By now politicians are well versed in anecdotal evidence on the importance of investing in education — from early development classes through college. But what about definitive numbers on the return on investment of higher education?

Over the past few years, we have seen that when colleges and universities are equipped with hard and objective data on their total economic contribution they can often stave off or reduce crippling funding losses. This case study from Wisconsin is just one example.

Consider this quote from Paul Gabriel, Executive Director of the Wisconsin Technical College District Boards Association:

The results of our statewide economic benefits study were instrumental in the unprecedented restoration of millions in state funding cuts already made by the legislature’s budget committee to our colleges. A vote to restore funding by the same committee that cut it just weeks before was simply unheard of.

EMSI has now conducted nearly 1,000 economic impact studies for various types of education institutions. The response has been positive, largely because the studies go beyond the traditional impact analysis by providing a qualitative assessment of how education improves the overall quality and earning potential of the regional workforce.

But that’s not all. EMSI’s studies highlight how colleges are accountable to the regions they serve and measure whether it makes economic sense for the students to attend the colleges, and/or if the returns to the taxpayers warrant continued funding at the same or different levels.

One of EMSI’s latest projects is an impact study for the University of Idaho, where leadership recently presented preliminary results to the state’s chief budget committee. EMSI’s analysis showed the University of Idaho contributes nearly $1 billion to the state’s economy every year, accounting for 1.9% of total economy.

According to Idaho President Duane Nellis, “Higher education is even more important to Idaho when the state is facing economic difficulties. … This is an impressive figure and it demonstrates the beneficial impact that we have on Idaho’s economy.”

Kent State University President Lester A. Lefton expressed a similar sentiment recently after EMSI showed his institution contributes $1.9 billion per year to Northeast Ohio. Said Lefton, “At a time when companies and organizations are asked to be more accountable and quantify their worth, this report documents the value of a Kent State education for not only our students, but also our alumni, the communities we serve and our regional economy.”

If you are interested in an economic impact study for your institution, please contact Rob Sentz at 208.883.3500.

To make it simple and because there is so much data out there to consider, let us suggest two major data components that ought to play a key role in program evaluation and planning. First, data on the occupations that a specific program trains for gives a college or university a solid framework to evaluate how well it’s staying in touch with local, regional, state, and national demand for that occupation. Second, data on the current availability of those training programs allows educators to see whether anyone else in the region already offers training for those occupations. Looking at occupational data seems too obvious to mention, but seeing it next to data on the actual training programs in the region offers an essential perspective on the program’s viability.

To demonstrate how an analysis like this might work, we’ll look at Larimer County, Colorado.

Let’s say that a college in Larimer County has identified Computer Support Specialists as an occupation it would like to evaluate. Initially we’ll want to look at the occupational data. Historical data from the region looks like this:

The occupation shows a growth of 47 jobs over the period. However, we also show a strong number of replacement jobs over the period. Replacement jobs are openings that come about due to attrition within the industry. By looking at both of these numbers together, over the 2006-2009 period we see there are 51 average annual openings. This number ensures that we’ll be looking at an apples-to-apples comparison when we bring in the program data for the region.

In this instance, the area colleges have produced zero graduates in the programs that train for this occupation—good news for the school interested in serving the occupation.

Next we’ll pull information on an occupation (Dental Hygienists) that does have training in the region and go a little more in depth on the programs that train for it. In Larimer County the historical data looks like this:

Because those replacement jobs figures are so low, we get an annual openings figure of 27. As we turn to the program data to see how the region will handle these openings we find that in 2008, area colleges produced 35 graduates in programs related to these occupations. So, at first glance, the need for this occupation is already being met by the existing training in the region—and actually creating a surplus of eight grads. However, let’s dig deeper into the program data and look at the award level.

The IPEDS information we’re using to measure the number of completers in the region indicates seven possible award levels: Awards of Less Than 2 years, Associate’s, Bachelor’s, Postbaccalaureate Certificate, Master’s, Doctor’s, and Professional. All of the awards in the region are at a Bachelor’s degree level, which doesn’t necessarily indicate a training need.

Next we’ll look to see the actual programs that train for the occupation:

This data shows us that all the registered completions for Dental Hygienists are actually completions in a more general degree, Health Services. While there might be some workers completing a degree in this program and then becoming Dental Hygienists, chances are good that this more general degree does not feed directly into the occupation.

This raises another important issue; even if we assume that some of the annual openings are filled by the Health Services completers, it’s still very likely that a graduate from a program focused on Dental Hygiene would be a much more competitive degree. Completers of that program would require less on the job training and orientation, making them more desirable for employers.

Viewing the program completer data like this helps to orient workforce training efforts, and to understand the viability of a given program in the region. However, there’s one final step—verifying on the ground that this program will succeed. This requires getting in touch with employers in the region. By looking at an inverse staffing pattern on the Dental Hygienist occupation in EMSI’s Economic Forecaster module we can find the industries that staff those workers.

Using this NAICS code we can pull the top businesses in the region—also in Economic Forecaster—under that code and find the employers it would make the most sense to contact. For this scenario the data looks like this:

Now we’ve essentially pulled back a list of employers we could contact to confirm the connection between our potential program and workforce demand.

Of course, evaluating any program in any region is going require its own tweaking of this process. If you’d like help performing this kind of program evaluation for your region, contact Josh Stevenson or call 866.999.3674.

