Graph and download economic data for Employment-Population Ratio - Black or African American (LNS12300006) from Jan 1972 to May 2025 about employment-population ratio, African-American, 16 years +, household survey, employment, population, and USA.

In 2023, **** percent of Black people living in the United States were living below the poverty line, compared to *** percent of white people. That year, the total poverty rate in the U.S. across all races and ethnicities was **** percent. Poverty in the United States Single people in the United States making less than ****** U.S. dollars a year and families of four making less than ****** U.S. dollars a year are considered to be below the poverty line. Women and children are more likely to suffer from poverty, due to women staying home more often than men to take care of children, and women suffering from the gender wage gap. Not only are women and children more likely to be affected, racial minorities are as well due to the discrimination they face. Poverty data Despite being one of the wealthiest nations in the world, the United States had the third highest poverty rate out of all OECD countries in 2019. However, the United States' poverty rate has been fluctuating since 1990, but has been decreasing since 2014. The average median household income in the U.S. has remained somewhat consistent since 1990, but has recently increased since 2014 until a slight decrease in 2020, potentially due to the pandemic. The state that had the highest number of people living below the poverty line in 2020 was California.

Designed to facilitate analysis of the status of Blacks around the turn of the century, this oversample of Black-headed households in the United States was drawn from the 1910 manuscript census schedules. The sample complements the 1/250 Public Use Sample of the 1910 census manuscripts collected by Samuel H. Preston at the University of Pennsylvania: CENSUS OF POPULATION, 1910 [UNITED STATES]: PUBLIC USE SAMPLE (ICPSR 9166). Part 1, Household Records, contains a record for each household selected in the sample and supplies variables describing the location, type, and composition of the households. Part 2, Individual Records, contains a record for each individual residing in the sampled households and includes information on demographic characteristics, occupation, literacy, nativity, ethnicity, and fertility. Manuscript census records for 1910 from counties with at least 10 percent of the population African-American (Negro, Black, or Mulatto) located in nine states where a large number of counties had at least this same proportion of African-Americans (Maryland, Virginia, North Carolina, Florida, Kentucky, Tennessee, Arkansas, Louisiana, and Texas). The four states with the largest population of Blacks (South Carolina, Alabama, Mississippi, and Georgia) were excluded from the oversample because the 1/250 Public Use Sample (referred to above) provided sufficient cases for most analyses. Sampling was carried out using computer software that randomly selected households based on the manuscript census microfilm reel number, sequence, and page and line number, with two different sampling fractions. Counties in Maryland, Kentucky, and Texas were sampled using a 0.01 sampling fraction, while a 0.005 sampling fraction was employed in Virginia, North Carolina, Florida, Tennessee, and Arkansas. In Louisiana, both fractions were utilized to test optimum sampling fractions. 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: Created variable labels and/or value labels.. The data contain blanks and alphabetic characters. This oversample can be combined with the 1/250 Public Use Sample by differential weighting of households (or individuals) by county of enumeration as described in the User's Guide. Datasets: DS0: Study-Level Files DS1: Household Records DS2: Individual Records

In 2023, the FBI reported that there were 9,284 Black murder victims in the United States and 7,289 white murder victims. In comparison, there were 554 murder victims of unknown race and 586 victims of another race. Victims of inequality? In recent years, the role of racial inequality in violent crimes such as robberies, assaults, and homicides has gained public attention. In particular, the issue of police brutality has led to increasing attention following the murder of George Floyd, an African American who was killed by a Minneapolis police officer. Studies show that the rate of fatal police shootings for Black Americans was more than double the rate reported of other races. Crime reporting National crime data in the United States is based off the Federal Bureau of Investigation’s new crime reporting system, which requires law enforcement agencies to self-report their data in detail. Due to the recent implementation of this system, less crime data has been reported, with some states such as Delaware and Pennsylvania declining to report any data to the FBI at all in the last few years, suggesting that the Bureau's data may not fully reflect accurate information on crime in the United States.

Graph and download economic data for Homeownership Rates by Race and Ethnicity: Black Alone in the United States (BOAAAHORUSQ156N) from Q1 1994 to Q1 2025 about homeownership, African-American, rate, and USA.

