ECON 2200 – Principles of Microeconomics

Days and Times: Tuesday, Thursday 1:30-2:45 PM
Classroom: Mammel Hall 118

This course is designed to help you learn about the most important concepts in Microeconomics and how they apply to our interactions with firms and people every day. I expect you to come prepared to class. If you have any topics that you think are particularly interesting, please bring them up to me so that I can make sure to teach the topics you care most about. This class is for you!

Learning Objectives

This course is part of UNO’s General Education Curriculum within its Social Sciences category. As such, there are four UNO-determined and approved Student Learning Outcomes (i.e. learning goals and objectives). For your information, these goals are listed below:

  1. Demonstrate an understanding of the diversity of interactions between human motivations, institutional forces, and/or social behavior.
  2. Use critical thinking and reasoning skills to analyze theories, perspectives and/or concepts in Economics.
  3. Identify multiple methods and modes of inquiry and their appropriate application.
  4. Communicate ideas and explain concepts and analyses using the language of Economics.

Office Hours

I will hold office hours Mondays from 2:45-4:15 PM, or by appointment. I would love to get to know you and your goals, so please come by and talk to me!


This course will be graded as follows:

All assignments (aside from in-class exams) need to be submitted through the Blackboard course space. Final grades will be based on the weighted average of your work, and distributed according to the following scale.

Letter Percent
A 940-1000
A- 900-939
B+ 870 - 899
B 840 - 869
B- 800-839
C+ 770-799
C 740-769
C- 700-739
D+ 660-699
D 600-659
F < 600

Reading Assignments

The assignments for this class will be made up of reading assignments from two books: OpenStax Principles of Microeconomics (OpenStax Microeconomics), Naked Economics (ISBN: 978-0393337648). The book titles are abbreviated in this syllabus as OPM and NE, respectively. Each week, you need to answer questions based on the reading and class material in order to demonstrate that you have read the expected chapters.


In place of spending more time memorizing facts for quizzes and tests (sorry, we still need to have at least one midterm and a final…), we will focus instead on a favorite project of mine: estimating the value of baseball players. This project may be completed with a group (I encourage you to find a group for this project, since you will probably work on teams in the real world).

Project Part 1 - Due at 11:55 PM, October 10th

The first part of this project is to collect data on all 30 MLB teams during last year’s baseball season (this year, that means data from the 2015 season).

Please be sure to turn in a printout of any Excel sheets created by you/your group as well as a short write-up (2-3 pages) describing how you completed the project, answering the questions included below, and any other insights you have after completing the first half of the project.

Before you begin the project, read the Hakes & Sauer Moneyball Paper , as well as the article Billion Dollar Billy Beane . Why was it possible for Billy Beane (the General Manager of the Oakland Athletics) to do so well relative to other MLB teams?

For this part of the project, you will collect team-level data for all 30 MLB teams, and use it to assess the impact of offensive success in baseball on the revenue generated by the firms (teams) in MLB. The following instructions describe how to collect and organize the information in a spreadsheet.

  1. Place the team names in the first column of your spreadsheet, with the first cell labeled “Team.”

  2. Label the second column “OBP,” and collect the \textbf{team} On-Base Percentage (OBP) for each team in MLB, and enter the values in the cells corresponding to those teams (use the information from the \textbf{regular season}).

  3. Label the third column “SLG” and enter each team’s regular season Slugging Average (SLG) into the cells corresponding to each team.

  4. The paper by Hakes and Sauer (2006) lists On-Base Percentage and Slugging Average as the most important factors in determining the number of runs (and, ultimately, wins) that a team earns. The authors find that OBP is twice as important in determining these outcomes as SLG. Create an index of offensive production in the fourth column, and label the column “Index.” The index should be calculated using the following formula: Index = 100 x (2 x OBP + SLG) Note: We multiply by 100 in order to have numbers that are easier to read.

  5. Label column 5 “WinPercentage.” Collect the regular season win percentage of each team, multiply by 100 (so that a team with a .500 win percent has a value of 50.0), and enter the data in the cell corresponding to the correct team.

  6. Label column 6 “Attendance” and column 7 “TicketPrice.” Collect the total attendance for each team, as well as the average ticket price for each team during the most recent completed season, and enter the data in the corresponding cells of the spreadsheet.

