def run_reform(name, reform, epsilon):
puf = pd.read_csv("./puf.csv")
policy_base = Policy(start_year=2013)
records_base = Records(puf)
policy_reform = Policy()
records_reform = Records(puf)
calcbase = Calculator(policy=policy_base, records=records_base)
calcreform = Calculator(policy=policy_reform, records=records_reform)
policy_reform.implement_reform(reform)
calcbase.advance_to_year(CURRENT_YEAR)
calcreform.advance_to_year(CURRENT_YEAR)
calcbase.calc_all()
calcreform.calc_all()
diff_df = get_diff(calcbase, calcreform, name)
data_df = print_data(calcbase,
calcreform,
weights=weighted,
tab='c00100',
name=name)
equiv_tax_cut = reform_equiv(reform, epsilon)
total_rev_raise = agg_diff(calcbase, calcreform)
delta_num_filers, delta_percent_filers = agg_num_delta(
calcbase, calcreform)
#diff_df['equiv_rate_cut'] = len(diff_df)*[equiv_tax_cut]
return (diff_df, data_df, equiv_tax_cut, total_rev_raise, delta_num_filers,
delta_percent_filers)
def _calc_object(self, exact_calcs, emulate_taxsim_2441_logic,
output_records):
"""
Create and return Calculator object to conduct the tax calculations.
Parameters
----------
exact_calcs: boolean
emulate_taxsim_2441_logic: boolean
output_records: boolean
Returns
-------
calc: Calculator
"""
# create all-zeros dictionary and then list of all-zero dictionaries
Records.read_var_info()
zero_dict = {}
for varname in Records.USABLE_READ_VARS:
zero_dict[varname] = 0
dict_list = [zero_dict for _ in range(0, len(self._input))]
# use dict_list to create a Pandas DataFrame and Records object
recsdf = pd.DataFrame(dict_list, dtype='int64')
recsdf['MARS'] = recsdf['MARS'].add(1) # because MARS==0 is illegal
recs = Records(data=recsdf,
exact_calculations=exact_calcs,
gfactors=None,
weights=None,
start_year=self.policy.start_year)
assert recs.array_length == len(self._input)
# specify input for each tax filing unit in Records object
lnum = 0
for idx in range(0, recs.array_length):
lnum += 1
SimpleTaxIO._specify_input(recs, idx, self._input[lnum],
emulate_taxsim_2441_logic)
# optionally write Records.USABLE_READ_VARS content to file
if output_records:
recdf = pd.DataFrame() # pragma: no cover
for varname in Records.USABLE_READ_VARS: # pragma: no cover
vardata = getattr(recs, varname) # pragma: no cover
recdf[varname] = vardata # pragma: no cover
recdf.to_csv(
re.sub(
'out-simtax',
'records', # pragma: no cover
self._output_filename),
float_format='%.2f',
index=False)
# create Calculator object containing all tax filing units
return Calculator(policy=self.policy, records=recs, sync_years=False)
def dump_output(self, dump_varset, mtr_inctax, mtr_paytax):
"""
Extract dump output and return it as Pandas DataFrame.
"""
recs_vinfo = Records(data=None) # contains only Records VARINFO
if dump_varset is None:
varset = recs_vinfo.USABLE_READ_VARS | recs_vinfo.CALCULATED_VARS
else:
varset = dump_varset
# create and return dump output DataFrame
odf = pd.DataFrame()
for varname in varset:
vardata = self.calc.array(varname)
if varname in recs_vinfo.INTEGER_VARS:
odf[varname] = vardata
else:
odf[varname] = vardata.round(2) # rounded to nearest cent
# specify mtr values in percentage terms
if 'mtr_inctax' in varset:
odf['mtr_inctax'] = (mtr_inctax * 100).round(2)
if 'mtr_paytax' in varset:
odf['mtr_paytax'] = (mtr_paytax * 100).round(2)
# specify tax calculation year
odf['FLPDYR'] = self.tax_year()
return odf
def custom_dump_variables(self, tcdumpvars_str):
"""
Return set of variable names extracted from tcdumpvars_str, which
contains the contents of the tcdumpvars file in the current directory.
Also, builds self.errmsg if any custom variables are not valid.
"""
assert isinstance(tcdumpvars_str, str)
self.errmsg = ''
# change some common delimiter characters into spaces
dump_vars_str = tcdumpvars_str.replace(',', ' ')
dump_vars_str = dump_vars_str.replace(';', ' ')
dump_vars_str = dump_vars_str.replace('|', ' ')
# split dump_vars_str into a list of dump variables
dump_vars_list = dump_vars_str.split()
# check that all dump_vars_list items are valid
recs_vinfo = Records(data=None) # contains records VARINFO only
valid_set = recs_vinfo.USABLE_READ_VARS | recs_vinfo.CALCULATED_VARS
for var in dump_vars_list:
if var not in valid_set:
msg = 'invalid variable name in tcdumpvars file: {}'
msg = msg.format(var)
self.errmsg += 'ERROR: {}\n'.format(msg)
# add essential variables even if not on custom list
if 'RECID' not in dump_vars_list:
dump_vars_list.append('RECID')
if 'FLPDYR' not in dump_vars_list:
dump_vars_list.append('FLPDYR')
# convert list into a set and return
return set(dump_vars_list)
def diff_in_revenue(reform_on_II, orig_reform):
policy_func = Policy()
puf = pd.read_csv("./puf.csv")
records_func = Records(puf)
calc_func = Calculator(policy=policy_func, records=records_func)
policy_bench = Policy()
records_bench = Records(puf)
calc_bench = Calculator(policy=policy_bench, records=records_bench)
reform = {
CURRENT_YEAR: {
"_II_rt1":
[max(policy_bench._II_rt1[0] * (1 - reform_on_II), 0.0)],
"_II_rt2":
[max(policy_bench._II_rt2[0] * (1 - reform_on_II), 0.0)],
"_II_rt3":
[max(policy_bench._II_rt3[0] * (1 - reform_on_II), 0.0)],
"_II_rt4":
[max(policy_bench._II_rt4[0] * (1 - reform_on_II), 0.0)],
"_II_rt5":
[max(policy_bench._II_rt5[0] * (1 - reform_on_II), 0.0)],
"_II_rt6":
[max(policy_bench._II_rt6[0] * (1 - reform_on_II), 0.0)],
"_II_rt7":
[max(policy_bench._II_rt7[0] * (1 - reform_on_II), 0.0)]
}
}
policy_func.implement_reform(reform)
policy_func.implement_reform(orig_reform)
calc_func.advance_to_year(CURRENT_YEAR)
calc_bench.advance_to_year(CURRENT_YEAR)
calc_func.calc_all()
calc_bench.calc_all()
ans = ((calc_bench.records._combined * calc_bench.records.s006).sum() -
(calc_func.records._combined * calc_func.records.s006).sum())
print("diff in revenue is ", ans)
return ans
def init(self, input_data, tax_year, reform, assump, growdiff_response,
aging_input_data, exact_calculations):
"""
TaxCalcIO class post-constructor method that completes initialization.
