chrisconlon/sin_taxes

Replication for Sin Taxes (Conlon, Rao, Wang)

Replication for "Who Pays Sin Taxes? Understanding the Overlapping Burdens of Corrective Taxes"

Review of Economics and Statistics Conlon, Rao, Wang (2022)

Github Install Instructions

To download the repo simply type:

git clone https://github.com/chrisconlon/sin_taxes

Installation/Setup

1. This project runs almost entirely in Python (3.7 or above recommended).
2. The dependencies can be installed via:

    pip install -r ./code/requirements.txt

3. The only kiltsreader is the authors' own package:

    https://github.com/chrisconlon/kiltsnielsen

4. We anticipate most users will be running this replication package from within an Anaconda environment. To avoid making changes to your base environment you will want to create a separate environment for this replication package. To do that

    conda create --name sin_tax --file requirements.txt
    conda activate sin_tax

5. How to run the code

Change to the directory labeled code and run "./run_all.sh" on the terminal. The code should take approximately 20 minutes to run. Tables and figures will be produced as described below.

cd code
./runall.sh

6. After the files numbered 0_ through 3_, the remaining files that generate tables and figures can be run in any order.

7. Memory Requirements: 0_read_nielsen_data.py is designed for speed not for memory usage. It can use over 32GB of RAM. You may want to separately process year by year to conserve memory. Comments are provided in the code. The remaining files use neglible amounts of memory.

Kilts/NielsenIQ Data

• We cannot include the Kilts/NielsenIQ data directly in this package but information on acquiring the data for academic researchers is available at https://www.chicagobooth.edu/research/kilts/datasets/nielseniq-nielsen
• You only need to download the Consumer Panelist Data and this project does not require any scanner data.
• You will need to modify the path in ./code/0_read_nielsen_data.py to point the folder with your (unzipped) Kilts/NielsenIQ files
• You must have the pyarrow dependency installed to read and save the Kilts/NielsenIQ data.

Author Constructed files

data/raw_data:

The below files are publicly available Excel files constructed by the authors.

category_list.xlsx: mapping from NielsenIQ product_group_code to our category assignemnt nielsen_income_bins.xlsx: mapping from NielsenIQ income bin to income levels state_alcohol_rates.xlsx: Tax Policy Center panel of state excise taxes. state_cigarette_rates_5.xlsx Tax Policy Center panel of state excise taxes.

File of origin for tables and figures

Table/Figure Number	Generating File
Table 1	table1_revised.py
Table 2	table2_odds_ratio.py
Table 3	table3_by_year.py
Figure 1	Figure1_cdfs.py
Figure 2	Figure2_Correlation.py
Table A1	tableA1_alcohol_usage.py
Table A2	tableA2_ethanol_per_capita.py
Table A3	tableA3_tobacco_useage.py
Figure A1	FigureD1A1_DrinksperWeek.R
Table B2	tableB2_state_taxes.py
Table D1	tableD1_tax_burden_by_inc.py
Table D2	tableD2_sin_tax_by_Race.py
Table D3	tableD3_Regression.R
Table D4	tableD4_tax_inc_ratio.py
Table D5	table2_odds_ratio.py
Table D6	tableD6multiNM.py
Table D7	table2_odds_ratio.py
Figure D1	FigureD1A1_DrinksperWeek.R
Table E1	tableE1E2_Other_Cluster_Number.py
Table E2	tableE1E2_Other_Cluster_Number.py

Description of .parquet file format

We use the parquet format for:

Large data inputs (above) Most intermediary datasets Parquet files are compressed columnar storage binaries that are readable by several software packages (R, Python, Stata, Julia, C++, etc.) and platforms. The goal of the parquet project is to maintain good performance for large datasets as well as interoperability.

The storage method is stable and maintained by the Apache Foundation. https://parquet.apache.org/documentation/latest/

We use the python package pyarrow to read parquets and the package brotli for compression (listed in the requirements.txt).