Where Occupational Birds of a Feather Flock Together

Last week, I blogged about metropolitan areas where the health-care industry dominates. I looked at the trends in the average earnings of health-care professionals in those metros and, for comparison, the trends for health-care professionals nationwide. I found that the Great Recession had no effect on nationwide earnings—they continued to increase at a steady pace—but in the metros where health-care professionals are concentrated, these workers’ earnings took a noticeable dip during the recession years.

I thought it would be interesting to look at some other occupational categories and see how their experiences compared. So I turned to the same database, the Occupational Employment Survey, and identified the metros where occupations in education exceeded 10 percent of the wage-and-salary workforce; where computer and mathematical occupations exceeded 5 percent; and where engineering occupations exceeded 4 percent.

Here are the metros where these occupations dominate:

Metro Area	Education Occupations
Ithaca, NY	15.5%
Gainesville, FL	13.3%
Hinesville-Fort Stewart, GA	13.2%
Champaign-Urbana, IL	12.9%
Corvallis, OR	12.8%
Blacksburg-Christiansburg-Radford, VA	12.3%
Merced, CA	11.7%
Lafayette, IN	11.5%
College Station-Bryan, TX	10.8%
Auburn-Opelika, AL	10.7%
Ann Arbor, MI	10.6%
Athens-Clarke County, GA	10.5%
Yuba City, CA	10.4%
McAllen-Edinburg-Mission, TX	10.4%

Metro Area	Computer & Mathematical Occupations
San Jose-Sunnyvale-Santa Clara, CA	11.6%
Washington-Arlington-Alexandria, DC-VA-MD-WV	7.4%
Boulder, CO	6.7%
Seattle-Tacoma-Bellevue, WA	6.6%
Huntsville, AL	6.5%
Durham-Chapel Hill, NC	6.2%
Austin-Round Rock-San Marcos, TX	5.7%
Trenton-Ewing, NJ	5.5%
San Francisco-Oakland-Fremont, CA	5.5%
Madison, WI	5.5%
Raleigh-Cary, NC	5.2%
Colorado Springs, CO	5.1%

Metro Area	Engineering Occupations
Huntsville, AL	8.3%
Columbus, IN	8.1%
San Jose-Sunnyvale-Santa Clara, CA	5.7%
Warner Robins, GA	5.2%
Norwich-New London, CT-RI	5.0%
Bremerton-Silverdale, WA	5.0%
Kennewick-Pasco-Richland, WA	4.8%
Palm Bay-Melbourne-Titusville, FL	4.5%
Detroit-Warren-Livonia, MI	4.4%
Holland-Grand Haven, MI	4.3%

Next, I graphed the 2007–2014 earnings of professionals in the metros where they are concentrated and also nationwide. Here’s what I found:

 

 




The most obvious common element is that the workers in all of the occupationally-concentrated metros experienced earnings downturns during the recession years, whereas across the nation the same kinds of workers experienced no such downturns. Therefore, it appears that what I found for health-care workers last week is not unique to them. Perhaps any concentration of a particular type of worker (and therefore of an industry) increases the wage instability of a metro area.

But I found one interesting way in which the experiences of these occupations differed: For health-care and education professionals, average wages were higher nationwide than in the metros where the workers are concentrated. On the other hand, for computer and engineering professionals, areas where the workers are concentrated offer higher wages.

This finding is consistent with what the urban theorist Richard Florida has written about the “creative class”: Highly creative workers, such as engineering and computer professionals, tend to be most productive where they can work collaboratively. That is why, even with the marvels of 21^st-century communication, the industries that employ creative workers tend to concentrate geographically. Thus we find Silicon Valley for high tech, Hollywood for movies, and Nashville for music. And where these workers are concentrated and more productive, they earn more. The same does not seem to be true for educators and health-care professionals.

I’m not saying that educators and health-care professionals are not creative, but the nature of their work does not demand constant creativity to the degree that engineering and computer careers do. As a result, concentration of these workers may actually lower wages by increasing competition.

