Where I mix career information and career decision making in a test tube and see what happens

Wednesday, April 24, 2013

How Work Tasks Responded to the Recession

In a recent blog, I crunched some data to see which parts of the nation’s workforce lost the most jobs during the Great Recession and which gained the most during the recovery. I created graphs that looked at the workforce two ways: by occupational group and by industry. This week I think it would be interesting to look at the kinds of work tasks (and, by implication, the kinds of skills) that lost ground or regained it during the recent downturn and upswing.

I created the four graphs below by this method:
  • As in the previous blog, I looked at changes over two pairs of years: 2007 and 2009 for the recession, and 2010 and 2012 for the recovery.
  • I used workforce estimates from the Occupational Employment Statistics survey of the BLS.
  • For each occupation, I multiplied the workforce size at each year by the numerical ratings for the 41 generic work tasks in the O*NET database.
  • For each year and for each task, I summed the products for all occupations and summed the workforce sizes of all occupations.
  • For each year and for each task, I divided the sum of products by the sum of workforces to get an overall quotient that indicated the level at which that task was important to the nation’s workforce during that year.
  • For the recession and for the recovery, I computed the percentage change in the overall quotient for each task, thus getting a measure of how much each task became more or less salient during the recession and recovery.

(I suggest you click on each graph to see it in a format that is big enough to read easily.)

This first graph shows the work tasks that lost the most ground during the recession. Based on the types of tasks that appear here, you can see that this downturn really deserved its nickname “the mancession.” These tasks characterize the manufacturing and construction industries, which were among those hardest-hit by the slump. Note how every one of these tasks bounced back during the recovery, but not enough to make up in two years of recovery for the erosion during the two years of recession.

But some types of jobs actually gained workers during the recession, and the second graph shows the work tasks that reflect this. These work tasks, which gained the most ground during the recession, characterize the education, health-care, and government jobs that were not fazed by the downturn. However, half of these tasks proved to be countercyclical—that is, they slid downward while the economy recovered. And even those that showed gains during the recovery did not match the gains they made during recession.



Like the recession, the recovery did not affect all kinds of jobs the same way. Some jobs actually showed a net loss of workers during the recovery, and the work tasks in the following graph are those that lost the most ground during this period. Note that every one of these did quite well during the recession, but they suffered (although not to the same extent) while the economy as a whole rebounded. These tasks characterize bureaucratic and clerical jobs, which have been hurt by government cutbacks and by automation.


The last graph shows the work tasks that gained the most ground during the recovery. Many of these tasks appear as “mancession” victims in the first chart, but two of them characterize white-collar occupations. It’s especially interesting to note the job security indicated by the steady growth of work that involves Selling or Influencing Others.

Thursday, April 11, 2013

America’s Most Creative Cities and States

Many of the most promising careers involve a high degree of creativity. This is happening because in today’s economy, much routine work can easily be handed off to computers, robots, and offshore workers. A decade ago, it was mostly low-skill jobs that got lost this way, but computers have  become smart enough to make inroads into middle-skill jobs. For example, some of the research work of paralegals is being done faster and more cheaply by computer programs.

However, computers so far have demonstrated little ability to do truly creative work. To be sure, we have all seen haikus written by computers and similar machine output that seems creative. But an algorithm defines how such tasks will be accomplished, so the creative part of the process happens when the systems analyst or computer scientist devises the algorithm. As a result, the creative worker has some security from the threat of being replaced by a computer.

Offshoring is also less of a threat for a creative worker, because the United States still is home to many hotbeds of creative industries. They are geographically clustered, just as the energy-extraction industry is clustered in certain oil patches and coal belts. You can understand that petroleum extraction and coal mining need to be located where the resources are to be found in the ground, but why should creative work need to cluster? The urban theorist Richard Florida says that creative workers are most productive when they can collaborate, bouncing ideas off one another. And the communities where they tend to cluster for collaborative work tend to have research universities, a good communications infrastructure, nearby investors, a lively cultural scene, and tolerant attitudes. The United States has many communities that offer all the ingredients of this creativity-fostering recipe.

I decided to identify these highly creative geographical areas, not by looking for the presence of these ingredients, but rather by finding the presence of creative workers. To do so, I combined data from the O*NET database, which describes the characteristics of occupations, and employment figures from BLS’s newly-released Occupational Employment Statistics survey, which has estimates for May 2012.

