Choosing a Career: ASAP
Issue
I discuss the results of our economy from both an employment and unemployment perspective on a regular basis on this blog. It is fine to record and analyze market results, but how as individuals can we have a proactive positive impact on our careers; whether looking for new employment, enhancing our current situation, or looking toward future career objectives?
Process
Colleague and career consultant Gary Crossley has written a short article (PDF) about choosing a career. This four step process is about taking a measured approach to selecting the career right for a person. Using Assessment, Skills, Analysis, and Preparation (ASAP) can only lead to a more logical approach to discovering your best path to an interesting career field. (Workforce Links PDF)
Labor Market Information: An Overview
Issue
My friend and business associate Gary Crossley provides labor market information (LMI) to a variety of organizations nationwide. Recently he sent me one of his overview presentations to post on Moore Data.
Most analysts believe they have a grip on labor market data, but what Gary and I find is that this is not so. The reality is analysts tend not to stray very far from unemployment statistics, rarely giving any weight to other key data sets that fill in the labor market knowledge gap. Below is Gary’s presentation. I have provided the appropriate links to corresponding Bureau of Labor Statistics (BLS) web sites.
Presentation Analysis (PDF)
Slides 9-11: Employment: Provides a general definition for employed and unemployed along with basic calculations.
Slides 14,15: Quarterly Census of Employment and Wages (QCEW) discusses data sources and uses of data.
Slides 17,18: Current Employment and Statistics (CES) discusses data sources and uses of data.
Slides 20 – 23: Occupational Employment Statistics (OES) discusses data sources, uses and programs.
Slides 25, 26: Local Area Unemployment Statistics (LAUS) discusses data sources and uses of data. For detailed calculation of unemployment see Unemployment Calculation Post.
Slide 28: Mass Layoff Statistics (MLS) discusses general program.
Slides 29, 30: These slides provide a link to the Bureau of Labor Statistics Handbook of Methods. This is a particularly good resource for analysts. The handbook not only describes method, but also what programs use these data as an engine to drive their information programs. The handbook also provides a detailed account of data limitations, which is a plus when determining appropriateness of data for various uses. Slide 30 provides a nice “cheat sheet” to the different data sets.
Slides 33 – 45: These slides list the different entities that provide data portals. Of course you can analyze these data yourself, but the challenge is understanding the nuances of the data so that one does not come to the wrong conclusion.
Slides 45 – 47: These slides touch on supply and demand, training, and military data. However, one of the more interesting and eye-opening data sources is the Census data set of military service-related disabilities. These data can be found at Veterans.
Conclusion
LMI data is readily available on the web. Most competent analysts will use two or three data sets attempting to triangulate to find the “answer.” Gary has provided a basic reference that will assist the user in thinking about the different data sources and how they may help you answer your labor market question. Thanks Gary.
“Green” versus “Conservation” Jobs
Green Jobs: The Issue
I likely am one of the first and few to question the reality of “Green“ jobs. The current employment buzz focuses on jobs in Green industries, even though no definition exists for these industries or jobs for that matter. There have been a number of attempts to identify the “Green” job. Below is one example from the State of MN:
“We’re moving to a green economy, a sustainable economy. Energy in the future will come from the sun, the wind, the waves and even algae. There will be a need for trades-people who understand conservation, attorneys who comprehend cap trade, energy employees who “get” smart grids and co-generation.”
No real definition there, rather a list of principals all well and good. However, note in the last part of this statement there is a small reference to conservation!
Conservation is where the Money is, thus the Jobs will follow!
Recently I reviewed the sources and uses of energy, industries which define part of the Green job discussion. It occurred to me while viewing this graphic that we in the U.S. are missing a huge opportunity to conserve- currently not necessary part of our history, popular culture or thinking. But how can conservation create jobs, and lot of them. The answer lies through gains in productively and thus market shares.
