Coding for Applied Economists

Syllabus

Course

Coding for Applied Economists

AAE 497A/597A, 2 Credits, Spring 2026

Instructor

Dr. Jeffrey D. Michler (he/him)

Office: Room N215 Building ENR2

Email: jdmichler@arizona.edu

Location

Times

Fridays, 12:30-2:00 pm MST

Office Hours

Times: Tuesdays, 1:00-2:00 pm MST Thursdays, 1:00-2:00 pm MST Fridays, 10:00-11:00 am MST

Location: Room N215 Building ENR2 (and Zoom - link, by appointment)

Website

The syllabus and other relevant class information and resources will be posted on Brightspace and at https://github.io/jdavidm/learn-stata. Changes to the schedule will be posted to Slack and announced on Brightspace.

Course Communications

Slack: Messages in Slack are the preferred method for course communication

Course Description

This workshop course is designed to give students the skills they need to code for economics, while also providing bits and pieces of information about programming more generally. It’s suitable for complete beginners who have never written any code before. Throughout the course, programming assignments will focus on applications important for applied economists. The course also introduces techniques for causal inference.

Prerequisite Knowledge and Skills

AREC 339, ECON 376, or equivalent economic statistics course

AREC 496A/596A

Course Objectives

  1. Introduce students to coding languages used in economic analysis.
  2. Familiarize students with a specific language.
  3. Apply that language to answer quantitative economic research questions.
  4. Prepare students for job market and graduate school applications.

Course Learning Outcomes

Upon completing this course, students will be able to:

  1. Demonstrate the ability to analyze data using the coding language of instruction.
  2. Describe key concepts, methods, issues and approaches in applied economics.
  3. Create and communicate comprehension of a programming language.
  4. Apply practical and technical skills such as modeling (abstraction, logic, succinctness), quantitative analysis, and general IT literacy.

Instructional Methods

Classes will involve brief introductions to new concepts followed by working on exercises in class that cover that concept. While students are working on exercises the instructor will actively engage with students to help them understand material they find confusing, explain misunderstandings and help identify mistakes that are preventing students from completing the exercises, and discuss novel applications and alternative approaches to the data analysis challenges students are attempting to solve.

Required Texts

It is highly recommended that students purchase a physical copy of The Effect. However, both books are open access and available for free online.

Required Materials

Coding assignments

Students are required to read the assigned chapters, complete coding assignments, and take a final exam.

House style

All code submitted for grading must follow this course’s house style. Code not following the house style will be returned un-graded. Students will then have a chance to re-submit the assignment in the correct style. If assignments are re-submitted the same day, they will be graded without penalty. If they are submitted late, the earned grade will be reduced 10%. If the assignment is never re-submitted, it will be graded a 0.

Replication Project

Graduate students are expected to complete a replication project in which they replicate the results in a published paper. Students will log their work on the Social Science Reproduction Platform.

Final Exam Policy

Final exam in which you will be given buggy and inefficient code that you will need to debug and improve upon. Policies and scheduling of final exam can be found at https://registrar.arizona.edu/faculty-staff-resources/room-course-scheduling/schedule-classes/final-exams/final-examination.

Attendance Policy

Attendance will not be taken or factor into the grades for this class. However, experience suggests that students who regularly miss class often struggle to learn the material.

Grading Policies

The goal of grading in this class is to encourage practice, which is crucial to learning computing skills. Given that goal, grading is based on weekly (equally weighted) assignments, with the focus of each assignment on practicing new material learned each week and reinforcing information learned earlier in the semester. Assignments due 11:59 the day before class (Thursday night)

Because the goal is practice, exercises in assignments will be graded as follows:

  • Code runs, uses the requested approach, and produces approximately the correct answer: 100%
  • Attempts to solve the problem and makes some progress using the core concept, but does not produce approximately the correct answer or demonstrate basic comfort with the core concept: 50%
  • Not submitted or no meaningful effort demonstrated: 0%

Students also enter the class with different levels of experience. The goal of assignments to is help students grow as computational scientists whether they are complete novices or have some limited background in coding. Therefore, on each assignment there are Challenge exercises that are both more difficult and require integrating all of the weeks material.

  • Undergraduate students who are already familiar with the basics may choose to submit Challenge exercises in addition to regular exercises for up to 10 pts of extra credit. If you are an undergraduate, you will need to indicate via a tag in Github if you are submitting Challenge exercises.
  • Graduate students must complete Challenge exercises as part of their homework. For graduate students, Challenge exercises do not count as extra credit but are part of the overall point total on each graded assignment.

