This graph shows the educational attainment of the U.S. population from in 2018, according to ethnicity. Around 56.5 percent of Asians and Pacific Islanders in the U.S. have graduated from college or obtained a higher educational degree in 2018.

Researchers have examined racial and gender patterns of intergenerational education mobility, but less attention has been given to the ways that race and gender interact to further shape these relationships. Based on data from the General Social Survey, this study examined the trajectories of education mobility among Blacks and Whites by gender over the past century. Ordinary least squares and logistic regression models revealed three noteworthy patterns. First, Black men and women have closed substantial gaps with their White counterparts in intergenerational education mobility. At relatively low levels of parental education, these gains have been experienced equally among Black men and women. However, Black men are most disadvantaged at the highest levels of parental education relative to Black women and Whites in general. Finally, the advantages in education mobility experienced by White men in the early and midpart of the 20th century have largely eroded. White women, in contrast, have made steady gains in education mobility across a variety of parental education levels.

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.

In the 10 years to July 2024, the percentage of further education students who were from Asian, Black, Mixed and Other ethnic backgrounds went up from 19.7% to 27.9%.

A detailed explanation of how this dataset was put together, including data sources and methodologies, follows below.Please see the "Terms of Use" section below for the Data DictionaryDATA ACQUISITION AND CLEANING PROCESSThis dataset was built from 5 separate datasets queried during the months of April and May 2023 from the Census Microdata System (link below):https://data.census.gov/mdat/#/All datasets include information on Property Value (VALP) by: Educational Attainment (SCHL), Gender (SEX), a specified race or ethnicity (RAC or HISP), and are grouped by Public Use Microdata Areas (PUMAS). PUMAS are geographic areas created by the Census bureau; they are weighted by land area and population to facilitate data analysis. Data also Included totals for the state of New Mexico, so 19 total geographies are represented. Datasets were downloaded separately by race and ethnicity because this was the only way to obtain the VALP, SCHL, and SEX variables intersectionally with race or ethnicity data. Datasets were downloaded separately by race and ethnicity because this was the only way to obtain the VALP, SCHL, and SEX variables intersectionally with race or ethnicity data. Cleaning each dataset started with recoding the SCHL and HISP variables - details on recoding can be found below.After recoding, each dataset was transposed so that PUMAS were rows and SCHL, VALP, SEX, and Race or Ethnicity variables were the columns.Median values were calculated in every case that recoding was necessary. As a result, all Property Values in this dataset reflect median values.At times the ACS data downloaded with zeros instead of the 'null' values in initial query results. The VALP variable also included a "-1" variable to reflect N/A values (details in variable notes). Both zeros and "-1" values were removed before calculating median values, both to keep the data true to the original query and to generate accurate median values.Recoding the SCHL variable resulted in 5 rows for each PUMA, reflecting the different levels of educational attainment in each region. Columns grouped variables by race or ethnicity and gender. Cell values were property values.All 5 datasets were joined after recoding and cleaning the data. Original datasets all include 95 rows with 5 separate Educational Attainment variables for each PUMA, including New Mexico State totals.Because 1 row was needed for each PUMA in order to map this data, the data was split by Educational Attainment (SCHL), resulting in 110 columns reflecting median property values for each race or ethnicity by gender and level of educational attainment.A short, unique 2 to 5 letter alias was created for each PUMA area in anticipation of needing a unique identifier to join the data with. GIS AND MAPPING PROCESSA PUMA shapefile was downloaded from the ACS site. The Shapefile can be downloaded here: https://tigerweb.geo.census.gov/arcgis/rest/services/TIGERweb/PUMA_TAD_TAZ_UGA_ZCTA/MapServerThe DBF from the PUMA shapefile was exported to Excel; this shapefile data included needed geographic information for mapping such as: GEOID, PUMACE. The UIDs created for each PUMA were added to the shapefile data; the PUMA shapfile data and ACS data were then joined on UID in JMP.The data table was joined to the shapefile in