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

Wednesday, January 18, 2017

Will the Job Outlook be Great Again? (Part 2: Energy Extraction)

In my previous blog, I discussed the likely impact of the Trump administration on manufacturing jobs and concluded that he is unlikely to deliver on his promise of a renaissance in low-skill manufacturing jobs. Although in his campaign Trump gave the greatest emphasis to manufacturing, he also assured voters that he would boost employment in other industrial sectors. This blog is about another sector.

Energy Extraction. The government has considerable powers to regulate the energy-extractive industries: coal mining and petroleum. The majority of lawmakers in Donald Trump’s party have been saying for some time that global warming either does not exist or is not caused by human activities, and in doing so they have positioned themselves as champions of this industry sector. Although at times Trump has made vague concessions that there may be human causes, most of the time he hews to the more extreme version of the party line. He even tweeted that human causation is a myth concocted by the Chinese, although later he denied having said so.


 For both Trump and the Republican Congressional majority, two main policy recommendations result from this denial of the scientific evidence: (1) increased oil drilling and coal mining (“Drill, baby, drill!”); and (2) reduction or elimination of limits placed on carbon-releasing industrial activity (“Burn, baby, burn!”). Another result is encouragement of oil-pipeline construction, which has frequently been blocked by environmentalists.

Among the existing laws, regulations, and treaties that bind the United States to reducing carbon output, some will easier than others Trump and Congress to reverse. Trump has said he will “cancel” the Paris agreement, in which nations have promised to cut their output of greenhouse gases, and he can back out of this agreement by one of several ways. On the other hand, it will be difficult for him to roll back the regulations that make up the EPA’s Clean Power Plan and the fuel-efficiency standards for cars and trucks. To do so, he will need to propose alternative regulations and will face litigation from environmentalist groups that could delay or even block any changes. Any changes Congress wants to make will face possible filibustering from Democrats. If Trump and the Republican Congress succeed at rolling back regulations, it is possible that states will take a more active role. For example, California already has more stringent standards for vehicle emissions than the nation as a whole, and other states can adopt the California standards.

Market forces are already promoting the transition from dirty energy sources to cleaner sources. As cheaper natural gas has become available, power plants have been switching from coal to gas. The Energy Department found this transition the chief reason why carbon dioxide emissions in the first half of 2016 reached the lowest levels since 1991. During that period, in which the weather was relatively mild, consumption of coal fell by 18%, while consumption of natural gas fell by only 1%. Energy from renewable sources increased by 9% during that same period. Meanwhile, the cost of alternative-energy sources has continued to fall, and energy-saving consumer goods, including hybrid and electric cars, have become more affordable. If you haven’t shopped for LED light bulbs lately, you may be surprised at how cheap they have become.

Even if the government managed to walk away from the anti-carbon policies of the past few years, it is questionable whether market forces would allow the petroleum-extraction industry to expand and create jobs on a large scale. Employment in this sector was at 538,000 in October 2014 but is now at about 175,000 workers. Anti-carbon policies had very little to do with this slump. Instead, the main culprit was the glut of capacity that was created by America’s widespread adoption of fracking technologies, together with the development of new oilfields in other countries, such as Brazil. Opening up new areas (such as national parks and sensitive offshore ecosystems) for drilling will not produce a cornucopia of jobs as long as the glut of cheap oil and gas continues to make it uneconomical to set up new drilling rigs and pipelines.

Coal-mining jobs face a similar hurdle. Employment in 2015 reached the lowest levels since the Energy Information Administration began collecting data in 1978, and the number of mines decreased in all three major coal-producing regions.  The EIA reports (PDF) that between 2014 and 2015, “the average total number of employees at underground mines and surface mines  declined  by  13.6%  and  9.3%,  respectively.” More significantly, “the   average production per employee-hour increased by 5.4% to 6.3 short tons per employee hour.” Like the manufacturing sector, coal mining has increased its use of automation and other technologies for extracting coal more easily.  Coal is now extracted mainly by excavation from the surface (including mountaintop removal), using colossal machines operated by relatively few workers. In 2015, all of the top 10 coal-producing operations were surface extractions. The industry is vastly different from the labor-intensive days when Loretta Lynn’s father went to work with a pick and shovel down in a tunnel .