Undergraduate Point Total

Activity Points
Coding Assignments 1500
Final Exam 500
Total 2000

Graduate Point Total

Activity Points
Coding Assignments 1800
Final Exam 500
Replication project 700
Total 3000

Grading Scale: The grading scheme will be as follows (out of 100):

A: 90-100, B: 80-89, C: 70-79, D: 60-69, E: 0-59.

Using of Generative AI

The use of generative AI (ChatGPT, etc.) is allowed for the debugging of code, which we will talk about more when we talk about debugging, but first trying to fix bugs yourself is important to learning.

Dispute of Grade Policy:

Final grade must be disputed within two weeks of receiving the grade. In week 7 we will cover how best to use generative AI to debug. Students should refrain from using generative AI on assignments until that week.

Late Policy:

There is an automatic 24 hour extension if you need it. You don’t need to ask, you can just turn things in up to 24 hours late. That said, I will grade late assignments but will not provide any feedback. So if you want feedback on your work, you need to turn it in on time. If an assignment turned in after the 24 hour grace period, the grade on the assignment will be reduced by 10% of its earned value for every day it is late including weekends.

UA Policies

Information on UA academic policies, academic resources, and campus health and wellness resources is available on the Syllabus Policies & Recommended Statements webpage. Below I highlight some policies that I find particularly important for students to be aware of.

Threatening Behavior Policy

The UA Threatening Behavior by Students Policy prohibits threats of physical harm to any member of the University community, including to oneself. See http://policy.arizona.edu/education-and-student-affairs/threatening-behavior-students.

UA Nondiscrimination and Anti-harassment Policy

The University is committed to creating and maintaining an environment free of discrimination; see https://policy.arizona.edu/employment-human-resources/nondiscrimination-and-anti-harassment-policy.

Our classroom is a place where everyone is encouraged to express well-formed opinions and their reasons for those opinions. We also want to create a tolerant and open environment where such opinions can be expressed without resorting to bullying or discrimination of others.

Life challenges

If you are experiencing unexpected barriers to your success in your courses, please note the Dean of Students Office is a central support resource for all students and may be helpful. The Dean of Students Office can be reached at (520) 621-2057 or DOS-deanofstudents@email.arizona.edu.

If you are facing physical or mental health challenges this semester, please note that Campus Health provides quality medical and mental health care. For medical appointments, call (520) 621-9202. For After Hours care, call (520) 570-7898. For the Counseling & Psych Services (CAPS) 24/7 hotline, call (520) 621-3334.

Student Assistance and Advocacy information is available at https://deanofstudents.arizona.edu/student-assistance.

Lauren’s Promise:

I will listen and believe you if someone is threatening you.

Lauren McCluskey, a 21-year-old honors student athlete, was murdered on 22 October 2018, by a man she briefly dated on the University of Utah campus. We must all take actions to ensure that this never happens again.

If you are in immediate danger, call 911.

If you are experiencing sexual assault, domestic violence, and stalking, please report it to me and I will connect you to resources or call the National Alternatives to Violence at 877-334-2887 (24-hour crisis hotline).

Any form of sexual harassment or violence will not be excused or tolerated at the University of Arizona. The UA Threatening Behavior by Students Policy prohibits threats of physical harm to any member of the University community, including to oneself.

Resources for Domestic Violence, Sexual Assault, and Stalking:

Accessibility and Accommodations

Our goal in this classroom is that learning experiences be as accessible as possible. If you anticipate or experience physical or academic barriers based on disability, please let me know immediately so that we can discuss options. You are also welcome to contact the Disability Resource Center (520-621-3268) to establish reasonable accommodations. For additional information on the Disability Resource Center and reasonable accommodations, please visit http://drc.arizona.edu. If you have reasonable accommodation, please plan to meet with me by appointment or during office hours to discuss accommodations and how my course requirements and activities may impact your ability to fully participate.

Please be aware that the accessible table and chairs in this room should remain available for students who find that standard classroom seating is not usable.

Code of Academic Integrity

Students are encouraged to share intellectual views and discuss freely the principles and applications of course materials. However, graded work/exercises must be the product of independent effort unless otherwise instructed. Students are expected to adhere to the UA Code of Academic Integrity as described in the UA General Catalog. See: https://deanofstudents.arizona.edu/policies/code-academic-integrity.

The University Libraries have some excellent tips for avoiding plagiarism, available at http://new.library.arizona.edu/research/citing/plagiarism.

Selling class notes and/or other course materials to other students or to a third party for resale is not permitted without the instructor’s express written consent. Violations to this and other course rules are subject to the Code of Academic Integrity and may result in course sanctions. Additionally, students who use Brightspace or UA e-mail to sell or buy these copyrighted materials are subject to Code of Conduct Violations for misuse of student e-mail addresses. This conduct may also constitute copyright infringement.

Course Schedule

The detailed course schedule is available on this website.