A dataset that combines federal and state administrative data on employers and employees with core Census Bureau censuses and surveys, while protecting the confidentiality of people and firms that provide the data. This data infrastructure facilitates longitudinal research applications in both the household / individual and firm / establishment dimensions. The specific research is targeted at filling an important gap in the available data on older workers by providing information on the demand side of the labor market. These datasets comprise Title 13 protected data from the Current Population Surveys, Surveys of Income and Program Participation, Surveys of Program Dynamics, American Community Surveys, the Business Register, and Economic Censuses and Surveys. With few exceptions, states have partnered with the Census Bureau to share data. As of December 2008, Connecticut, Massachusetts, New Hampshire and Puerto Rico have not signed a partnership agreement, while a partnership with the Virgin Islands is pending. LEHD's second method of developing employer-employee data relations through the use of federal tax data has been completed. LEHD has produced summary tables on accessions, separation, job creation, destruction and earnings by age and sex of worker by industry and geographic area. The data files consist of longitudinal datasets on all firms in each participating state (quarterly data, 1991- 2003), with information on age, sex, turnover, and skill level of the workforce as well as standard information on employment, payroll, sales and location. These data can be accessed for all available states from the Project Website. Data Availability: Research conducted on the LEHD data and other products developed under this proposal at the Census Bureau takes place under a set of rules and limitations that are considerably more constraining than those prevailing in typical research environments. If state data are requested, the successful peer-reviewed proposals must also be approved by the participating state. If federal tax data are requested, the successful peer-reviewed proposals must also be approved by the Internal Revenue Service. Researchers using the LEHD data will be required to obtain Special Sworn Status from the Census Bureau and be subject to the same legal penalties as regular Census Bureau employees for disclosure of confidential information. Basic instructions on how to download the data files and restrictions can be found on the Project Website. * Dates of Study: 1991-present * Study Features: Longitudinal * Sample Size: 48 States or U.S. territories

A dataset that combines federal and state administrative data on employers and employees with core Census Bureau censuses and surveys, while protecting the confidentiality of people and firms that provide the data. This data infrastructure facilitates longitudinal research applications in both the household / individual and firm / establishment dimensions. The specific research is targeted at filling an important gap in the available data on older workers by providing information on the demand side of the labor market. These datasets comprise Title 13 protected data from the Current Population Surveys, Surveys of Income and Program Participation, Surveys of Program Dynamics, American Community Surveys, the Business Register, and Economic Censuses and Surveys. With few exceptions, states have partnered with the Census Bureau to share data. As of December 2008, Connecticut, Massachusetts, New Hampshire and Puerto Rico have not signed a partnership agreement, while a partnership with the Virgin Islands is pending. LEHD's second method of developing employer-employee data relations through the use of federal tax data has been completed. LEHD has produced summary tables on accessions, separation, job creation, destruction and earnings by age and sex of worker by industry and geographic area. The data files consist of longitudinal datasets on all firms in each participating state (quarterly data, 1991- 2003), with information on age, sex, turnover, and skill level of the workforce as well as standard information on employment, payroll, sales and location. These data can be accessed for all available states from the Project Website. Data Availability: Research conducted on the LEHD data and other products developed under this proposal at the Census Bureau takes place under a set of rules and limitations that are considerably more constraining than those prevailing in typical research environments. If state data are requested, the successful peer-reviewed proposals must also be approved by the participating state. If federal tax data are requested, the successful peer-reviewed proposals must also be approved by the Internal Revenue Service. Researchers using the LEHD data will be required to obtain Special Sworn Status from the Census Bureau and be subject to the same legal penalties as regular Census Bureau employees for disclosure of confidential information. Basic instructions on how to download the data files and restrictions can be found on the Project Website. * Dates of Study: 1991-present * Study Features: Longitudinal * Sample Size: 48 States or U.S. territories

Longitudinal Employer-Household Dynamics (LEHD), Origin-Destination Employment Statistics (LODES)

2015-2019 Origin-Destination (OD) for Virginia. LODES7 is based on 2010 Census Blocks.

LEHD makes available several data products that may be used to research and characterize workforce dynamics for specific groups. Learn more about this data at https://lehd.ces.census.gov/

Processing steps: Files downloaded from https://lehd.ces.census.gov/data/lodes/LODES7/va/od/ and merged into a single file for all job types, and all state parts by year. See technical document for more details on original file structure https://lehd.ces.census.gov/data/lodes/LODES7/LODESTechDoc7.5.pdf.

