Source code for taxcalc.calculator

"""
Tax-Calculator federal income and payroll tax Calculator class.
"""
# CODING-STYLE CHECKS:
# pycodestyle calculator.py
# pylint --disable=locally-disabled calculator.py
#
# pylint: disable=too-many-lines,no-value-for-parameter

import copy
import numpy as np
import pandas as pd
import paramtools
from taxcalc.calcfunctions import (TaxInc, SchXYZTax, GainsTax, AGIsurtax,
                                   NetInvIncTax, AMT, EI_PayrollTax, Adj,
                                   DependentCare, ALD_InvInc_ec_base, CapGains,
                                   SSBenefits, UBI, AGI, ItemDedCap, ItemDed,
                                   StdDed, AdditionalMedicareTax, F2441, EITC,
                                   RefundablePayrollTaxCredit,
                                   ChildDepTaxCredit, AdditionalCTC, CTC_new,
                                   PersonalTaxCredit, SchR,
                                   AmOppCreditParts, EducationTaxCredit,
                                   CharityCredit,
                                   NonrefundableCredits, C1040, IITAX,
                                   BenefitSurtax, BenefitLimitation,
                                   FairShareTax, LumpSumTax, BenefitPrograms,
                                   ExpandIncome, AfterTaxIncome)
from taxcalc.policy import Policy
from taxcalc.records import Records
from taxcalc.consumption import Consumption
from taxcalc.growdiff import GrowDiff
from taxcalc.growfactors import GrowFactors
from taxcalc.utils import (DIST_VARIABLES, create_distribution_table,
                           DIFF_VARIABLES, create_difference_table,
                           create_diagnostic_table,
                           ce_aftertax_expanded_income,
                           mtr_graph_data, atr_graph_data, xtr_graph_plot,
                           pch_graph_data, pch_graph_plot)
# import pdb


[docs]class Calculator(): """ Constructor for the Calculator class. Parameters ---------- policy: Policy class object this argument must be specified and object is copied for internal use records: Records class object this argument must be specified and object is copied for internal use verbose: boolean specifies whether or not to write to stdout data-loaded and data-extrapolated progress reports; default value is false. sync_years: boolean specifies whether or not to synchronize policy year and records year; default value is true. consumption: Consumption class object specifies consumption response assumptions used to calculate "effective" marginal tax rates; default is None, which implies no consumption responses assumed in marginal tax rate calculations; when argument is an object it is copied for internal use; also specifies consumption value of in-kind benefis with no in-kind consumption values specified implying consumption value is equal to government cost of providing the in-kind benefits Raises ------ ValueError: if parameters are not the appropriate type. Returns ------- class instance: Calculator Notes ----- The most efficient way to specify current-law and reform Calculator objects is as follows: pol = Policy() rec = Records() calc1 = Calculator(policy=pol, records=rec) # current-law pol.implement_reform(...) calc2 = Calculator(policy=pol, records=rec) # reform All calculations are done on the internal copies of the Policy and Records objects passed to each of the two Calculator constructors. """ # pylint: disable=too-many-public-methods def __init__(self, policy=None, records=None, verbose=False, sync_years=True, consumption=None): # pylint: disable=too-many-arguments,too-many-branches if isinstance(policy, Policy): self.__policy = copy.deepcopy(policy) else: raise ValueError('must specify policy as a Policy object') if isinstance(records, Records): self.__records = copy.deepcopy(records) else: raise ValueError('must specify records as a Records object') if self.__policy.current_year < self.__records.data_year: self.__policy.set_year(self.__records.data_year) if consumption is None: self.__consumption = Consumption() elif isinstance(consumption, Consumption): self.__consumption = copy.deepcopy(consumption) else: raise ValueError('consumption must be None or Consumption object') if self.__consumption.current_year < self.__policy.current_year: self.__consumption.set_year(self.__policy.current_year) if verbose: if self.__records.IGNORED_VARS: print('Your data include the following unused ' + 'variables that will be ignored:') for var in self.__records.IGNORED_VARS: print(' ' + var) current_year_is_data_year = ( self.__records.current_year == self.__records.data_year) if sync_years and current_year_is_data_year: if verbose: print('You loaded data for ' + str(self.__records.data_year) + '.') while self.__records.current_year < self.__policy.current_year: self.__records.increment_year() if verbose: print('Tax-Calculator startup automatically ' + 'extrapolated your data to ' + str(self.__records.current_year) + '.') else: if verbose: print('Tax-Calculator startup did not ' + 'extrapolate your data.') assert self.__policy.current_year == self.__records.current_year assert self.__policy.current_year == self.__consumption.current_year self.__stored_records = None
[docs] def increment_year(self): """ Advance all embedded objects to next year. """ next_year = self.__policy.current_year + 1 self.__records.increment_year() self.__policy.set_year(next_year) self.__consumption.set_year(next_year)
[docs] def advance_to_year(self, year): """ The advance_to_year function gives an optional way of implementing increment year functionality by immediately specifying the year as input. New year must be at least the current year. """ iteration = year - self.current_year if iteration < 0: raise ValueError('New current year must be ' + 'greater than or equal to current year!') for _ in range(iteration): self.increment_year() assert self.current_year == year
