The news of a layoff is not something any community wants to hear. But to be prepared, local leaders should take advantage of the wide array of data, information, and resources to help the local workforce quickly find new jobs, train for what is in demand, and understand what direction the local economy might take.
When these resources are in a local planner’s toolbox, the region will be more prepared and ready to act when the inevitable happens. Instead of being afraid of economic change, communities should seek to use it to their advantage. They can take encouragement from other communities that turned seeming impossible situations into great opportunities to transform the shape and character of their local economy and workforce.
To learn more, check out the following case studies:
1. Iowa town reshapes its economy with data-focused plan
2. Threats of job loss lead to innovation in south central PA
Finally, when working on regional strategies, always remember the old adage, “What’s good for the goose isn’t always good for the gander.” It is all too common for regional workforce and economic development strategies to be based on anecdotes, trendy issues, or vague terminology. This means that it is vitally important to know and understand your region, which means really understanding what the data is telling you.
In this case “the data” is information about your region’s employers (trends and stats related to industries) and people (trends and stats related to your region’s occupations, demographics, education, and skills). This will ultimately help you make better decisions and avoid derailing your current efforts for the newest trends, which ultimately might be the wrong thing for your region to chase. Conversely, the data can also reveal that the current hot trend is something that your region could certainly chase. A lot of communities have begun to capitalize on the “green jobs” issue because they already have a good industry and occupation mix that can adjust itself to this need.
If you have any questions or would like additional help crafting a strategy for your region please contact us. Rob Sentz (email@example.com), 866.999.3674.
Data & Methodology
Background on Occupational Compatibility
Occupation compatibility, or the similarity between two occupation’s competencies, is another term that is crucial for interpreting career transitions. This report calculates the compatibility of two occupations by comparing their O*NET skill and knowledge profiles, which contain two scores for each competency element (e.g., Mechanical knowledge): the attainment level and the importance level. Our algorithm uses the differences between competency levels, weighted by importance levels, to determine how compatible two occupations are. Several assumptions are made in calculating compatibility:
The desirability of transitioning workers from one occupation to another decreases exponentially as the distance increases between a source occupation’s O*NET score in one category and the target occupation’s score in that category.
Over-qualification is just as much of a problem as under-qualification for a worker moving from one job to another.
The compatibility formula first adds up squared differences in every competency category and weights each difference according to its importance to the target occupation’s job requirements. This number we might call “competency divergence.” Only occupations with a divergence in the lowest quartile are considered compatible, and the maximum divergence number in this group is used to create the scale of the compatibility index, which is a number from 0 (not compatible) to 100 (perfectly compatible).
The number of job openings (over a given timeframe) expected in an occupation as the result of turnover—e.g., employees changing occupations, retiring, etc., based on national per-occupation averages. It is derived by multiplying estimated annual turnover by the number of years in the given timeframe.
Standard Occupational Classification (SOC) Code
The federal system of classifying occupations using six-digit codes. See www.bls.gov/soc/.
North American Industry Classification System (NAICS)
A system of classifying North American industries using six-digit codes.
EMSI Staffing Pattern
A staffing pattern is a way of showing what percentage of jobs in a given industry are in a specific occupation. For example, a (simplified) staffing pattern for the industry “Hospitals” might show that 10% of jobs in the hospitals industry are occupied by surgeons, 15% by general practitioners, 20% by nurses, 5% by information technology support staff, 5% by janitors, 1% by chief executives, and so on. EMSI uses regionalized staffing patterns that are available by Occupational Employment Statistics (OES) region.
Worker Adjustment and Retraining Notification Act
WARN offers protection to workers, their families and communities by requiring employers to provide notice 60 days in advance of covered plant closings and covered mass layoffs. This notice must be provided to either affected workers or their representatives (e.g., a labor union); to the State dislocated worker unit; and to the appropriate unit of local government.
Employment projections of growth or decline are not equivalent to labor market “supply” or “demand.” They are based on past trends and economists’ consensus about the near future, and cannot account for unexpected regional or national events that may affect employment. Full supply/demand analysis requires local knowledge such as can be obtained by surveying employers and job seekers.