Peter J Wilcoxen > PAI 789 Advanced Policy Analysis

General Information, Textbook and Requirements

Spring 2020

General information that you may need at the beginning of the semester.

Contact Information

Office Location: 426 Eggers
Office Hours: Mon and Wed, 10:00-11:30, or by appointment.
Email Address: wilcoxen at syr.edu

Course Web Site

Most course materials, including instructions for assignments and the corresponding due dates, will be posted at the main URL below. In addition, GitHub will be used extensively for distributing and submitting assignments and Blackboard will be used for submitting memos and presentations. The GitHub link below will give you quick access to your repositories once your account is set up.

Lab Sessions

On most Fridays there will be an optional lab session from 3:00 to 4:30. The sessions are very informal and you're welcome to come and go at any time. It's a good time to stop by if you're running into problems with the assignments, especially if you're having trouble with your code. The lab will be in the conference room in the Center for Policy Research in 426 Eggers. During the first two weeks of the semester (1/17 and 1/24) I have a conflict on Friday afternoon and the lab will be on Thursdays at a time and location to be announced.

Course Content

This course focuses on developing two kinds of skills needed for advanced policy analysis: (1) economic modeling beyond that covered in Economics for Public Decisions, and (2) core computational techniques for building and analyzing complex datasets in a clear, reliable, and reproducible way. More detail is available from the Course Outline page.

Learning Outcomes

Through this course you will: (1) learn how to construct and apply advanced models for economic analysis; (2) learn key principles and practices for carrying out robust, reliable, and reproducible computational analysis; (3) learn how to apply those practices in developing code for building, managing, visualizing, and analyzing large datasets, including those with a spatial component.

Prerequisites

There are two formal prerequisites: PAI 721 Introduction to Statistics and PAI 723 Economics for Public Decisions. In addition, PAI 724 Data-Driven Decision Making would usually be taken before this course.

Textbook

Strictly speaking, there's no required textbook. However, we'll use Python and Python's Pandas module extensively. If you haven't used Pandas a lot and don't want to spend more time than necessary on Google trying to figure it out, you may want to buy a copy of the book below. Also, I'll mention sections of it from time to time when talking about particular topics. 

If you're new, or relatively new, to Python, you might want to use one of the free web sites below. These are nice because they let you see what happens when you run example code.

A book that's decidedly not required, but that you might want to consider as part of your professional library, is the following:

Exercises

Weight: 60% of the final grade. The core of the class will be many short exercises: often more than one per class session. They will be a mix of pencil-and-paper analysis and computational tasks. The pencil-and-paper exercises will usually be turned in during class, or at the start of the following class. Computational exercises will usually be due late on Friday evening to give you time to finish them outside of class, if necessary. Grading will be on a 10-point scale using the following rubric:

Exercise Attribute
Points
Honest Effort no=0, yes=6
Correct Results
low=0, partial=1, complete=2
Clarity and Documentation low=0, partial=1, high=2

It's probably clear that the scale is set up to encourage trying each exercise: an honest attempt, even if it is muddled and incorrect, will end up with a 6, which is much better than a 0.

Finally, working in groups on the exercises is encouraged. However, please limit the group to no more than three people and make a note of your collaborators on your assignment when you submit it. For pencil-and-paper exercises, it's fine to turn in one answer sheet for the group. For computational exercises, everyone will need to turn in their own copy. That's to make sure everyone leaves the class with a complete set of example code.

Practice Technical Memo

Weight: 10% of the final grade. Toward the middle of the semester you'll be asked to write a technical memo documenting an analysis done in class. A technical memo should do two things: (1) show the result of your analysis, and (2) provide full details about where the input data was acquired and how the subsequent calculations were done. The idea is to provide enough information that another analyst working a year or two down the road could update the input data and rerun the analysis with confidence in both the process and the result.

