As stated in the Employment and Training Administration (ETA), Workforce Information Core Products and Services Grant, “Grantees must implement and maintain the most current version of WID and populate all ARC-designated core tables following ARC procedures.”
In general, core tables should be maintained with data that is as current and as detailed as state publications.
See the ETA Guidelines page for more information regarding ETA requirements.
The following tables have been designated as the core Workforce Information Database tables. Additional information is available for those tables with links.
* Tables to be furnished to ARC for publication by CareerOneStop ** Need to populate additional lookup tables ~ Tables associated with the Employer Database
Census products use their own occupations and industries for questions about the workforce. Relating those back to SOC, ONET, and NAICS can be challenging because the categories are more intuitive to respondents and less narrowly defined. There are some existing efforts to standardize crosswalks from Census codes to other taxonomies.
This comes from Census tables B01001, B01001A, B01001B, B01001C, B01001D, B01001E, B01001F, B01001G, B01001I, B01002, and B03002. Geographic extent is dependent on ACS 5 or 1 year.
This is a python script to extract the data from the Census API and format it to load into the WID. The script contains parameters to define years and areas for extraction allowing users to pull the most current data as needed. It will require a user to obtain a Census API key and insert it at the line “api_key = ” ”
Table Format
In WID 2.8 fields for Margins of Error were removed from the table structure, but all others remained the same.
The Bureau of Labor Statistics’ Job Openings and Labor Turnover Survey (JOLTS) program produces data on job openings, hires, and separations. Although the content is non-standard and does not have a formal WID table, there has been a recent surge in interest in the data.
This is a non-standard table structure and state uses vary. Instead of a download file, there is a python script to extract the data from the BLS LABSTAT database and format it to load into the WID. The script contains parameters to define years and areas for extraction allowing users to pull the most current BLS data as needed.
Table Format
This is non-standard content but has been structured to work seamlessly with the WID.
The structure of related tables and the SQL are in the same files under Table Format above.
While JOLTS closely follows CES industries, it’s not perfectly aligned. The script above creates a new industry type to be added to the indcodes table, and one other lookup table (values in above documentation) is used to describe the components.
Many states have a use for information about higher education providers, either for analytical purposes or to be able to direct job seekers to appropriate programs. The National Center for Education Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) puts out data about providers and programs offered, organized by state but containing address-level data.
There are a number of sources of cost of living data with different methods and products. They’re useful for research applications and to provide a reference point for wage data. Although cost of living is non-standard and does not have a formal WID table, the content is useful enough to states to index here.
This is an annual calculation that produces annual costs for various components (food, childcare, etc) and a required wage value to cover those costs for 14 family structures. You can find the documentation here.
This is an annual calculation that produces annual costs for various components (food, childcare, etc) and a required wage value to cover those costs for many family structures. You can find the documentation here.
This is an adaptation of a method developed by Minnesota. It has both component costs and an index value but is relatively new. You can find the documentation here and a visualization here.
Table Format
This is non-standard content but has been structured to work seamlessly with the WID.
This is a new data source at the national level. If there’s interest, it will continue to be produced in future years, with updates occurring in January or early February.
Related Tables
The structure of related tables and the SQL are in the same files under Table Format above.
Lookup table contents: AgeCol FamilyChars
Classification of Instructional Programs (CIP) are crosswalked with SOC occupation codes to analyze educational outcomes, wage premiums, and workforce supply and demand. Job banks use them to match qualifications with job postings and labor market data.
The 2020 download file contains both the content for WID tables occcodes/cipcode and the CIP-SOC crosswalk to be loaded into occxocc.
– The values all use stfips 00, so for state use it may need to be duplicated for the state.
– The crosswalk goes both ways – from SOC to CIP and from CIP to SOC. In this table structure, the “from” is housed in codetype/code and the “to” is in codetype2/code2.
– Not all CIPs have a direct occupational match and not all SOCs have a direct CIP match.
Links to the table description in the current structure document. Note: Page anchors do not work in Microsoft Edge (the default browser for some states).
The occcodes table contains industry codes of all types. It references the occtypes table. Depending on what tables your state populates and how long the historical series is, which code types you need may vary quite a bit.
ONET has more detail than SOC and has two more digits to reflect that. In most cases, the two systems can be crosswalked by directly matching the first 6 digits of the code, but the identification of occupations with more detail and aggregating those detailed occupations requires caution
The Department of Labor (DOL) last revised DOT in 1991 and later replaced it with ONET. However, a number of specific applications still use DOT e.g. the USCIS and apprenticeships
Classification of Instructional Programs (CIP) is used by the Department of Education to describe classes, credentials, and courses of study. In recent years, many different approaches to matching CIP codes to occupational codes have been attempted in order to research labor force mismatches and the benefits of higher education.
Census, ACS, and CPS – surveys conducted by the Census Bureau – all use their own coding systems dictated by the survey design. They provide classification systems and crosswalks
The OES program bases its codes on SOC, but because of survey methodology it has additional codes and roll-ups. In prior years OES had its own coding structure
Links to the table description in the current structure document. Note: Page anchors do not work in Microsoft Edge (the default browser for some states).
