def get_pol(self, reform_file):
"""
Reads the specified reform and implements it
"""
REFORMS_URL = ("https://raw.githubusercontent.com/"
"PSLmodels/Tax-Calculator/master/taxcalc/reforms/")
CURRENT_PATH = os.path.abspath(os.path.dirname(__file__))
if reform_file == None:
pol = tc.Policy()
else:
# check to see if file path to reform_file exists
if isinstance(reform_file, str) and os.path.isfile(
os.path.join(CURRENT_PATH, reform_file)):
reform_path = os.path.join(CURRENT_PATH, reform_file)
pol = tc.Policy()
pol.implement_reform(tc.Policy.read_json_reform(reform_path))
# try reform_file as dictionary
elif isinstance(reform_file, dict):
reform = reform_file
pol = tc.Policy()
pol.implement_reform(reform)
# if file path does not exist, check Tax-Calculator reforms file
else:
try:
reform_url = REFORMS_URL + reform_file
pol = tc.Policy()
pol.implement_reform(
tc.Policy.read_json_reform(reform_url))
except:
raise 'Reform file does not exist'
return pol
def get_pol(self, reform_file):
"""
Reads the specified reform and implements it
"""
REFORMS_URL = ("https://raw.githubusercontent.com/"
"PSLmodels/Tax-Calculator/master/taxcalc/reforms/")
# check to see if file path to reform_file exists
if isinstance(reform_file, str) and os.path.isfile(reform_file):
pol = tc.Policy()
pol.implement_reform(tc.Policy.read_json_reform(reform_file))
# try reform_file as dictionary
elif isinstance(reform_file, dict):
pol = tc.Policy()
try:
pol.implement_reform(reform_file)
except:
# use adjust method for web app
pol.adjust(reform_file)
# if file path does not exist, check Tax-Calculator reforms file
else:
try:
reform_url = REFORMS_URL + reform_file
pol = tc.Policy()
pol.implement_reform(tc.Policy.read_json_reform(reform_url))
except:
raise "Reform file does not exist"
return pol
def choose_reform(self):
"""
Creates Tax-Calculator Policy object for reform analysis
Returns:
self.pol2: Tax-Calculator Policy object for reform analysis
"""
REFORMS_URL = (
"https://raw.githubusercontent.com/"
"PSLmodels/Tax-Calculator/master/taxcalc/reforms/"
)
CURRENT_PATH = os.path.abspath(os.path.dirname(__file__))
# if user specified a preset reform in their adjustment file, pull
# reform from Tax-Calculator reforms folder
if self.reform_options != "None" and self.custom_reform is None:
reform_name = self.reform_options
reform_url = REFORMS_URL + reform_name
reform = tc.Calculator.read_json_param_objects(reform_url, None)
self.pol2 = tc.Policy()
self.pol2.implement_reform(reform["policy"])
# otherwise, look for user-provided json reform file
# first as file path
elif self.reform_options == "None" and isinstance(self.custom_reform, str):
try:
reform_filename = os.path.join(
CURRENT_PATH, self.custom_reform)
reform = tc.Calculator.read_json_param_objects(
reform_filename, None)
self.pol2 = tc.Policy()
self.pol2.implement_reform(reform["policy"])
except:
print("Reform file path does not exist")
# then as dictionary
elif self.reform_options == "None" and isinstance(self.custom_reform, dict):
try:
reform = self.custom_reform
self.pol2 = tc.Policy()
self.pol2.implement_reform(reform)
except Exception as e:
import traceback
traceback.print_exc()
print("Reform dictionary does not exist")
# raise error if preset reform is chosen and custom reform is specified
elif self.reform_options != "None" and self.custom_reform is not None:
raise AttributeError(
"You have specified a preset reform and a custom reform. Please choose one reform."
)
# if no reform file was given, set reform to current law
else:
self.pol2 = tc.Policy()
return self.pol2
def _make_calculators(self):
"""
This function creates the baseline and reform calculators used when
the `run()` method is called
"""
# Create two microsimulation calculators
gd_base = tc.GrowDiff()
gf_base = tc.GrowFactors()
# apply user specified growdiff
if self.params["growdiff_baseline"]:
gd_base.update_growdiff(self.params["growdiff_baseline"])
gd_base.apply_to(gf_base)
# Baseline calculator
if self.use_cps:
records = tc.Records.cps_constructor(data=self.microdata,
gfactors=gf_base)
else:
records = tc.Records(self.microdata, gfactors=gf_base)
policy = tc.Policy(gf_base)
if self.params["base_policy"]:
update_policy(policy, self.params["base_policy"])
base_calc = tc.Calculator(policy=policy,
records=records,
verbose=self.verbose)
# Reform calculator
# Initialize a policy object
gd_reform = tc.GrowDiff()
gf_reform = tc.GrowFactors()
if self.params["growdiff_response"]:
gd_reform.update_growdiff(self.params["growdiff_response"])
gd_reform.apply_to(gf_reform)
if self.use_cps:
records = tc.Records.cps_constructor(data=self.microdata,
gfactors=gf_reform)
else:
records = tc.Records(self.microdata, gfactors=gf_reform)
policy = tc.Policy(gf_reform)
if self.params["base_policy"]:
update_policy(policy, self.params["base_policy"])
update_policy(policy, self.params["policy"])
# Initialize Calculator
reform_calc = tc.Calculator(policy=policy,
records=records,
verbose=self.verbose)
# delete all unneeded variables
del gd_base, gd_reform, records, gf_base, gf_reform, policy
return base_calc, reform_calc
def test_alternative_behavior_parameters(cps_subsample):
"""
Test alternative behavior parameters to improve code coverage.
