Over 20 million freshmen matriculate into college each year and the most common question we ask them is: Do you know what you’re going to major in? Colleges traditionally require students to declare their major during the second year and some colleges are requiring high school applicants to select a major, thus 18-20 year-olds make a decision that defines their college degree. But does this decision define a career?
To what extent do college graduates work in fields unrelated to their college degree? Luckily the National Survey of College Graduates asks respondents this question directly. Of college graduates, 54% report that their highest degree field of study is closely related to their job. Meanwhile, 25% report that their degree field is somewhat related to their job and 20% report that their field of study is not related to their current job. Demographically, more women than men report that their field of study is closely related to their job (56.3% and 52.5%, respectively).
This data is from the 2013 version of the survey, and the answer to the question “To what extent was your work on your principal job… related to your highest degree?” has remained relatively constant (see figure 1).
While this data only consists of responses from those who are employed (otherwise there is nothing to match), there may be individuals unemployed because of their college major choice.
Ultimately, many choose careers that do not match our formal education and learn on-the-job. Nothing says that salary or happiness is based solely on this match, so the 20% of individuals who report that their field of study is not related to their current job may be doing just fine!
(note: this is the first of three posts relating college majors and careers.)
These are the resources that will be presented on Saturday at 11:00 (room 108 in BCEC).
Handout: Economic Applications NCTM
Slides: NCTM economic applications
I am teaching a hybrid AP Microeconomics course this spring. This is the first of three posts describing my experience teaching this course. The first is the basics about the course. Attached is a FAQ that I sent to students before the class began.
About the Course: AP Microeconomics
- This is a one-semester course.
- It is a hybrid course where students meet once every six school days and complete online readings, activities, and assessments.
- The goal was to add the opportunity for students to fit the course into their schedule and to add the flexibility to students’ workload.
- Students are expected to complete about an hour’s worth of work each day (equivalent of 40 minute class plus 20 minute homework).
- Students use the Learning Management System Canvas.
- Students read from Mankiw’s Principles of Economics textbook.
- Students complete workbook activities from Stone’s AP Microeconomics Resource Manual.
- Students meet in small groups in occasionally non-traditional locations (ie. library or breakout room)
- The schedule was challenging to coordinate everyone’s availability, given that we did not have a dedicated meeting time.
- There are five major resources that we use:
- Videos (short bursts of information)
- Textbook (longer and more theoretical)
- Workbook & Handouts (focusing on mechanics)
- Class (connecting dots and filling in the blanks)
- Discussion Board (with threaded answers to questions, both conceptual and vocabulary)
- Students take online quizzes to test for basic comprehension and completion
- Students take in-person quizzes and tests by scheduling a convenient time.
An economic bubble can be defined as an overvaluation of a product or asset. In the case of Facebook and its looming IPO (initial public offering) this week, I want to describe three potential bubbles relating to facebook.
First is in the literal sense. By most estimations, Facebook will have the largest IPO in history: $100 Billion. Simply, Facebook stock may simply be overvalued. With an inflated price, a classic bubble burst may be on Facebook’s horizon. The rationale for the inflated value may be because of two social networking bubbles.
Facebook uses each user’s personal information to sell ads. That’s how Facebook makes its money. Their ads are targeted based on users’ likes and clicks; they can sell their ads for a higher price because of the targeted audience. The bubble occurs as more people become fed up by the exploitation of their personal information. Once this practice becomes more common knowledge, I believe there will be an exodus from social networking sites that are simply shills for data gathering.
The third potential Facebook bubble is a mass departure by the people who were Facebook’s initial constituents. Now approaching their thirty-somethings, the college students that Facebook originally targeted may be bored by a decade of the social networking site. Many adults (mainly forty-plus) are only on Facebook because of their children. Maybe the hype will catch up with users who are departing their roaring twenties where sharing everything was a way to connect. Those users are now entering into their adult lives and may no longer feel the need to constantly share or to delve into their friends’ every matter.
Whether Facebook repels users by sharing their information or users finally lose interest in social networking, the company faces many unknowns while it emerges as a public company. While twenty years from now Facebook may be the largest company in the world, if I had to put money on it, I would bet that we all look back and see Facebook as an another instance of a dot-com bubble.
In my first (found here) of a three part series on statistical analysis, I discussed how data can inform decisions within countless industries. My research in human capital, and specifically education, provides for various usages of data in decision making. Specifically, available data can be used for predictions of when, where, and which students may have trouble, determinants of parental satisfaction, admission decisions (at both secondary and collegiate levels), and student achievement both academically, on standardized tests, and in future wages.