  7. Label column 8 “TotalRevenue,” and calculate the cell according to the following equation: TotalRevenue = Attendance x TicketPrice

  8. Using the information you have collected and calculated, create three scatter plots. In each scatter plot, include the linear trendline and select the option to display the equation of the trendline. The three plots you should present are:

    • Team Revenue (y-axis) to Index (x-axis)

    • Team Revenue (y-axis) to Win Percentage (x-axis)

    • Win Percentage (y-axis) to Index (x-axis)

Each trendline will be formatted as a traditional linear equation of the form y = mx + b. For our purposes, we care about the slope of the trendline (the m in the equation above). This slope tells us the effect of an increase in our x-variable on the value of our y-variable. What is the effect of a one-unit increase in the Index on TotalRevenue? How about Win Percentage?

Is the value of the coefficient from the third plot (WinPercent vs Index) times the coefficient from the second plot (TotalRevenue vs WinPercent) close in value to the coefficient from the first plot? What does this suggest about the relationship between wins and On-Base Percentage?

Project Part 2 - Due at 11:55 PM, December 5th

For the second half of this project, you will be analyzing whether or not individual players in Major League Baseball were signed to appropriate contracts for the 2016 season.

Again, please be sure to turn in a printout of any Excel sheets created by you/your group as well as a short write-up (2-3 pages) describing how you completed the project, answering the questions included below, and any other insights you have after completing the first half of the project.

In this portion of the project, you will make use of the information about baseball franchises from Part 1 to estimate how valuable the contribution of free-agent baseball players should be to their new teams. This information will allow you to determine whether or not you believe that a given player is over- or under-valued according to their offensive statistics.

  1. Using the list of free agents for the current (or most recent) offseason, select 30 players randomly. Note that all players should be position players, not pitchers. Please be sure to explain the process that you use to select the 30 players at random. Place these 30 players’ names in the first column of a new spreadsheet (You may also use a column for first name and another for last name if you choose).

  2. Using the Player Batting Statistics Database listed at the top of the assignment, obtain the most recent OBP and SLG statistics for each of the 30 players on your list. Label the second column “OBP” and the third column “SLG,” and enter the values in the appropriate cells. Note: For some players, you may have to use minor league statistics to obtain recent OBP and SLG values. We will pretend for the purposes of this project that these numbers accurately reflect how the player will perform in the major leagues, even though this is a VERY strong assumption.

  3. Collect each player’s salary from the Player Salary Database. Use the salary for each player in the upcoming season, not their past salary. If a player has no salary, assign them a salary of $507,700 (current league minimum salary). Enter these values in the fourth column, and label the column “Salary.”

  4. In the fifth column, calculate the player’s offensive index using the same equation as you used for the team offensive index. (See Part 1 Step 4).

  5. Label the sixth column “Difference.” Divide the output of the player by ten, because a typical starter on an MLB team will receive approximately 1 in 10 at-bats for his team, and so his index value will represent about 10% of the team index value.

  6. In the seventh column, multiply the “Difference” value for each player by revenue generated at the team level for each additional unit of offensive output. This value is the slope of the trendline from the Index vs. Revenue plot in Part 1 of the project multiplied by the actual output. Label this column “MRP” (Marginal Revenue Product). This is an estimate of the revenue generated by a team that signs a particular player, and in a competitive market should be the amount paid to the player for signing with a team.

  7. In the eighth column, use an “if” statement to assign a value of “undervalued” to players whose MRP is greater than their Salary, and a value of “overvalued” to players whose MRP is less than their Salary. Remember, these values are stored in columns 4 and 7.

  8. Make a scatterplot of the true salary (x-axis) and the predicted salary (y-axis) of each player in your sample. How close are these estimates to the true salary of each player? What do you think might cause the differences?

Course Schedule

Week 1 (Starts January 9)

Introduction, Why Economics Matters – This week we will go over what we will learn in class, and talk about the syllabus and grading procedures for the course. Additionally, we will talk about how to make sure that you are successful in this class as well as your other classes by talking about how to learn, take notes, and study. Your reflection this week will be based on making goals for success during the semester. Read OPM: Ch. 1

Week 2 (Starts January 16)

Choices and Trade – This week we will start our discussion of Economics. We will talk about why Economics is an important field, and what Economists actually study. We will discuss how understanding the choices that people make can affect our business as well as our personal interactions.
Read OPM: Ch. 2, 19 and NE: Introduction

Week 3 (Starts January 23)

Demand and Supply – Now that we know what Economics is, let’s dig in! We will start by looking at what supply and demand are, and what the relationship is between the two. We will explore several examples of supply and demand.
Read OPM: Ch. 3, 4 and NE: Ch. 1

Week 4 (Starts January 30)

