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

Tuesday, March 31, 2015

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.

Friday, March 13, 2015

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
All Occupations
Transportation and Material Moving Occupations
Life, Physical, and Social Science Occupations
Installation, Maintenance, and Repair Occupations
Construction and Extraction Occupations
Office and Administrative Support Occupations
Computer and Mathematical Occupations
Business and Financial Operations Occupations
Management Occupations
Sales and Related Occupations
Architecture and Engineering Occupations
Legal Occupations
Production Occupations
Healthcare Support Occupations
Healthcare Practitioners and Technical Occupations
Arts, Design, Entertainment, Sports, and Media Occupations
Food Preparation and Serving Related Occupations
Building and Grounds Cleaning and Maintenance Occupations
Protective Service Occupations
Community and Social Service Occupations
Personal Care and Service Occupations
Education, Training, and Library Occupations
Farming, Fishing, and Forestry Occupations

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.

Thursday, February 26, 2015

Is “Breaking Bad” a Skill?

This blog was inspired by the very last episode of the popular television drama “Breaking Bad” [spoiler alert!]. Walter White, once a milquetoast chemistry teacher and now a ruthless drug baron, confesses to his wife that he did not persist in his life of crime for his purported reason, which was to acquire a nest egg for his family to live on after his death. “I did it for me. I liked it. I was good at it. And I was really—I was alive.” Setting aside the matter of his enjoyment—which, in career development, would fall into the category of interest—let’s consider what it means to be good at crime. Is criminality a skill?

If we consider what it means for criminality to be a career—which means setting aside crimes of passion—we see it takes several forms. It includes such illegal pursuits as armed robbery, counterfeiting, selling illegal drugs, confidence rackets, and identity theft, among others. However, it also includes crimes that some people commit as part of what otherwise would be a law-abiding career—for example, securities traders using insider information or oligarchs conspiring to strangle competition.

Both kinds of criminals need to be skilled at the particular type of crime that they commit. For example, the counterfeiter has to be good at producing a realistic imitation of genuine currency. The drug dealer needs to be good at making connections with buyers. The identity thief needs to have skills with computers or with some other way of obtaining personal information about victims. These skills therefore are highly specific to each kind of criminal enterprise. White-collar lawbreakers need to have the skills that establish them in the law-abiding careers from which they veer into criminality—again, highly specific skills that can’t be summarized as a skill at criminality.

All kinds of career criminals also need to be skilled at escaping detection by the authorities. Here again, the skills are specific: The counterfeiter has to be skilled at passing funny money in ways that will escape notice, at least in the short term. The drug dealer needs to know how to operate stealthily. The securities trader who makes a killing based on insider information has to be skilled at manipulating the source of information to keep quiet. Any of these criminals might also corrupt law enforcement by using interpersonal skills, in addition to bribes.

Although these various criminals have highly diverse skills, one skill that many criminals have in common is the ability to use violence. Violence can help the criminal escape detection—silencing potential informers by threats, mayhem, or extermination. It can also fend off competition and theft. After all, someone who, like Walter White, is earning money from a criminal enterprise cannot expect the law enforcement authorities to protect his assets. Other outlaws understand this and will victimize the criminal who does not defend himself and his loot. White-collar criminals are mostly unlikely to resort to violence, but it is one skill that cuts across a broad swath of criminals.

However, even violence is not a single skill. One criminal may be talented at personally using a gun or fists, whereas another may be skilled at identifying and recruiting thugs. (That is Walter White’s strong suit.)
The skill that perhaps is to be found most universally among criminals is the ability to live with themselves, knowing what crimes they have committed. True, some criminals don’t need this skill because they are sociopaths, without empathy and incapable of feeling guilt for their actions. But criminologists say that these people are rare. Most criminals understand that they are doing wrong and have to deal with that understanding.

Generally, they adopt defense mechanisms: I’m doing this for my family (that’s Walter White’s). I’m doing this because society has conspired against me and I’ll never succeed in the straight world. I’m doing this to sustain a great company that employs thousands of people. I give a lot of money to widows and orphans. I get a lot of respect (from some people). One more big score, and then I’ll retire and go straight. Everybody does it.

Ultimately, this skill amounts to an ability to rationalize. Or you might call it hypocrisy, which Francois de La Rochefoucauld called “the homage vice pays to virtue.” It’s not a skill to be proud of; criminality is not something to be proud of. It is often regarded as a weakness, but I maintain that it functions like a skill to the extent that it allows people to pursue one kind of career: crime.

Friday, February 13, 2015

Which Industries Are Recession-Proof?