Here’s the procedure I used.
  •  The O*NET rates occupations on the level of “Thinking Creatively” that they require. For each occupation, I multiplied this rating by the number of workers in that occupation within each metropolitan area in the United States.
  • Then I divided this product by the number of workers in all occupations in the same metro area.
  • Finally, I sorted the metro areas by this “creativity quotient” and ordered them from highest (San Jose-Sunnyvale–Santa Clara, CA: 3.38) to lowest (Ithaca, NY: 1.85).

As you might expect, these jobs tend to be concentrated in the Silicon Valley and in similar hotbeds of high-tech industries. Here are the top 20 metro areas where creative workers are clustered, listed with their creativity quotients:

San Jose-Sunnyvale-Santa Clara, CA: 3.38
Washington-Arlington-Alexandria, DC-VA-MD-WV: 3.31
Boston-Cambridge-Quincy, MA-NH: 3.21
Huntsville, AL: 3.21
San Francisco-Oakland-Fremont, CA: 3.20
Denver-Aurora-Broomfield, CO: 3.19
Austin-Round Rock-San Marcos, TX: 3.18
Hartford-West Hartford-East Hartford, CT: 3.18
Bridgeport-Stamford-Norwalk, CT: 3.17
Raleigh-Cary, NC: 3.15
Baltimore-Towson, MD: 3.15
New York-Northern New Jersey-Long Island, NY-NJ-PA: 3.14
Minneapolis-St. Paul-Bloomington, MN-WI: 3.14
Boulder, CO: 3.13
Atlanta-Sandy Springs-Marietta, GA: 3.13
Dallas-Fort Worth-Arlington, TX: 3.13
Albany-Schenectady-Troy, NY: 3.11
Charlotte-Gastonia-Rock Hill, NC-SC: 3.11
Seattle-Tacoma-Bellevue, WA: 3.11
St. Louis, MO-IL: 3.09

Here is a map that my friend Jeffrey Doshna of Temple University produced, using the data about metropolitan areas that I furnished:



I also used the same procedure to identify the states where creative work is clustered. On the map below, the darker the color of the state, the more creative work is concentrated there. (I couldn't find a colorable map with Alaska and Hawaii, but they would be among the very pale states.)



Wednesday, April 3, 2013

Real-World Data Showing Trends in Job Security

Nobody’s job is completely secure, but some jobs are more resistant than others to the ups and downs of the economy. I have written books and blogs on this subject and have also created a video about it. Now, here is some new, real-world data that provides additional insights into this matter. The data may give you some insights into what careers you might pursue or avoid.

Last week, the Bureau of Labor Statistics released estimates for employment and wages in May 2012. I decided to look at the changes in employment in various occupational groups over two time periods: the Great Recession, which for my purposes I define as the difference in employment between May 2007 and May 2009; and the recovery, which I define as the difference in employment between May 2010 and May 2012. I find these particular dates useful because they provide symmetry: Each is a two-year period, and the employment in all occupations decreased by 3 percent in the first period and increased by 3 percent in the second period.

This symmetry ceases when you look at specific groups of occupations, and that’s what makes the chart below so useful. (You can see it full-sized here.) Note that the bars indicate change in percentage terms, not in absolute terms. Keep reading below the chart for my comments on what it reveals.


As I noted, the workforce for all occupations, taken together, declined during the recession and expanded during the recovery by the same percentage. And some families of occupations show a similar behavior, more or less symmetrical. The Sales and Related occupations are a good example of how employment can fluctuate in response to how much disposable income consumers have and are willing to part with for nonessentials. The Building and Grounds Cleaning and Maintenance occupations and the Farming, Fishing, and Forestry occupations were probably responding to similar forces. Many of the jobs in these three categories are low-skill, and employers can lay off workers without worrying about how to replace them when the economy rallies. They lack security, but they have been able to bounce back.

There tends to be a higher level of skill among the Installation, Maintenance, and Repair occupations; the Arts, Design, Entertainment, Sports, and Media occupations; and the Architecture and Engineering occupations. Nevertheless, they show similar symmetrical behavior and illustrate how even some middle-skill occupations are sensitive to fluctuations in the economy. It’s interesting to note that among the Architecture and Engineering workers, the occupations that continued to decline even during the recovery tended to be either related to construction, which was especially hard-hit by a recession set off by the explosion of a housing bubble (for example, Architects, Landscape Architects, and Surveyors), or were at a middle-skill level at which workers could be replaced by automation (for examples, various kinds of drafters and engineering technicians). The high-skill engineering occupations tended to recover.

