EMSI’s latest research on middle-skill jobs and the spotlight on community colleges has garnered considerable interest. We hosted a well-attended webinar that summarized the report, discussed why middle-skill jobs are so relevant, and walked through two potential ways to produce the analysis regionally. For those who couldn’t attend the presentation, we’ve included a video overview of the report below.
Also included in this post are step-by-step instructions and recommendations on how to use Analyst, EMSI’s labor market research tool, to tap into middle-skill data for your area.
The following is a quick rundown of the steps to produce EMSI’s Mid-Skill Spotlight analysis (PDF) for your region. Please email Josh Wright, the author the report, or contact EMSI Customer Service if you have further questions.
Step 1: Set Up the Four Sectors As Groups in Analyst
The first thing you’ll need to do is set up the sectors as occupations groups in Analyst. For the four sectors we analyzed, you’ll need the overall group with all detailed SOC codes, as well as a group for mid-skill occupations only. (Download the SOC codes by sector.)
A Note on EMSI’s Middle-Skill Definition
For this report, EMSI defined mid-skill occupations as those in which at least 40% of the current workforce has an associate’s degree or some college, no degree. Some occupations we included have a slightly smaller share than that but typically require an associate’s degree or postsecondary non-degree award as the entry level of education, per the BLS. For previous analyses, like our mid-wage analysis by state, we’ve used wages as a proxy for skill, or combined wages and education.
Step 2: Define Your Region
The majority of data we highlighted in the report is available at the county, MSA, and ZIP code levels. The only exception is educational attainment data by occupation, which the BLS only releases at the national level.
Step 3: Gather Summary Stats from Occupation Report
Once you’ve created the occupation groups in Analyst and defined your region, most of the data in the report is available in two areas of Analyst: the occupation report and the occupation table.
We suggest toggling between occupation reports for mid-skill occupations and all occupations to get a good sense of the key summary statistics of each sector. Key nuggets you can extract from the occupation report include:
- How wages in your region compare to the national average (or how mid-skill wages stack up to wages in all occupations by sector)
- The male/female ratio of jobs in the sector
- The age breakdown, either for mid-skill jobs or all jobs
- Regional trends over the time frame you’ve selected
- Estimated annual openings vs. related educational completions to get a sense of the supply and demand for the selected occupations in your region
The occupation report is also the launching point for pulling industry data from the inverse staffing patterns section (see step 5). There will soon be other ways to get at staffing pattern and inverse staffing pattern data with the new Analyst interface.
Step 4: Gather Detailed Data from Occupation Table
The quickest way to compile the detailed data for all occupations is to dive into the occupation table in Analyst. It’s best to export data to Excel for each of your pre-built sector groups (the one with all SOC codes and the one with mid-skill SOC codes) to allow for easy data comparisons. Before exporting the tables, be sure to include all relevant data variables as table columns: 2013 jobs, median hourly wages, the percentage of the workforce 45 or 55 and older, etc.
The data you exported can be used to parse the occupations with a two-year training focus versus those with good career pathway opportunities, as we showed in the table at the bottom of each section of the report. (We’ve included educational attainment percentages in the spreadsheet with all SOC codes by sector.)
Step 5: Pull Industry Data from Inverse Staffing Patterns
For the industry focus section, you’ll need to go the inverse staffing patterns section of the occupation report and look at all detailed industries (we recommend 5- or 6-digit NAICS).
We filtered for the highest percentage growth industries that employ occupations in the mid-skill realm for each sector, using 2001 to 2013 as our time frame (for the skilled trades sector, we used 2010 to 2013). We also filtered out industries with a small number of jobs since we ranked the industries by percentage growth and not total job change.
Step 6: Compare Data Across Regions
This final step depends on your data package in Analyst. For the report, we looked at all metropolitan statistical areas (MSAs) in the United States to track down the fastest-growing metros for mid-skill jobs in each sector. We removed metros areas with fewer than 5,000 jobs for the health care and business & finance sectors and fewer than 2,500 jobs in the IT and skilled trades sectors.
You can access this data via the regional breakdown in the occupation report or the mapping section in Analyst.
Other compelling regional analysis ideas: the most concentrated areas for mid-skill jobs in these sectors, something you can do using location quotient; the metros with the highest wages for these jobs; or the metros with the highest regional competitive effect in these mid-skill sectors, using shift share.
For more on EMSI’s employment data—available at the county, MSA, and ZIP code level—or to see data for your region, email Josh Wright. Follow EMSI on Twitter (@DesktopEcon) or check us out on LinkedIn and Facebook.