class Calculator(object):
"""
Constructor for the Calculator class.
Parameters
----------
policy: Policy class object
this argument must be specified
IMPORTANT NOTE: never pass the same Policy object to more than one
Calculator. In other words, when specifying more
than one Calculator object, do this::
pol1 = Policy()
rec1 = Records()
calc1 = Calculator(policy=pol1, records=rec1)
pol2 = Policy()
rec2 = Records()
calc2 = Calculator(policy=pol2, records=rec2)
records: Records class object
this argument must be specified
IMPORTANT NOTE: never pass the same Records object to more than one
Calculator. In other words, when specifying more
than one Calculator object, do this::
pol1 = Policy()
rec1 = Records()
calc1 = Calculator(policy=pol1, records=rec1)
pol2 = Policy()
rec2 = Records()
calc2 = Calculator(policy=pol2, records=rec2)
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 syncronize 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.
behavior: Behavior class object
specifies behaviorial responses used by Calculator; default is None,
which implies no behavioral responses to policy reform.
Raises
------
ValueError:
if parameters are not the appropriate type.
Returns
-------
class instance: Calculator
"""
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 = policy
else:
raise ValueError('must specify policy as a Policy object')
if isinstance(records, Records):
self.records = records
else:
raise ValueError('must specify records as a Records object')
if consumption is None:
self.consumption = Consumption(start_year=policy.start_year)
elif isinstance(consumption, Consumption):
self.consumption = 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 = 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')
if sync_years and self.records.current_year == Records.PUF_YEAR:
if verbose:
print('You loaded data for ' + str(self.records.current_year) +
'.')
if len(self.records.IGNORED_VARS) > 0:
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
def calc_all(self, zero_out_calc_vars=False):
"""
Call all tax-calculation functions.
"""
# conducts static analysis of Calculator object for current_year
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)
def increment_year(self):
"""
Advance all 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)
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.records.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.records.current_year == year
@property
def current_year(self):
"""
Calculator class current calendar year property.
"""
return self.policy.current_year
MTR_VALID_VARIABLES = [
'e00200p', 'e00200s', 'e00900p', 'e00300', 'e00400', 'e00600',
'e00650', 'e01400', 'e01700', 'e02000', 'e02400', 'p22250', 'p23250',
'e18500', 'e19200', 'e26270', 'e19800', 'e20100'
]
def mtr(self,
variable_str='e00200p',
negative_finite_diff=False,
zero_out_calculated_vars=False,
wrt_full_compensation=True):
"""
Calculates the marginal payroll, individual income, and combined
tax rates for every tax filing unit.
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.
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
-------
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
-----
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 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-locals,too-many-statements,too-many-branches
# 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
# save records object in order to restore it after mtr computations
recs0 = copy.deepcopy(self.records)
# extract variable array(s) from embedded records object
variable = getattr(self.records, variable_str)
if variable_str == 'e00200p':
earnings_var = self.records.e00200
elif variable_str == 'e00200s':
earnings_var = self.records.e00200
elif variable_str == 'e00900p':
seincome_var = self.records.e00900
elif variable_str == 'e00650':
divincome_var = self.records.e00600
elif variable_str == 'e26270':
schEincome_var = self.records.e02000
# calculate level of taxes after a marginal increase in income
setattr(self.records, variable_str, variable + finite_diff)
if variable_str == 'e00200p':
self.records.e00200 = earnings_var + finite_diff
elif variable_str == 'e00200s':
self.records.e00200 = earnings_var + finite_diff
elif variable_str == 'e00900p':
self.records.e00900 = seincome_var + finite_diff
elif variable_str == 'e00650':
self.records.e00600 = divincome_var + finite_diff
elif variable_str == 'e26270':
self.records.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 = copy.deepcopy(self.records.payrolltax)
incometax_chng = copy.deepcopy(self.records.iitax)
combined_taxes_chng = incometax_chng + payrolltax_chng
# calculate base level of taxes after restoring records object
setattr(self, 'records', recs0)
self.calc_all(zero_out_calc_vars=zero_out_calculated_vars)
payrolltax_base = copy.deepcopy(self.records.payrolltax)
incometax_base = copy.deepcopy(self.records.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.policy.SS_Earnings_c,
0.5 * (self.policy.FICA_ss_trt + self.policy.FICA_mc_trt),
0.5 * self.policy.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':
mtr_payrolltax = np.where(self.records.MARS == 2, mtr_payrolltax,
np.nan)
mtr_incometax = np.where(self.records.MARS == 2, mtr_incometax,
np.nan)
mtr_combined = np.where(self.records.MARS == 2, mtr_combined,
np.nan)
# return the three marginal tax rate arrays
return (mtr_payrolltax, mtr_incometax, mtr_combined)
def current_law_version(self):
"""
Return Calculator object same as self except with current-law policy.
