Source code for taxcalc.calculate

"""
Tax-Calculator federal tax Calculator class.
"""
# CODING-STYLE CHECKS:
# pep8 --ignore=E402 calculate.py
# pylint --disable=locally-disabled calculate.py
#
# pylint: disable=invalid-name,no-value-for-parameter,too-many-lines

import os
import json
import re
import copy
import six
import numpy as np
import pandas as pd
from taxcalc.functions 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,
                               SchR, ChildTaxCredit, AdditionalCTC, CTC_new,
                               PersonalTaxCredit,
                               AmOppCreditParts, EducationTaxCredit,
                               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.behavior import Behavior
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,
                           dec_graph_data, dec_graph_plot,
                           qin_graph_data, qin_graph_plot)
# import pdb


[docs]class Calculator(object): """ 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 true. 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 behavior: Behavior class object specifies behavioral responses used by Calculator; default is None, which implies no behavioral responses to policy reform; when argument is an object it is copied for internal use 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=True, sync_years=True, consumption=None, behavior=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(start_year=policy.start_year) elif isinstance(consumption, Consumption): self.__consumption = copy.deepcopy(consumption) while self.__consumption.current_year < self.__policy.current_year: next_year = self.__consumption.current_year + 1 self.__consumption.set_year(next_year) else: raise ValueError('consumption must be None or Consumption object') if behavior is None: self.__behavior = Behavior(start_year=policy.start_year) elif isinstance(behavior, Behavior): self.__behavior = copy.deepcopy(behavior) while self.__behavior.current_year < self.__policy.current_year: next_year = self.__behavior.current_year + 1 self.__behavior.set_year(next_year) else: raise ValueError('behavior must be None or Behavior object') 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) + '.') 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) 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) + '.') assert self.__policy.current_year == self.__records.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)
self.__behavior.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 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 assert self.__records.current_year == self.__policy.current_year 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): """ Return pandas DataFrame containing the listed variables from embedded Records object. """ assert isinstance(variable_list, list) arys = [self.array(vname) for vname in variable_list]
return pd.DataFrame(data=np.column_stack(arys), columns=variable_list)
[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. """ if variable_value is None: return getattr(self.__records, variable_name) else: assert isinstance(variable_value, np.ndarray)
setattr(self.__records, variable_name, variable_value)
[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)
self.__stored_records = None
[docs] def records_current_year(self, year=None): """ If year is None, return current_year of embedded Records object. If year is not None, set embedded Records current_year to year. """ if year is None: return self.__records.current_year else: assert isinstance(year, int)
self.__records.set_current_year(year) @property def array_len(self): """ Length of arrays in embedded Records object. """ return self.__records.array_length
[docs] def 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. """ if param_value is None: return getattr(self.__policy, param_name) else:
setattr(self.__policy, param_name, param_value)
[docs] def consump_param(self, param_name): """ Return value of named parameter in embedded Consumption object. """
return getattr(self.__consumption, param_name)
[docs] def behavior_has_response(self): """ Return True if embedded Behavior object has response; otherwise return False. """
return self.__behavior.has_response()
[docs] def behavior(self, param_name, param_value=None): """ If param_value is None, return named parameter in embedded Behavior object. If param_value is not None, set named parameter in embedded Behavior object to specified param_value. """ if param_value is None: return getattr(self.__behavior, param_name) else:
setattr(self.__behavior, param_name, param_value)
