7 percent of the U.S. population had graduated from college.

Demographics

Educational attainment varies by gender, location, race, and age throughout the United States. Asian-American and Pacific Islanders had the highest level of education, on average, while Massachusetts and the District of Colombia are areas home to the highest rates of residents with a bachelor’s degree or higher. However, education levels are correlated with wealth. While public education is free up until the 12th grade, the cost of university is out of reach for many Americans, making social mobility increasingly difficult.

Earnings

White Americans with a professional degree earned the most money on average, compared to other educational levels and races. However, regardless of educational attainment, males typically earned far more on average compared to females. Despite the decreasing wage gap over the years in the country, it remains an issue to this day. Not only is there a large wage gap between males and females, but there is also a large income gap linked to race as well.

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from the U.S. Census Bureau’s American Community Survey 5-year estimates for 2013-2017, to show levels of educational attainment by State of Georgia in the Atlanta region. The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2013-2017). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website. Naming conventions: Prefixes:NoneCountpPercentrRatemMedianaMean (average)tAggregate (total)chChange in absolute terms (value in t2 - value in t1)pchPercent change ((value in t2 - value in t1) / value in t1)chpChange in percent (percent in t2 - percent in t1)Suffixes:NoneChange over two periods_eEstimate from most recent ACS_mMargin of Error from most recent ACS_00Decennial 2000 Attributes: SumLevelSummary level of geographic unit (e.g., County, Tract, NSA, NPU, DSNI, SuperDistrict, etc)GEOIDCensus tract Federal Information Processing Series (FIPS) code NAMEName of geographic unitPlanning_RegionPlanning region designation for ARC purposesAcresTotal area within the tract (in acres)SqMiTotal area within the tract (in square miles)CountyCounty identifier (combination of Federal Information Processing Series (FIPS) codes for state and county)CountyNameCounty NamePop25P_e# Population 25 years and over, 2017Pop25P_m# Population 25 years and over, 2017 (MOE)NoHS_e# Population 25 years and over, less than 9th grade education, 2017NoHS_m# Population 25 years and over, less than 9th grade education, 2017 (MOE)pNoHS_e% Population 25 years and over, less than 9th grade education, 2017pNoHS_m% Population 25 years and over, less than 9th grade education, 2017 (MOE)SomeHS_e# Population 25 years and over, 9th-12th grade, no diploma, 2017SomeHS_m# Population 25 years and over, 9th-12th grade, no diploma, 2017 (MOE)pSomeHS_e% Population 25 years and over, 9th-12th grade, no diploma, 2017pSomeHS_m% Population 25 years and over, 9th-12th grade, no diploma, 2017 (MOE)HSGrad_e# Population 25 years and over, high school graduate (includes GED), 2017HSGrad_m# Population 25 years and over, high school graduate (includes GED), 2017 (MOE)pHSGrad_e% Population 25 years and over, high school graduate (includes GED), 2017pHSGrad_m% Population 25 years and over, high school graduate (includes GED), 2017 (MOE)SomeColl_e# Population 25 years and over, some college, no degree, 2017SomeColl_m# Population 25 years and over, some college, no degree, 2017 (MOE)pSomeColl_e% Population 25 years and over, some college, no degree, 2017pSomeColl_m% Population 25 years and over, some college, no degree, 2017 (MOE)Associates_e# Population 25 years and over, associate's degree, 2017Associates_m# Population 25 years and over, associate's degree, 2017 (MOE)pAssociates_e% Population 25 years and over, associate's degree, 2017pAssociates_m% Population 25 years and over, associate's degree, 2017 (MOE)BA_e# Population 25 years and over, bachelor's degree, 2017BA_m# Population 25 years and over, bachelor's degree, 2017 (MOE)pBA_e% Population 25 years and over, bachelor's degree, 2017pBA_m% Population 25 years and over, bachelor's degree, 2017 (MOE)GradProf_e# Population 25 years and over, graduate or professional degree, 2017GradProf_m# Population 25 years and over, graduate or professional degree, 2017 (MOE)pGradProf_e% Population 25 years and over, graduate or professional degree, 2017pGradProf_m% Population 25 years and over, graduate or professional degree, 2017 (MOE)LtHS_e# Population 25 years and over, Less than high school graduate, 2017LtHS_m# Population 25 years and over, Less than high school graduate, 2017 (MOE)pLtHS_e% Population 25 years and over, Less than high school graduate, 2017pLtHS_m% Population 25 years and over, Less than high school graduate, 2017 (MOE)HSPlus_e# Population 25 years and over, high school graduate or higher, 2017HSPlus_m# Population 25 years and over, high school graduate or higher, 2017 (MOE)pHSPlus_e% Population 25 years and over, high school graduate or higher, 2017pHSPlus_m% Population 25 years and over, high school graduate or higher, 2017 (MOE)BAPlus_e# Population 25 years and over, bachelor's degree or higher, 2017BAPlus_m# Population 25 years and over, bachelor's degree or higher, 2017 (MOE)pBAPlus_e% Population 25 years and over, bachelor's degree or higher, 2017pBAPlus_m% Population 25 years and over, bachelor's degree or higher, 2017 (MOE)last_edited_dateLast date the feature was edited by ARC Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2013-2017 For additional information, please visit the Census ACS website.

Chapter 3 looks at the institutional factors that contribute to explaining the relationship between parent’s education and children’s education. Data for 48 countries in total, from multiple harmonized surveys, are utilised. A total of 149 surveys are included. Using multivariate regressions, we first present the correlation coefficients of the relationship between parent’s education and children’s education. These coefficients then serve as the dependent variable in the regression analysis with the institutional factors at the second stage. To this end, secondary data are obtained from the household Demographic and Health Surveys (DHS), and from the U.S. Agency for International Development (USAID) and the World Bank data catalogue. The DHS are nationally representative cross-sectional surveys where data on impact evaluation indicators on the population, health, and nutrition in over 90 countries are represented. The primary respondents of the surveys are women of reproductive age, between 15-49 years, who respond to a household questionnaire and a woman’s questionnaire (DHS Program, 2020). The man’s questionnaire is responded to by men of reproductive age (typically 15 to 49, 54, or 59). In the household questionnaire, the respondent provides information on household membership, individual characteristics, household head, health, housing, consumer goods, and living conditions (DHS Program, 2020). The factors from the USAID and the World Bank data catalogue are part of the world development indicators (WDI) and the worldwide governance indicators (WGI). Corruption estimates, political stability estimates, and voice and accountability estimates are taken from the WGI while the others (GDP, prevalence of HIV, life expectancy at birth, female-male labour force participation, government expenditure on education, pupil-teacher ratio, primary school starting age, primary school duration, secondary school duration, compulsory years of education, fixed telephone subscriptions, and mobile cellular subscriptions) are from the WDI. The WDI is a compilation of high-quality, relevant, and internationally comparable statistics about global development and the fight against poverty (World Bank, 2020b). 1600 time series indicators are contained in the database for 217 countries. These indicators are organized according to six main thematic areas that are poverty and inequality, people, environment, economy, states and markets, global links (World Bank, 2020b). The WGI are nationally comparable indicators of government selection, monitoring, replacement, effectiveness, and the respect of citizens and the state. The worldwide governance indicators generally report on six broad governance dimensions for over 215 countries and territories. These dimensions are government effectiveness, control of corruption, rule of law, voice and accountability, regulatory quality, and political stability and absence of violence (World Bank, 2019). Specifically, we focus on GDP, the prevalence of HIV, life expectancy at birth, female-male labour force participation, government expenditure on education, pupil-teacher ratio, primary school starting age, primary school duration, secondary school duration, compulsory years of education, fixed telephone subscriptions, mobile cellular subscriptions, the extent of corruption, the extent of political stability, and the extent of voice and accountability. The factors used in this chapter are selected based on data availability. The process looks at the correlation between these factors and the intergenerational correlation of education. The results show that these institutional factors account for 39% of the explained cross-country variation in the intergenerational correlation of education. The pupil-teacher ratio, primary school duration, and compulsory years of education reduce intergenerational correlation of education by 0.03 years, 0.03 years, and 0.02 years respectively, following a one standard deviation change in the variables. Besides these variables, GDP, female-male labour force participation, and extent of voice and accountability reduce intergenerational correlation of education by 0.01 years, 0.03 years, and 0.03 years respectively, following a one standard deviation change in the variables. This confirms our second hypothesis on favourable institutional characteristics being able to reduce intergenerational correlation of education.

