# Model income tax and project

#### Hugh Parsonage

#### 2019-03-23

The functions `model_income_tax` and `project` are the core of the grattan package. Grattan applies them to the ATO’s 2% sample files to produce costings of changes to tax policy. The functions are both \(X^n \to X^n\). That is, they take a sample file and return a mutated sample file.

With the mutated sample file, the costing for that particular tax year is the weighted sum of the difference between the `new_tax` and the `baseline_tax` columns. We can also use the mutated sample file to perform distributional analysis, such as the average change in tax by taxable income percentile.

Since the input data consists of tax returns and the grattan package does not purport to generate inferences about the wider Australian population, these functions cannot (directly) analyse the effect of policies on households or on the wider population. For example, policies affecting welfare payments, changes to the tax settings of businesses or super funds, or changes which would tax people who do not currently file tax returns are not amenable to the kind of analysis these functions perform.

## How to use `model_income_tax`

`model_income_tax` takes a sample file and returns a sample file under the settings given by the function arguments.

To start, let’s load the (minimal) packages we need. We’ll use the synthetic 2015-16 sample file contained in the suggested package `taxstats1516`. See `?install_taxstats` for installation instructions. For future years, use the latest sample file from the ATO.

    library(knitr)
    library(data.table)
    library(magrittr)
    library(hutils)
    library(grattan)
    require_taxstats1516()

    # Use the actual sample file if you've got it
    s1516 <- as.data.table(sample_file_1516_synth)
    s1516[, WEIGHT := 50L]

This function is purely cosmetic.

    #' @return Number formatted as dollar e.g. 30e3 => $30,000
    dollar <- function (x, digits = 0) {
      nsmall <- digits
      commaz <- format(abs(x), nsmall = nsmall, trim = TRUE, big.mark = ",",
                       scientific = FALSE, digits = 1L)
      if_else(x < 0,
              paste0("\U2212","$", commaz),
              paste0("$", commaz))
    }

All instances of `model_income_tax` have two mandatory arguments: `sample_file` and `baseline_fy`. These define the `baseline_tax` column in the result. When an argument is left as `NULL`, the `new_tax` column is calculated using the corresponding tax setting that applied in `baseline_fy`.

    s1516 %>%
      model_income_tax(baseline_fy = "2015-16") %>%
      select_grep("tax$", "Taxable_Income") %>%  # just look at relevant cols
      head %>%
      kable

Taxable_Income | baseline_tax | new_tax
--- | --- | ---
28849 | 2155 | 2155.29
210436 | 72060 | 72060.64
22285 | 426 | 426.15
58461 | 11592 | 11592.96
0 | 0 | 0.00
20078 | 0 | 0.00

Note that by default `new_tax` is a double precision vector, not rounded. You can use `return. = sample_file.int` to return rounded variables.

With the use of a simple function to test equality, we can see that `new_tax` is just the same as `baseline_tax`, as expected.

    is_all_equal <- function(x, y) {
      if (is.integer(x) && is.integer(y)) {
        all(x == y)
      } else {
        isTRUE(all.equal(x, y))
      }
    }

    s1516 %>%
      model_income_tax(baseline_fy = "2015-16",
                       return. = "sample_file.int") %>%
      select_grep("tax$", "Taxable_Income") %T>%
      .[, stopifnot(is_all_equal(baseline_tax, new_tax))] %>%
      head %>%
      kable

Taxable_Income | baseline_tax | new_tax
--- | --- | ---
28849 | 2155 | 2155
210436 | 72060 | 72060
22285 | 426 | 426
58461 | 11592 | 11592
0 | 0 | 0
20078 | 0 | 0

The choice of rounded, unrounded, or truncated values may be important for some analysis. For instance, tax liabilities are calculated using whole dollar amounts, so a truncated value may be appropriate when the values of `new_tax` for each row need to be very precise. Unrounded values may be important to determine changes in marginal tax rates. Rounded values may be the most appropriate choice for costings.

### Changing ordinary tax parameters

You can change how the ‘ordinary tax’ is calculated by changing the arguments `ordinary_tax_thresholds` and `ordinary_tax_rates`. To replicate the 2015-16 tax scales, one would use.

    s1516_no_changes <-
      # Temp budget repair levy not refundable against SBTO
      s1516 %>%
      model_income_tax(baseline_fy = "2015-16",
                       ordinary_tax_thresholds = c(0, 18200, 37000, 80000, 180000),
                                                                   # temp budget
                                                                   # repair levy
                       ordinary_tax_rates = c(0, 0.19, 0.325, 0.37, 0.45 + 0.02),
                       return. = "sample_file.int")

Note that the temporary budget repair levy is not included by default, so I simulated it by topping up the $180,000 marginal tax rate. This simulation is imperfect because the small business tax offset does not offset levies. As a result, `baseline_tax` and `new_tax` are slightly different in `s1516_no_changes`. This is not a problem for tax years including and beyond 2018-19.

### Changing Medicare levy parameters

The Medicare levy is more complex to calculate than ordinary income tax. There are parameters relating to two thresholds, as well as different thresholds for families and SAPTO-eligible individuals. Even the simplest modification require changes to multiple parameters. Warnings are emitted whenever parameters are not internally consistent.