Also, test response function's dump argument.
"""
# ... specify Records object and policy reform
rec = tc.Records.cps_constructor(data=cps_subsample)
refyear = 2020
reform = {'II_em': {refyear: 1500}}
# ... specify non-default response elasticities
elasticities_dict = {'inc': -0.1}
# ... calculate behavioral response to reform
pol = tc.Policy()
calc1 = tc.Calculator(records=rec, policy=pol)
pol.implement_reform(reform)
calc2 = tc.Calculator(records=rec, policy=pol)
del pol
calc1.advance_to_year(refyear)
calc2.advance_to_year(refyear)
df1, df2 = response(calc1, calc2, elasticities_dict)
del calc1
del calc2
itax1 = round((df1['iitax'] * df1['s006']).sum() * 1e-9, 3)
itax2 = round((df2['iitax'] * df2['s006']).sum() * 1e-9, 3)
del df1
del df2
assert np.allclose([itax1, itax2], [1355.556, 1302.09])
def get_inputs(meta_params_dict):
"""
Return default parameters for Tax-Brain
"""
meta_params = MetaParameters()
with meta_params.transaction(defer_validation=True):
meta_params.adjust(meta_params_dict)
# Year must be at least 2014 when using the CPS. This rule is validated
# in the validate_inputs function below.
# See: https://github.com/PSLmodels/Tax-Brain/issues/176
if meta_params.data_source == "CPS" and meta_params.year < 2014:
meta_params.adjust({"year": 2014})
policy_params = taxcalc.Policy()
policy_params.set_state(year=meta_params.year.tolist())
policy_defaults = cs2tc.convert_policy_defaults(meta_params, policy_params)
behavior_params = BehaviorParams()
default_params = {
"policy": policy_defaults,
"behavior": behavior_params.dump()
}
meta = meta_params.dump()
return {"meta_parameters": meta, "model_parameters": default_params}
def test_sub_effect_independence(stcg):
"""
Ensure that LTCG amount does not affect magnitude of substitution effect.
"""
# pylint: disable=too-many-locals
# specify reform that raises top-bracket marginal tax rate
refyear = 2020
reform = {'II_rt7': {refyear: 0.70}}
# specify a substitution effect behavioral response elasticity
elasticities_dict = {'sub': 0.25}
# specify several high-earning filing units
num_recs = 9
input_csv = (u'RECID,MARS,e00200,e00200p,p22250,p23250\n'
u'1,2,1000000,1000000,stcg, 0\n'
u'2,2,1000000,1000000,stcg, 4800\n'
u'3,2,1000000,1000000,stcg, 3600\n'
u'4,2,1000000,1000000,stcg, 2400\n'
u'5,2,1000000,1000000,stcg, 1200\n'
u'6,2,1000000,1000000,stcg, 0\n'
u'7,2,1000000,1000000,stcg,-1200\n'
u'8,2,1000000,1000000,stcg,-2400\n'
u'9,2,1000000,1000000,stcg,-3600\n')
inputcsv = input_csv.replace('stcg', str(stcg))
input_dataframe = pd.read_csv(StringIO(inputcsv))
assert len(input_dataframe.index) == num_recs
recs = tc.Records(data=input_dataframe,
start_year=refyear,
gfactors=None,
weights=None)
pol = tc.Policy()
calc1 = tc.Calculator(records=recs, policy=pol)
assert calc1.current_year == refyear
pol.implement_reform(reform)
calc2 = tc.Calculator(records=recs, policy=pol)
assert calc2.current_year == refyear
del pol
df1, df2 = response(calc1, calc2, elasticities_dict)
del calc1
del calc2
# compute change in taxable income for each of the filing units
chg_funit = dict()
for rid in range(1, num_recs + 1):
idx = rid - 1
chg_funit[rid] = df2['c04800'][idx] - df1['c04800'][idx]
del df1
del df2
# confirm reform reduces taxable income when assuming substitution effect
emsg = ''
for rid in range(1, num_recs + 1):
if not chg_funit[rid] < 0:
txt = '\nFAIL: stcg={} : chg[{}]={:.2f} is not negative'
emsg += txt.format(stcg, rid, chg_funit[rid])
# confirm change in taxable income is same for all filing units
for rid in range(2, num_recs + 1):
if not np.allclose(chg_funit[rid], chg_funit[1]):
txt = '\nFAIL: stcg={} : chg[{}]={:.2f} != chg[1]={:.2f}'
emsg += txt.format(stcg, rid, chg_funit[rid], chg_funit[1])
del chg_funit
if emsg:
raise ValueError(emsg)
def __init__(
self,
btax_policy_ref,
itax_policy_ref,
# baseline defaults are current-law policy
btax_policy_base=Policy(),
itax_policy_base=itax.Policy(),
investor_data='puf.csv'):
# Check policy argument types
assert isinstance(btax_policy_ref, Policy)
assert isinstance(itax_policy_ref, itax.Policy)
assert isinstance(btax_policy_base, Policy)
assert isinstance(itax_policy_base, itax.Policy)
# Create Investor objects incorporating itax policy and investor data
self.investor_base = Investor(itax_policy_base, investor_data)
self.investor_ref = Investor(itax_policy_ref, investor_data)
# Create btax policy parameters DataFrame objects
self.btax_params_base = btax_policy_base.parameters_dataframe()
self.btax_params_ref = btax_policy_ref.parameters_dataframe()
# Create Corporations
self.corp_base = Corporation(self.btax_params_base)
self.corp_ref = Corporation(self.btax_params_ref)
# Create PassThroughs
self.passthru_base = PassThrough(self.btax_params_base)
self.passthru_ref = PassThrough(self.btax_params_ref)
# Declare calculated results objects
self.multipliers = None
self.model_results = None
def test_default_response_function(cps_subsample):
"""
Test that default behavior parameters produce static results.