Among asymmetrical occupational groups, some lost workers both in the recession and in what should have been a recovery. One of the most extreme examples is the Construction and Extraction occupations, which were hurt badly by the overbuilding that preceding the recession. The good performance of the petroleum industry has been unable to offset the many job losses in this field. (Nevertheless, the long-term outlook for many construction occupations is considered good.) The non-recovery of the Office and Administrative Support occupations cannot be blamed on a similar sustained slow-down in business activity—for a contrast, look at how well the Business and Financial Operations occupations have recovered. Instead, the continuing job loss in this field is explained by the expanded use of office automation. Again, the middle-skill and especially the low-skill jobs are unlikely to come back.

Other asymmetrical occupational groups achieved some expansion during the recovery, but much less than what would be sufficient to restore the recession’s losses. Good examples are the Production occupations and the Transportation and Material Moving occupations. Both of these fields actually recovered quite well from the recession in terms of productivity but did not replace the large number of low-skill workers who were replaceable by automation. (Offshoring was also a major factor for Production jobs.)

A fortunate few categories actually experienced workforce growth during the recession and sustained this expansion during the recovery. These tend to be groups consisting mostly of high-skilled workers: the Management occupations; the Business and Financial Operations occupations; the Computer and Mathematical occupations; the Life, Physical, and Social Science occupations; the Postsecondary Teachers; and the Healthcare Practitioners and Technical occupations. The exception that is notable for the comparatively low skill of the workers is the Personal Care and Service occupations. This group is dominated by the Hairdressers, Hairstylists, and Cosmetologists; the Childcare Workers; and the Personal Care Aides—all of whom perform essential services that cannot be automated and are not easy to do without even during hard times. A mostly low-skill group comparable to the Personal Care and Service occupations is the Food Preparation and Serving Related occupations, which experienced only a very small loss in the recession. We Americans seem limited in our ability to switch from restaurants to brown-bagged and home-cooked meals, even during bad times.

Finally, several families of occupations exhibited what might be called countercyclical behavior: they gained workforce size during the recession but lost workers during what should have been a recovery. These include the Community and Social Service occupations and the Education, Training, and Library occupations. They are actually needed more during hard times than during good times, but because they are paid largely out of the public coffers, they tend to experience cutbacks a few years after the trough of the recession, when state and local governments have run out of rainy-day funds and stimulus support from Washington. They may be expected to recover as tax receipts start returning to normal levels with the acceleration of business activity, but the anti-tax climate that now dominates many parts of the nation is likely to continue to hobble these occupations. Something similar accounts for the lackluster recovery of the Protective Service occupations.

It may seem puzzling to find the Healthcare Support occupations showing countercyclical behavior, especially in contrast to the recession-be-damned growth of the Healthcare Practitioners and Technical occupations. Actually, most of the occupations in this group, with middle-skill workers such as Occupational Therapy Aides, Massage Therapists, Dental Assistants, and Medical Assistants, showed continuous growth during both time spans. What has dragged down this group as an aggregate is the nearly one-million-strong Home Health Aides, who have not recovered like the Personal Care Aides. Home Health Aides are low-skilled workers who are easy to replace when a fully recovered economy justifies hiring. Both of these care-aide occupations are projected to grow by about 70 percent between 2010 and 2020.

The take-away lesson from this chart is very similar to what I concluded in a recent blog about a similar but less-detailed chart: Your best bet is a high-skill job, or at least a middle-skill job that is difficult to automate (such as many in health care), because these not only pay well but tend to thrive in both good times and bad times. 

Our society has not yet devised a way to prevent future economic downturns. Nor are we able to agree on and apply what it takes to shorten them, as the experience of Europe (and, to a lesser extent, the United States) shows. Given that a roller coaster economy seems to be here to stay, doesn’t it make sense to pursue a career that has comparative security?

Update, following the 4/5/13 release of data from BLS: Here's a graph from the Washington Post site that uses lines instead of bars, a slightly different set of industries, and a slightly different timescale. But it shows the same trends.


Another update, because manipulating data is fun: This graph uses the same method as the bar graph above, but (like the Washington Post graph) it represents industries.