In HughParsonage/grattan: Australian Tax Policy Analysis

grattan

Utilities for costing and evaluating Australian tax policy, including high-performance tax and transfer calculators, a fast method of projecting tax collections from ATO sample files, and an interface to common indices from the Australian Bureau of Statistics. Written to support Grattan Institute's Australian Perspectives program.

library(knitr)
opts_chunk$set(dev = 'svg',
               fig.path = "man/figures/README_")

Overview

install.packages("grattan")

library(grattan)

income_tax

Calculates the income tax for a given taxable income and financial year:

income_tax(50e3, "2015-16")

With sample files

income_tax is designed to work well with the ATO's sample files. You can obtain the sample files from my repo:

# install.packages("taxstats", repos = "https://hughparsonage.github.io/tax-drat")
library(taxstats)

library(hutils)
library(data.table) 
library(magrittr)
library(ggplot2)

Simply pass the sample file to .dots.ATO and the complexities of things like Medicare levy and the Seniors and Pensioners Tax Offset are handled for you. For example:

s1314 <- as.data.table(sample_file_1314)
s1314 %>%
  .[, tax := income_tax(Taxable_Income, "2013-14", .dots.ATO = s1314)] %>%
  .[, .(Taxable_Income, tax)]

model_income_tax: modelling changes to personal income tax

While income_tax is designed to inflexibly return the tax payable as legislated, model_income_tax is designed to calculate income tax when changes are made. For example,

s1314 %>%
  # reduce top threshold from 180,000 to 150,000
  model_income_tax(ordinary_tax_thresholds = c(0, 18200, 37000, 80000, 
                                               150e3), 
                   baseline_fy = "2013-14") %>%
  .[, .(Taxable_Income, baseline_tax, new_tax)]

project

Given a sample file, we can project forward a number of years

s1617 <- project(s1314, h = 3L)

or to a particular financial year

s1718 <- project_to(s1314, "2017-18")

Together with model_income_tax, this allows us to make point-predictions of future years. The function revenue_foregone prettily prints the resultant revenue:

sample_file_1314 %>%
  project_to("2018-19") %>%
  model_income_tax(baseline_fy = "2017-18",
                   ordinary_tax_thresholds = c(0, 18200, 37000, 87000, 
                                               150e3)) %>%
  revenue_foregone

compare_avg_tax_rates:

Create comparison of average tax rates:

lapply(list("30k" = 30e3,
            "36k" = 36e3,
            "42k" = 42e3),
       function(T2) {
         model_income_tax(s1718,
                          baseline_fy = "2017-18",
                          ordinary_tax_thresholds = c(0, 
                                                      18200,
                                                      T2,
                                                      87000, 
                                                      180e3))
       }) %>%
  rbindlist(idcol = "id",
            use.names = TRUE,
            fill = TRUE) %>%
  compare_avg_tax_rates(baseDT = .[id %ein% "36k"]) %>%
  ggplot(aes(x = Taxable_Income_percentile,
             y = delta_avgTaxRate,
             color = id,
             group = id)) +
  geom_hline(yintercept = 0) +
  geom_line()

NEWS

cat(readLines("NEWS.md"), sep = "\n")

CRAN Notes

cat(readLines("cran-comments.md"), sep = "\n")

HughParsonage/grattan documentation built on April 1, 2024, 4:10 a.m.