Where the Health-Care Industry Dominates

Last week, The New York Times ran an article about how the health-care industry has become the lifeblood of many rural towns. Other industries are offering fewer and fewer jobs paying middle-class incomes. Manufacturers have either moved offshore, become highly mechanized, or been driven out of business; small retail stores have lost the competition to big-box stores employing mostly low-wage workers; even agriculture has become mostly mechanized. In these towns, health care is one of the few industries that continue to offer decent wages and steady employment, so the towns survive by serving as the health-care hub of their region.

As an example, the article looks at Beatrice, Nebraska, which has not only a hospital but also two centers for the developmentally disabled and several new nursing homes and assisted-living centers. The article points out that one of the reasons rural hospitals are able to survive, besides simple demand, is a federal program that guarantees better-than-average Medicare reimbursement rates at facilities designated “critical access hospitals,” provided they meet certain standards.

I thought it might be interesting to identify some other communities that are health-care hubs. I used data from the Occupational Employment Survey that estimates employment in metropolitan areas for May 2014. Note that because the data set is based on metro areas, it overlooks small towns, such as Beatrice, located in rural areas. That means that I found cities that are health-care hubs rather than towns.

I looked for the ratio of employment in health-care occupations (specifically those in the group Healthcare Practitioners and Technical Occupations) to employment in all occupations. Here are the 10 metro areas where more than 10 percent of the wage-and-salary employment is in these occupations:

Metro Area	Health Care Practitioners and Techs as Percentage of  Wage-and-Salary Workforce
Rochester, MN	15.9%
Rome, GA	12.2%
Huntington-Ashland, WV-KY-OH	10.7%
Gainesville, FL	10.6%
Johnson City, TN	10.5%
Ann Arbor, MI	10.4%
Morgantown, WV	10.2%
Kankakee-Bradley, IL	10.2%
Cape Girardeau-Jackson, MO-IL	10.2%
Greenville, NC	10.1%

It should come as no surprise that Rochester, Minnesota, tops the list. It is the home of the Mayo Clinic, which employs 4,200 staff physicians and scientists; 2,400 residents, fellows, and others; and 52,900 allied health staff. Although we must exclude the scientists from the health-care workers and must subtract the workers in satellite clinics outside the Rochester area, we also must add a large number of non-Mayo health-care workers in this area. The result is an impressive ratio of health-care workers for a metro area with a total employment of 104,000 workers.

You may be less familiar with Rome, Georgia, but it is home to several medical centers: Floyd Medical Center, Redmond Regional Medical Center, and the Harbin Clinic, plus the Northwest Georgia Clinical Campus of The Medical College of Georgia.

Similarly, in the Huntington-Ashland metro area, some of the largest employers are Cabell Huntington Hospital and St. Mary’s Medical Center (in Huntington, West Virginia) and King’s Daughters Medical Center (in Ashland, Kentucky).

Most of the other metro areas on this list host universities with hospitals and medical schools. Greenville, North Carolina, is a good example—one I know well because my sister lives there. East Carolina University has a medical school and, since only a few years ago, the state’s second school of dentistry. Like Rome, Georgia, Greenville once served mainly as a center of trade for the region’s agriculture, but now health care has supplanted cotton and tobacco as the basis of the economy.

Next, I was curious about whether the health-care-dominated metro areas have experienced recent economic trends differently from the United States as a whole. So I looked at the median earnings for Healthcare Practitioners and Technical Occupations from 2007 through 2014. In the chart below, you can see the trends for the earnings of these workers nationwide (the blue line) versus the earnings for these workers who were employed in seven of the ten metro areas with high ratios (the red line). (Data was not available for all 10.) Here’s what I found:



First, note that the earnings in the health-care-dominated metros started out below those nationwide. This is not surprising, because all seven of these metros are low-cost-of-living regions, largely rural, and the nationwide average seems to be boosted by the earnings of the large number of health-care workers in big cities.

More interesting to me is the fact that the recent recession caused a conspicuous dip in the earnings of health-care workers in the metros where the industry is dominant, whereas health-care workers nationwide experienced no such downturn. Furthermore, although the workers in the health-care-dominated metros saw their wages rebound rather quickly, their earnings trend seems to fall slightly short of the curve one would have expected at the beginning of this time period. In other words, they seem to be still suffering a little from the after-effects of the recession.

