2015-16 taxstats miscellany

Hugh Parsonage

2019-03-21

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

library(grattan)  # needs to be before require_taxstats..
require_taxstats()
require_taxstats1516()

s1516 <- 
  as.data.table(sample_file_1516_synth) %>%
  setkey(Taxable_Income) %>%
  mutate_ntile("Taxable_Income", n = 100L) %>%
  mutate_ntile("Taxable_Income", n = 10L) 
s1516[, tax := income_tax(Taxable_Income, "2015-16", .dots.ATO = .SD)]

Negative gearing

Tax benefits

s1516[, taxNoNG := income_tax(Taxable_Income - pminC(Net_rent_amt, 0), 
                              fy.year = "2015-16",
                              .dots.ATO = .SD)]
s1516[, benefit_due_NG := taxNoNG - tax]

by decile

benefitsNG_by_Decile <- 
  s1516[, .(avgBenefitNG = mean(benefit_due_NG)), keyby = "Taxable_IncomeDecile"]

copy(benefitsNG_by_Decile) %>%
  setnames("Taxable_IncomeDecile", "Taxable Income Decile") %>%
  .[, Share := paste0(round(100 * avgBenefitNG / sum(avgBenefitNG)), "%")] %>%
  .[, avgBenefitNG := round(avgBenefitNG, -2)] %>%
  setnames("avgBenefitNG", "Average benefit from NG / $") %>%
  kable(align = "rrr")

Taxable Income Decile	Average benefit from NG / $	Share
1	0	2%
2	100	2%
3	100	3%
4	100	5%
5	200	6%
6	200	8%
7	200	9%
8	300	12%
9	500	17%
10	1000	36%

Average taxable income, by age and gender, 2015

s1516[, .(avgTaxableIncome = mean(Taxable_Income)), keyby = .(age_range, Gender)] %>%
  .[age_range_decoder, on = "age_range"] %>%
  .[, Sex := if_else(Gender == 1, "Female", "Male")] %>%
  ggplot(aes(x = age_range_description,
             y = avgTaxableIncome,
             group = Sex, 
             color = Sex)) + 
  geom_line() + 
  scale_y_continuous(labels = function(x) paste0("$", prettyNum(x, big.mark = ",")))