Let’s try to increase the Medicare levy rate from 2% and 3%. Observe the warning messages.

    m1516a <-
      s1516 %>%
      model_income_tax("2015-16",
                       # Increase to 3%
                       medicare_levy_rate = 0.03)

    ## Warning: `medicare_levy_upper_threshold` was not specified, but its default value would be inconsistent with the parameters that were specified.
    ## Its value has been set to:
    ##  medicare_levy_upper_threshold = 30479

    ## Warning: `medicare_levy_upper_sapto_threshold` was not specified, but its default value would be inconsistent with the parameters that were specified.
    ## Its value has been set to:
    ##  medicare_levy_upper_sapto_threshold = 48197

Note the warning messsage says that the parameter has been changed. However, you should never tolerate the warning; instead, change the parameter to the suggested one (if you agree with the warning message’s advice).

    m1516a <-
      s1516 %>%
      model_income_tax("2015-16",
                       # Increase to 3%
                       medicare_levy_rate = 0.03,
                       medicare_levy_upper_threshold = 30479,
                       medicare_levy_upper_sapto_threshold = 48197)

Since there are many degrees of freedom, and since thresholds are generally the things that are actually contemplated when making changes, warnings will suggest changing thresholds over changes to the rate or taper if there is a conflict. Only when the thresholds have been manually selected and there is still a conflict is a change to the taper or rate suggested. For example, if we didn’t want to change the upper threshold, but keep it at its 2015-16 value of $26,670, we could insist:

    m1516a <-
      s1516 %>%
      model_income_tax("2015-16",
                       # Increase to 3%
                       medicare_levy_rate = 0.03,
                       # but keep the upper threshold the same
                       medicare_levy_upper_threshold = 26670,
                       medicare_levy_upper_sapto_threshold = 48197)

    ## Warning: `medicare_levy_lower_threshold` was not specified, but its default value would be inconsistent with the parameters that were specified.
    ## Its value has been set to:
    ##  medicare_levy_lower_threshold = 18668

The warning still assumes the taper and rate are the same, but it can no longer suggest a change to the upper threshold (since we provided it), so it suggests a change to the lower threshold. Only once we exhaust the thresholds it can adjust does the warning message start to include changing the taper:

    m1516a <-
      s1516 %>%
      model_income_tax("2015-16",
                       # Increase to 3%
                       medicare_levy_rate = 0.03,
                       # but keep the upper threshold the same
                       medicare_levy_lower_threshold = 21335,
                       medicare_levy_upper_threshold = 26670,
                       medicare_levy_upper_sapto_threshold = 48197)

    ## Warning: `medicare_levy_taper` was not specified, but its default value would be inconsistent with the parameters that were specified.
    ## Its value has been set to:
    ##  medicare_levy_taper = 0.15

### Changes to the Low Income Tax Offset

Here is a change to the LITO so that the maximum offset is $1000, rather than $445, with the 1.5% taper left as-is. Then we print the revenue foregone.

    L1516a <-
      s1516 %>%
      model_income_tax("2015-16",
                       lito_max_offset = 1000)
    revenue_foregone(L1516a)

    ## [1] "-$4 billion"

## How to use `project`

The function `project` takes a sample file and returns a sample file. The other mandatory argument is `h`, the number of integer years ahead of the sample file provided.

Thus, to get a forecast for the 2018-19 tax year:

    s1819 <- project(s1516, h = 3L)

This uses the internal forecast methods. To specify specific forecast outcomes, you can use the `wage.series` and `lf.series`

### Wage and labour series

    s1819_lf2pc_wage2pc <-
      s1516 %>%
      project(h = 3L,
              lf.series = 0.02,
              wage.series = 0.02)

To compare the tax collections under these different assumptions, one would use `income_tax` separately:

    tax_Grattan_1819 <-
      s1819 %$%
      income_tax(Taxable_Income, "2018-19", .dots.ATO = copy(s1819)) %>%
      sum %>%
      # Weight (equi-weighted so do now)
      multiply_by(s1819[["WEIGHT"]][1L])
    tax_2pc_1819 <-
      s1819_lf2pc_wage2pc %$%
      income_tax(Taxable_Income, "2018-19", .dots.ATO = copy(s1819)) %>%
      sum %>%
      # Weight (equi-weighted so do now)
      multiply_by(s1819[["WEIGHT"]][1L])

Currently there is no interface to using the upper or lower bounds of the labour force or wage price indices. If you wanted the 80% upper bound of the prediction interval for salary out to 2020-21, for instance, you would pass `Sw_amt` to `excl_vars` and manually inflate.