Data SourcesAmerican Community Survey (ACS):Conducted by: U.S. Census BureauDescription: The ACS is an ongoing survey that provides detailed demographic and socio-economic data on the population and housing characteristics of the United States.Content: The survey collects information on various topics such as income, education, employment, health insurance coverage, and housing costs and conditions.Frequency: The ACS offers more frequent and up-to-date information compared to the decennial census, with annual estimates produced based on a rolling sample of households.Purpose: ACS data is essential for policymakers, researchers, and communities to make informed decisions and address the evolving needs of the population.CDC/ATSDR Social Vulnerability Index (SVI):Created by: ATSDR’s Geospatial Research, Analysis & Services Program (GRASP)Utilized by: CDCDescription: The SVI is designed to identify and map communities that are most likely to need support before, during, and after hazardous events.Content: SVI ranks U.S. Census tracts based on 15 social factors, including unemployment, minority status, and disability, and groups them into four related themes. Each tract receives rankings for each Census variable and for each theme, as well as an overall ranking, indicating its relative vulnerability.Purpose: SVI data provides insights into the social vulnerability of communities at both the tract and zip code levels, helping public health officials and emergency response planners allocate resources effectively.Utilization and IntegrationBy integrating data from both the ACS and the SVI, this dataset enables an in-depth analysis and understanding of various socio-economic and demographic indicators at the census tract level. This integrated data is valuable for research, policymaking, and community planning purposes, allowing for a comprehensive understanding of social and economic dynamics across different geographical areas in the United States.ApplicationsTargeted Interventions: Facilitates the development of targeted interventions to address the needs of vulnerable populations within specific zip codes.Resource Allocation: Assists emergency response planners in allocating resources more effectively based on community vulnerability at the zip code level.Research: Provides a rich dataset for academic and applied research in socio-economic and demographic studies at a granular zip code level.Community Planning: Supports the planning and development of community programs and initiatives aimed at improving living conditions and reducing vulnerabilities within specific zip code areas.Note: Due to limitations in the data environment, variable names may be truncated. Refer to the provided table for a clear understanding of the variables. CSV Variable NameShapefile Variable NameDescriptionStateNameStateNameName of the stateStateFipsStateFipsState-level FIPS codeState nameStateNameName of the stateCountyNameCountyNameName of the countyCensusFipsCensusFipsCounty-level FIPS codeState abbreviationStateFipsState abbreviationCountyFipsCountyFipsCounty-level FIPS codeCensusFipsCensusFipsCounty-level FIPS codeCounty nameCountyNameName of the countyAREA_SQMIAREA_SQMITract area in square milesE_TOTPOPE_TOTPOPPopulation estimates, 2013-2017 ACSEP_POVEP_POVPercentage of persons below poverty estimateEP_UNEMPEP_UNEMPUnemployment Rate estimateEP_HBURDEP_HBURDHousing cost burdened occupied housing units with annual income less than $75,000EP_UNINSUREP_UNINSURUninsured in the total civilian noninstitutionalized population estimate, 2013-2017 ACSEP_PCIEP_PCIPer capita income estimate, 2013-2017 ACSEP_DISABLEP_DISABLPercentage of civilian noninstitutionalized population with a disability estimate, 2013-2017 ACSEP_SNGPNTEP_SNGPNTPercentage of single parent households with children under 18 estimate, 2013-2017 ACSEP_MINRTYEP_MINRTYPercentage minority (all persons except white, non-Hispanic) estimate, 2013-2017 ACSEP_LIMENGEP_LIMENGPercentage of persons (age 5+) who speak English "less than well" estimate, 2013-2017 ACSEP_MUNITEP_MUNITPercentage of housing in structures with 10 or more units estimateEP_MOBILEEP_MOBILEPercentage of mobile homes estimateEP_CROWDEP_CROWDPercentage of occupied housing units with more people than rooms estimateEP_NOVEHEP_NOVEHPercentage of households with no vehicle available estimateEP_GROUPQEP_GROUPQPercentage of persons in group quarters estimate, 2014-2018 ACSBelow_5_yrBelow_5_yrUnder 5 years: Percentage of Total populationBelow_18_yrBelow_18_yrUnder 18 years: Percentage of Total population18-39_yr18_39_yr18-39 years: Percentage of Total population40-64_yr40_64_yr40-64 years: Percentage of Total populationAbove_65_yrAbove_65_yrAbove 65 years: Percentage of Total populationPop_malePop_malePercentage of total population malePop_femalePop_femalePercentage of total population femaleWhitewhitePercentage population of white aloneBlackblackPercentage population of black or African American aloneAmerican_indianamerican_iPercentage population of American Indian and Alaska native aloneAsianasianPercentage population of Asian aloneHawaiian_pacific_islanderhawaiian_pPercentage population of Native Hawaiian and Other Pacific Islander aloneSome_othersome_otherPercentage population of some other race aloneMedian_tot_householdsmedian_totMedian household income in the past 12 months (in 2019 inflation-adjusted dollars) by household size – total householdsLess_than_high_schoolLess_than_Percentage of Educational attainment for the population less than 9th grades and 9th to 12th grade, no diploma estimateHigh_schoolHigh_schooPercentage of Educational attainment for the population of High school graduate (includes equivalency)Some_collegeSome_collePercentage of Educational attainment for the population of Some college, no degreeAssociates_degreeAssociatesPercentage of Educational attainment for the population of associate degreeBachelor’s_degreeBachelor_sPercentage of Educational attainment for the population of Bachelor’s degreeMaster’s_degreeMaster_s_dPercentage of Educational attainment for the population of Graduate or professional degreecomp_devicescomp_devicPercentage of Household having one or more types of computing devicesInternetInternetPercentage of Household with an Internet subscriptionBroadbandBroadbandPercentage of Household having Broadband of any typeSatelite_internetSatelite_iPercentage of Household having Satellite Internet serviceNo_internetNo_internePercentage of Household having No Internet accessNo_computerNo_computePercentage of Household having No computerThis table provides a mapping between the CSV variable names and the shapefile variable names, along with a brief description of each variable.

