Applied Economic Analysis

This website contains the material for the MSc course Applied Economics.

This website is under construction for 2020/2021

This year the course is taught by:

Python track AEA

For this course we use the following resources:

  • Jean Tirole, Economics for the Common Good,
  • for students following the python track in this course: We are very happy that we partner with datacamp for this course to teach you python. Datacamp offers great on-line courses for you to learn python and it allows us to track your progress…

Screencasts

For this course a series of screencasts is available. The screencasts can be found on this webpage.

Organisation of the course

Since we have recorded screencasts, we will not repeat these during the lectures. The lectures are meant for interaction (only).

For the python track, we will use the following format:

  • in the lectures we will answer questions that you ask us
  • in order to prepare these answers, it is useful if you create an issue via this link
    • using the link, create an "issue" where you explain what your question is: what did you try and why did it not work?
      • hence not a something like "I do not know the answer to: Question Calculate the total welfare that this planner can achieve. Denote this value max_welfare."
      • but "I tried to calculate total welfare in the following way … but then I get the following error …"
      • describing your question like this, helps you better understand what your problem is
      • and it helps us to answer the question in class.
  • for us to be able to prepare these answers, submit the issue on the Friday before the week in which we discuss the topic
    • to illustrate, if you had a problem in the section "The market", submit your question on Friday November 12th before noon (12:00) so that we can discuss it during the lecture of the November 17th.
    • hence, finish the sections in the week before they are discussed in class
    • if you can solve everything yourself, there is no need to attend the lecture (but you are, of course, welcome to do so)
    • this is one of the advantages of programming: you can see for yourself whether it works or not.

A suggested workflow for the python track is:

  • follow the Datacamp courses and make notes of what you learn at Datacamp in a notebook on the jupyterlab server
    • in this way it is easy to refer back to useful code snippets that you see on Datacamp
  • go through the notebook and try to answer the questions and assignments
  • if you get stuck with a problem, watch the corresponding screencast for this part. It gives hints and ways to solve the problems
  • if you still cannot figure it out, create an issue on github to ask us
  • we will answer your questions in class

Hence, sitting back and hoping to copy all answers during the lectures is not going to work. This is a bad strategy anyway: you learn python by trying (and failing) to code; not by copy/paste-ing the correct code.

For the canon track, we will use the following format:

Over the semester, you write – in a team – a "canon" in which you collect and explain insights from economics on the Corona pandemics.

To guide you in this work, the course teaches you how to "see economics everywhere" and communicate insights from economics to non-economists. You develop your skills through:

  • reading the book by Tirole, Economics for the common good https://press.princeton.edu/books/hardcover/9780691175164/economics-for-the-common-good,
  • watching videos and slides,
  • actively participating in our interactive ZOOM meetings.
  • In the lectures on ZOOM we will
    • have group discussions
    • develop material and practice economic analysis for your canon assignment
    • answer questions about the course resources (book and video material)
    • discuss progress and answer questions about assignments (proto-canon and canon)
  • In order to prepare these answers, it is useful if you …
    • send me an email with your question (j.a.smulders@uvt.nl)
    • be as specific as you can when formulating your question.
    • for me to be able to prepare these answers, submit the question on the Friday before the week in which we have our ZOOM meeting. I can then decide whether to deal with your question in the meeting or directly answer to you.

A suggested workflow for the canon track is:

  • read the Tirole book chapters, keep the order of the schedule below, corresponding to Canvas modules
  • watch the videos made available on canvas before you turn to the next set of chapters in Tirole.
    • find the videos in Canvas modules (one module for each of our ZOOM meeting); open the pdf of the slides with clickable links to all videos.
  • do the exercises discussed in the videos/slides to be prepared for the ZOOM meetings that are scheduled for the canon track (schedule here: https://janboone.github.io/applied-economics/#lecture_schedule_AEA)
  • if needed, send a question or material to j.a.smulders@uvt.nl
  • stick to deadlines (protocanon 16/9, team plan 15/10, Canon 29/1)

Lecture schedule

Due to corona restrictions the lectures will be on line via zoom. The links and passwords for these lectures are published on Canvas.

