Brian Kelsey primarily uses Strategic Advantage to perform efficient market research for his consulting business, as well as to supply his business blog with interesting data highlights and maps. Brian finds that EMSI’s GIS module helps him communicate information effectively to audiences with wide-ranging levels of technical experience, while both the Career Pathways and Educational Analyst modules streamlined his workflow for a recent market research project. Brian turned to CP and EA when a client recently expressed interest in understanding demographic trends and workforce availability in order to locate new business branches in strategic locations. With the key data at his fingertips, Brian was able to quickly provide the client with a data-driven site selection plan.

Rose Baker, Program Manager, University Outreach
Penn State University

With the time-saving features of Strategic Advantage, Rose is able to multi-task without being spread too thin. She helps small communities with questions on their economies, trains workforce professionals on data-driven decision making, and assists continuing education professionals with program expansion decisions. Recently, Rose was able to use SA to assist a small rural community that needed to estimate how many of its jobs were driven by tourism. Using SA’s Economic Impact input/output model, Rose created an economic base report which showed 3,800 jobs driven by visitors. “Because I have Strategic Advantage, I could assist this community,” she said. “Without it, helping them would have meant a costly sacrifice of time and resources, not to mention a laborious and inefficient workflow.”

Carolyn Arnold, Institutional Research and Grants
Chabot-Las Positas Community College District (California)

Using Strategic Advantage is one way that Carolyn maximizes the efficiency and productivity of her research time. As the Coordinator of Institutional Research and Grants in a large community college, her work demands detailed, current, easily-accessible data on local demographic and economic trends. Carolyn finds that SA is a perfect fit for her needs, both (1) to provide a strong data-driven foundation for grant proposals, since she can tailor an SA report to provide relevant facts in a short timeframe; and (2) to perform program alignment and identify new program opportunities. By examining projections for high-wage occupations in her community college district’s service area, Carolyn can identify gaps in program offerings, as well as ensure that the current programs are aligned with labor market realities.

Beth Sullivan, Director of Planning and Marketing
Workforce Connections, Inc. (Wisconsin)

Strategic Advantage’s flexible database simplifies regional planning for Beth Sullivan of Workforce Connections, Inc., which provides jobseeker and employer services to western Wisconsin. Because their service area is part of a 16-county functional labor market area that includes two adjoining states (Minnesota and Iowa), Beth Sullivan relies on Strategic Advantage for streamlined analysis that aids collaboration with regional partners. Manually pooling data for 16 counties in the three states would be laborious and time-consuming at best, but SA’s customizable region definitions allow Beth to see the big picture instantly, regardless of jurisdictional boundaries. She applies this regional perspective by creating up-to-date analysis of industry clusters and creating data-driven grant applications.

Tom Tveidt, Director of Research
Asheville Area Chamber of Commerce (North Carolina)

Tom Tveidt values Strategic Advantage “for the ease in which I can perform economic development centered around data.” Before he had SA, Tom didn’t have a practical way of summarizing his area’s workforce competencies (skills, knowledge, and abilities) to businesses considering a move there. He could see the potential benefits of utilizing O*NET competency data, but there was no platform to use it practically for his needs. Now he can use the Career Pathways module in SA for instant numbers on not only the “target occupations” of a particular business, but also all “compatible occupations” which represent a broader potential labor pool. This gives the prospective business a more realistic picture of the local workforce, and using SA for this task has proven to be an invaluable way to provide relevant data in a short timeframe.

Mark Clasby, Executive Director
Haywood County Economic Development Commission (North Carolina)

Capturing the impact of business is one part of Mark Clasby’s success as an economic developer. He says Strategic Advantage is a great resource for performing impact analyses that are time- and cost-effective. Whether a business is thinking of arriving, leaving, expanding, or downsizing, Mark can use SA’s input/output model to find out the full impact of the scenario in terms of regional jobs and earnings. The results help him build a case for supporting existing industries and assisting new industries as they move to the region. With the impact analysis as a foundation, Mark can promote regional growth with decisions founded on the data.

A. Actually, this is not a case of one being “right” and one being “wrong,” because the data sets have different sources, purposes, and coverage.