Practice Memo Due Date
TBD 

Individual Project

Weight: 30% of the final grade. This will be a semester-long project on a topic of your own choosing. There will be four components: (1) a brief prospectus due partway through the semester describing what you plan to do; (2) a technical memo like the one above describing what you did; (3) a short policy brief describing your results for a non-technical audience; and (4) a short presentation describing one or two of your key findings. The due dates and grade weights of the components are shown below:

Project Component Weight Due Date
Prospectus
--
Wed Mar 11
Draft Presentation 5% Wed Apr 15
Final Presentation 5% Wed Apr 22
Technical Memo
10% Fri May 1
Policy Brief 10% Fri May 1

Working in Groups and Learning from Others

As you'll learn very quickly if you don't know it already, working with large, complex data sets using sophisticated analytical tools is a highly collaborative activity. Learning when and how to look for help will be part of the course. With that said, it will be important in some circumstances to document the line between your work and that of others. Here are some guidelines:

  1. It is not necessary to document any help you get from me. For example, if I send you a snippet of code, you don't need to cite me as the author.
  2. It is not necessary to document general help you get from web sites. For example, you don't need to provide any kind of citation if you look up how to select a subset of elements in a Python dictionary based on their values and end up using a couple of lines of code similar to something you found on the web. However, detailed help beyond that probably falls under category 4 below.
  3. It is not necessary to document conversations you have with other members of the class about: general approaches to problems; the nitty-gritty details of programming syntax or invoking particular functions; or with minor help in debugging. Very broad help like this doesn't rise to the level of group work.
  4. However, if you collaborated more closely with others on an exercise (i.e., on the design of the solution, or on doing the algebra or coding jointly) you should indicate your collaborators on your answer as mentioned earlier.
  5. In general, other things should be cited or documented. For example, if you use a function or module written by someone else (other than those in the standard library) you should document the source. If you work very closely with someone else from class, you should document that as well.
  6. Check with me if you're not sure.

Academic Integrity

Syracuse University’s Academic Integrity Policy, found at the URL below, reflects the high value that we, as a university community, place on honesty in academic work. The policy defines our expectations for academic honesty and holds students accountable for the integrity of all work they submit. Students should understand that it is their responsibility to learn about course-specific expectations, as well as about university-wide academic integrity expectations. The policy governs appropriate citation and use of sources, the integrity of work submitted in exams and assignments, and the veracity of signatures on attendance sheets and other verification of participation in class activities. The policy also prohibits students from submitting the same work in more than one class without receiving written authorization in advance from both instructors. Under the policy, students found in violation are subject to grade sanctions determined by the course instructor and non-grade sanctions determined by the School or College where the course is offered as described in the Violation and Sanction Classification Rubric. SU students are required to read an online summary of the University’s academic integrity expectations and provide an electronic signature agreeing to abide by them twice a year during pre-term check-in on MySlice.

Disability-Related Accommodations

If you need accommodations for a disability, please contact the Office of Disability Services (ODS), visit the ODS website below, or visit the ODS office in Room 309 of 804 University Avenue, or call (315) 443-4498 or TDD: (315) 443-1371 for an appointment to discuss your needs and the process for requesting accommodations. ODS is responsible for coordinating disability-related accommodations and will issue students with documented Disabilities Accommodation Authorization Letters, as appropriate. Since accommodations may require early planning and generally are not provided retroactively, please contact ODS as soon as possible.

Religious Observances

SU religious observances notification and policy, found at the URL below, recognizes the diversity of faiths represented among the campus community and protects the rights of students, faculty, and staff to observe religious holidays according to their tradition.  Under the policy, students are provided an opportunity to make up any examination, study, or work requirements that may be missed due to a religious observance provided they notify their instructors before the end of the second week of classes for regular session classes and by the submission deadline for flexibly formatted classes. If you prefer, however, you can notify me directly by email. In either case, just check with me and we'll work out an arrangement that fits your schedule.

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Peter J Wilcoxen, The Maxwell School, Syracuse University
Revised 01/16/2020