The dataset is related to student data, from an educational research study focusing on student demographics, academic performance, and related factors. Here’s a general description of what each column likely represents:

Sex: The gender of the student (e.g., Male, Female). Age: The age of the student. Name: The name of the student. State: The state where the student resides or where the educational institution is located. Address: Indicates whether the student lives in an urban or rural area. Famsize: Family size category (e.g., LE3 for families with less than or equal to 3 members, GT3 for more than 3). Pstatus: Parental cohabitation status (e.g., 'T' for living together, 'A' for living apart). Medu: Mother's education level (e.g., Graduate, College). Fedu: Father's education level (similar categories to Medu). Mjob: Mother's job type. Fjob: Father's job type. Guardian: The primary guardian of the student. Math_Score: Score obtained by the student in Mathematics. Reading_Score: Score obtained by the student in Reading. Writing_Score: Score obtained by the student in Writing. Attendance_Rate: The percentage rate of the student’s attendance. Suspensions: Number of times the student has been suspended. Expulsions: Number of times the student has been expelled. Teacher_Support: Level of support the student receives from teachers (e.g., Low, Medium, High). Counseling: Indicates whether the student receives counseling services (Yes or No). Social_Worker_Visits: Number of times a social worker has visited the student. Parental_Involvement: The level of parental involvement in the student's academic life (e.g., Low, Medium, High). GPA: The student’s Grade Point Average, a standard measure of academic achievement in schools.

This dataset provides a comprehensive look at various factors that might influence a student's educational outcomes, including demographic factors, academic performance metrics, and support structures both at home and within the educational system. It can be used for statistical analysis to understand and improve student success rates, or for targeted interventions based on specific identified needs.

Abstract (en): The American College Catalog Study Database (CCS) contains academic data on 286 four-year colleges and universities in the United States. CCS is one of two databases produced by the Colleges and Universities 2000 project based at the University of California-Riverside. The CCS database comprises a sampled subset of institutions from the related Institutional Data Archive (IDA) on American Higher Education (ICPSR 34874). Coding for CCS was based on college catalogs obtained from College Source, Inc. The data are organized in a panel design, with measurements taken at five-year intervals: academic years 1975-76, 1980-81, 1985-86, 1990-91, 1995-96, 2000-01, 2005-06, and 2010-11. The database is based on information reported in each institution's college catalog, and includes data regarding changes in major academic units (schools and colleges), departments, interdisciplinary programs, and general education requirements. For schools and departments, changes in structure were coded, including new units, name changes, splits in units, units moved to new schools, reconstituted units, consolidated units, departments reduced to program status, and eliminated units. The American College Catalog Study Database (CCS) is intended to allow researchers to examine changes in the structure of institutionalized knowledge in four-year colleges and universities within the United States. For information on the study design, including detailed coding conventions, please see the Original P.I. Documentation section of the ICPSR Codebook. The data are not weighted. Dataset 1, Characteristics Variables, contains three weight variables (IDAWT, CCSWT, and CASEWEIGHT) which users may wish to apply during analysis. For additional information on weights, please see the Original P.I. Documentation section of the ICPSR Codebook. 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: Checked for undocumented or out-of-range codes.. Response Rates: Approximately 75 percent of IDA institutions are included in CCS. For additional information on response rates, please see the Original P.I. Documentation section of the ICPSR Codebook. Four-year not-for-profit colleges and universities in the United States. Smallest Geographic Unit: state CCS includes 286 institutions drawn from the IDA sample of 384 United States four-year colleges and universities. CCS contains every IDA institution for which a full set of catalogs could be located at the initiation of the project in 2000. CCS contains seven datasets that can be linked through an institutional identification number variable (PROJ_ID). Since the data are organized in a panel format, it is also necessary to use a second variable (YEAR) to link datasets. For a brief description of each CCS dataset, please see Appendix B within the Original P.I. Documentation section of the ICPSR Codebook.There are date discrepancies between the data and the Original P.I. Documentation. Study Time Periods and Collection Dates reflect dates that are present in the data. No additional information was provided.Please note that the related data collection featuring the Institutional Data Archive on American Higher Education, 1970-2011, will be available as ICPSR 34874. Additional information on the American College Catalog Study Database (CCS) and the Institutional Data Archive (IDA) database can be found on the Colleges and Universities 2000 Web site.