    s2021 <- project(s1516, h = 5L)

    s2021_wage80pc <-
      s1516 %>%
      copy %>%
      .[, Sw_amt := wage_inflator(Sw_amt,
                                  from_fy = "2015-16",
                                  to_fy = "2020-21",
                                  forecast.level = 80,
                                  forecast.series = "upper")] %>%
      .[] %>%
      project(h = 5L,
              excl_vars = "Sw_amt",
              .copyDT = FALSE) %>%  # just for memory frugality
      .[]

    s2021[, mean(Sw_amt)] %>% dollar

    ## [1] "$50,884"

    s2021_wage80pc[, mean(Sw_amt)] %>% dollar

    ## [1] "$51,648"

    s2021[, mean(Taxable_Income)] %>% dollar

    ## [1] "$65,782"

    s2021_wage80pc[, mean(Taxable_Income)] %>% dollar

    ## [1] "$66,544"

## Combining the two

To cost a reduction in the capital gains tax discount from 50% to 25% over the four years from 2018-19, we would run

    cgt_25pc_fwd_estimates <-
      lapply(yr2fy(2019:2022), function(fy) {
        s1516 %>%
          project_to(to_fy = fy) %>%
          model_income_tax("2018-19",
                           cgt_discount_rate = 0.25) %>%
          .[, fy_year := fy]
      }) %>%
      rbindlist

Note that this takes a few seconds, most of which is spent within `project`. We could improve the speed of this by caching the intermediate objects, either as objects in the environment or as files (say, `.fst` files). You should consider doing this when you find yourself running `project` many times – likely you are just repeating calculations.

    cgt_25pc_fwd_estimates %>%
      mutate_ntile("Taxable_Income", n = 5L, keyby = "fy_year") %>%
      .[, delta := new_tax - baseline_tax] %>%
      .[, .(totDelta = sum(delta),
            avgDelta = mean(delta)),
        keyby = .(fy_year, Taxable_IncomeQuintile)] %>%
      # cosmetic
      .[, lapply(.SD, round), keyby = key(.)] %>%
      kable

fy_year | Taxable_IncomeQuintile | totDelta | avgDelta
--- | --- | --- | ---
2018-19 | 1 | 0 | 0
2018-19 | 2 | 380973 | 7
2018-19 | 3 | 1686609 | 31
2018-19 | 4 | 3335203 | 62
2018-19 | 5 | 72763741 | 1349
2019-20 | 1 | 0 | 0
2019-20 | 2 | 398814 | 7
2019-20 | 3 | 1748443 | 32
2019-20 | 4 | 3452320 | 64
2019-20 | 5 | 76423334 | 1417
2020-21 | 1 | 0 | 0
2020-21 | 2 | 434734 | 8
2020-21 | 3 | 1767220 | 33
2020-21 | 4 | 3609380 | 67
2020-21 | 5 | 82787537 | 1535
2021-22 | 1 | 0 | 0
2021-22 | 2 | 455595 | 8
2021-22 | 3 | 1834093 | 34
2021-22 | 4 | 3694293 | 69
2021-22 | 5 | 86490642 | 1604

### `lito_multi` for custom offsets

While `model_income_tax` cannot account for the future imagination of tax policy makers, the argument `lito_multi` does provide a powerful mechanism for handling complicated offsets. The argument, if provided, must be a list of two components `x` and `y`. These can be used to define an offset: for every `(x_i, y_i)` defined the value of the offset for a taxable income `x_i` must be `y_i` with the points in between interpolated linearly.

For example to simply mimic `LITO` in 2015-16:

    s1516 %>%
      model_income_tax("2015-16",
                       lito_multi = list(x = c(-Inf, 37e3, 200e3/3, Inf),
                                         y = c(445, 445, 0, 0)),
                       return. = "sample_file.int") %>%
      .[new_tax != baseline_tax]

    ## Empty data.table (0 rows) of 67 cols: Gender,age_range,Occ_code,Partner_status,Region,Lodgment_method...

### `Budget_...` parameters

These were used to cost policies proposed in the 2018 Budget period by the Government and the Opposition. They’re unlikely to have much use except in reproducing past results.

### SAPTO

The Seniors and Pensioner Tax Offset (SAPTO) can also be modified. To cost the abolition of SAPTO, one would use:

    sapto_abolished1819 <-
      s1819 %>%
      model_income_tax("2018-19",
                       sapto_eligible = FALSE)

To model a change to lower the SAPTO threshold from $32,279 to $27,000:

    sapto_abolished_abv27k_1819 <-
      s1819 %>%
      model_income_tax("2018-19",
                       sapto_lower_threshold = 27000)

To cost the proposal in Age of entitlement: age-based tax breaks (2016)

    s1718_AgeOfEntitlement <-
        project(s1516,
                h = 2L) %>%
        model_income_tax("2017-18",
                         sapto_lower_threshold = 27e3,
                         sapto_lower_threshold_married = 42e3,
                         sapto_max_offset = 1160,
                         sapto_max_offset_married = 390,
                         medicare_levy_lower_sapto_threshold = 27000,
                         medicare_levy_upper_sapto_threshold = 33750,
                         medicare_levy_upper_family_threshold = 46361,
                         medicare_levy_lower_family_sapto_threshold = 42000,
                         medicare_levy_upper_family_sapto_threshold = 52500)
    revenue_foregone(s1718_AgeOfEntitlement)

    ## [1] "$383 million"