Calculating Federal and State Income Taxes

This article presents two use cases for usincometaxes. The first shows users how to estimate income taxes from a data frame containing financial information and other characteristics of tax payer units. This income could come from surveys such as the Consumer Expenditure survey or the Panel Study of Income Dynamics survey. The second use case focuses on running simulations.

Calculating income taxes from survey data

For the first example we will use an internal data set called taxpayer_finances. The data is randomly generated and formatted for use with usincometaxes. Guidance on formatting data can be found in the Description of Input Columns article.

The data set contains financial and other household characteristics that help estimate income taxes.

data(taxpayer_finances)

taxpayer_finances %>%
  head() %>%
  kable()

taxsimid	year	mstat	state	page	sage	depx	age1	age2	age3	pwages	swages	dividends	intrec	stcg	ltcg
1	2000	single	NC	37	0	4	6	7	8	26361.75	0.00	2260.86	4340.19	2280.16	2060.29
2	2000	single	NC	29	0	1	7	0	0	33966.34	0.00	1969.54	868.10	1064.50	2234.61
3	2000	married, jointly	NC	36	30	1	13	0	0	174191.53	102286.98	1972.47	2048.31	1009.11	1226.34
4	2000	married, jointly	NC	37	34	3	5	6	7	67604.57	53205.76	1173.95	881.67	3582.74	1405.74
5	2000	married, jointly	NC	38	39	0	0	0	0	21176.78	21687.72	4614.91	1588.52	560.93	825.04
6	2000	single	NC	36	0	1	2	0	0	53397.72	0.00	2067.41	1320.01	687.23	3548.07

Each row in the data set is a tax paying unit. Thus, each row files one tax return. Columns represent items reported on tax returns that impact taxes. Of course, the information in the data set does not represent everything people report on tax returns. For this reason, the income tax calculations are simply estimates.

We call taxsim_calculate_taxes() to estimate federal and state income taxes for each tax paying unit. We are only interested in federal and state tax liabilities, not line item credits and deduction, so we are using return_all_information = FALSE.

family_taxes <- taxsim_calculate_taxes(
  .data = taxpayer_finances,
  return_all_information = FALSE
)

family_taxes %>%
  head() %>%
  kable()

taxsimid	fiitax	siitax	fica	frate	srate	ficar	tfica
1	924.97	1078.23	4033.35	15.00	7.00	15.3	2016.67
2	3596.23	1919.22	5196.85	15.00	7.00	15.3	2598.42
3	78080.32	20429.27	26915.48	36.58	8.12	2.9	13457.74
4	23279.56	7783.72	18483.98	30.83	7.75	15.3	9241.99
5	5584.33	2619.27	6558.27	15.00	7.00	15.3	3279.13
6	8358.38	3383.43	8169.85	28.00	7.00	15.3	4084.93

The taxsimid column is required for any input data frame used in taxsim_calculate_taxes. This column is also returned in the output data frame containing tax calculations, allowing us to link the input and output data frames.

income_and_taxes <- taxpayer_finances %>%
  left_join(family_taxes, by = 'taxsimid')

income_and_taxes %>%
  head() %>%
  kable()

taxsimid	year	mstat	state	page	sage	depx	age1	age2	age3	pwages	swages	dividends	intrec	stcg	ltcg	fiitax	siitax	fica	frate	srate	ficar	tfica
1	2000	single	NC	37	0	4	6	7	8	26361.75	0.00	2260.86	4340.19	2280.16	2060.29	924.97	1078.23	4033.35	15.00	7.00	15.3	2016.67
2	2000	single	NC	29	0	1	7	0	0	33966.34	0.00	1969.54	868.10	1064.50	2234.61	3596.23	1919.22	5196.85	15.00	7.00	15.3	2598.42
3	2000	married, jointly	NC	36	30	1	13	0	0	174191.53	102286.98	1972.47	2048.31	1009.11	1226.34	78080.32	20429.27	26915.48	36.58	8.12	2.9	13457.74
4	2000	married, jointly	NC	37	34	3	5	6	7	67604.57	53205.76	1173.95	881.67	3582.74	1405.74	23279.56	7783.72	18483.98	30.83	7.75	15.3	9241.99
5	2000	married, jointly	NC	38	39	0	0	0	0	21176.78	21687.72	4614.91	1588.52	560.93	825.04	5584.33	2619.27	6558.27	15.00	7.00	15.3	3279.13
6	2000	single	NC	36	0	1	2	0	0	53397.72	0.00	2067.41	1320.01	687.23	3548.07	8358.38	3383.43	8169.85	28.00	7.00	15.3	4084.93

Now we have a single data frame containing both wages and income tax liabilities. Let’s take a look at the relationship between wages and estimated federal income taxes. The colors represent the number of children 18 or younger.