Parameters
----------
First four parameters are same as for TaxCalcIO constructor:
input_data, tax_year, reform, assump.
growdiff_response: Growdiff object or None
growdiff_response Growdiff object is used only by the
TaxCalcIO.growmodel_analysis method;
must be None in all other cases.
aging_input_data: boolean
whether or not to extrapolate Records data from data year to
tax_year.
exact_calculations: boolean
specifies whether or not exact tax calculations are done without
any smoothing of "stair-step" provisions in the tax law.
"""
# pylint: disable=too-many-arguments,too-many-locals
# pylint: disable=too-many-statements,too-many-branches
self.errmsg = ''
# get parameter dictionaries from --reform and --assump files
paramdict = Calculator.read_json_param_objects(reform, assump)
# create Behavior object
beh = Behavior()
beh.update_behavior(paramdict['behavior'])
self.behavior_has_any_response = beh.has_any_response()
# create gdiff_baseline object
gdiff_baseline = Growdiff()
gdiff_baseline.update_growdiff(paramdict['growdiff_baseline'])
# create Growfactors clp object that incorporates gdiff_baseline
gfactors_clp = Growfactors()
gdiff_baseline.apply_to(gfactors_clp)
# specify gdiff_response object
if growdiff_response is None:
gdiff_response = Growdiff()
gdiff_response.update_growdiff(paramdict['growdiff_response'])
elif isinstance(growdiff_response, Growdiff):
gdiff_response = growdiff_response
else:
gdiff_response = None
msg = 'TaxCalcIO.more_init: growdiff_response is neither None '
msg += 'nor a Growdiff object'
self.errmsg += 'ERROR: {}\n'.format(msg)
if gdiff_response is not None:
some_gdiff_response = gdiff_response.has_any_response()
if self.behavior_has_any_response and some_gdiff_response:
msg = 'ASSUMP file cannot specify any "behavior" when using '
msg += 'GrowModel or when ASSUMP file has "growdiff_response"'
self.errmsg += 'ERROR: {}\n'.format(msg)
# create Growfactors ref object that has both gdiff objects applied
gfactors_ref = Growfactors()
gdiff_baseline.apply_to(gfactors_ref)
if gdiff_response is not None:
gdiff_response.apply_to(gfactors_ref)
# create Policy objects
if self.specified_reform:
pol = Policy(gfactors=gfactors_ref)
try:
pol.implement_reform(paramdict['policy'])
self.errmsg += pol.reform_errors
except ValueError as valerr_msg:
self.errmsg += valerr_msg.__str__()
else:
pol = Policy(gfactors=gfactors_clp)
clp = Policy(gfactors=gfactors_clp)
# check for valid tax_year value
if tax_year < pol.start_year:
msg = 'tax_year {} less than policy.start_year {}'
msg = msg.format(tax_year, pol.start_year)
self.errmsg += 'ERROR: {}\n'.format(msg)
if tax_year > pol.end_year:
msg = 'tax_year {} greater than policy.end_year {}'
msg = msg.format(tax_year, pol.end_year)
self.errmsg += 'ERROR: {}\n'.format(msg)
# any errors imply cannot proceed with calculations
if self.errmsg:
return
# set policy to tax_year
pol.set_year(tax_year)
clp.set_year(tax_year)
# read input file contents into Records objects
if aging_input_data:
if self.cps_input_data:
recs = Records.cps_constructor(
gfactors=gfactors_ref,
exact_calculations=exact_calculations)
recs_clp = Records.cps_constructor(
gfactors=gfactors_clp,
exact_calculations=exact_calculations)
else: # if not cps_input_data
recs = Records(data=input_data,
gfactors=gfactors_ref,
exact_calculations=exact_calculations)
recs_clp = Records(data=input_data,
gfactors=gfactors_clp,
exact_calculations=exact_calculations)
else: # input_data are raw data that are not being aged
recs = Records(data=input_data,
gfactors=None,
exact_calculations=exact_calculations,
weights=None,
adjust_ratios=None,
start_year=tax_year)
recs_clp = copy.deepcopy(recs)
if tax_year < recs.data_year:
msg = 'tax_year {} less than records.data_year {}'
msg = msg.format(tax_year, recs.data_year)
self.errmsg += 'ERROR: {}\n'.format(msg)
# create Calculator objects
con = Consumption()
con.update_consumption(paramdict['consumption'])
self.calc = Calculator(policy=pol,
records=recs,
verbose=True,
consumption=con,
behavior=beh,
sync_years=aging_input_data)
self.calc_clp = Calculator(policy=clp,
records=recs_clp,
verbose=False,
consumption=con,
sync_years=aging_input_data)
# remember parameter dictionary for reform documentation
self.param_dict = paramdict
import pandas as pd
import numpy as np
import sys
from taxcalc.records import Records
from taxcalc import *
from taxcalc.utils import *
puf = pd.read_csv("puf.csv")
policy_base = Policy()
records_base = Records(puf)
consump = Consumption()
policy_T = Policy()
records_reform = Records(puf)
consump_T = Consumption()
policy_H = Policy()
records_org = Records(puf)
consump_H = Consumption()
calcbase = Calculator(policy = policy_base, records = records_base,consumption=consump)
calc_T = Calculator(policy = policy_T, records = records_reform,consumption=consump_T)
calc_H = Calculator(policy = policy_H, records = records_org,consumption=consump_H)
reform_H= {
2017: {'_AGI_surtax_thd': [[5000000, 5000000, 5000000, 5000000, 5000000, 5000000]],
'_AGI_surtax_trt': [0.04],
'_FST_AGI_trt':[0.3],
'_ID_BenefitCap_rt': [0.28],
'_ACTC_Income_thd': [0],
'_CTC_c': [2000]
def init(self, input_data, tax_year, baseline, reform, assump,
growdiff_growmodel, aging_input_data, exact_calculations):
"""
TaxCalcIO class post-constructor method that completes initialization.