The National Center for Education Statistics' (NCES) Education Demographic and Geographic Estimate (EDGE) program develops annually updated point locations (latitude and longitude) for public elementary and secondary schools included in the NCES Common Core of Data (CCD). The CCD program annually collects administrative and fiscal data about all public schools, school districts, and state education agencies in the United States. The data are supplied by state education agency officials and include basic directory and contact information for schools and school districts, as well as characteristics about student demographics, number of teachers, school grade span, and various other administrative conditions. School and agency point locations are derived from reported information about the physical location of schools and agency administrative offices. The point locations in this data layer represent the most current CCD collection. For more information about NCES school point data, see: https://nces.ed.gov/programs/edge/Geographic/SchoolLocations.Notes:-1 or MIndicates that the data are missing.-2 or NIndicates that the data are not applicable.-9Indicates that the data do not meet NCES data quality standards.Collections are available for the following years:2022-232021-222020-212019-202018-192017-182016-172015-16 All information contained in this file is in the public domain. Data users are advised to review NCES program documentation and feature class metadata to understand the limitations and appropriate use of these data.

The National Center for Education Statistics’ (NCES) Education Demographic and Geographic Estimate (EDGE) program develops annually updated school district boundary composite files that include public elementary, secondary, and unified school district boundaries clipped to the U.S. shoreline. School districts are special-purpose governments and administrative units designed by state and local officials to provide public education for local residents. District boundaries are collected for NCES by the U.S. Census Bureau to develop demographic estimates and to support educational research and program administration. The NCES Common Core of Data (CCD) program is an annual collection of basic administrative characteristics for all public schools, school districts, and state education agencies in the United States. These characteristics are reported by state education officials and include directory information, number of students, number of teachers, grade span, and other conditions. The administrative attributes in this layer were developed from the most current CCD collection available. For more information about NCES school district boundaries, see: https://nces.ed.gov/programs/edge/Geographic/DistrictBoundaries. For more information about CCD school district attributes, see: https://nces.ed.gov/ccd/files.asp.Notes:-1 or MIndicates that the data are missing.-2 or NIndicates that the data are not applicable.-9Indicates that the data do not meet NCES data quality standards.Collections are available for the following years:2021-222020-212019-202018-192017-18All information contained in this file is in the public _domain. Data users are advised to review NCES program documentation and feature class metadata to understand the limitations and appropriate use of these data.

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North America School Market Size 2024-2028

The North America school market size is forecast to increase by USD 49.21 billion, at a CAGR of 12.55% between 2023 and 2028.

In the North American school market, digital transformation is gaining significant traction in both public and private educational institutions. Schools are increasingly investing in formative learning tools to enhance student engagement and improve educational outcomes. However, the implementation of blended learning approaches poses challenges, with concerns surrounding the credibility of sources and the quality of content. As schools navigate this digital shift, they must ensure that the resources they adopt are reliable and effective in supporting student learning. To capitalize on this trend, companies offering digital educational solutions must prioritize the development of high-quality, trustworthy content and tools to meet the evolving needs of the North American school market.
Effective collaboration between schools, technology providers, and educational experts will be essential to address the challenges of credibility and content quality in the blended learning environment, ultimately driving innovation and growth in the sector.

What will be the size of the North America School Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2018-2022 and forecasts 2024-2028 - in the full report.
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School transportation in North America is a dynamic market, prioritizing sustainability and security. School buses are being upgraded with recycling programs, alternative fuel infrastructure, and green building practices. Security screening and anti-terrorism measures, including biometric authentication and facial recognition, are increasingly integrated into transportation systems. Real-time tracking, route planning, and dispatch management ensure efficient operations. Safety technology, such as automatic emergency braking, lane departure warning, and blind spot detection, is standard in modern bus designs. Special needs transportation caters to diverse student populations.
School bus manufacturing incorporates innovation in transportation, focusing on passenger comfort, driver fatigue monitoring, and noise reduction. Vibration damping and chassis types are considered in bus design for optimal performance. Fuel management and engine options cater to sustainability concerns. Safety is a top priority, with a focus on safety technology and maintenance schedules.

How is this market segmented?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Type

  Public
  Private


Product

  Elementary
  Senior high
  Middle and junior high


Geography

  North America

    US
    Canada
    Mexico

By Type Insights

The public segment is estimated to witness significant growth during the forecast period.

Public schools in North America are integral to the education system, funded and operated by local, state, or provincial governments. With a focus on accessibility and affordability, these institutions serve the community by providing free education to students. The market for public schools is driven by government initiatives to modernize technology infrastructure, enabling various learning modes like online education. Sustainability is also a growing priority, with initiatives to reduce carbon footprints through the adoption of renewable energy sources and electric school buses. Cost optimization is a key concern, leading to the implementation of fuel-efficient HVAC systems, telematics for fleet management, and route optimization.