Updated yearly using enrollment data, employment data, information from websites, phone calls, and any other resources as available. At time of update fields were added to include employment data, enrollment data, building code, school code, TAZ08, and school website. Please verify information before use as it will be updated on an ongoing basis. Please contact COMPASS with any questions or any knowledge of updates, alterations or modifications that need to be made. FIELDS:UpdateBy: Name or initials of last person to update the recordUpdateOn: Date the record was last updated onSchoolName: Name of the school at the pointSchoolDist: School district the point physically is withinType: Describes the nature of the building and grade/age range of students enrolledValues:PRE K: Preschool &/or Nursery school & Day CareELEMENTARY: Traditional Kindergarten through 6thgradeK-8: Kindergarten through 8th gradeK-12: Kindergarten through 12th grade MIDDLE: 6thgrade through 8thgradeJUNIOR HS: 7thgrade through 9th gradeSENIOR HS: 9th through 12thgradePOST SR: College, University, Technical or Professional SchoolsOTHER: Irregular range of grades or ages ADMIN: Administrative Building/ServicesRETAIL-EDU: Retailor or seller of educational materials or suppliesSiteAddres: Physical address of the school or buildingSiteCity: City the school or building is located inSiteState: State the school or building is located inSiteZip: Zip code the school or building is located inSiteCounty: County the school or building is located inBuilding_Code: Building Code assigned to the school according to the 2012 Enrollment data sheet, where the number is not available or this does not apply the value used is ‘N/A’School_Code:School Code assigned to the school according to the 2012 Enrollment data sheet, where the number is not available or this does not apply the value used is ‘N/A’School_JoinID: Concatonated field of Building Code + School Code as a 7 digit code assigned by the 2012 Enrollment data sheet. If the School Code is only a three digit code an additional ‘0’ is added before the code to achieve the full seven digits necessary for the field. Where the number is not available or this does not apply the value used is ‘N/A’Notes: Any pertinent information that was not suited for another fieldEmploy13:Number of employees according to the 2013 employment final point fileTAZ08: TAZ08 in which the point liesType_II:Describes the nature of the school – public vs private runValues:PUBLIC: Owned, operated, funded, governed and sanctioned by the Idaho Department of EducationPRIVATE: Owned, operated & funded by private donors, foundation, trust or other source. May or may not meet State or Federal curriculum requirements/standardsOPT_ENROLL: Y/N field indicating if there is an open enrollment boundary for the schoolType_III:Any further information or description about the school. Values:AG PRODUCTION & RESEARCH: U of I extension campuses with specific research focus and use intentionALTERNATIVE: Any alternative learning environment, field may contain a ‘-_’ for a further description about what the alternative style is; teen parents, night school, at risk, ect…CHARTER: Any public school classified as a charter by the State Board of EducationCOLLEGE, UNIVERSITY, TRADE SCHOOL: Any post-secondary education institution, includes graduate programs, law schools and vocational training programs.COMMUNITY EDUCATION – ENVIRONMENTAL: Nontraditional classroom facilities which offer courses for the community (child and adult) to promote higher learning and understanding of the environment, care of the environment and environmental issues.CULTURAL: Any school which offers cultural enrichment or a multi-cultural learning environment. Field may also contain ‘-_’ to describe what the specific culture the school educates in.DURRING INCARCERATION: Schools are run through the Juvenile Detention Centers. These schools are acknowledged by the State Department of Education, and are recognized by the State. Available to students during the time of their incarceration. FAITH BASED: Any school run by or affiliated with a religious organization or faith based system of beliefs, and incorporates values and beliefs into the curriculum.FAITH BASED BOARDING: Any school run by or affiliated with a religious organization or faith based system of beliefs, and incorporates values and beliefs into the curriculum. These school also offer a live in facility option to students.HEADSTART: Formal pre-kindergarten education programsINTERNATIONAL BACCALAUREATE: School which offers programs for International Baccalaureate credit for studentsLANGUAGE AND CULTURE: Private (non-charter) language and culture focused schools. Field may also contain ‘-_’ to describe what the specific culture the school educates in.MAGNET: Any school with a particular subject area focus intended to draw students with natural aptitudes or specific interests, these schools have open enrollment boundaries with an application process, as long as the student resides within the school district to which the school is a part of. MONTESSORI: Private schools with a focus on experiential learning rather than traditional learning methods. MUSIC: Schools with an additional focus on musical aptitude and methodsONLINE OR HOME SCHOOL: Virtual or online classroom optionsSPECIAL NEEDS: Schools with facilities and resources for students with special needs or additional assistance and attention. Access: Indicates whether the point is the actual building location itself or an access point. Building locations are coded as "Loc" and access points are coded as "PV" for pedestrian/vehicle access.Main_Acc: Identifies if an access point is the main entrance/exit location for each school.Source: Where the numbers for the employment data and/or student enrollment were gathered from.Enrollment: # of students enrolled according to the 2012 enrollment data, or based on best information we were otherwise able to obtain (if not on the 2012 enrollment data).Website:Most recent URL if able to locate, if unable to locate indicated in field with “UTL”Status: Used to describe if the school is currently active, closed, or planned (used to query out inactive schools for performance monitoring purposes)UniqueID: Made by combining District number and building number in from DDDBBBB. _Updated Fall 2013 From School District WebsitesUpdated 9/11/11 From School District WebsitesJuly 2010 . Canyon County has since requested a new data structure to match their address points. The new schools file has the new structure. The point location of this file is identical to the new schools point file May 2010 - Edited the Ada County schools to align with school sites on NAIP imagery and confirmed schools against respective school district websites Jan - March 2010 - Worked with Jay Young over a several month period and several renditions to reconcile the Canyon County side of this file. December 2009 - Merged with Jay Young's Canyon point file in order to build a new data structure that meets Emergency Service data standards. Went through point by point to ensure alignement with buildings on NAIP imagery and attribute values.

Results from multivariable logistic regression models estimating unadjusted and adjusted odds ratio for demographic characteristics associated with parents’ plans to not have their child return to school.

