Emsi Case Study (See Full Archive)
Summary: The Workforce Intelligence Network for Southeast Michigan (WIN) uses Emsi data to provide data, research, and analytics to its regional and state partners. Established in 2011 and an Emsi user since 2012, WIN has already set a high bar for strengthening partnerships between higher ed institutions, WIBS, economic development corporations, and companies.
Key takeaways: Using labor market data, WIN has achieved the following:
- Helped colleges develop workforce-aligned programs and win grants
- Assisted EDOs with business attraction and retention
- Worked with WIBs and businesses to set competitive wages
Introduction: Data the Pillar to Collaboration
Of all the clarion calls issued in the name of economic prosperity, the need for better collaboration between higher education, workforce development, economic development, and businesses has been one of the most urgent. And it’s a call that WIN has answered.
Founded in 2011, the Workforce Intelligence Network for Southeast Michigan is a joint effort between nine community colleges, seven workforce boards, and numerous economic development partners. Fulfilling its mission—creating a comprehensive, cohesive workforce system that provides businesses with the talent they need for success—requires not just teamwork, but also the strategic application of labor market data.
“Data is a pillar in our work,” said Colby Spencer-Cesaro, research director at WIN. In fact, “WIN was born out of the need to connect the talent system with data. More access to information was necessary for our community colleges and workforce investment boards; we wanted to make sure the talent system was more employer-driven; we wanted to increase and strengthen the connections between companies and community colleges.”
Using Emsi Analyst (among other sources), WIN provides data, research, and analytics to its regional and state partners. “Emsi is our favorite data source,” Cesaro said. “The analysis—bringing so many different data sources in one tool, whether it’s for projections or city-level analysis—is so helpful because you don’t want to analyze any piece of information in a vacuum.”
WIN may be young, but it has already set a high bar for using labor market data to strengthen partnerships between higher ed institutions, WIBs, economic development corporations, and companies. Here are a few examples of how WIN gets it done.
Data, But Verify
If you boiled WIN’s approach to labor market data down to a maxim, it could be Data, but verify.
“Dig in and have a reality check,” Cesaro advised in her list of data dos and don’ts. “Figure out what’s actually happening on the ground, because the data can only tell you so much.”
Her favorite example of this involves the apparent disparity in demand for engineers vs. engineering technicians. Job postings indicated high demand for engineers and low demand for engineering technicians, while completions data told a mirror story: More graduates were earning technician degrees.
“If you just look at the data, it would tell us that nobody should go into an engineering technician program and that universities and colleges should grow bigger four-year programs,” Cesaro said. “But there’s more nuance to be had.”
“Emsi is our favorite data source. The analysis—bringing so many different data sources in one tool, whether it’s for projections or city-level analysis—is so helpful because you don’t want to analyze any piece of information in a vacuum.” — Colby Spencer-Cesaro, WIN
Her solution was simple: Talk to the employers. Their reply was equally simple: “It’s not a four-year degree that matters, it’s calculus.”
Employers desired calculus, but since calculus was typically offered only by four-year programs, they had listed a four-year degree as “required” on their job postings. It was a curriculum and communication issue—nothing to do with the length of the degree. The solution now lies in adjusting the engineering technician program to include calculus.
“A lot of the information you need to get is from the companies,” Cesaro said. “Call them.”
Data for Community Colleges
Last January, the state of Michigan offered a grant to fund new equipment at community colleges. The two criteria were that the college 1) must justify why it needed the money and 2) must use the funding to train for jobs that required heavy machinery costing at least $5,000 per machine.
WIN’s community college partners came to WIN for analysis: What should they apply for? What should they train for?
WIN used Emsi’s detailed occupation data to project job growth for machinist occupations five to 10 years out in each college’s region, then analyzed employer needs, concentration, and job postings and replacement hires over time. Winnowing the results to occupations geared towards associates degrees or certificates, WIN produced a roster of jobs requiring expensive machinery that community colleges could train for.
The result of Cesaro’s data research, carefully applied, was a huge victory: WIN’s partners received $20M in equipment grants. “This successful project was based on the iterative process of analyzing data and helping our partners write about it,” Cesaro said. “Emsi to the rescue!”
Data for Economic Developers
Working alongside economic developers is a huge part of WIN’s role. “We get a lot of urgent data requests,” Cesaro said. “They call us and they need the data yesterday. Thank God Emsi exists, because all that data is at our fingertips.”
Business attraction and retention is a perennial goal. Cesaro uses modified shift share analysis based on data from on Emsi Analyst to identify regional strengths and weaknesses, comparing differences in concentration and differences in employment to highlight where a region is competitive…or falling behind.
“Thank God Emsi exists, because all that data is at our fingertips.”
“We give detailed analysis identifying occupations where you’re going to have a lot of bang for your buck if you’re talking to companies who are thinking about moving to the area,” she explained. “You can show that your workforce is growing, your LQ is high, or whatever. Look at jobs that offer high wages. Look at the number of graduates; is there a fresh workforce coming out of the schools? If your area doesn’t have a lot of universities or colleges, where are you going to produce your talent? Emsi has a lot of these data points.”
Data for Workforce Investment Boards & Businesses
How much should employers pay their workers? It’s more than an ethical question; it’s also a matter of self interest.
“Do you want average workers?” Cesaro challenged. “Nobody does. So why would you want to pay average wages? If you want a quality workforce, if you want stellar talent, then you need to pay for it.”
The average American male, she pointed out, was earning less in 2014 than in 1973. Forty years later and workers are no better off. “We always say we want our kids to be better off than we were, but that’s not really happening,” she said. “We have to help our company partners understand the important role they play.”
By providing WIBs with Emsi wage data, WIN helps employers determine their employees’ paychecks. How do regional wages compare to those elsewhere? What are the differences between entry-level, average, and high wages? Setting realistic but competitive numbers cuts turnover (satisfied employers tend to stay put) and enriches business in the long run because “you get what you pay for.”
“Do you want average workers? Nobody does…. If you want stellar talent, then you need to pay for it.”
“There’s a slippery slope to offering less competitive wages,” Cesaro said, noting that for several years, companies had the upper hand as the US came out of the recession; people were desperate for work and companies had their pick of whom to hire. But this is no longer the case. As the economy has improved, companies have been forced to step up their competition. The key? “Treat talent like an asset,” Cesaro said.
Conclusion: The Same Team
WIN’s raison d’être is shared by everyone working in the various worlds of higher education, business, and workforce and economic development: It’s all about improving our economies and blessing our communities by educating students, employing workers, and ensuring businesses find champion talent. But for this vision to work, everyone must see themselves as WIN does—not as silos, but as players in an ecosystem that requires collaboration as much as robust intel (shrewdly applied).
“Data is a great conversation starter, but what it brings us to is stronger partnerships and more informed decisions,” Cesaro said. “So partner with everyone on your team—and we’re all on the same team.”
The Workforce Intelligence Network is a southeast Michigan collaborative effort between nine community colleges, seven workforce boards and economic development partners. Its mission is to create a comprehensive and cohesive workforce development system in Southeast Michigan that provides employers with the talent they need for success. Within this, its goals are to provide current and actionable labor market intelligence to allow for greater regional talent system effectiveness; strengthen and sustain an employer-driven talent system that serves as a resource hub and connection point for regional businesses, industries, and other stakeholders; and improve institutional, local, state, and federal talent development policy through research, thought leadership, and innovative practice.
Economic Modeling Specialists Intl. provides comprehensive, user-friendly labor market data that helps educational institutions, workforce planners, and regional developers (such as workforce development boards and economic development organizations) build a better workforce and improve the economic conditions in their regions.