First, let’s clarify that EMSI actually offers two different data sets: “EMSI Complete” and “EMSI Covered.” You’ll probably see significant differences between EMSI Complete and your state’s LMI data, but only minor differences between state data and EMSI Covered. That’s because EMSI Covered and state LMI are based on the same single data source: the Quarterly Census of Employment and Wages (QCEW; formerly ES-202). This federal program, with participation from all the states, collects data on all workers who are covered by unemployment insurance (UI), which is why we call it “EMSI Covered.” You will often hear reporters and economy-watchers talk about these numbers as “payrolls,” because nearly all payroll employees are covered by UI. Interested readers can take a look at the BLS Handbook of Methods for more information.

There are only two differences between EMSI Covered and state LMI data:

1. EMSI “unsuppresses” non-disclosed data. The QCEW program collects and releases data with the promise that published data cannot be tied to any single business establishment. So whenever it determines there is a chance of this (and the chance increases with more geographic and industry detail), it “suppresses” those data points—e.g., number of jobs and total wages for industry X in county A. You will often see these as “(D)” or “(ND)” symbols in state data. Since EMSI’s philosophy is to achieve the most complete and detailed data possible, we use sophisticated techniques and additional data sources to estimate these suppressed values.
2. EMSI distributes jobs coded at the state level to individual counties. A small percentage of QCEW-reported jobs are coded only at the state level rather than in a particular county. For our EMSI Covered data set, we have chosen to redistribute these jobs proportionally by industry to individual counties, rather than leaving them at the state level.

(Updated: Moreover, keep in mind that EMSI Covered aligns with private-sector only employment reported in QCEW; EMSI puts all government employment, regardless of industry, into separate categories based on total figures from the Bureau of Economic Analysis.)

So, if you use EMSI Covered data, you’ll get results that line up very closely with your state’s LMI data for private-sector employment. (You can toggle between EMSI Covered and Complete in EMSI’s Strategic Advantage suite by choosing Home > Preferences > Data Options.)

We also produce the “EMSI Complete” data set because a significant portion of the workforce is not covered by traditional QCEW labor market data. Here are just a few examples of non-covered workers:

• Self-employed workers (sole proprietors, partnerships, tax-exempt cooperatives)
• Railroad employees
• Military employees
• Farm workers
• Insurance and real estate agents receiving commissions
• Private schools and religious organizations (partially reported)
• Nonprofit organizations with fewer than four employees
• And more….

Because EMSI is interested in creating the most “complete” possible picture of local economies, we estimate jobs and earnings for all these workers using additional data from the U.S. Bureau of Economic Analysis and the U.S. Census Bureau (QCEW is produced by the U.S. Bureau of Labor Statistics).

Because the number of non-covered workers in a given area can be large, job figures in EMSI Complete will often be much larger than those in state LMI data. This is natural considering the expanded coverage of EMSI Complete. Data users should also remember that labor market data normally counts jobs, not headcount of workers (some Census data counts workers). A single worker holding two half-time jobs would cause two jobs to appear in the data. Although our clients sometimes request it, there is currently no reliable method for translating these raw job figures into full-time equivalent (FTE) job figures.

Choosing the right data source for your research project depends on your purposes and goals, but knowing the basic differences between various sources is essential. EMSI’s philosophy is to produce integrated data that is as complete and detailed as possible, locally focused, and internally self-consistent. Public agencies more focused on producing separate data sets collected from individual programs, while preserving the confidentiality of underlying records.

People who are new to EMSI’s Strategic Advantage web-based analysis suite can sometimes get lost in its bewildering array of data sets, features, and terminology–and lose sight of its time-saving, productivity-boosting capabilities. That’s why we’ve collected real-world examples of how our clients use Strategic Advantage to become more efficient and more informed while saving their organizations money and leveraging additional resources. [Note: The following names and case studies are used by permission for informational purposes. They do not necessarily constitute an explicit or implicit endorsement of EMSI by the people or organizations listed.]

Workforce Investment & Development

David Blumenthal, Workforce Associate for Research and Development
Center of Workforce Innovations, Inc., Indiana
The need for accessible and accurate data is one constant in the ever-changing landscape of workforce development. To meet the Center of Workforce Innovations’ needs for occupation, industry, and demographic data, David Blumenthal uses Strategic Advantage. Its broad scope helps him perform multi-county research like the “State of the Workforce” report for Northwest Indiana, while the ability to drill down to ZIP code data helps him with detailed reports for individual companies. David says he likes to use Strategic Advantage because “its user-friendly layout provides me the capability to shift between occupations and industries, with the ability to sort, filter, or trim any report I choose.” For David and many more workforce developers like him, Strategic Advantage has become an integral part in the strategic planning process.