US: GERD per Capita Population: Current PPP data was reported at 2,767.780 % in 2022. This records an increase from the previous number of 2,472.601 % for 2021. US: GERD per Capita Population: Current PPP data is updated yearly, averaging 972.628 % from Dec 1981 (Median) to 2022, with 42 observations. The data reached an all-time high of 2,767.780 % in 2022 and a record low of 316.293 % in 1981. US: GERD per Capita Population: Current PPP data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s United States – Table US.OECD.MSTI: Gross Domestic Expenditure on Research and Development: OECD Member: Annual.

For the United States, from 2021 onwards, changes to the US BERD survey questionnaire allowed for more exhaustive identification of acquisition costs for ‘identifiable intangible assets’ used for R&D. This has resulted in a substantial increase in reported R&D capital expenditure within BERD. In the business sector, the funds from the rest of the world previously included in the business-financed BERD, are available separately from 2008. From 2006 onwards, GOVERD includes state government intramural performance (most of which being financed by the federal government and state government own funds). From 2016 onwards, PNPERD data are based on a new R&D performer survey. In the higher education sector all fields of SSH are included from 2003 onwards.

Following a survey of federally-funded research and development centers (FFRDCs) in 2005, it was concluded that FFRDC R&D belongs in the government sector - rather than the sector of the FFRDC administrator, as had been reported in the past. R&D expenditures by FFRDCs were reclassified from the other three R&D performing sectors to the Government sector; previously published data were revised accordingly. Between 2003 and 2004, the method used to classify data by industry has been revised. This particularly affects the ISIC category “wholesale trade” and consequently the BERD for total services.

U.S. R&D data are generally comparable, but there are some areas of underestimation:

1. i) Up to 2008, Government sector R&D performance covers only federal government activities. That by State and local government establishments is excluded;
2. ii) Except for the Government and the Business Enterprise sectors, the R&D data exclude most capital expenditures. For the Business Enterprise sector, depreciation is reported in place of gross capital expenditures up to 2014. Higher education (and national total) data were revised back to 1998 due to an improved methodology that corrects for double-counting of R&D funds passed between institutions.

Breakdown by type of R&D (basic research, applied research, etc.) was also revised back to 1998 in the business enterprise and higher education sectors due to improved estimation procedures.

The methodology for estimating researchers was changed as of 1985. In the Government, Higher Education and PNP sectors the data since then refer to employed doctoral scientists and engineers who report their primary work activity as research, development or the management of R&D, plus, for the Higher Education sector, the number of full-time equivalent graduate students with research assistantships averaging an estimated 50 % of their time engaged in R&D activities. As of 1985 researchers in the Government sector exclude military personnel. As of 1987, Higher education R&D personnel also include those who report their primary work activity as design.

Due to lack of official data for the different employment sectors, the total researchers figure is an OECD estimate up to 2019. Comprehensive reporting of R&D personnel statistics by the United States has resumed with records available since 2020, reflecting the addition of official figures for the number of researchers and total R&D personnel for the higher education sector and the Private non-profit sector; as well as the number of researchers for the government sector. The new data revise downwards previous OECD estimates as the OECD extrapolation methods drawing on historical US data, required to produce a consistent OECD aggregate, appear to have previously overestimated the growth in the number of researchers in the higher education sector.

Pre-production development is excluded from Defence GBARD (in accordance with the Frascati Manual) as of 2000. 2009 GBARD data also includes the one time incremental R&D funding legislated in the American Recovery and Reinvestment Act of 2009. Beginning with the 2000 GBARD data, budgets for capital expenditure – “R&D plant” in national terminology - are included. GBARD data for earlier years relate to budgets for current costs only.

The total consumer spending on education in Egypt was forecast to continuously increase between 2024 and 2029 by in total 5.9 billion U.S. dollars (+46.18 percent). After the fourth consecutive increasing year, the education-related spending is estimated to reach 18.8 billion U.S. dollars and therefore a new peak in 2029. Consumer spending, in this case eduction-related spending, refers to the domestic demand of private households and non-profit institutions serving households (NPISHs). Spending by corporations and the state is not included. The forecast has been adjusted for the expected impact of COVID-19.Consumer spending is the biggest component of the gross domestic product as computed on an expenditure basis in the context of national accounts. The other components in this approach are consumption expenditure of the state, gross domestic investment as well as the net exports of goods and services. Consumer spending is broken down according to the United Nations' Classification of Individual Consumption By Purpose (COICOP). The shown data adheres broadly to group tenth As not all countries and regions report data in a harmonized way, all data shown here has been processed by Statista to allow the greatest level of comparability possible. The underlying input data are usually household budget surveys conducted by government agencies that track spending of selected households over a given period.The data is shown in nominal terms which means that monetary data is valued at prices of the respective year and has not been adjusted for inflation. For future years the price level has been projected as well. The data has been converted from local currencies to US$ using the average exchange rate of the respective year. For forecast years, the exchange rate has been projected as well. The timelines therefore incorporate currency effects.Find more key insights for the total consumer spending on education in countries like Morocco and Sudan.

AP VoteCast is a survey of the American electorate conducted by NORC at the University of Chicago for Fox News, NPR, PBS NewsHour, Univision News, USA Today Network, The Wall Street Journal and The Associated Press.

AP VoteCast combines interviews with a random sample of registered voters drawn from state voter files with self-identified registered voters selected using nonprobability approaches. In general elections, it also includes interviews with self-identified registered voters conducted using NORC’s probability-based AmeriSpeak® panel, which is designed to be representative of the U.S. population.

Interviews are conducted in English and Spanish. Respondents may receive a small monetary incentive for completing the survey. Participants selected as part of the random sample can be contacted by phone and mail and can take the survey by phone or online. Participants selected as part of the nonprobability sample complete the survey online.

In the 2020 general election, the survey of 133,103 interviews with registered voters was conducted between Oct. 26 and Nov. 3, concluding as polls closed on Election Day. AP VoteCast delivered data about the presidential election in all 50 states as well as all Senate and governors’ races in 2020.

Using this Data - IMPORTANT

This is survey data and must be properly weighted during analysis: DO NOT REPORT THIS DATA AS RAW OR AGGREGATE NUMBERS!!

Instead, use statistical software such as R or SPSS to weight the data.

National Survey

The national AP VoteCast survey of voters and nonvoters in 2020 is based on the results of the 50 state-based surveys and a nationally representative survey of 4,141 registered voters conducted between Nov. 1 and Nov. 3 on the probability-based AmeriSpeak panel. It included 41,776 probability interviews completed online and via telephone, and 87,186 nonprobability interviews completed online. The margin of sampling error is plus or minus 0.4 percentage points for voters and 0.9 percentage points for nonvoters.

State Surveys

In 20 states in 2020, AP VoteCast is based on roughly 1,000 probability-based interviews conducted online and by phone, and roughly 3,000 nonprobability interviews conducted online. In these states, the margin of sampling error is about plus or minus 2.3 percentage points for voters and 5.5 percentage points for nonvoters.

In an additional 20 states, AP VoteCast is based on roughly 500 probability-based interviews conducted online and by phone, and roughly 2,000 nonprobability interviews conducted online. In these states, the margin of sampling error is about plus or minus 2.9 percentage points for voters and 6.9 percentage points for nonvoters.

In the remaining 10 states, AP VoteCast is based on about 1,000 nonprobability interviews conducted online. In these states, the margin of sampling error is about plus or minus 4.5 percentage points for voters and 11.0 percentage points for nonvoters.