[docs] def records_include_behavioral_responses(self): """ Mark embedded Records object as including behavioral responses """
self.__records.behavioral_responses_are_included = True @property def reform_warnings(self): """ Calculator class embedded Policy object's reform_warnings. """ return self.__policy.reform_warnings
[docs] def policy_current_year(self, year=None): """ If year is None, return current_year of embedded Policy object. If year is not None, set embedded Policy current_year to year. """ if year is None: return self.__policy.current_year else: assert isinstance(year, int)
self.__policy.set_year(year) @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; 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) tlist = list() for iyr in range(1, num_years + 1): assert calc.behavior_has_response() is False calc.calc_all() diag = create_diagnostic_table(calc.dataframe(DIST_VARIABLES), calc.current_year) tlist.append(diag) if iyr < num_years: calc.increment_year()
return pd.concat(tlist, axis=1)
[docs] def distribution_tables(self, calc, groupby='weighted_deciles', income_measure='expanded_income', result_type='weighted_sum'): """ Get results from self and calc, sort them based on groupby using income_measure, manipulate grouped statistics based on result_type, and return tables as a pair of Pandas dataframes. Note that the returned tables have consistent income groups (based on the self income_measure) even though the income_measure in self and the income_measure 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', 'webapp_income_bins', 'large_income_bins', 'small_income_bins'; determines how the columns in returned tables are sorted 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). income_measure : String object options for input: 'expanded_income' or 'c00100'(AGI) result_type : String object options for input: 'weighted_sum' or 'weighted_avg'; determines how whether or not table entries are averages or totals Typical usage ------------- dist1, dist2 = calc1.distribution_tables(calc2) OR dist1, _ = calc1.distribution_tables(None) (where calc1 is a baseline Calculator object and calc2 is a reform Calculator object) """ # nested function used only by this method def have_same_income_measure(calc1, calc2, income_measure): """ Return true if calc1 and calc2 contain the same income_measure; otherwise, return false. (Note that "same" means nobody's income_measure differs by more than one cent.) """ im1 = calc1.array(income_measure) im2 = calc2.array(income_measure) return np.allclose(im1, im2, rtol=0.0, atol=0.01) # main logic of method assert calc is None or isinstance(calc, Calculator) assert (groupby == 'weighted_deciles' or groupby == 'webapp_income_bins' or groupby == 'large_income_bins' or groupby == 'small_income_bins') assert (income_measure == 'expanded_income' or income_measure == 'c00100') assert (result_type == 'weighted_sum' or result_type == 'weighted_avg') dt1 = create_distribution_table(self.dataframe(DIST_VARIABLES), groupby=groupby, income_measure=income_measure, result_type=result_type) if calc is None: dt2 = None else: assert calc.current_year == self.current_year assert calc.array_len == self.array_len var_dataframe = calc.dataframe(DIST_VARIABLES) if have_same_income_measure(self, calc, income_measure): imeasure = income_measure else: imeasure = income_measure + '_baseline' var_dataframe[imeasure] = self.array(income_measure) dt2 = create_distribution_table(var_dataframe, groupby=groupby, income_measure=imeasure, result_type=result_type)
return dt1, dt2
[docs] def difference_table(self, calc, groupby='weighted_deciles', income_measure='expanded_income', tax_to_diff='combined'): """ Get results from self and calc, sort them based on groupby using income_measure, and return tax-difference table as a Pandas dataframe. Parameters ---------- calc : Calculator object calc represents the reform while self represents the baseline groupby : String object options for input: 'weighted_deciles', 'webapp_income_bins', 'large_income_bins', 'small_income_bins'; determines how the columns in returned tables are sorted 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). income_measure : String object options for input: 'expanded_income' or 'c00100'(AGI) tax_to_diff : String object options for input: 'iitax', 'payrolltax', 'combined' specifies which tax to difference Typical usage ------------- diff = calc1.difference_table(calc2) (where calc1 is a baseline Calculator object and calc2 is a reform Calculator object) """ assert isinstance(calc, Calculator) assert calc.current_year == self.current_year assert calc.array_len == self.array_len diff = create_difference_table(self.dataframe(DIFF_VARIABLES), calc.dataframe(DIFF_VARIABLES), groupby=groupby, income_measure=income_measure, tax_to_diff=tax_to_diff)