Student safety remains a top priority, with regulations mandating safety inspections, emergency response plans, and the use of two-way radios for communication. Compliance with industry standards for seat comfort, ADA accessibility, and emergency evacuation procedures is essential. Technology integration, including wi-fi connectivity, digital signage, and data analytics, enhances the learning experience and streamlines operations. School bus procurement involves considering factors like passenger capacity, bus safety features, and financing options. Partnerships with school districts and labor unions contribute to operational efficiency, while bus driver training and communication systems ensure effective student transportation.

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The Public segment was valued at USD 25.52 billion in 2018 and showed a gradual increase during the forecast period.

Market Dynamics

Our researchers analyzed the data with 2023 as the base year, along with the key drivers, trends, and challenges. A holistic analysis of drivers will help companies refine their marketing strat

This layer shows education level for adults (25+) by race by sex. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percent of adults age 25+ who have a bachelor's degree or higher as their highest education level. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B15002, C15002B, C15002C, C15002D, C15002E, C15002F, C15002G, C15002H, C15002I (Not all lines of these ACS tables are available in this layer.)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census: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 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 Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

United States US: Labour Force With Advanced Education: Female: % of Female Working-age Population data was reported at 77.809 % in 2017. This records a decrease from the previous number of 78.336 % for 2016. United States US: Labour Force With Advanced Education: Female: % of Female Working-age Population data is updated yearly, averaging 82.936 % from Dec 1994 (Median) to 2017, with 24 observations. The data reached an all-time high of 86.467 % in 1994 and a record low of 77.809 % in 2017. United States US: Labour Force With Advanced Education: Female: % of Female Working-age Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Labour Force. The percentage of the working age population with an advanced level of education who are in the labor force. Advanced education comprises short-cycle tertiary education, a bachelor’s degree or equivalent education level, a master’s degree or equivalent education level, or doctoral degree or equivalent education level according to the International Standard Classification of Education 2011 (ISCED 2011).; ; International Labour Organization, ILOSTAT database. Data retrieved in September 2018.; Weighted average;

In an impressive increase from years past, 39 percent of women in the United States had completed four years or more of college in 2022. This figure is up from 3.8 percent of women in 1940. A significant increase can also be seen in males, with 36.2 percent of the U.S. male population having completed four years or more of college in 2022, up from 5.5 percent in 1940.

4- and 2-year colleges

In the United States, college students are able to choose between attending a 2-year postsecondary program and a 4-year postsecondary program. Generally, attending a 2-year program results in an Associate’s Degree, and 4-year programs result in a Bachelor’s Degree.

Many 2-year programs are designed so that attendees can transfer to a college or university offering a 4-year program upon completing their Associate’s. Completion of a 4-year program is the generally accepted standard for entry-level positions when looking for a job.

Earnings after college

Factors such as gender, degree achieved, and the level of postsecondary education can have an impact on employment and earnings later in life. Some Bachelor’s degrees continue to attract more male students than female, particularly in STEM fields, while liberal arts degrees such as education, languages and literatures, and communication tend to see higher female attendance.

All of these factors have an impact on earnings after college, and despite nearly the same rate of attendance within the American population between males and females, men with a Bachelor’s Degree continue to have higher weekly earnings on average than their female counterparts.

Quality of life is a measure of comfort, health, and happiness by a person or a group of people. Quality of life is determined by both material factors, such as income and housing, and broader considerations like health, education, and freedom. Each year, US & World News releases its “Best States to Live in” report, which ranks states on the quality of life each state provides its residents. In order to determine rankings, U.S. News & World Report considers a wide range of factors, including healthcare, education, economy, infrastructure, opportunity, fiscal stability, crime and corrections, and the natural environment. More information on these categories and what is measured in each can be found below:

Healthcare includes access, quality, and affordability of healthcare, as well as health measurements, such as obesity rates and rates of smoking. Education measures how well public schools perform in terms of testing and graduation rates, as well as tuition costs associated with higher education and college debt load. Economy looks at GDP growth, migration to the state, and new business. Infrastructure includes transportation availability, road quality, communications, and internet access. Opportunity includes poverty rates, cost of living, housing costs and gender and racial equality. Fiscal Stability considers the health of the government's finances, including how well the state balances its budget. Crime and Corrections ranks a state’s public safety and measures prison systems and their populations. Natural Environment looks at the quality of air and water and exposure to pollution.

