Graph and download economic data for Ratio of Female to Male Secondary School Enrollment for Low Income Countries (SEENRSECOFMZSLIC) from 1970 to 2020 about enrolled, secondary schooling, secondary, females, males, ratio, education, and income.

The data and syntax files for the Journal of Research on Educational Effectiveness paper titled "The Effects of Two Mindset Interventions on Low-Income Students’ Academic and Psychological Outcomes" are presented here. Please download and read the "CSRP-Two_Mindset-Analysis-README" file before working with the data. Any questions can be directed to Jill Gandhi, the corresponding author of the study.

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 2023, 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 25.1 percent in 2023.

Data includes: board and school information, grade 3 and 6 EQAO student achievements for reading, writing and mathematics, and grade 9 mathematics EQAO and OSSLT. Data excludes private schools, Education and Community Partnership Programs (ECPP), summer, night and continuing education schools.

How Are We Protecting Privacy?

Results for OnSIS and Statistics Canada variables are suppressed based on school population size to better protect student privacy. In order to achieve this additional level of protection, the Ministry has used a methodology that randomly rounds a percentage either up or down depending on school enrolment. In order to protect privacy, the ministry does not publicly report on data when there are fewer than 10 individuals represented.

* Percentages depicted as 0 may not always be 0 values as in certain situations the values have been randomly rounded down or there are no reported results at a school for the respective indicator. * Percentages depicted as 100 are not always 100, in certain situations the values have been randomly rounded up.

The information in the School Information Finder is the most current available to the Ministry of Education at this time, as reported by schools, school boards, EQAO and Statistics Canada. The information is updated as frequently as possible.

This information is also available on the Ministry of Education's School Information Finder website by individual school.

Descriptions for some of the data types can be found in our glossary.

School/school board and school authority contact information are updated and maintained by school boards and may not be the most current version. For the most recent information please visit: https://data.ontario.ca/dataset/ontario-public-school-contact-information.

These files contain the programs and the public data for the journal article: "Centralized Admissions, Affirmative Action and Access of Low-income Students to Higher Education", American Economic Journal: Economic Policy.

United States - of Female to Male Tertiary School Enrollment for Low Income Countries was 0.67036 Ratio in January of 2021, according to the United States Federal Reserve. Historically, United States - of Female to Male Tertiary School Enrollment for Low Income Countries reached a record high of 0.67036 in January of 2021 and a record low of 0.48351 in January of 1979. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - of Female to Male Tertiary School Enrollment for Low Income Countries - last updated from the United States Federal Reserve on July of 2025.

United States - of Female to Male Secondary School Enrollment for Low Income Countries was 0.81871 Ratio in January of 2020, according to the United States Federal Reserve. Historically, United States - of Female to Male Secondary School Enrollment for Low Income Countries reached a record high of 0.81871 in January of 2020 and a record low of 0.60995 in January of 1979. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - of Female to Male Secondary School Enrollment for Low Income Countries - last updated from the United States Federal Reserve on July of 2025.

Number of persons in low income, low income rate and average gap ratio by age, sex and economic family type, annual.

Existing studies from the United States, Latin America, and Asia provide scant evidence that private schools dramatically improve academic performance relative to public schools. Using data from Kenya—a poor country with weak public institutions—we find a large effect of private schooling on test scores, equivalent to one full standard deviation. This finding is robust to endogenous sorting of more able pupils into private schools. The magnitude of the effect dwarfs the impact of any rigorously tested intervention to raise performance within public schools. Furthermore, nearly two-thirds of private schools operate at lower cost than the median government school.

This dataset provides data about classes by subject in Massachusetts public schools and districts since 2011. It includes the number of classes in a given subject, the average class size, and demographic indicators of students taking those classes.

This data allows for analyzing trends in class size distribution across different subjects and student subgroups. It can support research on educational equity, resource allocation within schools, and potential disparities in learning environments.

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 contains the same data that is also published on our DESE Profiles site: Class Size by Race/Ethnicity Class Size by Gender and Selected Population

Changhua County Elementary School Children's Afterschool Care Service Program (low-income, disabled, indigenous, and general students)

Student enrollment data disaggregated by students from low-income families, students from each racial and ethnic group, gender, English learners, children with disabilities, children experiencing homelessness, children in foster care, and migratory students for each mode of instruction.

CONTEXT

Practice Scenario: The UIW School of Engineering wants to recruit more students into their program. They will recruit students with great math scores. Also, to increase the chances of recruitment,​ the department will look for students who qualify for financial aid. Students who qualify for financial aid more than likely come from low socio-economic backgrounds. One way to indicate this is to view how much federal revenue a school district receives through its state. High federal revenue for a school indicates that a large portion of the student base comes from low incomes families.

The question we wish to ask is as follows: Name the school districts across the nation where their Child Nutrition Programs(c25) are federally funded between the amounts $30,000 and $50,000. And where the average math score for the school districts corresponding state is greater than or equal to the nations average score of 282.

The SQL query below in 'Top5MathTarget.sql' can be used to answer this question in MySQL. To execute this process, one would need to install MySQL to their local system and load the attached datasets below from Kaggle into their MySQL schema. The SQL query below will then join the separate tables on various key identifiers.

DATA SOURCE Data is sourced from The U.S Census Bureau and The Nations Report Card (using the NAEP Data Explorer).

Finance: https://www.census.gov/programs-surveys/school-finances/data/tables.html

Math Scores: https://www.nationsreportcard.gov/ndecore/xplore/NDE

COLUMN NOTES

All data comes from the school year 2017. Individual schools are not represented, only school districts within each state.