"""
# ... specify Records object and policy reform
rec = tc.Records.cps_constructor(data=cps_subsample)
refyear = 2020
assert refyear >= 2018
reform = {'II_em': {refyear: 1500}}
# ... construct pre-reform calculator
pol = tc.Policy()
calc1 = tc.Calculator(records=rec, policy=pol)
calc1.advance_to_year(refyear)
# ... construct two post-reform calculators
pol.implement_reform(reform)
calc2s = tc.Calculator(records=rec, policy=pol) # for static assumptions
calc2s.advance_to_year(refyear)
calc2d = tc.Calculator(records=rec, policy=pol) # for default behavior
calc2d.advance_to_year(refyear)
del pol
# ... calculate aggregate inctax using static assumptions
calc2s.calc_all()
df2s = calc2s.dataframe(['iitax', 's006'])
itax2s = round((df2s['iitax'] * df2s['s006']).sum() * 1e-9, 3)
# ... calculate aggregate inctax using zero response elasticities
_, df2d = response(calc1, calc2d, elasticities={}, dump=True)
itax2d = round((df2d['iitax'] * df2d['s006']).sum() * 1e-9, 3)
assert np.allclose(itax2d, itax2s)
# ... clean up
del calc1
del calc2s
del calc2d
del df2s
del df2d
def test_nondefault_response_function(be_inc, cps_subsample):
"""
Test that non-default behavior parameters produce expected results.
"""
# ... specify Records object and policy reform
rec = tc.Records.cps_constructor(data=cps_subsample)
refyear = 2020
reform = {'II_em': {refyear: 1500}}
# ... specify non-default1 response elasticities
elasticities_dict = {'sub': 0.25, 'inc': be_inc, 'cg': -0.79}
# ... calculate behavioral response to reform
pol = tc.Policy()
calc1 = tc.Calculator(records=rec, policy=pol)
pol.implement_reform(reform)
calc2 = tc.Calculator(records=rec, policy=pol)
del pol
calc1.advance_to_year(refyear)
calc2.advance_to_year(refyear)
df1, df2 = response(calc1, calc2, elasticities_dict)
del calc1
del calc2
itax1 = round((df1['iitax'] * df1['s006']).sum() * 1e-9, 3)
itax2 = round((df2['iitax'] * df2['s006']).sum() * 1e-9, 3)
del df1
del df2
if be_inc == 0.0:
assert np.allclose([itax1, itax2], [1355.556, 1304.984])
elif be_inc == -0.1:
assert np.allclose([itax1, itax2], [1355.556, 1303.898])
def test_incorrect_instantiation():
"""
Test incorrect Investor instantiation
"""
with pytest.raises(ValueError):
Investor(Policy())
with pytest.raises(ValueError):
Investor(itax.Policy(), list())
def _make_stacked_objects(self):
"""
This method makes the base calculator and policy and records objects
for stacked reforms. The difference between this and the standard
_make_calcuators method is that this method
only fully creates the baseline calculator. For the reform, it creates
policy and records objects and implements any growth assumptions
provided by the user.
"""
# Create two microsimulation calculators
gd_base = tc.GrowDiff()
gf_base = tc.GrowFactors()
# apply user specified growdiff
if self.params["growdiff_baseline"]:
gd_base.update_growdiff(self.params["growdiff_baseline"])
gd_base.apply_to(gf_base)
# Baseline calculator
if self.use_cps:
records = tc.Records.cps_constructor(data=self.microdata,
gfactors=gf_base)
else:
records = tc.Records(self.microdata, gfactors=gf_base)
policy = tc.Policy(gf_base)
if self.params["base_policy"]:
update_policy(policy, self.params["base_policy"])
base_calc = tc.Calculator(policy=policy,
records=records,
verbose=self.verbose)
# Reform calculator
# Initialize a policy object
gd_reform = tc.GrowDiff()
gf_reform = tc.GrowFactors()
if self.params["growdiff_response"]:
gd_reform.update_growdiff(self.params["growdiff_response"])
gd_reform.apply_to(gf_reform)
if self.use_cps:
records = tc.Records.cps_constructor(data=self.microdata,
gfactors=gf_reform)
else:
records = tc.Records(self.microdata, gfactors=gf_reform)
policy = tc.Policy(gf_reform)
return base_calc, policy, records
def choose_baseline(self):
"""
Creates Tax-Calculator Policy object for baseline policy
The default baseline policy is current law.