Higher Education

Tom Prendergast, Director of Institutional Research
North Central State College, Ohio
Tom Prendergast uses Strategic Advantage (SA) for successful grant writing. Recently, he and North Central State College used SA to support the application and subsequent gain of a Regional Innovation Grant (RIG) for his region. Armed with data on the aging workforce, dislocated workers, and the increasing demand for health care services, North Central State College and the other regional partners were able to prove their case for funding with conclusive data-driven findings on their region. Tom also used SA data to support his applications for two other recent grant successes, which brought 1 million dollars to the region for bioscience research and workforce training. Currently, Tom is focusing on the implementation phase of the RIG, and he is working closely with an EMSI consulting team to formulate a plan for transitioning laid-off workers into two focus industry sectors.

Nancy Benziger Brown, Dean of Workforce Development
Walter State Community College Center for Workforce Development, Morristown TN
Nancy Brown used EMSI data to help win a $1.95 Million Community-Based Job Training Grant. In order to do this, she used Strategic Advantage to provide data on: local emerging industries, demographics, and growth rates for several different occupations. This data helped establish need, as the per capita income was below poverty level compared to the state and nation. By identifying need and focusing on Advanced Manufacturing, especially Automotive Parts Manufacturing, Nancy and her team received the CBJTG to develop training, purchase state-of-the-art equipment and begin a career center on campus.

Nancy Ness, Tech Prep Coordinator, Selland College of Applied Technology
Boise State University, Idaho
Nancy is currently using Strategic Advantage to support a data-driven framework for program planning and program justification. With a new Southwest Idaho community college set to open in 2009, Nancy and the college program managers have been using occupation data from SA to guide program offerings. As the infrastructure for this community college is created, occupation data from SA will be used to evaluate the demand for the occupations that these programs train for. In the past, Nancy has used SA to help Southwest Idaho high schools meet Perkins IV accountability requirements and for grant writing for Boise State University Selland College of Applied Technology.

Terry Newman, Director, Contract and Community Education
Gavilian College, California
Terry Newman understands the power of detailed regional data. Gavilian College is a recent recipient of a Rural Opportunities Studies Grant, and the focus of the grant is on a rural county where detailed and up-to-date data is hard to come by. Terry is using Strategic Advantage ZIP code data to better understand this underserved region. In looking at workforce projections for this county, she has unearthed several growing occupations that went unnoticed previous to the detailed analysis. With this knowledge, Gavilian College can proactively address the competencies workers will need in order to remain in the region. This analysis will become the basis for new programs in the future. Terry’s example of data-driven regional analysis proves the importance for utilizing detailed data to address the workforce and education needs of rural communities.

The basic answer is that in EMSI data, hospitals and schools operated by the state or local government are classified under the “state government” or “local government” categories rather than the categories of “hospitals” or “schools.”
EMSI’s information for hospitals and schools comes from several sources, including the Quarterly Census of Employment and Wages (QCEW; Bureau of Labor Statistics), the Regional Economic Information System (REIS; Bureau of Economic Analysis), and County Business Patterns (CBP; Census Bureau). QCEW reports both private and government employment within “schools” and “hospitals” categories. (Note, however, that all American Indian Tribal Councils’ employment is reported as “local government,” which may affect hospital and school job totals in some areas.) REIS, which covers more types of workers, places all government employees, regardless of the type of establishment where they work, under state and local government categories. CBP is almost entirely private-sector only.

The central problem with reporting hospital and school jobs under their intuitive categories is that we have found QCEW’s state and local government employment numbers difficult to integrate with other sources. Not only does QCEW not cover a significant number of state and local government employees (due to nuances of unemployment insurance regulations), it also has a very high percentage of nondisclosed or “suppressed” figures at the county level. Since we use CBP to estimate nondisclosed figures in QCEW, and CBP excludes almost all government employment, we could not show the majority of QCEW’s county-level state and local government employment anyway. So it makes more sense for us to include only private-sector QCEW under the main industry categories and put state and local government employment, totaled from REIS, in separate categories. (Notice that if you run a staffing pattern on the “local government” industry, you will frequently see large numbers of teachers and nurses.)

So, EMSI follows the BEA’s method of counting public schools and hospitals under state and local government. At some point in the future we would like to resolve this issue, but we would require better source data in order to do it with confidence.

