# Canonical replications: top 1% share, corporate ETR, tax gap trend

Three canonical tax-research exercises, each in around 20 lines.

## 1. Top 1 per cent income share (Atkinson-Leigh style)

The approach below loosely mirrors Atkinson and Leigh (2007), “The Distribution of Top Incomes in Australia,” Economic Record, 83(262), 247-261 (https://doi.org/10.1111/j.1475-4932.2007.00412.x). The Atkinson-Leigh method reconstructs top-income shares from ATO published tabulations; a postcode-based approximation is a cruder but data-only proxy. For rigorous top-share work use Pareto interpolation on the ATO top-percentile table or apply for ALife microdata access. See also Burkhauser, Hahn and Wilkins (2015) for caveats (https://doi.org/10.1007/s10888-014-9281-z).

    library(ato)
    ato_snapshot("2026-04-24")

    pc_panel <- ato_individuals_postcode(
      year = c("2015-16", "2016-17", "2017-18", "2018-19",
               "2019-20", "2020-21", "2021-22", "2022-23")
    )
    pc_panel <- ato_harmonise(pc_panel)

    # For each year, rank postcodes by mean taxable income per return,
    # take top 1% of returns, compute their share of total income.
    top1 <- function(df) {
      df <- df[order(-df$taxable_income / df$number_of_individuals), ]
      cum_returns <- cumsum(df$number_of_individuals)
      total_returns <- sum(df$number_of_individuals, na.rm = TRUE)
      cutoff <- which(cum_returns >= 0.01 * total_returns)[1]
      sum(df$taxable_income[seq_len(cutoff)], na.rm = TRUE) /
        sum(df$taxable_income, na.rm = TRUE)
    }

    shares <- by(pc_panel, pc_panel$year, top1)
    shares

## 2. Corporate effective tax rate by industry (transparency data)

    ctt <- ato_top_taxpayers(year = "2022-23")

    # Effective tax rate = tax payable / taxable income, for entities
    # with positive taxable income. Drop zero-taxable rows (they bias
    # the ratio; rely on loss-makers analysis separately).
    ctt <- ctt[!is.na(ctt$taxable_income) & ctt$taxable_income > 0, ]
    ctt$etr <- ctt$tax_payable / ctt$taxable_income

    by_industry <- aggregate(etr ~ entity_type, data = ctt, FUN = median)
    by_industry[order(-by_industry$etr), ]

## 3. Tax gap trend and confidence context

    tg <- ato_tax_gaps()

    library(ggplot2)
    ggplot(tg, aes(x = year, y = tax_gap_estimate,
                   colour = tax_gap_type)) +
      geom_line() +
      labs(title = "ATO estimated tax gaps over time",
           x = NULL, y = "Estimated tax gap (AUD million)",
           colour = "Gap type",
           caption = "Source: ATO Taxation Statistics. Retrieved via ato package.") +
      theme_minimal()

## 4. HELP debt by age cohort

    help_data <- ato_help()

    # Bucketed by age range; real-terms deflation to 2022-23
    help_data$real <- ato_deflate(help_data$total_debt,
                                   year = help_data$year,
                                   base = "2022-23")
    head(help_data)

Each of these replications takes an ATO published release, a `harmonise/deflate/reconcile` transformation, and a small computation. The provenance header (snapshot pin + SHA-256) makes the result fully auditable.