Elasticity – This week will focus on a relatively tricky concept: elasticity. We will talk about what elasticity means, how to calculate it, and the many different relationships that it can help us to understand.
Read OPM: Ch. 5 and NE: Ch. 2

Week 5 (Starts February 6)

How Consumers Behave – We typically view ourselves as consumers, and it turns out that Economics is just as important to consumers as it is to producers (people who make stuff). This week we will talk about the choices that consumers frequently face, and about making decisions at the margin, which is one of the most fundamental aspects of Economics.
Read OPM: Ch. 6 and NE: Ch. 3

Week 6 (Starts February 13)

How Producers Behave – Producers also make their decisions at the margin. We will discuss how costs vary in the short and long run, and why we need to distinguish between the two. After understanding how consumers and producers make decisions, we will be ready to explore markets in greater detail.
Read OPM: Ch. 7 and NE: Ch. 4

Week 7 (Starts February 20)

Perfectly Competitive Markets – Perfect competition is rare in reality, but is the fundamental market structure. All other market structures are best described by explaining in what ways they differ from perfect competition. We will talk about what makes markets perfectly competitive, and what this means for both producers and consumers.
Read OPM: Ch. 8 and NE: Ch. 5

Week 8 (Starts February 27)

Catch-up, Review and Midterm Exam – This week we will catch up if we have fallen behind, and will take the Midterm Exam. Come ready on review day with questions you want me to answer, so that I can best help you to be ready for the Midterm.

Week 9 (Starts March 6)

Monopoly – Monopolies are the extreme opposite of Perfect Competition. We will spend this week discussing how monopolies happen, and what it means for both producers and consumers when monopolies exist.Read OPM: Ch. 9 and NE: Ch. 6

Week 10 (Starts March 13)

Antitrust Policy and Monopolistic Competition – How do we take advantage of the best parts of monopoly (if you think there are any), while still protecting consumers from firms with market power? This week we will explore how monopolies are regulated through Antitrust policies. We will also look at a market structure that looks a little bit like monopoly and a little bit like perfect competition called monopolistic competition.
Read OPM: Ch. 10.1, 11 and NE: Ch. 7

Week 11 (Starts March 27)

Game Theory and Oligopoly – What happens when there are only a few firms in a market? It turns out that these markets are called Oligopolies, and are very hard to explore without using Game Theory. We will spend some time discussing game theory as well as what it tells us about interactions between firms in an Oligopoly.
Read OPM: Ch. 10.2 and NE: Ch. 8

Week 12 (Starts April 3)

Externalities – After spending the past few weeks exploring our data, and getting our heads around the questions that we might want to ask of our data, we will talk about classical methods of data analysis. We will spend some time talking about what correlations and regressions are, and about when they are the best tools for the job. In lab, we will take our data, and explore the effects of variables that we choose on some outcome that we care about. We will be able to combine this information with our visualizations and summary statistics from previous weeks into a nice briefing for our “boss” about what we learned from our data, and why he/she should believe our discovery.
Read OPM: Ch. 12, 13 and NE: Ch. 9

Week 13 (Starts April 10)

Poverty and Inequality – Are markets the perfect solution to all of society’s problems? This week we will talk about Income Inequality and Poverty measures, as we seek to understand why the government gets involved in attempting to reduce poverty.
Read OPM: Ch. 14 and NE: Ch. 10

Week 14 (Starts April 17)

Public Economics – It turns out that Economics can tell us a LOT about how government works. This week we will look at how Economics predicts special interest groups, as well as many of the other complicated problems that arise in democratic (or even non-democratic) states.
Read OPM: Ch. 18 and NE: Ch. 11

Week 15 (Starts April 24)

Labor Markets – Unless you are independently wealthy, you will probably participate in Labor Markets at some point in your life. This week we will talk about insights that Economics provides into topics such as discrimination, unions, and immigration as they relate to Labor Markets.
Read OPM: Ch. 15 and NE: Ch. 12, 13


UNO’s requirements for Academic Integrity and Behavior All students are required to adhere to the highest standards of academic integrity and behavior and must satisfy the UNO Academic Integrity Policy and Student Code of Conduct It is the student’s responsibility to read, understand and abide by these policies.

If I find that you have plagiarized, been dishonest in completing your assignments, or cheated an an exam or assignment, then I reserve the right to award you no points on the entire exam, project, or assignment and to report the behavior to the university. If this behavior is repeated, I reserve the right to award a failing grade, independent of your score on other assignments. Academic integrity is essential to education, and I take it very seriously.