Occupations vs. Jobs, Part II

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 middleclass 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

Rank

Occupation, SOC Code

Projected Percent Change

In 2000 SOC

1

Personal care aides, 39-9021

70.5%

X

2

Home health aides, 31-1011

69.4%

X

3

Biomedical engineers, 17-2031

61.7%

X

4

Helpers: brick masons, block masons,

stone masons, and tile & marble setters, 47-3011

60.1%

X

5

Helpers: carpenters, 47-3012

55.7%

X

6

Veterinary technologists & technicians, 29-2056

52.0%

X

7

Reinforcing iron & rebar workers, 47-2171

48.6%

X

8

Physical therapist assistants, 31-2021

45.7%

X

9

Helpers: pipe layers, plumbers, pipe fitters, and steamfitters, 47-3015

45.4%

X

10

Meeting, convention, and event planners, 13-1121

43.7%

X

 

The 10 Occupations with the largest projected employment growth, 2010-20 (in thousands)

Rank

Occupation, SOC Code

Projected Number Change

In 2000 SOC

1

Registered nurses, 29-1141

711.9

X

2

Retail salespersons, 41-2031

     706.8

X

3

Home health aides, 31-1011

706.3

X

4

Personal care aides, 39-9021

607.0

X

5

Office clerks, general, 43-9061

489.5

X

6

Combined food preparation and serving workers, including fast food,

35-3021

398.0

X

7

Customer service representative, 43-4051

338.4

X

8

Heavy and tractor-trailer truck drivers, 53-3032

330.1

X

9

Laborers and freight, stock, and material movers, hand, 53-7062

319.1

X

10

Postsecondary teachers, 25-1000

305.7

X

     

The 10 Occupations with the largest projected number of total job openings due to growth and replacements, 2010-20 (in thousands)

Rank

Occupation, SOC Code

Projected Number Change

In 2000 SOC

1

Retail salespersons, 41-2031

1,958.7

X

2

Cashiers, 41-2010

1,775.9

X

3

Waiters and Waitresses, 35-3030

1,324.3

X

4

Registered Nurses, 29-1141

1,207.4

X

5

Combined food preparation and serving workers, including fast food,

35-3021

1,146.5

X

6

Office clerks, general, 43-9061

1,011.5

X

7

Laborers and freight, stock, and material movers, hand, 53-7062

980.2

X

8

Customer service representative, 43-4051

959.6

X

9

Home health aides, 31-1011

837.5

X

10

Janitors and Cleaners, Except Maids and Housekeeping Cleaners, 37-2011

682.0

X


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.

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christopher laubenthal

Christopher Laubenthal is a program officer in Education for the Ewing Marion Kauffman Foundation, where he works to explore topics around data, education, and human capital through grants and programs.