week day date time Staff Topic Tirole Datacamp
36 Tue 2020-09-01 08:45-10:30 Boone* Intro python, markdown, github, jupyterlab    
36 Fri 2020-09-04 08:45-10:30 Smulders* Canon 0: intro   Intro (1,2)
37 Tue 2020-09-08 08:45-10:30 Smulders Canon 1: The economist 1,2,3,4 Intro (3)
38 Tue 2020-09-15 10:45-12:30 Smulders Canon 2: Jobs 6,7,9 Intro (4)
38 Wed 2020-09-16 12:45-14:30 Ladenstein* Plenary Career Session   Intermediate (1,2)
39 Thu 2020-09-24 10:45-14:30 * Online Workshop by Student Career Services   Intermediate (3,4)
39 Fri 2020-09-25 12:45-16:30 * Online Workshop by Student Career Services   Intermediate (5)
40 Wed 2020-09-30 10:30-14:15 Asset* Inside the Business Day    
41 Tue 2020-10-06 08:45-10:30 Boone Q&A python/Datacamp    
42 Tue 2020-10-13 08:45-10:30 Smulders Canon 3: Climate 8,16 Pandas foundation (1,2)
46 Tue 2020-11-10 08:45-10:30 Smulders Canon 4: Finance 10,11,12 Pandas foundation (3,4)
47 Tue 2020-11-17 08:45-10:30 Boone The market    
47 Thu 2020-11-19 10:45-12:30 Boone Asymmetric information    
48 Tue 2020-11-24 08:45-10:30 Smulders Canon 5: regulation 13,17  
49 Tue 2020-12-01 08:45-10:30 Boone Financial crisis    
49 Thu 2020-12-03 10:45-12:30 Boone Empirical research    
50 Tue 2020-12-08 08:45-10:30 Smulders Canon 6: Behavior 5,15  
50 Thu 2020-12-10 10:45-12:30 Boone Health care regulation    

"*" indicates that everyone has to attend this lecture (irrespective of whether you do the canon or python track).

First Python Lecture

Assignment 1

Do the following four steps:

  • create a github account on github (you need this for the next step)
  • fill in this google form before Friday September 18, 2020
    • note that you need to login on google with your TiU credentials when you fill in the form
    • e.g. make sure that your browser is not logged in with your gmail-address
    • if necessary, create an incognito/private window
  • go to
    • jupyter lab
      • IT suggests that you use the Firefox browser to access jupyter lab
      • sometimes it helps to access jupyter lab with an incognito/private window
    • or –if all else fails– you can use google's colab
  • create a new python notebook and type the following code in the first cell:
%%bash

git clone https://github.com/janboone/applied-economics
  • then press the Shift key and Enter key as the same time
  • this creates a folder on the server `applied-economics` that contains the material for the python part of the course.
  • Note: you can only run this command once. If you run it again, you get an error since the folder already exists.

Final assignment

  • instructions for the final assignment can be found below.

Datacamp

You can get access to Datacamp via the university website.

From Datacamp, do the following courses:

It is up to you how to combine the Datacamp courses with the Screencasts. We suggest to finish the Datacamp Intro course first. As there is no economics on Datacamp, some students prefer to start with the screencasts after the Intro. Others prefer to finish all the Datacamp courses before they start on the screencasts, so that they have seen all the python and pandas material.

Deadlines

We recommend that you finish the Intermediate course before September 25, 2020 and the Pandas foundations course before November 10, 2020.

The deadline for the final python assignment is: Friday January 29th, 2021. However, if you need your grade before the end of January, you need to submit your final assignment by Friday January 15th, 2021. Let us know by email that your assignment needs to be graded early.

If you do the python track in this course, your grade is determined by the final assignment (only).

The resit deadline for the python assignment is: Friday August 27th, 2021. Let us know by email that you have submitted your assignment for the resit. Further, follow the instructions below on how to submit an assignment on github and fill in the google form etc.

Questions

If you have questions/comments about this course, go to the issues page open a new issue (with the green "New issue" button) and type your question. Use a title that is informative (e.g. not "question", but "question about the second assignment"). Go to the next box ("Leave a comment") and type your question. Then click on "Submit new issue". We will answer your question as quickly as possible.