"""
clp = self.policy.current_law_version()
recs = copy.deepcopy(self.records)
cons = copy.deepcopy(self.consumption)
behv = copy.deepcopy(self.behavior)
calc = Calculator(policy=clp,
records=recs,
sync_years=False,
consumption=cons,
behavior=behv)
return calc
@staticmethod
def read_json_param_files(reform_filename,
assump_filename,
arrays_not_lists=True):
"""
Read JSON files and call Calculator.read_json_*_text methods
returning a single dictionary containing five key:dict pairs:
'policy':dict, 'consumption':dict, 'behavior':dict,
'growdiff_baseline':dict and 'growdiff_response':dict.
"""
if reform_filename is None:
rpol_dict = dict()
elif os.path.isfile(reform_filename):
txt = open(reform_filename, 'r').read()
rpol_dict = (Calculator._read_json_policy_reform_text(
txt, arrays_not_lists))
else:
msg = 'policy reform file {} could not be found'
raise ValueError(msg.format(reform_filename))
if assump_filename is None:
cons_dict = dict()
behv_dict = dict()
gdiff_base_dict = dict()
gdiff_resp_dict = dict()
elif os.path.isfile(assump_filename):
txt = open(assump_filename, 'r').read()
(cons_dict, behv_dict, gdiff_base_dict,
gdiff_resp_dict) = (Calculator._read_json_econ_assump_text(
txt, arrays_not_lists))
else:
msg = 'economic assumption file {} could not be found'
raise ValueError(msg.format(assump_filename))
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'])
# ----- 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)
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.records.standard)
item = copy.deepcopy(self.records.c04470)
item_no_limit = copy.deepcopy(self.records.c21060)
item_phaseout = copy.deepcopy(self.records.c21040)
self.records.c04470 = np.zeros(self.records.dim)
self.records.c21060 = np.zeros(self.records.dim)
self.records.c21040 = np.zeros(self.records.dim)
self._taxinc_to_amt()
std_taxes = copy.deepcopy(self.records.c05800)
# Set standard deduction to zero, calculate taxes w/o
# standard deduction, and store AMT + Regular Tax
self.records.standard = np.zeros(self.records.dim)
self.records.c21060 = item_no_limit
self.records.c21040 = item_phaseout
self.records.c04470 = item
self._taxinc_to_amt()
item_taxes = copy.deepcopy(self.records.c05800)
# Replace standard deduction with zero where the taxpayer
# would be better off itemizing
self.records.standard[:] = np.where(item_taxes < std_taxes, 0., std)
self.records.c04470[:] = np.where(item_taxes < std_taxes, item, 0.)
self.records.c21060[:] = np.where(item_taxes < std_taxes,
item_no_limit, 0.)
self.records.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)
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, arrays_not_lists):
"""
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 rpol_dict 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(rpol_dict)
method ONLY if the function argument arrays_not_lists is True.
"""
# 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 the policy dictionary in raw_dict
rpol_dict = Calculator._convert_parameter_dict(raw_dict['policy'],
arrays_not_lists)
return rpol_dict
@staticmethod
def _read_json_econ_assump_text(text_string, arrays_not_lists):
"""
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,
but ONLY if the function argument arrays_not_lists is True.
"""
# 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],
arrays_not_lists)
key = 'behavior'
behv_dict = Calculator._convert_parameter_dict(raw_dict[key],
arrays_not_lists)
key = 'growdiff_baseline'
gdiff_base_dict = Calculator._convert_parameter_dict(
raw_dict[key], arrays_not_lists)
key = 'growdiff_response'
gdiff_resp_dict = Calculator._convert_parameter_dict(
raw_dict[key], arrays_not_lists)
return (cons_dict, behv_dict, gdiff_base_dict, gdiff_resp_dict)
@staticmethod
def _convert_parameter_dict(param_key_dict, arrays_not_lists):
"""
Converts specified param_key_dict into a dictionary whose primary
keys are calendary 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,
but only if function argument is arrays_not_lists=True.
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)
if isinstance(val, list) and arrays_not_lists:
rdict[year] = np.array(val)
else:
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
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
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.
"""
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')
if sync_years and self.records.current_year == self.records.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
def calc_all(self, zero_out_calc_vars=False):
"""
Call all tax-calculation functions.