So the policies advocated by Trump (most of the time) and by the Republicans in Congress are unlikely to create a boom in employment in the energy-extractive industries. That’s not to mention the damage that these policies will do to the alternative-energy industries, to the extent that solar and wind power are still being subsidized.

There is one more wild card that may influence the energy-extraction policies of the new administration: Trump’s relationship with Russia. Oil and natural gas are Russia’s major exports, accounting for 68% of total export revenues in 2013. For reasons that are not entirely clear, Trump has made good relations with Russia a high priority. So although removing the barriers to petroleum output in the United States might create a limited number of jobs here, it would depress worldwide prices for petroleum and thus inflict a blow to Russia’s economy. Perhaps Congress will be less eager than President Trump to prop up Vladimir Putin.

Of course, unemployment in the oil patch and especially in the coal belt is no trivial matter that either party can afford to ignore. But the answer is not to turn back the clock on energy policy as if the energy markets and extractive technologies have not changed since the 1970s energy crises. Wayne Gretzky used to say that you skate to where the puck is going, not to where it’s been. As was true for manufacturing, the answer is to retrain displaced workers and train young people for the changed economy. But, as I pointed out in the previous blog, Trump and the Republicans are not advocating policies that will expand the availability of low-cost, high-skill training.

Tuesday, January 3, 2017

Will the Job Outlook be Great Again? (Part 1)

As a new presidential administration approaches, you may be wondering about the impact that the change in leadership will have on job prospects in the United States. I don’t claim to have a crystal ball, but I believe I can glean useful insights from what economists are saying and from past employment trends. And, in fact, I have a record of forecasting trends brought in by a previous sea change.

Eight years ago, when a major recession was looming and I was working for JIST Publishing, my editor—Susan Pines—assigned me to write a book that eventually was called 150 Best Recession-Proof Jobs. The book came out just as the full force of the Great Recession came crashing down on the U.S. economy. Thanks to this good timing, the book was so newsworthy that I was being interviewed on television approximately once a week for a month and more. (A lot of credit goes to JIST’s crackerjack publicist at the time, Selena Dehne.)

I selected the occupations that I featured as “recession-proof” by mathematically comparing the past ups and downs in the gross domestic product with the ups and downs in the workforce size of each occupation. Thus I was able to identify occupations that were least impacted by past downturns. The main limitation of this approach is that each recession is different from previous recessions. For example, the Great Recession was caused by a sudden drop in the value of real estate after a long bubble of overbuilding, so certain industries related to real estate—especially construction—suffered more than they did in previous downturns. Nevertheless, over the following several years my predictions were more often confirmed than disconfirmed.

The pending change in administrations presents a similar problem for those who would forecast employment trends. It might seem sensible to look at the employment effects of past transitions when a Democrat was succeeded by a Republican. However, each such transition, like each recession, is different in many ways. The year 2017 can’t be expected to repeat 2001 precisely. For example, while the last part of Barack Obama’s administration has seen a long streak of growth, it is not comparable to the technology bubble that ended Bill Clinton’s second term. And Donald J. Trump is not as conventional a Republican as George W. Bush.

In fact, it’s difficult to tell what kind of Republican—indeed, what kind of politician—Trump is. His policy pronouncements tend to lack specifics and frequently change depending on his audience. Some of the policy goals he has stated at various times run counter to the stated goals of Congressional leaders of his own party, raising the question of who will prevail if he tries to bend Congress to his will.

Because of these uncertainties, I am basing my forecasts on Trump’s general goals, plus an analysis of possible policies that he might or might not employ in pursuit of these goals. And I am organizing my forecasts by industries. In this blog, Part 1, I discuss only manufacturing.

