title	README
author	Andrew Pederson
date	2022-09-08
output	html_document

Evaluating Service Quality vs. Revenue and Spending Between U.S. States

This analysis was inspired by a 2021 WalletHub analysis that purported to show a 'tax ROI' between states. That is, a comparison between the amount of taxes paid and the quality of services received. This project began as an experiment to statistically evaluate the WalletHub analysis and expand it by using U.S. Census data on state tax revenues as well as state and local expenditures on different service areas to explore those relationships.

This project found no significant statistical relationship between partisan control and service quality and also prompted questions about the original methodology. These questions provide a solid basis for reinterpreting the original measurement indicators and experimenting with new ways to gauge the efficacy and effectiveness of government spending as it relates to specific citizen outcomes.

The final report is in this directory as a .pdf file generated from 'State_Performance_Party.Rmd', and the raw data files are the in ./data directory. The 'Data_Clean_Munge.py' script was used to acquire, clean, format and output data files, and the 'state_ranks_analysis.R' script was used to conduct the statistical analyses and create visualizations.

Data Sources

• WalletHub state tax ROI from https://wallethub.com/edu/state-taxpayer-roi-report/3283
• Party control of the Legislature and Governorship in 2020 was manually input from https://ballotpedia.org/State_legislative_elections,_2020 and https://ballotpedia.org/Election_results,_2020:_Partisan_balance_of_governors
• Tax Burden data from https://taxfoundation.org/tax-burden-by-state-2022/
• 2020 population estimates from the US Census API from http://api.census.gov/data/2021/pep/population
• Census data on state and local taxes and expenditures from https://www.census.gov/data/datasets/2020/econ/local/public-use-datasets.html. The two Excel files were combined to include all states.
• National Assessment of Educational Progress (NAEP) test scores for most recent available year from https://www.nationsreportcard.gov/profiles/stateprofile
• National Center for Education Statistics student enrollment data from https://nces.ed.gov/programs/digest/d22/tables/dt22_203.20.asp _
• CDC life expectancy data from https://www.cdc.gov/nchs/pressroom/sosmap/life_expectancy/life_expectancy.htm
• CDC infant mortality data from https://www.cdc.gov/nchs/pressroom/sosmap/infant_mortality_rates/infant_mortality.htm
• CDC obesity data from https://www.cdc.gov/obesity/data/prevalence-maps.html

add-venv-additional-instructions

Development Setup

Prerequisites:

• Python 3.x (for generating data)
• R

Python environment setup for data generation

The following steps are recommended for running Data_Clean_Munge.py.

1. Create a new virtual environment (in a local directory called venv at the project top-level):

    python -m venv venv
   

2. Activate the virtual environment:

     source ./venv/bin/activate
   

3. Install project dependencies:

     pip install -r requirements.txt
   

R setup for report processing and statistical analysis