1.  Expressing impacts in terms of sales rather than income
2. Ignoring the “with and without” principle (also known as the “Broken Window Principle”)
3. Failing to account for general equilibrium effects

Click here to view/download the full document (PDF): Economic Impact Analysis: Common Pitfalls

With that context, here at EMSI we thought this provided a good opportunity to show how you can analyze your region’s STEM workforce with EMSI’s Strategic Advantage (SA) web-based tool, which integrates dozens of sources to make custom regional labor market research fast, flexible, and simple. With SA, you can see vital statistics on jobs, wages, trends, projections, education levels, skills, and more for any occupation or occupation group, with comparisons to larger areas like states or the whole U.S. You can also see which regional industries are likely to employ workers in these occupations and and further analyze those industries. For a more education-oriented view, the tool allows you to see which regional training providers are offering courses and producing graduates in those occupations. And finally, you can view the occupations as a “career cluster” with various career ladders, or pathways, within it having various specializations.

For quick analysis, go to SA’s Educational Analyst module, and select the “Career Clusters” tool, then the “Career Cluster Data” report. Select timeframe and other options, making sure you choose to aggregate data by cluster. Generate the report and drill down in the “Science, Technology, Engineering & Mathematics” cluster for regional labor market information for STEM pathways and occupations. (Click the thumbnail for full-size image.)

To get started with more analysis, you’ll need to find a list of STEM occupations using the standard codes and titles used by the federal government. A great place to do this is at the Dept. of Labor’s O*NET site. In Strategic Advantage, go to the Career Pathways module and select the Occupational Analysis > Occupational Programs tool. This will allow you to create an occupation group using O*NET-SOC codes. Find and select the occupations and save the group as “STEM”.

With this list of STEM occupations (and with the individual occupations within it), you can use SA’s Career Pathways module to:

1. View O*NET data on knowledge, skills, and abilities (Occupational Analysis > Occupational Competencies)
2. Compare two occupations’ knowledge, skill, and ability requirements (Occupational Analysis > Compare Occupations)
3. See regional educational institutions that offer related programs (Occupational Analysis > Occupational Programs)
4. See the regional “labor pool” of occupations are compatible with certain STEM occupations (Transition Workers > Into Occupation)
5. Create maps of postsecondary completions in related programs (EMSI GIS)

To just get labor market information, you’ll have to define the list using plain SOC codes in Strategic Advantage’s Economic Forecaster module. These are the same as O*NET-SOC codes except without the 2-digit decimal extension, which allows O*NET to have slightly more detailed occupational categories. With this list, you’ll be able to:

1. See which postsecondary courses of study are linked to these occupations (Educational Analyst > Find Programs)
2. View extensive regional labor market reports with jobs, wages, trends, and projections (Economic Forecaster > Occupation tool > Jobs by Occupation, Occupation Report)
3. See which regional industries employ the most workers in STEM (Economic Forecaster > Occupation tool > Inverse Staffing Patterns)
4. Create maps using occupational data (EMSI GIS).

Here’s an example of chemical engineering jobs in California:

This is just a quick introduction to some of the capabilities of Strategic Advantage. To learn more, current users are encouraged to browse the online help within the tool, or take advantage of EMSI’s free, unlimited Customer Solutions support service. Prospective users can browse our main site for more information or tour videos.

For either Sales or Customer Solutions, feel free to call EMSI at (866) 999-3674 (8am to 5pm Pacific).

Current and prospective clients sometimes ask EMSI how their peers are using Strategic Advantage. To help with this knowledge-sharing, we’re pleased to publish a handful of customer experiences that demonstrate some of the real-world solutions enabled by Strategic Advantage (SA) in the fields of economic development, workforce investment, and education. We hope that current and prospective SA users find them useful and inspiring. [Disclaimer: EMSI has received permission to publish these case studies for informational purposes. They should not be interpreted as constituting an official or unofficial endorsement from the organizations listed.]

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Shift share is a standard regional analysis method that attempts to determine how much of regional job growth can be attributed to national trends and how much is due to unique regional factors. The shift share section of various reports in EMSI’s Strategic Advantage helps to answer the question “Why is employment growing or declining in this regional industry, cluster, or occupation?”

Download the full document (PDF): Understanding Shift Share

Location quotient (LQ) is basically a way of quantifying how concentrated a particular industry, cluster, occupation, or demographic group is in a region as compared to the nation. It can reveal what makes a particular region “unique” in comparison to the national average.

Download the full document (PDF): Understanding Location Quotient

Click here to view the video in Flash format.