Parameters
----------
First five are same as the first five of the TaxCalcIO constructor:
input_data, tax_year, baseline, reform, assump.
growdiff_growmodel: GrowDiff object or None
growdiff_growmodel GrowDiff object is used only in the
TaxCalcIO.growmodel_analysis method.
aging_input_data: boolean
whether or not to extrapolate Records data from data year to
tax_year.
exact_calculations: boolean
specifies whether or not exact tax calculations are done without
any smoothing of "stair-step" provisions in the tax law.
"""
# pylint: disable=too-many-arguments,too-many-locals
# pylint: disable=too-many-statements,too-many-branches
self.errmsg = ''
# get policy parameter dictionary from --baseline file
basedict = Calculator.read_json_param_objects(baseline, None)
# get assumption sub-dictionaries
paramdict = Calculator.read_json_param_objects(None, assump)
# get policy parameter dictionaries from --reform file(s)
policydicts = list()
if self.specified_reform:
reforms = reform.split('+')
for ref in reforms:
pdict = Calculator.read_json_param_objects(ref, None)
policydicts.append(pdict['policy'])
paramdict['policy'] = policydicts[0]
# remember parameters for reform documentation
self.param_dict = paramdict
self.policy_dicts = policydicts
# create Behavior object
beh = Behavior()
try:
beh.update_behavior(paramdict['behavior'])
except ValueError as valerr_msg:
self.errmsg += valerr_msg.__str__()
self.behavior_has_any_response = beh.has_any_response()
# create gdiff_baseline object
gdiff_baseline = GrowDiff()
try:
gdiff_baseline.update_growdiff(paramdict['growdiff_baseline'])
except ValueError as valerr_msg:
self.errmsg += valerr_msg.__str__()
# create GrowFactors base object that incorporates gdiff_baseline
gfactors_base = GrowFactors()
gdiff_baseline.apply_to(gfactors_base)
# specify gdiff_response object
gdiff_response = GrowDiff()
try:
gdiff_response.update_growdiff(paramdict['growdiff_response'])
except ValueError as valerr_msg:
self.errmsg += valerr_msg.__str__()
# create GrowFactors ref object that has all gdiff objects applied
gfactors_ref = GrowFactors()
gdiff_baseline.apply_to(gfactors_ref)
gdiff_response.apply_to(gfactors_ref)
if growdiff_growmodel:
growdiff_growmodel.apply_to(gfactors_ref)
# create Policy objects:
# ... the baseline Policy object
base = Policy(gfactors=gfactors_base)
try:
base.implement_reform(basedict['policy'],
print_warnings=False,
raise_errors=False)
self.errmsg += base.parameter_errors
except ValueError as valerr_msg:
self.errmsg += valerr_msg.__str__()
# ... the reform Policy object
if self.specified_reform:
pol = Policy(gfactors=gfactors_ref)
for poldict in policydicts:
try:
pol.implement_reform(poldict,
print_warnings=False,
raise_errors=False)
self.errmsg += pol.parameter_errors
except ValueError as valerr_msg:
self.errmsg += valerr_msg.__str__()
else:
pol = Policy(gfactors=gfactors_base)
# create Consumption object
con = Consumption()
try:
con.update_consumption(paramdict['consumption'])
except ValueError as valerr_msg:
self.errmsg += valerr_msg.__str__()
# create GrowModel object
self.growmodel = GrowModel()
try:
self.growmodel.update_growmodel(paramdict['growmodel'])
except ValueError as valerr_msg:
self.errmsg += valerr_msg.__str__()
# check for valid tax_year value
if tax_year < pol.start_year:
msg = 'tax_year {} less than policy.start_year {}'
msg = msg.format(tax_year, pol.start_year)
self.errmsg += 'ERROR: {}\n'.format(msg)
if tax_year > pol.end_year:
msg = 'tax_year {} greater than policy.end_year {}'
msg = msg.format(tax_year, pol.end_year)
self.errmsg += 'ERROR: {}\n'.format(msg)
# any errors imply cannot proceed with calculations
if self.errmsg:
return
# set policy to tax_year
pol.set_year(tax_year)
base.set_year(tax_year)
# read input file contents into Records objects
if aging_input_data:
if self.cps_input_data:
recs = Records.cps_constructor(
gfactors=gfactors_ref,
exact_calculations=exact_calculations)
recs_base = Records.cps_constructor(
gfactors=gfactors_base,
exact_calculations=exact_calculations)
else: # if not cps_input_data but aging_input_data
recs = Records(data=input_data,
gfactors=gfactors_ref,
exact_calculations=exact_calculations)
recs_base = Records(data=input_data,
gfactors=gfactors_base,
exact_calculations=exact_calculations)
else: # input_data are raw data that are not being aged
recs = Records(data=input_data,
gfactors=None,
exact_calculations=exact_calculations,
weights=None,
adjust_ratios=None,
start_year=tax_year)
recs_base = copy.deepcopy(recs)
if tax_year < recs.data_year:
msg = 'tax_year {} less than records.data_year {}'
msg = msg.format(tax_year, recs.data_year)
self.errmsg += 'ERROR: {}\n'.format(msg)
# create Calculator objects
self.calc = Calculator(policy=pol,
records=recs,
verbose=True,
consumption=con,
behavior=beh,
sync_years=aging_input_data)
self.calc_base = Calculator(policy=base,
records=recs_base,
verbose=False,
consumption=con,
sync_years=aging_input_data)
def init(self, input_data, tax_year, baseline, reform, assump,
aging_input_data, exact_calculations):
"""
TaxCalcIO class post-constructor method that completes initialization.
