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.

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

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, ratio, females, males, education, and income.

The gap between the proportion of 15 year olds eligible for FSM and those not eligible for FSM progressing to HE at age 18.

The programs replicate tables and figures from "Marginal Effects of Merit Aid for Low-Income Students", by Angrist, Autor, and Pallais. Please see the README_STBF file for additional details.

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.

This report reviews the collection, availability and quality of system-level data and metadata on education from countries participating in the PISA for Development project: Cambodia, Ecuador, Guatemala, Paraguay, Senegal and Zambia. PISA for Development aims to increase low income countries’ use of PISA assessments for monitoring progress towards national goals for improving education and for analysing the factors associated with student learning outcomes, particularly among poor and marginalised populations. The project also helps track progress towards the international education targets defined in the Education 2030 Framework for Action, which the international community adopted in 2015 as the strategy for achieving the Education Sustainable Development Goal (SDG). The report suggests technically sound and viable options for improving data quality, completeness and international comparability in the six countries that are reviewed. It also provides insights into overcoming some of the challenges common to countries that participate in PISA for Development and to other middle income and low income countries.

The number of children who lived in households where a parent or guardian claims an out-of-work benefit. https://www.gov.uk/government/statistics/children-in-low-income-families-local-area-statistics-201415-to-201819 Source agency: Work and Pensions Designation: Experimental Official Statistics Language: English Alternative title: CiLIF

How does your organization use this dataset? What other NYSERDA or energy-related datasets would you like to see on Open NY? Let us know by emailing OpenNY@nyserda.ny.gov. The Low- to Moderate-Income (LMI) New York State (NYS) Census Population Analysis dataset is resultant from the LMI market database designed by APPRISE as part of the NYSERDA LMI Market Characterization Study (https://www.nyserda.ny.gov/lmi-tool). All data are derived from the U.S. Census Bureau’s American Community Survey (ACS) 1-year Public Use Microdata Sample (PUMS) files for 2013, 2014, and 2015. Each row in the LMI dataset is an individual record for a household that responded to the survey and each column is a variable of interest for analyzing the low- to moderate-income population. The LMI dataset includes: county/county group, households with elderly, households with children, economic development region, income groups, percent of poverty level, low- to moderate-income groups, household type, non-elderly disabled indicator, race/ethnicity, linguistic isolation, housing unit type, owner-renter status, main heating fuel type, home energy payment method, housing vintage, LMI study region, LMI population segment, mortgage indicator, time in home, head of household education level, head of household age, and household weight. The LMI NYS Census Population Analysis dataset is intended for users who want to explore the underlying data that supports the LMI Analysis Tool. The majority of those interested in LMI statistics and generating custom charts should use the interactive LMI Analysis Tool at https://www.nyserda.ny.gov/lmi-tool. This underlying LMI dataset is intended for users with experience working with survey data files and producing weighted survey estimates using statistical software packages (such as SAS, SPSS, or Stata).

Users can customize data applying to low-income families and children. Tables and statistics generated from interactive tools can be downloaded. BackgroundThe National Center for Children in Poverty (NCCP), a division of the Columbia Mailman School of Public Health, is a non-partisan public interest research center. NCCP generates research regarding economic security and the health and development of low-income American families, children and adolescents to inform public health policy and practice. The NCCP site contains state profiles and analyses of the impact of state-level policies on families. Topics include: polici es designed to assist low-income families, the impact of work supports on family resources and statistics about low-income families and children. User FunctionalityUsers can build and compare customized tables of state low-income demographics and policies; compare the impact of work support policies on family resources by state; calculate a minimum family budget according to household size by state; and convert income to annual income, percent of federal poverty level (%FPL) and percent of state median income (%SMI) by state. Users can download tables regarding state demographics and policies into SAS statistical software. Users have great flexibility in terms of which demographics they use to view data. Users can view demographic information by: family structure, age group, race/ethnicity, income level, parental education, parental employment, marital status, parental nativity, homeownership, and family size. Data NotesData included in the Data Tools are derived from multiple sources. Data sources include: Current Population Survey; National Conference of State Legislators; National Partnership for Women and Families; US Department of Agriculture, Food and Nutrition Service; US Department of Labor, Office of Workforce Security; U.S. Department of Labor, Employment Standards Administration Wage and Hour Division; U.S. Department of Health and Human Services, Centers for Medicare and Med icaid Services; U.S. Census Bureau; State EITC Online Resource Center; Community Resources Information, Inc.; National Employment Law Project; Department of Treasury Internal Revenue Service, Center on Budget and Policy Priorities; The Urban Institute; The Center for Law and Social Policy; Department of Health and Human Services, Administration for Children and Families; National Immigration Law Center; Kaiser Commission on Medicaid and the Uninsured; and National Women’s Law Center. Years to which the data apply is noted under “Data Notes and Sources.” Depending on the tool, users can view information on national, regional, state, county, or city levels.

This dataset represents the geospatial extent as polygons and the corresponding attribution for census block groups that meet the definition of low-income communities according to the Virginia 2020 Environmental Justice Act: “Low-income community” definition: “’Low-income community’ means any census block group in which 30 percent or more of the population is composed of people with low income.” The referenced “low income” definition is also provided below: “Low income” definition: “’Low income’ means having an annual household income equal to or less than the greater of (i) an amount equal to 80 percent of the median income of the area in which the household is located, as reported by the Department of Housing and Urban Development, and (ii) 200 percent of the Federal Poverty Level.”Click Here to view Data Fact Sheet.

Between 2019 and 2023, people living in households in the Asian and ‘Other’ ethnic groups were most likely to be in persistent low income before and after housing costs

This repository contains data and replication code for the following article: "A Summer Bridge Program for First-Generation Low-Income Students Stretches Academic Ambitions with No Adverse Impacts on First-year GPA" The data is deidentified in a way that protects student privacy.

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

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

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.

We study the impact of post-1990 school finance reforms, during the so-called "adequacy" era, on absolute and relative spending and achievement in low-income school districts. Using an event study research design that exploits the apparent randomness of reform timing, we show that reforms lead to sharp, immediate, and sustained increases in spending in low-income school districts. Using representative samples from the National Assessment of Educational Progress, we find that reforms cause increases in the achievement of students in these districts, phasing in gradually over the years following the reform. The implied effect of school resources on educational achievement is large.

This table contains regional statistics on relatively low-income households. The data are broken down by household characteristics such as gender and age of the main breadwinner, and the household composition and main source of income of the household. Two income limits are used for the classification by level of income: the low-income limit and the policy minimum. For these classifications, the number of households is published, both in absolute terms and as a percentage of the total population per region. The table also contains data on the number of households that had to survive on an income below the used income threshold for a long period of time (4 years and more). The results are used, among other things, in reports on poverty. The data relate to all private households with income as at 1 January of the year under review. Student households and households that only had an income for part of the year were not taken into account. The reference date for the municipal division is January 1, 2020. Data available from 2011 to 2019. Status of the figures: The figures in this table for 2011 to 2018 are final. The figures for 2019 are provisional. Changes as of December 2, 2022: None, this table has been discontinued. When will new numbers come out? Not applicable anymore.