Returns:
self.pol: Tax-Calculator Policy object for baseline policy
"""
REFORMS_URL = (
"https://raw.githubusercontent.com/"
"PSLmodels/Tax-Calculator/master/taxcalc/reforms/"
)
CURRENT_PATH = os.path.abspath(os.path.dirname(__file__))
# if no baseline policy is specified, baseline is current law
if self.baseline is None:
self.pol = tc.Policy()
# if a baseline policy is specified, first see if user created json
# policy file
else:
exists = os.path.isfile(os.path.join(CURRENT_PATH, self.baseline))
if exists:
baseline_file = os.path.join(CURRENT_PATH, self.baseline)
# baseline = tc.Calculator.read_json_param_objects(baseline_file, None)
self.pol = tc.Policy()
self.pol.implement_reform(
tc.Policy.read_json_reform(baseline_file))
# if the user did not create a json file, try the Tax-Calculator
# reforms file
else:
try:
baseline_file = self.baseline
baseline_url = REFORMS_URL + baseline_file
baseline = tc.Calculator.read_json_param_objects(
baseline_url, None)
self.pol = tc.Policy()
self.pol.implement_reform(baseline["policy"])
except:
print("Baseline file does not exist")
return self.pol
def test_simple_adj(taxparams):
pol = taxcalc.Policy()
pol.implement_reform({
"EITC_c": {
2020: [10000, 10001, 10002, 10003],
2023: [20000, 20001, 20002, 20003],
}
})
taxparams.adjust({
"EITC_c": [
{
"year": 2020,
"EIC": "0kids",
"value": 10000
},
{
"year": 2020,
"EIC": "1kid",
"value": 10001
},
{
"year": 2020,
"EIC": "2kids",
"value": 10002
},
{
"year": 2020,
"EIC": "3+kids",
"value": 10003
},
{
"year": 2023,
"EIC": "0kids",
"value": 20000
},
{
"year": 2023,
"EIC": "1kid",
"value": 20001
},
{
"year": 2023,
"EIC": "2kids",
"value": 20002
},
{
"year": 2023,
"EIC": "3+kids",
"value": 20003
},
]
})
cmp_with_taxcalc_values(taxparams, pol)
def test_adj_CPI_offset(year):
def convert(arr):
return {2013 + i: arr[i] for i in range(len(arr))}
taxparams_cpi = TaxParams()
taxparams_cpi.adjust({"CPI_offset": [{"year": year, "value": -0.001}]})
pol = taxcalc.Policy()
pol.set_year(year)
pol.implement_reform({"CPI_offset": {year: -0.001}})
cmp_with_taxcalc_values(taxparams_cpi, pol)
def test_incorrect_calc_all():
"""
Test incorrect call of calc_all method.
"""
# Do a quick "simulation" of executing response.calc_all(...) by setting
# one of the calculated responses to anything other than None
pre_calc_response = Response()
pre_calc_response.investment_response = 9.99
# Try to use pre_calc_response as argument to BusinessModel.calc_all method
bizmod = BusinessModel(Policy(), itax.Policy(),
investor_data='nodata.csv')
with pytest.raises(ValueError):
bizmod.calc_all(response=pre_calc_response)
def validate_inputs(meta_params_dict, adjustment, errors_warnings):
"""
Function to validate COMP inputs
"""
pol_params = cs2tc.convert_policy_adjustment(adjustment["policy"])
policy_params = taxcalc.Policy()
policy_params.adjust(pol_params, raise_errors=False, ignore_warnings=True)
errors_warnings["policy"]["errors"].update(policy_params.errors)
behavior_params = BehaviorParams()
behavior_params.adjust(adjustment["behavior"], raise_errors=False)
errors_warnings["behavior"]["errors"].update(behavior_params.errors)
return {"errors_warnings": errors_warnings}
def test_reforms(reform_number, reforms, puf_subsample, actual_vs_expect):
"""
Test BusinessModel corporate tax return results under reforms
with no response.
