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.
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. taxsim_calculate_taxes()
sends the data to the NBER’s TAXSIM 35 program for calculation and returns the results as a data frame.
We are only interested in federal and state tax liabilities, not line item credits and deduction, so we are using return_all_information = FALSE
.
<- taxsim_calculate_taxes(
family_taxes .data = taxpayer_finances,
return_all_information = FALSE
)
%>%
family_taxes head() %>%
kable()
taxsimid | fiitax | siitax | fica | frate | srate | ficar | tfica |
---|---|---|---|---|---|---|---|
1 | 924.97 | 1046.23 | 4033.35 | 15.00 | 7.00 | 15.3 | 2016.67 |
2 | 3596.23 | 1947.22 | 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 | 3411.43 | 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.
<- taxpayer_finances %>%
income_and_taxes 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 | 1046.23 | 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 | 1947.22 | 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 | 3411.43 | 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
<- function() {
plt_theme
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
<- rev(c('#4B0055','#353E7C','#007094','#009B95','#00BE7D','#96D84B'))
dep_color_palette
%>%
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.
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
<- seq(0, 200000, 100)
wage_linespace
<- 4
n_kids
<- data.frame(
base_family_income 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
<- base_family_income %>%
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 | swages |
---|---|---|---|---|---|---|---|---|---|---|---|
1 | 2020 | married, jointly | NC | 40 | 40 | 4 | 4 | 4 | 4 | 0 | 0 |
2 | 2020 | married, jointly | NC | 40 | 40 | 4 | 4 | 4 | 4 | 100 | 0 |
3 | 2020 | married, jointly | NC | 40 | 40 | 4 | 4 | 4 | 4 | 200 | 0 |
4 | 2020 | married, jointly | NC | 40 | 40 | 4 | 4 | 4 | 4 | 300 | 0 |
5 | 2020 | married, jointly | NC | 40 | 40 | 4 | 4 | 4 | 4 | 400 | 0 |
6 | 2020 | married, jointly | NC | 40 | 40 | 4 | 4 | 4 | 4 | 500 | 0 |
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).
<- taxsim_calculate_taxes(
family_income_taxes .data = family_income,
return_all_information = TRUE
)
%>%
family_income_taxes head() %>%
kable()
taxsimid | fiitax | siitax | fica | frate | srate | ficar | tfica | v10_federal_agi | v11_ui_agi | v12_soc_sec_agi | v13_zero_bracket_amount | v14_personal_exemptions | v15_exemption_phaseout | v16_deduction_phaseout | v17_itemized_deductions | v18_federal_taxable_income | v19_tax_on_taxable_income | v20_exemption_surtax | v21_general_tax_credit | v22_child_tax_credit_adjusted | v23_child_tax_credit_refundable | v24_child_care_credit | v25_eitc | v26_amt_income | v27_amt_liability | v28_fed_income_tax_before_credit | v29_fica | v30_state_household_income | v31_state_rent_expense | v32_state_agi | v33_state_exemption_amount | v34_state_std_deduction_amount | v35_state_itemized_deduction | v36_state_taxable_income | v37_state_property_tax_credit | v38_state_child_care_credit | v39_state_eitc | v40_state_total_credits | v41_state_bracket_rate | v42_self_emp_income | v43_medicare_tax_unearned_income | v44_medicare_tax_earned_income | v45_cares_recovery_rebate |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | -6900 | 0 | 0.0 | -45 | 0 | 0.0 | 0.00 | 0 | 0 | 0 | 24800 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.01 | 0 | 0.01 | 0 | 21500 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6900 |
2 | -6945 | 0 | 15.3 | -45 | 0 | 15.3 | 7.65 | 100 | 0 | 0 | 24800 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 45 | 100 | 0 | 0 | 15.3 | 101.01 | 0 | 100.01 | 0 | 21500 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 100 | 0 | 0 | 6900 |
3 | -6990 | 0 | 30.6 | -45 | 0 | 15.3 | 15.30 | 200 | 0 | 0 | 24800 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 90 | 200 | 0 | 0 | 30.6 | 201.01 | 0 | 200.01 | 0 | 21500 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 200 | 0 | 0 | 6900 |
4 | -7035 | 0 | 45.9 | -45 | 0 | 15.3 | 22.95 | 300 | 0 | 0 | 24800 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 135 | 300 | 0 | 0 | 45.9 | 301.01 | 0 | 300.01 | 0 | 21500 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 300 | 0 | 0 | 6900 |
5 | -7080 | 0 | 61.2 | -45 | 0 | 15.3 | 30.60 | 400 | 0 | 0 | 24800 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 180 | 400 | 0 | 0 | 61.2 | 401.01 | 0 | 400.01 | 0 | 21500 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 400 | 0 | 0 | 6900 |
6 | -7125 | 0 | 76.5 | -45 | 0 | 15.3 | 38.25 | 500 | 0 | 0 | 24800 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 225 | 500 | 0 | 0 | 76.5 | 501.01 | 0 | 500.01 | 0 | 21500 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 500 | 0 | 0 | 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 %>%
family_income_long 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)
<- function(.data, x_var, y_var, color_var) {
taxes_line_plot 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:'
)
Note that North Carolina had a flat tax of 5.25% in 2020. That’s why their taxes increase linearly.
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:'
)
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.
<- c(
tax_items_mapping 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
)