Manufacturing Jobs. One of Trump’s most consistent messages has been his desire to bring manufacturing jobs back to the United States. What’s often overlooked in his rhetoric is that manufacturing output in the United States is now at nearly an all-time high. The kind of manufacturing that is now done here uses highly automated processes and employs a comparatively small workforce of highly skilled technicians. (Since recovering from the Great Recession by 2015, the number of employees has leveled off at about 12.3 million.) Most low-skill manufacturing tasks are performed either here by robots or overseas by low-paid workers, such as the ones who are making the garments branded with Trump’s name and his daughter’s.

That leaves the next president with these policy choices:

·         He might impose high tariffs that make it uneconomical for offshore manufacturers (whether American-owned or otherwise) to export to the U.S.  market—a policy that Trump has explicitly endorsed at times. But the policy could backfire. To the extent that imposing or raising tariffs is feasible under existing trade agreements, it would encourage other nations to retaliate with their own tariffs, reducing exports of our own manufactured goods and increasing the costs of the supply chain—imported parts and raw materials that U.S. manufacturers use. Also, once American manufacturers are protected from foreign competition and start hiring low-skill American workers, they will not be able to pay the rock-bottom wages that third-world workers earn. So, although many jobs will open in manufacturing, consumers will find American-made manufactured goods costing a lot more than the cheap foreign-made goods that now fill the shelves at Walmart. In summary, this policy would hurt high-skill manufacturing workers and create an economy where the low-skill manufacturing workers might have no more purchasing power than is now possible from work in service industries. And, finally, almost all Republican lawmakers, as well as many Democrats, are committed to tariff-free trade (although Republican voters have become more hostile to it), so this policy stands little chance of getting through Congress.
·          
·         He might mandate that American-made products and components be used when federal funds are expended. He hinted at this policy at the third presidential debate with Hillary Clinton when he said that he had used Chinese steel in one of his building projects because Congress had done nothing to stop him. It seems unlikely, however, that Congress would go along with this policy. Congressional leadership has not changed since House Republicans defeated an amendment to impose just such a mandate on infrastructure projects for the nation’s waterways.
·          
·         He might offer tax breaks to American manufacturers so that their operations here are more profitable. Trump actually tried this approach in October when he persuaded Indiana to give tax breaks to the Carrier division of United Technologies as a way of preventing jobs from being shifted to Mexico. However, Carrier has indicated that it intends to use the resulting savings to increase the company’s use of automation—thus subsequently cutting more low-skill manufacturing jobs. Cutting labor costs produces savings that are more dependable than easily-reversible tax breaks. So tax cuts seem unlikely to be an effective solution.
·          
·         He might reduce federal regulations on manufacturers that add to the costs of doing business in the United States. (I am not saying I favor this policy, but it is one that is sometimes proposed.) For example, it has been argued that the main reason Carrier wanted to relocate jobs to Mexico was regulations, not wages, and Trump is in agreement with Republican leadership in Congress on the need to reduce regulations on industry. It is not clear that Trump can get enough cooperation from Congress to make sufficient reductions in regulations to bring a true renaissance of manufacturing. And for some regulations (e.g., on the formaldehyde levels in plywood), the way to reduce competition by offshore manufacturers is to apply the same regulations to imports that are imposed on goods manufactured here, rather than rolling back regulations.
·          
·         He might accept the reality that low-skill manufacturing jobs are gone for good and instead focus on preparing (or retraining) workers for high-skill jobs in the industry. One way to accomplish this is to make community college as free of charge as high school. This is what President Obama proposed and that became the America’s College Promise Act of 2015, but Congress sent the bill to die in committee. I have been unable to find any statement from Trump himself about this proposal, but Trump’s campaign co-chair Sam Clovis, in an article in Inside Higher Education, stated that the campaign rejected the call for free community college.
·          
·         A related policy to encourage manufacturing would be to find a way to increase the number of manufacturing engineers working here. According to Walter Isaacson’s biography of Steve Jobs, the Apple  CEO once told President Obama that Apple’s suppliers in China are able to employ 700,000 factory workers because they have ”30,000 engineers on-site to support those workers. ‘You can’t find that many in America to hire.’” The skill level that Jobs was referring to seems to have been closer to engineering technicians than to what we normally think of as engineers. So, again, increased funding of community colleges and technology schools would be a way to achieve this goal, but that was not a priority of the Trump campaign. Alternatively (or additionally), a reformed immigration system might welcome engineers and engineering technicians trained abroad or coming from abroad for training here. Trump has both welcomed and rejected immigration of high-skill workers at various times. Congressional leadership has generally favored it despite the reluctance of many Republicans and therefore has sometimes had to find underhanded ways to encourage it. Therefore, it is difficult to predict whether or not this policy will be pursued in the coming administration.
·          
·         Yet another way to create a high-skilled manufacturing workforce is to encourage unions to partner with manufacturers on forming apprenticeship programs. Although Trump starred in a TV series called “The Apprentice,” I have not been able to find any statements from his campaign about his attitude toward actual apprenticeships, with the meaningless exception of a tweet he issued in October: “Did Hillary just say she wants more Apprenticeships? I created The Apprentice!” Judging by Trump’s past record as an employer, his attacks on an Indiana union leader, and his appointment of a foe of unions for Labor Secretary, he seems no friend of unions, and Congress certainly has not encouraged them, so this last outcome seems unlikely.
·          
In conclusion, then, I do not expect the Trump administration to deliver on its promise of a renaissance in low-skill manufacturing jobs. I expect employment in these jobs to remain at its current low level—or worsen if there is an economic downturn.