[docs] def calc_all(self, zero_out_calc_vars=False): """ Call all tax-calculation functions for the current_year. """ # conducts static analysis of Calculator object for current_year UBI(self.__policy, self.__records) BenefitPrograms(self) self._calc_one_year(zero_out_calc_vars) BenefitSurtax(self) BenefitLimitation(self) FairShareTax(self.__policy, self.__records) LumpSumTax(self.__policy, self.__records) ExpandIncome(self.__policy, self.__records) AfterTaxIncome(self.__policy, self.__records)
[docs] def weighted_total(self, variable_name): """ Return all-filing-unit weighted total of named Records variable. """ return (self.array(variable_name) * self.array('s006')).sum()
[docs] def total_weight(self): """ Return all-filing-unit total of sampling weights. NOTE: var_weighted_mean = calc.weighted_total(var)/calc.total_weight() """ return self.array('s006').sum()
[docs] def dataframe(self, variable_list, all_vars=False): """ Return Pandas DataFrame containing the listed variables from the embedded Records object. If all_vars is True, then the variable_list is ignored and all variables used as input to and calculated by the Calculator.calc_all() method (which does not include marginal tax rates) are included in the returned Pandas DataFrame. """ if all_vars: varlist = list(self.__records.USABLE_READ_VARS | self.__records.CALCULATED_VARS) else: assert isinstance(variable_list, list) varlist = variable_list arys = [self.array(varname) for varname in varlist] dframe = pd.DataFrame(data=np.column_stack(arys), columns=varlist) del arys del varlist return dframe
[docs] def array(self, variable_name, variable_value=None): """ If variable_value is None, return numpy ndarray containing the named variable in embedded Records object. If variable_value is not None, set named variable in embedded Records object to specified variable_value and return None (which can be ignored). """ if variable_value is None: return getattr(self.__records, variable_name) assert isinstance(variable_value, np.ndarray) setattr(self.__records, variable_name, variable_value) return None
[docs] def n65(self): """ Return numpy ndarray containing the number of individuals age 65+ in each filing unit. """ vdf = self.dataframe(['age_head', 'age_spouse', 'elderly_dependents']) return ((vdf['age_head'] >= 65).astype(int) + (vdf['age_spouse'] >= 65).astype(int) + vdf['elderly_dependents'])
[docs] def incarray(self, variable_name, variable_add): """ Add variable_add to named variable in embedded Records object. """ assert isinstance(variable_add, np.ndarray) setattr(self.__records, variable_name, self.array(variable_name) + variable_add)
[docs] def zeroarray(self, variable_name): """ Set named variable in embedded Records object to zeros. """ setattr(self.__records, variable_name, np.zeros(self.array_len))
[docs] def store_records(self): """ Make internal copy of embedded Records object that can then be restored after interim calculations that make temporary changes to the embedded Records object. """ assert self.__stored_records is None self.__stored_records = copy.deepcopy(self.__records)
[docs] def restore_records(self): """ Set the embedded Records object to the stored Records object that was saved in the last call to the store_records() method. """ assert isinstance(self.__stored_records, Records) self.__records = copy.deepcopy(self.__stored_records) del self.__stored_records self.__stored_records = None
@property def array_len(self): """ Length of arrays in embedded Records object. """ return self.__records.array_length
[docs] def policy_param(self, param_name, param_value=None): """ If param_value is None, return named parameter in embedded Policy object. If param_value is not None, set named parameter in embedded Policy object to specified param_value and return None (which can be ignored). """ if param_value is None: val = getattr(self.__policy, param_name) if param_name.startswith("_"): return val else: return val[0] # drop down a dimension. setattr(self.__policy, param_name, param_value) return None
[docs] def consump_param(self, param_name): """ Return value of named parameter in embedded Consumption object. """ return getattr(self.__consumption, param_name)
[docs] def consump_benval_params(self): """ Return list of benefit-consumption-value parameter values in embedded Consumption object. """ return self.__consumption.benval_params()
@property def reform_warnings(self): """ Calculator class embedded Policy object's parameter_warnings. """ return self.__policy.parameter_warnings @property def current_year(self): """ Calculator class current calendar year property. """ return self.__policy.current_year @property def data_year(self): """ Calculator class initial (i.e., first) records data year property. """ return self.__records.data_year
[docs] def diagnostic_table(self, num_years): """ Generate multi-year diagnostic table containing aggregate statistics; this method leaves the Calculator object unchanged. Parameters ---------- num_years : Integer number of years to include in diagnostic table starting with the Calculator object's current_year (must be at least one and no more than what would exceed Policy end_year) Returns ------- Pandas DataFrame object containing the multi-year diagnostic table """ assert num_years >= 1 max_num_years = self.__policy.end_year - self.__policy.current_year + 1 assert num_years <= max_num_years calc = copy.deepcopy(self) yearlist = list() varlist = list() for iyr in range(1, num_years + 1): calc.calc_all() yearlist.append(calc.current_year) varlist.append(calc.dataframe(DIST_VARIABLES)) if iyr < num_years: calc.increment_year() del calc return create_diagnostic_table(varlist, yearlist)
