The 2020-2021 School Neighborhood Poverty Estimates are based on school locations from the 2020-2021 Common Core of Data (CCD) school file and income data from families with children ages 5 to 17 in the U.S. Census Bureau’s 2017-2021 American Community Survey (ACS) 5-year collection. The ACS is a continuous household survey that collects social, demographic, economic, and housing information from the population in the United States each month. The Census Bureau calculates the income-to-poverty ratio (IPR) based on money income reported for families relative to the poverty thresholds, which are determined based on the family size and structure. Noncash benefits (such as food stamps and housing subsidies) are excluded, as are capital gains and losses. The IPR is the percentage of family income that is above or below the federal poverty level. The IPR indicator ranges from 0 to a top-coded value of 999. A family with income at the poverty threshold has an IPR value of 100. The estimates in this file reflect the IPR for the neighborhoods around schools which may be different from the neighborhood conditions of students enrolled in schools. 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.

In 2024, about four percent of the people with a Bachelor's degree or higher were living below the poverty line in the United States. This is far below the poverty rate of those without a high school diploma, which was 23.1 percent that year.

Will all children be able to read by 2030? The ability to read with comprehension is a foundational skill that every education system around the world strives to impart by late in primary school—generally by age 10. Moreover, attaining the ambitious Sustainable Development Goals (SDGs) in education requires first achieving this basic building block, and so does improving countries’ Human Capital Index scores. Yet past evidence from many low- and middle-income countries has shown that many children are not learning to read with comprehension in primary school. To understand the global picture better, we have worked with the UNESCO Institute for Statistics (UIS) to assemble a new dataset with the most comprehensive measures of this foundational skill yet developed, by linking together data from credible cross-national and national assessments of reading. This dataset covers 115 countries, accounting for 81% of children worldwide and 79% of children in low- and middle-income countries. The new data allow us to estimate the reading proficiency of late-primary-age children, and we also provide what are among the first estimates (and the most comprehensive, for low- and middle-income countries) of the historical rate of progress in improving reading proficiency globally (for the 2000-17 period). The results show that 53% of all children in low- and middle-income countries cannot read age-appropriate material by age 10, and that at current rates of improvement, this “learning poverty” rate will have fallen only to 43% by 2030. Indeed, we find that the goal of all children reading by 2030 will be attainable only with historically unprecedented progress. The high rate of “learning poverty” and slow progress in low- and middle-income countries is an early warning that all the ambitious SDG targets in education (and likely of social progress) are at risk. Based on this evidence, we suggest a new medium-term target to guide the World Bank’s work in low- and middle- income countries: cut learning poverty by at least half by 2030. This target, together with improved measurement of learning, can be as an evidence-based tool to accelerate progress to get all children reading by age 10.

For further details, please refer to https://thedocs.worldbank.org/en/doc/e52f55322528903b27f1b7e61238e416-0200022022/original/Learning-poverty-report-2022-06-21-final-V7-0-conferenceEdition.pdf

The 2015-2016 School Neighborhood Poverty Estimates are based on school locations from the 2015-2016 Common Core of Data (CCD) school file and income data from families with children ages 5 to 17 in the U.S. Census Bureau’s 2012-2016 American Community Survey (ACS) 5-year collection. The ACS is a continuous household survey that collects social, demographic, economic, and housing information from the population in the United States each month. The Census Bureau calculates the income-to-poverty ratio (IPR) based on money income reported for families relative to the poverty thresholds, which are determined based on the family size and structure. Noncash benefits (such as food stamps and housing subsidies) are excluded, as are capital gains and losses. The IPR is the percentage of family income that is above or below the federal poverty level. The IPR indicator ranges from 0 to a top-coded value of 999. A family with income at the poverty threshold has an IPR value of 100. The estimates in this file reflect the IPR for the neighborhoods around schools which may be different from the neighborhood conditions of students enrolled in schools. 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.

Finland - At Risk of Poverty-rate: Tertiary education (levels 5-8) was 5.10% in December of 2024, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Finland - At Risk of Poverty-rate: Tertiary education (levels 5-8) - last updated from the EUROSTAT on March of 2026. Historically, Finland - At Risk of Poverty-rate: Tertiary education (levels 5-8) reached a record high of 5.40% in December of 2020 and a record low of 4.40% in December of 2012.

The Common Core of Data (CCD) is a program of the U.S. Department of Education’s National Center for Education Statistics that collects selected data about all public schools, public school districts and state education agencies in the United States every year. Data are supplied by state education agency officials.

Some schools may have very low student counts. According to the NCES “A student may attend more than one school, but each student is counted only once, in the school where he/she spends most of the school day—the “home school” or “school of record.” For example, a student may attend a regular high school for most of the day and a career/technical (CTE) high school part time. That student is typically counted in the membership of the regular high school, not the CTE high school.” Some schools may have no student counts. This is because they contract with other schools or agencies to provide services for some students. Those students are not reported for the receiving school in order to avoid duplication. However, where all services are provided by a contracting school, no student counts are reported for the sending school.

