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Calculate Federal and State Income Taxes

usincometaxes is an R package that calculates federal and state income taxes in the United States. It relies on the National Bureau of Economic Research’s (NBER) TAXSIM 35 tax simulator for calculations. The package takes care of the behind-the-scenes work of getting the data in the proper format, converting it to the proper file type for uploading to the NBER server, uploading the data, downloading the results, and placing the results into a tidy data frame.

NOTE: This package is not associated with the NBER. It is a private creation that uses their wonderful tax calculator.


You can install usincometaxes from GitHub with:


Quick example

usincometaxes helps users estimate household income taxes from data sets containing financial and household data. This allows users to estimate income taxes from surveys with financial information, as the United States Census Public Use Micro Data (PUMS).

The short example below uses taxsim_calculate_taxes() to calculate income taxes.


family_income <- data.frame(
  taxsimid = c(1, 2),
  state = c('North Carolina', 'NY'),
  year = c(2015, 2020),
  mstat = c('married, jointly', 'single'),
  pwages = c(50000, 100000), # primary wages
  page = c(26, 36) # primary age

family_taxes <- taxsim_calculate_taxes(
  .data = family_income,
  marginal_tax_rates = 'Wages',
  return_all_information = FALSE
taxsimid fiitax siitax fica frate srate ficar tfica
1 3487.5 2012.50 7650 15 5.75 15.3 3825
2 15103.5 5377.86 15300 24 6.41 15.3 7650

Users can use the taxsimid column to join the tax data with the original data set. Every taxsimid in the input data is represented in the output tax data.

family_income %>%
  left_join(family_taxes, by = 'taxsimid') %>%
taxsimid state year mstat pwages page fiitax siitax fica frate srate ficar tfica
1 North Carolina 2015 married, jointly 5e+04 26 3487.5 2012.50 7650 15 5.75 15.3 3825
2 NY 2020 single 1e+05 36 15103.5 5377.86 15300 24 6.41 15.3 7650


taxsim_calculate_taxes() returns a data frame where each row corresponds to a row in .data and each column is a piece of tax information. The output and .data can be linked by the taxsimid column.

The amount of output (tax information) received is controlled by the return_all_information parameter to taxsim_calculate_taxes(). Setting return_all_information to FALSE returns minimal information such as federal and state tax liabilities and FICA taxes. When return_all_information is TRUE 44 different tax items are returned.

usoncometax’s output contains the same information and column names as TAXSIM 35. Therefore, please consult either the Description of Output Columns vignette or TAXSIM 35 documentation for more output information.

family_taxes_full_output <- taxsim_calculate_taxes(
  .data = family_income,
  marginal_tax_rates = 'Wages',
  return_all_information = TRUE

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_deducation 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 3487.5 2012.50 7650 15 5.75 15.3 3825 5e+04 0 0 12600 8000 0 0 0 29400 3487.5 0 0 0 0 0 0 5e+04 0 3487.5 7650 50000.01 0 50000.01 0 15000 0 35000.01 0 0 0 0 0.00 5e+04 0 0 0
2 15103.5 5377.86 15300 24 6.41 15.3 7650 1e+05 0 0 12400 0 0 0 0 87600 15103.5 0 0 0 0 0 0 1e+05 0 15103.5 15300 100001.01 0 100000.01 0 8000 0 92000.01 0 0 0 0 6.41 1e+05 0 0 0


Taxes are calculated with taxsim_calculate_taxes() using the financial and household characteristics found in the data frame represented by the .data parameter. Each column is a different piece of information and each row contains a tax payer unit.

All columns must have the column names and data types listed in the Description of Input Columns vignette. These are the same column names found in the TAXSIM 35 documentation. Therefore, you can consult the package documentation or TAXSIM 35 documentation for more information on input columns. There are two differences between usincometaxes and TAXSIM 35:

  1. usincometaxes allows users to specify the state with either the two letter abbreviation or state SOI code. usincometaxes will convert the abbreviation to an SOI code for TAXSIM 35.
  2. For filing status, mstat users can either use the TAXSIM 35 integer found in TAXSIM 35’s documentation or one of the following descriptions:
    • “single” or 1 for single;
    • “married, jointly” or 2 for married, filing jointly;
    • “married, separately” or 6 for married, filing separately;
    • “dependent child” or 8 for dependent, usually a child with income; or
    • “head of household” or 1 for head of household filing status.

The input data frame, .data, can contain columns beyond those listed in the vignette. The additional columns will be ignored.

Marginal tax rates

By default, marginal tax rates are calculated using wages. The default can be changed with the marginal_tax_rates parameter to taxsim_calculate_taxes(). Possible options are: ‘Wages’ (default), ‘Long Term Capital Gains’, ‘Primary Wage Earner’, or ‘Secondary Wage Earner’.

Giving credit

The NBER’s TAXSIM 35 tax simulator does all tax calculations. This package simply lets users interact with the tax simulator through R. Therefore, users should cite the TAXSIM 35 tax simulator when they use this package in their work:

          Feenberg, Daniel Richard, and Elizabeth Coutts, An Introduction to the TAXSIM Model, Journal of Policy Analysis and Management vol 12 no 1, Winter 1993, pages 189-194.