Thursday, September 1, 2016

Trends in Job Satisfaction

Much of my work is aimed at helping people find satisfying jobs. That’s why I was interested to find a survey report (PDFhere) that looked at job satisfaction, among other fulfilling aspects of life. The report, “Trends in Psychological Well-Being, 1972–2014,” by Tom W. Smith, Jaesok Son, and Benjamin Schapiro, was published by the research institute NORC at the University of Chicago. (I earned my master’s degree there, but at that time I was researching English literature.)

The researchers looked at surveys that have measured people’s level of satisfaction in general, with one’s marriage, with one’s financial situation, with the level of excitement, and (most interesting to me) with one’s job. Specifically, they looked for trends in how people’s satisfaction changed over the past four decades.

It turns out that of all the kinds of satisfaction that they looked at, job satisfaction was the most stable over the time period that they examined. Here is a graph showing the trend for those reporting they were “very satisfied” with their job or housework:


Note the contrast with this graph of satisfaction with one’s financial situation—which shows a long-term decline and a notable dip apparently caused by the Great Recession:


In addition to the trends, note the levels of satisfaction shown here. At its very peak, in the late 1970s, financial satisfaction reached only 35 percent, whereas job satisfaction came close to 90 percent at times and never sank below 80 percent.

Using data from the report, I created the following graph showing trends in job satisfaction separately for men and women. You can see that the general trend is that women used to be less satisfied than men but lately have been more satisfied. My guess is that this is the result of growing opportunities for women in the workplace:



The researchers found that job satisfaction tends to increase with age; this was true for all years that they studied. It seems likely that as people age, they gain greater mastery over their job demands, they may get greater recognition for their skills, and they may learn which job environments suit them best and thus move into more satisfying situations. Here is a graph based on the average percentages of those “very satisfied” over the entire span of the study:

Finally, here are the trends for job satisfaction, with separate trend lines based on the level of education of the respondent: less than high school, high school, or college (or beyond).

Overall, those with more education tend to be more satisfied with their jobs. A notable exception occurs just at the beginning of this century, when those with less than high school showed the greatest satisfaction—evidently the result of the tail end of the tech boom. Conversely, the Great Recession seems to have dampened, at least temporarily, the satisfaction of those with more education.

