kvsroyal/funding-analysis

An attempt to replicate the findings and methodology of the EdBuild Analysis of National Public School Funding found here: https://edbuild.org/content/23-billion/methodology.

funding-analysis's Issues

Adjustment calculations and exclusions

Subtract the following from total state and local revenues for each school district:

• Revenue for capital from the calculation of state revenues.
• Money generated from the sale of property from local revenues.
• A proportional share (based on the percent of each districts’ revenues that come from local, state and federal sources) of the total amount of money sent to outside charter LEAs—an expenditure category included in the F33 survey.

Exclude the following districts:

• Districts that are of types 5 (vocational or special education), 6 (nonoperating) or 7 (educational service agency) in the F33 data
• If F33 school type is missing, districts that are of types 4 (regional education service agency), 5 (state agency), 6 (federal agency), 7 (charter agency) or 8 (other education agency) based on Common Core of Data
• Districts with missing or zero total enrollments
• Districts that have missing or zero operational schools
• Districts that have missing revenues
• Districts that have very low revenues (<$500)
• Districts that have very high revenues (>$100,000)
• Districts from the US territories
• School districts that intersect with Native American Reservations because federal dollars are a much larger proportion of revenue for Bureau of Indian Affairs (BIA) schools and the federal dollars are not always intended to supplement funds from BIA.

Finally:

• Calculate per-pupil state and local revenues by dividing state and local revenues (adjusted to exclude the monies described above) by fall enrollment counts as reported in the F33 survey.

This is in line with the methodology found here: https://edbuild.org/content/23-billion/methodology

Title Explanations

Each Spreadsheet has poor titles (acronyms) as column titles. Creating some support for figuring out what these terms mean would make using the large data sets in this project easier. Recommended goals:

• Add another Spreadsheet or data construct containing all the acronyms and their descriptions
• Add a function that will use this construct to return a meaningful description of a given title

Summary Table Computations

The summary tables are a result of selective sampling of data found in the relevant_raw_data file. Instead of parsing them, we should create some functionality that will allow a user to selectively sample whatever they would like more easily.

• Create functionality that will allow sampling of relevant_raw_data files
• Create a small example / testing class for this

data_flags.xls is missing

The data_flags.xls file is too large to be uploaded directly to GitHub. The following need to be done to integrate it:

• Separate it into two smaller files
• Adjust loading_data.py to work with two data_flags files

Cost of Living Index Values Supplied

The Cost of Living Index adjustments were calculated using commercial software. The adjusted values have been provided to us, so it would be simplest for us to just load them for use.