Are You a City Mouse or a Country Mouse?

If you’re making career plans, you may have a definite preference for the urban lifestyle or the rural lifestyle. Some people prefer the diversity, lively cultural scene, public transportation, really good restaurants, and fast pace of city life. Others would rather enjoy the big horizons, closeness to nature, traditional values, quiet, and slow pace of rural life.

If you have already made your choice of a career goal, you may have already settled this issue. Some careers, such as those in the performing arts, are very difficult to sustain in a rural area. On the other hand, many occupations in agriculture and mining require the open countryside that is scarce in urban areas.

But let’s assume that you have not yet made your career choice. One factor to consider is that, all things being equal, there tend to be more job openings in urban areas simply because there are so many businesses. Of course, you also face more competition in cities because there are so many workers with skills like yours. Another two-edged sword is the higher pay that urban jobs usually command; this may be offset by the higher cost of living (especially for housing and locally provided services) in urban areas.

So I’m not going to try to influence your thinking on this issue. I’ll assume that you have a definite preference for either urban or rural living but have not yet settled on a career goal, either as a first occupation or as a midcareer shift. So let me show to you which occupations have a high concentration in either urban or rural settings, and maybe you can find one that matches your skills and not just your preferences for location.

To calculate the urban percentage for each occupation for which I could get information, I identified the 38 largest metropolitan areas out of all 380 metro areas for which the Bureau of Labor Statistics reports workforce size (in the Occupational Employment Statistics data). For each occupation, 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.

For the following list, I set the cutoff for this urban percentage at 70. In other words, this list shows those occupations for which at least 70 percent of the workers are employed in the largest cities. The occupations are ordered to put those with the highest urban percentage at the top of the list.

Occupation                                                                   Urban Percentage

Fashion Designers                                                     85%

Agents and Business Managers of Artists, Performers, and Athletes   82%

Parking Lot Attendants                                               80%

Film and Video Editors                                               79%

Media and Communication Workers, All Other     78%

Art Directors                                                                  78%

Political Scientists                                                       75%

Sound Engineering Technicians                              74%

Multimedia Artists and Animators                            74%

Software Developers, Applications                          74%

Producers and Directors                                            74%

Economists                                                                  73%

Financial Analysts                                                       72%

Sales Engineers                                                          72%

Securities, Commodities, and Financial Services Sales Agents            72%

Brokerage Clerks                                                        72%

Medical Scientists, Except Epidemiologists          72%

Manicurists and Pedicurists                                      71%

Software Developers, Systems Software               71%

Marketing Managers                                                   71%

Writers and Authors                                                    71%

Actors                                                                             71%

Computer Network Architects                                   70%

Information Security Analysts                                    70%

Market Research Analysts and Marketing Specialists               70%

Computer and Information Systems Managers    70%

Baggage Porters and Bellhops                                70%

To calculate the rural percentage for occupations, I used a procedure similar to what I used for the urban percentage. However, instead of using workforce figures that applied to metropolitan areas, I used figures for the 172 nonmetropolitan areas for which the BLS reports occupational earnings. These nonmetro areas are regions such as east central Pennsylvania, the Low Country of South Carolina, coastal Oregon, and the Upper Peninsula of Michigan.

In the following list, the cutoff percentage is 25, which means that at least 25 percent of the workforce of each occupation is employed in the 172 nonmetropolitan areas. The occupations with the highest rural percentages are at the top of the list.

Occupation                                                                   Rural Percentage

Mine Shuttle Car Operators                                       71%

Roof Bolters, Mining                                                    66%

Logging Equipment Operators                                 63%

Postmasters and Mail Superintendents                 53%

Forest and Conservation Technicians                    51%

Roustabouts, Oil and Gas                                         51%

Farm Equipment Mechanics and Service Technicians             47%

Sawing Machine Setters, Operators, and Tenders, Wood        45%

Loading Machine Operators, Underground Mining    43%

Service Unit Operators, Oil, Gas, and Mining         40%

Continuous Mining Machine Operators                  40%

Wellhead Pumpers                                                     40%

Slaughterers and Meat Packers                               40%

Highway Maintenance Workers                                39%

Helpers--Extraction Workers                                     39%

Rotary Drill Operators, Oil and Gas                         38%

Log Graders and Scalers                                          38%

Woodworking Machine Setters, Operators, and Tenders, Except Sawing            35%