United States US: Unemployment with Intermediate Education: Female: % of Female Labour Force data was reported at 5.791 % in 2017. This records a decrease from the previous number of 6.524 % for 2016. United States US: Unemployment with Intermediate Education: Female: % of Female Labour Force data is updated yearly, averaging 6.771 % from Dec 1994 (Median) to 2017, with 24 observations. The data reached an all-time high of 13.389 % in 2010 and a record low of 4.732 % in 2000. United States US: Unemployment with Intermediate Education: Female: % of Female Labour Force data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Employment and Unemployment. The percentage of the labor force with an intermediate level of education who are unemployed. Intermediate education comprises upper secondary or post-secondary non tertiary education according to the International Standard Classification of Education 2011 (ISCED 2011).; ; International Labour Organization, ILOSTAT database. Data retrieved in September 2018.; Weighted average;

Report on Demographic Data in New York City Public Schools, 2020-21Enrollment counts are based on the November 13 Audited Register for 2020. Categories with total enrollment values of zero were omitted. Pre-K data includes students in 3-K. Data on students with disabilities, English language learners, and student poverty status are as of March 19, 2021. Due to missing demographic information in rare cases and suppression rules, demographic categories do not always add up to total enrollment and/or citywide totals. NYC DOE "Eligible for free or reduced-price lunch” counts are based on the number of students with families who have qualified for free or reduced-price lunch or are eligible for Human Resources Administration (HRA) benefits. English Language Arts and Math state assessment results for students in grade 9 are not available for inclusion in this report, as the spring 2020 exams did not take place. Spring 2021 ELA and Math test results are not included in this report for K-8 students in 2020-21. Due to the COVID-19 pandemic’s complete transformation of New York City’s school system during the 2020-21 school year, and in accordance with New York State guidance, the 2021 ELA and Math assessments were optional for students to take. As a result, 21.6% of students in grades 3-8 took the English assessment in 2021 and 20.5% of students in grades 3-8 took the Math assessment. These participation rates are not representative of New York City students and schools and are not comparable to prior years, so results are not included in this report. Dual Language enrollment includes English Language Learners and non-English Language Learners. Dual Language data are based on data from STARS; as a result, school participation and student enrollment in Dual Language programs may differ from the data in this report. STARS course scheduling and grade management software applications provide a dynamic internal data system for school use; while standard course codes exist, data are not always consistent from school to school. This report does not include enrollment at District 75 & 79 programs. Students enrolled at Young Adult Borough Centers are represented in the 9-12 District data but not the 9-12 School data. “Prior Year” data included in Comparison tabs refers to data from 2019-20. “Year-to-Year Change” data included in Comparison tabs indicates whether the demographics of a school or special program have grown more or less similar to its district or attendance zone (or school, for special programs) since 2019-20. Year-to-year changes must have been at least 1 percentage point to qualify as “More Similar” or “Less Similar”; changes less than 1 percentage point are categorized as “No Change”. The admissions method tab contains information on the admissions methods used for elementary, middle, and high school programs during the Fall 2020 admissions process. Fall 2020 selection criteria are included for all programs with academic screens, including middle and high school programs. Selection criteria data is based on school-reported information. Fall 2020 Diversity in Admissions priorities is included for applicable middle and high school programs. Note that the data on each school’s demographics and performance includes all students of the given subgroup who were enrolled in the school on November 13, 2020. Some of these students may not have been admitted under the admissions method(s) shown, as some students may have enrolled in the school outside the centralized admissions process (via waitlist, over-the-counter, or transfer), and schools may have changed admissions methods over the past few years. Admissions methods are only reported for grades K-12. "3K and Pre-Kindergarten data are reported at the site level. See below for definitions of site types included in this report. Additionally, please note that this report excludes all students at District 75 sites, reflecting slightly lower enrollment than our total of 60,265 students

United States US: Labour Force With Advanced Education: Male: % of Male Working-age Population data was reported at 73.250 % in 2017. This records a decrease from the previous number of 73.560 % for 2016. United States US: Labour Force With Advanced Education: Male: % of Male Working-age Population data is updated yearly, averaging 77.826 % from Dec 1994 (Median) to 2017, with 24 observations. The data reached an all-time high of 81.128 % in 1994 and a record low of 73.250 % in 2017. United States US: Labour Force With Advanced Education: Male: % of Male Working-age Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Labour Force. The percentage of the working age population with an advanced level of education who are in the labor force. Advanced education comprises short-cycle tertiary education, a bachelor’s degree or equivalent education level, a master’s degree or equivalent education level, or doctoral degree or equivalent education level according to the International Standard Classification of Education 2011 (ISCED 2011).; ; International Labour Organization, ILOSTAT database. Data retrieved in September 2018.; Weighted average;