This is a collection of the US Census Bureau's Longitudinal Employer-Household Dynamics (LEHD) for the Cleveland metro area.The data reflects characteristics of workplaces by census block (2020 vintage) shown as centroid points. It is based on unemployment insurance data and Quarterly Census of Employment and Wages (QCEW). This is a useful longitudinal source for understanding economic activity and relationships between where people work and live.This dataset was exported from the Census' powerful tool called OnTheMap and merged into one dataset with a year field. Note: This includes multiple years of data for each block, so specific years must be filtered for mapping. By default, the layer loads with the latest year of data.Data GlossaryClick here, then click on "Fields" to view documentation.Consult U.S. Census LEHD technical document for additional technical information. This extract is Primary Jobs.Update FrequencyAnnually with new data releases.ContactsDro Sohrabian, Urban Analytics and Innovation, dsohrabian@clevelandohio.gov

The census LEHD program makes data about workers available anually that show where a worker lives and where they work. D3 aggregated the job location information by block group to get a total jobs per Block Group in Detroit. More information on the LODES data can be found here: https://lehd.ces.census.gov/data/ Click here for a description of fields.

Analysis of ‘Veteran Employment Outcomes’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/mpwolke/cusersmarildownloadsvetcsv on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

Veteran Employment Outcomes (VEO) are new experimental U.S. Census Bureau statistics on labor market outcomes for recently discharged Army veterans. These statistics are tabulated by military specialization, service characteristics, employer industry (if employed), and veteran demographics. They are generated by matching service member information with a national database of jobs, using state-of-the-art confidentiality protection mechanisms to protect the underlying data.

https://lehd.ces.census.gov/data/veo_experimental.html

Content

"The VEO are made possible through data sharing partnerships between the U.S. Army, State Labor Market Information offices, and the U.S. Census Bureau. VEO data are currently available at the state and national level."

"Veteran Employment Outcomes (VEO) are experimental tabulations developed by the Longitudinal Employer-Household Dynamics (LEHD) program in collaboration with the U.S. Army and state agencies. VEO data provides earnings and employment outcomes for Army veterans by rank and military occupation, as well as veteran and employer characteristics. VEO are currently released as a research data product in "experimental" form."

"The source of veteran information in the VEO is administrative record data from the Department of the Army, Office of Economic and Manpower Analysis. This personnel data contains fields on service member characteristics, such as service start and end dates, occupation, pay grade, characteristics at entry (e.g. education and test scores), and demographic characteristics (e.g. sex, race, and ethnicity). Once service member records are transferred to the Census Bureau, personally-identifying information is stripped and veterans are assigned a Protected Identification Key (PIK) that allows for them to be matched with their employment outcomes in Census Bureau jobs data."

Earnings, and Employment Concepts

Earnings "Earnings are total annual earnings for attached workers from all jobs, converted to 2018 dollars using the CPI-U. For the annual earnings tabulations, we impose two labor force attachment restrictions. First, we drop veterans who earn less than the annual equivalent of full-time work at the prevailing federal minimum wage. Additionally, we drop veterans with two or more quarters with no earnings in the reference year. These workers are likely to be either marginally attached to the labor force or employed in non-covered employment."

Employment

"While most VEO tabulations include earnings from all jobs, tabulations by employer characteristics only consider the veteran's main job for that year. Main jobs are defined as the job for which veterans had the highest earnings in the reference year. To attach employer characteristics to that job, we assign industry and geography from the highest earnings quarter with that employer in the year. For multi-establishment firms, we use LEHD unit-to-worker imputations to assign workers to establishments, and then assign industry and geography."

https://lehd.ces.census.gov/data/veo_experimental.html

Acknowledgements

United States Census Bureau

https://lehd.ces.census.gov/data/veo_experimental.html

Photo by Robert Linder on Unsplash

Inspiration

U.S. Veterans.

--- Original source retains full ownership of the source dataset ---

Dataset, GDB, and Online Map created by Renee Haley, NMCDC, May 2023 DATA ACQUISITION PROCESS

Scope and purpose of project: New Mexico is struggling to maintain its healthcare workforce, particularly in Rural areas. This project was undertaken with the intent of looking at flows of healthcare workers into and out of New Mexico at the most granular geographic level possible. This dataset, in combination with others (such as housing cost and availability data) may help us understand where our healthcare workforce is relocating and why.

The most relevant and detailed data on workforce indicators in the United States is housed by the Census Bureau's Longitudinal Employer-Household Dynamics, LEHD, System. Information on this system is available here:

https://lehd.ces.census.gov/

The Job-to-Job flows explorer within this system was used to download the data. Information on the J2J explorer can ve found here:

https://j2jexplorer.ces.census.gov/explore.html#1432012

The dataset was built from data queried with the LED Extraction Tool, which allows for the query of more intersectional and detailed data than the explorer. This is a link to the LED extraction tool:

https://ledextract.ces.census.gov/

The geographies used are US Metro areas as determined by the Census, (N=389). The shapefile is named lehd_shp_gb.zip, and can be downloaded under this section of the following webpage: 5.5. Job-to-Job Flow Geographies, 5.5.1. Metropolitan (Complete). A link to the download site is available below:

https://lehd.ces.census.gov/data/schema/j2j_latest/lehd_shapefiles.html

DATA CLEANING PROCESS

This dataset was built from 8 non intersectional datasets downloaded from the LED Extraction Tool.