Thursday, July 21, 2016

The Importance of the Sample

In my work researching and writing about occupations, I encounter a lot of statistics. And this year, with an election coming ever closer, we are likely to see the results of many surveys of voters. I want to emphasize that numbers reported from surveys tell less than half of the story. They are the results of mere tabulation. What makes the numbers meaningful is the nature of the sample. Or, to put it another way, you can’t understand what a study tells you unless you understand the sample it’s based on.
To illustrate this point, I like to bring up two anecdotes. I think you’ll find them interesting even if (maybe especially if) you’ve never taken a course in statistics.
The first anecdote is based on the research that social scientists did when they essentially invented the science of jury selection. This happened in 1972, when seven radicals were about to go on trial in Harrisburg, Pennsylvania, for conspiracy to raid draft boards and destroy records, among other planned antiwar actions. This was a time of great political polarization and in a place that is characterized by political conservatism. The researchers, working on behalf of the antiwar activists’ lawyers, wanted to find a way to predict the political leanings of jurors so the lawyers could seat a jury that would be less conservative than one chosen at random from the Harrisburg population. The lawyers would not be able to ask the potential jurors flat-out about their politics; instead, they needed an indirect way to assess this.
The social scientists surveyed citizens of that community to identify their political attitudes and then correlated these attitudes with other facts about the jurors. They discovered that the surest way to predict a Harrisburger’s politics was to ask how much education the person had: The more educated the person was, the more conservative that person’s politics.
The researchers eventually realized why this was so: Young people in Harrisburg who became highly educated acquired the occupational mobility to leave the region if they were not conservative; therefore, the sample of highly educated people who remained had to be quite conservative. If the results of their survey surprised you, it’s because you didn’t stop to think about what the sample really was: not everyone who ever lived in Harrisburg, but rather those who remained—by choice or because they were less able to move out.
The second anecdote is from the Second World War. British bomber planes flying missions over Germany were often shot down by anti-aircraft fire. The Royal Air Force wanted to shield vulnerable parts of the aircraft with armor, but they wanted to use a minimal amount of armor to avoid weighing down (and slowing down) the planes. The RAF commissioned the statistician Abraham Wald to examine the planes after bombing missions to determine where on the planes’ undersides it was most critical to apply anti-flak armor.
Wald counted bullet holes in the planes and recommended that armor be applied where there were the fewest bullet holes.
This may seem like a mistake to you. Maybe you’re thinking that armor is supposed to protect against anti-aircraft fire, so shouldn’t the RAF have armored the places that got hit the most?
Again, consider the sample: Wald was not looking at every bomber that flew a mission, but rather those that returned from missions. Bombers that got shot down were removed from the sample. The bombers that returned and made up the sample were the ones that were hit only in places that were not critical for staying airborne. The places where the surviving planes were not hit, therefore, were the most likely to be critical and in need of armor.
If you’re wondering why I’m writing about this subject in a blog about careers, consider this blog entry a look at how complicated statisticians’ work can be, not so much in terms of the mathematics, but rather in terms of the concepts that must be understood.
The nonstatistical lesson to take away from these anecdotes is that you have to be careful when you make a generalization about a population—for example, the notion that educated people are more liberal politically (or, to draw on today’s politics, the notion that people of one religion are a greater threat to security). Such generalizations may be true in some global sense, but the particular population you are dealing with may really be a subset of the global population, either self-selecting or selected by some exterior factor you have not considered. The global generalization may be a poor fit for this subset, or the subset may be a misleading basis for a global generalization.

Thursday, July 7, 2016

Should I Sign That Noncompete?

It is a paradox of today’s job market that employers want ever-greater flexibility in their ability to shed workers but simultaneously want to reduce workers’ flexibility in seeking employment. Specifically, employers increasingly are imposing noncompetition agreements (“noncompetes”) that can seriously limit workers’ ability to find jobs elsewhere. According to a White House report (PDF), an estimated 30 million Americans, nearly one-fifth of the workforce, are bound by these agreements, and roughly 37 percent have been so bound at some time during their careers. Perhaps the agreements themselves have not proliferated but merely their enforcement. Whichever is the case, “The law firm Beck Reed Riden LLP found a 61 percent rise from 2002 to 2013 in the number of employees getting sued by former companies for breach of non-compete agreements.”