Legislators                                                                    35%

Textile Winding, Twisting, and Drawing Out Machine Setters, Operators, and Tenders    34%

Agricultural Equipment Operators                            33%

Meat, Poultry, and Fish Cutters and Trimmers      33%

Farmworkers, Farm, Ranch, and Aquacultural Animals           32%

Explosives Workers, Ordnance Handling Experts, and Blasters            31%

Derrick Operators, Oil and Gas                                29%

Correctional Officers and Jailers                              28%

Textile Knitting and Weaving Machine Setters, Operators, and Tenders               28%

Water and Wastewater Treatment Plant and System Operators             28%

Electrical Power-Line Installers and Repairers    27%

Operating Engineers and Other Construction Equipment Operators    27%

Fallers                                                                            26%

Furnace, Kiln, Oven, Drier, and Kettle Operators and Tenders                26%

Foresters                                                                       25%

Welders, Cutters, Solderers, and Brazers              25%

Mine Cutting and Channeling Machine Operators     25%

First-Line Supervisors of Farming, Fishing, and Forestry Workers        25%

Excavating and Loading Machine and Dragline Operators      25%

Racial Disparity in Earnings

Today, even as I am writing this, people are gathered on the Mall in Washington, DC, to mark the fiftieth anniversary of the march on Washington at which Dr. Martin Luther King, Jr., gave his most famous speech. It’s useful to remember that the actual name of that event was the March on Washington for Jobs and Freedom. I thought this would be a good occasion to take a look at the employment situation of African American workers.

Figures on unemployment are relatively easy to obtain. For example, you can quickly find that 13.8 percent of the African American population in the labor force is unemployed, compared to 7.2 percent in the White population. (These are actually 2012 figures.)

What I thought would be more interesting would be to look at the racial mix of various occupations and see the impact on earnings. Recently, I have been doing a lot of analysis using correlations, so I decided to see how racial presence in occupations is correlated with median earnings in those occupations. Understand that correlation is not the same as causation, but it indicates that two things are happening together, for whatever reason. In this case, I was trying to determine whether concentration of any race tends to happen together with the level of median earnings.

What I found was not surprising but also not pleasant to contemplate. The correlation between percentage of African American workers in occupations and median income in those same occupations was –0.37. If you’re not familiar with correlation, let me explain what this negative correlation means: To some extent (specifically, 37 on a scale of 0 to 100), the greater the percentage of African Americans working in an occupation, the lower the median income of that occupation is likely to be. For Hispanic/Latino workers, the negative correlation is actually even greater: –0.48.

For Whites and Asians, however, the correlations are positive: 0.24 for Asians and 0.43 for Whites.

Note that this simple analysis masks a lot of information. It does not tell us what positions the workers of each races are holding within the occupations. It also does not tell us the full-time or part-time status of the workers. (Actually, a slightly higher percentage of White workers are part-timers.) It does not include earnings of self-employed workers. It does not account for loss of earnings among those who are counted in an occupation but who currently are unemployed. For actual earnings comparisons, a better indicator might be that full-time African American male workers are currently earning 75.3 percent of the earnings of White male workers. For women, the figure is 85.0 percent. These actual earnings comparisons are consistent with what I found about tendencies in occupations.

These statistics are one more indication that we do not yet live in a postracial society. Understand that the solution to this situation is not simply a matter of achieving colorblindness in hiring, although that certainly would help and is something we have not yet achieved. In an experiment described in a paper (PDF) called “Are Emily and Greg More Employable than Lakisha and Jamal?,” resumes were randomly assigned African American– or White-sounding names and sent to employers in the Chicago and Boston areas. The resumes with White-sounding names resulted in 50 percent more callbacks for interviews.