Although there is no statistically agreed upon approach for calculating margins of error for nonprobability samples, these margins of error were estimated using a measure of uncertainty that incorporates the variability associated with the poll estimates, as well as the variability associated with the survey weights as a result of calibration. After calibration, the nonprobability sample yields approximately unbiased estimates.

As with all surveys, AP VoteCast is subject to multiple sources of error, including from sampling, question wording and order, and nonresponse.

Sampling Details

Probability-based Registered Voter Sample

In each of the 40 states in which AP VoteCast included a probability-based sample, NORC obtained a sample of registered voters from Catalist LLC’s registered voter database. This database includes demographic information, as well as addresses and phone numbers for registered voters, allowing potential respondents to be contacted via mail and telephone. The sample is stratified by state, partisanship, and a modeled likelihood to respond to the postcard based on factors such as age, race, gender, voting history, and census block group education. In addition, NORC attempted to match sampled records to a registered voter database maintained by L2, which provided additional phone numbers and demographic information.

Prior to dialing, all probability sample records were mailed a postcard inviting them to complete the survey either online using a unique PIN or via telephone by calling a toll-free number. Postcards were addressed by name to the sampled registered voter if that individual was under age 35; postcards were addressed to “registered voter” in all other cases. Telephone interviews were conducted with the adult that answered the phone following confirmation of registered voter status in the state.

Nonprobability Sample

Nonprobability participants include panelists from Dynata or Lucid, including members of its third-party panels. In addition, some registered voters were selected from the voter file, matched to email addresses by V12, and recruited via an email invitation to the survey. Digital fingerprint software and panel-level ID validation is used to prevent respondents from completing the AP VoteCast survey multiple times.

AmeriSpeak Sample

During the initial recruitment phase of the AmeriSpeak panel, randomly selected U.S. households were sampled with a known, non-zero probability of selection from the NORC National Sample Frame and then contacted by mail, email, telephone and field interviewers (face-to-face). The panel provides sample coverage of approximately 97% of the U.S. household population. Those excluded from the sample include people with P.O. Box-only addresses, some addresses not listed in the U.S. Postal Service Delivery Sequence File and some newly constructed dwellings. Registered voter status was confirmed in field for all sampled panelists.

Weighting Details

AP VoteCast employs a four-step weighting approach that combines the probability sample with the nonprobability sample and refines estimates at a subregional level within each state. In a general election, the 50 state surveys and the AmeriSpeak survey are weighted separately and then combined into a survey representative of voters in all 50 states.

State Surveys

First, weights are constructed separately for the probability sample (when available) and the nonprobability sample for each state survey. These weights are adjusted to population totals to correct for demographic imbalances in age, gender, education and race/ethnicity of the responding sample compared to the population of registered voters in each state. In 2020, the adjustment targets are derived from a combination of data from the U.S. Census Bureau’s November 2018 Current Population Survey Voting and Registration Supplement, Catalist’s voter file and the Census Bureau’s 2018 American Community Survey. Prior to adjusting to population totals, the probability-based registered voter list sample weights are adjusted for differential non-response related to factors such as availability of phone numbers, age, race and partisanship.

Second, all respondents receive a calibration weight. The calibration weight is designed to ensure the nonprobability sample is similar to the probability sample in regard to variables that are predictive of vote choice, such as partisanship or direction of the country, which cannot be fully captured through the prior demographic adjustments. The calibration benchmarks are based on regional level estimates from regression models that incorporate all probability and nonprobability cases nationwide.

Third, all respondents in each state are weighted to improve estimates for substate geographic regions. This weight combines the weighted probability (if available) and nonprobability samples, and then uses a small area model to improve the estimate within subregions of a state.

Fourth, the survey results are weighted to the actual vote count following the completion of the election. This weighting is done in 10–30 subregions within each state.

National Survey

In a general election, the national survey is weighted to combine the 50 state surveys with the nationwide AmeriSpeak survey. Each of the state surveys is weighted as described. The AmeriSpeak survey receives a nonresponse-adjusted weight that is then adjusted to national totals for registered voters that in 2020 were derived from the U.S. Census Bureau’s November 2018 Current Population Survey Voting and Registration Supplement, the Catalist voter file and the Census Bureau’s 2018 American Community Survey. The state surveys are further adjusted to represent their appropriate proportion of the registered voter population for the country and combined with the AmeriSpeak survey. After all votes are counted, the national data file is adjusted to match the national popular vote for president.

The per capita consumer spending on education in Egypt was forecast to continuously increase between 2024 and 2029 by in total 39.5 U.S. dollars (+35.86 percent). After the fourth consecutive increasing year, the education-related per capita spending is estimated to reach 149.69 U.S. dollars and therefore a new peak in 2029. Consumer spending, in this case education-related spending per capita, refers to the domestic demand of private households and non-profit institutions serving households (NPISHs). Spending by corporations and the state is not included. The forecast has been adjusted for the expected impact of COVID-19.Consumer spending is the biggest component of the gross domestic product as computed on an expenditure basis in the context of national accounts. The other components in this approach are consumption expenditure of the state, gross domestic investment as well as the net exports of goods and services. Consumer spending is broken down according to the United Nations' Classification of Individual Consumption By Purpose (COICOP). The shown data adheres broadly to group tenth As not all countries and regions report data in a harmonized way, all data shown here has been processed by Statista to allow the greatest level of comparability possible. The underlying input data are usually household budget surveys conducted by government agencies that track spending of selected households over a given period.The data is shown in nominal terms which means that monetary data is valued at prices of the respective year and has not been adjusted for inflation. For future years the price level has been projected as well. The data has been converted from local currencies to US$ using the average exchange rate of the respective year. For forecast years, the exchange rate has been projected as well. The timelines therefore incorporate currency effects.Find more key insights for the per capita consumer spending on education in countries like Algeria and Morocco.