NOTE: After 5/20/2021, this dataset will no longer be updated and will be replaced by the new dataset: "COVID-19 Vaccinations by Race/Ethnicity" (https://data.ct.gov/Health-and-Human-Services/COVID-19-Vaccinations-by-Race-Ethnicity/4z97-pa4q).

Cumulative number and percent of people who initiated COVID-19 vaccination and who are fully vaccinated by race/ethnicity for select age groups (ages 16+, ages 65-74, and ages 75+) as reported by providers.

Population estimates are based on 2019 CT population estimates. The 2019 CT population data which is the most recent year available. The tables that show the percent vaccinated by town and age group are an exception. These tables use 2014 CT population estimates. This the most recent year for which reliable estimates by town and age are available.

A person who has received one dose of any vaccine is considered to have received at least one dose. A person is considered fully vaccinated if they have received 2 doses of the Pfizer or Moderna vaccines or 1 dose of the Johnson & Johnson vaccine. The fully vaccinated are a subset of the number who have received at least one dose. The number with At Least One Dose and the number Fully Vaccinated add up to more than the total number of doses because people who received the Johnson & Johnson vaccine fit into both categories.

In this data, a person with reported Hispanic or Latino ethnicity is considered Hispanic regardless of reported race. The category Unknown includes unknown race and/or ethnicity.

The percent of people classified as Other race (not specified) and Multiple race in CT WiZ (for COVID-19 vaccine records and all other vaccine records) are higher than would be expected based on census data. Other race, Multiple race and Unknown include people who should be classified as Asian, Black, Hispanic and White. Therefore, the coverage of these groups may be underestimated and should be interpreted with caution.

The estimates for the category Multiple Races are considered unreliable

All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected.

Note: As part of continuous data quality improvement efforts, duplicate records were removed from the COVID-19 vaccination data during the weeks of 4/19/2021 and 4/26/2021.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2019-2023 American Community Survey 5-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..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 roughly 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 ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..The Hispanic origin and race codes were updated in 2020. For more information on the Hispanic origin and race code changes, please visit the American Community Survey Technical Documentation website..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Created for the 2023-2025 State of Black Los Angeles County (SBLA) interactive report. Countywide Statistical Areas (CSA) are current as of October 2023.

Fields ending in _yr1 were calculated for the original 2021-2022 SBLA report, while fields ending in _yr2 or without a year suffix were calculated for the 2023-2025 version. Eviction Filings per 100 (eviction_filings_per100) and Life Expectancy (life_expectancy) did not have updated data and are the same data shown in the Year 1 report.

Population and demographic data are from US Census American Community Survey (ACS) 5-year estimates, aggregated up from census tract or block group to CSA. Year 1 data are from 2020, year 2 data are from 2022.

Poverty Data (200% FPL) are from LA County ISD-eGIS Demographics. Year 1 data are from 2021, Year 2 are from 2022.