[docs] def distribution_tables(self, calc, groupby, pop_quantiles=False, scaling=True): """ Get results from self and calc, sort them by expanded_income into table rows defined by groupby, compute grouped statistics, and return tables as a pair of Pandas dataframes. This method leaves the Calculator object(s) unchanged. Note that the returned tables have consistent income groups (based on the self expanded_income) even though the baseline expanded_income in self and the reform expanded_income in calc are different. Parameters ---------- calc : Calculator object or None typically represents the reform while self represents the baseline; if calc is None, the second returned table is None groupby : String object options for input: 'weighted_deciles', 'standard_income_bins', 'soi_agi_bins' determines how the columns in resulting Pandas DataFrame are sorted pop_quantiles : boolean specifies whether or not weighted_deciles contain an equal number of people (True) or an equal number of filing units (False) scaling : boolean specifies create_distribution_table utility function argument that determines whether table entry values are scaled or not Return and typical usage ------------------------ dist1, dist2 = calc1.distribution_tables(calc2, 'weighted_deciles') OR dist1, _ = calc1.distribution_tables(None, 'weighted_deciles') (where calc1 is a baseline Calculator object and calc2 is a reform Calculator object). Each of the dist1 and optional dist2 is a distribution table as a Pandas DataFrame with DIST_TABLE_COLUMNS and groupby rows. NOTE: when groupby is 'weighted_deciles', the returned tables have 3 extra rows containing top-decile detail consisting of statistics for the 0.90-0.95 quantile range (bottom half of top decile), for the 0.95-0.99 quantile range, and for the 0.99-1.00 quantile range (top one percent); and the returned table splits the bottom decile into filing units with negative (denoted by a 0-10n row label), zero (denoted by a 0-10z row label), and positive (denoted by a 0-10p row label) values of the specified income_measure. """ # nested functions used only by this method def distribution_table_dataframe(calcobj): """ Return pandas DataFrame containing the DIST_TABLE_COLUMNS variables from specified Calculator object, calcobj. """ dframe = calcobj.dataframe(DIST_VARIABLES) # weighted count of all people or filing units if pop_quantiles: dframe['count'] = np.multiply(dframe['s006'], dframe['XTOT']) else: dframe['count'] = dframe['s006'] # weighted count of those with itemized-deduction returns dframe['count_ItemDed'] = dframe['count'].where( dframe['c04470'] > 0., 0.) # weighted count of those with standard-deduction returns dframe['count_StandardDed'] = dframe['count'].where( dframe['standard'] > 0., 0.) # weight count of those with positive Alternative Minimum Tax (AMT) dframe['count_AMT'] = dframe['count'].where( dframe['c09600'] > 0., 0.) return dframe def have_same_income_measure(calc1, calc2): """ Return true if calc1 and calc2 contain the same expanded_income; otherwise, return false. (Note that "same" means nobody's expanded_income differs by more than one cent.) """ im1 = calc1.array('expanded_income') im2 = calc2.array('expanded_income') return np.allclose(im1, im2, rtol=0.0, atol=0.01) # main logic of distribution_tables method assert calc is None or isinstance(calc, Calculator) assert groupby in ('weighted_deciles', 'standard_income_bins', 'soi_agi_bins') if calc is not None: assert np.allclose(self.array('s006'), calc.array('s006')) # check rows in same order var_dataframe = distribution_table_dataframe(self) imeasure = 'expanded_income' dt1 = create_distribution_table(var_dataframe, groupby, imeasure, pop_quantiles, scaling) del var_dataframe if calc is None: dt2 = None else: assert calc.current_year == self.current_year assert calc.array_len == self.array_len assert np.allclose(self.consump_benval_params(), calc.consump_benval_params()) var_dataframe = distribution_table_dataframe(calc) if have_same_income_measure(self, calc): imeasure = 'expanded_income' else: imeasure = 'expanded_income_baseline' var_dataframe[imeasure] = self.array('expanded_income') dt2 = create_distribution_table(var_dataframe, groupby, imeasure, pop_quantiles, scaling) del var_dataframe return (dt1, dt2)
[docs] def difference_table(self, calc, groupby, tax_to_diff, pop_quantiles=False): """ Get results from self and calc, sort them by expanded_income into table rows defined by groupby, compute grouped statistics, and return tax-difference table as a Pandas dataframe. This method leaves the Calculator objects unchanged. Note that the returned tables have consistent income groups (based on the self expanded_income) even though the baseline expanded_income in self and the reform expanded_income in calc are different. Parameters ---------- calc : Calculator object calc represents the reform while self represents the baseline groupby : String object options for input: 'weighted_deciles', 'standard_income_bins' determines how the columns in resulting Pandas DataFrame are sorted tax_to_diff : String object options for input: 'iitax', 'payrolltax', 'combined' specifies which tax to difference pop_quantiles : boolean specifies whether or not weighted_deciles contain an equal number of people (True) or an equal number of filing units (False) Returns and typical usage ------------------------- diff = calc1.difference_table(calc2, 'weighted_deciles', 'iitax') (where calc1 is a baseline Calculator object and calc2 is a reform Calculator object). The returned diff is a difference table as a Pandas DataFrame with DIST_TABLE_COLUMNS and groupby rows. NOTE: when groupby is 'weighted_deciles', the returned table has three extra rows containing top-decile detail consisting of statistics for the 0.90-0.95 quantile range (bottom half of top decile), for the 0.95-0.99 quantile range, and for the 0.99-1.00 quantile range (top one percent); and the returned table splits the bottom decile into filing units with negative (denoted by a 0-10n row label), zero (denoted by a 0-10z row label), and positive (denoted by a 0-10p row label) values of the specified income_measure. """ assert isinstance(calc, Calculator) assert calc.current_year == self.current_year assert calc.array_len == self.array_len assert np.allclose(self.consump_benval_params(), calc.consump_benval_params()) self_var_dframe = self.dataframe(DIFF_VARIABLES) calc_var_dframe = calc.dataframe(DIFF_VARIABLES) diff = create_difference_table(self_var_dframe, calc_var_dframe, groupby, tax_to_diff, pop_quantiles) del self_var_dframe del calc_var_dframe return diff
MTR_VALID_VARIABLES = ['e00200p', 'e00200s', 'e00900p', 'e00300', 'e00400', 'e00600', 'e00650', 'e01400', 'e01700', 'e02000', 'e02400', 'p22250', 'p23250', 'e18500', 'e19200', 'e26270', 'e19800', 'e20100', 'k1bx14p']