ARC GiIS, based on PUMA region (specifically GEOID text).The resulting shapefile was exported as a GDB (geodatabase) in order to keep 'Null' values in the data. GDBs are capable of including a rule allowing null values where shapefiles are not. This GDB was uploaded to NMCDCs Arc Gis platform. SYSTEMS USEDMS Excel was used for data cleaning, recoding, and deriving values. Recoding was done directly in the Microdata system when possible - but because the system is was in beta at the time of use some features were not functional at times.JMP was used to transpose, join, and split data. ARC GIS Desktop was used to create the shapefile uploaded to NMCDC's online platform. VARIABLE AND RECODING NOTESTIMEFRAME: Data was queried for the 5 year period of 2015 to 2019 because ACS changed its definiton for and methods of collecting data on race and ethinicity in 2020. The change resulted in greater aggregation and les granular data on variables from 2020 onward.Note: All Race Data reflects that respondants identified as the specified race alone or in combination with one or more other races.VARIABLE:ACS VARIABLE DEFINITIONACS VARIABLE NOTESDETAILS OR URL FOR RAW DATA DOWNLOADRACBLKBlack or African American ACS Query: RACBLK, SCHL, SEX, VALP 2019 5yrRACAIANAmerican Indian and Alaska Native ACS Query: RACAIAN, SCHL, SEX, VALP 2019 5yrRACASNAsian ACS Query: RACASN, SCHL, SEX, VALP 2019 5yrRACWHTWhite ACS Query: RACWHT, SCHL, SEX, VALP 2019 5yrHISPHispanic Origin ACS Query: HISP ORG, SCHL, SEX, VALP 2019 5yrHISP RECODE: 24 original separate variablesThe Hispanic Origin (HISP) variable originally included 24 subcategories reflecting Mexican, Central American, South American, and Caribbean Latino, and Spanish identities from each Latin American counry. 7 recoded VariablesThese 24 variables were recoded (grouped) into 7 simpler categories for data analysis: Not Spanish/Hispanic/Latino, Mexican, Caribbean Latino, Central American, South American, Spaniard, All other Spanish/Hispanic/Latino Female. Not Spanish/Hispanic/Latino was not really used in the final dataset as the race datasets provided that information.SCHLEducational Attainment25 original separate variablesThe Educational Attainment (SCHL) variable originally included 25 subcategories reflecting the education levels of adults (over 18) surveyed by the ACS. These include: Kindergarten, Grades 1 through 12 separately, 12th grade with no diploma, Highschool Diploma, GED or credential, less than 1 year of college, more than 1 year of college with no degree, Associate's Degree, Bachelor's Degree, Master's Degree, Professional Degree, and Doctorate Degree.SCHL RECODE: 5 recoded variablesThese 25 variables were recoded (grouped) into 5 simpler categories for data analysis: No High School Diploma, High School Diploma or GED, Some College, Bachelor's Degree, and Advanced or Professional DegreeSEXGender2 variables1 - Male, 2 - FemaleVALPProperty Value1 variableValues were rounded and top-coded by ACS for anonymity. The "-1" variable is defined as N/A (GQ/ Vacant lots except 'for sale only' and 'sold, not occupied' / not owned or being bought.) This variable reflects the median value of property owned by individuals of each race, ethnicity, gender, and educational attainment category.PUMAPublic Use Microdata Area18 PUMAsPUMAs in New Mexico can be viewed here:https://nmcdc.maps.arcgis.com/apps/mapviewer/index.html?webmap=d9fed35f558948ea9051efe9aa529eafData includes 19 total regions: 18 Pumas and NM State TotalsNOTES AND RESOURCESThe following resources and documentation were used to navigate the ACS PUMS system and to answer questions about variables:Census Microdata API User Guide:https://www.census.gov/data/developers/guidance/microdata-api-user-guide.Additional_Concepts.html#list-tab-1433961450Accessing PUMS Data:https://www.census.gov/programs-surveys/acs/microdata/access.htmlHow to use PUMS on data.census.govhttps://www.census.gov/programs-surveys/acs/microdata/mdat.html2019 PUMS Documentation:https://www.census.gov/programs-surveys/acs/microdata/documentation.2019.html#list-tab-13709392012014 to 2018 ACS PUMS Data Dictionary:https://www2.census.gov/programs-surveys/acs/tech_docs/pums/data_dict/PUMS_Data_Dictionary_2014-2018.pdf2019 PUMS Tiger/Line Shapefileshttps://www.census.gov/cgi-bin/geo/shapefiles/index.php?year=2019&layergroup=Public+Use+Microdata+Areas Note 1: NMCDC attemepted to contact analysts with the ACS system to clarify questions about variables, but did not receive a timely response. Documentation was then consulted.Note 2: All relevant documentation was reviewed and seems to imply that all survey questions were answered by adults, age 18 or over. Youth who have inherited property could potentially be reflected in this data.Dataset and feature service created in May 2023 by Renee Haley, Data Specialist, NMCDC.

