It will mostly include graphs and data visualizations, but will also use other sources–such as art and movies–for ideas, such as color palette. Essentially, I hope it will serve as a sort of free style magazine for those starting new projects. I’m also accepting submissions of any interactive, static graphing style, data visualization project, or anything that might help someone choose a color palette, graphing style, or layout for their own project.

This paper explores the relationship between student major and industry of employment and its application to higher education accountability. Data provided by statewide longitudinal data systems (SLDS) have enabled state educational agencies and colleges to follow students into the workforce. While most studies have focused on wage outcomes, this study shows how to use SLDS data to understand the correlation between major and industry. The transition into the workforce is an important outcome since it is an assessment of a college’s ability to develop specific, targeted sectors of the economy. We use SLDS data from Iowa to follow community college alumni from 2002 through 2008.

Find it here.

Darker areas represent a higher proportion of kids in poverty. Pockets of high poverty, 30%+, are predominately in southern Iowa. Not surprisingly, educational attainment for adults in these same school districts also tend to be lower.

The lower-left graphic–dependence on manufacturing jobs–is one of the first items I mention to folks when discussing Iowa’s economy. Rural areas rely more on manufacturing than agriculture as a percentage of employment and as a percentage of income after the 1982 recession–most assume it’s agriculture. The Federal Reserve Bank of Chicago noted the greater reliance of non-metro areas on manufacturing in the past decades. In the graph below, the orange areas represent counties which the share of manufacturing income is greater than the statewide average. The white areas are metropolitan areas. In 2009, two-thirds of non-metro counties are highlighted in orange.

Scholars have noted the decline in rural Midwestern states. Often, they point to the high concentration of manufacturing as a perilous position for rural counties. Manufacturing, one could argue, is often outsourced, which would be catastrophic for these communities if a manufacturer left. It is critical to note, however, manufacturing in these areas are often forms of food manufacturing. For instance, corn is rarely sold for corn, but is now used to create products such as high fructose corn syrup and, of course, ethanol (40% of all corn production). Though ethanol is not considered food manufacturing, it is similar insofar as it is the processing of sugars. Manufacturing provides greater gross value-added than the agriculture cultivation process which contributes to a greater proportion of income coming from manufacturing as opposed to agriculture. In reality, manufacturing is still tied to Iowa’s agricultural base, making it harder for those firms to be simply exported.

Iowa has had increasing reliance on food manufacturing for employment. A graph from the aforementioned Federal Reserve Bank of Chicago shows the increase in Iowa in relation to neighboring states:

The rise of manufacturing is notable, but it is a mistake to separate it from Iowa’s agriculture. Fortunately, it means rural Iowa is not just waiting to be outsourced, but it doesn’t guarantee economic growth either. Notable books and articles have touched on the “death” of the rural Midwest–search for any book with “Middle” in its title–which is mostly tied to the shift “away” from agriculture and large migration outflow.

Iowa, where research in Hollowing Out the Middle was based, has transformed from agriculture as a final good to agriculture as an input into manufacturing. The greater value-added from manufacturing–such a Marxian concept, I know–has helped keep real income higher in the non-metro areas.

This data does not negate the other concerns raised by the authors of the aforementioned books, but non-metro areas can provide a base for manufacturing that may not be easily outsourced.

There is a relative floor at $40,000 with a wide variation of unemployment (poor clinical psychology). There is a negative correlation between earnings and unemployment rate, but it might be too presumptuous to presume that high-paying majors get their cake and eat it too. Depending on the methodology, the higher earnings might simply reflect the fact they have a job.

Of course, median earnings is only one dimension. Below shows a messy graph that also incorporates the first and third quartiles:

On average, the third quartile was about 44 percent above the median values. Most of the majors had median earnings in the center of the inter-quartile range.

Similar data is also available for Iowa community colleges.

There are important threads in the data with are important to notice. The top 10 paying jobs are:

1. Petroleum engineering
2. Pharmacy/Pharmaceutical sciences & administration
3. Mining and mineral engineering
4. Naval architecture & marine engineering
5. Nuclear engineering
6. Mathematics and computer science
7. Chemical engineering
8. Electrical engineering
9. Metallurgical engineering
10. Military technologies

H1-B visa applications contain the company name, the occupation they’re attempting to hire, the “target” salary for the worker, and prevailing salary taken from the Census or Bureau of Labor Services. Each visa application is a public record which you can use to search salaries for a new job or promotion. Immihelp is one site where you can search a database of H1-B applications, below is a screenshot for a Senior IT Application Analyst at Principal Life Insurance

If the same occupation as you’re applying for is listed, then you should at least aim for the target salary. The prevailing wage amount gives you a sense of the minimum acceptable wage (unless you’re demanding more) since it’s the typical wage for that particular job in the U.S. In the above example, the prevailing wage data was taken from the Occupational Employment Statistics survey.

There are a few sites which attempt to provide the same service, such as GlassDoor and PayScale. These are limited since they rely on self-reported data and seldom contain all occupations. The H1-B visa database has it’s own limitations since it only contains data from applications. This is especially applicable if you’re in a field that doesn’t require a Bachelor’s degree or your company hasn’t filed for a visa application in recent years.

In describing the human body we must realize the whole and particulars. The whole is the body we see and touch. It is what we assign names, such as Scott or Tara. However, there are particulars, namely atoms that comprise the whole.

Now let’s look at the forces on the body. One may ask, how can such a body stay together here on Earth? Why not when we walk do our legs move and our torso remain in place? That being so, why are we not being pulled apart, but instead remain steady on Earth.

The answer is two-fold. For one, we stay together because bones and tissue make us continguous. Bone and tissue are comprised of cells which are built by atoms. We, therefore, stay in one piece becasue of atoms and the strong and weak forces of physics.

The second part, why we are not floating away, rests on gravity.

So to answer the question, how can we physically exist on Earth, is answered by two parts, both necessary and yet not sufficient. We stay as a whole because of the strong and weak forces of atomic physics. We remain together in one piece here on Earth because of gravity. Is this not two theories to answer one question which can also be phrased as a hypothesis? If it is a bad hypothesis/question, what makes it so?

It seems to be an introduction to a paper I never finished writing. I think it was going to address the Duhem-Quine thesis with some elements relating to a good or bad hypothesis. Probably on the separability of the two concepts. I wonder if it was going to be a good paper…

GOOD Magazine has put together an infographic on the education demands for next generation’s workforce. It’s a very timely topic for me and always a good reminder for students. Unfortunately, students who need to know this the most often do not understand.

Some common problems are abound in this infographic that are also apparent in other analysis. The “completion rate” of two-year colleges is underestimated since many students transfer to four-year universities instead of completing a degree. There is also a lot of pressure on colleges here, but there is an entire pipeline–from birth through school–that needs to be addressed.