The New York State calculation method was first adopted for the Cohort of 2001 (Class 2005). The cohort consists of al students who first entered 9th grade in a given school year (e.g., the cohort of 2006 entered 9th grade in 2006-2007 school year). Graduates are defined as those students earning either a local or regents diploma and exclude those earning either a special education (IEP) diploma for GED. "The NYSED defined English/Math Aspirational Performance Measure (APM) is the ercentage of students that after their fourth year in high school have met NYSED standards: Graduated by August with a Regents or Local diploma, AND Earned a 75 or higher on the English Regents, AND Earned an 80 or higher on one Math Regents." In order to comply with FERPA regulations on public reporting of education outcomes, rows with a cohort of 20 or fewer students are suppressed. Due to small number of students identified as Native American or Multi-Racial these ethnicities are not reported on the Ethnicity tab, however these students are included in the counts on all other tabs.

The TIGER/Line Files are shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The primary legal divisions of most States are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, and municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four States (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their States. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The 2010 Census boundaries for counties and equivalent entities are as of January 1, 2010, primarily as reported through the Census Bureau's Boundary and Annexation Survey (BAS).

This table contains data on educational attainment from the American Community Survey 2006-2010 database for counties. The American Community Survey (ACS) is a household survey conducted by the U.S. Census Bureau that currently has an annual sample size of about 3.5 million addresses. ACS estimates provides communities with the current information they need to plan investments and services. Information from the survey generates estimates that help determine how more than $400 billion in federal and state funds are distributed annually. Each year the survey produces data that cover the periods of 1-year, 3-year, and 5-year estimates for geographic areas in the United States and Puerto Rico, ranging from neighborhoods to Congressional districts to the entire nation. This table also has a companion table (Same table name with MOE Suffix) with the margin of error (MOE) values for each estimated element. MOE is expressed as a measure value for each estimated element. So a value of 25 and an MOE of 5 means 25 +/- 5 (or statistical certainty between 20 and 30). There are also special cases of MOE. An MOE of -1 means the associated estimates do not have a measured error. An MOE of 0 means that error calculation is not appropriate for the associated value. An MOE of 109 is set whenever an estimate value is 0. The MOEs of aggregated elements and percentages must be calculated. This process means using standard error calculations as described in "American Community Survey Multiyear Accuracy of the Data (3-year 2008-2010 and 5-year 2006-2010)". Also, following Census guidelines, aggregated MOEs do not use more than 1 0-element MOE (109) to prevent over estimation of the error. Due to the complexity of the calculations, some percentage MOEs cannot be calculated (these are set to null in the summary-level MOE tables).

The name for table 'ACS10EDUCNTYMOE' was added as a prefix to all field names imported from that table. Be sure to turn off 'Show Field Aliases' to see complete field names in the Attribute Table of this feature layer. This can be done in the 'Table Options' drop-down menu in the Attribute Table or with key sequence '[CTRL]+[SHIFT]+N'. Due to database restrictions, the prefix may have been abbreviated if the field name exceded the maximum allowed characters.

Abstract (en): The Higher Education General Information Survey (HEGIS) series was designed to provide comprehensive information on various aspects of postsecondary education in the United States and its territories (American Samoa, Guam, Puerto Rico, the Virgin Islands, and the Marshall Islands) and Department of Defense schools outside the United States. A component of the HEGIS XVIII survey package, this data collection furnishes final finance data for 3,325 colleges and universities and their branches throughout the United States. The final edited file contains a sample of 3,287 institutions, of which 386 have been imputed. The data serve as a basis for making decisions concerning the extent of involvement of the nation in postsecondary education, as well as provide a perspective on current trends in education. This file contains final, edited data for 91 percent of these institutions and estimates for the remaining 9 percent. The data are in an institution-by-institution format and include current funds revenues by source, current funds expenditures by function, physical plant assets, indebtedness on physical plant, endowment assets, and change in fund balances. 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: Checked for undocumented or out-of-range codes.. All postsecondary institutions operating during 1983 in the United States and its territories (American Samoa, Guam, Puerto Rico, the Virgin Islands, and the Marshall Islands) and Department of Defense schools outside of the United States. self-enumerated questionnairesThe items in Part G of the survey form require input from publicly controlled institutions. However, these data were not entered into the HEGIS XVIII Financial Database, but were used by the Bureau of Census, U.S. Department of Commerce. No data are published or otherwise released containing personally identifiable data on any individual.

