Tag Archives: Unemployment

Earnings and Unemployment by College Major

The Wall Street Journal recently published a table of income and unemployment data  that presented pay and employment rates for various college majors. The original study by Georgetown University’s Center on Education and the Workforce contained enough additional details that I thought it might be worth trying to incorporate the information into a Tableau visualization.

After a little data massaging, I created charts for both the high-level fields of study and the more detailed individual majors. Each level contains unemployment rates, income levels, and popularity of major measured by number of enrollees.

One of the first things you notice is that, despite frequent claims to the contrary, college graduates with a degree in Education have the lowest median earnings overall. The Education field also has the narrowest range of income and includes four of the ten majors with the lowest median earnings. On the plus side, fifteen of the sixteen Education majors have (or had at the time of the study) unemployment rates below 5.5% — the weighted average rate of unemployment for all majors in the study.

Graduates with an Engineering degree have the highest median earnings overall and a relatively low unemployment rate compared to other disciplines. In addition, seven of the ten majors with the highest median earnings were found in Engineering.

Other majors with good earnings potential included the usual suspects (Computers & Mathematics, Health, and Business) while the best employment prospects were found in Education, Health, Physical Sciences, and Agriculture & Natural Resources.

As for individual majors, the winners in my completely fictitious categories are as follows:

  • Most Popular –  Business Management & Administration takes this category with nearly 2.8 million grads holding this degree. The next two majors in line (also in the Business field) weren’t even close — trailing by over a million people.
  • Best Prospects –  Actuarial Science beat out four other fully-employed competitors by coming in with a median income of over $80K.
  • Worst Prospects –  Clinical Psychology tops this category with an estimated unemployment rate of nearly 20%. Yikes! I also noticed that a number of other majors in the Psychology field had unemployment rates above 10%, which means that intra-discipline career changes for people with this major would be difficult.
  • Most Deceptive – The “winner” here is Architecture, an outlier with the lowest median earnings and the highest unemployment rate of all of the Engineering majors. For this category, I wanted a relatively popular major with an uncommonly high unemployment rate … the kind of major that churns out grads and then strands them in the unemployment line. An educational Judas, if you will. (Full disclosure: I have an Architecture degree, but I can’t say I wasn’t warned.)
  • Hidden Gem – I’m going to call this one a tie between Petroleum Engineering and Pharmacy Pharmaceutical Sciences & Administration. Petroleum Engineering has a slight edge on median earnings ($127K vs. $105K) but the Pharma major has a lower overall unemployment rate (3.2% vs. 4.4%). You probably can’t go wrong with either one but keep on eye on the horizon … Petroleum Engineering is notoriously dependent on the boom/bust cycles of the oil and gas industry while workers in the pharmaceutical industry are facing major changes as companies try to adjust to globalization and increasing costs of product development. 

Unemployment vs. Underemployment

The Bureau of Labor Statistics releases the results of two major surveys on the first Friday of every month (the Current Employment Statistics or CES and the Current Population Statistics or CPS). Although the amount of information in these two surveys is quite extensive, the general public is probably familiar with only a few specific metrics.

First and foremost among these is the unemployment rate, which represents the ratio of unemployed workers to the overall civilian labor force. As with anything involving the government, this simple number is more complex than it than it seems. For one thing, the BLS has no less than six different methods of calculating unemployment … and each one comes in a seasonally adjusted and unadjusted format. The standard unemployment rate — the one that makes all the headlines — is called U-3 and it is usually seasonally adjusted.

Many economists feel that U-3 is misleading because, over they years, it has slowly excluded many of the factors that used to go into how the U.S. reported unemployment. They prefer to use the “underemployment” rate or U-6, which is the BLS’s broadest measure of unemployment.

The basic definitions:

  • U-3 – Total unemployed persons, as a percent of the civilian labor force (the official unemployment rate).
  • U-6 – Includes those people counted by U-3, plus marginally attached workers (not looking, but want and are available for a job and have looked for work sometime in the recent past), as well as persons employed part time for economic reasons (they want and are available for full-time work but have had to settle for a part-time schedule).

Keeping all of these terms straight can be difficult for the average person, so — despite Stephen Few’s objections — I have created a pie chart that attempts to explain all of the various relationships. The central pie shows the  basic division of the working age population into the civilian labor force and people who are outside of the labor force. Each subsequent pie divides these categories into smaller and more specific subcategories. 

The calculations for U-3 and U-6 can then be represented as slices of the pie:


Right off the bat you can see that there is a problem with some of the various categories. For one thing, there is an entire group of people who are listed as Want a Job Now but aren’t working and aren’t counted as unemployed. This category includes people who have been out of work for over a year and have officially fallen out of the civilian labor force. Although the U-6 figure includes a portion of this group, many critics still feel that this practice understates unemployment.

Another way to show the calculation of the two metrics is graphically, using the color coding of the legend from the chart to show the details for each metric:

This excercise highlights another potential issue for measurement of the economy by showing the importance of the denominator (in this case, the Civilian Labor Force). Variations in this number have a tremendous effect on the outcome of both calculations. By reclassifying certain groups of unemployed (the Want a Job Now crowd), people are siphoned off from both the numerator and the denominator. The end result is a slight reduction of both the U-3 and U-6 rates. Not a big deal … unless you happen to be running for office.

Fighting Insidious Business Jargon with Design

One of the biggest barriers to introducing new concepts to people is that they often have old, preconceived notions about those concepts that are just close enough to the truth to cause confusion. For example, my company recently started a sales program that establishes performance incentives by “vertical” — one of those vague business terms that could mean just about anything to anybody. Tracking such an ambiguous concept can cause a lot of angst when someone’s paycheck is on the line so it fell to me to come up with some ideas to help clarify the definition.

The first problem I needed to address was the fact that most people already thought they knew what vertical meant. When you try and find a definition online, you’ll usually come across terms like “vertical industry” or “vertical market” which both refer to groups of companies that serve specific, related  industries (i.e. a niche market). In contrast, a “horizontal market” refers to companies that meet more general business needs.

The differences between these two definitions are pretty subtle and, as a result, most people tend to associate the term vertical with almost any industry, departmental function or even groups of occupations. In our business, we need to keep such categories distinct so I decided to create a matrix that placed our two main areas of focus — jobs and industries — on two separate axes. This would provide a simple visual cue to the differences during future discussions and presentations. The basic distinctions are:

  1. Industry (based on the NAICS standard) applies to a company or client.
  2. Occupation (based on the SOC standard) applies to a person or individual.

Unfortunately, a standard table would still present the information in columns and rows — leaving the vague association with “vertical” unresolved. To address this, I decided to take a cue from a common Scandinavian holiday decoration and rotate the table 45 degrees. This eliminates all vertical and horizontal lines in the diagram and forces the observer to abandon the concept altogether. In the diagrams, the industry information appears in the orange axis, while the occupation appears in the blue axis.

 

Once this basic structure is established, unique industry/occupation combinations can be “mapped” to demonstrate situations that are familiar to the audience. These examples help reinforce the concepts while emphasizing the difference between the two categories. It can be particularly helpful explaining examples where industries and occupations share some elements in their names (i.e. health services vs. healthcare practitioners).