anthonyokc/automation-and-the-minimum-wage

Automation and the Minimum Wage

WARNING:

The CPS data (cps.csv) are a random sample of 4% of the full data set, which has 70 million observations. Accordingly, the sample only has 2.8 million. This is for your convenience in having the code run more quickly. However, estimates are far off that of the full sample and not statistically significant. For the actual full results, see the end of the real paper: https://anthonyfloresokc.myportfolio.com/raising-the-minimum-wage-slightly-increases-automation

You can access the full data set here: https://cps.ipums.org/cps-action/variables/group

Use the variables selected during importing here as a guide for which variables to checkout of IPUMS.

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Requirements:

-- R 4.1.1

Instructions:

(Automated)

• RunAllCode.R: Run the entire analysis from start to finish, including the resulting .pdf.

• RunDirectory.R: Each folder in /src/ contains this file, which will run that section of code in the correct order.

NOTE: Using these scripts clears corresponding /out/ directories to ensure output is produced by current code.

(Manual)

The following describes the order to run the analysis from start to finish.

0. Run install_r_packages.R if first-time running analysis.
1. Clear output from corresponding ./out folders to ensure all output is new.
2. ./src/data-management
   1. rti_clean_join.R
   2. mw_clean_join_genvar.R
   3. cps_clean_join_genvar.R
3. ./src/analysis/
   1. compute_rti_rsh.R
   2. regression_aggregated_rsh.R
   3. regression_disaggregated_rsh.R
4. ./src/paper/
   1. Knit to PDF -> Automation-and-the-Minimum-Wage.Rmd

Directory Guide:

• ./ the Root Folder: The root directory.

• ./src folder: This is the 'source' folder. It contains all of our code files we develop during our analysis and the original datasets we begin your analysis with.

• ./out folder: This is the output directory. We will put anything that we create by running a R script. For example, it can contain new datasets we create by cleaning and merging our original data, saved regression results and saved figures or summary tables. The main point is that anything we can recreate by running R scripts will get saved here - whether they be 'temporary' or 'intermediate' outputs that will get used by another R script later in our project, or 'final' outputs that we will want to insert into a paper, report or slide deck.

• ./sandbox folder: As we work through our project, we will want to explore new ideas and test out how to code them. While we play with these bits and pieces of code, we save them in the sandbox. Separating them from src means we know that R scripts in here are 'under development'. When they are finalized, we can move them into src.

Subfolders Guide:

• data/ contains all of the project's original/raw data.

• data-management/ contains all R scripts to clean and merge datasets together

• data-specs/ contains any special parameterizations used in cleaning or analysis.

• analysis/ contains all R scripts that are our main analysis. For example, our regression scripts

• figures/ contains R scripts that produce figures. One script per figure.

• tables/ contains R scripts that produce summary tables and regression tables. One script per table.

• lib/ contains R scripts that contain functions that can be used more generally. For example helper functions that can be used in both data cleaning and analysis could be put here. So can scripts that contain functions that can be portable across multiple projects.

• slides/ contains the Rmarkdown files to write up project results as a slide deck, i.e. the text of the slides.