Parameters
----------
First five are same as the first five of the TaxCalcIO constructor:
input_data, tax_year, baseline, reform, assump.
aging_input_data: boolean
whether or not to extrapolate Records data from data year to
tax_year.
exact_calculations: boolean
specifies whether or not exact tax calculations are done without
any smoothing of "stair-step" provisions in the tax law.
"""
# pylint: disable=too-many-arguments,too-many-locals
# pylint: disable=too-many-statements,too-many-branches
self.errmsg = ''
# get policy parameter dictionary from --baseline file
basedict = Calculator.read_json_param_objects(baseline, None)
# get assumption sub-dictionaries
paramdict = Calculator.read_json_param_objects(None, assump)
# get policy parameter dictionaries from --reform file(s)
policydicts = list()
if self.specified_reform:
reforms = reform.split('+')
for ref in reforms:
pdict = Calculator.read_json_param_objects(ref, None)
policydicts.append(pdict['policy'])
paramdict['policy'] = policydicts[0]
# remember parameters for reform documentation
self.param_dict = paramdict
self.policy_dicts = policydicts
# create gdiff_baseline object
gdiff_baseline = GrowDiff()
try:
gdiff_baseline.update_growdiff(paramdict['growdiff_baseline'])
except ValueError as valerr_msg:
self.errmsg += valerr_msg.__str__()
# create GrowFactors base object that incorporates gdiff_baseline
gfactors_base = GrowFactors()
gdiff_baseline.apply_to(gfactors_base)
# specify gdiff_response object
gdiff_response = GrowDiff()
try:
gdiff_response.update_growdiff(paramdict['growdiff_response'])
except ValueError as valerr_msg:
self.errmsg += valerr_msg.__str__()
# create GrowFactors ref object that has all gdiff objects applied
gfactors_ref = GrowFactors()
gdiff_baseline.apply_to(gfactors_ref)
gdiff_response.apply_to(gfactors_ref)
# create Policy objects:
# ... the baseline Policy object
base = Policy(gfactors=gfactors_base)
try:
base.implement_reform(basedict['policy'],
print_warnings=False,
raise_errors=False)
self.errmsg += base.parameter_errors
except ValueError as valerr_msg:
self.errmsg += valerr_msg.__str__()
# ... the reform Policy object
if self.specified_reform:
pol = Policy(gfactors=gfactors_ref)
for poldict in policydicts:
try:
pol.implement_reform(poldict,
print_warnings=False,
raise_errors=False)
self.errmsg += pol.parameter_errors
except ValueError as valerr_msg:
self.errmsg += valerr_msg.__str__()
else:
pol = Policy(gfactors=gfactors_base)
# create Consumption object
con = Consumption()
try:
con.update_consumption(paramdict['consumption'])
except ValueError as valerr_msg:
self.errmsg += valerr_msg.__str__()
# check for valid tax_year value
if tax_year < pol.start_year:
msg = 'tax_year {} less than policy.start_year {}'
msg = msg.format(tax_year, pol.start_year)
self.errmsg += 'ERROR: {}\n'.format(msg)
if tax_year > pol.end_year:
msg = 'tax_year {} greater than policy.end_year {}'
msg = msg.format(tax_year, pol.end_year)
self.errmsg += 'ERROR: {}\n'.format(msg)
# any errors imply cannot proceed with calculations
if self.errmsg:
return
# set policy to tax_year
pol.set_year(tax_year)
base.set_year(tax_year)
# read input file contents into Records objects
if aging_input_data:
if self.cps_input_data:
recs = Records.cps_constructor(
gfactors=gfactors_ref,
exact_calculations=exact_calculations
)
recs_base = Records.cps_constructor(
gfactors=gfactors_base,
exact_calculations=exact_calculations
)
else: # if not cps_input_data but aging_input_data
recs = Records(
data=input_data,
gfactors=gfactors_ref,
exact_calculations=exact_calculations
)
recs_base = Records(
data=input_data,
gfactors=gfactors_base,
exact_calculations=exact_calculations
)
else: # input_data are raw data that are not being aged
recs = Records(data=input_data,
gfactors=None,
exact_calculations=exact_calculations,
weights=None,
adjust_ratios=None,
start_year=tax_year)
recs_base = copy.deepcopy(recs)
if tax_year < recs.data_year:
msg = 'tax_year {} less than records.data_year {}'
msg = msg.format(tax_year, recs.data_year)
self.errmsg += 'ERROR: {}\n'.format(msg)
# create Calculator objects
self.calc = Calculator(policy=pol, records=recs,
verbose=True,
consumption=con,
sync_years=aging_input_data)
self.calc_base = Calculator(policy=base, records=recs_base,
verbose=False,
consumption=con,
sync_years=aging_input_data)
def generate_policy_revenues():
from taxcalc.growfactors import GrowFactors
from taxcalc.policy import Policy
from taxcalc.records import Records
from taxcalc.gstrecords import GSTRecords
from taxcalc.corprecords import CorpRecords
from taxcalc.parameters import ParametersBase
from taxcalc.calculator import Calculator
"""
for num in range(1, num_reforms):
block_selected_dict[num]['selected_item']= block_widget_dict[num][1].get()
block_selected_dict[num]['selected_value']= block_widget_dict[num][3].get()
block_selected_dict[num]['selected_year']= block_widget_dict[num][2].get()
print(block_selected_dict)
"""
f = open('reform.json')