Abstract (en): The primary purpose of the State Nonfiscal Survey is to provide basic information on public elementary and secondary school students and staff for each of the 50 states, the District of Columbia, and outlying territories (American Samoa, Guam, Puerto Rico, the Virgin Islands, and the Marshall Islands). The database provides the following information on students and staff: general information (name, address, and telephone number of the state education agency), staffing information (number of FTEs on the instructional staff, guidance counselor staff, library staff, support staff, and administrative staff), and student information (membership counts by grade, counts of high school completers, counts of high school completers by racial/ethnic breakouts, and breakouts for dropouts by grade, sex, race). 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: Checked for undocumented or out-of-range codes.. All public elementary and secondary education agencies in the 50 states, the District of Columbia, United States territories (American Samoa, Guam, Puerto Rico, the Virgin Islands, and the Marshall Islands), and Department of Defense schools outside of the United States. 2006-01-18 File DOC2450.ALL.PDF was removed from any previous datasets and flagged as a study-level file, so that it will accompany all downloads.2006-01-18 File CB2450.ALL.PDF was removed from any previous datasets and flagged as a study-level file, so that it will accompany all downloads. (1) Part 2, Imputed Data, is a different version of the data in Part 1, Reported Data. The National Center for Education Statistics (NCES) imputed and adjusted some reported values in order to create a data file (Part 2) that more accurately reflects student and staff counts and improves comparability between states. Imputations are defined as cases where the missing value is not reported at all, indicating that subtotals for the category are under-reported. An imputation by NCES assigns a value to the missing item, and the subtotals containing this item increase by the amount of the imputation. Imputations and adjustments were performed on the 50 states and Washington, DC, only. Since all states and Washington, DC, reported data in this survey, these imputations and adjustments were implemented to correct for item nonresponse only. This process consisted of several stages and steps, and varied as to the nature of the missing data. No adjustments or imputations were made to high school graduates or other high school completer categories, nor were any adjustments or imputations performed on the race/ethnicity data. (2) The Instruction Manual that is included with this data collection also applies to COMMON CORE OF DATA: PUBLIC EDUCATION AGENCY UNIVERSE, 1995-1996 (ICPSR 2468) and COMMON CORE OF DATA: PUBLIC SCHOOL UNIVERSE, 1995-1996 (ICPSR 2470). (3) The codebook, data collection instrument, and instruction manual are provided as two Portable Document Format (PDF) files. The PDF file format was developed by Adobe Systems Incorporated and can be accessed using the Adobe Acrobat Reader (version 3.0 or later). Information on how to obtain a copy of the Acrobat Reader is provided through the ICPSR Website on the Internet.

U.S. citizens with a professional degree had the highest median household income in 2023, at 172,100 U.S. dollars. In comparison, those with less than a 9th grade education made significantly less money, at 35,690 U.S. dollars. Household income The median household income in the United States has fluctuated since 1990, but rose to around 70,000 U.S. dollars in 2021. Maryland had the highest median household income in the United States in 2021. Maryland’s high levels of wealth is due to several reasons, and includes the state's proximity to the nation's capital. Household income and ethnicity The median income of white non-Hispanic households in the United States had been on the rise since 1990, but declining since 2019. While income has also been on the rise, the median income of Hispanic households was much lower than those of white, non-Hispanic private households. However, the median income of Black households is even lower than Hispanic households. Income inequality is a problem without an easy solution in the United States, especially since ethnicity is a contributing factor. Systemic racism contributes to the non-White population suffering from income inequality, which causes the opportunity for growth to stagnate.