Everyone knows that the nation is now recovering from the worst recession since the Great Depression. But not every industry sank into recession or recovered from it on the same schedule. In fact, some did not sink at all, and some have not yet recovered. I thought it would be interesting to look at employment in the major industries and see how their experiences varied over the course of the years from 2007 to 2013.

I obtained figures from the Occupational Employment Survey of the Bureau of Labor Statistics and graphed them. (Note that for the second and third graphs below, the y axis starts at a number greater than zero. I used scales that would emphasize vertical shifts. If you find the graphs below rather small to read, just click on the links that pull up larger versions.)

The most important lesson to take away from all three graphs is that the low-tide mark of employment in most industries did not happen right after the financial crisis of 2008, but rather two years later, in 2010. When recession first strikes, employers generally try to hold onto skilled workers and hope for a quick turnaround to better times. They may have a cushion of resources that enables them to postpone layoffs

But when the economic slump drags on and the rainy-day funds are expended, employers start to shed workers. And as unemployed people draw down their own rainy-day funds cut back on their own expenditures, the slackening demand adds to the woes of employers. This is the recessionary spiral that can take a few years to hit bottom. Europe, which chose the path of austerity rather than stimulus, seems stuck in a valley, but the American economy, as a whole, started to turn upward after 2010.

But different industries have followed different paths. Let's start by looking at the first chart below. (Larger image here.)

Of these seven industries, only three follow the classic U-shaped curve of loss and recovery: the purple line, Mining, the pale blue line, Real Estate and Rental and Leasing, and the orange line, Arts, Entertainment, and Recreation. It's not surprising that Real Estate follows this U curve, because a dramatic loss of real estate value is what precipitated the financial crisis. (Note that, like property values in most locales, employment in this industry has not recovered the pre-recession level.) It's also to be expected that the Arts would lose business during recession, although the downward dip of the curve is really quite shallow. These jobs seem to be less sensitive than you might expect. Mining, on the other hand, actually owes its dramatic uptick in recent years not as much to the economic recovery as to the developing technology of fracking.

Management of Companies and Enterprises, the turquoise line, hardly declined at all during the recession and more recently  has been making impressive gains. Everyone knows managers who lost their jobs in that downturn, but apparently jobs in management are less sensitive than those of rank-and-file workers.

The two lines at the bottom--for Utilities (green) and Agriculture, Forestry, Fishing, and Hunting (dark red) are almost level rather than curving. People need electricity, water, and other utilities no matter how the economy is doing, and a cutback in volume (for example, less use of electricity for manufacturing) has little effect on the number of people needed to work at the utility plant. Agriculture sometimes suffers from downturns in prices because of overproduction, but the rapidly rising world population and some diversion of crops to biofuel production seem have kept employment steady in recent years.

The really dismal story here is what happened to the Information industry--which consists mostly of the media, not computer technology. Like Mining, this industry has been affected mostly by technology, but not in a good way. As more and more media content migrates to the Internet and media companies consolidate, the need for workers has reached a permanently lower level than before the recession.


Now, let's look at the second chart. (Larger image here.) All of these medium-sized industries follow the classic U-shaped curve, although there are two interesting variants on the U shape: Construction (pale blue, second from the top) dropped off very steeply after the housing bubble popped and is still a very long way from recovering to its pre-recession level. Professional, Scientific, and Technical Services (orange, at the top), by contrast, has already recouped all the lost jobs and is reaching new heights. This industry includes America's premier field, high tech.


The third chart (larger image here) shows the largest industries. Here, the most dramatic curve is the light red line at the top, Health Care and Social Assistance. As I predicted in my 2008 book 150 Best Recession-Proof Jobs, this industry was unfazed by the recession because it is vital to life and has been gaining in demand because of the aging Baby Boomers. Expect this upward path to continue.

Educational Services (turquoise) is actually an upside-down U, peaking when other industries bottomed out and falling slightly thereafter, although this curve is very shallow. This is another industry that I predicted would be recession-proof, because people seek additional education during an economic downturn--displaced workers retooling for new jobs or young people postponing their entry to the workforce. The slight downturn in more recent years is largely explained by cuts in public education budgets, a consequence of the political movement to lower taxes, and by the increasing use of adjunct teachers in postsecondary education.

The orange curve for Manufacturing resembles the path taken by Construction in the previous chart: a steep decline into recession, followed by a slow and inadequate recovery. In dollar terms, Manufacturing has actually bounced back, but employers in this industry rely more heavily on automation than they did a decade ago, so fewer human workers are needed.

The path for Retail Trade (pale blue) is a much shallower version of this same curve. As consumers have opened their wallets, retail purchases have returned, but automation--especially the large amount of buying that gets done on websites--has reduced the number of workers.


