State data systems must be designed to provide community college leaders with access to data, including labor market information (LMI), to better understand their local economies and support students to make informed decisions about career pathways. In this guest blog post, Iris Palmer explores where LMI comes from, its strengths and weaknesses, and how to apply it. Iris is director for community colleges with the Education Policy program at New America. This piece was originally posted on the New America blog on May 30, 2024.
Labor market information (LMI) includes various data such as unemployment rates, labor force participation, economic indicators, industry trends, occupational data, wage statistics, and employment forecasts. Community colleges can use this information to plan new programs, update current programs, support fundraising, and provide career counseling to students. However, it can be challenging for community college leaders to collect and use this data. In this post, I will outline where LMI comes from, its strengths and weaknesses, and how to apply it.
LMI has two sources: publicly available information collected by state and federal governments using surveys and administrative data like unemployment claims and real-time labor market data provided by private companies. Real-time data enhances public data by scraping the web for job postings, online salary boards such as Glassdoor, and information on workers through LinkedIn data. The publicly available data comes from the US Bureau of Labor Statistics, state agencies, and the US Census Bureau. Some state agencies might use real-time data to enhance traditional labor market information. Private companies such as Lightcast (formerly Burning Glass & Emsi), Chmura Economics & Analytics, Revelio Labs, and the Conference Board provide real-time labor market data.
Publicly available data collected by the government has some drawbacks. First, it typically lags by a year or more, leaving users behind the curve. There is also a lack of consistent definitions of skills, and updating the typologies of occupations to keep up with emerging jobs is difficult. Real-time labor market data can be useful for addressing some of these gaps. These tools generate more up-to-date data and can help identify emerging labor market trends like demand for skills around artificial intelligence. They could also help update the Education and Training Administration’s Occupational Information Network (O*NET) and the Bureau of Labor Statistics’ Standard Occupational Classification (SOC) system.
But there are gaps in real-time data, too. There is an underrepresentation of middle-skill jobs and people without bachelor’s degrees in the source data: online job postings, Glassdoor, and LinkedIn profiles. (See page 52 here on underrepresentation.) This can lead to bias in the types of programs, occupations, and credentials in this data. The underrepresentation of people and jobs with requirements below the bachelor’s degree can create challenges for leaders at community colleges trying to use real-time LMI, where the associate degree and certificates are the predominant credentials awarded. We don’t know the extent of the underrepresentation, and the gap needs more study.
At the same time, Glassdoor and LinkedIn data are self-reported, so salary data or worker and career profile data used by real-time LMI vendors can be unreliable, particularly when the sample size in the middle-skill labor market may be small. Another known issue is the inclusion of postings that do not represent actual job vacancies. Some employers use job postings to collect resumes or maintain an online labor market presence when they are not hiring. The widespread use of vague language and incomplete information in many job postings exacerbates these challenges. Additionally, many employers are dropping the requirements of a bachelor’s degree in their job postings but hire people with a BA anyway.
Some community colleges maintain public-facing dashboards of both real-time and traditional LMI. Northern Virginia Community College (NOVA) and Monroe Community College are good examples. NOVA’s career exploration tool includes data from Lightcast, O*NET OnLine, the U.S. Bureau of Labor Statistics, the Employment Projections Program, 2022, and the U.S. Census Bureau. These dashboards can be used to support students in selecting a career pathway. Some colleges are adding this context to programs or college web pages to help students make decisions about programs of study. For example, the Maricopa Colleges in Arizona embed information about the occupation in program descriptions, and the Program Pathways Mapper presents LMI in an attractive way for many California college students selecting a program.
There are some things to remember for community colleges looking to use LMI for the first time. First, everyone can access public data, but that doesn’t make it easy to use. It is best to have analysts who understand the surveys and their limitations interpret the data. Some state agencies have better interfaces to access data than others. The private real-time data may have more intuitive interfaces, but having an analyst who knows the sources well is still better. These platforms tend to be very complicated with lots of functionality. For this reason, NOVA’s Office of Strategic Insights has a full-time staff member dedicated to labor market intelligence.
Next, no matter where the LMI is from, community colleges should be sure to pressure test this data with employer partners, including through advisory boards. Real-time labor market information is an excellent conversation starter but an insufficient metric to drive decisionmaking in isolation. Community college systems, districts, and associations can also consider purchasing licenses for private real-time LMI products through regional consortia to encourage collaboration and maximize efficiency.
Labor market information is a wonderful tool for community colleges looking to better understand their local economy. Knowing who collects it, how they collect it, and what its limitations are is key to using labor market information effectively.