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from the U.S. Census Bureau’s American Community Survey 5-year estimates for 2013-2017, to show levels of educational attainment by Zip Code Tabulation Area in the Atlanta region. The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2013-2017). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website. Naming conventions: Prefixes:NoneCountpPercentrRatemMedianaMean (average)tAggregate (total)chChange in absolute terms (value in t2 - value in t1)pchPercent change ((value in t2 - value in t1) / value in t1)chpChange in percent (percent in t2 - percent in t1)Suffixes:NoneChange over two periods_eEstimate from most recent ACS_mMargin of Error from most recent ACS_00Decennial 2000 Attributes: SumLevelSummary level of geographic unit (e.g., County, Tract, NSA, NPU, DSNI, SuperDistrict, etc)GEOIDCensus tract Federal Information Processing Series (FIPS) code NAMEName of geographic unitPlanning_RegionPlanning region designation for ARC purposesAcresTotal area within the tract (in acres)SqMiTotal area within the tract (in square miles)CountyCounty identifier (combination of Federal Information Processing Series (FIPS) codes for state and county)CountyNameCounty NamePop25P_e# Population 25 years and over, 2017Pop25P_m# Population 25 years and over, 2017 (MOE)NoHS_e# Population 25 years and over, less than 9th grade education, 2017NoHS_m# Population 25 years and over, less than 9th grade education, 2017 (MOE)pNoHS_e% Population 25 years and over, less than 9th grade education, 2017pNoHS_m% Population 25 years and over, less than 9th grade education, 2017 (MOE)SomeHS_e# Population 25 years and over, 9th-12th grade, no diploma, 2017SomeHS_m# Population 25 years and over, 9th-12th grade, no diploma, 2017 (MOE)pSomeHS_e% Population 25 years and over, 9th-12th grade, no diploma, 2017pSomeHS_m% Population 25 years and over, 9th-12th grade, no diploma, 2017 (MOE)HSGrad_e# Population 25 years and over, high school graduate (includes GED), 2017HSGrad_m# Population 25 years and over, high school graduate (includes GED), 2017 (MOE)pHSGrad_e% Population 25 years and over, high school graduate (includes GED), 2017pHSGrad_m% Population 25 years and over, high school graduate (includes GED), 2017 (MOE)SomeColl_e# Population 25 years and over, some college, no degree, 2017SomeColl_m# Population 25 years and over, some college, no degree, 2017 (MOE)pSomeColl_e% Population 25 years and over, some college, no degree, 2017pSomeColl_m% Population 25 years and over, some college, no degree, 2017 (MOE)Associates_e# Population 25 years and over, associate's degree, 2017Associates_m# Population 25 years and over, associate's degree, 2017 (MOE)pAssociates_e% Population 25 years and over, associate's degree, 2017pAssociates_m% Population 25 years and over, associate's degree, 2017 (MOE)BA_e# Population 25 years and over, bachelor's degree, 2017BA_m# Population 25 years and over, bachelor's degree, 2017 (MOE)pBA_e% Population 25 years and over, bachelor's degree, 2017pBA_m% Population 25 years and over, bachelor's degree, 2017 (MOE)GradProf_e# Population 25 years and over, graduate or professional degree, 2017GradProf_m# Population 25 years and over, graduate or professional degree, 2017 (MOE)pGradProf_e% Population 25 years and over, graduate or professional degree, 2017pGradProf_m% Population 25 years and over, graduate or professional degree, 2017 (MOE)LtHS_e# Population 25 years and over, Less than high school graduate, 2017LtHS_m# Population 25 years and over, Less than high school graduate, 2017 (MOE)pLtHS_e% Population 25 years and over, Less than high school graduate, 2017pLtHS_m% Population 25 years and over, Less than high school graduate, 2017 (MOE)HSPlus_e# Population 25 years and over, high school graduate or higher, 2017HSPlus_m# Population 25 years and over, high school graduate or higher, 2017 (MOE)pHSPlus_e% Population 25 years and over, high school graduate or higher, 2017pHSPlus_m% Population 25 years and over, high school graduate or higher, 2017 (MOE)BAPlus_e# Population 25 years and over, bachelor's degree or higher, 2017BAPlus_m# Population 25 years and over, bachelor's degree or higher, 2017 (MOE)pBAPlus_e% Population 25 years and over, bachelor's degree or higher, 2017pBAPlus_m% Population 25 years and over, bachelor's degree or higher, 2017 (MOE)last_edited_dateLast date the feature was edited by ARC Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2013-2017 For additional information, please visit the Census ACS website.

The real total consumer spending on education in Egypt was forecast to continuously increase between 2024 and 2029 by in total 8.5 billion U.S. dollars (+68.06 percent). After the eighth consecutive increasing year, the real education-related spending is estimated to reach 21.1 billion U.S. dollars and therefore a new peak in 2029. Consumer spending, in this case eduction-related spending, refers to the domestic demand of private households and non-profit institutions serving households (NPISHs). Spending by corporations and the state is not included. The forecast has been adjusted for the expected impact of COVID-19.Consumer spending is the biggest component of the gross domestic product as computed on an expenditure basis in the context of national accounts. The other components in this approach are consumption expenditure of the state, gross domestic investment as well as the net exports of goods and services. Consumer spending is broken down according to the United Nations' Classification of Individual Consumption By Purpose (COICOP).The shown data adheres broadly to group tenth As not all countries and regions report data in a harmonized way, all data shown here has been processed by Statista to allow the greatest level of comparability possible. The underlying input data are usually household budget surveys conducted by government agencies that track spending of selected households over a given period.The data has been converted from local currencies to US$ using the average constant exchange rate of the base year 2017. The timelines therefore do not incorporate currency effects. The data is shown in real terms which means that monetary data is valued at constant prices of a given base year (in this case: 2017). To attain constant prices the nominal forecast has been deflated with the projected consumer price index for the respective category.Find more key insights for the real total consumer spending on education in countries like Morocco and Sudan.

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from the U.S. Census Bureau’s American Community Survey 5-year estimates for 2013-2017, to show counts and percentages for school enrollment by education level by State of Georgia in the Atlanta region.

The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent.

The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2013-2017). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available.

For further explanation of ACS estimates and margin of error, visit Census ACS website.

Naming conventions:

Prefixes:

None

Count

p

Percent

r

Rate

m

Median

a

Mean (average)

t

Aggregate (total)

ch

Change in absolute terms (value in t2 - value in t1)

pch

Percent change ((value in t2 - value in t1) / value in t1)

chp

Change in percent (percent in t2 - percent in t1)

Suffixes:

None

Change over two periods

_e

Estimate from most recent ACS

_m

Margin of Error from most recent ACS

_00

Decennial 2000

Attributes:

SumLevel

Summary level of geographic unit (e.g., County, Tract, NSA, NPU, DSNI, SuperDistrict, etc)

GEOID

Census tract Federal Information Processing Series (FIPS) code

NAME

Name of geographic unit

Planning_Region

Planning region designation for ARC purposes

Acres

Total area within the tract (in acres)

SqMi

Total area within the tract (in square miles)

County

County identifier (combination of Federal Information Processing Series (FIPS) codes for state and county)

CountyName

County Name

Pop3P_e

# Population ages 3 and over, 2017

Pop3P_m

# Population ages 3 and over, 2017 (MOE)

InSchool_e

# Population 3 years and over enrolled in school, 2017

InSchool_m

# Population 3 years and over enrolled in school, 2017 (MOE)

InPreSchool_e

# Enrolled in nursery school, preschool, 2017

InPreSchool_m

# Enrolled in nursery school, preschool, 2017 (MOE)

pInPreSchool_e

% Enrolled in nursery school, preschool, 2017

pInPreSchool_m

% Enrolled in nursery school, preschool, 2017 (MOE)

InKindergarten_e

# Enrolled in kindergarten, 2017

InKindergarten_m

# Enrolled in kindergarten, 2017 (MOE)

pInKindergarten_e

% Enrolled in kindergarten, 2017

pInKindergarten_m

% Enrolled in kindergarten, 2017 (MOE)

InElementary_e

# Enrolled in elementary school (grades 1-8), 2017

InElementary_m

# Enrolled in elementary school (grades 1-8), 2017 (MOE)

pInElementary_e

% Enrolled in elementary school (grades 1-8), 2017

pInElementary_m

% Enrolled in elementary school (grades 1-8), 2017 (MOE)

InHS_e

# Enrolled in high school (grades 9-12), 2017

InHS_m

# Enrolled in high school (grades 9-12), 2017 (MOE)

pInHS_e

% Enrolled in high school (grades 9-12), 2017

pInHS_m

% Enrolled in high school (grades 9-12), 2017 (MOE)

InCollegeGradSch_e

# Enrolled in college or graduate school, 2017

InCollegeGradSch_m

# Enrolled in college or graduate school, 2017 (MOE)

last_edited_date

Last date the feature was edited by ARC

Source: U.S. Census Bureau, Atlanta Regional Commission

Date: 2013-2017

For additional information, please visit the Census ACS website.