[docs] def mtr(self, variable_str='e00200p', negative_finite_diff=False, zero_out_calculated_vars=False, calc_all_already_called=False, wrt_full_compensation=True): """ Calculates the marginal payroll, individual income, and combined tax rates for every tax filing unit, leaving the Calculator object in exactly the same state as it would be in after a calc_all() call. The marginal tax rates are approximated as the change in tax liability caused by a small increase (the finite_diff) in the variable specified by the variable_str divided by that small increase in the variable, when wrt_full_compensation is false. If wrt_full_compensation is true, then the marginal tax rates are computed as the change in tax liability divided by the change in total compensation caused by the small increase in the variable (where the change in total compensation is the sum of the small increase in the variable and any increase in the employer share of payroll taxes caused by the small increase in the variable). If using 'e00200s' as variable_str, the marginal tax rate for all records where MARS != 2 will be missing. If you want to perform a function such as np.mean() on the returned arrays, you will need to account for this. Parameters ---------- variable_str: string specifies type of income or expense that is increased to compute the marginal tax rates. See Notes for list of valid variables. negative_finite_diff: boolean specifies whether or not marginal tax rates are computed by subtracting (rather than adding) a small finite_diff amount to the specified variable. zero_out_calculated_vars: boolean specifies value of zero_out_calc_vars parameter used in calls of Calculator.calc_all() method. calc_all_already_called: boolean specifies whether self has already had its Calculor.calc_all() method called, in which case this method will not do a final calc_all() call but use the incoming embedded Records object as the outgoing Records object embedding in self. wrt_full_compensation: boolean specifies whether or not marginal tax rates on earned income are computed with respect to (wrt) changes in total compensation that includes the employer share of OASDI and HI payroll taxes. Returns ------- A tuple of numpy arrays in the following order: mtr_payrolltax: an array of marginal payroll tax rates. mtr_incometax: an array of marginal individual income tax rates. mtr_combined: an array of marginal combined tax rates, which is the sum of mtr_payrolltax and mtr_incometax. Notes ----- The arguments zero_out_calculated_vars and calc_all_already_called cannot both be true. Valid variable_str values are: 'e00200p', taxpayer wage/salary earnings (also included in e00200); 'e00200s', spouse wage/salary earnings (also included in e00200); 'e00900p', taxpayer Schedule C self-employment income (also in e00900); 'e00300', taxable interest income; 'e00400', federally-tax-exempt interest income; 'e00600', all dividends included in AGI 'e00650', qualified dividends (also included in e00600) 'e01400', federally-taxable IRA distribution; 'e01700', federally-taxable pension benefits; 'e02000', Schedule E total net income/loss 'e02400', all social security (OASDI) benefits; 'p22250', short-term capital gains; 'p23250', long-term capital gains; 'e18500', Schedule A real-estate-tax paid; 'e19200', Schedule A interest paid; 'e26270', S-corporation/partnership income (also included in e02000); 'e19800', Charity cash contributions; 'e20100', Charity non-cash contributions; 'k1bx14p', Partnership income (also included in e26270 and e02000). """ # pylint: disable=too-many-arguments,too-many-statements # pylint: disable=too-many-locals,too-many-branches assert not zero_out_calculated_vars or not calc_all_already_called # check validity of variable_str parameter if variable_str not in Calculator.MTR_VALID_VARIABLES: msg = 'mtr variable_str="{}" is not valid' raise ValueError(msg.format(variable_str)) # specify value for finite_diff parameter finite_diff = 0.01 # a one-cent difference if negative_finite_diff: finite_diff *= -1.0 # remember records object in order to restore it after mtr computations self.store_records() # extract variable array(s) from embedded records object variable = self.array(variable_str) if variable_str == 'e00200p': earnings_var = self.array('e00200') elif variable_str == 'e00200s': earnings_var = self.array('e00200') elif variable_str == 'e00900p': seincome_var = self.array('e00900') elif variable_str == 'e00650': divincome_var = self.array('e00600') elif variable_str == 'e26270': scheincome_var = self.array('e02000') elif variable_str == 'k1bx14p': scheincome_var = self.array('e02000') scorpincome_var = self.array('e26270') # calculate level of taxes after a marginal increase in income self.array(variable_str, variable + finite_diff) if variable_str == 'e00200p': self.array('e00200', earnings_var + finite_diff) elif variable_str == 'e00200s': self.array('e00200', earnings_var + finite_diff) elif variable_str == 'e00900p': self.array('e00900', seincome_var + finite_diff) elif variable_str == 'e00650': self.array('e00600', divincome_var + finite_diff) elif variable_str == 'e26270': self.array('e02000', scheincome_var + finite_diff) elif variable_str == 'k1bx14p': self.array('e02000', scheincome_var + finite_diff) self.array('e26270', scorpincome_var + finite_diff) if self.__consumption.has_response(): self.__consumption.response(self.