The 2023-2025 report includes several new indicators that are calculated as the percent of countywide population by race that resides in a geographic area of interest. Population for these indicators is estimated based on intersection with census block group centroids. These indicators are:

Indicator

Fields

Source

Health Professional Shortage Areas (HPSA) for Primary Care

hpsa_primary_pct hpsa_primary_black_pct

LA County DPH https://data.lacounty.gov/datasets/lacounty::health-professional-shortage-area-primary-care/about

Health Professional Shortage Areas (HPSA) for Mental Health

hpsa_mental_pct hpsa_mental_black_pct

LA County DPH https://data.lacounty.gov/datasets/lacounty::health-professional-shortage-area-mental-health/about

Concentrated Disadvantage

cd_pct cd_black_pct

LA County ISD-Enterprise GIS https://egis-lacounty.hub.arcgis.com/datasets/lacounty::concentrated-disadvantage-index-2022/explore

Firearm Dealers

firearm_dl_count (count of dealers in CSA) firearm_dl_per10000 (rate of dealers per 10,000)

LA County DPH Office of Violence Prevention (OVP)

High and Very High Park Need Areas

parks_need_pct parks_need_black_pct

LA County Parks Needs Assessment Plus (PNA+) https://lacounty.maps.arcgis.com/apps/instant/media/index.html?appid=3d0ef36720b447dcade1ab87a2cc80b9

High Quality Transit Areas

hqta_pct hqta_black_pct

SCAG https://lacounty.maps.arcgis.com/home/item.html?id=43e6fef395d041c09deaeb369a513ca1

High Walkability Areas

walk_total_pct walk_black_pct

EPA Walkability Index https://www.epa.gov/smartgrowth/smart-location-mapping#walkability

High Poverty and High Segregation Areas

highpovseg_total_pct highpovseg_black_pct

CTCAC/HCD Opportunity Area Maps https://www.treasurer.ca.gov/ctcac/opportunity.asp

LA County Arts Investments

arts_dollars (total $$ for CSA) arts_dollars_percap (investment dollars per capita)

LA County Department of Arts and Culture https://lacountyartsdata.org/#maps

Strong Start (areas with at least 9 Strong Start indicators)

strongstart_total_pct strongstart_black_pct

CA Strong Start Index https://strongstartindex.org/map

For more information about the purpose of this data, please contact CEO-ARDI.

For more information about the configuration of this data, please contact ISD-Enterprise GIS.

This statistic shows the number of black men and women in the US from 1820 until 1880. Slavery was legal in the Southern States of the US until 1865, when the Thirteenth Amendment was added to the US Constitution after the American Civil War. Until that time all of the slaves included in this statistic were registered as living in the South, whereas the majority of the free, black men and women lived in the Northern States. From the data we can see that, while the slave experience was very different for men and women, there was relatively little difference between their numbers in each respective category. While female slaves were more likely to serve in domestic roles, they were also more likely to be working in the lowest and unskilled jobs on plantations, whereas men were given more skilled and physically demanding roles. As slavery was abolished in 1870, all black people from this point were considered free in the census data. It is also worth noticing that in these years the difference in the number of men and women increased, most likely as a result of all the black male soldiers who fell fighting in the American Civil War.

United States - Unemployment Rate - Black or African American was 6.00% in May of 2025, according to the United States Federal Reserve. Historically, United States - Unemployment Rate - Black or African American reached a record high of 21.20 in January of 1983 and a record low of 4.80 in April of 2023. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Unemployment Rate - Black or African American - last updated from the United States Federal Reserve on July of 2025.

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 contains information about the demographics of all US cities and census-designated places with a population greater or equal to 65,000. This data comes from the US Census Bureau's 2015 American Community Survey. This product uses the Census Bureau Data API but is not endorsed or certified by the Census Bureau.

THIS DATASET WAS LAST UPDATED AT 2:11 AM EASTERN ON JULY 12

OVERVIEW

2019 had the most mass killings since at least the 1970s, according to the Associated Press/USA TODAY/Northeastern University Mass Killings Database.

In all, there were 45 mass killings, defined as when four or more people are killed excluding the perpetrator. Of those, 33 were mass shootings . This summer was especially violent, with three high-profile public mass shootings occurring in the span of just four weeks, leaving 38 killed and 66 injured.

A total of 229 people died in mass killings in 2019.

The AP's analysis found that more than 50% of the incidents were family annihilations, which is similar to prior years. Although they are far less common, the 9 public mass shootings during the year were the most deadly type of mass murder, resulting in 73 people's deaths, not including the assailants.

One-third of the offenders died at the scene of the killing or soon after, half from suicides.

About this Dataset

The Associated Press/USA TODAY/Northeastern University Mass Killings database tracks all U.S. homicides since 2006 involving four or more people killed (not including the offender) over a short period of time (24 hours) regardless of weapon, location, victim-offender relationship or motive. The database includes information on these and other characteristics concerning the incidents, offenders, and victims.