The study on the modern state was conducted by the Forschungsgruppe Wahlen Telefonfeld on behalf of the Press and Information Office of the German Federal Government. During the survey period 23.05.2022 to 08.06.2022, the German-speaking population aged 18 and over was surveyed in telephone interviews (CATI) on the following topics: citizens´ expectations of a modern, digital and effective state: satisfaction with democracy and state responsibility, official matters online, information on applicable laws and regulations and the importance of various Internet-based applications, the census in Germany and digitization in healthcare. Respondents were selected using a multistage random sample according to the RDD method, including fixed-network and mobile phone numbers (dual-frame sample).
Satisfaction with democracy and state responsibility: satisfaction with democracy; state responsibility or ownership in various areas (protection against crime, protection against the spread of epidemics and diseases, environmental and climate protection, protection against impoverishment, protection in the event of illness, financial security in old age); preference for a country with high taxes and extensive social services vs. country with low taxes and low social benefits; in the economy, too many vs. too few things are regulated by the state; better results by the state vs. private companies in the provision of services in various areas (health care, nursing care, local public transport, road construction, waste disposal).

State responsibility: preference with regard to decision-making processes in the administration (always the same rules for everyone vs. deviating from these rules in individual cases, implementing state projects as quickly as possible vs. extensive citizen participation, examining each individual case as closely as possible vs. keep administrative burden low); agreement with various statements (the state interferes too much in our lives, the opinion of the population is not taken into account enough in important political decisions, citizens are well informed in advance of important political decisions, people should not rely so much on the state but tackle their problems more themselves); opinion on the extent of government spending on various tasks (police, education, health care, defense, social affairs and climate protection); satisfaction with last contact with a government agency or administration; last contact with a government agency or administration online, in writing, by telephone or in person; preferred method of contact; evaluation of the effort spent on government agency matters; change in the effort spent on government agency matters in recent years.

Public authority matters online: Importance of the possibility of handling official matters online or on the Internet; expected problems for citizens if more official matters are handled online in the future; type of expected problems (data not safe from hacker attacks, no longer able to control one´s own data, insufficient help in operating the system, technical malfunctions delay matters, other problems); have already handled matters with authorities and offices via e-mail or the Internet (e.g., child benefit application); rather good or rather bad experiences; possession of an electronic ID card; use of electronic ID card to identify oneself on the Internet.

Information on applicable laws and regulations and importance of various Internet-based applications: Being informed about applicable laws and regulations; importance of public authorities and public institutions offering information about applicable laws and regulations via the Internet; having already searched for information about applicable laws and regulations on the Internet at public authorities and public institutions; having had rather good or rather bad experiences in this regard; importance of the Internet for the economy and society in various areas (expansion of fast Internet for all, greater promotion of the use of the Internet and computers in schools and further education institutions, greater promotion and development in the area of the Internet and information technology, greater promotion of business start-ups and combating cybercrime).

Census in Germany and digitalization in healthcare: Knowledge that the population census (Zensus) is taking place in Germany this year; evaluation of the Zensus; reasons for a negative evaluation of the Zensus (state uses data for purposes other than it claims, transparent citizen, misuse of data, compulsion to participate, waste of money, other); rather advantages or rather disadvantages of digitization in healthcare; evaluation of the electronic patient record; willingness to make own health data available for research purposes; party preference.

Demography: sex; age; education: school-leaving qualification or intended school-leaving qualification; university degree; occupation; professional position; simple, higher or...

According to exit polling in ten key states of the 2024 presidential election in the United States, almost two-thirds of voters who had never attended college reported voting for Donald Trump. In comparison, a similar share of voters with advanced degrees reported voting for Kamala Harris.

Abstract (en): This special topic poll, conducted March 31-April 9, 2005, is part of a continuing series of monthly surveys that solicit public opinion on the presidency and on a range of other political and social issues. A national sample of 1,586 respondents aged 14 to 24 years was surveyed, including oversamples of African American youth, Hispanic youth, and 14- to 20-year olds. Despite being termed a monthly poll, the foci of this poll were the opinions and judgments of teenagers and young adults about various aspects of the education system and process in the United States. Views were sought on the most important problem facing young people, the highest level of education respondents hoped to achieve, the highest level they expected to actually achieve, and whether a college degree was necessary to "get ahead". Respondents were asked about their plans after high school, the quality of their high school and its teachers and staff, whether their high school education was adequately preparing them for college and/or the job market, what measures respondents took or would like take to improve their chances of getting into the college of their choice, the importance of grade point averages and performance on standardized tests in getting into college, and their ability to get information about educational opportunities. Similar questions were asked of those respondents who were college students, regarding assistance received from college professors, the importance of internships, and whether college was adequately preparing them to get a well-paying job after graduation. Additional questions addressed MTV's involvement in issues concerning young people and how much impact MTV could have in raising awareness among young people about the importance of education. Demographic information includes age, race, sex, education, employment status, ethnicity, parents' education, perceived social class, level of religious participation, religious preference, whether respondents considered themselves to be an evangelical or born-again Christian, and the presence of other household members between the ages of 14 and 24. The data contain weights that should be used for analysis. All oversampled groups were weighted to their proper proportion in the total sample. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Created online analysis version with question text.; Checked for undocumented or out-of-range codes.. Teen and young adult population of the United States aged 14 through 24 who have a telephone at home. A national sample of 1,586 respondents aged 14 to 24 years was surveyed. These respondents were part of nationwide representative samples identified in households previously interviewed by CBS News and from RDD samples drawn from targeted areas. The 262 African American respondents, 200 Hispanic respondents, and 1,200 respondents aged 14 to 20 years in this poll included an oversample to provide larger bases for analysis. 2010-04-27 Corrected the dates of collection, PI, and oversample information and releasing the full product suite including question text. telephone interviewThe data contain oversamples of African Americans, Hispanics, and 14- to 20-years olds, as identified in the OSMP variable.Interviews were collected in both English and Spanish, as indicated in the variable HISP.The CASEID variable was reformatted in order to make it a unique identifier.Truncated value label in variables Q2, Q4, and Q5 were corrected.This data collection was produced by CBS News, New York, NY.

Abstract

The 2006 Azerbaijan Demographic and Health Survey (2006 AzDHS) is a nationally representative sample survey designed to provide information on population and health issues in Azerbaijan. The primary goal of the survey was to develop a single integrated set of demographic and health data pertaining to the population of the Republic of Azerbaijan.

The 2006 AzDHS was conducted from July to November by the State Statistical Committee (SSC) of the Republic of Azerbaijan. Macro International Inc. provided technical support for the survey through the MEASURE DHS project. USAID Caucasus, Azerbaijan provided funding for the survey through the MEASURE DHS project. MEASURE DHS is sponsored by the United States Agency for International Development (USAID) to assist countries worldwide in obtaining information on key population and health indicators. The UNICEF/Azerbaijan country office was instrumental for political mobilization during the early stages of the 2006 AzDHS negotiation with the Government of Azerbaijan and also supported the survey through in-kind contributions.

The 2006 AzDHS collected national- and regional-level data on fertility and contraceptive use, maternal and child health, adult health, tuberculosis, and HIV/AIDS and other sexually transmitted diseases. The survey obtained detailed information on these issues from women of reproductive age and, on certain topics, from men as well.

