Over the last couple of years, the Lancaster County (Pa.) Workforce Investment Board has been making use of “career pathways,” an important framework for discussing the variety of career options that are available to workers entering (or re-entering) the job market. Instead of simply listing available jobs, the Lancaster County WIB takes a more comprehensive approach by using Emsi data to study local industry dynamics. Then, it lets that data shape the way it organizes data on high-potential occupations into a roadmap for jobseekers; a sort of ladder that easily explains how workers can potentially progress through a series of occupations, and what skills and training they’ll need in order to do so. This is exciting work, and the Lancaster WIB’s executive director, Scott Sheely, will be hosting a webinar to demonstrate his methods on March 24. Sheely has also written a white paper summarizing how the workforce board uses labor market data to chart career pathways effectively.
Using LMI To Understand the Economic Environment
Sheely (pictured, left) notes that “many folks [in workforce planning] understand the importance of economic and labor market information.” But, at the same time, work on this subject has too often neglected the need to understand the broader economy. Meanwhile, he adds, those who do understand the importance of LMI are prone “use the data to justify decisions that have already been made… instead of letting [decisions] flow out of a broader data analysis.”
So how should WIBs use LMI to effectively construct career pathways and make a real difference in the economy? Sheely outlined his organization’s approach in the white paper. In its most basic form, it has two steps: first, industry analysis and then, proceeding from the results of that analysis, the identification of possible career progressions that match both the workforce talent available and the talent demand that employers are expected to generate.
Since the pathways that the WIB uses need to be grounded in a thorough understanding of the industry mix of the local economy, Sheely’s process begins not by building careers out of occupation data, but by analyzing Emsi data on local industry clusters to see what sectors are driving the regional economy. For example, building a career pathway around production jobs began with the recognition that, in 2012, more than 12% of the jobs in the Lancaster area were in manufacturing, an industry that accounted for 42% of the regional economy’s exports that year.
After understanding those pivotal industry clusters, the Lancaster County WIB explores the occupational mix in those industries by looking at staffing patterns in Analyst. Combining those patterns with a big-picture analysis of how industries will grow in the future, Sheely is then able to show not only which occupations are currently in demand, but also which will be in demand in the future.
Using Data To Make Skills-Based Connections
That isn’t the end of the career pathways process that Sheely lays out, though. Instead, with this complete data-driven picture of economic realities and future needs in their hands, Sheely’s plan suggests that local educators will be able to easily compare the region’s skill needs with the actual skills with which they are providing workers, and to find the skills gaps that can prevent workers from moving between jobs. Those occupations can then be used to map out the skills that workers will need in order to get hired in the future, since standardized occupations allow data on skills needs to be aggregated.
And, more importantly, the combination of occupation and industry data also allows workforce advisors and workers themselves to see get perspective on jobs and career pathways are available, as well as the skills workers will need to have or learn in order to progress along those pathways. After all, Sheely points out, “Experience says that many dislocated workers, for example, may have as many as 80-90% of the skills that they will need to get into a job.” What they need, he writes, is a data-informed advisor who can tell them about this compatibility and, if necessary, point them toward ways of getting that last 10%.
Using O*NET skills compatibility data from Emsi, Sheely and the Lancaster County WIB have been able to easily generate radar charts like the illustration on the right. It overlays the skills needs of two different occupations in a number of standardized categories, each represented by a wedge of the circle; the size of the wedge represents the importance of the skill to the occupation, and the difference between the sizes of two overlapping sections shows how similar the needs of two different occupations are for that skill. This makes it immediately clear where a worker’s existing skills are enough to qualify him or her for a job in another occupation, and where more training in an area is needed.
In many cases, this training can even be on-the-job; although formal education is currently considered more valuable by many, the kind of tacit education that comes from observing work being done and learning by doing is essential to many jobs. And, according to Sheely, this is especially true of the sort of middle-skill jobs he expects to see in short supply over coming decades.
Regardless of how the economy develops in the near future, though, the ability to use data to make well-informed decisions and recommendations at both the individual and regional levels makes Scott Sheely’s approach unique and exciting. By allowing the reality of what connections can actually be made between similarly skilled occupations and the growing industries that need them, the Lancaster County WIB’s career pathways-based approach is a great example of the success that data-driven decisions can lead to. Career pathways don’t just lead to careers. They lead to success — for workers, for industries, and for local economies as a whole.
To read more about how Scott Sheely and the Lancaster County WIB use career pathways and Emsi data to create success for their area, be sure to sign up for Sheely’s webinar, which you can read about here. You can also download the original white paper here.