block_selected_dict = json.load(f)
print("block_selected_dict from json",block_selected_dict)
#print(block_selected_dict)
# create Records object containing pit.csv and pit_weights.csv input data
#print("growfactors filename ", growfactors_filename)
#recs = Records(data=data_filename, weights=weights_filename, gfactors=GrowFactors(growfactors_filename=growfactors_filename))
#recs = Records(data=data_filename, weights=weights_filename, gfactors=GrowFactors(growfactors_filename=growfactors_filename))
#recs.increment_year1(3.0)
#grecs = GSTRecords()
f = open('global_vars.json')
vars = json.load(f)
print("data_filename: ", vars['cit_data_filename'])
print("weights_filename: ", vars['cit_weights_filename'])
print("growfactors_filename: ", vars['GROWFACTORS_FILENAME'])
print("policy_filename: ", vars['DEFAULTS_FILENAME'])
# create CorpRecords object using cross-section data
#crecs1 = CorpRecords(data='cit_cross.csv', weights='cit_cross_wgts1.csv')
crecs1 = CorpRecords(data=vars['cit_data_filename'], weights=vars['cit_weights_filename'], gfactors=GrowFactors(growfactors_filename=vars['GROWFACTORS_FILENAME']))
#crecs1 = CorpRecords(data=vars['cit_weights_filename'], weights=vars['cit_weights_filename'])
# Note: weights argument is optional
assert isinstance(crecs1, CorpRecords)
assert crecs1.current_year == 2017
# create Policy object containing current-law policy
pol = Policy(DEFAULTS_FILENAME=vars['DEFAULTS_FILENAME'])
# specify Calculator objects for current-law policy
#calc1 = Calculator(policy=pol, records=recs, corprecords=crecs1,
# gstrecords=grecs, verbose=False)
calc1 = Calculator(policy=pol, corprecords=crecs1, verbose=False)
#calc1.increment_year1(3.8)
assert isinstance(calc1, Calculator)
assert calc1.current_year == 2017
np.seterr(divide='ignore', invalid='ignore')
pol2 = Policy(DEFAULTS_FILENAME=vars['DEFAULTS_FILENAME'])
years, reform=read_reform_dict(block_selected_dict)
print("reform dictionary: ",reform)
#reform = Calculator.read_json_param_objects('app01_reform.json', None)
pol2.implement_reform(reform['policy'])
#calc2 = Calculator(policy=pol2, records=recs, corprecords=crecs1,
# gstrecords=grecs, verbose=False)
calc2 = Calculator(policy=pol2, corprecords=crecs1, verbose=False)
pit_adjustment_factor={}
revenue_dict_cit={}
revenue_amount_dict = {}
calc1.calc_all()
for year in range(2019, 2024):
cols = []
calc1.advance_to_year(year)
calc2.advance_to_year(year)
# NOTE: calc1 now contains a PRIVATE COPY of pol and a PRIVATE COPY of recs,
# so we can continue to use pol and recs in this script without any
# concern about side effects from Calculator method calls on calc1.
# Produce DataFrame of results using the calculator
# First run the calculator for the corporate income tax
calc1.calc_all()
print("***** Year ", year)
weighted_citax1 = calc1.weighted_total_cit('citax')
citax_collection_billions1 = weighted_citax1/10**9
citax_collection_str1 = '{0:.2f}'.format(citax_collection_billions1)
print("The CIT Collection in billions is: ", citax_collection_billions1)
# Produce DataFrame of results using cross-section
calc2.calc_all()
weighted_citax2 = calc2.weighted_total_cit('citax')
citax_collection_billions2 = weighted_citax2/10**9
citax_collection_str2 = '{0:.2f}'.format(citax_collection_billions2)
# This is the difference in the collection due to the reform
# This amount will now be allocated to dividends of PIT
citax_diff_collection_billions2 = (citax_collection_billions2-citax_collection_billions1)
citax_diff_collection_str2 = '{0:.2f}'.format(citax_diff_collection_billions2)
print("The CIT Collection after reform billions is: ", citax_collection_billions2)
print("The difference in CIT Collection in billions is: ", citax_diff_collection_billions2)
# Process of allocation of difference in CIT profits to PIT
# in the form of Dividends
# Dividends in this case is reported as Income from Other Sources
# TOTAL_INCOME_OS in the PIT form
# First get the unadjusted amounts
# Now calculate the adjusted amounts
# contribution to PIT Dividends
proportion_change_dividend = (weighted_citax1 - weighted_citax2)/weighted_citax1
new_dividend_proportion_of_old = (1 + proportion_change_dividend)
pit_adjustment_factor[year]=new_dividend_proportion_of_old
# Store Results
revenue_dict_cit[year]={}
revenue_dict_cit[year]['current_law']=citax_collection_str1
revenue_dict_cit[year]['reform']=citax_collection_str2
revenue_dict_cit[year]['difference']=citax_diff_collection_str2
print(revenue_dict_cit)
print("new_dividend_proportion_of_old ", pit_adjustment_factor)
# now update pit.csv with this proportion
# start a new round of simulation for pit
recs = Records(data=vars['pit_data_filename'], weights=vars['pit_weights_filename'], gfactors=GrowFactors(growfactors_filename=vars['GROWFACTORS_FILENAME']))
# create Policy object containing current-law policy
pol = Policy(DEFAULTS_FILENAME=vars['DEFAULTS_FILENAME'])
# specify Calculator objects for current-law policy
#calc1 = Calculator(policy=pol, records=recs, corprecords=crecs1,