This post is intended to shed light on the science of data analysis, specifically conditional expectations. The mathematical approach to conditional expectation is based on heavy statistical concepts, some of which are not appropriate for this venue. That being said, my intention is to provide an accessible explanation of the techniques used in statistical analysis. For a mathematical approach, I recommend the seminal text Econometric Analysis, by William Greene, 2002; or Econometric Analysis of Cross Section and Panel Data, by Jeffrey Wooldridge.
Econometrics, and essentially any data analysis, is based on determining a prediction. In statistical terms, this is called an expectation. A conditional expectation is a prediction based on available information.
For instance, consider guessing the height of a random human being. The average human height is 5 foot 6 inches, so, this would be a logical starting point for our prediction. But if we know more information about this random person, we can improve our expectation. Specifically, if we knew the person was male, we would want to change our expectation conditional on that fact. The average height of an adult man is 5 foot 9.5 inches, so that would be our new prediction given some information.
If we also knew that the person weighed 240 pounds, we may want to increase our expected height. Note here that there is no causal assumption, just a change in our expectation, given some piece of information. This is correlation: the taller a person is the more, on average, we expect that person to weigh. We may predict 6 foot 1 inch for our random male weighing 240 pounds. Data analysis can help us with our predictions, given some imperfect information.
Next, consider that we also knew this random person’s SAT score was 1200. We probably would not consider changing our expectation of height. If we had data on a sample of people with the variables height, sex, weight, and SAT score, some variables may be good predictors of height and be statistically significant while others, SAT score in this example, would not be statistically significant and would not persuade us to change our expectation.
Multiple Regression Analysis essentially considers a sample of data and determines the predictive success of each variable. Using the information from a statistical data program (even excel can do this reasonably well), we can arrive at a predictive equation for the most logical expectation conditional on the information we have at our disposal.
The data will find the coefficients- the “B’s”- and also determine the likelihood that each “B” is a significant predictor. In this case, I suspect B3 would not be statistically significant.
In part three, I will discuss an example of student achievement data from a nation-wide sample and how conditional expectations can be used to inform decisions in many fields.
In the past few days, I have come across this question multiple times: “What would happen if there was a negative interest rate?”
First, let me define a positive, then a negative, interest rate. Banks want us to give them our money so that they can lend it to others. The bank pays us to give it our money then charges a premium to others to borrow that same money. A negative interest rate would result in us paying the bank while we give it our money. This is not entirely unfeasible; in fact, with the recent rise in bank fees, this may be the case for some smaller accounts.
In a piece in Slate Online Magazine by Matthew Yglesias, which I commented on in my last post, Yglesias describes a negative interest rate as “in effect a tax on holding cash in the bank”. He continues with the logic that if this were the case, we would all store money in shoeboxes. That is, unless there was no physical money only “electronic” currency that we were forced to pay this “tax” on. Then, he argues we could stimulate demand by “raising this tax” or equivalently making the interest rate more negative.
But even in the simple model economy I described in my last post (The Economic Overlapping Generations Model), a negative interest rate results in saving. Specifically, by the need to store the value of current production. The Value of Money is based on our need to store value not in the interest rate that we receive from the bank.
This past week Matthew Yglesias wrote two pieces for Slate that underestimate the value of money in our society. His first piece was about eliminating paper money, where he argued that we could end recessions by eliminating paper currency. In a blog post this Friday, he argued about the “Irrelevance of Money.” None of Yglesias’ comments are directly untrue; in fact, the first paints a clear picture of how the cash we hold and the interest rate we receive drive spending decisions. Yglesias does however, hide the other two main sources of value of money.
The origins of money are as a trading tool. A currency, whether gold, silver, or printed paper, facilitated and continues to facilitate trade. My cart full of corn for your week’s labor is a difficult trade if there is no means of payment beyond barter. This is the most obvious use and value of money.
But money as a storage of value over time is money’s main source of value, and opposite to Yglesias’ claims, its obvious relevance. Economists model the value of money using a model called “Overlapping Generations,” first formulated by Irving Fisher, then expanded upon by Paul Samuelson and Peter Diamond.
The non-mathematical description of the model and subsequent formula for the value of money goes like this: When we are in our working age (the first generation), we generate income but in our later years we do not generate income. Subsequently, we need to store our income from our younger years to our older year. The way we do that is money. Food rots and houses deteriorate, when we are not generating an income, we need to be able to provide by delaying consumption from our early to latter years. In the model, the young “sell” services and goods to the old for “money,” and when the young turn old, they buy services from the new young generation, and so on. In the “Overlapping Generations Model,” money increases utility and that increase has subsequent value.
Yes, money is the cause of many evils in the world, but money is not the sole cause of recessions as Yglesias claims. Besides the everyday ease money creates in facilitating transactions, money is a necessity as a means of transferring wealth over time.