"""
bizmod = BusinessModel(reforms[reform_number]['policy_obj'],
itax.Policy(),
investor_data=puf_subsample)
bizmod.calc_all(response=None)
# compare actual and expected results
dec = 3
results = bizmod.corp_ref.taxreturn.combined_return.round(dec)
fname = 'bizmod_corp_ref{}_expect.csv'.format(reform_number)
actual_vs_expect(results, fname, precision=dec)
def calc_mtr(self, reform_file):
"""
Calculates income tax, payroll tax, and combined marginal rates
"""
year = self.invar["FLPDYR"][0]
year = int(year.item())
recs_base = tc.Records(
data=self.invar,
start_year=year,
gfactors=None,
weights=None,
adjust_ratios=None,
)
if reform_file is None:
pol = tc.Policy()
else:
pol = self.get_pol(reform_file)
calc_base = tc.Calculator(policy=pol, records=recs_base)
calc_base.advance_to_year(year)
calc_base.calc_all()
payrolltax_base = calc_base.array("payrolltax")
incometax_base = calc_base.array("iitax")
combined_taxes_base = incometax_base + payrolltax_base
recs_marg = tc.Records(
data=self.invar_marg,
start_year=year,
gfactors=None,
weights=None,
adjust_ratios=None,
)
calc_marg = tc.Calculator(policy=pol, records=recs_marg)
calc_marg.advance_to_year(year)
calc_marg.calc_all()
payrolltax_marg = calc_marg.array("payrolltax")
incometax_marg = calc_marg.array("iitax")
combined_taxes_marg = incometax_marg + payrolltax_marg
payrolltax_diff = payrolltax_marg - payrolltax_base
incometax_diff = incometax_marg - incometax_base
combined_diff = combined_taxes_marg - combined_taxes_base
mtr_payrolltax = payrolltax_diff / FINITE_DIFF
mtr_incometax = incometax_diff / FINITE_DIFF
mtr_combined = combined_diff / FINITE_DIFF
return (mtr_payrolltax, mtr_incometax, mtr_combined)
def cmp_with_taxcalc_values(taxparams, pol=None):
if pol is None:
pol = taxcalc.Policy()
# test all keys are the same.
assert set(map(lambda x: x[1:],
pol._vals.keys())) == set(taxparams._data.keys())
# test all values are the same.
pol.set_year(2029)
# breakpoint()
for param in taxparams._data:
np.testing.assert_allclose(getattr(pol, f"_{param}"),
getattr(taxparams, param))
taxparams.set_state()
for param in taxparams._data:
np.testing.assert_allclose(getattr(pol, f"_{param}"),
getattr(taxparams, param))
def test_multiple_cpi_swaps():
pol = taxcalc.Policy()
pol.implement_reform({
"II_em": {
2016: 6000,
2018: 7500,
2020: 9000
},
"II_em-indexed": {
2016: False,
2018: True
},
})
taxparams = TaxParams()
taxparams.adjust({
"II_em": [
{
"year": 2016,
"value": 6000
},
{
"year": 2018,
"value": 7500
},
{
"year": 2020,
"value": 9000
},
],
"II_em-indexed": [
{
"year": 2016,
"value": False
},
{
"year": 2018,
"value": True
},
],
})
cmp_with_taxcalc_values(taxparams, pol)
def convert_params(params):
"""
Convert ParamTools style parameter inputs to traditional taxcalc style
parameters for report policy table creation
Parameters
----------
params: dict
ParamTools style reform dictionary
Returns
-------
reform: dict
a dictionary in traditional taxcalc style
"""
pol = tc.Policy()
pol.adjust(params)
indexed_params = []
reform = defaultdict(dict)
first_yr = pol.LAST_BUDGET_YEAR
for param in params.keys():
yrs = []
if param.endswith("-indexed"):
indexed_params.append(param)
continue
for adj in params[param]:
yrs.append(adj["year"])
yrs = set(yrs)
values = getattr(pol, f"_{param}")
for yr in yrs:
idx = yr - pol.JSON_START_YEAR
vals = values[idx]
if isinstance(vals, np.ndarray):
vals = list(vals)
first_yr = min(yr, first_yr)
reform[param][yr] = vals
# add indexed parameters as being implemented in first year of reform
for param in indexed_params:
val = params[param][0]['value']
reform[param][first_yr] = val
return reform
def get_inputs(meta_params_dict):
"""
Return default parameters for Tax-Brain
"""
meta_params = MetaParameters()
meta_params.adjust(meta_params_dict)
policy_params = taxcalc.Policy()
policy_params.set_state(year=meta_params.year.tolist(), )
policy_defaults = cs2tc.convert_policy_defaults(meta_params, policy_params)
behavior_params = BehaviorParams()
default_params = {
"policy": policy_defaults,
"behavior": behavior_params.dump()
}
meta = meta_params.dump()
