The conventional wisdom is dished out to millions of students every year: You “need” a four-year degree in order to get a good job. However, with employers begging for more skilled (but not necessarily university-educated) workers, and with many bachelor’s grads finding themselves with few job prospects and a mountain of student loans, workforce and education professionals have begun to take issue with the common wisdom. Instead, they’re focusing on badly-needed “middle-skill” jobs—that is, jobs requiring some postsecondary education or training, but not a 4-year degree. An excellent overview of the issues can be found in the report “America’s Forgotten Middle-Skill Jobs,” produced by Skills2Compete and the Urban Institute.

Read the rest of this entry »

The Bureau of Economic Analysis has released advance estimates for 2007 state gross products. Overall, GDP growth slowed or remained unchanged in all U.S. regions, while national GDP grew by 2% in 2007 compared to 3.1% in 2006.

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.

Q. EMSI’s data is different from data I get from my state’s labor market information (LMI) agency. Which is right?

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.

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.

With the advent of EMSI’s affordable, easy-to-use economic modeling tools, many more people are able to conduct economic impact analyses without an extensive background in economics. This document describes some of the basic pitfalls that more inexperienced researchers should avoid when conducting an economic impact study. The three primary pitfalls are:

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

In this whitepaper, EMSI explains the importance of  labor market analysis for businesses, site selectors, and economic development professionals. The paper covers types of key information needed and the challenges of local labor market research using public sources, and some basic features of EMSI’s Strategic Advantage web-based system that make it the top labor market analysis solution for many policy professionals, researchers, and consultants. An appendix reviews the most commonly used public data sources.

Read the full whitepaper (PDF):  Labor Market Analysis for Site Selection

Update: This whitepaper has been expanded — read the latest version.

DoL/ETA Catalogue of Workforce Information Sources: A comprehensive list of public and private sources for labor market and related data.

The Department of Labor’s Employment and Training Administration has released a 90+ page document detailing the various public and private workforce information sources available to researchers. Subtitled “Decision Making Assistance for Regional Economic Development,” the catalogue provides brief introductions to dozens of sources, along with comparison tables to help users identify the right source for their needs.

EMSI’s Strategic Advantage is highlighted on page 90 of the report, in the category “Data Integration and Analysis Tools and Services.”

Read the press release or download the catalogue in PDF.

On September 26th, the Bureau of Economic Analysis released prototype estimates of gross domestic product (GDP) for the nation’s metropolitan statistical areas (MSAs). Representing the total market value of all goods and services produced in the area, metro GDP is available for 363 areas and 61 NAICS industries for each area.

This detailed information is an exciting new metric for analyzing regional performance, either in a time-series analysis or for regional benchmarking.

Predictably, the New York City MSA tops the list with a GDP of $1.1 trillion, nearly 9% of the GDP for the entire U.S.