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.