The ongoing privatization of higher education in the USA is driven largely by a view of education as a private good (of benefit primarily to the one receiving the education) rather than a public good (where society as a whole reaps the benefit of an educated populace). To make the “private good” view work, one has to convince people that there is a substantial benefit to the recipients of the education that far exceeds any benefit to society. This has generally been done in crassly monetary terms, talking about the earnings of graduates compared to those with less education (generally in lifetime earning terms, to make the differences appear as large as possible). By using a purely monetary assessment, one can conveniently ignore all the other effects on society, and pretend that education is purely a private investment in increasing earning potential.
Where there is a demand for data that can be fairly easily collected, someone will supply it. One of the most thorough ones I’ve seen on the economic value of a college degree to the recipient is “What’s It Worth: The Economic Value of College Majors” by Anthony P. Carnevale, Jeff Strohl, and Michelle Melton, published by Georgetown University in May 2011. The report relies on US census data from the 2009 American Community Survey, so has a large sample size (over half a million people and about 320,000 people with bachelor’s degrees), but is a little dated.
The authors calculated summary statistics from the census data, looking mainly at median income for bachelor’s degree holders in various fields. They also looked at 25th and 75th percentile earnings, earnings boost from graduate degrees, employment status, gender, race, and occupation classification.
There is no mention in the methods section of any correction for age, which means that the numbers are not very good predictions of either starting salaries or eventual salaries for people entering the field. Old fields in which everyone is nearing retirement age will have much higher salaries in the report than people entering the field will see, while new or rapidly growing fields will have reported numbers closer to starting salaries.
Comparing male and female salaries without correcting for years of experience can also lead to some major distortions of the data. In engineering fields where the “leaky pipeline” leads to much greater losses of experienced females than experienced males, one would expect the data to show higher median salaries for the males even if there is no salary discrimination. But even in the fields where one would expect the distortions to be inflating the female salaries relative to the male salaries (like nursing), males are still earning more than females—so there probably is some gender-based salary discrimination in the census data, but one would need a different analysis of the raw data to determine how much.
There are very few surprises in the data. Engineering and computer science are near the top of the salary scale, followed by business, health, and physical science. There are a whole bunch of fields in the middle, then humanities, arts, education, and psychology at the bottom. Some of the clustering is a bit idiosyncratic (like putting computer science and computer engineering with math, rather than with engineering), but the individual fields can be examined by looking within the separate chapters.
For engineers and computer scientists, median earnings are around $70k–75k, with men earning about 25% more than women (remember, this median is over all employees, not comparing individuals with the same amount of experience). There is about a 1/3 boost in salary from earning a graduate degree in these fields. Physical sciences BS degrees result in lower salaries, but the boost from a graduate degree is much higher. This report did not distinguish between MS and PhD degrees, and I suspect that the engineering salary boost comes mainly from the lower cost MS, while the physical science boost comes from the more expensive PhD. Again, a different analysis would be needed to compare the salary boost for different graduate degrees.
I noticed that in computer science and computer engineering there is low unemployment (5–6%), about 55% of the degree holders were working in a computer job with 15% having moved into management, while in other engineering fields, there is about 4–5% unemployment, but only about 35% of engineers worked in engineering, and 20% had moved into management. I suspect that the high number of computer science degree holders remaining in computer work reflects both the demand for the degree holders (jobs are available) and a fairly high level of satisfaction with the work (people aren’t burning out).
At the other extreme, theater arts majors have very low salaries ($40k) and high unemployment (9%), and most are in management or office work, with only 12% in arts jobs. I suspect that the low salaries, high unemployment, and employment outside the field is due to the very small number of theater jobs compared to theater majors.
For students (and parents) looking at likely eventual outcomes of different courses of study, the report is informative, but pay attention to the wide spread of salaries in most fields. Only half the degree holders earn more than the median, and a quarter earn less than the 25%ile level, so there is no guarantee that completing a particular degree will result in the reported levels of salaries.