The Formula
This is an old rule in manufacturing (knowledge and skill sets we are losing in this country). If one can make a product or deliver a service at less cost, market share will follow as long as there is demand. As you note from reviewing the graphic, the immediate opportunity available in “green” industries is small. Contrary to common knowledge, is the market opportunity to reduce wasted energy. If we were able to conserve/reduce waste, the result would be increased efficiency leading to higher productivity and ultimately increased market share the final result being significant numbers of new jobs.
The BLS is starting the process of defining the green job. I wish them luck. In the mean time it will be interesting to see where the real jobs show up in our economy- I am putting my bet on where the money is.
Workforce Professionals and O*NET
International Association of Workforce Professionals (IAWP)
O*NET Data Set (xlsx)
Presentation (ppt-pdf)
Presentation Live Live Scribe
The Occupational Information Network (O*NET) is sponsored by the US Department of Labor/Employment and Training Administration (USDOL/ETA). I primarily use O*NET as an engine in other databases I develop. I also use O*NET data as the basic building blocks for statistical analysis on a variety of subjects. The data is found in the developers corner on the O*NET site.
This post compares O*NET data bases over the past seven years. In particular, we wanted to know if there was a significant difference in occupational educational requirements from 2003, (version 5) compared to 2009 (version 14). We did not complete any specific statistical significance calculations, mainly as a result of sample size; however, the results are interesting.
Example
We used Dental Assistant as our test sample. We found that there was a decrease in the number of persons which worked as Dental Assistants with only a high school degree in 2009 versus 2003. To off-set that decline there was an increase in Post High School Certificates, AA, and Masters Degrees. See Data Sets, Dental Assistant Calculation.
This indicates there is a different level of education required in 2009 than 2003 for this occupation. O*NET does not tell us why the variance which could be related to increased competition, new technology, certification requirements or other regulatory (insurance) requirements.
For person that work with other trying to locate new work, this is a good place to determine if the applicant has the necessary educational requirements to compete in the job market. For you convenience, I have completed all the calculations on occupations re-surveyed by O*NET between these two time periods. As a result of limited funding, O*NET has only updated approximately 50 occupations. This limited data, still provide UI and human resource persons general guidelines to the change education requirements in both service and goods producing occupations.
O*NET Work Activities Report
For some increased education is not an option. For UI staff, O*NET provides a variety of useful tools to access an applicant’s compatibility with new and likely different work. One such too is the CUSTOM report. See presentation slides. The customer report allows for an analysis of O*NET Descriptors, and in particular WORK ACTIVITIES. When we check work activities and related occupations, and select GO, at the bottom is a list of occupations, with similar work activities and the occupations outlook. This is just one example of the many ways O*NET can assist in breaking through the skills gap barrier.
Developers
If you are a developer, especially in the area of human resources, take another look at O*NET data. I believe you will find it intuitive, relational, and compatible with most of your current database designs enabling you to assist your HR staff and company being more affective in resource development and allocation.
O*NET
The Occupational Information Network, (O*NET) is being developed under the sponsorship of the US Department of Labor/Employment and Training Administration (USDOL/ETA) through a grant to the North Carolina Employment Security Commission which operates the National Center for O*NET Development.
This particular data base is especially useful to private business as a result of the DOL making the process and data open to the public (Developers Corner). In particular I have used the developers corner to match occupation characteristics to private industry databases.
Example: Using the occupation Registered Nurse, go to the home page of O*NET and for the keyword type “Registered Nurse” (RN). Click on RN, click on details, and go to work activities. Here is an objective measure of the top work activities for a RN, based on a statistical sample of RN’s. Note the different categories that define RN work. These data are then available in their raw form for analysis (Sample PDF) . These data are relational, therefore they can be connected through an MS Access or sql database to your organizations data.
With this information one is able to identify unique occupational criteria allowing the organization to create employment ladders, compensation plans based on skills and abilities, and of course training programs. More importantly the data can be linked to safety programs, production, or other metrics used to operate the business. The possibilities are endless.
In addition to those applications, the data can be used as a research tool to forecast occupational training and education based on industry growth or change. Here is an example of O*NET data used by the State of Minnesota in their Occupations in Demand Model (OID).