According to a survey conducted in 2024, Black Americans were found slightly less likely than white or Hispanic Americans to say that they think that children in their area have an opportunity to get a good education in the United States, with ** percent of Black Americans sharing this belief. In comparison, ** percent of white Americans and ** percent of Hispanic Americans said that they thought children in their area have the opportunity to get a good education.

Graph and download economic data for Expenditures: Education by Race: White and All Other Races, Not Including Black or African American (CXUEDUCATNLB0903M) from 2003 to 2023 about white, education, expenditures, and USA.

Students from the Chinese ethnic group had the highest entry rate into higher education in every year from 2006 to 2024.

In 2023, the mean income of Black Bachelor's degree holders was ****** U.S. dollars, compared to ****** U.S. dollars for White Americans with a Bachelor's degree.

The Fall Enrollment survey is conducted annually by the National Center for Education Statistics (NCES) as part of the Integrated Postsecondary Education Data System (IPEDS). The survey is sent to accredited institutions of higher education and to all other institutions offering bachelor's, master's, doctoral, or first-professional degrees. In odd years beginning in 1987, the Fall Enrollment survey was expanded to collect student enrollment data by 11 age categories. In even years, the survey was expanded to collect residence data for first-time freshmen. There are three data files in this collection. Part 1, Institutional Characteristics, includes variables on control and level of institution, highest level of offering, accreditation status, Carnegie classification, and state FIPS codes and abbreviations. Part 2 covers enrollment data by race/ethnicity and level of student, while the focus of Part 3 is enrollment data for first-time, first-year students by state residence of student.

According to a 2023 survey, ** percent of undergraduate students who were studying online in the United States were White, while ** percent were Black or African-American. In comparison, ** percent of graduate students studying online in the United States in that year were White, while ** percent were Black or African American.

In large urban districts, schools enrolling more white students tend to have higher performance ratings. We use an instrumental variables strategy leveraging centralized school assignment to explore the drivers of this relationship. Estimates from Denver and New York City suggest the correlation between widely-reported school performance ratings and white enrollment shares reflects selection bias rather than causal school value-added. In fact, value-added in these two cities is essentially unrelated to white enrollment shares. A simple regression adjustment is shown to yield school ratings that are uncorrelated with race, while predicting value-added as well or better than the corresponding unadjusted measures.

Graph and download economic data for Consumer Unit Characteristics: Percent High School (9-12) by Race: White, Asian, and All Other Races, Not Including Black or African American (CXU980300LB0902M) from 1984 to 2023 about asian, consumer unit, secondary schooling, secondary, white, education, percent, and USA.

Further education and skills learner participation and achievements detailed demographic breakdowns. Breakdowns for adult (19+) Education and training can also be obtained. Academic years: 2018/19 to 2023/24 full academic yearsIndicators: Total Participation and Achievement by Level Filters: Minority Ethnic, LLDD, Sex, Ethnicity Major, Ethnicity Minor, Age group, Provision type