Even if hiring were not biased, the career prospects of African Americans are damaged by a justice system that stops, arrests, and imprisons African Americans at a much higher rate than Whites, even for offenses that are known to be committed at equal rates. Imprisoning people not only puts a stain on the convict’s record that reduces employability but, especially for young people, breaks up families and thus damages the prospects of the next generation.

I am not blind to the advances in racial justice that have been made over the past 50 years. But our nation has a lot further to go to realize Dr. King’s dream, and doing so will take positive action, not passive waiting around for change to occur.

Changes in Skill-Income Payoff Over 10 Years

Everybody knows that a high level of skills is associated with high earnings, but perhaps you have been wondering which skills have the highest payoff. I actually answered that question just about a year ago in a blog that I wrote called “Transferable Skills with the Biggest Payoff.”  When I did the research for that blog, I wanted to use a statistical approach to seeing which skills are linked to the highest earnings, so I computed the correlations between the skill ratings of occupations and their median income. In the blog, you can see that Judgment and Decision Making, Complex Problem Solving, and Active Learning were the transferable skills with the highest payoff.

Now, a year later, I have been thinking about career trends and decided it would be useful to see whether these correlations changed over time. So I ran correlations again, using 2002 and 2012 earnings data from the Bureau of Labor Statistics. If you want to understand how I calculated correlations, as well as the significance of correlations as a technique, I suggest you look at the earlier blog.

As you might expect, the payoff for some skills increased over that ten-year span and decreased for others. Even the largest differences were not very big: the correlation of one skill gained by .06. Keep in mind that correlations are computed on a scale in which 1.0 means total correlation. You may think of the numbers as equivalent to percentages, which means that the biggest gain in correlation was equivalent to 6 percent.

So here are the skills that gained the most from 2002 to 2012, which is to say that their connection to earnings increased the most:

	2002 Correlation	2012 Correlation	Gain
Technology Design	0.34	0.40	0.06
Management of Material Resources	0.41	0.45	0.05
Management of Financial Resources	0.46	0.50	0.04
Installation	-0.08	-0.05	0.04
Management of Personnel Resources	0.57	0.60	0.03

I find it really interesting that three of these skills are managerial. What I take away from this is that compensation for management has probably been increasing over the last decade relative to compensation for other occupations.

I’m not surprised to see Technology Design posted the largest gains. Here’s how this skill is defined: “Generating or adapting equipment and technology to serve user needs.” It is well known that technology jobs are in high demand, but this skill is about practical uses of technology rather than scientific principles. The increasing correlation of this skill with earnings gives reinforcement to the idea that what increasingly drives the U.S. economy is innovation in applications of technology—think of the iPhone, for example.

I’m intrigued by the large gain for Installation. You’ll notice that the correlation is still in negative territory, which means that the more of this skill you use, the lower your earnings are likely to be. However, over the past decade the correlation got .04 closer to zero, which means that having this skill as an important part of your work has become less of an income liability. Perhaps the occupations with a heavy emphasis on installation are becoming better compensated as the level of technology that they use increases. In other words, as technology becomes simultaneously more complex and more important in everyone’s lives, it is becoming increasingly necessary to pay good wages to people who can install the sophisticated software and hardware that we use constantly.

Only four skills had lower correlations to earnings in 2012 than in 2002, meaning that there is less of a payoff now than there was then. These are the four:

	2002 Correlation	2012 Correlation	Loss
Repairing	-0.18	-0.19	-0.02
Operation and Control	-0.18	-0.19	-0.02
Equipment Maintenance	-0.19	-0.20	-0.01
Science	0.58	0.57	-0.01

Note that three of the four have negative correlations, meaning that they already were associated more with low earnings than with high earnings and only got more so. These three skills—Repairing, Operation and Control, and Equipment Maintenance—are characteristic of rust-belt occupations that are being replaced by robots and foreign workers.