return {"meta_parameters": meta, "model_parameters": default_params}
def test_adj_indexed_status_and_param_value(taxparams, year):
pol = taxcalc.Policy()
pol.implement_reform({
"EITC_c": {
year: [10000, 10001, 10002, 10003]
},
"EITC_c-indexed": {
year: False
},
})
taxparams.adjust({
"EITC_c": [
{
"year": year,
"EIC": "0kids",
"value": 10000
},
{
"year": year,
"EIC": "1kid",
"value": 10001
},
{
"year": year,
"EIC": "2kids",
"value": 10002
},
{
"year": year,
"EIC": "3+kids",
"value": 10003
},
],
"EITC_c-indexed": [{
"year": year,
"value": False
}],
})
cmp_with_taxcalc_values(taxparams, pol)
def test_adj_activates_index(taxparams, year):
pol = taxcalc.Policy()
pol.implement_reform({
"CTC_c": {
year: 1005
},
"CTC_c-indexed": {
year: True
}
})
taxparams.adjust({
"CTC_c": [{
"year": year,
"value": 1005
}],
"CTC_c-indexed": [{
"year": year,
"value": True
}],
})
cmp_with_taxcalc_values(taxparams, pol)
def reformat_params():
"""
Translates ParamTools-style policy_current_law.json
to a dictionary that resembles the old Tax-Calculator
parameter schema
"""
# Parameters that were changed by TCJA will be extended through
# 2026 in the uguide
tcja = tc.Policy.read_json_reform(TCJA_PATH)
pol = tc.Policy()
pol.clear_state()
years_short = list(range(START_YEAR, END_YEAR_SHORT))
years_long = list(range(START_YEAR, END_YEAR_LONG))
pol.set_year(years_long)
params = pol.specification(serializable=True, sort_values=True)
# Create parameter dictionary that resembles old Tax-Calculator
# parameter schema
params_dict = {}
for param in params.keys():
if param in tcja.keys():
years = years_long
else:
years = years_short
params_dict[param] = {}
params_dict[param]['years'] = years
list_vals2 = []
for year in years:
list_vals1 = []
for idx in range(0, len(params[param])):
if params[param][idx]['year'] == year:
list_vals1.append(params[param][idx]['value'])
if (params[param][idx]['year'] !=
params[param][idx - 1]['year']):
list_vals2.append(list_vals1)
params_dict[param]['values'] = list_vals2
return params_dict
def test_adj_CPI_offset_and_index_status():
taxparams = TaxParams()
taxparams.adjust({
"CPI_offset": [{
"year": 2020,
"value": -0.005
}],
"CTC_c-indexed": [{
"year": 2020,
"value": True
}],
})
pol = taxcalc.Policy()
pol.implement_reform({
"CTC_c-indexed": {
2020: True
},
"CPI_offset": {
2020: -0.005
}
})
cmp_with_taxcalc_values(taxparams, pol)
def __init__(self,
start_year,
end_year=LAST_BUDGET_YEAR,
microdata=None,
use_cps=False,
reform=None,
behavior=None,
assump=None,
verbose=False):
"""
Constructor for the TaxBrain class
Parameters
----------
start_year: First year in the analysis. Must be no earlier than the
first year allowed in Tax-Calculator.
end_year: Last year in the analysis. Must be no later than the last
year allowed in Tax-Calculator.
microdata: Either a path to a micro-data file or a Pandas DataFrame
containing micro-data.
use_cps: A boolean value to indicate whether or not the analysis should
be run using the CPS file included in Tax-Calculator.
Note: use_cps cannot be True if a file was also specified with
the microdata parameter.
reform: Individual income tax policy reform. Can be either a string
pointing to a JSON reform file, or the contents of a JSON file.
behavior: Individual behavior assumptions use by the Behavior-Response
package.
assump: A string pointing to a JSON file containing user specified
economic assumptions.
verbose: A boolean value indicated whether or not to write model
progress reports.
"""
if not use_cps and microdata is None:
raise ValueError("Must specify microdata or set 'use_cps' to True")
assert isinstance(start_year, int) & isinstance(end_year, int), (
"Start and end years must be integers")
assert start_year <= end_year, (
f"Specified end year, {end_year}, is before specified start year, "
f"{start_year}.")
assert TaxBrain.FIRST_BUDGET_YEAR <= start_year, (
f"Specified start_year, {start_year}, comes before first known "
f"budget year, {TaxBrain.FIRST_BUDGET_YEAR}.")
assert end_year <= TaxBrain.LAST_BUDGET_YEAR, (
f"Specified end_year, {end_year}, comes after last known "
f"budget year, {TaxBrain.LAST_BUDGET_YEAR}.")