__records, finite_diff) self.calc_all(zero_out_calc_vars=zero_out_calculated_vars) payrolltax_chng = self.array('payrolltax') incometax_chng = self.array('iitax') combined_taxes_chng = incometax_chng + payrolltax_chng # calculate base level of taxes after restoring records object self.restore_records() if not calc_all_already_called or zero_out_calculated_vars: self.calc_all(zero_out_calc_vars=zero_out_calculated_vars) payrolltax_base = self.array('payrolltax') incometax_base = self.array('iitax') combined_taxes_base = incometax_base + payrolltax_base # compute marginal changes in combined tax liability payrolltax_diff = payrolltax_chng - payrolltax_base incometax_diff = incometax_chng - incometax_base combined_diff = combined_taxes_chng - combined_taxes_base # specify optional adjustment for employer (er) OASDI+HI payroll taxes mtr_on_earnings = variable_str in ('e00200p', 'e00200s') if wrt_full_compensation and mtr_on_earnings: oasdi_taxed = np.logical_or( variable < self.policy_param('SS_Earnings_c'), variable >= self.policy_param('SS_Earnings_thd') ) adj = np.where(oasdi_taxed, 0.5 * (self.policy_param('FICA_ss_trt') + self.policy_param('FICA_mc_trt')), 0.5 * self.policy_param('FICA_mc_trt')) else: adj = 0.0 # compute marginal tax rates mtr_payrolltax = payrolltax_diff / (finite_diff * (1.0 + adj)) mtr_incometax = incometax_diff / (finite_diff * (1.0 + adj)) mtr_combined = combined_diff / (finite_diff * (1.0 + adj)) # if variable_str is e00200s, set MTR to NaN for units without a spouse if variable_str == 'e00200s': mars = self.array('MARS') mtr_payrolltax = np.where(mars == 2, mtr_payrolltax, np.nan) mtr_incometax = np.where(mars == 2, mtr_incometax, np.nan) mtr_combined = np.where(mars == 2, mtr_combined, np.nan) # delete intermediate variables del variable if variable_str in ('e00200p', 'e00200s'): del earnings_var elif variable_str == 'e00900p': del seincome_var elif variable_str == 'e00650': del divincome_var elif variable_str == 'e26270': del scheincome_var elif variable_str == 'k1bx14p': del scheincome_var del scorpincome_var del payrolltax_chng del incometax_chng del combined_taxes_chng del payrolltax_base del incometax_base del combined_taxes_base del payrolltax_diff del incometax_diff del combined_diff del adj # return the three marginal tax rate arrays return (mtr_payrolltax, mtr_incometax, mtr_combined)
[docs] def mtr_graph(self, calc, mars='ALL', mtr_measure='combined', mtr_variable='e00200p', alt_e00200p_text='', mtr_wrt_full_compen=False, income_measure='expanded_income', pop_quantiles=False, dollar_weighting=False): """ Create marginal tax rate graph that can be written to an HTML file (using the write_graph_file utility function) or shown on the screen immediately in an interactive or notebook session (following the instructions in the documentation of the xtr_graph_plot utility function). Parameters ---------- calc : Calculator object calc represents the reform while self represents the baseline mars : integer or string specifies which filing status subgroup to show in the graph - 'ALL': include all filing units in sample - 1: include only single filing units - 2: include only married-filing-jointly filing units - 3: include only married-filing-separately filing units - 4: include only head-of-household filing units mtr_measure : string specifies which marginal tax rate to show on graph's y axis - 'itax': marginal individual income tax rate - 'ptax': marginal payroll tax rate - 'combined': sum of marginal income and payroll tax rates mtr_variable : string any string in the Calculator.VALID_MTR_VARS set specifies variable to change in order to compute marginal tax rates alt_e00200p_text : string text to use in place of mtr_variable when mtr_variable is 'e00200p'; if empty string then use 'e00200p' mtr_wrt_full_compen : boolean see documentation of Calculator.mtr() argument wrt_full_compensation (value has an effect only if mtr_variable is 'e00200p') income_measure : string specifies which income variable to show on the graph's x axis - 'wages': wage and salary income (e00200) - 'agi': adjusted gross income, AGI (c00100) - 'expanded_income': broader than AGI (see definition in calcfunctions.py file). pop_quantiles : boolean specifies whether or not weighted_deciles contain an equal number of people (True) or an equal number of filing units (False) dollar_weighting : boolean False implies both income_measure percentiles on x axis and mtr values for each percentile on the y axis are computed without using dollar income_measure weights (just sampling weights); True implies both income_measure percentiles on x axis and mtr values for each percentile on the y axis are computed using dollar income_measure weights (in addition to sampling weights). Specifying True produces a graph x axis that shows income_measure (not filing unit) percentiles. Returns ------- graph that is a bokeh.plotting figure object """ # pylint: disable=too-many-arguments,too-many-locals # check that two Calculator objects are comparable assert isinstance(calc, Calculator) assert calc.current_year == self.current_year assert calc.array_len == self.array_len # check validity of mars parameter assert mars == 'ALL' or 1 <= mars <= 4 # check validity of income_measure assert income_measure in ('expanded_income', 'agi', 'wages') if income_measure == 'expanded_income': income_variable = 'expanded_income' elif income_measure == 'agi': income_variable = 'c00100' elif income_measure == 'wages': income_variable = 'e00200' # check validity of mtr_measure parameter assert mtr_measure in ('combined', 'itax', 'ptax') # calculate marginal tax rates (mtr1_ptax, mtr1_itax, mtr1_combined) = self.mtr(variable_str=mtr_variable, wrt_full_compensation=mtr_wrt_full_compen) (mtr2_ptax, mtr2_itax, mtr2_combined) = calc.mtr(variable_str=mtr_variable, wrt_full_compensation=mtr_wrt_full_compen) if mtr_measure == 'combined': mtr1 = mtr1_combined mtr2 = mtr2_combined elif mtr_measure == 'itax': mtr1 = mtr1_itax mtr2 = mtr2_itax elif mtr_measure == 'ptax': mtr1 = mtr1_ptax mtr2 = mtr2_ptax # extract datafames needed by mtr_graph_data utility function record_variables = ['s006', 'XTOT'] if mars != 'ALL': record_variables.append('MARS') record_variables.append(income_variable) vdf = self.dataframe(record_variables) vdf['mtr1'] = mtr1 vdf['mtr2'] = mtr2 # select filing-status subgroup, if any if mars != 'ALL': vdf = vdf[vdf['MARS'] == mars] # construct data for graph data = mtr_graph_data(vdf, year=self.current_year, mars=mars, mtr_measure=mtr_measure, alt_e00200p_text=alt_e00200p_text, mtr_wrt_full_compen=mtr_wrt_full_compen, income_measure=income_measure, pop_quantiles=pop_quantiles, dollar_weighting=dollar_weighting) # delete intermediate variables del vdf del mtr1_ptax del mtr1_itax del mtr1_combined del mtr1 del mtr2_ptax del mtr2_itax del mtr2_combined del mtr2 del record_variables # construct figure from data fig = xtr_graph_plot(data, width=850, height=500, xlabel='', ylabel='', title='', legendloc='bottom_right') del data return fig