The White House looked into this matter out of concern that noncompetition agreements can hamper the economic recovery. “Non-competes can reduce workers’ ability to use job switching or the threat of job switching to negotiate for better conditions and higher wages, reflecting their value to employers. Furthermore, non-competes could result in unemployment if workers must leave a job and are unable to find a new job that meets the requirements of their non-compete contract. In addition to reducing job mobility and worker bargaining power, non-competes can negatively impact other companies by constricting the labor pool from which to hire. Non-competes may also prevent workers from launching new companies.”

In some states, most notably California, employment laws make noncompetition agreements essentially unenforceable.  It is thought that the absence of noncompetes is one of the factors that have contributed to the towering success of the Silicon Valley. Job-hopping is a normal part of career building in the tech industry there. In fact, job-hopping is one of the reasons that employers have traditionally tended to cluster together geographically with others in the same industry, even when access to natural resources or transportation infrastructure is not a factor. Think of New York for finance, Nashville for music, or Detroit for automobiles.

Noncompetes reduce the efficiency of these industry clusters. As a result, The New York Times reports that some states are trying to limit the reach of noncompetes in hopes of duplicating one of the factors of the Silicon Valley environment: “Hawaii banned noncompete agreements for technology jobs last year, while New Mexico passed a law prohibiting noncompetes for health care workers. And Oregon and Utah have limited the duration of noncompete arrangements.”

I live in New Jersey and have personal experience with this kind of shackling. In the late 1990s, my employer required that I sign a noncompetition agreement as a condition for receiving a raise. I complied, although it bound me not to compete for one year, and after a downsizing only a few years later, the agreement seriously limited my work as a consultant. The crowning irony was that only a few years after I began consulting, my old employer came back to me in need of my consulting services and presented me with a contract that contained another noncompetition agreement—this one binding me for two years.

I refused to sign it, and with no hesitation or bargaining, they struck that paragraph from the contract. Since then, I have been asked by another employer to sign a noncompete and have again refused, with no adverse consequences.

What should you do if an employer confronts you with a noncompetition agreement? First, you should investigate whether it is enforceable in your state and for your occupation. To be totally sure, you may want to consult a lawyer, but you can get useful preliminary information from a downloadable chart at the website of Beck Reed Ridin, LLP.

It’s usually a good idea to negotiate with your employer over the terms of the noncompete. If you’re lucky enough to have some bargaining power, such as a very desirable skill set, you may be able to convince the employer to strike the agreement entirely. If not, you may be able to get the employer to relax some of the terms. For example, you may suggest altering the agreement to restrict you only in a certain geographic area or only from working for certain employers. You may be able to reduce the duration of the restriction. You may get the employer to accept wording based on the conditions of your future separation—for example, that the restriction will apply only if you quit, not if you are terminated.

Be sure to examine the fine print of any noncompetition clause. (Again, a lawyer may be helpful.) For example, some agreements include the onerous requirement that the ex-employee will have to pay any legal fees that the employer incurs as part of enforcing the agreement. Such additional burdens may also be negotiable before you sign.

Understand that one reason employers like to impose noncompetition agreements is that they fear you will carry company secrets to a competing organization. It is reasonable for the employer to ask you to sign a nondisclosure or confidentiality agreement with wording that is separate from noncompetition.

Monday, June 20, 2016

A Three-Angle View on Your Career

When you think about how to improve your career, it helps to view it from several different angles. I find it useful to employ the approach called tagmemics. Please let me define this term for you before it scares you away, and then you’ll start to see its usefulness.

The idea of tagmemics is that any unit of human experience can be viewed in three forms: as a particle, as a wave, and as a field. This approach was originated by a linguist, Kenneth Pike, so it may or may not be very sound as physics, but I find it very useful for achieving insights into ideas such as the one I’m discussing here: improving your career.