IDEA Section 618 Data Products: Static Tables Part B Assessment Number and percent of students grades 3 through 8 and high school, served under IDEA, Part B, who participated in reading and math assessments, by assessment type and state. Number and percent of students grades 3 through 8 and high school served under IDEA, Part B, who received a valid and proficient score on assessments for math, by assessment type, grade level, and state. Number and percent of students grades 3 through 8 and high school served under IDEA, Part B, who received a valid and proficient score on assessments for reading, by assessment type, grade level, and state. Part B Child Count and Educational Environments Number of children and students served under IDEA, Part B, by age group and state. Number of children ages 3 through 5 (not in kindergarten) served under IDEA, Part B, by disability and state. Number of students ages 5 (in kindergarten) through 21 served under IDEA, Part B, by disability and state. Number and percent of children ages 3 through 5 (not in kindergarten) and students ages 5 (in kindergarten) through 21 served under IDEA, Part B, by EL status and state. Number and percent of children ages 3 through 5 (not in kindergarten) served under IDEA, Part B, by race/ethnicity and state. Number and percent of students ages 5 (in kindergarten) through 21 served under IDEA, Part B, by race/ethnicity and state. Children ages 3 through 5 (not in kindergarten) served under IDEA, Part B, as a percentage of population, by disability category and state. Students ages 5 (in kindergarten) through 21 served under IDEA, Part B, as a percentage of population, by disability category and state. Children and students ages 3 through 21 served under IDEA, Part B, as a percentage of population, by age and state. Number and percent of children in race/ethnicity category ages 3 through 5 (not in kindergarten) with disabilities served under IDEA, Part B, by disability category and state. Number and percent of children in race/ethnicity category ages 5 (in kindergarten) through 21 with disabilities served under IDEA, Part B, by disability category and state. Number and percent of children ages 3 through 5 (not in kindergarten) served under IDEA, Part B, by educational environment and state. Number and percent of students ages 5 (in kindergarten) through 21 served under IDEA, Part B, by educational environment and state. Number and percent of female/male children ages 3 through 5 (not in kindergarten) served under IDEA, Part B, by educational environment and state. Number and percent of female/male students ages 5 (in kindergarten) through 21 served under IDEA, Part B, by educational environment and state. Number and percent of non-English Learner (non-EL) and English Learner (EL) children ages 3 through 5 (not in kindergarten) served under IDEA, Part B, by educational environment and state. Number and percent of non-English Learner (non-EL) and English Learner (EL) students ages 5 (in kindergarten) through 21 served under IDEA, Part B, by educational environment and state. Number and percent of children in race/ethnicity category ages 3 through 5 (not in kindergarten) with disabilities served under IDEA, Part B, by educational environment and state. Number and percent of students in race/ethnicity category ages 5 (in kindergarten) through 21 with disabilities served under IDEA, Part B, by educational environment and state. Number of children and students served under IDEA, Part B, in the US, Outlying Areas, and Freely Associated States by age and disability category. Part B Discipline Number of children and students ages 3 through 21 with disabilities served under IDEA, Part B, removed to an Interim Alternative Educational Setting by type of removal and state by disability. Number of children and students ages 3 through 21 served under IDEA, Part B, suspended/expelled by total number of days removed and state by disability. Number of children and students ages 3 through 21 served under IDEA, Part B, subject to disciplinary removal by total cumulative number of days removed during school year and state by type of disability. Number of children and students ages 3 through 21 with disabilities served under IDEA, Part B, removed to an Interim Alternative Educational Setting by type of removal and state by race/ethnicity. Number of children and students ages 3 through 21 with disabilities served under IDEA, Part B, suspended/expelled by total number of days removed and state by race/ethnicity. Number of children and students ages 3 through 21 with disabilities served under IDEA, Part B, subject to disciplinary removal by total cumulative number of days removed during school year, and state by race/ethnicity. Number and percent of female and male children and students ages 3 through 21 with disabilities served under IDEA, Part B, removed to an Interim Alternative Educational Setting by type of removal and state. Number and percent of female and male children and students ages 3 through 21 with disabilities served under IDEA, Part B, suspended/expelled by total number of days removed and state. Number and percent of female and male children and students ages 3 through 21 with disabilities served under IDEA, Part B, subject to disciplinary removal by total cumulative number of days removed during school year and state. Number and percent of non-English Learner (non-EL) and English Learner (EL) children and students ages 3 through 21 with disabilities served under IDEA, Part B, removed to an Interim Alternative Educational Setting by type of removal and state. Number and percent of non-English Learner (non-EL) and English Learner (EL) children and students ages 3 through 21 with disabilities served under IDEA, Part B, suspended/expelled by total number of removed and state. Number and percent of non-English Learner (non-EL) and English Learner (EL) children and students ages 3 through 21 with disabilities served under IDEA, Part B, subject to disciplinary removal by total cumulative number of days removed during school year and state. Number of children and students, ages 3 through 21, subject to expulsion, by disability status, receipt of educational services and state. Percent of children and students ages 3 through 21 with disabilities served under IDEA, Part B, removed to an Interim Alternative Educational Setting by type of removal, disability, and state. Percent of children and students ages 3 through 21 with disabilities served under IDEA, Part B, suspended/expelled by total number of days removed, disability, and state. Percent of children and students ages 3 through 21 with disabilities served under IDEA, Part B, subject to disciplinary removal by total cumulative number of days removed during school year, disability, and state. Percent of children and students ages 3 through 21 with disabilities served under IDEA, Part B, removed to an Interim Alternative Educational Setting by type of removal, race/ethnicity, and state. Percent of children and students ages 3 through 21 with disabilities served under IDEA, Part B, suspended/expelled by total number of days removed, race/ethnicity, and state. Percent of children and students ages 3 through 21 with disabilities served under IDEA, Part B, subject to disciplinary removal by total cumulative number of days removed during school year, race/ ethnicity, and state. Part B Dispute Resolution Number and percent of written, signed complaints initiated through dispute resolution procedures for children ages 3 through 21 served under IDEA, Part B, by case status and state. Number and percent of mediations held through dispute resolution procedures for children ages 3 through 21 served under IDEA, Part B, by case status and state. Number and percent of hearings (fully adjudicated) through dispute resolution procedures for children ages 3 through 21 served under IDEA, Part B, by case status and state. Number of expedited hearing requests (related to disciplinary decision) filed through dispute resolution procedures for children ages 3 through 21 served under IDEA, Part B, by case status and state. Part B Exiting Number of students ages 14 through 21 with disabilities served under IDEA, Part B, who exited special education, by exit reason and state. Number of students ages 14 through 21 with disabilities served under IDEA, Part B, in the U.S., Outlying Areas, and Freely Associated States who exited special education, by exit reason and age. Number and percent of students ages 14 through 21 with disabilities served under IDEA, Part B, who exited special education, by exit reason, race/ethnicity, and state. Number and percent of female and male students ages 14 through 21 with disabilities served under IDEA, Part B, who exited special education, by exit reason and state. Number and percent of non-English Learner (non-EL) and English Learner (EL) students ages 14 through 21 with disabilities served under IDEA, Part B, who exited special education, by exit reason and state. Part B Maintenance of Effort Reduction and Coordinated Early Intervening Services Number and percent of LEAs reported under each determination level that controls whether the LEA may be able to reduce MOE Amount reduced under the IDEA MOE provision in IDEA §613(a)(2)(C) Number and percent of LEAs that met requirements and had an increase in 611 allocations and took the MOE reduction Number and percent of LEAs required to use 15% of funds for CEIS due to significant disproportionality or voluntarily reserved funds for CEIS Number of children who received CEIS anytime in the past two years and who received special education and related services Number and percent of LEAs/ESAs that were determined to meet the MOE compliance standard in SY 2016-17 Part B Personnel Teachers employed (FTE) to work with children, ages 3 through 5, who are receiving special education under IDEA, Part B, by qualification status and state. Teachers employed