I’m not at all sure why this is the case, but I will hazard a guess. There’s an old joke that the poor will never starve because they can always find work doing each other’s laundry. By that logic, health-care workers can always find work diagnosing and treating each other’s diseases. But this does not seem to be what actually happens during hard times. It appears that when a region does not have a diverse base of industries and is dominated by health care, a downturn in the economy reduces the number of patients available to health-care workers, causing their earnings to suffer. So although health care may save a community from becoming a ghost town after other industries have departed, the community seems likely to be less stable than it was when it hosted a more diverse set of industries.

A Tribute to Martin R. Katz

As I was growing up, I often thought of making a career in writing, but it never occurred to me to write about careers. This career path, which has been so rewarding to me, I owe to Marty Katz. He died March 17, age 98.

In 1979, he hired me to develop career information for the SIGI program at Educational Testing Service. The System of Interactive Guidance and Information was his brainchild and was one of the pioneering systems in the early days of computer-based career development. His ideas were so far ahead of his time that, initially, the available computers were incapable of storing the amount of information necessary to make the system work. He proposed using a carousel of slides to display the fixed information (such as occupational descriptions) so that the computer’s limited storage would be freed up for the interactive textual elements. He also conceived a radical layout for the display, making it a mosaic of text blocks rather than a solid page of text.

But SIGI was more than a technological breakthrough. It also was the implementation of his unique values-based approach to career development. Instead of basing career choice on a single domain, such as interests or skills, or encouraging birds of a feather to flock together, he posited that people choose careers in order to obtain rewards that they value. These rewards could be extrinsic, such as high income or prestige, or intrinsic, such as helping others or the opportunity to work in a field in which one has a strong interest. He did considerable research to identify which values are most widely held and most readily understood. In fact, by recording user interactions, he was able to employ SIGI itself as a research tool to gather statistics on values preferences. Besides confirming his selection of values for the system, it enabled him to study values differences between the sexes.

His values-based philosophy of career development represented a break with the theories that were the legacy of the Second World War, a time when national mobilization was more important than individual self-actualization. He also emphasized self-assessment, as opposed to testing—ironically, his employer’s main line of business. His approach was perfectly in tune with the 1960s decade of self-exploration and the 1970s “me decade.”

I did not know or understand any of these ideas when he hired me. Until then, my education had been in English literature and my work experience had been mostly in teaching English composition. I had learned a tiny bit about career development from trying to get my own career started, and especially from reading (and doing all the exercises in) What Color Is Your Parachute? From one of these exercises (a self-assessment), I discovered that I derived the greatest satisfaction and feelings of competence from research and writing. So when Marty advertised a job opening for someone to research and write career information, I applied and submitted a writing sample. He liked my writing and, when I said I hadn’t yet given up on a career as an English professor, he asked me whether I would give the SIGI job three years before moving on. I agreed. Thirty-five years have passed, and in a way I have not moved on yet.

Marty recognized potential in me and served as a valuable mentor. Although for the first two years my work for SIGI was focused almost entirely on researching salaries—using primary sources, such as the salary surveys of professional organizations, and following well-established procedures—in the following year Marty entrusted a new research project to me: developing descriptions of college majors. In its field test, SIGI had asked each college to provide descriptions of the majors they offered. This was a powerful way to help career decision makers with their planning, but the institution-specific information proved to be too costly for subsequent institutional subscribers to develop and maintain. SIGI needed generic descriptions of college majors, a kind of information that—compared to occupational information—was (and still is) very scarce. Marty assigned me a two-year research and development project that not only created a new module for SIGI but also laid the groundwork for the R&D strategies I have used in more recent years for several books—such as my next book for Meyer & Meyer Publishing, Choose Your College Major in a Day. Marty also entrusted me to key in the large amount of text that I was developing. A new text-entry program allowed relatively unskilled workers like me to enter and edit text, if only one line at a time.

