Want to know what is going on in key clusters of our region’s job base (i.e. those targeted by workforce professionals)? Better yet, do you work in the workforce or economic development space yourself? Well (as for other topics like Census data, forecast data, comparative metro data, and small-area mapping), do we have a visualization and analysis tool for YOU!

The CareerRise Economic Mobility Dashboard presents labor demand and supply data for the 10-county metro Atlanta region, also known as the Atlanta Regional Commission (ARC) region. The tool aggregates private sector data to summarize key elements of our workforce ecosystem, by profiling job and worker characteristics. It focuses on the characteristics of ‘top jobs’ within five high-demand industry clusters, as identified by and in the WorkSource Atlanta Regional Plan, and also as refined by CareerRise outreach efforts (via the Metro Atlanta Industry Partnership) to local employers. These high-demand clusters are Transportation, Distribution and Logistics (TDL), Healthcare, Information Technology (IT), Advanced Manufacturing, and Skilled Trades. Important note: you will also be able to get a lot of the same information for occupations NOT within the five clusters–that is, for the region’s largest occupations (those with 1,000 or more jobs).

How to get the most out of this resource? As you see below, and as you will see better when you click on the image and go to the actual tool, the dashboard is organized by tabs down the left of the page. Each tab provides information on particular groupings of job and occupation characteristics. The user has the ability to focus on these groupings either across all occupations (as mentioned above) or in any of the five high- demand clusters. While the tab titles are pretty self-explanatory, be sure to check out the Data Explorer tab (to the top right of the page). There you will be able to access the nuts and bolts, allowing the user to drill down into pre-pandemic and initial pandemic period growth patterns for specific large occupations and distinct top jobs within each of the five high-demand clusters.

So what are some highlights/takeaways that give you a preview of what you will find on the dashboard. The most important finding is that as far as defining clusters on which to focus training, recruitment, and retention, we are looking in the right place. Chart 2 below summarizes these trends economy-wide and by cluster. Pre-pandemic (2010-2019), job growth in all but one of the clusters exceeded that seen across the entire economy. In a period including the initial impacts of the pandemic period (Q2 2019 through Q2 2020), while the economy’s overall job base declined by eight percent, jobs grew in three of the five high-demand clusters.

Chart 3 moves on to comparative wages, and shows that the high-demand clusters, with the unsurprising exception of IT, have average wages below that of the broader economy. On the plus side of this, these relatively low average wages do indicate that certain occupations within the clusters may have a relatively low barrier of entry. Then, with the Data Explorer portion of the tool, we can see the wide variation in average and median wages within the high-demand clusters, by specific top job—which speaks to the viability of earnings progression through top jobs (and experience levels) by cluster. For example, in the Skilled Trade cluster, median wages range from $21,000 for brick mason helpers to $97,000 for construction managers.

So overall cluster trends look pretty good, but as in so many socioeconomic variables, the details can be devils. Equity in opportunity across race and gender has always been and remains a challenge.

Chart 4 compares racial distribution of the general workforce to related shares within the high-demand clusters. And here, the picture is better than might be expected.  The worker pool in the high-demand clusters, with the exception of Skilled Trades, is more diverse (i.e., a relatively higher share of minorities) than that in the broader economy. These relatively high shares suggest that an equity goal for training within these target clusters may be relatively easier to achieve than in other segments of the economy. Yet, looking at the Data Explorer tab we can find some areas of real concern v.v. equity. In IT, non-whites have a higher share than Whites only in one job title: Software Developer jobs—and that is driven by 40% Asian workers across that entire occupation. In the Skilled Trade cluster, no top job has under a 60 percent share of White employees.

In Chart 5, though, we see that gender distribution in the target clusters is anything but balanced. In the high-demand clusters, with the exception of Healthcare, the share of male employment is far higher (and female share lower) than in the broader economy. A dive into the Data Explorer tab shows the gender variation within the high-demand clusters. And in TDL, only in Packaging do we find a majority-female workforce. Conversely, in the Healthcare industry, only for EMT/ paramedics is the share of males over 30%.

In closing, some key things to know: (1) all these data are aggregated and analyzed by ARC Research & Analytics and Neighborhood Nexus; (2) the raw data is sourced in the case of supply data from JobsEQ®   and demand (job postings) information from BurningGlass® Labor Insight –both of which are proprietary, private sector tools. Data are in most cases from various quarters in 2020 (primarily the second and third quarters) as well as for comparable periods in 2019 and earlier years.

Want to know more about Atlanta jobs and workers? Be on the lookout for a Regional Snapshot on these and many other workforce issues in our region. And check out some of our recent blogs: on labor force participation and educational attainment.