With the Census coming up we thought it might be neat to have a look at how migration has been changing over the past ten years. We put together this interactive map to show the net migration into and out of the 48 lower states.

The data has been compiled from the IRS Tax Stats for 1998-2008.

To enter into the fray, below is a table that shows the 15 fastest-growing occupations in the US based on EMSI’s fourth-quarter 2009 figures for 2009-2010. The list is sorted by new and replacement percentage growth (see shaded column). Keep in mind we filtered out occupations paying $15 per hour or less, and these are county-level numbers.

• The top three occupations on this list are tied to the energy sector (technically classified as transportation and material moving occupations).
• Six of the 15 jobs on this list are construction and extraction occupations. All pay fairly well and require, on average, moderate-term on-the-job training.
• Surprisingly, only one of these (podiatrists) would be considered to be in the health care industry.
• Math and science occupations figure heavily on the list. That follows closely with occupations highlighted in the above WSJ article.

For more on this data or to find out the fastest-growing occupations in your region, contact Josh Wright.

First, here are some of the highlights of the new numbers:

• The fastest growth is projected to come from occupations requiring an associate degree. This, of course, puts community colleges front and center when it comes to training.
• There is a definite parallel between education and job growth — 17 of the 30 occupations projected to decline the most require, on average, short-term on-the-job training while 14 of the 30 fastest-growing occupations require a bachelor’s degree.
• Registered nurses are projected to add 582,000 jobs, hardly a surprise as the top expected gainer.
• Service sectors are projected to make up 96% of new jobs (14.8 million) from ‘08-18. Professional and business services and health care/social assistance are two largest industries in that category.
• Goods-producing industries are projected to show no growth, with manufacturing and mining being the hardest hit through 2018 and construction showing modest growth. According to the press release, “By 2018, the goods-producing sector is expected to account for 12.9 percent of total jobs, down from 17.3 percent in 1998 and 14.2 percent in 2008.”

If you compare the latest projections to the 2006-16 numbers, some trends stand out:

• Construction is actually projected to gain substantially more jobs through 2018 than what was expected in the last projections (781,000 to 1.3 million). However, the percentage increase (5.1) is the same.
• The steady increase in health care and social assistance is well documented but nonetheless staggering. From 2006-16, BLS projected an 11.4% increase in employment. In the latest numbers, it is 11.9%.

That which is not defined cannot be measured. That which is not measured cannot be improved. That which is not improved will languish. Without a clear definition, green policy initiatives will be too diffused to be effective and success will be impossible to measure.

You will only see the new data if you specifically choose that option. The first time you log in after the Monday release, you will see a splash screen informing you of the new dataset. Click through to reach the Strategic Advantage home screen, then under “Account Management” select “Preferences.” At the bottom of the options page under “Data Options,” you will be able to choose “Fall 2008 Release (BETA).” Please be advised that we intend this dataset to be used for testing/evaluation only at this time; since it has not been fully tested we do not yet recommend using it for decision making, publications, or wide distribution.

What’s new in EMSI’s Fall 2008 data? We’ve made a few important additions to our methodology in order to improve the timeliness of our data:

1. Current Employment Statistics (CES) now informs current year projections. Until now, the time lag inherent in our main data sources as well as our own biannual data release schedule meant that our current year of data was always projected, and the most recent year of data was often a partial projection (for Spring data releases). We’re now filling that gap by using CES, which is released monthly with month-old data. While CES does not contain nearly the industry or geographic detail of other sources (or EMSI’s final data), it is still very useful to benchmark our more detailed current-year projections, especially in times of rapid economic change. We also now use the current year as the base year for future projections. Given the economic turmoil of the past year, you will find that many of the major differences between the current and new data releases (for current and future years) are a result of these changes.
2. Nearly all of our major data sources have been updated since our last release, including the Quarterly Census of Employment and Wages, the BEA’s Regional Economic Information System, County Business Patterns, Nonemployer Statistics, Occupational Employment Statistics, and about half of the states’ own employment projections. We have included these updates and they will affect 2006-2008 data as well as future-year projections.

If you have any questions about this beta release, or if you would like to offer your feedback on the new data and methodology, please contact EMSI Customer Solutions at 866-999-3674. Thank you!

So what are the most plentiful and well-paid “middle-skill” jobs? Using EMSI’s Strategic Advantage, we were able to get the answer in minutes. Starting with occupations having an average education level no higher than an associate’s degree, we filtered out those having hourly earnings lower than $25 and ranked the remaining occupations by estimated job openings (new plus replacement jobs) over the next five years.

These numbers offer some valuable insights into America’s best middle-skill job market:

1. Recent high school grads, beware: These jobs may not require a four-year college education, but the majority (6 of 10) require significant experience, so be prepared to start at the bottom and work your way up.
2. It pays to be a first-line manager or supervisor, especially in construction, mining, technical and mechanical fields, sales, and manufacturing—all fields where work experience generally counts more than college.
3. It’s already common knowledge that middle-skill health care workers are in high demand, but the numbers are still stunning: US employers will hire more than half a million nurses in the next five years. There’s also strong demand for dental hygienists, who can earn even more than nurses.
4. People in a sales career, or considering one, should get some scientific or technical background—the demand and wages are both higher than in other sales fields.
5. If you have experience in construction or manufacturing and are good with numbers, you could advance your career with training in cost estimation—the art of calculating the actual or predicted cost of a big project. Why construction and manufacturing? Those industries employ the vast majority—about 87%—of cost estimators.
6. If you’re into computers but not software development, a two-year stint at the local community college can qualify you for a high-paying job (although a four-year degree and/or experience won’t hurt). The above category of “computer specialists, all other” includes network designers, software testers, web developers, web administrators, and computer systems architects.

We’ve seen that most of the top middle-skill jobs require experience in the field. But what about jobs for recent high school graduates who don’t have years of experience? Using our criterion of $25/hour or higher earnings, they are harder to find. But if we lower the cutoff to $20/hour (equivalent to almost $42,000/year), more possibilities open up:

Still, high school grads should not expect to just walk into these jobs, especially at the given earnings levels. All except mail carriers require a two-year degree or significant on-the-job training. Some (especially electricians and plumbers) may use apprenticeships combined with classroom work, and increasingly, community colleges are offering degrees or certificates for jobs that once only required on-the-job training. Certificates from industry groups can also be highly desirable. For more information about education and training trends for specific jobs, you can consult the BLS Occupational Outlook Handbook.

After crunching the numbers for middle-skill jobs, it is clear that high-paying, middle-skill jobs are plentiful in a diverse array of fields. However, nearly all require some kind of training and/or education beyond high school. This means that for the majority of today’s youth, the two years immediately after high school will be crucial for making transitions into college, career/tech training, and workplace training such as apprenticeships and internships.

Regions with the most state GDP growth in 2007 were:

• The Pacific Northwest & Rocky Mountain  (WA, OR, ID, + MT and UT)
• South Central (TX, OK, KS)
• Northern Plains (ND, SD, MN)

Low-growth regions included:

• The southern Far West (CA, NV, + AZ)
• The Great Lakes (WI, MI, IL, IN, OH)
• The Southeast (mixed — higher-growth states were NC, LA, KY, GA)
• The Northeast (except NY and DC)

(Note: these “regions” don’t necessarily match region boundaries or names used by the BEA.)

The slowdown was led by Construction and Finance & Insurance industries, consistent with the “credit crunch” widely discussed in the media in past months. CA, NV, AZ, and FL (all states that experienced a significant subprime-related housing bubble) experienced extreme deceleration in growth and in 2007 were in the mid to lower quintiles of all states.

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.

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.

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.