FEDERAL FINANCE DATA DEFINITIONS

t_fed_rev: Total federal revenue through the state to each school district.

C14- Federal revenue through the state- Title 1 (no child left behind act).

C25- Federal revenue through the state- Child Nutrition Act.

Title 1 is a program implemented in schools to help raise academic achievement ​for all students. The program is available to schools where at least 40% of the students come from low inccom​​e families.

Child Nutrition Programs ensure the children are getting the food they need to grow and learn. Schools with high federal revenue to these programs indicate students that also come from low income​ families.

MATH SCORES DATA DEFINITIONS

Note: Mathematics, Grade 8, 2017, All Students (Total)

average_scale_score - The state's average score for eighth graders taking the NAEP math exam.

Poverty and low-income statistics by disability status, age group, sex and economic family type, Canada, annual.

These geospatial data resources and the linked mapping tool below reflect currently available data on three categories of potentially qualifying Low-Income communities: Census tracts that meet the CDFI's New Market Tax Credit Program's threshold for Low Income, thereby are able to apply to Category 1. Census tracts that meet the White House's Climate and Economic Justice Screening Tool's threshold for disadvantage in the 'Energy' category, thereby are able to apply for Additional Selection Criteria Geography. Counties that meet the USDA's threshold for Persistent Poverty, thereby are able to apply for Additional Selection Criteria Geography. Note that Category 2 - Indian Lands are not shown on this map. Note that Persistent Poverty is not calculated for US Territories. Note that CEJST Energy disadvantage is not calculated for US Territories besides Puerto Rico. The excel tool provides the land area percentage of each 2023 census tract meeting each of the above categories. To examine geographic eligibility for a specific address or latitude and longitude, visit the program's mapping tool. Additional information on this tax credit program can be found on the DOE Landing Page for the 48e program at https://www.energy.gov/diversity/low-income-communities-bonus-credit-program or the IRS Landing Page at https://www.irs.gov/credits-deductions/low-income-communities-bonus-credit. Maps last updated: September 1st, 2024 Next map update expected: December 7th, 2024 Disclaimer: The spatial data and mapping tool is intended for geolocation purposes. It should not be relied upon by taxpayers to determine eligibility for the Low-Income Communities Bonus Credit Program. Source Acknowledgements: The New Market Tax Credit (NMTC) Tract layer using data from the 2016-2020 ACS is from the CDFI Information Mapping System (CIMS) and is created by the U.S. Department of Treasury Community Development Financial Institutions Fund. To learn more, visit CDFI Information Mapping System (CIMS) | Community Development Financial Institutions Fund (cdfifund.gov). https://www.cdfifund.gov/mapping-system. Tracts are displayed that meet the threshold for the New Market Tax Credit Program. The 'Energy' Category Tract layer from the Climate and Economic Justice Screening Tool (CEJST) is created by the Council on Environmental Quality (CEQ) within the Executive Office of the President. To learn more, visit https://screeningtool.geoplatform.gov/en/. Tracts are displayed that meet the threshold for the 'Energy' Category of burden. I.e., census tracts that are at or above the 90th percentile for (energy burden OR PM2.5 in the air) AND are at or above the 65th percentile for low income. The Persistent Poverty County layer is created by joining the U.S. Department of Agriculture, Economic Research Service's Poverty Area Official Measures dataset, with relevant county TIGER/Line Shapefiles from the US Census Bureau. To learn more, visit https://www.ers.usda.gov/data-products/poverty-area-measures/. Counties are displayed that meet the thresholds for Persistent Poverty according to 'Official' USDA updates. i.e. areas with a poverty rate of 20.0 percent or more for 4 consecutive time periods, about 10 years apart, spanning approximately 30 years (baseline time period plus 3 evaluation time periods). Until Dec 7th, 2024 both the USDA estimates using 2007-2011 and 2017-2021 ACS 5-year data. On Dec 8th, 2024, only the USDA estimates using 2017-2021 data will be accepted for program eligibility.

Percentage of the population aged 0 to 24 in low income, by age group and type of living arrangement. This table is included in Section A: A portrait of the school-age population: Low income of the Pan Canadian Education Indicators Program (PCEIP). PCEIP draws from a wide variety of data sources to provide information on the school-age population, elementary, secondary and postsecondary education, transitions, education finance and labour market outcomes. The program presents indicators for all of Canada, the provinces, the territories, as well as selected international comparisons and comparisons over time. PCEIP is an ongoing initiative of the Canadian Education Statistics Council, a partnership between Statistics Canada and the Council of Ministers of Education, Canada that provides a set of statistical measures on education systems in Canada.

Poverty and low-income statistics by visible minority group, Indigenous group and immigration status, Canada and provinces.

This dataset includes expenditure data reported by school districts, charter schools, and virtual schools starting with fiscal year 2009. It also includes student enrollment, demographic, and performance indicators as well as teacher salary and staffing data.

In addition to showing the overall cost per pupil, this dataset provides detail about how much districts spend in major functional areas such as administration, teaching, and maintenance. For more information about the data and how to interpret it, please visit the School Finance Dashboard.

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

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

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

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

• Administration
• Instructional Leadership
• Teachers
• Other Teaching Services
• Professional Development
• Instructional Materials, Equipment and Technology
• Guidance, Counseling and Testing
• Pupil Services
• Operations and Maintenance
• Insurance, Retirement Programs and Other
• Total In-District Expenditures
• Total Expenditures

In 2023, around 52 percent of Indonesians with the lowest income levels completed their senior high school. In comparison, the completion rate of senior high school for those with the highest economic level was 77.76 percent. As education levels rise and economic levels fall in Indonesia, the percentage of people who complete their schooling decreases.