For previously posted customer snapshots, click here.

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.

With all the headlines talking about a recession linked to the subprime crisis, we thought it would be interesting to take a look at where all those high-risk mortgages are. By combining data from the Home Mortgage Disclosure Act (HMDA) and the U.S. Department of Housing and Urban Development (HUD), let’s look at where in the U.S. the most 2006 loans were originated through lenders who specialize in subprime loans.

In the following map, which uses 2006 mortgage data for home purchases (i.e., excluding improvement or refinance), each dot represents at most 372 loans and the dot density provides a good visualization of the counties where subprime loans are most concentrated. Note that the data set does not specifically mark loans as subprime, so as a proxy we count loans provided by certain institutions which HUD has identified as subprime lenders.

The major problem areas are in southern California, Florida, Phoenix Arizona, Dallas and Houston in Texas, and Chicago. Here is a list of the 15 counties with the highest numbers of these loans (8 of which are in California or Florida):

However, totals can be deceiving—what about subprime lenders’ loans as a percentage of all home purchase loans originated in the county? The following list shows just that for the top 15 “worst” subprime counties (only counties with 250 or more total home purchase loans in 2006 are included):

Again, California and Florida counties make up 10 of the 15 listed. For comparison, the national average percentage of subprime home purchase loans was 12.6%.

Finally, let’s go beyond just purchase loans and include loans for refinance and improvement as well. After all, purchase loans accounted for less than half of all subprime loans in 2006 (see graphic).

Here, then, are the counties with the highest percentage of all types of loans from subprime lenders (counties with fewer than 500 total loans excluded):

The list isn’t quite the same, is it? For comparison, the U.S. average rate for subprime loans of all types was 14.1% in 2006.

When using industry-based labor market data, it is important to understand the data’s sources and limitations. One question we sometimes get from our clients goes like this: “We have a big hospital in our county, and yet your data shows no jobs in the “hospitals” industry. What’s going on?” Similar questions arise for public elementary, secondary, and postsecondary schools.

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.

A free information seminar is being co-hosted by EMSI and Warwickshire College on 23 April in at Warwickshire College’s Henley-in-Arden Centre. The seminar, intended for FE college principals or their representatives, will provide an overview of the Socioeconomic Impact (SEIM) Study, including the underlying approach, types of information and analysis included, its benefits for colleges, its relationship to the Foster Report, and the kinds of data that college must furnish to EMSI in order to complete the study. In addition, EMSI will provide a prototype demonstration of its labour market analysis tools for FE colleges. A complimentary lunch will be served.

Please fill out the form on this page to reserve your place and receive more information.

The introductory pricing period for EMSI’s Socioeconomic Impact (SEIM) study is approaching its end. The special pricing of £7500 has been available since autumn 2007 following the successful pilot study conducted by Warwickshire College. In the meantime, some 40 FE colleges in the UK have taken advantage of this introductory rate to commission SEIM studies of their own.

Any FE college that signs a contract for the SEIM before 31 May 2008 will receive the lower rate, but after that date the price will increase to the standard rate of £8500.

For more information about SEIM pricing, please contact EMSI.

Everyone knows that manufacturing is on the decline both nationwide and in states like Ohio, right? Well, not exactly—the story is actually much more complicated.

It is true that Ohio’s manufacturing sector saw a loss of some 93,000 jobs (about 10 percent) from 2002 to 2007, even as other portions of the economy were pulling out of the 2001 recession. Nationally, the manufacturing sector lost more than a million jobs, or 7 percent, over the same period. However, this does not at all mean that manufacturing should be neglected by economic development efforts. Rather, the sector is now undergoing intense restructuring that will require innovative, targeted investments. In particular, Ohio economic development professionals should be aware of four major trends in the state’s manufacturing sector:

1. The sector is actually adding new jobs in many non-urban areas.
2. The sector is becoming smaller, more productive, and higher-paying.
3. An aging workforce and rapid skill changes will tighten the sector’s job market.
4. The weak dollar will benefit manufacturers who can export products.

Read on to examine each of these trends in more detail: Recent Trends in Ohio Manufacturing (PDF)

The Kauffman Foundation has released a new report on increasing entrepreneurship in the US economy. The report’s foundational assumption is that entrepreneurs have created most of the new technologies and business models that have fueled our nation’s staggering productivity growth in the past few decades.

The central policy recommendations of the report are

1. “Ensuring a skilled workforce” by improving K-16 education and beyond in math, science, and entrepreneurial thinking, as well as making it easier for skilled and educated immigrants to work in the US.
2. “Reforming health care” to reduce costs and risks for the self-employed and small businesses.
3. “Promoting innovation” by reforming the patent system, increasing commercialization of university research, and monitoring foreign R&D activity for new ideas.
4. “Limiting overly burdensome regulation and liability litigation,” which have a greater effect on entrepreneurial firms than on large established corporations.