The takeaway from this set of charts is that it is possible to ride out a downturn with minimal risk of job loss, provided you establish a career in a recession-proof industry. But it also shows that technology can cause long-term, perhaps permanent changes in the need for workers in some industries. As technology advances, it may increasingly affect employment in industries that previously have not felt its impact, such as education, health care, and professional services. One of the best ways to find job security is to work with technology in any field at a high level of skill.

Thursday, February 5, 2015

The Changing Expectations of Employers and Employees

As teenagers turn into adults, they and their parents need to renegotiate the terms of their relationship. The parents have to let go of the control that they had over their children’s’ lives, and the new adults have to learn how to fend for themselves without the parental safety net. The transition usually takes several years, during which there are always awkward, even painful moments when the two parties’ expectations don’t totally match.

Something similar has happened as the employer-employee relationship has changed in recent decades. Traditionally, employers have had certain expectations for their employees: high-quality work output, low absenteeism, a good attitude, and reasonable cost. Employees have had their own expectations: a reasonably good work environment, respect, steady employment, and at least a living wages. Starting in the late 1970s, these expectations began to weaken or even fall away, but during this period of transition, the two parties have often found their expectations were not in synch.

The first big dislocation was the wave of downsizing that began in the 1970s and 1980s. Companies decided that their need to contain costs was more important than meeting their employees’ expectation of steady work. Loyalty became a thing of the past. In making this move, employees banked on the notion that the quality of work output would not diminish with employees’ loss of security. My generation, the Baby Boomers, never completely adjusted to the new reality. Books like What Color is Your Parachute? taught many of us how to deal with this situation, but we did not and still largely do not accept that this is the way jobs ought to be. Our expectations are stuck in the model of the 1950s and 1960s economy.

However, it’s also true that many employers did not fully appreciate how they had changed the nature of the relationship. I experienced this when I returned as a consultant to work for a company where I had been downsized after many years as a salaried employee. The company presented me with a contract that included a two-year noncompetition clause. I was flabbergasted. The company was saying, in effect, that they had no loyalty to me but that I had to be 100 percent loyal to them. I refused to sign the contract unless that clause was removed. They needed my skills badly enough that they conceded on this point.

In the 1990s, automation killed off some jobs but added so much productivity to other jobs that the economy as a whole did very well, so the employer-employee relationship did not change greatly. But as the century turned, computers got smarter and began to displace more workers and, worse yet, globalization resulted in the offshoring of hundreds of thousands of manufacturing jobs. Employers were happy with the reduced cost of foreign workers and the quality of their work, while the absenteeism and attitude of these foreign workers were somebody else’s problem. To find steady work, many former employees of manufacturing jobs now had to shift to service jobs and lower their expectations for the work environment, for respect, and even for living wages.

The latest dislocation is the arrival of what is often called on-demand work or the “Uberization” of the workforce. Technology now makes it possible for employers to take on workers for assignments that last only a few hours or less. The work may be driving passengers for Uber or Lyft, performing cleaning or other home services for Handy (formerly Handybook), or doing almost any kind of low- to moderate-skill task for TaskRabbit or Mechanical Turk. By using these temporary workers, who are classified as independent contractors rather than employees, employers can still meet their expectations for low cost, and customer feedback about individual workers ensures that the quality of work output and perhaps workers’ displayed attitude will not suffer. Absenteeism is not a problem as long as there is a sufficient pool of interchangeable workers. Some workers are able to earn better than a living wage in these arrangements, but many do not, and few can count on steady employment or respect in this relationship.

It is possible that many workers have become so beaten down, especially following the Great Recession, that they have lowered their expectations to the point where they can accept this new relationship. But several lawsuits are revealing small but significant instances in which employers’ expectations are out of synch with the realities of using on-demand workers. Last year, FedEx drivers won an appeals court ruling that they are not independent contractors, because the employer requires them to wear company uniforms, drive company vehicles, and maintain company standards for grooming. A current lawsuit against Handy makes a similar argument, stating that the employer does not treat its workers as contractors because it requires them to adhere to strict guidelines on matters such as what clothes to wear, when to ring customers’ doorbells, when to listen to music, and how to use the bathroom. Still another lawsuit, this one also against Handy, happened when the company stopped using a contractor because she subcontracted the work to her sister.

In the traditional employer-employee relationship, the worker’s expectations of good work conditions and a living wage were guaranteed by laws that governed workplace safety, minimum wage, overtime pay, Social Security payments, unemployment insurance, and the right to unionization. These laws mostly do not apply to on-demand work situations. On-demand workers may also face new kinds of liabilities. For example, when I was a full-time employee and drove from my office to a meeting at another site, my employer provided insurance coverage for the trip. Uber drivers, on the other hand, are insured by Uber only for the time when they have a passenger in their car; they are not covered when they drive to, from, or between assignments.