"""
# conducts static analysis of Calculator object for current_year
assert self.records.current_year == self.policy.current_year
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)
def increment_year(self):
"""
Advance all 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)
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.records.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.records.current_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
MTR_VALID_VARIABLES = [
'e00200p', 'e00200s', 'e00900p', 'e00300', 'e00400', 'e00600',
'e00650', 'e01400', 'e01700', 'e02000', 'e02400', 'p22250', 'p23250',
'e18500', 'e19200', 'e26270', 'e19800', 'e20100'
]
def mtr(self,
variable_str='e00200p',
negative_finite_diff=False,
zero_out_calculated_vars=False,
wrt_full_compensation=True):
"""
Calculates the marginal payroll, individual income, and combined
tax rates for every tax filing unit.
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.
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
-------
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
-----
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-locals,too-many-statements,too-many-branches
# 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
# save records object in order to restore it after mtr computations
recs0 = copy.deepcopy(self.records)
# extract variable array(s) from embedded records object
variable = getattr(self.records, variable_str)
if variable_str == 'e00200p':
earnings_var = self.records.e00200
elif variable_str == 'e00200s':
earnings_var = self.records.e00200
elif variable_str == 'e00900p':
seincome_var = self.records.e00900
elif variable_str == 'e00650':
divincome_var = self.records.e00600
elif variable_str == 'e26270':
schEincome_var = self.records.e02000
# calculate level of taxes after a marginal increase in income
setattr(self.records, variable_str, variable + finite_diff)
if variable_str == 'e00200p':
self.records.e00200 = earnings_var + finite_diff
elif variable_str == 'e00200s':
self.records.e00200 = earnings_var + finite_diff
elif variable_str == 'e00900p':
self.records.e00900 = seincome_var + finite_diff
elif variable_str == 'e00650':
self.records.e00600 = divincome_var + finite_diff
elif variable_str == 'e26270':
self.records.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 = copy.deepcopy(self.records.payrolltax)
incometax_chng = copy.deepcopy(self.records.iitax)
combined_taxes_chng = incometax_chng + payrolltax_chng
# calculate base level of taxes after restoring records object
setattr(self, 'records', recs0)
self.calc_all(zero_out_calc_vars=zero_out_calculated_vars)
payrolltax_base = copy.deepcopy(self.records.payrolltax)
incometax_base = copy.deepcopy(self.records.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.policy.SS_Earnings_c,
0.5 * (self.policy.FICA_ss_trt + self.policy.FICA_mc_trt),
0.5 * self.policy.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':
mtr_payrolltax = np.where(self.records.MARS == 2, mtr_payrolltax,
np.nan)
mtr_incometax = np.where(self.records.MARS == 2, mtr_incometax,
np.nan)
mtr_combined = np.where(self.records.MARS == 2, mtr_combined,
np.nan)
# return the three marginal tax rate arrays
return (mtr_payrolltax, mtr_incometax, mtr_combined)
def current_law_version(self):
"""
Return Calculator object same as self except with current-law policy.
"""
return Calculator(policy=self.policy.current_law_version(),
records=copy.deepcopy(self.records),
sync_years=False,
consumption=copy.deepcopy(self.consumption),
behavior=copy.deepcopy(self.behavior))
@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'])
@staticmethod
def reform_documentation(params):
"""
Generate reform documentation.
Parameters
----------
params: dict
compound dictionary structured as dict returned from
the static Calculator method read_json_param_objects()
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
"""
# 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())
basevals = getattr(base, '_vals', None)
assert isinstance(basevals, dict)
doc = ''
for year in years:
# write year
base.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(base, Policy):
if param.endswith('_cpi'):
rootparam = param[:-4]
bval = basevals[rootparam].get(
'cpi_inflated', False)
else:
bval = getattr(base, param[1:], None)
if isinstance(bval, np.ndarray):
# pylint: disable=no-member
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 base 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'
return doc
# ----- 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.records.standard)
item = copy.deepcopy(self.records.c04470)
item_no_limit = copy.deepcopy(self.records.c21060)
item_phaseout = copy.deepcopy(self.records.c21040)
self.records.c04470 = np.zeros(self.records.dim)
self.records.c21060 = np.zeros(self.records.dim)
self.records.c21040 = np.zeros(self.records.dim)
self._taxinc_to_amt()
std_taxes = copy.deepcopy(self.records.c05800)
# Set standard deduction to zero, calculate taxes w/o
# standard deduction, and store AMT + Regular Tax
self.records.standard = np.zeros(self.records.dim)
self.records.c21060 = item_no_limit
self.records.c21040 = item_phaseout
self.records.c04470 = item
self._taxinc_to_amt()
item_taxes = copy.deepcopy(self.records.c05800)
# Replace standard deduction with zero where the taxpayer
# would be better off itemizing
self.records.standard[:] = np.where(item_taxes < std_taxes, 0., std)
self.records.c04470[:] = np.where(item_taxes < std_taxes, item, 0.)
self.records.c21060[:] = np.where(item_taxes < std_taxes,
item_no_limit, 0.)
self.records.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