Lab Instructions
Use this .R script to add federal program data to your models. Save a local copy within your labs/wk05/ sub-directory.
Then within a separate .Rmd file, use the difference-in-difference framework introduced in the tutorial to estimate the impact of each federal program: the NMTC and the LIHTC.
- Use your baseline model predicting tract change from Lab 04 as the starting point.
- Create a log-linear diff-in-diff model following these steps and add your control variables from Lab 04.
Report and interpret the main results from the models. Are the programs effective at catalyzing neighborhood improvement? We are assuming median home value captures the increased market demand that reflects the desirability of improvements.
REFLECTION:
How can we test the parallel lines assumption in this model? We know that growth rates change significantly between periods. The market for urban homes from 1990-2000 looks very different from the market in 2000 to 2010.
Submission Instructions
- your separate
.Rfile that stores your data processing steps- You do not have to call it
utilities.Ranymore, feel free to call it whatever you like.
- You do not have to call it
- your knitted RMD file that answers the questions posed above.
- If your RMD file looks exactly like the tutorial, you will not get full credit.
- This is because the tutorial is meant to show you what the data preprocessing steps should look like; not where they should live (which is in a separate
.Rfile). - This tutorial also has extra material meant to teach you concepts. The non technical audience does not need to see this information so exclude it from your work when you submit your lab.
- You need to use
import::here()function and import specific objects rather than doing all of your data processing within the.rmdfile. If you need a tutorial on how to use this, please see the “Project Data Steps” tutorial.
- your HTML output of your RMD file
Record your work in an RMD file where you can document your code and responses to the questions. Knit your RMD file and include your rendered HTML file.
Note that this lab will become one chapter in your final report. You will save time by drafting the lab as if it is an external report chapter rather than a regular lab.
Login to Canvas at http://canvas.asu.edu and navigate to the assignments tab in the course repository. Upload your zipped folder to the appropriate lab submission link.
Remember to:
- name your files according to the convention: Lab-##-LastName.Rmd
- show your solution, include your code.
- do not print excessive output (like a full data set).
- follow appropriate style guidelines (spaces between arguments, etc.).
See Google’s R Style Guide for examples.
Notes on Knitting
If you are having problems with your RMD file, visit the RMD File Styles and Knitting Tips manual.
Note that when you knit a file, it starts from a blank slate. You might have packages loaded or datasets active on your local machine, so you can run code chunks fine. But when you knit you might get errors that functions cannot be located or datasets don’t exist. Be sure that you have included chunks to load these in your RMD file.
Your RMD file will not knit if you have errors in your code. If you get stuck on a question, just add eval=F to the code chunk and it will be ignored when you knit your file.
However, you cannot make every chunk eval=F. Only resort to this if you truly are stuck: that way I can give you credit for attempting the question and provide guidance on fixing the problem.