An excerpt from the report’s introduction:

Following are examples of the widespread use of O*NET products, including O*NET OnLine, the O*NET database, the Toolkit for Business, and the O*NET Career Exploration Tools. Among the many users of O*NET products are:

• assessment and career information delivery systems
• educational and research institutions
• federal and state government agencies
• international users
• private companies and commercial products
• public workforce investment systems and workforce investment board
• U.S. Armed Forces

The use of O*NET products and tools continues to grow. The O*NET program, through continuous improvement efforts based on user needs and advancing technology, makes every effort to efficiently develop products that meet customer demands in both the public and private sectors.

Click here to access the report, entitled “O*NET Products At Work.”

“The commonly held belief that entrepreneurs are young college students working out of their dorms is simply wrong,” says author [Vivek] Wadhwa, the associate director of the Center for Entrepreneurship and Research Commercialization at Duke University and a senior research associate at Harvard Law School. “Rather, on average, they tend to be highly experienced, well-educated workers who have families. They have come to a stage in their lives when they are simply tired of working for others and want to build real wealth before they retire.” Wadhwa’s research team hopes to uncover not only entrepreneurs’ backgrounds, but also “the deeper formative factors that influence this select and incredibly important class of individuals.”

Researchers surveyed 549 entrepreneurs (i.e., founders of successful businesses in high-growth industries) for the study, and 95.1% of them had earned bachelor’s degrees while 47% had more advanced degrees.

Follow this link for more.

• More than a third of respondents (33.9%) say newly hired high school grads are deficient in their preparation when they enter the workforce. That number dropped to 21.7% for two-year college graduates and 17.4% for four-year grads.
• “High need” workforce readiness training gaps include: creativity/innovation, ethics/social responsibility, professionalism/work ethic, and critical thinking/problem solving.
• Half the employers surveyed provide remedial training programs to overcome deficiencies.

As ASTD emphasizes on its web site, “The widening skills gap among new entrants to the workforce makes it clear that everyone involved in employment, education, and the public workforce system must collaborate to effectively prepare workers to be successful on the job.”

Click here for more on the report and here to access a PDF version of the study (free registration required).

Further, the “Seattle Jobs Initiative (SJI) has sought to advance career pathways in its own work and believes that a broader adoption of this framework within the community colleges would be of significant benefit to low-income, low-skill adults.”

The report is useful and direct — real examples are given on state-level strategies for engaging employers, supporting and retaining pathway participants, and measuring outcomes. For those who aspire to create or re-shape career pathways efforts in their own states, the last third of the report will be a lot of help. The challenges of implementing a statewide model are discussed: establishing partnerships, funding, and shifting from planning to actual implementation.

For more information please contact Mark Beauchamp.

In order to run labor market analysis, link to staffing industries, and establish trends over time, we have translated the coding over to the Standard Occupational Classification system, or SOC codes. Because O*NET allows for further detail not captured with SOC codes, there will be occasional instances where there is not an exact one-to-one correspondence between the two datasets. For instance, O*NET coding for Heating and Air Conditioning Mechanics and Installers, (49-9021.01) and Refrigeration Mechanics and Installers (49-9021.02) are lumped together under the SOC code of 49-9021, Heating, Air Conditioning, and Refrigeration Mechanics and Installers. These overlaps are rare, but should be noted when trying to assess regional growth or demand.

Click on the links below for cluster analysis using national county-level data. Also, check out this post to learn how the clusters can be used in the application process for the recently announced DOL-ETA job training grants.

• Renewable Energy Generation (see below)
• Transportation
• Energy Efficiency
• Green Construction
• Energy and Carbon Capture and Storage
• Research, Design, and Consulting Services
• Environmental Protection
• Agriculture and Forestry
• Manufacturing
• Recycling and Waste Reduction
• Government and Regulatory Administration

The first table (1-A) deals with “green increased demand” occupations, as classified by O*NET, for the Renewable Energy Generation cluster; the second table (1-B) relates to “green enhanced skills” occupations for the same cluster.

Click on the tables for a full-sized image

Findings:

• While overall there’s been a loss of nearly 120,000 jobs in this cluster from 2007-2009, there are a several occupations showing positive growth. The largest, when new and replacement jobs are factored in, is Geological and petroleum technicians (15%).
• A general rule of thumb is that the more education a worker has, the better chance he or she will have landing a job. However, that’s not always the case with this cluster. Most engineering occupations are showing negative or small growth, while Geological and petroleum technicians (which requires an Associate’s degree on average) and Service unit operators (moderate on-the-job training) have been growing at a good pace. Both are middle-skill occupations.
• Meanwhile, educational attainment is tied to increased wages in this example. Most of these occupations that pay $30 per hour or more require lots of experience or a Bachelor’s degree.

For sector 2 data, click here

The study found that more than half of the companies on the 2009 Fortune 500 list were launched during a recession or bear market, along with nearly half of the firms on the 2008 Inc. list of America’s fastest-growing companies. The report also suggests a broader economic trend, with job creation from startup companies proving to be less volatile and sensitive to downturns when compared to the overall economy.

Another Kauffman study, “The Entrepreneurial Boom,” focuses on how baby boomers have been and are projected to be the chief catalyst for new economic activity. Fewer boomers are staying in lifetime jobs, and many are exploring new business possibilities in light of the current recession.

The study does an admirable job outlining the different natures, purposes, and goals that ED and WD possess, as well drawing broader principles from real-life case studies, thus avoiding both ditches that befall these kinds of studies: either falling into a disjointed morass of details, or falling into vague platitudes with no reality checks. It is not exhaustive (it features examples drawn from three states: Pennsylvania, North Carolina, and Illinois), and its study time frame was from 2006-07. But instead of detracting from its merits, it shows a profitable direction for further study.

For more, check out Brian Kelsey’s blog post on the report here.

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.