Separate datasets were downloaded in order to obtain detailed information on the race, ethnicity, and educational attainment levels of healthcare workers and where they are migrating.

Datasets included information for the four separate quarters of 2021. It was not possible to download annual data, only quarterly. Quarterly data was summed in a later step to derive annual totals for 2021.

4 datasets for healthcare workers moving OUT OF New Mexico, with details on race, ethnicity, and educational attainment, were downloaded. 1 contained information on educational attainment, 2 contained information on 7 racial categories identifying as non- Hispanic, 3 contained information on those same 7 categories also identifying as Hispanic, and 4 contained information for workers identifying as white and Hispanic.

4 datasets for healthcare worker moving INTO New Mexico, with details on race, ethnicity, and educational attainment, were downloaded with the same details outlined above.

Each dataset was cleaned according to Data Template which kept key attributes and discarded excess information. Within each dataset, the J2J Indicators reflecting 6 different types of job migration were totaled in order to simplify analysis, as this information was not needed in detail.

After cleaning, each set of 4 datasets for workers moving INTO New Mexico were joined. The process was repeated for workers moving OUT OF New Mexico. This resulted 2 main datasets.

These 2 main datasets still listed all of the variables by each quarter of 2021. Because of this the data was split in JMP, so that attributes of educational attainment, race and ethnicity, of workers migrating by quarter were moved from rows to columns. After this, summary columns for the year of 2021 were derived. This resulted in totals columns for workers identifying as: 6 separate races and all ethnicities, all races and Hispanic, white-Hispanic, and workers of 6 different education levels, reflecting how many workers of each indicator migrated to and from metro areas in New Mexico in 2021.

The data split transposed duplicate rows reflecting differing worker attributes within the same metro area, resulting in one row for each metro area and reflecting the attributes in columns, thus resulting in a mappable dataset.

The 2 datasets were joined (on Metro Area) resulting in one master file containing information on healthcare workers entering and leaving New Mexico.

Rows (N=389) reflect all of the metro areas across the US, and each state. Rows include the 5 metro areas within New Mexico, and New Mexico State.

Columns (N=99) contain information on worker race, ethnicity and educational attainment, specific to each metro area in New Mexico.

78 of these rows reflect workers of specific attributes moving OUT OF the 5 specific Metro Areas in New Mexico and totals for NM State. This level of detail is intended for analyzing who is leaving what area of New Mexico, where they are going to, and why.

13 Columns reflect each worker attribute for healthcare workers moving INTO New Mexico by race, ethnicity and education level. Because all 5 metro areas and New Mexico state are contained in the rows, this information for incoming workers is available by metro area and at the state level - there is less possability for mapping these attributes since it was not realistic or possible to create a dataset reflecting all of these variables for every healthcare worker from every metro area in the US also coming into New Mexico (that dataset would have over 1,000 columns and be unmappable). Therefore this dataset is easier to utilize in looking at why workers are leaving the state but also includes detailed information on who is coming in.

The remaining 8 columns contain geographic information.

GIS AND MAPPING PROCESS

The master file was opened in Arc GIS Pro and the Shapefile of US Metro Areas was also imported

The excel file was joined to the shapefile by Metro Area Name as they matched exactly

The resulting layer was exported as a GDB in order to retain null values which would turn to zeros if exported as a shapefile.

This GDB was uploaded to Arc GIS Online, Aliases were inserted as column header names, and the layer was visualized as desired.

SYSTEMS USED

MS Excel was used for data cleaning, summing NM state totals, and summing quarterly to annual data.

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 NOTES

Summary of variables selected for datasets downloaded focused on educational attainment:

J2J Flows by Educational Attainment

Summary of variables selected for datasets downloaded focused on race and ethnicity:

J2J Flows by Race and Ethnicity

Note: Variables in Datasets 1 through 4 downloaded twice, once for workers coming into New Mexico and once for those leaving NM. VARIABLE: LEHD VARIABLE DEFINITION LEHD VARIABLE NOTES DETAILS OR URL FOR RAW DATA DOWNLOAD

Geography Type - State Origin and Destination State

Data downloaded for worker migration into and out of all US States

Geography Type - Metropolitan Areas Origin and Dest Metro Area

Data downloaded for worker migration into and out of all US Metro Areas

NAICS sectors North American Industry Classification System Under Firm Characteristics Only downloaded for Healthcare and Social Assistance Sectors

Other Firm Characteristics No Firm Age / Size Detail Under Firm Characteristics Downloaded data on all firm ages, sizes, and other details.