First, let’s look at your career as a particle—as a static entity. To do that, you need to move away from the word career (which implies development over time) and focus instead on the word job. (If you’re still in school, consider that your job.) Ask yourself these questions about your job as it is right now:

  • Does your job have a title that you’re happy with?
  • During the workday, do you find the work tasks interesting and engaging, or do they involve knowledge or tasks that don’t interest you?
  • Are your skills a good match for the job, or do you feel overwhelmed (or unchallenged)?
  • Is the stress level one that is comfortable to you?
  • Are you satisfied with the physical requirements of your job?
  • Is the amount of structure in your job too loose or too confining?
  • Do you enjoy the level of creativity in your work?
  • At the end of a typical workday, do you have a feeling of satisfaction?
  • Do you have a way of assessing your work and therefore taking pride in what you have accomplished?
  • When you’re not working, is your job providing a sufficiently comfortable lifestyle and amount of leisure?
If some of your answers indicate a situation that is not totally satisfactory, this may be an indication that you need to make some changes to your job. But first, you need to consider the wave and field perspectives on your career.

Your career is a wave in that it is a dynamic process. It is unfolding over time; it has a past and a future. Here are some questions that reflect on this dynamic nature:

  • Over the course of time—whether it’s a day or a year of work—does your job offer a level of variety in tasks, locations, or people that you find satisfactory?
  • Do you make career choices by planning, by seizing opportunities, or by following the path of least resistance?
  • Are your past career preparation and experiences a good match for your present job, or would they be a better match for something else?
  • Does your job provide opportunities for advancement?
  • Are you knowledgeable about future developments in your career field and the job opportunities (or threats to job security) that they will create?
  • What have you done or are willing to do to prepare for these job opportunities or to counteract any threats?
  • Will your career allow you to deal adequately with future changes in your lifestyle, such as marriage, child-rearing, or retirement?
  • If you’re still in school, will you be able to get through the program?
Your answers to this second set of questions (wave-based) may help you plan for ways to remedy shortcomings revealed by your answers to the first set (particle-based). But you should also consider the field aspect of your career, which has implications for both your current situation and your plans.

Your career is a field in that it involves relationships. It occurs in a spatial and interpersonal context. Answer these questions:

  • Is your job allowing you to live in a community that satisfies you?
  • How do you feel about your workday commute and the amount of travel?
  • Do you enjoy the physical setting of your work?
  • How comfortable are you with your boss, your co-workers, and members of the public whom you deal with?
  • Are you satisfied with the job’s ratio of solitary work to working with or dealing with other people?
  • Do you desire more or fewer opportunities for leadership in your job?
  • Are you knowledgeable about your industry, not just your job?
  • Do you have credentials that have value in your industry (or another industry)?
  • Are you known to people in your industry (or another industry) and, if not, do you know how to make yourself known?
  • Do you feel good about the extent to which your work contributes to the well-being of other people, of animals, or of the natural environment?
  • Do you worry about the possible impact of an on-the-job error on your organization or on other people?
  • Does your work create stress between you and your family or community?
  • Are you satisfied with the level of prestige that your work confers on you?
If you have read this far, I hope that you understand that you usually need to consider all three aspects of your career to solve any problems that you have detected in it. For example, if your work is too stressful (a particle issue), you need to think about what is causing this stress. It might be another particle issue, such as the necessity of following a restrictive rulebook, but it could also be a wave issue, such as a feeling of being in a dead-end job or worry about future threats to job security. It could also be a field issue, such as concern about making decisions that could bankrupt your employer or the perception that your long hours at work are making you lose touch with your family.

When you evaluate a possible change to your career, be sure to consider the change from the perspectives of all three aspects. For example, if you decide to get a degree or certification to improve your future employability (which you may think of as a wave-related change because it happens over time), consider the particle issues that this will raise, such as how well your skills and aptitudes will match the demands of the program. Consider also such wave issues as how the program’s demands on your time will affect your family relationships or how you can leverage your new credentials to achieve greater recognition in your industry.

Your career affects so many aspects of your life that you need to be multidimensional in your thinking when you assess your satisfaction or make plans for improving your situation. Tagmemics can provide a structure to help you expand your thinking.

Tuesday, May 31, 2016

Is the World of Work Really Hexagonal?