The AP/USA TODAY/Northeastern database represents the most complete tracking of mass murders by the above definition currently available. Other efforts, such as the Gun Violence Archive or Everytown for Gun Safety may include events that do not meet our criteria, but a review of these sites and others indicates that this database contains every event that matches the definition, including some not tracked by other organizations.

This data will be updated periodically and can be used as an ongoing resource to help cover these events.

Using this Dataset

To get basic counts of incidents of mass killings and mass shootings by year nationwide, use these queries:

Mass killings by year

Mass shootings by year

To get these counts just for your state:

Filter killings by state

Definition of "mass murder"

Mass murder is defined as the intentional killing of four or more victims by any means within a 24-hour period, excluding the deaths of unborn children and the offender(s). The standard of four or more dead was initially set by the FBI.

This definition does not exclude cases based on method (e.g., shootings only), type or motivation (e.g., public only), victim-offender relationship (e.g., strangers only), or number of locations (e.g., one). The time frame of 24 hours was chosen to eliminate conflation with spree killers, who kill multiple victims in quick succession in different locations or incidents, and to satisfy the traditional requirement of occurring in a “single incident.”

Offenders who commit mass murder during a spree (before or after committing additional homicides) are included in the database, and all victims within seven days of the mass murder are included in the victim count. Negligent homicides related to driving under the influence or accidental fires are excluded due to the lack of offender intent. Only incidents occurring within the 50 states and Washington D.C. are considered.

Methodology

Project researchers first identified potential incidents using the Federal Bureau of Investigation’s Supplementary Homicide Reports (SHR). Homicide incidents in the SHR were flagged as potential mass murder cases if four or more victims were reported on the same record, and the type of death was murder or non-negligent manslaughter.

Cases were subsequently verified utilizing media accounts, court documents, academic journal articles, books, and local law enforcement records obtained through Freedom of Information Act (FOIA) requests. Each data point was corroborated by multiple sources, which were compiled into a single document to assess the quality of information.

In case(s) of contradiction among sources, official law enforcement or court records were used, when available, followed by the most recent media or academic source.

Case information was subsequently compared with every other known mass murder database to ensure reliability and validity. Incidents listed in the SHR that could not be independently verified were excluded from the database.

Project researchers also conducted extensive searches for incidents not reported in the SHR during the time period, utilizing internet search engines, Lexis-Nexis, and Newspapers.com. Search terms include: [number] dead, [number] killed, [number] slain, [number] murdered, [number] homicide, mass murder, mass shooting, massacre, rampage, family killing, familicide, and arson murder. Offender, victim, and location names were also directly searched when available.

This project started at USA TODAY in 2012.

Contacts

Contact AP Data Editor Justin Myers with questions, suggestions or comments about this dataset at jmyers@ap.org. The Northeastern University researcher working with AP and USA TODAY is Professor James Alan Fox, who can be reached at j.fox@northeastern.edu or 617-416-4400.

United States Unemployment Rate: Black or African American: Female data was reported at 5.500 % in Apr 2025. This records an increase from the previous number of 5.400 % for Mar 2025. United States Unemployment Rate: Black or African American: Female data is updated monthly, averaging 10.900 % from Jan 1972 (Median) to Apr 2025, with 640 observations. The data reached an all-time high of 21.100 % in Jun 1983 and a record low of 4.000 % in Apr 2023. United States Unemployment Rate: Black or African American: Female data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G: Current Population Survey: Unemployment Rate.

PLEASE NOTE: This is an index of a historical collection that contains words and phrases that may be offensive or harmful to individuals investigating these records. In order to preserve the objectivity and historical accuracy of the index, State Archives staff took what would today be considered archaic and offensive descriptions concerning race, ethnicity, and gender directly from the original court papers. For more information on appropriate description, please consult the Diversity Style Guide and Archives for Black Lives in Philadelphia: Anti-Racist Description Resources.

The Litchfield County Court African Americans and Native Americans Collection is an artificial collection consisting of photocopies of cases involving persons of African descent and indigenous people from the Files and Papers by Subject series of Litchfield County Court records. This collection was created in order to highlight the lives and experiences of underrepresented groups in early America, and make them more easily accessible to researchers.