# gstrecords=grecs, verbose=False)
calc1 = Calculator(policy=pol, records=recs, verbose=False)
#calc1.increment_year1(3.8)
assert isinstance(calc1, Calculator)
assert calc1.current_year == 2017
np.seterr(divide='ignore', invalid='ignore')
pol2 = Policy(DEFAULTS_FILENAME=vars['DEFAULTS_FILENAME'])
#years, reform=read_reform_dict(block_selected_dict)
#print("reform dictionary: ", reform)
#reform = Calculator.read_json_param_objects('app01_reform.json', None)
pol2.implement_reform(reform['policy'])
#calc2 = Calculator(policy=pol2, records=recs, corprecords=crecs1,
# gstrecords=grecs, verbose=False)
calc2 = Calculator(policy=pol2, records=recs, verbose=False)
total_revenue_text={}
reform_revenue_text={}
revenue_dict_pit={}
revenue_amount_dict = {}
num = 1
first_time = True
i=1
j=0
#rows = []
window = tk.Toplevel()
window.geometry("800x400+140+140")
display_table(window, revenue_dict_cit, revenue_dict_pit, header=True)
#for year in range(years[0], years[-1]+1):
for year in range(2019, 2024):
cols = []
calc1.advance_to_year(year)
calc2.advance_to_year(year)
# NOTE: calc1 now contains a PRIVATE COPY of pol and a PRIVATE COPY of recs,
# so we can continue to use pol and recs in this script without any
# concern about side effects from Calculator method calls on calc1.
# Produce DataFrame of results using the calculator
# First run the calculator for the corporate income tax
calc1.calc_all()
weighted_pitax1 = calc1.weighted_total_pit('pitax')
pitax_collection_billions1 = weighted_pitax1/10**9
pitax_collection_str1 = '{0:.2f}'.format(pitax_collection_billions1)
print('\n\n\n')
print(f'TAX COLLECTION FOR THE YEAR - {year} \n')
print("The PIT Collection in billions is: ", pitax_collection_billions1)
#total_revenue_text[year] = "PIT COLLECTION UNDER CURRENT LAW FOR THE YEAR - " + str(year)+" : "+str(pitax_collection_str1)+" bill"
# Produce DataFrame of results using cross-section
calc2.calc_all()
weighted_pitax2 = calc2.weighted_total_pit('pitax')
pitax_collection_billions2 = weighted_pitax2/10**9
pitax_collection_str2 = '{0:.2f}'.format(pitax_collection_billions2)
pitax_diff_collection_billions2 = (pitax_collection_billions2-pitax_collection_billions1)
pitax_diff_collection_str2 = '{0:.2f}'.format(pitax_diff_collection_billions2)
# Now calculate the adjusted amounts
# contribution to PIT Dividends
print("Total Income from Other Sources (bill) no adjustment is ", calc2.weighted_total_pit('TOTAL_INCOME_OS')/10**9 )
calc2.adjust_pit(pit_adjustment_factor[year])
print("Total Income from Other Sources (bill) after adjustment is ", calc2.weighted_total_pit('TOTAL_INCOME_OS')/10**9 )
calc2.calc_all()
weighted_pitax3 = calc2.weighted_total_pit('pitax')
pitax_collection_billions3 = weighted_pitax3/10**9
pitax_collection_str3 = '{0:.2f}'.format(pitax_collection_billions3)
pitax_diff_collection_billions3 = (pitax_collection_billions3-pitax_collection_billions1)
pitax_diff_collection_str3 = '{0:.2f}'.format(pitax_diff_collection_billions3)
pitax_diff_collection_billions4 = (pitax_collection_billions3-pitax_collection_billions2)
pitax_diff_collection_str4 = '{0:.2f}'.format(pitax_diff_collection_billions4)
#save the results
revenue_dict_pit[year]={}
revenue_dict_pit[year]['current_law']=pitax_collection_str1
revenue_dict_pit[year]['reform']={}
revenue_dict_pit[year]['reform']['unadjusted']=pitax_collection_str2
revenue_dict_pit[year]['reform']['adjusted']=pitax_collection_str3
revenue_dict_pit[year]['difference']=pitax_diff_collection_str3
print('\n\n\n')
print(f'TAX COLLECTION FOR THE YEAR UNDER REFORM - {year} \n')
print("The PIT Collection in billions is: ", pitax_collection_billions2)
print("The difference in PIT Collection in billions is: ", pitax_diff_collection_billions2)
print('****AFTER ADJUSTMENT \n\n\n')
print('TAX COLLECTION FOR THE YEAR UNDER REFORM WITH ADJUSTMENT \n')
print("The PIT Collection in billions after adjusting for the impact of CIT is: ", pitax_collection_billions3)
print("The difference in PIT Collection in billions after adjusting for the impact of CIT is: ", pitax_diff_collection_billions3)
print("The impact of adjustment is: ", pitax_diff_collection_billions4)
display_table(window, revenue_dict_cit, revenue_dict_pit, year=year, row=i)
i=i+1
#reverse the adjustment to obtain baseline
calc2.adjust_pit(1/pit_adjustment_factor[year])
display_table(window, revenue_dict_cit, revenue_dict_pit, footer=i)
"""
def generate_revenues():
from taxcalc.growfactors import GrowFactors
from taxcalc.policy import Policy
from taxcalc.records import Records
from taxcalc.gstrecords import GSTRecords
from taxcalc.corprecords import CorpRecords
from taxcalc.parameters import ParametersBase
from taxcalc.calculator import Calculator
"""
for num in range(1, num_reforms):
block_selected_dict[num]['selected_item']= block_widget_dict[num][1].get()
block_selected_dict[num]['selected_value']= block_widget_dict[num][3].get()
block_selected_dict[num]['selected_year']= block_widget_dict[num][2].get()