[docs] def atr_graph(self, calc, mars='ALL', atr_measure='combined', pop_quantiles=False): """ Create average tax rate graph that can be written to an HTML file (using the write_graph_file utility function) or shown on the screen immediately in an interactive or notebook session (following the instructions in the documentation of the xtr_graph_plot utility function). The graph shows the mean average tax rate for each expanded-income percentile excluding any percentile that includes a filing unit with negative or zero basline (self) expanded income. Parameters ---------- calc : Calculator object calc represents the reform while self represents the baseline, where both self and calc have calculated taxes for this year before being used by this method mars : integer or string specifies which filing status subgroup to show in the graph - 'ALL': include all filing units in sample - 1: include only single filing units - 2: include only married-filing-jointly filing units - 3: include only married-filing-separately filing units - 4: include only head-of-household filing units atr_measure : string specifies which average tax rate to show on graph's y axis - 'itax': average individual income tax rate - 'ptax': average payroll tax rate - 'combined': sum of average income and payroll tax rates pop_quantiles : boolean specifies whether or not weighted_deciles contain an equal number of people (True) or an equal number of filing units (False) Returns ------- graph that is a bokeh.plotting figure object """ # check that two Calculator objects are comparable assert isinstance(calc, Calculator) assert calc.current_year == self.current_year assert calc.array_len == self.array_len # check validity of function arguments assert mars == 'ALL' or 1 <= mars <= 4 assert atr_measure in ('combined', 'itax', 'ptax') # extract needed output that is assumed unchanged by reform from self record_variables = ['s006', 'XTOT'] if mars != 'ALL': record_variables.append('MARS') record_variables.append('expanded_income') vdf = self.dataframe(record_variables) # create 'tax1' and 'tax2' columns given specified atr_measure if atr_measure == 'combined': vdf['tax1'] = self.array('combined') vdf['tax2'] = calc.array('combined') elif atr_measure == 'itax': vdf['tax1'] = self.array('iitax') vdf['tax2'] = calc.array('iitax') elif atr_measure == 'ptax': vdf['tax1'] = self.array('payrolltax') vdf['tax2'] = calc.array('payrolltax') # select filing-status subgroup, if any if mars != 'ALL': vdf = vdf[vdf['MARS'] == mars] # construct data for graph data = atr_graph_data(vdf, year=self.current_year, mars=mars, atr_measure=atr_measure, pop_quantiles=pop_quantiles) # delete intermediate variables del vdf del record_variables # construct figure from data fig = xtr_graph_plot(data, width=850, height=500, xlabel='', ylabel='', title='', legendloc='bottom_right') del data return fig
[docs] def pch_graph(self, calc, pop_quantiles=False): """ Create percentage change in after-tax expanded income graph that can be written to an HTML file (using the write_graph_file utility function) or shown on the screen immediately in an interactive or notebook session (following the instructions in the documentation of the xtr_graph_plot utility function). The graph shows the dollar-weighted mean percentage change in after-tax expanded income for each expanded-income percentile excluding any percentile that includes a filing unit with negative or zero basline (self) expanded income. Parameters ---------- calc : Calculator object calc represents the reform while self represents the baseline, where both self and calc have calculated taxes for this year before being used by this method pop_quantiles : boolean specifies whether or not weighted_deciles contain an equal number of people (True) or an equal number of filing units (False) Returns ------- graph that is a bokeh.plotting figure object """ # check that two Calculator objects are comparable assert isinstance(calc, Calculator) assert calc.current_year == self.current_year assert calc.array_len == self.array_len # extract needed output from baseline and reform Calculator objects vdf1 = self.dataframe(['s006', 'XTOT', 'aftertax_income', 'expanded_income']) vdf2 = calc.dataframe(['s006', 'XTOT', 'aftertax_income']) assert np.allclose(vdf1['s006'], vdf2['s006']) assert np.allclose(vdf1['XTOT'], vdf2['XTOT']) vdf = pd.DataFrame() vdf['s006'] = vdf1['s006'] vdf['XTOT'] = vdf1['XTOT'] vdf['expanded_income'] = vdf1['expanded_income'] vdf['chg_aftinc'] = vdf2['aftertax_income'] - vdf1['aftertax_income'] # construct data for graph data = pch_graph_data(vdf, year=self.current_year, pop_quantiles=pop_quantiles) del vdf del vdf1 del vdf2 # construct figure from data fig = pch_graph_plot(data, width=850, height=500, xlabel='', ylabel='', title='') del data return fig
REQUIRED_REFORM_KEYS = set(['policy']) REQUIRED_ASSUMP_KEYS = set(['consumption', 'growdiff_baseline', 'growdiff_response'])