Perhaps what is needed is a new category of worker, “dependent contractor,” who would have some protections that independent contractors lack. A court in Canada ruled that this kind of worker has the right to reasonable notice of termination. Germany recognizes several rights for these workers.

Can the United States protect on-demand workers? I am pessimistic. The major constituencies that influence workplace policy are the corporations and the unions. Most corporations are not interested in making these concessions to workers, and few on-demand workers are union members.

Wednesday, January 28, 2015

Will a Drone and a Robot Take Your Job?

In the past, when talking about the use of robots to replace human workers, I have often given the example of ground transportation at the airport. To get from Terminal A to Terminal B at many airports, you take a robot-controlled trolley. No human judgments are needed to navigate the rails, make the stops, and open and close the doors. However, to get from the airport to your hotel, you take a shuttle driven by a human, because a robot cannot make the many judgments that are required to navigate through traffic out on the streets.

This example used to be a way I would indicate that some types of jobs may never be replaced by robots. But recently I am using this example to illustrate how robots may soon be extending their reach. Google has been experimenting with robot-driven cars for several years and has already logged hundreds of thousands of accident-free miles. The self-driven cars use GPS to understand their route and can consult a database of information to learn about speed limits and other considerations that we human drivers learn from signage. They avoid accidents with other cars or careless pedestrians by means of the same radar technology that is now being offered as an accessory in human-driven cars. Google’s technology is still experimental, but in a few years we may see it being used in airport shuttles, probably beginning with trips that involve the fewest variables, such as to and from the airport’s rent-a-car lot. I suppose the robot shuttle vans will also need to provide some mechanism that lifts heavy suitcases in and out of rear storage. And you won’t need to tip the robots.

When will these robot drivers take over? First, jurisdictions will need to change traffic laws that do not presently allow driverless vehicles on the roads, and you can expect some pushback from the Teamsters Union and other representatives of the people who earn their living by driving. Secondly, the cost of the technology will need to come down to the point where companies that deploy fleets of cars and trucks will save money by switching to robots. Besides saving on wages and benefits, fleet owners may realize savings if robot drivers prove to be safer than human drivers, as preliminary data indicates. It may take many years before all of these stars align, so human drivers can probably expect at least a decade’s reprieve.

The outlook changes, however, when you look at occupations with a shortage of human workers. There are lots of people who are qualified to drive airport vans. As far as I can tell, most states do not require a special license for the drivers, although employers look for a clean driving record. A modest level of fitness is necessary to handle passengers’ luggage, and the driver must speak English well enough to understand passengers’ destinations. But millions of Americans have these qualifications, so it is not hard to find workers to fill these jobs.

Long-distance truck driving requires a higher level of skill, and there currently is a shortage of qualified drivers. However, the higher skill requirements, which are reflected in the special licensure needed for this work, also mean that robots will probably take longer to make inroads into this occupation.

Japan furnishes a fine example of how a shortage of human workers can accelerate the adoption of robots. You may have already read about how Japan is using robots to perform certain routine health-care tasks, such as moving a patient from a bed to a wheelchair. Japan’s aging population means there is a growing number of elderly patients and a diminishing number of health-care workers with the physical strength needed to do the work. This provides the opening for robots.

Japan also has a shortage of workers who can drive heavy construction vehicles, probably also largely because of the physical demands of the work. The Komatsu company is planning to fill this employment gap by using self-driven bulldozers and excavators. Unlike long-haul trucks or even airport shuttles, construction vehicles function in a closed location and don’t have to deal with traffic or random pedestrians.

One thing that is particularly intriguing about Komatsu’s plan is that it also involves another new technology: drones. At a construction site, drones made by the San Francisco company Skycatch will survey the terrain from above, and the mapping data the drones gather on the actual lay of the land will be compared to a computerized map of how the site is meant to be shaped. The self-driving construction vehicles will then move earth as needed to achieve the desired result; their work will be periodically monitored by the drones.

Note that this arrangement displaces not only heavy-vehicle operators, but also surveyors. The Komatsu manager overseeing this project notes that the old way of surveying a site typically required a week’s work by two people, whereas the drones can acquire the data in only an hour or two.

Understand that this kind of construction will require some highly skilled human operators to program the machines, monitor their progress, and sometimes jump in to take control of a machine as needed. So consider this an example of how yet one more industry, construction, is seeing a trend toward eliminating many low-skill jobs and creating a smaller number of high-skill jobs. I have often said that construction jobs can’t be offshored, but the other trend eroding jobs—automation—is about to take its toll.