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Abstract (en): The Recent College Graduates (RCG) survey estimates the potential supply of newly qualified teachers in the United States and explores the immediate post-degree employment and education experiences of individuals obtaining bachelor's or master's degrees from American colleges and universities. The RCG survey, which focuses heavily, but not exclusively, on those graduates qualified to teach at the elementary and secondary levels, is designed to meet the following objectives: (1) to determine how many graduates become eligible or qualified to teach for the first time and how many are employed as teachers in the year following graduation, by teaching field, (2) to examine the relationships among courses taken, student achievement, and occupational outcomes, and (3) to monitor unemployment rates and average salaries of graduates by field of study. The RCG survey collects information on education and employment of all graduates (date of graduation, field of study, whether newly qualified to teach, further enrollment, financial aid, employment status, and teacher employment characteristics) as well as standard demographic characteristics such as earnings, age, marital status, sex, and race/ethnicity. 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: Checked for undocumented or out-of-range codes.. Students within one year of attaining a bachelor's or a master's degree from an American college or university. A two-stage stratified sampling approach was employed. The first stage consisted of drawing a sample of bachelor's and master's degree-granting institutions from Higher Education General Information Survey (HEGIS)/Integrated Postsecondary Education Data System (IPEDS) completions files. Institutions were stratified by control (public or private), by region, and by the proportion of degrees awarded in the field of education (over or under a specified number). Within each of these strata, institutions were selected according to size (size being measured by the sum of bachelor's and master's degrees awarded that year). The second stage consisted of the selection of a core sample of graduates (bachelor's and master's degree recipients) who received their degrees from the sampled institutions during the 1976-1977 academic year. Sampling rates of graduates differed by major field of study. The institution sample consisted of 300 institutions of which 30 were Historically Black Colleges (HBCs). The graduate sample was stratified by degree received and major field of study (vocational education, special education, other education, and noneducation). Data are representative at the national level. 2001-01-05 SAS and SPSS data definition statements have been created for this collection. Also, the codebook and data collection instrument were converted to a PDF file. The codebook and data collection instrument are provided by ICPSR as a Portable Document Format (PDF) file. The PDF file format was developed by Adobe Systems Incorporated and can be accessed using PDF reader software, such as the Adobe Acrobat Reader. Information on how to obtain a copy of the Acrobat Reader is provided on the ICPSR Web site.