Data for the Next Move Forward

Our Data Sources

Traditional Labor Market Information (LMI)

Source: Government sources (Bureau of Labor Statistics, US Census Bureau, etc.)

What it is: Industry, occupation, education, and demographics data

What it can do: Traditional LMI is great for understanding the structure of an economy and getting a full picture of the major trends in jobs and wages

Word to the wise: Traditional LMI lacks a certain level of detail and isn’t collected very often, so it is strongest when used in concert with job posting analytics and profile data (see below)

Job Posting Analytics (JPA)

Source: Online job postings

What it is: Data collected from hundreds of millions of job postings created by employers

What it can do: JPA can help measure the demand for talent in a given region. It is more granular than traditional LMI, providing details about the labor market (e.g. specific skills requested by employers) that LMI simply can’t. Another of JPA’s strengths is that it has virtually no time lag, since job postings are live; hence it is also sometimes known as “real-time labor market data.”

Word to the wise: The number of postings may be either higher or lower than the number of actual hires. Postings might outnumber hires when a company is trying hard to find talent, or postings may be significantly fewer than hires because certain types of jobs (e.g., roofers, welders, and other blue-collar jobs) aren’t typically advertised online. When this happens, we deduplicate the postings as much as possible and then provide a realistic ratio of unique postings to hires.

Profile Data

Source: Online profiles and resumes

What it is: Public, self-reported information about individuals’ city/state/nation of residence, job history, education history, and skills

What it can do: Lightcast's profile data can help you measure the supply of talent in a region

Word to the wise: Profile data is a great complement to LMI because it includes unprecedented levels of detail impossible for LMI to provide

Compensation Data

Source: Government sources + postings data

What it is: Wage estimations based on wage data (from government sources) plus self-reported wage data from our postings data.

What it can do: Compensation data can help you estimate how much a position should be paid based on a worker’s actual experience and skills, rather than their mere job title

Word to the wise: Compensation data contains estimations, not hard numbers; nevertheless these estimations are reliable due to our rigorous methodology which allows us to reduce the rampant sample bias in self-reported compensation

Global Data

Source: Government and profile data sources around the world

What it is: Industry, occupation, and profile data collected from various countries

What it can do: Global data helps you compare markets across borders, determine where your company might expand, and understand the skills landscape

Word to the wise: Countries use all kinds of different names and definitions when they report their talent supply, so we solve this disparity by using our own taxonomy that unifies the occupation categories between countries and helps everyone speak the same language

Skills

Source: Job postings, resumes, online profiles

What it is: Skills possessed by real people in the real world

What it can do: Lightcast Skills creates a common language for students & jobseekers, colleges & universities, employers, and communities. Students & jobseekers can use Lightcast Skills to write better resumes and discover the top skills in demand. Colleges and universities can use Lightcast Skills to design and market work-relevant programs that help students communicate their skills to potential employers. Communities can use skills to describe the talent that businesses need, the abilities that local people have, and any gaps between the two.

Word to the wise: Skills are the basic units which define the economy. They often describe a person’s job much more accurately than the raw job title. This is why skills make such an effective common language.