The following has been adapted from EMSI’s recently published guidebook, Contextualizing Real-Time and Traditional Labor Market Data. To download the full report, visit this page.
It’s a simple point. Job posting analytics (or real-time labor market information) come from job advertisements posted online. This means that some occupations are going to be underrepresented and others overrepresented in postings, based on how businesses recruit talent. We can see this clearly when comparing postings to hires by occupation—something EMSI does in our labor market research tool, Analyst, by bringing together data from Quarterly Workforce Indicators with industry-to-occupation staffing patterns and other data.*
In general, companies hunting for, say, information technology talent lean heavily on job postings. The same goes for professional services and managerial positions. But the skilled trades? Not so much. Take electricians. Average hires for electricians exceeded 52,000 per month from November 2013 to October 2014, easily the most hires over that time of any skilled trade. But employers posted just 4,500 unique postings for electricians per month during that timeframe. That means average monthly hires outnumbered postings 11.5 to 1.
| Occupation | Avg Monthly Postings (Nov 2013 - Oct 2014) | Avg Monthly Hires (Nov 2013 - Oct 2014) | Hires-to-Postings Ratio |
|---|---|---|---|
| All Skilled Trades (Some Occupations Excluded Above) | 27,249 | 140,118 | |
| Electricians | 4,548 | 52,344 | 11.5 |
| Welders, Cutters, Solderers, and Brazers | 3,774 | 23,729 | 6.3 |
| Machinists | 4,381 | 18,927 | 4.3 |
| Industrial Machinery Mechanics | 5,553 | 14,597 | 2.6 |
| Cutting, Punching, and Press Machine Setters, Operators, and Tenders, Metal and Plastic | 736 | 10,607 | 14.4 |
| Electrical and Electronics Engineering Technicians | 5,517 | 5,675 | 1.0 |
| Computer-Controlled Machine Tool Operators, Metal and Plastic | 2,192 | 4,427 | 2.0 |
| Maintenance Workers, Machinery | 61 | 3,859 | 63.6 |
| Multiple Machine Tool Setters, Operators, and Tenders, Metal and Plastic | 60 | 3,207 | 53.1 |
| Tool and Die Makers | 427 | 2,747 | 6.4 |
Companies searching for electricians and other trade positions are unlikely to advertise online, largely because many prospective candidates don’t look on the Internet for jobs. It’s also worth mentioning that some companies will advertise for one job and hire multiple people off of a single listing. Postings vary by educational level, too. The Georgetown University Center for Education and the Workforce, in a technical report last year, noted that only 30% to 40% of job openings for workers with some college or an associate degree were posted online, compared to 80% to 90% of openings for workers with at least a bachelor’s degree.
National postings trends are illuminating. But since there’s so much regional variation with postings, it’s also useful to look at specific examples from metro to metro.
Examples of Underrepresented Jobs
From November 2013 to October 2014, Houston was the top metro area in the nation for welder job postings. Employers posted an average of 200 unique job ads for welders, more than double the number in Chicago or Los Angeles.
Yet all that posting activity in Houston pales in comparison to the number of welding hires—an average of more than 1,000 per month. So for every posting, companies hired five welders (see chart below).
Underrepresentation in postings isn’t isolated to welders and other skilled trades positions. Consider the case of dental assistants in Seattle, where there were six average hires for every unique posting. The significantly bigger number of hires than postings could stem partly from the churn among dental assistants, some of whom could be seeking to become dental hygienists. From 2010 to 2013, the churn rate for dental assistants averaged 51%, lower than the average for all occupations (68.1%) but still significant. These examples illustrate why you wouldn’t want to use postings data by itself to assess occupations that are growing, shrinking, or stagnant.
Examples of Overrepresented Jobs
When postings far outweigh hires, it could indicate actual vacancies in the market. That seems to be the case with heavy and tractor-trailer truck drivers and possibly registered nurses and select IT occupations.