The advantages of the issue page include:

  • if you have a question, other students may have it as well; in this way we answer the questions in a way that everyone can see it. Also before asking the question, you may want to check whether it was asked/answered before on the issue page
  • we answer your question more quickly than when you email us
  • you increase your knowledge of github!

Only when you need to include privately sensitive information ("my cat has passed away"), you can send an email.

In order to post issues, you need to create a github account (which you need anyway to follow this course).

Note that if your question is related to another issue, you can react to the earlier issue and leave a comment in that "conversation".

Final Assignment

  • The python assignment you can do alone or with at max. one other student (i.e. max group size is 2).
  • for the deadline of the python assignment, see Deadlines above
  • on Canvas we give you the link to the github repos. with the assignment_template.ipynb
  • once you have "cloned" the applied-economics repository, you can see there the assignment_template.ipynb notebook. This gives you an idea of the template for the final assignment
  • to submit your final assignment:
    • do not change the name of the assignment_template.ipynb notebook
    • fill in this google form
    • push the final notebook on the github classroom repository

what we are looking for

The idea of the assignment is that you report your findings in a transparent way that can easily be verified/reproduced by others. The intended audience is your fellow students. They should be able to understand the code you write together with the explanations that you give for this code.

The following ingredients will be important when we evaluate your assignment:

  • Start with a clear and transparent question.
  • There are a lot of interesting economic questions for which you do not need programming to find the answer. However, as an important part of this course is programming in python, we want to see some programming. So choose a question where you can show off your programming skills!
  • Briefly motivate why this question is interesting.
  • Explain the method or data that you use to answer the question.
  • Give the answer that you find (as a preview).
  • Mention the main assumptions that you need to get this answer.
  • When you use information, create a link to this information. The reader then only needs to click to find the relevant information.
  • If you use data, present graphs of the data.
  • Include the data in your github respository so that we can replicate your analysis.
  • If you use equations, use latex to make them easy to read.
  • Explain your code, the reader –think of your fellow students– must be able to easily follow what you are doing.
  • Present a clear conclusion/answer to your question.
  • Programming is great to do sensitivity analysis, just do the same you did before, but now with different parameter values.
  • Include some discussion of what you find and elements on which you need additional information.

Two remarks:

  • you can copy code from the web; but
    • make sure that you explain the code that you use so that another student of the course understands it and can use it;
    • give the reference of the code that you copy;
  • use common sense: it is not always necessary to have a full blown economic model, but we do expect you to think!
    • in the past we had students looking at the effect of age on income in sports; "theory" suggests that this relation is hump-shaped: 5 year olds and 80 year olds tend not to earn a lot of money as elite athletes; the students presented a scatter plot with a clear hump-shape; then they wrote "now we do a linear regression". For each step that you program, ask yourself why this step makes sense and then explain this in your notebook.

resit of final assignment

The resit of the final assignment needs to be a new project compared to the one you handed in before. The easiest way to achieve this is to choose a new research question; if you are using data, choose a new data set. Simply adjusting your first submission based on our feedback will be not be enough.

Otherwise, follow the procedure above on how to submit the assignment and fill in the google form.

examples of assignments from previous years

Python as programming language

We use python as programming language. I guess, a fair question is: why python? The non-scientific answer is: because I like it a lot.

Other answers, better motivated than my own, include:

We will program python using the jupyter notebook. One motivation to use the jupyter notebook is based on a paper in the AER:

Note that this is a presentation on a python conference having nothing to do with economics. The fact that this presentation uses an AER paper to motivate using the notebook, may induce a wee pause in which you ponder the state of our discipline.

After this pause, take a look at some notebooks:

https://github.com/jupyter/jupyter/wiki/A-gallery-of-interesting-Jupyter-Notebooks

Links and resources for python:

Jupyter notebooks

Markdown

For the assignment it is useful to know a bit of markdown. You can either google "markdown tutorial" or use one of the following websites:

Latex

For the python assignment it is useful to familiarize yourself with latex. Note that you do not need to type a whole document in latex (so don't worry about preambles etc.), you just need to know how to type \(x^2\), \(\alpha,\beta\) or have math displayed like

\begin{equation} a^2 + b^2 = c^2 \end{equation}

Google "latex tutorial" or go to pages like:

and focus on typesetting.

Author: Jan Boone