print(block_selected_dict)
"""
f = open('reform.json')
block_selected_dict = json.load(f)
print("block_selected_dict from json", block_selected_dict)
#print(block_selected_dict)
# create Records object containing pit.csv and pit_weights.csv input data
#print("growfactors filename ", growfactors_filename)
#recs = Records(data=data_filename, weights=weights_filename, gfactors=GrowFactors(growfactors_filename=growfactors_filename))
#recs = Records(data=data_filename, weights=weights_filename, gfactors=GrowFactors(growfactors_filename=growfactors_filename))
#recs.increment_year1(3.0)
#grecs = GSTRecords()
f = open('global_vars.json')
vars = json.load(f)
print("data_filename: ", vars['cit_data_filename'])
print("weights_filename: ", vars['cit_weights_filename'])
print("growfactors_filename: ", vars['GROWFACTORS_FILENAME'])
print("policy_filename: ", vars['DEFAULTS_FILENAME'])
# create CorpRecords object using cross-section data
#crecs1 = CorpRecords(data='cit_cross.csv', weights='cit_cross_wgts1.csv')
crecs1 = CorpRecords(
data=vars['cit_data_filename'],
weights=vars['cit_weights_filename'],
gfactors=GrowFactors(
growfactors_filename=vars['GROWFACTORS_FILENAME']))
#crecs1 = CorpRecords(data=vars['cit_weights_filename'], weights=vars['cit_weights_filename'])
# Note: weights argument is optional
assert isinstance(crecs1, CorpRecords)
assert crecs1.current_year == 2017
# create Policy object containing current-law policy
pol = Policy(DEFAULTS_FILENAME=vars['DEFAULTS_FILENAME'])
# specify Calculator objects for current-law policy
#calc1 = Calculator(policy=pol, records=recs, corprecords=crecs1,
# gstrecords=grecs, verbose=False)
calc1 = Calculator(policy=pol, corprecords=crecs1, verbose=False)
#calc1.increment_year1(3.8)
assert isinstance(calc1, Calculator)
assert calc1.current_year == 2017
np.seterr(divide='ignore', invalid='ignore')
calc1.calc_all()
revenue_dict_cit = {}
for year in range(2019, 2024):
cols = []
calc1.advance_to_year(year)
# NOTE: calc1 now contains a PRIVATE COPY of pol and a PRIVATE COPY of recs,
# so we can continue to use pol and recs in this script without any
# concern about side effects from Calculator method calls on calc1.
# Produce DataFrame of results using the calculator
# First run the calculator for the corporate income tax
calc1.calc_all()
print("***** Year ", year)
weighted_citax1 = calc1.weighted_total_cit('citax')
citax_collection_billions1 = weighted_citax1 / 10**9
citax_collection_str1 = '{0:.2f}'.format(citax_collection_billions1)
print("The CIT Collection in billions is: ",
citax_collection_billions1)
# Store Results
revenue_dict_cit[year] = {}
revenue_dict_cit[year]['current_law'] = citax_collection_str1
# start a new round of simulation for pit
recs = Records(data=vars['pit_data_filename'],
weights=vars['pit_weights_filename'],
gfactors=GrowFactors(
growfactors_filename=vars['GROWFACTORS_FILENAME']))
# create Policy object containing current-law policy
pol = Policy(DEFAULTS_FILENAME=vars['DEFAULTS_FILENAME'])
# specify Calculator objects for current-law policy
#calc1 = Calculator(policy=pol, records=recs, corprecords=crecs1,
# gstrecords=grecs, verbose=False)
calc1 = Calculator(policy=pol, records=recs, verbose=False)
#calc1.increment_year1(3.8)
assert isinstance(calc1, Calculator)
assert calc1.current_year == 2017
np.seterr(divide='ignore', invalid='ignore')
total_revenue_text = {}
reform_revenue_text = {}
revenue_dict_pit = {}
revenue_amount_dict = {}
num = 1
first_time = True
i = 1
j = 0
#rows = []
window = tk.Toplevel()
window.geometry("800x400+140+140")
display_table(window, revenue_dict_cit, revenue_dict_pit, header=True)
#for year in range(years[0], years[-1]+1):
for year in range(2019, 2024):
cols = []
calc1.advance_to_year(year)
# NOTE: calc1 now contains a PRIVATE COPY of pol and a PRIVATE COPY of recs,
# so we can continue to use pol and recs in this script without any
# concern about side effects from Calculator method calls on calc1.
# Produce DataFrame of results using the calculator
# First run the calculator for the corporate income tax
calc1.calc_all()
weighted_pitax1 = calc1.weighted_total_pit('pitax')
pitax_collection_billions1 = weighted_pitax1 / 10**9
pitax_collection_str1 = '{0:.2f}'.format(pitax_collection_billions1)
print('\n\n\n')
print(f'TAX COLLECTION FOR THE YEAR - {year} \n')
print("The PIT Collection in billions is: ",
pitax_collection_billions1)
#total_revenue_text[year] = "PIT COLLECTION UNDER CURRENT LAW FOR THE YEAR - " + str(year)+" : "+str(pitax_collection_str1)+" bill"
#save the results
revenue_dict_pit[year] = {}
revenue_dict_pit[year]['current_law'] = pitax_collection_str1
display_table(window,
revenue_dict_cit,
revenue_dict_pit,
year=year,
row=i)
i = i + 1
display_table(window, revenue_dict_cit, revenue_dict_pit, footer=i)
"""
def __init__(
self,
input_data,
tax_year,
reform,
assump,
growdiff_response, # =None in static analysis
aging_input_data,
exact_calculations):
"""
TaxCalcIO class constructor.
