Mayor's Order 2017-115. District of Columbia Data Policy. Originating Agency: Office of the Mayor. A comprehensive data policy for the District of Columbia government. The data created and managed by the District government are valuable assets and are independent of the information systems in which the data reside.

Graph and download economic data for Employed Persons in the District of Columbia (LAUST110000000000005) from Jan 1976 to Aug 2025 about DC, household survey, persons, employment, and USA.

Amendment. A comprehensive data policy for the District of Columbia government. The data created and managed by the District government are valuable assets and are independent of the information systems in which the data reside.

In 2024, the real gross domestic product (GDP) of the District of Columbia was 148.02 billion U.S. dollars. This is an increase from the previous year, when the GDP stood at 146.02 billion U.S. dollars.

This dataset contains polygons that represent the boundaries of statistical neighborhoods as defined by the DC Department of Health (DC Health). DC Health delineates statistical neighborhoods to facilitate small-area analyses and visualization of health, economic, social, and other indicators to display and uncover disparate outcomes among populations across the city. The neighborhoods are also used to determine eligibility for some health services programs and support research by various entities within and outside of government. DC Health Planning Neighborhood boundaries follow census tract 2010 lines defined by the US Census Bureau. Each neighborhood is a group of between one and seven different, contiguous census tracts. This allows for easier comparison to Census data and calculation of rates per population (including estimates from the American Community Survey and Annual Population Estimates). These do not reflect precise neighborhood locations and do not necessarily include all commonly-used neighborhood designations. There is no formal set of standards that describes which neighborhoods are included in this dataset. Note that the District of Columbia does not have official neighborhood boundaries. Origin of boundaries: each neighborhood is a group of between one and seven different, contiguous census tracts. They were originally determined in 2015 as part of an analytical research project with technical assistance from the Centers for Disease Control and Prevention (CDC) and the Council for State and Territorial Epidemiologists (CSTE) to define small area estimates of life expectancy. Census tracts were grouped roughly following the Office of Planning Neighborhood Cluster boundaries, where possible, and were made just large enough to achieve standard errors of less than 2 for each neighborhood's calculation of life expectancy. The resulting neighborhoods were used in the DC Health Equity Report (2018) with updated names. HPNs were modified slightly in 2019, incorporating one census tract that was consistently suppressed due to low numbers into a neighboring HPN (Lincoln Park incorporated into Capitol Hill). Demographic information were analyzed to identify the bordering group with the most similarities to the single census tract. A second change split a neighborhood (GWU/National Mall) into two to facilitate separate analysis.

Graph and download economic data for Labor Force Participation Rate for District Of Columbia (LBSSA11) from Jan 1976 to Aug 2025 about DC, participation, labor force, labor, rate, and USA.

Age, Sex, Race, Ethnicity, Total Housing Units, and Voting Age Population. This service is updated annually with American Community Survey (ACS) 5-year data. Contact: District of Columbia, Office of Planning. Email: planning@dc.gov. Geography: 2022 Wards (State Legislative Districts [Upper Chamber]). Current Vintage: 2019-2023. ACS Table(s): DP05. Data downloaded from: Census Bureau's API for American Community Survey. Date of API call: January 2, 2025. National Figures: data.census.gov. 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. Boundaries come from the US Census TIGER geodatabases. 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 clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2020 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page. Data processed using R statistical package and ArcGIS Desktop. Margin of Error was not included in this layer but is available from the Census Bureau. Contact the Office of Planning for more information about obtaining Margin of Error values.

Abstract: The dataset contains a subset of locations and attributes of incidents reported in the ASAP (Analytical Services Application) crime report database by the District of Columbia Metropolitan Police Department (MPD). Visit crimecards.dc.gov for more information. This data is shared via an automated process where addresses are geocoded to the District's Master Address Repository and assigned to the appropriate street block. Block locations for some crime points could not be automatically assigned resulting in 0,0 for x,y coordinates. These can be interactively assigned using the MAR Geocoder.On February 1 2020, the methodology of geography assignments of crime data was modified to increase accuracy. From January 1 2020 going forward, all crime data will have Ward, ANC, SMD, BID, Neighborhood Cluster, Voting Precinct, Block Group and Census Tract values calculated prior to, rather than after, anonymization to the block level. This change impacts approximately one percent of Ward assignments.Purpose: On February 1 2020, the methodology of geography assignments of crime data was modified to increase accuracy. From January 1 2020 going forward, all crime data will have Ward, ANC, SMD, BID, Neighborhood Cluster, Voting Precinct, Block Group and Census Tract values calculated prior to, rather than after, anonymization to the block level. This change impacts approximately one percent of Ward assignments. This change was not applied to datasets pre-2020.Supplemental Information: All statistics presented in Crime Cards are based on preliminary DC criminal code offense definitions. The data do not represent official statistics submitted to the FBI under the Uniform Crime Reporting program (UCR) or National Incident Based Reporting System (NIBRS). All preliminary offenses are coded based on DC criminal code and not the FBI offense classifications. Please understand that any comparisons between MPD preliminary data as published on this website and the official crime statistics published by the FBI under the Uniform Crime Reporting Program (UCR) are inaccurate and misleading. The MPD does not guarantee (either expressed or implied) the accuracy, completeness, timeliness, or correct sequencing of the information. The MPD will not be responsible for any error or omission, or for the use of, or the results obtained from the use of this information. Please note that changes to MPD's PSA and police district boundaries occasionally occur. The statistics provided through DC Crime Mapping Application are based on current police boundaries as of January 3, 2017. Sex Assault Data Availability: In an effort to provide more clear information about the most serious sex assaults that are most closely aligned with the public's perception of rape and attempted rape, the most serious sex abuse categories are included in the reports of DC Code Index Violent Crimes: Sex Assault. The figures reported in this category include First Degree Sex Abuse, Second Degree Sex Abuse, Attempted First Degree Sex Abuse and Assault with Intent to Commit First Degree Sex Abuse against adults. Data in this format is available online from 2011. Similar to the other offense data, the sex assault statistics are subject to change due to a variety of reasons, such as a change in classification, the determination that certain offense reports were unfounded, or late reporting. Please understand that any comparisons between MPD preliminary data as published on this website and the official crime statistics published by the FBI under the Uniform Crime Reporting Program (UCR) are inaccurate and misleading. Please also be aware that on Sunday, August 23, 2015, the MPD implemented a new records management system called Cobalt. The offense categories presented within this application have remained the same; however, all statistics are subject to change due to a variety of reasons, such as a change in classification, the determination that certain offense reports were unfounded, or late reporting. All statistics presented in Crime Cards are based on preliminary DC Index crime data reported from 2009 up to a second before midnight today (that's 11:59:59 pm yesterday) . They are compiled based on the date the offense was reported ( Report Date) to the police department. The date and time window of the crime’s occurrence is provided in the See a det