I was quite surprised, however, to see that Science had lost ground. The contrast with the performance of Technology Design is instructive and tells me that applied scientific knowledge has gained in earnings even while abstract scientific knowledge has lost slightly. It is consistent with the disappointing national trend toward diminished funding for basic scientific research and reminds me of how a member of my family recently quit her job in medical research and became a software developer. I looked at how the correlation for Science changed on a year-by-year basis and found that it actually climbed during the first half of the decade, peaking in 2008, and then began its downward slide. This suggests that the Great Recession brought on the comparative decrease in pay for scientific research jobs.

It’s important to understand that none of the nine skills I focus on in this blog is among those with particularly high correlations with income. The movement you see here happened in the middle and bottom of the pack. If income is very important to you, I suggest you look at the earlier blog and aim at occupations that involve Judgment and Decision Making, Complex Problem Solving, Active Learning, and other skills with the highest correlations.

Another Take on Careers: Industries

In the 30-plus years that I have been writing about careers, I have focused mostly on occupations and, to a lesser extent, on college majors. But lately I have achieved a new appreciation for the value of considering careers from the viewpoint of industries. If you are thinking about a career move, you may want to think about industries and consult the sources I have found useful.

One reason that I have directed most of my attention to occupations is that this is where a great wealth of information is to be found. Resources such as the O*NET database, the Occupational Outlook Handbook, the Occupational Employment Statistics wage estimates, and the FedScope database of government jobs all present information in terms of occupations and offer valuable information about career options.

Of course, part of a career decision is learning how to prepare for a career move, and the preparation pathway to many occupations runs through a college degree. As a result, I have also researched college majors for many of my books. Not as many information resources are available for college majors as for occupations—I have relied on college catalogues and the National Survey of College Graduates—and these require a lot of analysis to yield useful facts.

By comparison, I have written very little about industries and done much less research in that field until this summer, when I undertook a project for Vault.com to write descriptions of 19 industries, such as Architecture, Animation, Elder Care, and Wholesale. If you have noticed that my blogs have been appearing less frequently of late, it was because my work schedule on this project has kept me very busy. But let me share some of what I have learned.

(If the following looks too tedious to you, go directly to Vault, where the most relevant of this information is distilled.)

One of the most valuable resources for learning about industries is the Census Bureau’s Industry Statistics Sampler pages. On the main page, click on an industry sector and drill down to the particular one that interests you. For example, if you’re interested in the software publishing industry, click the "More" arrow next to 51 Information. (The 51 is a classification number from the North American Industry Classification System [NAICS], the taxonomy that the government uses to classify all industries.) This takes you to the Industry Statistics Sampler for the Information industry. Like all two-digit industries in NAICS, it is a very large group, but you’ll find a tab called 2007 Census: Employers & Nonemployers. Click this, and you’ll see a table of information that covers not only the two-digit industry Information, but also all the three-digit, more-detailed industries within Information.

Click on 511 Publishing Industries (except Internet), and you’ll drill down to the Industry Statistics Sampler for that more-detailed industry. Once again, the tab called 2007 Census: Employers & Nonemployers will lead you to a table, this time covering all the four-digit industry sectors of the publishing industry. One of these is 5112 Software publishers. Click this, and you’ll finally get down to the Industry Statistics Sampler with the very specific level of detail for that industry sector. Click on the various tabs to retrieve tables with information such as the number of establishments, the dollar volume of sales, the payroll, the number of paid employees, and historical data on these topics. The last of these may be most interesting to you because it indicates trends. Try playing with the figures—for example, seeing whether the average number of workers per establishment has gone up or down.

The tab called 2007 Economic Census: Links to AFF (i.e., American Fact Finder) leads to links that can retrieve some very informative tables. For example, you can find where firms are clustered geographically or how business is divided among the various size firms—for example, whether industry sales are dominated by a few very big players.

The tab called 2007 Economic Census: Product Lines provides helpful information about the mix of products the industry outputs, both as dollar figures and percentages.