"""
# pylint: disable=too-many-arguments
# pylint: disable=too-many-locals
# pylint: disable=too-many-branches
# pylint: disable=too-many-statements
# check for existence of INPUT file
if isinstance(input_data, six.string_types):
# remove any leading directory path from INPUT filename
fname = os.path.basename(input_data)
# check if fname ends with ".csv"
if fname.endswith('.csv'):
inp = '{}-{}'.format(fname[:-4], str(tax_year)[2:])
else:
msg = 'INPUT file named {} does not end in .csv'
raise ValueError(msg.format(fname))
# check existence of INPUT file
if not os.path.isfile(input_data):
msg = 'INPUT file named {} could not be found'
raise ValueError(msg.format(input_data))
elif isinstance(input_data, pd.DataFrame):
inp = 'df-{}'.format(str(tax_year)[2:])
else:
msg = 'INPUT is neither string nor Pandas DataFrame'
raise ValueError(msg)
# construct output_filename and delete old output file if it exists
if reform is None:
self._reform = False
ref = ''
elif isinstance(reform, six.string_types):
self._reform = True
# remove any leading directory path from REFORM filename
fname = os.path.basename(reform)
# check if fname ends with ".json"
if fname.endswith('.json'):
ref = '-{}'.format(fname[:-5])
else:
msg = 'REFORM file named {} does not end in .json'
raise ValueError(msg.format(fname))
else:
msg = 'TaxCalcIO.ctor reform is neither None nor str'
raise ValueError(msg)
if assump is None:
asm = ''
elif isinstance(assump, six.string_types):
# remove any leading directory path from ASSUMP filename
fname = os.path.basename(assump)
# check if fname ends with ".json"
if fname.endswith('.json'):
asm = '-{}'.format(fname[:-5])
else:
msg = 'ASSUMP file named {} does not end in .json'
raise ValueError(msg.format(fname))
else:
msg = 'TaxCalcIO.ctor assump is neither None nor str'
raise ValueError(msg)
self._output_filename = '{}{}{}.csv'.format(inp, ref, asm)
delete_file(self._output_filename)
# get parameter dictionaries from --reform and --assump files
param_dict = Calculator.read_json_param_files(reform, assump)
# make sure no behavioral response is specified in --assump
beh = Behavior()
beh.update_behavior(param_dict['behavior'])
if beh.has_any_response():
msg = '--assump ASSUMP cannot assume any "behavior"'
raise ValueError(msg)
# make sure no growdiff_response is specified in --assump
gdiff_response = Growdiff()
gdiff_response.update_growdiff(param_dict['growdiff_response'])
if gdiff_response.has_any_response():
msg = '--assump ASSUMP cannot assume any "growdiff_response"'
raise ValueError(msg)
# create gdiff_baseline object
gdiff_baseline = Growdiff()
gdiff_baseline.update_growdiff(param_dict['growdiff_baseline'])
# create Growfactors clp object that incorporates gdiff_baseline
gfactors_clp = Growfactors()
gdiff_baseline.apply_to(gfactors_clp)
# specify gdiff_response object
if growdiff_response is None:
gdiff_response = Growdiff()
elif isinstance(growdiff_response, Growdiff):
gdiff_response = growdiff_response
else:
msg = 'TaxCalcIO.ctor growdiff_response is neither None nor {}'
raise ValueError(msg.format('a Growdiff object'))
# create Growfactors ref object that has both gdiff objects applied
gfactors_ref = Growfactors()
gdiff_baseline.apply_to(gfactors_ref)
gdiff_response.apply_to(gfactors_ref)
# create Policy object and implement reform if specified
if self._reform:
pol = Policy(gfactors=gfactors_ref)
pol.implement_reform(param_dict['policy'])
clp = Policy(gfactors=gfactors_clp)
else:
pol = Policy(gfactors=gfactors_clp)
# check for valid tax_year value
if tax_year < pol.start_year:
msg = 'tax_year {} less than policy.start_year {}'
raise ValueError(msg.format(tax_year, pol.start_year))
if tax_year > pol.end_year:
msg = 'tax_year {} greater than policy.end_year {}'
raise ValueError(msg.format(tax_year, pol.end_year))
# set policy to tax_year
pol.set_year(tax_year)
if self._reform:
clp.set_year(tax_year)
# read input file contents into Records object(s)
if aging_input_data:
if self._reform:
recs = Records(data=input_data,
gfactors=gfactors_ref,
exact_calculations=exact_calculations)
recs_clp = Records(data=input_data,
gfactors=gfactors_clp,
exact_calculations=exact_calculations)
else:
recs = Records(data=input_data,
gfactors=gfactors_clp,
exact_calculations=exact_calculations)
else: # input_data are raw data that are not being aged
recs = Records(data=input_data,
exact_calculations=exact_calculations,
gfactors=None,
adjust_ratios=None,
weights=None,
start_year=tax_year)
if self._reform:
recs_clp = copy.deepcopy(recs)
# create Calculator object(s)
con = Consumption()
con.update_consumption(param_dict['consumption'])
self._calc = Calculator(policy=pol,
records=recs,
verbose=True,
consumption=con,
sync_years=aging_input_data)
if self._reform:
self._calc_clp = Calculator(policy=clp,
records=recs_clp,
verbose=False,
consumption=con,
sync_years=aging_input_data)