return diff MTR_VALID_VARIABLES = ['e00200p', 'e00200s', 'e00900p', 'e00300', 'e00400', 'e00600', 'e00650', 'e01400', 'e01700', 'e02000', 'e02400', 'p22250', 'p23250', 'e18500', 'e19200', 'e26270', 'e19800', 'e20100']
[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. """ # 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') # 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) 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 == 'e00200p' or variable_str == 'e00200s') if wrt_full_compensation and mtr_on_earnings: adj = np.where(variable < self.param('SS_Earnings_c'), 0.5 * (self.param('FICA_ss_trt') + self.param('FICA_mc_trt')), 0.5 * self.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) # 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', 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': sum of AGI, non-taxable interest income, non-taxable social security benefits, and employer share of FICA taxes. 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 (mars >= 1 and mars <= 4) # check validity of income_measure assert (income_measure == 'expanded_income' or income_measure == 'agi' or income_measure == '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 == 'combined' or mtr_measure == 'itax' or mtr_measure == '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'] 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, dollar_weighting=dollar_weighting) # construct figure from data fig = xtr_graph_plot(data, width=850, height=500, xlabel='', ylabel='', title='', legendloc='bottom_right')
return fig
[docs] def atr_graph(self, calc, mars='ALL', atr_measure='combined', min_avginc=1000): """ 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. 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 min_avginc : float specifies the minimum average expanded income for a percentile to be included in the graph data; value must be positive 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 (mars >= 1 and mars <= 4) assert (atr_measure == 'combined' or atr_measure == 'itax' or atr_measure == 'ptax') assert min_avginc > 0 # extract needed output that is assumed unchanged by reform from self record_variables = ['s006'] 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, min_avginc=min_avginc) # construct figure from data fig = xtr_graph_plot(data, width=850, height=500, xlabel='', ylabel='', title='', legendloc='bottom_right')
return fig
[docs] def decile_graph(self, calc): """ Create graph that shows percentage change in aftertax expanded income (from going from policy in self to policy in calc) for each expanded-income decile and subgroups of the top decile. The graph 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, where both self and calc have calculated taxes for this year before being used by this method 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 diff_table = self.difference_table(calc, groupby='weighted_deciles', income_measure='expanded_income', tax_to_diff='combined') # construct data for graph data = dec_graph_data(diff_table, year=self.current_year) # construct figure from data fig = dec_graph_plot(data, width=850, height=500, xlabel='', ylabel='', title='')
return fig
[docs] def quintile_graph(self, calc): """ Create graph that shows percentage change in aftertax expanded income (from going from policy in self to policy in calc) for each expanded-income quintile and subgroups of the top quintile. The graph 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, where both self and calc have calculated taxes for this year before being used by this method 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 diff_table = self.difference_table(calc, groupby='weighted_deciles', income_measure='expanded_income', tax_to_diff='combined') # construct data for graph data = qin_graph_data(diff_table, year=self.current_year) # construct figure from data fig = qin_graph_plot(data, width=850, height=500, xlabel='', ylabel='', title='')
return fig