UPDATE.: Drones strike again: An Israeli company is marketing a self-piloted drone that reads water meters remotely. Also, a Dutch student has prototyped a drone that delivers a defibrillator to a heart-attack victim much faster than an ambulance could. Such drones presumably could also deliver other medical supplies needed in an emergency, plus a webcam to allow on-the-spot diagnosis that would enable helpful bystanders to be coached and thus provide better-informed first aid. Such drones certainly would not replace the need for EMTs, but they might mean that fewer EMTs would be needed to cover a geographic area because proximity would no longer be quite as important.

Wednesday, January 21, 2015

Young People and Urban Careers

Yesterday’s Wall Street Journal ran an article titled “More Young Stay Put in the Biggest Cities.” Drawing on an analysis of census figures, it noted that between 2004 and 2007, “before the recession, an average of about 50,000 adults aged 25 to 34 left both the New York and Los Angeles metro areas annually, after accounting for new arrivals.” But this turnover of young people diminished after the recession. “Fewer than 23,000 young adults left New York annually between 2010 and 2013. Only about 12,000 left Los Angeles—a drop of nearly 80% from before the recession. Chicago’s departures dropped about 60%.”

The article cites a demographer at the Brookings Institution who believes that young people may now be stuck in the cities for economic reasons: They are having more trouble getting their careers (and families) started, establishing a credit rating good enough to snag a mortgage on a suburban house, and paying off student debt. I agree that these factors are true for a lot of young people, but I also wonder why the demographer and the writer of the article did not consider another possible factor: that urban life may simply have become more attractive to young people.

The article does acknowledge one reason why young people flock to the cities: “In tough times, finding well-paying jobs may be easier in big cities, offsetting their relatively high costs of living.” Actually, there is a longstanding trend of college graduates concentrating in cities. One economist traced this trend from 1980 to 2000, so it is not just the result of temporary economic stress. As the percentage of young people with bachelor’s degrees keeps increasing, we should expect a greater percentage of young urban arrivals to make their permanent homes there.

And cities have other attractions besides job opportunities that might make young people less eager to leave. The crime rate in most large cities has plunged in recent years. Young people are showing diminishing interest in owning automobiles, a necessity of suburban life. And popular culture has changed the image of cities from the gritty and drab environment of “The Honeymooners” to the glamorous setting of “Sex and the City.”

You may be interested in which particular occupations are concentrated in cities. As it happens, in my recent book Your Guide to High-Paying Careers, I include a relevant list. Here’s how I created it: First, I identified the 38 largest metropolitan areas out of all 380 metro areas for which the BLS reports workforce size. For each of the high-paying occupations in the book (those with a median income greater than the 75th percentile for all salaried workers), I summed the number of workers employed in these 38 metro areas and then divided it by the total number of workers in that same occupation throughout the United States. This yielded a figure I call the “urban percentage.”

I thought it would be interesting to see how much better the wages for these occupations are in large cities than in the country as a whole. To do this, I computed the weighted average of the median earnings in the 38 largest metropolitan areas. (In a weighted average, the pay in each city is given a weight proportionate to the number of workers in that city.)

Understand that this single figure for the urban wage conceals the variation that may often be found among different regions. For example, look at the first occupation on the list: Agents and Business Managers of Artists, Performers, and Athletes. You’ll note that for this occupation (as for all the others on this list), the figure for average urban earnings is higher than the figure for the national average. No surprise here: Pay tends to be higher in big cities, partly to offset the higher costs of living there. But for the best pay, you may want to look for work in a particular city where your targeted industry has a large presence. This occupation earns an average of $103,380 in Los Angeles-Long Beach-Santa Ana, CA, the urban area that includes Hollywood, whereas it averages only $28,460 in Tampa-St. Petersburg-Clearwater, FL.

Here are the 20 high-paying occupations with the highest concentration in cities:

Urban Percentage
Urban Earnings
Nationwide Earnings
Agents and Business Managers of Artists, Performers, and Athletes
Art Directors
Multimedia Artists and Animators
Producers and Directors
Software Developers, Applications
Financial Analysts
Medical Scientists, Except Epidemiologists
Sales Engineers
Securities, Commodities, and Financial Services Sales Agents
Marketing Managers
Software Developers, Systems Software
Computer and Information Systems Managers
Market Research Analysts and Marketing Specialists
Architects, Except Landscape and Naval
Computer Systems Analysts
Advertising and Promotions Managers
Financial Examiners
Operations Research Analysts