The 2006 AzDHS results are intended to provide the information needed to evaluate existing social programs and to design new strategies for improving the health of Azerbaijanis and health services for the people of Azerbaijan. The 2006 AzDHS also contributes to the growing international database on demographic and health-related variables.

Geographic coverage

The 2006 Azerbaijan Demographic and Health Survey (2006 AzDHS) is a nationally representative sample survey.

Analysis unit

• Household
• Children under five years
• Women age 15-49
• Men age 15-49

Kind of data

Sample survey data

Sampling procedure

The sample was designed to permit detailed analysis, including the estimation of rates of fertility, infant/child mortality, and abortion, for the national level, for Baku, and for urban and rural areas separately. Many indicators are available separately for each of the economic regions in Azerbaijan except the Autonomous Republic of Nakhichevan (conducting the survey in Nakhichevan was complicated, since this region is in the blockade).

A representative probability sample of households was selected for the 2006 AzDHS sample. The sample was selected in two stages. In the first stage, 318 clusters in Baku and 8 other economic regions were selected from a list of enumeration areas from the master sample frame that was designed for the 1999 Population Census. In the second stage, a complete listing of households was carried out in each selected cluster. Households were then systematically selected from each cluster for participation in the survey. This design resulted in a final sample of 7,619 households.

Because of the non-proportional allocation of the sample to the different economic regions, sampling weights will be required in all analysis using the DHS data to ensure the actual representativity of the sample at both the national and regional levels. The sampling weight for each household is the inverse of its overall selection probability with correction for household non-response; the individual weight is the household weight with correction of individual non-response. Sampling weights are further normalized in order to give the total number of unweighted cases equal to the total number of weighted cases at the national level, for both household weights and individual weights.

All women age 15-49 who were either permanent residents of the households in the 2006 AzDHS sample or visitors present in the household on the night before the survey were eligible to be interviewed. In addition, all men age 15-59 in one-third of the households selected for the survey were eligible to be interviewed if they were either permanent residents or visitors present in the household on the night before the survey. Interviews were completed with 8,444 women and 2,558 men.

Note: See detailed description of sample design in APPENDIX A of the Final Report.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were used in the AzDHS: Household Questionnaire, Women’s Questionnaire, and Men’s Questionnaire. The household and individual questionnaires were based on model survey instruments developed in the MEASURE DHS program. The model questionnaires were adapted for use by experts from the SSC and Ministry of Health (MOH). Input was also sought from a number of nongovernmental organizations. Additionally, at the request of UNICEF, the Multiple Indicator Cluster Survey (MICS) modules on early child education and development, birth registration, and child discipline were adapted for the 2006 AzDHS instrument. The questionnaires were prepared in English and translated into Azerbaijani and Russian. The household and individual questionnaires were pretested in May 2006.

The Household Questionnaire was used to list all usual members of and visitors to the selected households and to collect information on the socioeconomic status of the household. The first part of the Household Questionnaire collected information on the age, sex, educational attainment, and relationship of each household member or visitor to the household. This information provides basic demographic data for Azerbaijan households. It also was used to identify the women and men who were eligible for the individual interview (i.e., women age 15-49 and men age 15-59). In the second part of the Household Questionnaire, there were questions on housing characteristics (e.g., the flooring material, the source of water, and the type of toilet facilities), on ownership of a variety of consumer goods, and other questions relating to the socioeconomic status of the household. In addition, the Household Questionnaire was used to obtain information on child discipline, education, and development; to record height and weight measurements of women, men, and children under age five; and to record hemoglobin measurements of women and children under age five.

The Women’s Questionnaire obtained information from women age 15-49 on the following topics:- - Background characteristics - Pregnancy history - Abortion history - Antenatal, delivery, and postnatal care - Knowledge, attitudes, and use of contraception - Reproductive and adult health - Vaccinations, birth registration, and childhood illness and treatment - Breastfeeding and weaning practices - Marriage and recent sexual activity - Fertility preferences - Knowledge of and attitudes toward AIDS and other sexually transmitted diseases - Knowledge of and attitudes toward tuberculosis - Hypertension and other

The Men’s Questionnaire, administered to men age 15-59, covered the following topics: - Background characteristics - Reproductive health - Marriage and recent sexual activity - Attitudes toward and use of condoms - Fertility preferences - Employment and gender roles - Attitudes toward women’s status - Knowledge of and attitudes toward AIDS and other sexually transmitted diseases - Knowledge of and attitudes toward tuberculosis - Hypertension and other adult health issues - Smoking and alcohol consumption

Blood pressure measurements of women and men were recorded in their individual questionnaires.

Cleaning operations

The processing of the Azerbaijan DHS results began shortly after the fieldwork commenced. Completed questionnaires were returned regularly from the field to SSC headquarters in Baku, where they were entered and edited by data processing personnel who were specially trained for this task. The data processing personnel included a supervisor, a questionnaire administrator, several office editors, 10 data entry operators, and a secondary editor. The concurrent processing of the data was an advantage since the survey technical staff was able to advise field teams of problems detected during the data entry using tables generated to check various data quality parameters. As a result, specific feedback was given to the teams to improve their performance. The data entry and editing phase of the survey was completed in late January 2007.

Response rate

A total of 7,619 households were selected for the sample, of which 7,341 were found at the time of fieldwork. The main reason for the difference is that some of the dwelling units that were occupied during the household listing operation were either vacant or the household was away for an extended period at the time of interview. Of the households that were found, 98 percent were successfully interviewed.

In these households, 8,652 women were identified as eligible for the individual interview. Interviews were completed with 98 percent of the women. Of the 2,717 eligible men identified, 94 percent were successfully interviewed.

Note: See summarized response rates by residence (urban/rural) in Table 1.1 of the Final Report.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: nonsampling errors and sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the

The real per capita consumer spending on education in Egypt was forecast to continuously increase between 2024 and 2029 by in total 60.5 U.S. dollars (+56.27 percent). After the eighth consecutive increasing year, the real education-related per capita spending is estimated to reach 167.96 U.S. dollars and therefore a new peak in 2029. Consumer spending, in this case education-related spending per capita, refers to the domestic demand of private households and non-profit institutions serving households (NPISHs). Spending by corporations and the state is not included. The forecast has been adjusted for the expected impact of COVID-19.Consumer spending is the biggest component of the gross domestic product as computed on an expenditure basis in the context of national accounts. The other components in this approach are consumption expenditure of the state, gross domestic investment as well as the net exports of goods and services. Consumer spending is broken down according to the United Nations' Classification of Individual Consumption By Purpose (COICOP). The shown data adheres broadly to group tenth As not all countries and regions report data in a harmonized way, all data shown here has been processed by Statista to allow the greatest level of comparability possible. The underlying input data are usually household budget surveys conducted by government agencies that track spending of selected households over a given period.The data has been converted from local currencies to US$ using the average constant exchange rate of the base year 2017. The timelines therefore do not incorporate currency effects. The data is shown in real terms which means that monetary data is valued at constant prices of a given base year (in this case: 2017). To attain constant prices the nominal forecast has been deflated with the projected consumer price index for the respective category.Find more key insights for the real per capita consumer spending on education in countries like Algeria and Sudan.