[docs] @staticmethod def read_json_param_objects(reform, assump): """ Read JSON reform and assump objects and return a single dictionary containing five key:dict pairs: 'policy':dict, 'consumption':dict, 'behavior':dict, 'growdiff_baseline':dict and 'growdiff_response':dict. Note that either of the first two parameters may be None. If reform is None, the dict in the 'policy':dict pair is empty. If assump is None, the dict in the 'consumption':dict pair, in the 'behavior':dict pair, in the 'growdiff_baseline':dict pair, and in the 'growdiff_response':dict pair, are all empty. Also note that either of the first two parameters can be strings containing a valid JSON string (rather than a filename), in which case the file reading is skipped and the appropriate read_json_*_text method is called. The reform file contents or JSON string must be like this: {"policy": {...}} and the assump file contents or JSON string must be like: {"consumption": {...}, "behavior": {...}, "growdiff_baseline": {...}, "growdiff_response": {...} } The returned dictionary contains parameter lists (not arrays). """ # first process second assump parameter if assump is None: cons_dict = dict() behv_dict = dict() gdiff_base_dict = dict() gdiff_resp_dict = dict() elif isinstance(assump, six.string_types): if os.path.isfile(assump): txt = open(assump, 'r').read() else: txt = assump (cons_dict, behv_dict, gdiff_base_dict, gdiff_resp_dict) = Calculator._read_json_econ_assump_text(txt) else: raise ValueError('assump is neither None nor string') # next process first reform parameter if reform is None: rpol_dict = dict() elif isinstance(reform, six.string_types): if os.path.isfile(reform): txt = open(reform, 'r').read() else: txt = reform rpol_dict = ( Calculator._read_json_policy_reform_text(txt, gdiff_base_dict, gdiff_resp_dict) ) else: raise ValueError('reform is neither None nor string') # finally construct and return single composite dictionary param_dict = dict() param_dict['policy'] = rpol_dict param_dict['consumption'] = cons_dict param_dict['behavior'] = behv_dict param_dict['growdiff_baseline'] = gdiff_base_dict param_dict['growdiff_response'] = gdiff_resp_dict
return param_dict REQUIRED_REFORM_KEYS = set(['policy']) REQUIRED_ASSUMP_KEYS = set(['consumption', 'behavior', 'growdiff_baseline', 'growdiff_response'])
[docs] @staticmethod def reform_documentation(params, policy_dicts=None): """ Generate reform documentation. Parameters ---------- params: dict dictionary is structured like dict returned from the static Calculator method read_json_param_objects() policy_dicts : list of dict or None each dictionary in list is a params['policy'] dictionary representing second or 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 policy reform specified in params """ # pylint: disable=too-many-statements,too-many-branches # nested function used only in reform_documentation def param_doc(years, change, base): """ Parameters ---------- years: list of change years change: dictionary of parameter changes base: Policy or Growdiff object with baseline values syear: parameter start calendar year 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 param_doc # pylint: disable=too-many-nested-blocks assert len(years) == len(change.keys()) basex = copy.deepcopy(base) basevals = getattr(basex, '_vals', None) assert isinstance(basevals, dict) doc = '' for year in years: # write year basex.set_year(year) doc += '{}:\n'.format(year) # write info for each param in year for param in sorted(change[year].keys()): # ... write param:value line pval = change[year][param] if isinstance(pval, list): pval = pval[0] if basevals[param]['boolean_value']: if isinstance(pval, list): pval = [True if item else False for item in pval] else: pval = bool(pval) doc += ' {} : {}\n'.format(param, pval) # ... write optional param-index line if isinstance(pval, list): pval = basevals[param]['col_label'] pval = [str(item) for item in pval] doc += ' ' * (4 + len(param)) + '{}\n'.format(pval) # ... write name line if param.endswith('_cpi'): rootparam = param[:-4] name = '{} inflation indexing status'.format(rootparam) else: name = basevals[param]['long_name'] for line in lines('name: ' + name, 6): doc += ' ' + line # ... write optional desc line if not param.endswith('_cpi'): desc = basevals[param]['description'] for line in lines('desc: ' + desc, 6): doc += ' ' + line # ... write baseline_value line if isinstance(basex, Policy): if param.endswith('_cpi'): rootparam = param[:-4] bval = basevals[rootparam].get('cpi_inflated', False) else: bval = getattr(basex, param[1:], None) if isinstance(bval, np.ndarray): bval = bval.tolist() if basevals[param]['boolean_value']: bval = [True if item else False for item in bval] elif basevals[param]['boolean_value']: bval = bool(bval) doc += ' baseline_value: {}\n'.format(bval) else: # if basex is Growdiff object # all Growdiff parameters have zero as default value doc += ' baseline_value: 0.0\n' return doc # begin main logic of reform_documentation # create