[docs] @staticmethod def read_json_param_objects(reform, assump): """ Read JSON reform and assump objects and return a composite dictionary containing four key:dict pairs: 'policy':dict, 'consumption':dict, 'growdiff_baseline':dict, and 'growdiff_response':dict. Note that either of the two function arguments can be None. If reform is None, the dict in the 'policy':dict pair is empty. If assump is None, the dict in all the other key:dict pairs is empty. Also note that either of the two function arguments can be strings containing a valid JSON string (rather than a local filename). Either of the two function arguments can also be a valid URL string beginning with 'http' and pointing to a valid JSON file hosted online. The reform file/URL contents or JSON string must be like this: {"policy": {...}} OR {...} (in other words, the top-level policy key is optional) and the assump file/URL contents or JSON string must be like this: {"consumption": {...}, "growdiff_baseline": {...}, "growdiff_response": {...}} The {...} should be empty like this {} if not specifying a policy reform or if not specifying any non-default economic assumptions of that type. The 'policy' subdictionary of the returned dictionary is suitable as input into the Policy.implement_reform method. The 'consumption' subdictionary of the returned dictionary is suitable as input into the Consumption.update_consumption method. The 'growdiff_baseline' subdictionary of the returned dictionary is suitable as input into the GrowDiff.update_growdiff method. The 'growdiff_response' subdictionary of the returned dictionary is suitable as input into the GrowDiff.update_growdiff method. """ # construct the composite dictionary param_dict = dict() param_dict['policy'] = Policy.read_json_reform(reform) param_dict['consumption'] = Consumption.read_json_update(assump) for topkey in ['growdiff_baseline', 'growdiff_response']: param_dict[topkey] = GrowDiff.read_json_update(assump, topkey) # return the composite dictionary return param_dict
[docs] @staticmethod def reform_documentation(params, policy_dicts=None): """ Generate reform documentation versus current-law policy. Parameters ---------- params: dict dictionary is structured like dict returned from the static Calculator.read_json_param_objects() method policy_dicts : list of dict or None each dictionary in list is a params['policy'] dictionary representing second and subsequent elements of a compound reform; None implies no compound reform with the simple reform characterized in the params['policy'] dictionary Returns ------- doc: String the documentation for the specified policy reform """ # pylint: disable=too-many-statements,too-many-branches,too-many-locals # nested function used only in reform_documentation function def param_doc(years_list, updated, baseline): """ Parameters ---------- years_list: list of parameter-change years updated: reform Policy or updated GrowDiff object base: current-law Policy or default GrowDiff object Returns ------- doc: String """ # pylint: disable=too-many-locals # nested function used only in param_doc def lines(text, num_indent_spaces, max_line_length=77): """ Return list of text lines, each one of which is no longer than max_line_length, with the second and subsequent lines being indented by the number of specified num_indent_spaces; each line in the list ends with the '\n' character """ if len(text) < max_line_length: # all text fits on one line line = text + '\n' return [line] # all text does not fix on one line first_line = True line_list = list() words = text.split() while words: if first_line: line = '' first_line = False else: line = ' ' * num_indent_spaces while (words and (len(words[0]) + len(line)) < max_line_length): line += words.pop(0) + ' ' line = line[:-1] + '\n' line_list.append(line) return line_list # begin main logic of nested function param_doc # pylint: disable=too-many-nested-blocks doc = '' assert isinstance(years_list, list) years = sorted(years_list) for year in years: baseline.set_year(year) updated.set_year(year) assert set(baseline.keys()) == set(updated.keys()) params_with_diff = list() for pname in baseline.keys(): upda_value = getattr(updated, pname) base_value = getattr(baseline, pname) if ( (isinstance(upda_value, np.ndarray) and np.allclose(upda_value, base_value)) or (not isinstance(upda_value, np.ndarray) and upda_value != base_value) ): params_with_diff.append(pname) if params_with_diff: mdata_base = baseline.specification(meta_data=True) # write year doc += '{}:\n'.format(year) for pname in sorted(params_with_diff): # write updated value line pval = getattr(updated, pname).tolist()[0] if mdata_base[pname]['type'] == 'bool': if isinstance(pval, list): pval = [bool(item) for item in pval] else: pval = bool(pval) doc += ' {} : {}\n'.format(pname, pval) # ... write optional param-vector-index line if isinstance(pval, list): labels = paramtools.consistent_labels( [mdata_base[pname]["value"][0]] ) label = None for _label in labels: if _label not in ("value", "year"): label = _label break if label: lv = baseline._stateless_label_grid[label] lv = [ str(item) for item in lv ] doc += ' ' * ( 4 + len(pname) ) + '{}\n'.format(lv) # ... write param-name line name = mdata_base[pname]['title'] for line in lines('name: ' + name, 6): doc += ' ' + line # ... write param-description line desc = mdata_base[pname]['description'] for line in lines('desc: ' + desc, 6): doc += ' ' + line # ... write param-baseline-value line if isinstance(baseline, Policy): pval = getattr(baseline, pname).tolist()[0] ptype = mdata_base[pname]['type'] if isinstance(pval, list): if ptype == 'bool': pval = [bool(item) for item in pval] elif ptype == 'bool': pval = bool(pval) doc += ' baseline_value: {}\n'.format(pval) else: # if baseline is GrowDiff object # each GrowDiff parameter has zero as default value doc += ' baseline_value: 0.0\n' del mdata_base return doc # begin main logic of reform_documentation # create Policy object with current-law-policy values gdiff_base = GrowDiff() gdiff_base.update_growdiff(params['growdiff_baseline']) gfactors_clp = GrowFactors() gdiff_base.apply_to(gfactors_clp) clp = Policy(gfactors=gfactors_clp) # create Policy object with post-reform values gdiff_resp = GrowDiff() gdiff_resp.update_growdiff(params['growdiff_response']) gfactors_ref = GrowFactors() gdiff_base.apply_to(gfactors_ref) gdiff_resp.apply_to(gfactors_ref) ref = Policy(gfactors=gfactors_ref) ref.implement_reform(params['policy']) reform_years = Policy.years_in_revision(params['policy']) if policy_dicts is not None: # compound reform has been specified assert isinstance(policy_dicts, list) for policy_dict in policy_dicts: ref.implement_reform(policy_dict) xyears = Policy.years_in_revision(policy_dict) for year in xyears: if year not in reform_years: reform_years.append(year) # generate documentation text doc = 'REFORM DOCUMENTATION\n' # ... documentation for baseline growdiff assumptions doc += 'Baseline Growth-Difference Assumption Values by