But more postings than hires doesn’t always indicate a skills gap. It could have something to do with the way postings are de-duplicated or priced; if the cost drops dramatically, employers could flood the market with listings. These fluctuations in the market are one reason postings are more volatile than average hiring. (Postings data is also volatile because of several artifacts in the nature of the data itself: It is difficult to collect, clean, de-duplicate, and then normalize and categorize by company and position.)
For these and other reasons, it’s hard to draw concrete conclusions from the data alone. Local knowledge and information from businesses is very important in these cases, but you can also investigate further with data.
For example, unique monthly postings for web developers in the New York City metro outnumbered average monthly hires 8 to 1 from November 2013 to October 2014. Now, this could signal that employers can’t find the developers they need, but also consider that there were nearly five times as many postings as hires nationwide for web developers over this time. And tech occupations in general are heavily represented in postings. Employers in New York City haven’t posted as intensely for web developers as the metro average for all occupations; if they had, it might suggest they’re facing a talent shortage. But as it is, New York City appears to be oversaturated with postings for developers.
The same goes for database administrators in the Washington, D.C., metro. There were 737 unique postings from November 2013 to October 2014 and just 269 average monthly hires. Like web developers, this trend plays out nationally for database administrators. It’s also worth noting that postings tend to be concentrated in major metro areas like D.C. and New York City.
In Provo-Orem, Utah, a small but still bustling tech metro, the script was flipped for database administrators and web developers—there were more hires than postings for both occupations, albeit at a much smaller scale (16 hires to 9 postings for administrators and 57 hires to 55 postings for developers).
*EMSI has developed staffing patterns by age and gender to account for different churn (hires and separations) for each occupation within any given industry. Further, we annualize hiring data and divide by 12 to arrive at monthly averages.
To learn more about EMSI’s job posting analytics and our approach in contextualizing JPA and traditional LMI, download our guidebook or email Josh Wright (jwright@economicmodeling.com.)
There is no doubt that some occupations, and some industries, are underrepresented in the online job posting data. All of the providers of such data acknowledge the limitation. The reason it is valuable none-the-less is that it serves as a proxy for real time demand absent any better, or more comprehensive source. The LED QWI are also a useful data set, with their own strengths and weaknesses. While it is not explicit in this article, I suspect that you have taken the industry new hire numbers from LED and run them through an industry-occupation matrix to ‘occupationalize’ the new hire data set. Technically quite possible, as you have likely proven here. However this approach assumes that real time hiring (flow) matches a labor market at equilibrium (an industry staffing pattern, which is a stock concept). It would be extraordinarily unusual for a company to be hiring at any point in time in the same percentages represented by each occupation in an industry staffing pattern; which is, by definition, an occupational employment distribution of an industry at equilibrium and thus represents a long term balance and not a representation of short term occupational demand. Given the caliber of EMSI talent I suspect you already know this and thus it is disingenuous to write an article that emphasizes the limits of one data set (online postings) without similarly representing the most spurious part of the analysis which is the application of static staffing patterns to industry new hire data and passing it off as an efficient proxy for real time hiring flows.
Richard, thanks for your comment. We totally agree that applying static staffing patterns to industry hires data is problematic and we have accounted for that in our model. In short, we’ve developed a staffing pattern by age and gender, which winds up accounting for different hires and separations for each occupation within any given industry.
We should have been more clear in the article and our report that our data is modeled. We’ll go back and make sure to include discussion about this weakness in this and future articles. That said, with both postings and hires by occupation, the data is either imperfect or does not exist via public sources. Postings only represent a slice of total job activity and hires by occupation are a modeled number. Also, instead of thinking of hires as a proxy for real-time hiring flows, we view them as a way to provide much-needed context with postings, which can fluctuate for a long list of reasons, some of which we explore in our report.
Again, thanks for bringing up an important point. Please feel free to contact us (my email is jwright@economicmodeling.com) if you’d like to further this discussion.