Turning away from the Census pages, another very useful database of information about industries is the National Employment Matrix of the Bureau of Labor Statistics, which can tell you what growth is projected for the workforce of the industry. The figures tell not only what growth to expect for the industry as a whole but also for individual occupations within the industry. These occupational projections can differ markedly; for example, the outlook for Accountants and Auditors in nursing care facilities is 11.7 percent growth, compared to –21.7 percent in newspaper publishing.

Another database within BLS is the Occupational Employment Statistics survey, which despite the name does have figures for industries. Go to the May 2012 National Industry-Specific Occupational Employment and Wage Estimates page and drill down to the specific industry that interest you. This tells you not only what the wages are across the whole industry and for individual occupations within it, but also how many people from each occupation are employed in the industry.

Information like this might help you decide what specialization to pursue within an occupation. It might guide your selection of a minor in college or which employers you focus on in your job search.

My final recommendation for researching industries is to turn to industry associations. Search the Web for “[name of industry] association” and you are likely to uncover one or several groups that represent the field. Understand that these groups are in business mostly to promote the industry, and they usually are eager for you to become a dues-paying member or take one of their certification courses. In fact, some industry association websites do little else but offer these options. On the other hand, many industry association websites feature news and even career tips about the industry. Filter the information through your critical thinking skills, recognizing that the industry representatives may be projecting a biased viewpoint. If more than one group represents the industry, you probably should compare their differing takes on the industry and its environment.

Career Paths of Veterans Show the Value of Real-World Information

Using historical data about career development—developing information based on the career experiences of real people—has both advantages and disadvantages. The advantage is that these career experiences are real, so when large samples of people’s career experiences are compiled into lists that show the most probable career outcomes, the implicit advice is realistic. However, such lists (unless they are exhaustive) do not show all possible outcomes. The result is that such lists tend to discourage people from seeking less-probable career paths, which is not always a good idea. Sometimes the status quo needs to be shaken up rather than be reinforced, and people who seek unconventional career paths are often important for creating progress in society.

I was confronted with this two-edged sword when I developed information for my book 150 Best Jobs for the Military-to-Civilian Transition. I based the selection of occupations on the actual career experiences of recent veterans, as reported by the Census Bureau’s American Community Survey for the years 2005–09. I identified a “recent veteran” as someone not presently in the armed forces who had been on active duty (not just having received Reserves or National Guard training) since September 2001. Therefore, these vets were in the early stages of their post-military careers, and the occupations they held were representative of this transition. The weighted sampled represented 1.5 million recent veterans: 1.3 million men and 200,000 women.

Of the 459 unique occupations that they held, 224 were held by more than 1,000 vets, and these were equivalent to 446 occupations in the Standard Occupational Taxonomy, for which the Department of Labor provides useful information.

It was interesting to find that vets averaged a 21 percent earnings advantage over nonvets in the same occupation, even though the median age of the vets (36) was significantly lower than that of the nonvets (42).

But the real-world data told a more discouraging story when I looked at the most popular occupations held by male and female vets. Many of the top 10 occupations held by the male vets showed an obvious connection to military training and experience:

1. Police Officers
2. Security Guards and Gaming Surveillance Officers
3. Driver/Sales Workers and Truck Drivers
4. Retail Salespersons
5. Aircraft Mechanics and Service Technicians
6. Bailiffs, Correctional Officers, and Jailers
7. Laborers and Freight, Stock, and Material Movers, Hand
8. First-Line Supervisors of Retail Sales Workers
9. Stock Clerks and Order Fillers
10. First-Line Supervisors of Office and Administrative Support Workers

On the other hand, the list with the top 10 occupations held by the female vets amounts to a roster of “pink-collar” jobs:

1. Secretaries and Administrative Assistants
2. Customer Service Representatives
3. Human Resources Workers
4. Cashiers
5. Nursing, Psychiatric, and Home Health Aides
6. Office Clerks, General
7. Waiters and Waitresses
8. Stock Clerks and Order Fillers
9. First-Line Supervisors of Office and Administrative Support Workers
10. Retail Salespersons

It’s hard to read this second list without concluding that military service does little to boost the career options of women. On the other hand, it’s important to recognize some of the career barriers that the female vets face. Employers may not be used to the idea of women in the military and therefore may overlook female vets’ military-acquired skills. Also, most recent female vets are of child-bearing age and may not be focused on (or have ready access to) a long-term career path. It’s significant that 13 percent of the female vets were working part-time, as opposed to 1 percent of the male vets. Most of the occupations in the second list use many part-time workers.