Year:\n' years = GrowDiff.years_in_revision(params['growdiff_baseline']) if years: doc += param_doc(years, gdiff_base, GrowDiff()) else: doc += 'none: no baseline GrowDiff assumptions specified\n' # ... documentation for reform growdiff assumptions doc += 'Response Growth-Difference Assumption Values by Year:\n' years = GrowDiff.years_in_revision(params['growdiff_response']) if years: doc += param_doc(years, gdiff_resp, GrowDiff()) else: doc += 'none: no response GrowDiff assumptions specified\n' # ... documentation for (possibly compound) policy reform if policy_dicts is None: doc += 'Policy Reform Parameter Values by Year:\n' else: doc += 'Compound Policy Reform Parameter Values by Year:\n' # ... use clp and ref Policy objects to generate documentation if reform_years: doc += param_doc(reform_years, ref, clp) else: doc += 'none: using current-law policy parameters\n' # cleanup local objects del gdiff_base del gfactors_clp del gdiff_resp del gfactors_ref del clp del ref del years del reform_years # return documentation string return doc
[docs] def ce_aftertax_income(self, calc, custom_params=None, require_no_agg_tax_change=True): """ Return dictionary that contains certainty-equivalent of the expected utility of after-tax expanded income computed for several constant-relative-risk-aversion parameter values for each of two Calculator objects: self, which represents the pre-reform situation, and calc, which represents the post-reform situation, both of which MUST have had calc_call() called before being passed to this function. IMPORTANT NOTES: These normative welfare calculations are very simple. It is assumed that utility is a function of only consumption, and that consumption is equal to after-tax income. This means that any assumed responses that change work effort will not affect utility via the correpsonding change in leisure. And any saving response to changes in after-tax income do not affect consumption. The cmin value is the consumption level below which marginal utility is considered to be constant. This allows the handling of filing units with very low or even negative after-tax expanded income in the expected-utility and certainty-equivalent calculations. """ # check that calc and self are consistent assert isinstance(calc, Calculator) assert calc.array_len == self.array_len assert calc.current_year == self.current_year assert np.allclose(calc.consump_benval_params(), self.consump_benval_params()) # extract data from self and calc records_variables = ['s006', 'combined', 'expanded_income'] df1 = self.dataframe(records_variables) df2 = calc.dataframe(records_variables) cedict = ce_aftertax_expanded_income( df1, df2, custom_params=custom_params, require_no_agg_tax_change=require_no_agg_tax_change) cedict['year'] = self.current_year return cedict
# ----- begin private methods of Calculator class -----
[docs] def _taxinc_to_amt(self): """ Call TaxInc through AMT functions. """ TaxInc(self.__policy, self.__records) SchXYZTax(self.__policy, self.__records) GainsTax(self.__policy, self.__records) AGIsurtax(self.__policy, self.__records) NetInvIncTax(self.__policy, self.__records) AMT(self.__policy, self.__records)
[docs] def _calc_one_year(self, zero_out_calc_vars=False): """ Call all the functions except those in the calc_all() method. """ # pylint: disable=too-many-statements if zero_out_calc_vars: self.__records.zero_out_changing_calculated_vars() # pdb.set_trace() EI_PayrollTax(self.__policy, self.__records) DependentCare(self.__policy, self.__records) Adj(self.__policy, self.__records) ALD_InvInc_ec_base(self.__policy, self.__records) CapGains(self.__policy, self.__records) SSBenefits(self.__policy, self.__records) AGI(self.__policy, self.__records) ItemDedCap(self.__policy, self.__records) ItemDed(self.__policy, self.__records) AdditionalMedicareTax(self.__policy, self.__records) StdDed(self.__policy, self.__records) # Store calculated standard deduction, calculate # taxes with standard deduction, store AMT + Regular Tax std = self.array('standard').copy() item = self.array('c04470').copy() item_no_limit = self.array('c21060').copy() item_phaseout = self.array('c21040').copy() item_component_variable_names = ['c17000', 'c18300', 'c19200', 'c19700', 'c20500', 'c20800'] item_cvar = dict() for cvname in item_component_variable_names: item_cvar[cvname] = self.array(cvname).copy() self.zeroarray('c04470') self.zeroarray('c21060') self.zeroarray('c21040') for cvname in item_component_variable_names: self.zeroarray(cvname) self._taxinc_to_amt() std_taxes = self.array('c05800').copy() # Set standard deduction to zero, calculate taxes w/o # standard deduction, and store AMT + Regular Tax self.zeroarray('standard') self.array('c21060', item_no_limit) self.array('c21040', item_phaseout) self.array('c04470', item) self._taxinc_to_amt() item_taxes = self.array('c05800').copy() # Replace standard deduction with zero so the filing unit # would always be better off itemizing self.array('standard', np.where(item_taxes < std_taxes, 0., std)) self.array('c04470', np.where(item_taxes < std_taxes, item, 0.)) self.array('c21060', np.where(item_taxes < std_taxes, item_no_limit, 0.)) self.array('c21040', np.where(item_taxes < std_taxes, item_phaseout, 0.)) for cvname in item_component_variable_names: self.array(cvname, np.where(item_taxes < std_taxes, item_cvar[cvname], 0.)) del std del item del item_no_limit del item_phaseout del item_cvar # Calculate taxes with optimal itemized deduction self._taxinc_to_amt() F2441(self.__policy, self.__records) EITC(self.__policy, self.__records) RefundablePayrollTaxCredit(self.__policy, self.__records) PersonalTaxCredit(self.__policy, self.__records) AmOppCreditParts(self.__policy, self.__records) SchR(self.__policy, self.__records) EducationTaxCredit(self.__policy, self.__records) CharityCredit(self.__policy, self.__records) ChildDepTaxCredit(self.__policy, self.__records) NonrefundableCredits(self.__policy, self.__records) AdditionalCTC(self.__policy, self.__records) C1040(self.__policy, self.__records) CTC_new(self.__policy, self.__records) IITAX(self.__policy, self.__records)