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.
Technology drives the economy. Strictly speaking, technology is any improvement to the status quo. Economic production functions display economic growth as driven by technology, or in more common terms, innovation.
Throughout the world and throughout time, innovation is not solely the responsibility of the private sector. A common misconception is that privately owned corporations, through research and development, propel innovation. Yes, Google (a private company) is touted as the leader in innovation and Facebook, Microsoft, and Apple are all private firms on the cutting edge of technology and innovation, but the government has and should continue to provide both protection and incentive for innovation. In a time when the loudest voices are calling for smaller government as a solution to economic woes, government activism focused on innovation is the solution.
Governments drive innovation and technology, which subsequently drive economic growth in two major ways. The first is to protect intellectual property and the subsequent profits through the patent system, which provides value to ideas. If a patent is granted in The United States, the owner of the patent is granted a monopoly over the innovation for a set number of years (roughly 20 years). Currently, many rival companies are purchasing patents and creating legal troubles for their competitors (mostly wasting resources: time and money).
The current patent law is dated and does not facilitate innovation growth as intended. There is a distinct difference in ideas as inventions and ideas that are methods. The former deserve protection; a patent on a new drug provides incentive for the development of the drug and new programs (Microsoft’s Windows or Office computer programs) will only be created if financial protection exists. But methods and processes should be allowed to flourish and facilitate more growth, not be held back by court proceedings. In the words of President Obama:
“Through patent reform, we can cut the red tape that stops too many inventors and entrepreneurs from quickly turning new ideas into thriving businesses — which holds our whole economy back.”
Along with financial protection through patents, the government can directly sponsor innovation. There would be no Google, Microsoft, Apple, or Facebook without the government’s Defense Advanced Research Projects Agency (DARPA)… they were responsible for the internet. Check out what DARPA is currently working on.
Government intervention to create innovation is nothing new. Never mind the advances of government agencies such as DARPA and NASA, consider the eighteenth century problem of determining a ship’s longitude at sea (the North Star took care of Latitude). The problem had been around even before Columbus thought he landed in India, but in the mid 1700s, “the rulers of Spain, Holland, and Britain offered large monetary prizes for the solution. [The problem was solved] by a poorly educated but eminently skilled clockmaker.” He used the chronometer (from a watch) to set two watches: one to Greenwich Time and one to noon on the ship when the sun was directly overhead. Nevertheless, government sponsored financial incentive was the means of innovation. (Anecdote from Charles Jones’ “Economic Growth”)
This primitive version of “crowd sourcing” should be replicated today. We have problems in this country… too many to name, but the government should provide a means for entrepreneurs and innovators to find solutions and reap financial rewards.