Occupations vs. Jobs, Part II
This blog post originally appeared in Growthology on December 12, 2014 and is posted here for posterity and portfolio purposes.
In a continuation of my previous post on the difference between jobs and occupations when technology is concerned, let’s consider what current researchers think about technologies’ impact on occupations. The consensus is that there is and has been some impact. Responses can be categorized, broadly, by severity. An example of a measured response is the 2005 book The New Division of Labor: How Computers Are Creating the Next Job Market written by Frank Levy and Richard J. Murname.
The New Division of Labor: How Computers Are Creating the Next Job Market outlines the impact of computers on the workplace and where humans still fit into that picture. Levy and Murname are quick to identify that computers excel at rule-based thinking — executing a set of actions, given a set of rules. A problem easily addressed by rule based-thinking would be processing low level banking requests or summation of large groups of numbers.
They argue that the implementation of computer and rule-based thinking has affected: employment, the mix of jobs, the distribution of wages, and which skills are important. The cumulative effect of these changes has been a hollowing out of middle skill jobs. When middle class or middle-skill jobs disappear, people move into either menial labor or high skill jobs. Ultimately it is the last change, in which skills are important, that provides an opportunity for humans.
The weakness of rule-based thinking is that it does not address unanticipated problems not programmed into the system, nor does it do well with uncommunicative insights, e.g. those things humans know, but cannot articulate. This is where humans are superior and where the focus of skills has been shifting. Humans fill this window with expert thinking and complex communication. Levy and Murname flesh out expert thinking as pattern recognition and case-based reasoning, using schemas to sort and link data and metacognition to know when to drop a strategy or line of thinking. Complex communication is about building understanding, negotiating outcomes, and managing people.
For Levy and Murname, it is more likely that jobs will disappear than appear and that topical changes to tools or tasks within an occupation will be far more likely. This moderated approach also appears in a 1999 Department of Labor study on the future employment, Futurework , “In the midst of the creation of these new high-tech jobs, most current jobs will endure, albeit in altered form…The fundamental skills used by these workers will endure but they will also need new skills to function effectively.”
A more severe view can be found in myths populating the internet and becoming established in public conversation. The myth reads, “The Top Ten [descriptor] Jobs in YYYY did not exist in XXXX.” This myth seems to come from The Jobs Revolution: Changing How America Works by Steve Gunderson, Jones Roberts, and Kathryn Scanland, “Former Secretary of Education Richard Riley recently noted that none of the top 10 jobs that will exist in 2010 exist today, and that these jobs will employ technology that hasn’t yet been invented to solve problem we don’t know.”
Andrew Old, in his British blog Scenes from the Battleground, provides the logic to address this statement. He pulled a qualitative listing of in-demand jobs for 2009 from an HR magazine. The jobs on the list were obvious enough to be in existence from 2004 if not earlier. Using information from the Bureau of Labor Statistics (BLS) we can verify his counterargument.
Addressing this myth hinges upon the definition of “in demand” or “top,” first let’s look at defining “top” in terms of fastest growing. The argument would be that the fastest growing jobs of 2010 did not exist in 2014. To get this information we access the BLS Employment Projections 2010–20, which BLS describes as a tool for “high school students and their teachers and parents, college students, career changes, and career development and guidance specialists.” The intended audience gives an extra level of credence to this data, because the Richard Riley quote and its derivations are in the context of life planning and preparation.
Below is a table of the top ten BLS-projected “fastest growing occupations” for 2010 and 2020 in the United States. The occupations were then cross-referenced against the 2000 Standard Occupational Classification System. If the occupation was listed in both areas, it would argue against the myth. As the table will show, all of the “fastest growing occupations” in 2010 existed in 2000.
Within the same 2010 report there was a measure for “largest projected growth” and “largest projected number of total job openings due to growth and replacements.” When combined with the “fastest growing occupations,” these three measures comprise what the Occupational Information Network (O*NET) uses to label an occupation “Bright Outlook.” Given their O*NET status and the fact that these measures identify largest growth and openings; they could also be employed as a definition for “in-demand.” Tables from these measures are also below, and they too include occupations found in the 2000 Standard Occupational Classification System. Please note that repeat occupations (appearing on multiple tables) have been bolded.
The 10 Occupations with the fastest projected employment growth, 2010–20.
The 10 Occupations with the largest projected employment growth, 2010–20 (in thousands).
The 10 Occupations with the largest projected number of total job openings due to growth and replacements, 2010–20 (in thousands).
It is apparent that the 2010 projections show the fastest growing, largest growing, and most hiring occupations have not only been in existence since 2000, but frequently much before that time.
Just for example, the field of Carpentry in America has been around since the time of the colonies and much longer than that outside of America. These tables and this example speak to the ability of technology to update skills, augment tasks, add or change tools, and even remove jobs, but perhaps not wholesale create them.
Myths like this one are pervasive and can create a bullish view on the impact of technology. Namely that technology leads to massive growth in new jobs or even new occupations, which is simply not the case. Skills or tasks may rise and fall, jobs may get new titles and tools, but ultimately change is incremental and slow.