Certain indicators provided in this data on PolicyMap do not come from the NCES. Student/teacher ratio was calculated by PolicyMap by dividing the total number of students by the number of full-time-equivalent classroom teachers. The percentages of students of a given race were calculated by PolicyMap. The links to the GreatSchools school pages were made using a table from GreatSchools.

Three indicators are included for consideration in matters related to the Office of Minority and Women Inclusion (OMWI). These indicators label a school as being all-female, majority-minority, or in an inner city. All-female schools were calculated by summing the number of female students in each race category (the NCES does not provide total numbers of students by sex). If that number is equal to the number of students in the school, it is label as all female. This simply means that all the students in the school in the given school year were female; it does not mean that the school is by policy an all-female school.

Majority-minority was calculated by dividing the number of white students by the number of total students. If the percent of students who are white is less than 50%, the school is labeled majority minority.

The inner city label was not calculated using NCES data. A spatial calculation was made using ACS data, using methodology similar to that developed by the Initiative for a Competitive City (see http://www.icic.org/research-and-analysis/research-definitions). Using this methodology, a census tract in a metropolitan statistical area is considered to be part of an inner city if its poverty rate is 20% or higher, or it meets at least two of the three following criteria:

Poverty rate 1.5 times or more than that of the MSA Median household income .5 or less than that of the MSA Unemployment rate (using ACS) of 1.5 times or more than that of the MSA

Schools without coordinates are excluded from the data.

The 2018-2019 School Neighborhood Poverty Estimates are based on school locations from the 2018-2019 Common Core of Data (CCD) school file and income data from families with children ages 5 to 17 in the U.S. Census Bureau’s 2015-2019 American Community Survey (ACS) 5-year collection. The ACS is a continuous household survey that collects social, demographic, economic, and housing information from the population in the United States each month. The Census Bureau calculates the income-to-poverty ratio (IPR) based on money income reported for families relative to the poverty thresholds, which are determined based on the family size and structure. Noncash benefits (such as food stamps and housing subsidies) are excluded, as are capital gains and losses. The IPR is the percentage of family income that is above or below the federal poverty level. The IPR indicator ranges from 0 to a top-coded value of 999. A family with income at the poverty threshold has an IPR value of 100. The estimates in this file reflect the IPR for the neighborhoods around schools which may be different from the neighborhood conditions of students enrolled in schools. 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 EDGE School Neighborhood Poverty Estimates rely on household economic data from the Census Bureau’s American Community Survey (ACS) and public school point locations developed by NCES to estimate the income-to-poverty ratio for neighborhoods around school buildings. Unlike neighborhood poverty estimates created from survey responses collected for predefined geographic areas like census tracts, Spatially Interpolated Demographic Estimates (SIDE) predict conditions at specific point locations based on the survey responses nearest to those locations. This approach allows SIDE estimates to extract new value from existing data sources to provide indicators of neighborhood conditions. The economic conditions of school neighborhoods may be different from the economic conditions in neighborhoods where students live. However, the economic condition of the neighborhood around a school may impact schools, just as the condition of neighborhood schools may impact local neighborhoods. The school neighborhood poverty estimates provide an additional indicator to help identify these local conditions.

The 2021-2022 School Neighborhood Poverty Estimates are based on school locations from the 2021-2022 Common Core of Data (CCD) school file and income data from families with children ages 5 to 17 in the U.S. Census Bureau’s 2018-2022 American Community Survey (ACS) 5-year collection. The ACS is a continuous household survey that collects social, demographic, economic, and housing information from the population in the United States each month. The Census Bureau calculates the income-to-poverty ratio (IPR) based on money income reported for families relative to the poverty thresholds, which are determined based on the family size and structure. Noncash benefits (such as food stamps and housing subsidies) are excluded, as are capital gains and losses. The IPR is the percentage of family income that is above or below the federal poverty level. The IPR indicator ranges from 0 to a top-coded value of 999. A family with income at the poverty threshold has an IPR value of 100. The estimates in this file reflect the IPR for the neighborhoods around schools which may be different from the neighborhood conditions of students enrolled in schools. For more information about School Neighborhood Poverty Estimates, see: https://nces.ed.gov/programs/edge/Economic/NeighborhoodPoverty.Collections are available for the following years:2020-212019-202018-192017-182016-172015-16All 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 2016-2017 School Neighborhood Poverty Estimates are based on school locations from the 2016-2017 Common Core of Data (CCD) school file and income data from families with children ages 5 to 17 in the U.S. Census Bureau’s 2013-2017 American Community Survey (ACS) 5-year collection. The ACS is a continuous household survey that collects social, demographic, economic, and housing information from the population in the United States each month. The Census Bureau calculates the income-to-poverty ratio (IPR) based on money income reported for families relative to the poverty thresholds, which are determined based on the family size and structure. Noncash benefits (such as food stamps and housing subsidies) are excluded, as are capital gains and losses. The IPR is the percentage of family income that is above or below the federal poverty level. The IPR indicator ranges from 0 to a top-coded value of 999. A family with income at the poverty threshold has an IPR value of 100. The estimates in this file reflect the IPR for the neighborhoods around schools which may be different from the neighborhood conditions of students enrolled in schools.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.