# custom theme for all plots in the vignette
plt_theme <- function() {
  
    theme_minimal() +
    theme(
      legend.text = element_text(size = 11),
      axis.text = element_text(size = 10),
      axis.title=element_text(size=11,face="bold"),
      strip.text = element_text(size = 11),
      panel.grid.minor = element_blank(),
      plot.title = element_text(face = "bold"),
      plot.subtitle = element_text(size = 12),
      legend.position = 'bottom'
    )
}
# color palettes for number of children
dep_color_palette <- rev(c('#4B0055','#353E7C','#007094','#009B95','#00BE7D','#96D84B'))

income_and_taxes %>%
  mutate(
    tax_unit_income = pwages + swages,
    num_dependents_eitc = factor(depx, levels = as.character(0:5)),
    filing_status = tools::toTitleCase(mstat)
  ) %>%
  ggplot(aes(tax_unit_income, fiitax, color = num_dependents_eitc)) +
    geom_point(alpha = .5) +
    scale_x_continuous(labels = scales::label_dollar(scale = .001, suffix = "K"), limits = c(0, 200000)) +
    scale_y_continuous(labels = scales::label_dollar(scale = .001, suffix = "K"), limits = c(-10000, 50000)) +
    scale_color_discrete(type = dep_color_palette) +
    facet_grid(rows = vars(mstat), cols = vars(year)) +
    labs(
      title = "Federal Income Taxes by Filing Status, Year, and Number of Children",
      x = "\nHousehold Wages",
      y = "Federal Income Taxes"
    ) +
    plt_theme() +
    guides(color = guide_legend(title = "Number of Childern 18 or Younger", title.position = "top", byrow = TRUE))
#> Warning: Removed 134 rows containing missing values (`geom_point()`).

The plots shows what we would expect: higher income families pay more in taxes and households pay less the more children they have. We also see the reduction in federal marginal tax rates from 2000 to 2020, as shown by the decrease in income tax liabilities when comparing the two years.

Income tax simulations

Association between income taxes paid and household wages

An additional use of usincometaxes is to run simulations. This could be as simple as plotting the relationship between wages and income taxes paid. To do this, we first need to create a data set that holds everything constant except for wages. The code block below does this, except it also creates different data sets for households with zero and four children 18 or younger, so we can compare differences on this characteristic as well.

# calculate taxes from 0 to 200,000 in wages
wage_linespace <- seq(0, 200000, 100)

n_kids <- 4

base_family_income <- data.frame(
  year = 2020,
  mstat = 'married, jointly',
  state = 'NC',
  page = 40,
  sage = 40,
  depx = n_kids,
  age1 = n_kids,
  age2 = n_kids,
  age3 = n_kids,
  pwages = wage_linespace,
  swages = 0
)

# create an additional data set with no dependents and add it to the original
family_income <- base_family_income %>%
  bind_rows(
    # make all numeber of dependent columns 0
    base_family_income %>%
      mutate(across(c(depx, age1, age2, age3), ~0))
  ) %>%
  # add unique ID to each row
  mutate(taxsimid = row_number()) %>%
  select(taxsimid, everything())

family_income %>%
  head() %>%
  kable()

taxsimid	year	mstat	state	page	sage	depx	age1	age2	age3	pwages
1	2020	married, jointly	NC	40	40	4	4	4	4	0
2	2020	married, jointly	NC	40	40	4	4	4	4	100
3	2020	married, jointly	NC	40	40	4	4	4	4	200
4	2020	married, jointly	NC	40	40	4	4	4	4	300
5	2020	married, jointly	NC	40	40	4	4	4	4	400
6	2020	married, jointly	NC	40	40	4	4	4	4	500

Now, we will calculate federal and state income taxes for our simulated data set. Note that return_all_information = TRUE. This allows us to examine credit amounts like the Child Tax Credit and Earned Income Tax Credit (EITC).