The purpose of this data collection was to provide a more accurate measure of the racial/ethnic enrollment in postsecondary institutions in the United States than was previously available. The National Center for Education Statistics (NCES) collects racial/ethnic enrollment data from higher education institutions on an annual basis. Some institutions do not report these data, and their "unknown" categories have previously been distributed in direct proportion to the "knowns." This resulted in lower than accurate figures for the racial/ethnic categories. With the advent of the Integrated Postsecondary Education Data System (IPEDS), NCES has attempted to eliminate this problem by distributing all "race/ethnicity unknown" students through a two-stage process. First, the differences between reported totals and racial/ethnic details were allocated on a gender and institutional basis by distributing the differences in direct proportion to reported distributions. The second-stage distribution was designed to eliminate the remaining instances of "race/ethnicity unknown." The procedure was to accumulate the reported racial/ethnic total enrollments by state, level, control, and gender, calculate the percentage distributions, and apply these percentages to the reported total enrollments of institutional respondents (in the same state, level, and control) that did not supply race/ethnicity detail. In addition, the original "race/ethnicity unknown" data were also left unaltered for those who wish to review the numbers actually distributed. The racial/ethnic status was broken down into nonresident alien, Black non-Hispanic, American Indian or Alaskan Native, Asian or Pacific Islander, Hispanic, and White non-Hispanic. There are six data files. Part 1, Institutional Characteristics, includes variables on control and level of institution, religious affiliation, highest level of offering, Carnegie classification, and state FIPS code and abbreviation. Variables in Part 2 cover total original enrollment by race/ethnicity and sex and by level and year of study of student. Race/ethnicity data were not imputed for institutions that only reported total enrollment. The "race ethnicity unknown" category was not distributed among the race/ethnicity categories. In Part 3, enrollment data are presented by race/ethnicity and sex of student, and by level and year of study for the following selected major field of studies: architecture, education, engineering, law, biological/life sciences, mathematics, physical sciences, dentistry, medicine, veterinary medicine, and business management and administrative services. This file contains data for four-year institutions only. Part 4 provides summary enrollment data by adjusted race/ethnicity and sex of student and by level and year of study of student. The "race/ethnicity unknown" category data were distributed across all known race categories in this file. Also, race data were imputed for institutions that did not report enrollment by race. Part 5, Residence and Migration, contains enrollment data for first-time freshmen, by state of residence. Part 6, Clarifying Questions on Enrollments, provides information on students enrolled in remedial courses, extension divisions, and branches of schools, and numbers of transfer students from in-state, out of state, and other countries. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR02447.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

Abstract This article aims to present an overview of the existing comparative studies on anti-racist educational policies in Latin America, focusing on the black population. The research was conducted on online databases and found 06 papers on the subject, focusing mainly on Brazil and Colombia. Although studies point out that black people in Latin America face similar difficulties in the educational field, the lack of comparative studies on this population in the region is alarming and may be related to the lack of statistical data based on racial criteria.

The Civil Rights Data Collection, 2017-18 (CRDC 2017-18) is part of the Civil Rights Data Collection (CRDC) program; program data are available beginning with the 2000 collection at https://civilrightsdata.ed.gov/data. CRDC 2017-18 is a cross-sectional survey that collects data on key education and civil rights issues in the nation's public schools, which include student enrollment and educational programs and services, disaggregated by race/ethnicity, sex, limited English proficiency, and disability. LEAs submit administrative records about schools in the district. CRDC 2017-18 is a universe survey. Key statistics produced from CRDC 2017-18 can provide information about critical civil rights issues as well as contextual information on the state of civil rights in the nation, including enrollment demographics, advanced placement, school discipline, and special education services.

The data in this repository includes a database of Black Economists created for reference in the Francis, Hardy, and Jones (2021) article "Black Economists on Race and Policy: Contributions to Education, Poverty and Mobility, and Public Finance" in the Journal of Economic Literature. The database is stored in a comma separated file, i.e. .csv. The data were all collected from publicly available data online via scholar websites, biographical entries, and curricula vitae.

This dataset provides population 25 years and over estimates by sex, race and educational attainment for State of Iowa, individual Iowa counties, Iowa places and census tracts within Iowa. Data is from the American Community Survey, Five Year Estimates, Tables C15002A, C15002B, C15002C, C15002D, C15002E, C15002F, and C15002G.

Sex categories: Male, Female, and Both.

Race categories: White Alone, Black or African American Alone, American Indian and Alaska Native, Asian Alone, Native Hawaiian and Other Pacific Islander Alone, Some Other Race, and Two or More Races.

Educational attainment categories: Less than High School, High School Graduate, Some College or Associates Degree, and Bachelors Degree or Higher.

This dataset includes the attendance rate for public school students PK-12 by student group and by district during the 2021-2022 school year. Student groups include: Students experiencing homelessness Students with disabilities Students who qualify for free/reduced lunch English learners All high needs students Non-high needs students Students by race/ethnicity (Hispanic/Latino of any race, Black or African American, White, All other races) Attendance rates are provided for each student group by district and for the state. Students who are considered high needs include students who are English language learners, who receive special education, or who qualify for free and reduced lunch. When no attendance data is displayed in a cell, data have been suppressed to safeguard student confidentiality, or to ensure that statistics based on a very small sample size are not interpreted as equally representative as those based on a sufficiently larger sample size. For more information on CSDE data suppression policies, please visit http://edsight.ct.gov/relatedreports/BDCRE%20Data%20Suppression%20Rules.pdf.