Policy object with pre-reform (i.e., baseline) values # ... create gdiff_baseline object gdb = Growdiff() gdb.update_growdiff(params['growdiff_baseline']) # ... create Growfactors clp object that incorporates gdiff_baseline gfactors_clp = Growfactors() gdb.apply_to(gfactors_clp) # ... create Policy object containing pre-reform parameter values clp = Policy(gfactors=gfactors_clp) # generate documentation text doc = 'REFORM DOCUMENTATION\n' doc += 'Baseline Growth-Difference Assumption Values by Year:\n' years = sorted(params['growdiff_baseline'].keys()) if years: doc += param_doc(years, params['growdiff_baseline'], gdb) else: doc += 'none: using default baseline growth assumptions\n' doc += 'Policy Reform Parameter Values by Year:\n' years = sorted(params['policy'].keys()) if years: doc += param_doc(years, params['policy'], clp) else: doc += 'none: using current-law policy parameters\n' if policy_dicts is not None: assert isinstance(policy_dicts, list) base = clp base.implement_reform(params['policy']) assert not base.reform_errors for policy_dict in policy_dicts: assert isinstance(policy_dict, dict) doc += 'Policy Reform Parameter Values by Year:\n' years = sorted(policy_dict.keys()) doc += param_doc(years, policy_dict, base) base.implement_reform(policy_dict) assert not base.reform_errors
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 behavioral 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 # 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 ----- 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) def _calc_one_year(self, zero_out_calc_vars=False): """ Call all the functions except those in the calc_all() method. """ 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) UBI(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 = copy.deepcopy(self.array('standard')) item = copy.deepcopy(self.array('c04470')) item_no_limit = copy.deepcopy(self.array('c21060')) item_phaseout = copy.deepcopy(self.array('c21040')) self.zeroarray('c04470') self.zeroarray('c21060') self.zeroarray('c21040') self._taxinc_to_amt() std_taxes = copy.deepcopy(self.array('c05800')) # 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 = copy.deepcopy(self.array('c05800')) # Replace standard deduction with zero where the taxpayer # would 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.)) # Calculate taxes with optimal itemized deduction self._taxinc_to_amt() F2441(self.__policy, self.__records) EITC(self.__policy, self.__records) ChildTaxCredit(self.__policy, self.__records) PersonalTaxCredit(self.__policy, self.__records) AmOppCreditParts(self.__policy, self.__records) SchR(self.__policy, self.__records) EducationTaxCredit(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) @staticmethod def _read_json_policy_reform_text(text_string, growdiff_baseline_dict, growdiff_response_dict): """ Strip //-comments from text_string and return 1 dict based on the JSON. Specified text is JSON with at least 1 high-level string:object pair: a "policy": {...} pair. Other high-level pairs will be ignored by this method, except that a "consumption", "behavior", "growdiff_baseline" or "growdiff_response" key will raise a ValueError. The {...} object may be empty (that is, be {}), or may contain one or more pairs with parameter string primary keys and string years as secondary keys. See tests/test_calculate.py for an extended example of a commented JSON policy reform text that can be read by this method. Returned dictionary prdict has integer years as primary keys and string parameters as secondary keys. This returned dictionary is suitable as the argument to the Policy implement_reform(prdict) method. """ # strip out //-comments without changing line numbers json_str = re.sub('//.*', ' ', text_string) # convert JSON text into a Python dictionary try: raw_dict = json.loads(json_str) except ValueError as valerr: msg = 'Policy reform text below contains invalid JSON:\n' msg += str(valerr) + '\n' msg += 'Above location of the first error may be approximate.\n' msg += 'The invalid JSON reform text is between the lines:\n' bline = 'XX----.----1----.----2----.----3----.----4' bline += '----.----5----.----6----.