A more encouraging way to look at the experiences of female vets is to consider the occupations with the highest proportion of recent vets among the female workers:

1. Aircraft Mechanics and Service Technicians (19.5%)
2. Air Traffic Controllers/Airfield Operations Specialists (18.7%)
3. Avionics Technicians (18.1%)
4. Electrical and Electronics Installers and Repairers (11.3%)
5. Electrical Power-Line Installers and Repairers (8.0%)
6. Sailors and Marine Oilers/Ship Engineers (7.7%)
7. Earth Drillers, Except Oil and Gas (7.0%)
8. Electric Motor, Power Tool, and Related Repairers (6.9%)
9. Logisticians (6.4%)
10. Atmospheric and Space Scientists (6.3%)

In other words, when you find a female aircraft mechanic, the odds are 1 in 5 that she is a recent vet. This list demonstrates that women can and do use their military training to enter careers that traditionally have been dominated by men. They may not do so in large numbers, but this kind of career movement is possible.

I’m glad that I compiled this last list, because it shows that one can use historical data about career experiences not only to show what is probable but also what is possible. Historical data, used in imaginative ways, does not have to reinforce the status quo.

The Chinese Skills Disconnect: An Opportunity for Us?

The New York Times headline read “Degrees, but No Guarantees.” However, the story was not about the students graduating from American universities this season. Instead, it was about Chinese grads. It seems that Chinese businesses are swamped by job applications from graduating students but have few jobs to offer. At the risk of seeming to express schadenfreude, I want to point out that as bad as our economy seems for our own grads, their prospects are better than China’s.

The problem is not just that growth of the Chinese economy has slowed from its fever pitch of the previous several years. More fundamental is the nature of that economy. Like the postwar United States economy, it is growing mainly from manufacturing. You only have to prowl the aisles of your local Walmart to see the fruits of that manufacturing prowess. However, in an economy that has a very large share of low-tech manufacturing, such as the plants that produce plastic toys, rubber tires, kitchen utensils, or even iPads and other high-tech devices that are assembled by hand, there is a limited need for college-educated workers.

In the postwar American economy, millions of workers with only a high school diploma or even less education were able to find low-tech work in manufacturing plants and earn middle-class incomes. But more and more young people are getting college degrees now, despite the fast-climbing expense, because those low-skill jobs have largely vanished, exported to China, and the low-skill service jobs that remain are very low-paying.

But consider the implications for China. Think of it this way: We have not only exported the 1950s-style manufacturing capability, but also the accompanying 1950s-style skill requirements. Chinese universities are capable of churning out hordes of graduates, having quadrupled the number of college students in the past decade. But at this point in its development, China’s economy does not particularly need these grads.

A survey released last winter of Chinese young people age 21 to 25 found 16 percent of the college grads unemployed, but only 4 percent unemployment among those with only an elementary school education. This is the reverse of what you’ll find in the United States, where the May 2013 unemployment rate was 11.1 percent for those with less than a high school diploma and only 3.8 percent for those with a bachelor’s degree or higher.

There certainly is a case to be made that China is preparing human capital for the economic transition that will occur as wages rise, as manufacturing shifts to still lower-paying countries, and as their economy starts looking more like ours does now rather than our 1950s model. But what will the young grads pouring out of the universities find to occupy themselves until that shift occurs? And if that shift is somehow accelerated—for example, if China’s government invests very heavily in research and development activity—what will happen to today’s huge cohort of low-skilled Chinese workers?