Collection Overview

The collection consists of records of 188 court cases involving either African Americans or Native Americans. A careful search of the Files for the Litchfield County Court discovered 165 on African Americans and 23 on Native Americans, about one third of the total that was found in Files for the New London County Court for the period up to the American Revolution. A couple of reasons exist for this vast difference in numbers. First, Litchfield County was organized much later than New London, one of Connecticut's four original counties. New London was the home of four of seven recognized tribes, was a trading center, and an area of much greater wealth. Second, minority population in the New London County region has been tracked and tabulated by Barbara Brown and James Rose in Black Roots of Southeastern Connecticut.¹ Although this valuable work does not include all of Negro or Indian background, it provides a wonderful starting point and it has proven to be of some assistance in tracking down minorities in Litchfield County. In most instances, however, identification is based upon language in the documents and knowledge of surnames or first names.² Neither surname nor first name provides an invariably reliable guide so it is possible that some minorities have been missed and some persons included that are erroneous.

In thirteen of 188 court cases, the person of African or Native American background cannot be identified even by first name. He or she is noted as "my Negro," a slave girl, or an Indian. In twenty-three lawsuits, a person with a first name is identified as a Negro, as an Indian in two other cases, and Mulatto in one. In the remaining 151 cases, a least one African American or Native American is identified by complete name.³ Thirteen surnames recur in three or more cases.⁴ A total of seventy surnames, some with more than one spelling, are represented in the records.

The Jacklin surname appears most frequently represented in the records. Seven different Jacklins are found in eighteen cases, two for debt and the remaining sixteen for more serious crimes like assault, breach of peace, keeping a bawdy house, and trespass.⁵ Ten cases concern Cuff Kingsbury of Canaan between 1808 and 1812, all involving debts against Kingsbury and the attempts of plaintiffs to secure writs of execution against him. Cyrus, Daniel, Ebenezer, Jude, Luke, Martin, Nathaniel, Pomp, Titus, and William Freeman are found in nine cases, some for debt, others for theft, and one concerning a petition to appoint a guardian for aged and incompetent Titus Freeman.⁶ Six persons with the surname Caesar are found in seven court cases.

Sixty-one of 188 cases concern debt.⁷ Litchfield County minorities were plaintiffs in only about ten of these lawsuits, half debt by book and half debt by note. The largest single category of court proceedings concern cases of crimes against person or property. They include assault (32 cases), theft (30), breach of peace (5), and breaking out of jail (1). In cases of assault, the Negro or Indian was the perpetrator in about two thirds of the cases and victim in one third. In State v. Alexander Kelson, the defendant was accused of assaulting Eunice Mawwee.⁸ Minority defendants in assault cases included Daniel K. Boham, William Cable, Prince Comyns, Adonijah Coxel, Homer Dolphin, Jack Jacklin, Pompey Lepean, John Mawwee, Zack Negro, and Jarvis Phillips. One breach of peace case, State v. Frederic Way, the defendant, "a transient Indian man," was accused of breach of the peace for threatening Jonathan Rossetter and the family of Samuel Wilson of Harwinton.⁹

In cases of theft, African Americans appeared as defendants in 27 of 30 cases, the only exceptions being two instances in which Negroes were illegally seized by whites and the case of State v. William Pratt of Salisbury. The State charged Pratt with stealing $35 from the house of George Ceasor.¹⁰ More typical, however, are such cases as State v. Prince Cummins for the theft of a dining room table and State v. Nathaniel Freeman for the theft of clothes.¹¹

Another major category of lawsuits revolves around the subject of slaves as property. The number and percentage of such cases is much lower than that for New London County due to the fact that the county was only organized one generation before the American Revolution and the weaker grip the institution of slavery had in that county. The cases may be characterized as conversion to own use (4), fraudulent contract (3), fraudulent sale (3), runaways (3), illegal enslavement (2), and trespass (2).¹² The Litchfield County Court in April 1765 heard George Catling v. Moses Willcocks, a case in which Willcocks was accused of converting a slave girl and household goods to his own use.¹³ In the 1774 fraudulent contract case of Josiah Willoughby v. Elisha Bigelow, the plaintiff accused Bigelow of lying about York Negro's age and condition. Willoughby stated that York Negro was twenty years older that he was reputed to be, was blind in one eye, and "very intemperate in the use of Speretuous Lickor." He sued to recover the purchase price of £45, the court agreed, and the defendant appealed.¹⁴ Cash Africa sued Deborah Marsh of Litchfield in 1777 for illegal enslavement. He claimed that he was unlawfully seized with force and arms and compelled to labor for the defendant for three years.¹⁵ In another case, David Buckingham v. Jonathan Prindle, the defendant was accused of persuading Jack Adolphus to run away from his master. The plaintiff claimed that Adolphus was about twenty years old and bound to service until age twenty-five, when he would be freed under terms of Connecticut's gradual emancipation law.¹⁶