In 2023, the government and government enterprises industry added the most real value to the gross domestic product (GDP) of the District of Columbia, amounting to around 44.91 billion U.S. dollars. Comparatively, the information industry contributed around 12.89 billion U.S. dollars to the district's real GDP.

Comprehensive demographic dataset for District Of Columbia, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

In 2023, **** percent of residents of the District of Columbia were white. A further **** percent of the population were Black or African American, and ** percent of D.C. residents were Hispanic or Latino in that same year.

About this dataset

Context

The dataset tabulates the District of Columbia population by age. The dataset can be utilized to understand the age distribution and demographics of District of Columbia.

Content

The dataset constitues the following three datasets

• District of Columbia, DC Age Group Population Dataset: A complete breakdown of District of Columbia age demographics from 0 to 85 years, distributed across 18 age groups
• District of Columbia, DC Age Cohorts Dataset: Children, Working Adults, and Seniors in District of Columbia - Population and Percentage Analysis
• District of Columbia, DC Population Pyramid Dataset: Age Groups, Male and Female Population, and Total Population for Demographics Analysis

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

About this dataset

Context

The dataset tabulates the District of Columbia population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of District of Columbia. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 to 64 years with a poulation of 460,903 (68.58% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the District of Columbia population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in District of Columbia is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the District of Columbia is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for District of Columbia Population by Age. You can refer the same here

Age, Sex, Race, Ethnicity, Total Housing Units, and Voting Age Population. This service is updated annually with American Community Survey (ACS) 5-year data. Contact: District of Columbia, Office of Planning. Email: planning@dc.gov. Geography: Census Tracts. Current Vintage: 2019-2023. ACS Table(s): DP05. Data downloaded from: Census Bureau's API for American Community Survey. Date of API call: January 2, 2025. National Figures: data.census.gov. 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. Boundaries come from the US Census TIGER geodatabases. 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 clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2020 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page. Data processed using R statistical package and ArcGIS Desktop. Margin of Error was not included in this layer but is available from the Census Bureau. Contact the Office of Planning for more information about obtaining Margin of Error values.

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Washington. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Washington, the median income for all workers aged 15 years and older, regardless of work hours, was $77,818 for males and $64,201 for females.

These income figures indicate a substantial gender-based pay disparity, showcasing a gap of approximately 17% between the median incomes of males and females in Washington. With women, regardless of work hours, earning 83 cents to each dollar earned by men, this income disparity reveals a concerning trend toward wage inequality that demands attention in thecity of Washington.

- Full-time workers, aged 15 years and older: In Washington, among full-time, year-round workers aged 15 years and older, males earned a median income of $110,582, while females earned $95,422, resulting in a 14% gender pay gap among full-time workers. This illustrates that women earn 86 cents for each dollar earned by men in full-time positions. While this gap shows a trend where women are inching closer to wage parity with men, it also exhibits a noticeable income difference for women working full-time in the city of Washington.

Interestingly, when analyzing income across all roles, including non-full-time employment, the gender pay gap percentage was higher for women compared to men. It appears that full-time employment presents a more favorable income scenario for women compared to other employment patterns in Washington.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Washington median household income by race. You can refer the same here

Graph and download economic data for Unemployment Rate in the District of Columbia (DCURN) from Jan 1990 to Aug 2025 about DC, unemployment, rate, and USA.

Financial overview and grant giving statistics of Columbia University Club Of Washington Dc

Graph and download economic data for Unemployment Rate in the District of Columbia (LAUST110000000000003A) from 1976 to 2024 about DC, household survey, unemployment, rate, and USA.

District of Columbia, DC has a A wealth grade. Median household income: $107,226. Unemployment rate: 6.4%. Income grows 5.6% yearly.

United States Unemployment Rate: District of Columbia data was reported at 5.600 % in Oct 2018. This records a decrease from the previous number of 6.000 % for Sep 2018. United States Unemployment Rate: District of Columbia data is updated monthly, averaging 7.700 % from Jan 1976 (Median) to Oct 2018, with 514 observations. The data reached an all-time high of 11.700 % in Jun 1983 and a record low of 4.400 % in Apr 1989. United States Unemployment Rate: District of Columbia data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G057: Unemployment Rate: By State.