self.use_cps = use_cps
self.start_year = start_year
self.end_year = end_year
self.base_data = {yr: {} for yr in range(start_year, end_year + 1)}
self.reform_data = {yr: {} for yr in range(start_year, end_year + 1)}
self.verbose = verbose
# Process user inputs early to throw any errors quickly
self.params = self._process_user_mods(reform, assump)
self.params["behavior"] = behavior
# Create two microsimulation calculators
gd_base = tc.GrowDiff()
gf_base = tc.GrowFactors()
# apply user specified growdiff
if self.params["growdiff_baseline"]:
gd_base.update_growdiff(self.params["growdiff_baseline"])
gd_base.apply_to(gf_base)
# Baseline calculator
if use_cps:
records = tc.Records.cps_constructor(data=microdata,
gfactors=gf_base)
else:
records = tc.Records(microdata, gfactors=gf_base)
self.base_calc = tc.Calculator(policy=tc.Policy(gf_base),
records=records,
verbose=self.verbose)
# Reform calculator
# Initialize a policy object
gd_reform = tc.GrowDiff()
gf_reform = tc.GrowFactors()
if self.params["growdiff_response"]:
gd_reform.update_growdiff(self.params["growdiff_response"])
gd_reform.apply_to(gf_reform)
if use_cps:
records = tc.Records.cps_constructor(data=microdata,
gfactors=gf_reform)
else:
records = tc.Records(microdata, gfactors=gf_reform)
policy = tc.Policy(gf_reform)
policy.implement_reform(self.params['policy'])
# Initialize Calculator
self.reform_calc = tc.Calculator(policy=policy,
records=records,
verbose=self.verbose)
def report(tb,
name=None,
change_threshold=0.05,
description=None,
outdir=None,
author=None,
css=None,
verbose=False):
"""
Create a PDF report based on TaxBrain results
Parameters
----------
tb: TaxBrain object
name: Name you want used for the title of the report
change_threshold: Percentage change (expressed as a decimal fraction) in
an aggregate variable for it to be considered notable
description: A description of the reform being run
outdir: Output directory
author: Person or persons to be listed as the author of the report
css: Path to a CSS file used to format the final report
verbose: boolean indicating whether or not to write progress as report is
created
"""
def format_table(df):
"""
Apply formatting to a given table
"""
for col in df.columns:
df.update(df[col].astype(float).apply("{:,.2f}".format))
return df
def export_plot(plot, graph):
"""
Export a bokeh plot based on Cairo version
"""
# export graph as a PNG or SVG depending on Cairo version is installed
# higher quality SVG only works with Cairo version >= 1.15.4
cairo_version = cairo.cairo_version()
if cairo_version < 11504:
filename = f"{graph}_graph.png"
full_filename = Path(output_path, filename)
export_png(plot, full_filename)
print("For a higher quality SVG image file, install Cairo 1.15.4")
else:
filename = f"{graph}_graph.svg"
full_filename = Path(output_path, filename)
export_svgs(plot, full_filename)
return filename
if not tb.has_run:
tb.run()
if not name:
name = f"Policy Report-{date()}"
if not outdir:
outdir = "-".join(name)
if author:
author = f"Report Prepared by {author.title()}"
# create directory to hold report contents
output_path = Path(outdir)
if not output_path.exists():
output_path.mkdir()
# dictionary to hold pieces of the final text
text_args = {
"start_year": tb.start_year,
"end_year": tb.end_year,
"title": name,
"date": date(),
"author": author
}
if verbose:
print("Writing Introduction")
# find policy areas used in the reform
pol = tc.Policy()
pol_meta = pol.metadata()
pol_areas = set()
for var in tb.params["policy"].keys():
area = pol_meta[var]["section_1"]
if area != "":
pol_areas.add(area)
pol_areas = list(pol_areas)
# add policy areas to the intro text
text_args["introduction"] = form_intro(pol_areas, description)
# write final sentance of introduction
current_law = tb.params["base_policy"]
text_args["baseline_intro"] = form_baseline_intro(current_law)
if verbose:
print("Writing Summary")
agg_table = tb.weighted_totals("combined")
rev_change = agg_table.loc["Difference"].sum()
rev_direction = "increase"
if rev_change < 0:
rev_direction = "decrease"
text_args["rev_direction"] = rev_direction
text_args["rev_change"] = f"{rev_change:,.0f}"
# create differences table
if verbose:
print("Creating distribution table")
diff_table = tb.differences_table(tb.start_year, "standard_income_bins",
"combined")
# find which income bin sees the largest change in tax liability
largest_change = largest_tax_change(diff_table)
text_args["largest_change_group"] = largest_change[0]
text_args["largest_change_str"] = largest_change[1]
diff_table.columns = tc.DIFF_TABLE_LABELS
# drop certain columns to save space
drop_cols = [
"Share of Overall Change", "Count with Tax Cut",
"Count with Tax Increase"
]
sub_diff_table = diff_table.drop(columns=drop_cols)
# convert DataFrame to Markdown table
diff_table.index.name = "_Income Bin_"
# apply formatting
diff_table = format_table(diff_table)
diff_md = convert_table(sub_diff_table)
text_args["differences_table"] = diff_md
# aggregate results
if verbose:
print("Compiling aggregate results")
# format aggregate table
agg_table *= 1e-9
agg_table = format_table(agg_table)
agg_md = convert_table(agg_table)
text_args["agg_table"] = agg_md
# aggregate table by tax type
tax_vars = ["iitax", "payrolltax", "combined"]
agg_base = tb.multi_var_table(tax_vars, "base")
agg_reform = tb.multi_var_table(tax_vars, "reform")
agg_diff = agg_reform - agg_base
agg_diff.index = ["Income Tax", "Payroll Tax", "Combined"]
agg_diff *= 1e-9
agg_diff = format_table(agg_diff)
text_args["agg_tax_type"] = convert_table(agg_diff)
# summary of policy changes
text_args["reform_summary"] = policy_table(tb.params["policy"])
# policy baseline
if tb.params["base_policy"]:
text_args["policy_baseline"] = policy_table(tb.params["base_policy"])
else:
text_args["policy_baseline"] = (
f"This report is based on current law as of {date()}.")