Other subjects found in Litchfield County Minorities include defamation, gambling, keeping a bawdy house, and lascivious carriage. The defamation cases all included the charge of sexual intercourse with an Indian or Negro. In one such case, Henry S. Atwood v. Norman Atwood, both of Watertown, the defendant defamed and slandered the plaintiff by charging that he was "guilty of the crime of fornication or adultery with [a] Black or Negro woman," the wife of Peter Deming.¹⁷ Three cases, two from 1814 and one from 1821, accuse several Negroes accuse Harry Fitch, Polly Gorley, Violet Jacklin, Betsy Mead, and Jack Peck alias Jacklin, of running houses of ill repute.¹⁸

The records on African Americans and Native Americans from Litchfield County are relatively sparse, but they do provide some indication of the difficulties encountered by minorities in white society. They also provide some useful genealogical data on a handful of families in northwestern Connecticut.

1. Barbara W. Brown and James M. Rose, joint authors, Black Roots in Southeastern Connecticut, 1650-1900 (Detroit: Gale Research Co., 1980).
2. The court cases often identify minorities by the words Negro, mulatto, colored, or Indian.
3. Two or more African Americans or Native Americans are found in 27 lawsuits, but a maximum of two people are included in the Litchfield County Minorities database.
4. Surnames with spelling variations: Boston (3), Botsford (4), Caesar (7), Coxel (3), Freedom (3), Freeman (9), Gauson (5), Jacklin (17), Kingsbury (10), Leopen (4), Mawwee (5), Quomenor (4), and Smith (3).
5. George, Harvey, Isaac, Jack, Philip, Violet, and William Jacklin. Also included is Jack Peck, alias Jack Jacklin.
6. For the last case, see Conservators and Guardians, Box 2, folder 42.
7. Fifty-seven suits for debt, the vast majority of which a minority was plaintiff or defendant, and four concerning writs of execution to recover debt owed.
8. Dec. 1836, Box 3, folder 16.
9. Sep. 1796, Box 3, folder 6.
10. David King v. Stephen Walton, Mar. 1791, Box 1, folder 17;Simon Mitchel v. Edward Hinman, Dec. 1793, Box 1, folder 18; State v. William Pratt, Oct. 1848, Box 2, folder 37.
11. Apr. 1828, Box 2, folder 23; Oct. 1837, Box 2, folder 29.
12. Three additional conversion cases concern livestock and hay.
13. Apr. 1765, Box 1, folder 5.
14. Dec. 1774, Box 1, folder 9.
15. Sep. 1777, Box 1, folder 9.
16. Dec. 1813, Box 1, folder 49.
17. Dec. 1814, Box 2, folder 2.
18. Sep. 1814, Box 2, folder 3, Sep. 1814, Box 2, folder 4; Sep. 1821, Box 2, folder 15.

If a record of interest is found, and a reproduction of the original record is desired, you may submit a request via <a

Graph and download economic data for Unemployment Rate - 16-19 Yrs., Black or African American (LNS14000018) from Jan 1972 to Jun 2025 about 16 to 19 years, African-American, household survey, unemployment, rate, and USA.

There were almost 700 thousand slaves in the US in 1790, which equated to approximately 18 percent of the total population, or roughly one in every six people. By 1860, the final census taken before the American Civil War, there were four million slaves in the South, compared with less than 0.5 million free African Americans in all of the US. Of the 4.4 million African Americans in the US before the war, almost four million of these people were held as slaves; meaning that for all African Americans living in the US in 1860, there was an 89 percent* chance that they lived in slavery. A brief history Trans-Atlantic slavery began in the early sixteenth century, when the Portuguese and Spanish forcefully brought captured African slaves to the New World, in order to work for them. The British Empire introduced slavery to North America on a large scale, and the economy of the British colonies there depended on slave labor, particularly regarding cotton, sugar and tobacco output. In the seventeenth and eighteenth century the number of slaves being brought to the Americas increased exponentially, and at the time of American independence it was legal in all thirteen colonies. Although slavery became increasingly prohibited in the north, the number of slaves remained high during this time as they were simply relocated or sold from the north to the south. It is also important to remember that the children of slaves were also viewed as property, and (apart from some very rare cases) were born into a life of slavery. Abolition and the American Civil War In the years that followed independence, the Northern States began gradually prohibiting slavery, and it was officially abolished there by 1805, and the importation of slave labor was prohibited nationwide from 1808 (although both still existed in practice after this). Business owners in the Southern States however depended on slave labor in order to meet the demand of their rapidly expanding industries, and the issue of slavery continued to polarize American society in the decades to come. This culminated in the election of President Abraham Lincoln in 1860, who promised to prohibit slavery in the newly acquired territories to the west, leading to the American Civil War from 1861 to 1865. Although the Confederacy (south) were victorious in much of the early stages of the war, the strength in numbers of the northern states (including many free, black men), eventually resulted in a victory for the Union (north), and the nationwide abolishment of slavery with the Thirteenth Amendment in 1865. Legacy In total, an estimated twelve to thirteen million Africans were transported to the Americas as slaves, and this does not include the high number who did not survive the journey (which was as high as 23 percent in some years). In the 150 years since the abolishment of slavery in the US, the African-American community have continuously campaigned for equal rights and opportunities that were not afforded to them along with freedom. The most prominent themes have been the Civil Rights Movement, voter suppression, mass incarceration and the relationship between the police and the African-American community has taken the spotlight in recent years.

