1 2 3 4 5 What do we need to get skilled workers? 6 7 8 As we begin to develop career pathways, the first question is where to start? Do we begin by offering training that seem like they would be a good fit and assume that if we deliver it well, then we are helping jobseekers? Or do we being with a more detailed analysis of the labor market as a whole? 9 That was a rhetorical question, though you would be surprised how many educators like to lead with the education piece instead of analyzing what the market needs. Speaking of the labor markets, let’s jump into a quick overview of what I mean by that, and how it will inform the way we develop career pathways that will lead to long term success for our jobseekers. 10 11 If you remember anything from your econ classes in college you will remember some of this. 12 LMI data ends up producing three perspectives on the economy: Industries, Occupations, and Programs. 13 Industries are groups of businesses that do or produce similar things. 14 For example, McCoy Plumbing is a small local business in Moscow that is classified, along with all other similar businesses, in the industry of “plumbing, heating, and air-conditioning contractors.” When we look at industry data for Plumbing Contractors, we see job counts, earnings, and job change for ALL of those businesses together. And those numbers also reflect ALL of the jobs working there, from the owner to the journeyman plumber. 15 Occupations are those specific job titles. When we look at the labor market from the occupation perspective we can get detailed information on those jobs, regardless of what industry employs them. 16 “Industry Staffing Patterns” – all the occupations that work for a specific industry sector (janitors to CEOs) 17 Last, programs are fields of study offered by colleges, universities, and other training centers. By connecting occupations to the programs that train for them, we can see how well regions are supplying trained workers to their economy. There are about 1700 different program codes (CIP) in use. 18 Those are the three perspectives on the labor market. A “job” can describe industry employment, like people working in hospitals. It can describe the actual occupation they work in, like nurses or lab techs. And it can also describe the jobs that are connected to specific training, so something like 30 jobs are connected to the training programs in your town. 19 20 More and more they’re being aggregated and sorted by third parties, who try to make analytical data out of the info found in the ads. 21 Here’s how to think about how those two work together: Data is like a map. It’s an abstraction of reality. It’s good for telling you certain things, and worthless for telling you other things. In this case, LMI helps us get a great structure in place like this map of the entire US. This map is good for showing where the borders are, the shape of the country, and how big Texas is compared to Michigan. It’s bad for telling us what the streets look like in Seattle. This map is good for knowing where the states are in relation to one another. It’s great at telling us how big Texas is compared to Michigan, but it’s worthless for telling us how to drive from Portland to Seattle. 22 This is a map of Washington state. It’s good for showing us the major cities in the area and the major highways connecting them. We can also see where there are national forests, mountain ranges, and rivers. But this map doesn’t tell me what the houses look like in Seattle, or how long it might take to drive from Yakima to Phoenix. As the scale changes, you lose stuff on both ends, fine detail and big picture. But each map is still useful for certain things. 23 Here’s a Google Street View image. The level of detail in this is amazing! I can tell you what kind of tree is growing in the front yard, or if your driveway needs to be repaired. But at this level, we lose a lot of the context because we don’t know where we are in relation to other things. Google has got the right idea by giving you that context with an address (top left) and a minimap (bottom left). The point is that none of these maps are better or worse than the others, they just specialize in different things. 24 Google street view is REALLY detailed and shows us what a street in Seattle looks like. But that map isn’t good for other things, like knowing where the closest coffee shop is, or how long it will take to get to Portland. And as cool as Street View is, it’s not available everywhere. Job postings are a lot like this. They can provide astounding detail, but it’s often not clear where you are in the big picture. And they’re not available everywhere for every job. 25 The point is that you wouldn’t say one of these maps is better than other one. They’re useful for different things, and we think LMI and postings are both important enough to be used together. 26 For our workflow today, we will be talking mostly about EMSI’s proprietary data set which is a synthesis of about 90+ data sources which has been built to overcome some of the flaws of the publicly available data you have access to. EMSI’s numbers will be different (and we think far superior) than the public sources you can access for free, but you should know that everyone has access to the public sources they use. 27 Using the order of the labor market that we just talked about, let’s fill in the initial questions that will drive out career development process. 28 Going back to this slide again, this should make a little more sense now. IF we start with training, and build programs for jobs that are not in high demand, then we’re setting up jobseekers for failure, and we’ll end up seeing them cycle through the system again. 29 Here’s a look at the basic process we will follow. Chances are that you know who the most important businesses in your area are, then we sort for all the jobs paying more than a certain threshold, find the jobs in highest demand in terms of openings, look for commonalities between them that we could leverage, and then review with employers 30 Again, you probably know who these are, but it never hurts to look at the actual data on this point. You might be surprised at what you find, especially in larger metro areas. Look for industries with the highest Earnings and growth rate, high location quotient, high competitive effect and components of STEM. 31 Annual openings include new jobs due to growth (end year minus start year all divided by number of projected years) as well as replacement jobs (based on published replacement rates by occupation). 32 33 34 35 There are some exceptions to this rule. 36 37 38 39 40 41 42 43 44 Technical support includes professional services: lawyers, architects, etc. 45 46 47 48 49 50 51