# notable changes
if verbose:
print("Finding notable changes")
text_args["notable_changes"] = notable_changes(tb, change_threshold)
# behavioral assumptions
if verbose:
print("Compiling assumptions")
text_args["behavior_assumps"] = behavioral_assumptions(tb)
# consumption asssumptions
text_args["consump_assumps"] = consumption_assumptions(tb)
# growth assumptions
text_args["growth_assumps"] = growth_assumptions(tb)
# determine model versions
text_args["model_versions"] = [{
"name": "Tax-Brain",
"release": taxbrain.__version__
}, {
"name": "Tax-Calculator",
"release": tc.__version__
}, {
"name": "Behavioral-Responses",
"release": behresp.__version__
}]
# create graphs
if verbose:
print("Creating graphs")
dist_graph = taxbrain.distribution_plot(tb, tb.start_year, width=650)
dist_graph.background_fill_color = None
dist_graph.border_fill_color = None
text_args["distribution_graph"] = export_plot(dist_graph, "dist")
# differences graph
diff_graph = taxbrain.differences_plot(tb, "combined", width=640)
diff_graph.background_fill_color = None
diff_graph.border_fill_color = None
text_args["agg_graph"] = export_plot(diff_graph, "difference")
# fill in the report template
if verbose:
print("Compiling report")
template_path = Path(CUR_PATH, "report_files", "report_template.md")
report_md = write_text(template_path, **text_args)
# create PDF and HTML used to create the PDF
wpdf, html = md_to_pdf(report_md, str(output_path), css)
# write PDF, markdown files, HTML
filename = name.replace(" ", "-")
pdf_path = Path(output_path, f"{filename}.pdf")
pdf_path.write_bytes(wpdf)
md_path = Path(output_path, f"{filename}.md")
md_path.write_text(report_md)
html_path = Path(output_path, f"{filename}.html")
html_path.write_text(html)
def test_bm_corp0(with_response, actual_vs_expect,
puf_subsample, tests_path):
"""
Test BusinessModel corporate results under a corporate-income-tax reform
using calc_all(response=None) and calc_all(response=zero_elasticities),
checking that the two sets of results are exactly the same, which is what
is expected.
"""
# ensure that expected results in the two with_response cases are the same
assert filecmp.cmp(os.path.join(tests_path,
'bm_corp0_base_nresp_expect.csv'),
os.path.join(tests_path,
'bm_corp0_base_wresp_expect.csv'),
shallow=False)
assert filecmp.cmp(os.path.join(tests_path,
'bm_corp0_refm_nresp_expect.csv'),
os.path.join(tests_path,
'bm_corp0_refm_wresp_expect.csv'),
shallow=False)
# specify corporate-income-tax reform policy with these provisions:
# - apply a 28% corporate tax rate
# - eliminate bonus depreciation
# - establish 50% haircut on the deductibility of interest on new debt
btax_reform = {
'tau_c': {2018: 0.28},
'depr_3yr_bonus': {2018: 0.0},
'depr_5yr_bonus': {2018: 0.0},
'depr_7yr_bonus': {2018: 0.0},
'depr_10yr_bonus': {2018: 0.0},
'depr_15yr_bonus': {2018: 0.0},
'depr_20yr_bonus': {2018: 0.0},
'depr_25yr_bonus': {2018: 0.0},
'depr_275yr_bonus': {2018: 0.0},
'depr_39yr_bonus': {2018: 0.0},
'pymtc_hc': {2018: 1.0},
'newIntPaid_corp_hc': {2018: 1.0},
'newIntPaid_corp_hcyear': {2018: 2018},
'oldIntPaid_corp_hc': {2018: 1.0},
'oldIntPaid_corp_hcyear': {2018: 2018},
'newIntPaid_noncorp_hc': {2018: 1.0},
'newIntPaid_noncorp_hcyear': {2018: 2018},
'oldIntPaid_noncorp_hc': {2018: 1.0},
'oldIntPaid_noncorp_hcyear': {2018: 2018}
}
btax_policy_ref = Policy()
btax_policy_ref.implement_reform(btax_reform)
# specify individual-tax reform dictionary with no reform provisions
itax_reform = {}
itax_policy_ref = itax.Policy()
itax_policy_ref.implement_reform(itax_reform)
# calculate results in different ways depending on value of with_response
if with_response:
zero_elast_response = Response()
zero_elast_response.update_elasticities({}) # all zero elasticities
bizmod = BusinessModel(btax_policy_ref, itax_policy_ref,
investor_data=puf_subsample)
bizmod.calc_all(response=zero_elast_response)
else:
bizmod = BusinessModel(btax_policy_ref, itax_policy_ref,
investor_data=puf_subsample)
bizmod.calc_all(response=None)
# compare actual and expected results
resp = 'wresp' if with_response else 'nresp'
dec = 4
results = bizmod.corp_base.taxreturn.combined_return.round(dec)
fname = 'bm_corp0_base_{}_expect.csv'.format(resp)
actual_vs_expect(results, fname, precision=dec)
results = bizmod.corp_ref.taxreturn.combined_return.round(dec)
fname = 'bm_corp0_refm_{}_expect.csv'.format(resp)
actual_vs_expect(results, fname, precision=dec)