family_income_taxes <- taxsim_calculate_taxes(
  .data = family_income,
  return_all_information = TRUE
)

family_income_taxes %>%
  head() %>%
  kable()

taxsimid	fiitax	fica	frate	ficar	tfica	v10_federal_agi	v13_zero_bracket_amount	v25_eitc	v26_amt_income	v29_fica	v30_state_household_income	v32_state_agi	v34_state_std_deduction_amount	v45_cares_recovery_rebate
1	-6900	0.0	-45	15.3	0.00	0	24800	0	0	0.0	0.01	0.01	21500	6900
2	-6945	15.3	-45	15.3	7.65	100	24800	45	100	15.3	101.01	100.01	21500	6900
3	-6990	30.6	-45	15.3	15.30	200	24800	90	200	30.6	201.01	200.01	21500	6900
4	-7035	45.9	-45	15.3	22.95	300	24800	135	300	45.9	301.01	300.01	21500	6900
5	-7080	61.2	-45	15.3	30.60	400	24800	180	400	61.2	401.01	400.01	21500	6900
6	-7125	76.5	-45	15.3	38.25	500	24800	225	500	76.5	501.01	500.01	21500	6900

As before, let’s merge our tax data with the original input data set.

family_income <- family_income %>%
  left_join(family_income_taxes, by = 'taxsimid')

Now, let’s look at the relationship between household wages and estimated income tax liabilities.

family_income_long <- family_income %>%
  select(pwages, depx, fiitax, siitax) %>%
  pivot_longer(cols = c('fiitax', 'siitax'), 
               names_to = 'jurisdiction', values_to = 'taxes_paid') %>%
  mutate(
    jurisdiction = recode(jurisdiction, 'fiitax' = 'Federal Income Taxes', 'siitax' = 'NC State Income Taxes'),
    num_dependents_eitc = factor(depx, levels = as.character(0:5)),
    post_tax_wages = pwages - taxes_paid
  )
# primary_wages, taxes_paid, color = as.character(num_dependents_eitc)
taxes_line_plot <- function(.data, x_var, y_var, color_var) {
  ggplot(.data, aes({{x_var}}, {{y_var}}, color = {{color_var}})) +
    geom_line(size = 1, alpha = .8) +
    geom_hline(yintercept = 0) +
    scale_x_continuous(labels = scales::label_dollar(scale = .001, suffix = "K")) +
    scale_y_continuous(labels = scales::label_dollar(scale = .001, suffix = "K")) +
    scale_color_brewer(type = 'seq', palette = 'Set2')  +
    plt_theme()
  
}
taxes_line_plot(family_income_long, pwages, taxes_paid, num_dependents_eitc) +
  facet_wrap(vars(jurisdiction)) +
  labs(
    title = "Relationship Between Wages and Income Taxes Paid",
    subtitle = "Taxpayer is married, filing jointly, in 2020",
    x = "\nPre-Tax Household Wages",
    y = "Federal Income Taxes",
    color = 'Number of Children 18 or Younger:'
  )
#> Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
#> ℹ Please use `linewidth` instead.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.

Note that North Carolina had a flat tax of 5.25% in 2020. That’s why their taxes increase linearly.

Relationship Between Pre and Post-Tax Wages

We’ll create a additional plot comparing pre-tax and post-tax household wages.

taxes_line_plot(family_income_long, pwages, post_tax_wages, num_dependents_eitc) +
  facet_wrap(vars(jurisdiction)) +
  labs(
    title = "Relationship Between Pre and Post-Tax Wages",
    subtitle = "Taxpayer is married, filing jointly, in 2020",
    x = "\nPre-Tax Household Wages",
    y = "Post-Tax Hosuehold Wages",
    color = 'Number of Children 18 or Younger:'
  )

Child Tax Credit and Earned Income Tax Credit (EITC)

As noted previously, setting return_all_information = TRUE lets us retrieve additional output. Included in this additional output are amounts for the Child Tax Credit and EITC. Let’s look at the amounts for both credits, while varying household wages. The values reflect a household with four children 18 or younger.

tax_items_mapping <- c(
  v25_eitc = 'Earned Income Tax Credit',
  child_tax_credit = 'Child Tax Credit'
)

family_income %>%
  filter(depx == 4) %>%
  mutate(child_tax_credit = v22_child_tax_credit_adjusted + v23_child_tax_credit_refundable) %>%
  select(pwages, fiitax, v25_eitc, child_tax_credit) %>%
  pivot_longer(cols = names(tax_items_mapping), names_to = 'tax_item', values_to = 'amount') %>%
  mutate(tax_item = recode(tax_item, !!!tax_items_mapping)) %>%
  taxes_line_plot(pwages, amount, tax_item) +
  labs(
    title = "Relationship Between Wages and Credits",
    subtitle = "Taxpayer is married, filing jointly, in 2020 and has four children under 19",
    x = "\nPre-Tax Wages",
    y = "Credit Amount",
    color = NULL
  )

Shane Orr

Calculating income taxes from survey data

Income tax simulations

Association between income taxes paid and household wages

Relationship Between Pre and Post-Tax Wages

Child Tax Credit and Earned Income Tax Credit (EITC)