The Holland hexagon is perhaps the best-known schematic representation of people's work-related interests. This interest scheme is used with the understanding that interests on adjacent angles of the hexagon are more closely related--i.e., more likely for people to share--than interests on more widely separated angles. For example, people are thought to be more likely to have the profile RI than RS. A lot of research has been done to confirm or refute this layout, based on how people have responded to interest inventories that report out the RIASEC Holland codes. For example, I might examine people's scores to see whether their I scores indeed correlate more strongly with their R and A scores than they do with their E scores. Several researchers have done this; a good bibliography of these studies may be found in "The Structure of Vocational Interests," by Itamar Gati.

Keep in mind, however, that the Holland theory is based on the principle of congruence: that people should seek types of work that are good fits for their interests--in terms of tasks, settings, and the personalities of co-workers. Congruence makes sense as a goal only if the world of work can be described in the same terms as people's interests. With regard to the Holland scheme, this means that the opportunities for satisfaction of interests that exist in the world of work should also be describable by the same hexagon.

But are they? I'm not aware of any studies that have tested this hypothesis. To do so, one needs a data set that describes the world of work--that is, it describes a comprehensive set of occupations--in RIASEC terms. Then one can see whether the occupations really do distribute themselves around a hexagonal shape.

Most data sets of this kind provide one-, two-, and three-letter RIASEC codes for occupations. For example, one might consult the Dictionary of Holland Occupational Codes, co-authored by Holland himself. I decided instead to use data from the O*NET database, which rates 974 occupations on the RIASEC interests. This data set is not only more readily available at no cost, but it also provides numerical ratings that represent differences among occupations that are more nuanced than just permutations of six letters. In the O*NET database, two occupations that have the same Holland code might have somewhat different numerical ratings. For example, take Educational, Guidance, School, and Vocational Counselors and Recreation and Fitness Studies Teachers, Postsecondary, both of which are coded S. In the O*NET database, the former has an S rating of 7, while the latter's S rating is only 6.67. In each case, the S rating is so much higher than the ratings for the other five Holland types that the occupation is given only the single S code; nevertheless, the ratings indicate that one occupation is a bit more Social than the other.

I used the numerical ratings from the most recent release of the O*NET database (20.3). When I ran correlations between occupations' ratings on the six RIASEC interests, I found the figures illustrated on the hexagon below:

Among five of the interests--IASEC--the correlations support the prediction that interests will have a positive correlation with interests on adjacent angles and a negative or negligible positive correlation with interests at a distance of two angles--and, furthermore, that correlations between interests at opposite angles will be more strongly negative. But the sixth interest, Realistic, is anomalous; it shows only a negligible (and negative) correlation with the two interests (C and I) that are supposed to be adjacent. To be sure, it shows negative correlations with the opposite interest (S) and the two-angles-away interests (E and A). In any diagram, it should be placed distant from them; but it should also be placed farther from C and I than any other pairs of adjacent angles are distant from each other.

Because Realistic shows no positive correlation with any other interest, a hexagon does not adequately describe its relationship to the other interests. I suggest that if we must use a geometrical shape to describe the layout of the six interests, we need one that allows Realistic to sit away from the others. Perhaps this is best shown as a diagram resembling a frying pan:

On the other hand, there is a good argument for using a hierarchical arrangement of the six types, as Gati has proposed in the article cited above. A hierarchical model is based on the assumption that people make a first-cut decision between one group of interests and all other groups, and then make a second-cut decision between one interest and all others within that first-choice group. Gati created groups by observing that certain pairs of RIASEC codes had the highest correlations (in score data from assessments). Here is his hierarchical diagram:
My analysis of data from the world of work (specifically, O*NET data) suggests a somewhat different hierarchy: 


The first cut consists of choosing between R and everything else. The second cut recognizes two clusters (A and S, with a correlation of 0.31; and E and C, with a correlation of 0.27), and I standing off by itself because its best correlation (0.20, with A), is as weak as the E-S correlation.

The frying pan model has one advantage over the hierarchical model: It makes clear that diametrically opposed interests are more distant from each other than interests that are closer on the circumference.