America’s “greatest generation,” which prospered during the postwar period, has already left the workforce. They had several decades in which to enjoy the match between their skills and their nation’s economy. Among the baby boomers, who got their start in that postwar economy, many now are suffering from the shifted economy but at least had many good years of opportunity. What about China? With worldwide economic development now moving so fast, will millions of Chinese factory workers have the rug pulled out from under them as their 1950’s-style economy disappears? Or will China find some way to slow this transition and continue to disappoint the college grads? Either of these alternatives promises to cause social unrest.

There is still one more possibility: that China will do what we did with community colleges in the 1970s, only on a much bigger scale—that is, invest heavily in adult education and retool the hordes of low-skill workers for the inevitable economic shift. Online courses, in particular, could facilitate a golden age of Chinese adult education in the next decade. But consider that the online lessons won’t need to be based in China. Thousands of Chinese students are now coming to brick-and-ivy universities in the United States because of our sterling reputation for higher education. We are now pioneering online education. If we play our cards right, perhaps Chinese-language online education can be one of our hottest export industries in the next decade.

No, Friedman; Here's the Better Way to Get a Job

I was appalled by Thomas Friedman’s column of May 28, “How to Get a Job.”  The title indicated that the column was aimed at job-seekers and would provide advice about job-finding, but the content that followed was quite misguided. It might as well have been called, “How Not to Get a Job.”

In fairness, Friedman made one valid point: He quoted a Harvard education expert, Tony Wagner, who says, “The world doesn’t care anymore what you know; all it cares ‘is what you can do with what you know’.” Employers are losing respect for a degree as an indicator of value and instead want to know only “Can you add value?”

The root problem with the  rest of Friedman’s analysis is the unspoken assumption that all jobs are found through advertisements. Of course, in the present economy, posted jobs tend to attract floods of applicants, so the hordes of job-seekers who respond to these postings have a tough time demonstrating to employers what value they can add, and employers have a similarly tough time identifying the applicants who can add the most value. Therefore, Friedman goes into a lengthy discussion of a start-up company that claims it can devise tests to identify the most promising employees in the avalanche of resumes.

But consider that, according to some research, fewer than 15 percent of job-seekers find work through job postings. (This research was done when the economy was in better shape; I suspect the odds are worse now.) The most successful way to find jobs is to tap into the hidden job market—to identify the jobs that open and get filled without being advertised. Every job opening goes through several steps between the time when it first becomes apparent that a new hire is needed and the time when the job gets advertised. By making yourself and your skills known at any instant between those two times, you can get the job.

The procedures for connecting with the hidden job market are thoroughly spelled out in books such as What Color Is Your Parachute? by Richard Bolles and Getting the Job You Really Want by  J. Michael Farr. Briefly, it means networking. Specifically, the strategy consists of reaching out to two kinds of contacts: warm contacts, the people who already know you; and cold contacts, the people who don’t know you yet. With both types of people, it helps to focus on contacts who have good connections to the kinds of employers that hire people like you. However, unexpected connections can occur, so you should also give special consideration to anybody who knows a lot of people.

The immediate goal is not a job but an interview. In fact, the interview process is a lot easier on all parties concerned when it’s not part of a formal hiring process, so when you cold-call and ask for an interview, it’s to your advantage to say you are not looking for a job with the employer but simply want information about the kind of work the employer does. In fact, the employer may never hire you but will then be part of your network and may be able to direct you toward or refer you to another employer who is expecting to hire. So redefine what counts as an interview and don’t think it has to be for a posted job opening.

Another advantage of face-to-face encounters in the networking process is that they allow you opportunities to make a thorough case—much better than a resume could—for what value you can bring to an employer. You may make your case through any combination of what you say, the professional appearance you show, the interpersonal skills you demonstrate, and a portfolio of key accomplishments that demonstrates your skills.

This job-seeking method is not just theoretically effective; it has a proven record of success. My former boss, the late J. Michael Farr, built his publishing company on the workbook that he wrote for classes that taught this technique. A minuscule fraction of job-seekers will benefit from the high-tech, gee-whiz methods used by the start-up company that Friedman describes, but for most of us there's no substitute for networking through phone calls and face-to-face contacts.