In the obituary that ran in The Times of Trenton, you can read a lot more about his achievements, including the 1992 Eminent Career Award from the National Career Development Association. He probably would have achieved greater recognition in his field if he had done more self-promotion, but he seldom spoke at conferences because he was very hard of hearing as long as I have known him. And when he retired, he walked away from all involvement in the field.

He once remarked that he had taught college-level statistics although he had never taken a course in statistics. He said, “Most of what I’ve done has been without special training. I live by my wits.” This statement, with its tone of self-deprecation masking well-earned pride, strikes me as very characteristic of his personality.

Which Boats Get Lifted Fastest by a Rising Tide?

The old saying goes, “A rising tide lifts all boats.” In today’s context, this means that the recovering economy should be improving the lot of all workers. I was wondering, however, whether some boats are rising faster as the tide comes in. In other words, which types of occupations are getting the biggest boost from the improving economy?

I decided it would perhaps be most revealing to look at the places where the tide is coming in fastest—the metropolitan areas that have seen the largest gains in real personal income. Thanks to a dataset from the Bureau of Economic Analysis, I was able identify 20 metro areas in which real personal income increased by more than 6 percent between 2011 and 2012. I then looked at the increases in wage-and-salary occupational employment, for each metro area, over the same time period. Rather than deal with hundreds of occupations, I looked at the increases for major groups of occupations.

Then I computed the correlations between these employment increases for occupational groups and the real-personal-income gains in the 20 fastest-rising metro areas. Here’s what I found:

Occupational Group	Correlation
All Occupations	0.71
Transportation and Material Moving Occupations	0.70
Life, Physical, and Social Science Occupations	0.69
Installation, Maintenance, and Repair Occupations	0.67
Construction and Extraction Occupations	0.59
Office and Administrative Support Occupations	0.57
Computer and Mathematical Occupations	0.53
Business and Financial Operations Occupations	0.51
Management Occupations	0.35
Sales and Related Occupations	0.30
Architecture and Engineering Occupations	0.29
Legal Occupations	0.22
Production Occupations	0.17
Healthcare Support Occupations	0.14
Healthcare Practitioners and Technical Occupations	0.04
Arts, Design, Entertainment, Sports, and Media Occupations	-0.04
Food Preparation and Serving Related Occupations	-0.05
Building and Grounds Cleaning and Maintenance Occupations	-0.09
Protective Service Occupations	-0.11
Community and Social Service Occupations	-0.17
Personal Care and Service Occupations	-0.31
Education, Training, and Library Occupations	-0.33
Farming, Fishing, and Forestry Occupations	-0.34

These results make more sense if you’re aware that several of the 20 metro areas that figure into these calculations are in the oil patch: Odessa, Texas (10.2 percent real-income growth); Midland, Texas (9.6 percent); and Victoria, Texas (6.9 percent); and Grand Forks, North Dakota (7.3 percent). The occupational groups that are growing fastest are those that are important for getting oil out of the ground and moving it to refineries.

It’s also interesting to note that some occupational groups that grew fastest nationwide over this same time period show low correlations to income growth in these metro areas. For example, Personal Care and Service Occupations grew by 5.3 percent nationwide, faster than any other group, yet it grew by only 1.2 percent in these 20 metro areas and shows a negative correlation with income gains there. Farming, Fishing, and Forestry Occupations grew at the same rate nationwide and in these 20 metros (4.4 percent), but it also shows a negative correlation to income gains there. Food Preparation and Serving Related Occupations actually grew faster in these 20 metros (4.4 percent) than nationwide (2.9 percent), but it also shows a negative correlation to income gains there.

These anomalies can be explained partly by the difference between the economies of these 20 metro areas and that of the nation as a whole. But understand that a rising tide of income in an occupation does not necessarily bring a commensurate increase in employment for the same occupation—at least, in the short run. In many occupations, income can rise because existing workers are able to put in longer hours. Eventually, the rising income should attract new workers, but there is always a lag because of barriers to job entry, such as licensure and other credentialing, plus (at the regional level) geographical distance.