Worker Characteristics Education, Race, Ethnicity

Non Intersectional data aside from Race / Ethnicity data.

Sex Gender

0 - All Sexes Selected

Age Age

A00 All Ages (14-99)

Education Education Level E0, E1, E2, E3, 34, E5 E0 - All Education Categories, E1 - Less than high school, E2 - High school or equivalent, no college, E3 - Some college or Associate’s degree, E4 - Bachelor's degree or advanced degree, E5 - Educational attainment not available (workers aged 24 or younger)

Dataset 1 All Education Levels, E1, E2, E3, E4, and E5

RACE

A0, A1, A2, A3, A4, A5 OPTIONS: A0 All Races, A1 White Alone, A2 Black or African American Alone, A3 American Indian or Alaska Native Alone, A4 Asian Alone, A5 Native Hawaiian or Other Pacific Islander Alone, SDA7 Two or More Race Groups

ETHNICITY

A0, A1, A2 OPTIONS: A0 All Ethnicities, A1 Not Hispanic or Latino, A2 Hispanic or Latino

Dataset 2 All Races (A0) and All Ethnicities (A0)

Dataset 3 6 Races (A1 through A5) and All Ethnicities (A0)

Dataset 4 White (A1) and Hispanic or Latino (A1)

Quarter Quarter and Year

Data from all quarters of 2021 to sum into annual numbers; yearly data was not available

Employer type Sector: Private or Governmental

Query included all healthcare sector workflows from all employer types and firm sizes from every quarter of 2021

J2J indicator categories Detailed types of job migration

All options were selected for all datasets and totaled: AQHire, AQHireS, EE, EES, J2J, J2JS. Counts were selected vs. earnings, and data was not seasonally adjusted (unavailable).

NOTES AND RESOURCES

The following resources and documentation were used to navigate the LEHD and J2J Worker Flows system and to answer questions about variables:

https://lehd.ces.census.gov/data/schema/j2j_latest/lehd_public_use_schema.html

https://www.census.gov/history/www/programs/geography/metropolitan_areas.html

https://lehd.ces.census.gov/data/schema/j2j_latest/lehd_csv_naming.html

Statewide (New

This research studies the potential of bikeshare services to bridge the gap between Affordable Housing Communities (AHC) and transit services to improve transport accessibility for the residents. In doing so, the study develops an agent-based simulation optimization modeling (ABM) framework for the optimal design of the bikesharing station network considering improving accessibility as the objective. The study discusses measures of accessibility and uses travel times in a multi-modal network. Focusing on the city of Sacramento, CA, the study gathered information related to affordable housing communities, detailed transit services, demographic information, and other relevant data. This ABM framework is used to run three stages of travel demand modeling: trip generation, trip distribution, and mode split to find the travel time differences under the availability of new bikesharing stations. The model is solved with a genetic algorithm approach. The results of the optimization and ABM- based simulation indicate the share of bike and bike & transit trips in the network under different scenarios. Key results indicate that about 60% of the AHCs are within 25-minute active travel time when the number of stations ranges from 25 to 75, and when the number of stations is increased to 100, most AHCs are within 40 mins of active mode distance and all of them are less than an hour away. In terms of accessibility, for example, having a larger network of stations (e.g., 100) increases by 70% the number of Points of Interest (for work, health, recreation, and other) within a 30-minute travel time. This report then provides some general recommendations for the planning of the bikesharing network considering information about destination choices as well as highlighting the past and current challenges in housing and transit planning. Methods The dataset for this study was collected from various sources including Affordable Housing Communities Data, demographic information from the American Community Survey, OpenStreetMap road network, Points of Interest (POIs) data, General Transit Feed Specification (GTFS) data, and Longitudinal Employer-Household Dynamics (LEHD) data. You will need to download all the data from their sources except for the LEHD data. The data was collected from the Sacramento Housing and Redevelopment Agency and the office of the State Treasurer which maintains a list of affordable housing projects. The data was then processed to identify and eliminate duplicates and old projects that were scrapped. The final list consisted of 149 affordable housing projects spread across the city. Demographic information like household income, number of family and non-family households, and number of occupied and vacant housing units were sourced from the 5-year estimates of 2020 American Community Survey data. The LEHD Origin-Destination Employment Statistics or LODES data were used to identify origin-destination matrix with census blocks with residences/homes as the origin and the census blocks with workplaces/ offices as the destination. Bike and Walk Network Data from OpenStreetMap and POIs Data from OpenStreetMap were also used in the study.