Abstract

The Nigeria Teacher Development Programme (TDP) In-Service Training Component Impact Evaluation 2014 is a DFID-funded programme, managed by a consortium led by Mott Macdonald. Its aim is to increase the effectiveness of teachers and thus raise primary and junior secondary school (JSS) pupil learning levels, through support to the Federal and State institutions responsible for pre- and in-service training and development of basic education teachers. The programme has the following three components:

• In-service teacher training in the three core curriculum subjects of English, maths, and science & technology (S&T) for primary and JSS teachers;
• Pre-service teacher training; and
• Results & evidence-based research.

Schools eligible for TDP intervention are state (public) schools, excluding Integrated Quranic, Tsangaya Education (IQTE) schools, and special schools for children with disabilities.

The programme is being implemented in two phases (phase 1 covering Jigawa, Katsina and Zamfara (2013-2019) and phase 2 covering Kaduna and Kano (2016-2019)). For more information on the programme, please visit: www.tdpnigeria.org

The quantitative survey described in this documentation forms part of a DFID-funded phase 1 Impact Evaluation (IE) of the TDP in-service training component, which is the largest component of the programme (approximately 80% of total programme resources). The IE is being conducted by the Education Data, Research and Evaluation in Nigeria programme (EDOREN) which is a DFID-funded education research programme managed by Oxford Policy Management Ltd (OPM), and will focus on TDP activities in primary schools (since JSS activities do not start until the school year 2016/17).

The IE uses a theory-based, mixed-methods approach, and the results will be used to inform the implementation of the TDP for the remainder of phase 1 and the design and implementation in phase 2, as well as to assess whether the in-service training has contributed to improving teacher effectiveness and what could work otherwise and/or elsewhere in improving teacher effectiveness.

The three key objectives of the phase 1 IE quantitative baseline survey were to: - Establish baseline levels of teacher effectiveness and pupil learning before the start of TDP's in-service training, and to check that the IE's randomisation design yielded a valid counterfactual. - Inform and allow for potential adjustments to TDP design and implementation as deemed appropriate by the programme and DFID-Nigeria; and - Guide and interact with the qualitative baseline research by providing information on the situation prior to the start of the TDP as well as to allow for more in-depth examination of unanticipated quantitative findings.

Geographic coverage

The survey was carried out in the TDP phase 1 states (Jigawa, Katsina and Zamfara) but the results are NOT representative at the state-level, i.e. state-level estimates do not represent the average situation in a given state.

The data are not representative at any geographical level.

Analysis unit

The Primary Sampling Units (PSUs) of the survey are TDP-eligible state primary schools, at which level some analysis is performed (for example, characteristics of schools and head teachers).

However, the main units of analysis are:

· Teachers (selected prior to the PSUs and teaching grades 1-3 in any of the three subjects: English, maths, or science) · Pupils (in grade 3 at baseline, and taught English, math or science by at least one of the 'selected' teachers). · Lessons taught by the selected teachers (not sampled) · TDNAs administered to the selected teachers (not sampled)

Please refer to the 'Sampling Procedure' section for more details.

Universe

The target populations (the groups for which one would like to generalise the study findings) are the schools eligible for the TDP in treatment and control groups in the three states, and the eligible teachers and pupils within these schools.

Please refer to the 'Sampling Procedure' section for more details on the definition of eligibility.

Kind of data

Sample survey data [ssd]

Sampling procedure

***------------------------------------------------------------------------------------------------------------------- *** (1) Aim of sampling design ***-------------------------------------------------------------------------------------------------------------------

The aim of the sampling design was to define a valid counterfactual 'control' group from which comparisons could be made with a 'treatment' group that participate in the TDP. The control group would not participate in the TDP in-service training but would have background characteristics which are, on average, similar to the treatment group that do participate in TDP in-service training.

The sampling design of the IE was based on a quasi-experimental 'constrained randomisation' approach. 'Constrained randomisation' means that certain parameters of the IE were already fixed - for example, the Local Government Areas (LGAs) where the programme operates. In addition, pre-determined groups of schools fulfilling certain criteria (described below) would constitute the sampling frame - this is in contrast to a fully randomised design approach where one might expect the random drawing of groups (or clusters) of schools from a list of all state primary schools in the region under study.

Randomisation was conducted only in allocating groups of schools to 'treatment' or 'control' status.

The sample design was determined to a large extent by practical programme considerations, and also by the available budget.

***------------------------------------------------------------------------------------------------------------------- *** (2) Construction of sampling frame: Eligible primary schools ***-------------------------------------------------------------------------------------------------------------------

The sampling frame was constructed from scratch through the stages described below. The intended size of the frame was 1008 primary schools eligible for the TDP (504 'treatment' schools and 504 'control' schools) and would constitute the target population (or universe) of eligible schools, from which a sample of treatment and control schools would be drawn for the survey.

***--------------------------------------------------------------------------------- *** Stage 1: Selection of LGAs ***---------------------------------------------------------------------------------

In each state, 14 LGAs where the programme would operate had already been pre-determined by the TDP as per arrangements with the States.

· Jigawa: 14 out of 27 LGAs · Katsina: 14 out of 34 LGAs · Zamfara: 14 out of 14 LGAs

***--------------------------------------------------------------------------------- *** Stage 2: Selection of sets of primary schools ***---------------------------------------------------------------------------------

In each of the 14 LGAs in each state, 2 sets of 12 eligible primary schools each were to be selected;

To be eligible for the TDP: (1) each school should have one head teacher and at least another three teachers; (2) each school should have at least 8 grade-3 pupils.

Schools within each set were identified according to geographical proximity in order to facilitate any training and periodic meetings of teachers within each set, and to create a broader peer network within the locality.

It was the intention that the two sets of schools within each LGA would be selected to be broadly similar. State Education Boards (SUBEBs) were responsible for the selection and were provided with guidelines to assist them, such as taking into account the location of the schools (urban/rural), the size of the schools in terms of classrooms and pupils, presence of a School Based Management Committee (SBMC), and state of school infrastructure. In the case of Jigawa, nearly all schools would have had exposure to the also DFID-funded Education Sector Support Programme in Nigeria (ESSPIN). Therefore, care was taken to balance the level of exposure to ESSPIN across the pairs of sets in each LGA.

***--------------------------------------------------------------------------------- *** Stage 3: Selection of eligible teachers ***---------------------------------------------------------------------------------

Before the selection of schools which would participate in the TDP or not, the LGEA and head teacher from each school in every set was required to identify three other teachers who would potentially receive TDP support in addition to him/herself, based on the following criteria:

· Classroom teaching at early grade-level (grades 1-3); and · Classroom teaching in any of the three subjects: English, maths, or science.

***------------------------------------------------------------------------ *** Stage 4: Random allocation of treatment/control sets ***------------------------------------------------------------------------

After receiving lists of school sets and teachers from the TDP coordinators, the IE team randomly assigned one set of schools among every pair of sets to TDP 'treatment' status using a random number generator. The other set would therefore be assigned 'control' status.

This would result in 14 x 3 = 42 'treatment' sets of 12 schools each (504 'treatment' schools in total) and correspondingly 42 'control' sets of 12 schools each (504 'control' schools in total). In 'treatment' schools, all head teachers and identified teachers in the previous stage would receive TDP support.

***------------------------------------------------------------------------------------------------------------------- *** (3) Drawing of the samples for the baseline