----7' msg += bline + '\n' linenum = 0 for line in json_str.split('\n'): linenum += 1 msg += '{:02d}{}'.format(linenum, line) + '\n' msg += bline + '\n' raise ValueError(msg) # check key contents of dictionary actual_keys = raw_dict.keys() for rkey in Calculator.REQUIRED_REFORM_KEYS: if rkey not in actual_keys: msg = 'key "{}" is not in policy reform file' raise ValueError(msg.format(rkey)) for rkey in actual_keys: if rkey in Calculator.REQUIRED_ASSUMP_KEYS: msg = 'key "{}" should be in economic assumption file' raise ValueError(msg.format(rkey)) # convert raw_dict['policy'] dictionary into prdict tdict = Policy.translate_json_reform_suffixes(raw_dict['policy'], growdiff_baseline_dict, growdiff_response_dict) prdict = Calculator._convert_parameter_dict(tdict) return prdict @staticmethod def _read_json_econ_assump_text(text_string): """ Strip //-comments from text_string and return 4 dict based on the JSON. Specified text is JSON with at least 4 high-level string:object pairs: a "consumption": {...} pair, a "behavior": {...} pair, a "growdiff_baseline": {...} pair, and a "growdiff_response": {...} pair. Other high-level pairs will be ignored by this method, except that a "policy" key will raise a ValueError. The {...} object may be empty (that is, be {}), or may contain one or more pairs with parameter string primary keys and string years as secondary keys. See tests/test_calculate.py for an extended example of a commented JSON economic assumption text that can be read by this method. Note that an example is shown in the ASSUMP_CONTENTS string in tests/test_calculate.py file. Returned dictionaries (cons_dict, behv_dict, gdiff_baseline_dict, gdiff_respose_dict) have integer years as primary keys and string parameters as secondary keys. These returned dictionaries are suitable as the arguments to the Consumption.update_consumption(cons_dict) method, or the Behavior.update_behavior(behv_dict) method, or the Growdiff.update_growdiff(gdiff_dict) method. """ # pylint: disable=too-many-locals # strip out //-comments without changing line numbers json_str = re.sub('//.*', ' ', text_string) # convert JSON text into a Python dictionary try: raw_dict = json.loads(json_str) except ValueError as valerr: msg = 'Economic assumption text below contains invalid JSON:\n' msg += str(valerr) + '\n' msg += 'Above location of the first error may be approximate.\n' msg += 'The invalid JSON asssump text is between the lines:\n' bline = 'XX----.----1----.----2----.----3----.----4' bline += '----.----5----.----6----.----7' msg += bline + '\n' linenum = 0 for line in json_str.split('\n'): linenum += 1 msg += '{:02d}{}'.format(linenum, line) + '\n' msg += bline + '\n' raise ValueError(msg) # check key contents of dictionary actual_keys = raw_dict.keys() for rkey in Calculator.REQUIRED_ASSUMP_KEYS: if rkey not in actual_keys: msg = 'key "{}" is not in economic assumption file' raise ValueError(msg.format(rkey)) for rkey in actual_keys: if rkey in Calculator.REQUIRED_REFORM_KEYS: msg = 'key "{}" should be in policy reform file' raise ValueError(msg.format(rkey)) # convert the assumption dictionaries in raw_dict key = 'consumption' cons_dict = Calculator._convert_parameter_dict(raw_dict[key]) key = 'behavior' behv_dict = Calculator._convert_parameter_dict(raw_dict[key]) key = 'growdiff_baseline' gdiff_base_dict = Calculator._convert_parameter_dict(raw_dict[key]) key = 'growdiff_response' gdiff_resp_dict = Calculator._convert_parameter_dict(raw_dict[key]) return (cons_dict, behv_dict, gdiff_base_dict, gdiff_resp_dict) @staticmethod def _convert_parameter_dict(param_key_dict): """ Converts specified param_key_dict into a dictionary whose primary keys are calendar years, and hence, is suitable as the argument to the Policy.implement_reform() method, or the Consumption.update_consumption() method, or the Behavior.update_behavior() method, or the Growdiff.update_growdiff() method. Specified input dictionary has string parameter primary keys and string years as secondary keys. Returned dictionary has integer years as primary keys and string parameters as secondary keys. """ # convert year skey strings into integers and # optionally convert lists into np.arrays year_param = dict() for pkey, sdict in param_key_dict.items(): if not isinstance(pkey, six.string_types): msg = 'pkey {} in reform is not a string' raise ValueError(msg.format(pkey)) rdict = dict() if not isinstance(sdict, dict): msg = 'pkey {} in reform is not paired with a dict' raise ValueError(msg.format(pkey)) for skey, val in sdict.items(): if not isinstance(skey, six.string_types): msg = 'skey {} in reform is not a string' raise ValueError(msg.format(skey)) else: year = int(skey) rdict[year] = val year_param[pkey] = rdict # convert year_param dictionary to year_key_dict dictionary year_key_dict = dict() years = set() for param, sdict in year_param.items(): for year, val in sdict.items(): if year not in years: years.add(year) year_key_dict[year] = dict() year_key_dict[year][param] = val
return year_key_dict