"""
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_employer') +
self.policy_param('FICA_ss_trt_employee') +
self.policy_param('FICA_mc_trt_employer') +
self.policy_param('FICA_mc_trt_employee')),
0.5 * (self.policy_param('FICA_mc_trt_employer') +
self.policy_param('FICA_mc_trt_employee')))
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]
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)