In 2023, about 36.79 million Americans were living below the national poverty line in the United States. Of those Americans, around 4.04 million had a four-year degree or higher. This means they have an income below 100 percent of the national poverty level as defined by the U.S. Census Bureau.

The U.S. Census Bureau's Small Area Income and Poverty Estimates (SAIPE) program provides annual estimates of income and poverty statistics for all school districts, counties, and states. The main objective of this program is to provide estimates of income and poverty for the administration of federal programs and the allocation of federal funds to local jurisdictions. In addition to these federal programs, state and local programs use the income and poverty estimates for distributing funds and managing programs. In order to implement provisions under Title I of the Elementary and Secondary Education Act as amended, we produce total population, number of children ages 5 to 17, and number of related children ages 5 to 17 in families in poverty estimates for school districts.

Socioeconomic indicators like the poverty rate, population change, unemployment rate, and education levels vary across the nation. ERS has compiled the latest data on these measures into a mapping and data display/download application that allows users to identify and compare States and counties on these indicators.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: Poverty Population Unemployment Education Web page with links to Excel files For complete information, please visit https://data.gov.

Context

Education is a fundamental driver of development and a critical instrument for reducing poverty and improving health, gender equality, and global stability. This dataset, sourced from the World Bank's EdStats database, provides a comprehensive, multi-dimensional view of the state of education worldwide.

It contains over 4,000 internationally comparable indicators covering education access, progression, completion, literacy, teacher availability, population demographics, and government expenditures. The data spans more than 200 countries and territories from $1970$ to the present.

Dataset Content

This upload represents a relational view of the global education landscape through four core files:

• EdStatsCountry.csv: Metadata for each country, including Region, Income Group (e.g., "Low income", "High income: non-OECD"), Currency Unit, and the System of National Accounts used for reporting.

• EdStatsSeries.csv: The dataset's "Master Dictionary." It defines thousands of indicator codes (e.g., BAR.NOED.1519.ZS Percentage of population age 15-19 with no education) and provides long-form definitions and sources.

• EdStatsCountry-Series.csv: Maps specific series indicators to country-specific data sources and collection methodologies.

• EdStatsFootNote.csv: Essential for data cleaning and integrity. It contains year-specific notes explaining outliers, estimation methods (e.g., "UIS estimation"), or shifts in national reporting standards.

Research & AI Use Cases

This dataset is an exceptional resource for Time-Series Analysis, Global Clustering, and Predictive Modeling:

• EdTech Strategy & Planning: Identify regions with rapidly growing primary school enrollment to forecast demand for digital learning infrastructure.

• Gender Equality Modeling: Analyze the Attainment and Literacy series to track and predict the closing of the gender gap in education over the last half-century.

• Economic Impact Analysis: Correlate government expenditure on education (Expenditures topic) with future economic indicators like GDP growth.

• Data Integrity Research: Use the FootNote file to build robust pipelines that account for reporting irregularities and estimated data points in developing nations.

Title I of the 1965 Elementary and Secondary Education Act explicitly directed more federal aid for K-12 education to poorer areas for the first time in US history, with a goal of promoting regional convergence in school spending. Using newly collected data, we find some evidence that Title I narrowed the gap in per-pupil school spending between richer and poorer states in the short- to medium-run. However, the program was small relative to then-existing poverty gaps in school spending; even in the absence of crowd-out by local or state governments, the program could have reduced the gap by only 15 percent.

Financial overview and grant giving statistics of Out of Poverty Thru Education Inc.

This statistic shows the percentage of the population aged 25 and over that live in households in poverty, as distinguished by their education level and household type. 47 percent of female householders with related children under the age of 18 who had never graduated from high school were living in poverty as of 2018.

The eradication of poverty and the provision of equitable and inclusive quality education for all are two intricately linked Sustainable Development Goals (SDGs). As this year’s High Level Political Forum focuses on prosperity and poverty reduction, this paper, jointly released by the UNESCO Institute for Statistics (UIS) and the Global Education Monitoring (GEM) Report, shows why education is so central to the achievement of the SDGs and presents the latest estimates on out-of school children, adolescents and youth to demonstrate how much is at stake. The out-of school rate has not budged since 2008 at the primary level, since 2012 at the lower secondary level and since 2013 at the upper secondary level. The consequences are grave: if all adults completed secondary school, the global poverty rate would be more than halved.

The files in the data directory contain estimates of population and poverty.

The school districts for which we have estimates were 
identified in the **2022 school district mapping survey**,
which asked about all school districts as of January 1, 2023 and 
used school district boundaries for the 2021-2022 school year.

The 2022 estimates are consistent with the population controls and 
income concepts used in the American Community Survey single-year 
estimates.  

There is one file for each of the states, the District of Columbia, and 
the entire United States. Each file contains the FIPS state code, 
Department of Education Common Core of Data (CCD) ID numbers, District names, 
the total population, population of school-age children, and estimated 
number of school-age children in poverty related to the head of the household.

This dataset contains Zanzibar Census, Survey and Statistics data.