Quantifying Skills Gaps with the Economic Graph

At LinkedIn, we are focused on creating economic opportunity for every member of the global workforce—all three billion people. As our community grows, the Economic Graph gets better and better at helping us understand what’s happening in the global economy in near real-time. This becomes a truly unique source of data to understand complex labor market dynamics. We work to transform this data into actionable insights—and create a new source of information for individuals, companies, governments and nonprofits to make decisions on jobs, learning, and skills investments.

Many employers say they cannot find people with the skills they need, even for entry level jobs. Skills gaps are an impediment to workers getting jobs, and to companies gaining essential talent. Everyone talks about skills gaps, but we have very little insight into how skills gaps present themselves at the local level, and manifest themselves among and within organizations, states or regions.

That’s a problem, because skills are the building blocks of human capital. Job titles can vary widely across companies and industries, so skills tend to have a stronger signal for what a job actually entails. After all, the skills needed to be a data analyst in the oil and energy industry vary greatly from the skills needed in market research.

Figure 1: Skills diversity of the data analyst job title across industries. (Source.)

At LinkedIn, we measure the supply and demand of 50,000 unique skills: the skills that LinkedIn members add to their profiles. This includes the supply of members with them, and the demand from employers who need them. One of the advantages of our skills data is that it allows us to see trends unfold in near real time, as members add new skills to their profiles. In aggregate, we can start to see when new skills emerge, or when demand for a certain skill suddenly spikes. This dataset is incredibly dynamic, and we are continuously uncovering new ways to leverage it to better understand the economy.

Our definition of a skills gap is:
A gap between supply and demand for a specific skill, in a specific local labor market, at a specific point in time. It may be positive, which is a shortage, or negative, which is a surplus.

At LinkedIn, our granular skills data makes it abundantly clear that there is not one monolithic Skills Gap. Rather, there are many skills gaps.

We can now measure these multitudes of skills gaps very precisely: in specific geographic locations, for specific skills, over specific periods of time—in absolute headcount numbers.

We measure the supply of skills as the number of LinkedIn members who have a particular skill on their profile, in a given location. And we measure employer demand for skills by a weighted average of the skills employers list in their job postings, plus the skills they actually hire for: the frequency at which members with a particular skill are hired.

This is unique because it’s not based on survey data or job descriptions alone, but on the skills people actually have, and the skills that employers are actually hiring for. And the skills gaps that we’re measuring correspond with exactly what you’d expect to see with unemployment rates. Cities and time periods that tend to have more skills shortages also tend to have lower unemployment rates—because those skills are highly in demand.

We believe this new skills gap capability is incredibly valuable for the policy community and others who are trying to close gaps. It can provide more transparency into what the actual needs are, so that we as a community can go about addressing them more efficiently. If you run a school, or are trying to direct funding to new training programs, it can help you to know what skills are actually in demand in your local market. Or if you are struggling to find a job, and find that your skills are overly abundant in your city, you can see which cities do have high demand for your skills and consider relocation for better opportunities.

Skills gaps can be narrowed in a variety of ways: by people moving to cities where their skills are in demand; by businesses opening up shop in cities where there’s an abundance of the skills they need; by training people to learn the skills that are in demand from employers; by employers offering higher pay for in-demand skills.

In order to narrow skills gaps, cities should seek to understand the dynamics of their own labor markets and create policies to align education and training with employer needs.

That’s why we think this work matters. Our hope is that quantifying skills gaps , and diagnosing skills gaps on a super local level, can help policymakers, employers, teachers, and workers understand the dynamics that they’re operating in. We’re just getting started, and will have lots more to share here in coming months.