Graph and download economic data for Unemployment Rate - Black or African American from Jan 1972 to May 2025 about African-American, 16 years +, household survey, unemployment, rate, and USA.

This data is pulled from the U.S. Census website. This data is for years Calendar Years 2009-2014. Product: SAHIE File Layout Overview Small Area Health Insurance Estimates Program - SAHIE Filenames: SAHIE Text and SAHIE CSV files 2009 – 2014 Source: Small Area Health Insurance Estimates Program, U.S. Census Bureau. Internet Release Date: May 2016 Description: Model‐based Small Area Health Insurance Estimates (SAHIE) for Counties and States File Layout and Definitions

The Small Area Health Insurance Estimates (SAHIE) program was created to develop model-based estimates of health insurance coverage for counties and states. This program builds on the work of the Small Area Income and Poverty Estimates (SAIPE) program. SAHIE is only source of single-year health insurance coverage estimates for all U.S. counties.

For 2008-2014, SAHIE publishes STATE and COUNTY estimates of population with and without health insurance coverage, along with measures of uncertainty, for the full cross-classification of: •5 age categories: 0-64, 18-64, 21-64, 40-64, and 50-64

•3 sex categories: both sexes, male, and female

•6 income categories: all incomes, as well as income-to-poverty ratio (IPR) categories 0-138%, 0-200%, 0-250%, 0-400%, and 138-400% of the poverty threshold

•4 races/ethnicities (for states only): all races/ethnicities, White not Hispanic, Black not Hispanic, and Hispanic (any race).

In addition, estimates for age category 0-18 by the income categories listed above are published.

Each year’s estimates are adjusted so that, before rounding, the county estimates sum to their respective state totals and for key demographics the state estimates sum to the national ACS numbers insured and uninsured.

This program is partially funded by the Centers for Disease Control and Prevention's (CDC), National Breast and Cervical Cancer Early Detection ProgramLink to a non-federal Web site (NBCCEDP). The CDC have a congressional mandate to provide screening services for breast and cervical cancer to low-income, uninsured, and underserved women through the NBCCEDP. Most state NBCCEDP programs define low-income as 200 or 250 percent of the poverty threshold. Also included are IPR categories relevant to the Affordable Care Act (ACA). In 2014, the ACA will help families gain access to health care by allowing Medicaid to cover families with incomes less than or equal to 138 percent of the poverty line. Families with incomes above the level needed to qualify for Medicaid, but less than or equal to 400 percent of the poverty line can receive tax credits that will help them pay for health coverage in the new health insurance exchanges.

We welcome your feedback as we continue to research and improve our estimation methods. The SAHIE program's age model methodology and estimates have undergone internal U.S. Census Bureau review as well as external review. See the SAHIE Methodological Review page for more details and a summary of the comments and our response.

The SAHIE program models health insurance coverage by combining survey data from several sources, including: •The American Community Survey (ACS) •Demographic population estimates •Aggregated federal tax returns •Participation records for the Supplemental Nutrition Assistance Program (SNAP), formerly known as the Food Stamp program •County Business Patterns •Medicaid •Children's Health Insurance Program (CHIP) participation records •Census 2010

Margin of error (MOE). Some ACS products provide an MOE instead of confidence intervals. An MOE is the difference between an estimate and its upper or lower confidence bounds. Confidence bounds can be created by adding the margin of error to the estimate (for the upper bound) and subtracting the margin of error from the estimate (for the lower bound). All published ACS margins of error are based on a 90-percent confidence level.