Read the full report here.

Update 2 (5/1/2008): EMSI has released the revised dataset, now incorporating state projections from New York (the last state to provide them).

Update (4/16/2008): We have temporarily reverted to the previous version of our data pending a review, testing, and revision of the new industry/occupation projections. The new data, with revised projections, will be re-released on Thursday, May 1, 2008. We apologize for any inconvenience to our users.

Economic Modeling Specialists Inc. (EMSI) is pleased to announce the release of its spring 2008 data set. All of our industry, occupation, demographic, indicator, and regional input-output modeling data have been rebuilt using our most sophisticated integration process ever and the latest available data from our 80-plus government sources. This update affects past, current, and projected data, so numbers will differ slightly from those contained in past EMSI data releases.

Here is an overview of the additions and changes in the new data (note that a few elements are still being added but will be available by the end of the week):

New County-Level Indicators

In this data release, we have continued to expand our portfolio of county-level economic indicators. We’ve added:

• Mortgage data from the Home Mortgage Disclosure Act (HMDA), including number of loans and total loan amounts from regular and subprime lenders (identified by the US Dept. of Housing and Urban Development).
• (Coming soon) Overall workforce indicators from the Census LEHD data set, including job churn (turnover) and percentage of workforce by age group and gender.
• Social Security beneficiaries and benefit amounts, both total and for retirees only.
• Building permits issued by year for both single- and multi-family units.
• Annual and monthly overall unemployment rate from the Bureau of Labor Statistics (BLS) - monthly figures coming soon.
• Estimates of total persons in poverty by county, children in poverty, and median household income, all from the Census’s Small Area Income and Poverty Estimates (SAIPE) program.
• (Coming soon) Personal income information from the US Bureau of Economic Analysis (BEA), including dividend/interest/rent income, transfer payments (Social Security, welfare payments, veterans’ benefits, government grants and loans, unemployment benefits, etc), and overall per-capita personal income.

Methodology Updates for Published Industry/Occupation Data

• We use the latest data sources, especially 1st and 2nd quarter 2007 covered employment from the Bureau of Labor Statistics (BLS).
• The Economic Impact input-output model uses updated national 2002 benchmark and 2006 annual tables from the BEA, inclusion of Census of Governments data, and an improved regionalization methodology for a more updated and accurate model.
• More sophisticated combination of covered employment (BLS) and complete employment (BEA), to account for BEA’s occasional re-classifications of jobs from one industry or county to another. This results in a more accurate count of proprietors and non-covered wage and salary jobs in cases where the BEA’s “complete” employment numbers are actually lower than the BLS’s covered employment numbers. This creates better local data, although it means that our state and national totals are no longer strictly benchmarked to BEA totals.

Improved, More Localized Projections

In the past, we controlled our own local projections first to statewide and then to national projections. We have since found that this sometimes places too much weight on national trends, which could override local trends. We now put more weight on our own local projections, official sub-state area projections (where available), and official statewide projections. (We still use national projections to adjust our own local projections initially, mainly because national projections are the most up-to-date and have the most industry-by-industry detail.) Overall, this results in more localized industry/occupation projections.

Finally, we have made minor adjustments to our demographics projection methodology to improve projections in certain counties where different racial/ethnic populations have rapidly divergent growth rates compared to statewide trends.

Workforce3one has an informative collection of documents that summarize the lessons of Workforce Innovation Networks (WINs) — an initiative to build capacity of employer organizations (such as chambers) to serve as market-driven workforce intermediaries. Documents include:

• Organizing and Supporting the Employer Role in Workforce Development: A Guide for Employer Organizations
• Creating Community Advancement Initiatives: A “How To” Manual
• Building Employer-Responsive Workforce Systems at the State Level: A “How To” Manual
• Providing Business Services: A “How To” Manual
• Partnering with One-Stop Career Centers-Strategies for Recruiting and Training Employees

Download the PDFs from Workforce3one’s page (free reg. req’d), or at Jobs for the Future.

Community College Bridges to Opportunity Initiative: Started by the Ford Foundation, the goals of this initiative are (1) “Promote policy innovation which supports the integration of the multiple community college missions through selected engagement of policymakers, institutional practitioners, and multi-stakeholder coalitions of support from business, labor, and community groups, educational leaders and others; (2) Research ways in which state and local policies can enhance community colleges’ efforts to expand educational and economic opportunities for disadvantaged students; and, (3) Develop models of effective institutional classroom and administrative practice for use by policymakers, college administrators, and advocacy coalitions.” Be sure to check out their Community College Career Pathways Toolkit.