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"""
Partial-equilibrium elasticity-based Behavioral-Responses logic.
"""
# CODING-STYLE CHECKS:
# pycodestyle behavior.py
# pylint --disable=locally-disabled behavior.py
import copy
import numpy as np
import taxcalc as tc
# Behavioral-Response parameter information
PARAM_INFO = {
'BE_sub': {
'long_name': 'Substitution elasticity of taxable income',
'description': ('Defined as proportional change in taxable income '
'divided by proportional change in marginal '
'net-of-tax rate (1-MTR) on taxpayer earnings '
'caused by the reform. Must be zero or positive.'),
'default_value': 0.0,
'minimum_value': 0.0,
'maximum_value': 9e99
},
'BE_inc': {
'long_name': 'Income elasticity of taxable income',
'description': ('Defined as dollar change in taxable income '
'divided by dollar change in after-tax income '
'caused by the reform. Must be zero or negative.'),
'default_value': 0.0,
'minimum_value': -9e99,
'maximum_value': 0.0
},
'BE_cg': {
'long_name': 'Semi-elasticity of long-term capital gains',
'description': ('Defined as change in logarithm of long-term '
'capital gains divided by change in marginal tax '
'rate (MTR) on long-term capital gains caused by '
'the reform. Must be zero or negative. Read '
'response function documentation (see below) for '
'discussion of appropriate values.'),
'default_value': 0.0,
'minimum_value': -9e99,
'maximum_value': 0.0
}
}
def response(calc_1, calc_2, behavior, dump=False):
"""
Implements TaxBrain "Partial Equilibrium Simulation" dynamic analysis
returning results as a tuple of Pandas DataFrame objects (df1, df2) where:
df1 is extracted from a baseline-policy calc_1 copy, and
df2 is extracted from a reform-policy calc_2 copy that incorporates the
behavioral responses given by the nature of the baseline-to-reform
change in policy and elasticities in the specified behavior dictionary.
Note: this function internally modifies a copy of calc_2 records to account
for behavioral responses that arise from the policy reform that involves
moving from calc1 policy to calc2 policy. Neither calc_1 nor calc_2 need
to have had calc_all() executed before calling the response function.
And neither calc_1 nor calc_2 are affected by this response function.
The behavior argument is a dictionary returned from the Tax-Calculator
Calculator.read_json_assumptions method.
The optional dump argument controls the number of variables included
in the two returned DataFrame objects. When dump=False (its default
value), the variables in the two returned DataFrame objects include
just the variables in the Tax-Calculator DIST_VARIABLES list, which
is sufficient for constructing the standard Tax-Calculator tables.
When dump=True, the variables in the two returned DataFrame objects
include all the Tax-Calculator input and calculated output variables,
which is the same output as produced by the Tax-Calculator tc --dump
option except for one difference: the tc --dump option provides two
calculated variables, mtr_inctax and mtr_paytax, that are replaced
in the dump output of this response function by mtr_combined, which
is the sum of mtr_inctax and mtr_paytax.
Note: the use here of a dollar-change income elasticity (rather than
a proportional-change elasticity) is consistent with Feldstein and
Feenberg, "The Taxation of Two Earner Families", NBER Working Paper
No. 5155 (June 1995). A proportional-change elasticity was used by
Gruber and Saez, "The elasticity of taxable income: evidence and
implications", Journal of Public Economics 84:1-32 (2002) [see
equation 2 on page 10].
Note: the substitution effect here is calculated from a taxable income
base that excludes long-term capital gains, since long-term capital
gains behavior may be modeled separately using the capital-gains
elasticity. You should consider this when applying estimates of the
elasticity of taxable income from the literature.
Note: the nature of the capital-gains elasticity used here is similar
to that used in Joint Committee on Taxation, "New Evidence on the
Tax Elasticity of Capital Gains: A Joint Working Paper of the Staff
of the Joint Committee on Taxation and the Congressional Budget
Office", (JCX-56-12), June 2012. In particular, the elasticity
use here is equivalent to the term inside the square brackets on
the right-hand side of equation (4) on page 11 --- not the epsilon
variable on the left-hand side of equation (4), which is equal to
the elasticity used here times the weighted average marginal tax
rate on long-term capital gains. So, the JCT-CBO estimate of
-0.792 for the epsilon elasticity (see JCT-CBO, Table 5) translates
into a much larger absolute value for the BE_cg semi-elasticity
used by Tax-Calculator.
To calculate the elasticity from a semi-elasticity, we multiply by
MTRs from TC and weight by shares of taxable gains. To avoid those
with zero MTRs, we restrict this to the top 40% of tax units by AGI.
Using this function, a semi-elasticity of -3.45 corresponds to a tax
rate elasticity of -0.792.
"""
# pylint: disable=too-many-locals,too-many-statements,too-many-branches
calc1 = copy.deepcopy(calc_1)
calc2 = copy.deepcopy(calc_2)
assert isinstance(calc1, tc.Calculator)
assert isinstance(calc2, tc.Calculator)
assert isinstance(behavior, dict)
# Begin nested functions used only in this response function
def _update_ordinary_income(taxinc_change, calc):
"""
Implement total taxable income change induced by behavioral response.
"""
# compute AGI minus itemized deductions, agi_m_ided
agi = calc.array('c00100')
ided = np.where(calc.array('c04470') < calc.array('standard'),
0., calc.array('c04470'))
agi_m_ided = agi - ided
# assume behv response only for filing units with positive agi_m_ided
pos = np.array(agi_m_ided > 0., dtype=bool)
delta_income = np.where(pos, taxinc_change, 0.)
# allocate delta_income into three parts
winc = calc.array('e00200')
delta_winc = np.zeros_like(agi)
delta_winc[pos] = delta_income[pos] * winc[pos] / agi_m_ided[pos]
oinc = agi - winc
delta_oinc = np.zeros_like(agi)
delta_oinc[pos] = delta_income[pos] * oinc[pos] / agi_m_ided[pos]
delta_ided = np.zeros_like(agi)
delta_ided[pos] = delta_income[pos] * ided[pos] / agi_m_ided[pos]
# confirm that the three parts are consistent with delta_income
assert np.allclose(delta_income, delta_winc + delta_oinc - delta_ided)
# add the three parts to different records variables embedded in calc
calc.incarray('e00200', delta_winc)
calc.incarray('e00200p', delta_winc)
calc.incarray('e00300', delta_oinc)
calc.incarray('e19200', delta_ided)
return calc
def _update_cap_gain_income(cap_gain_change, calc):
"""
Implement capital gain change induced by behavioral responses.
"""
calc.incarray('p23250', cap_gain_change)
return calc
def _mtr12(calc__1, calc__2, mtr_of='e00200p', tax_type='combined'):
"""
Computes marginal tax rates for Calculator objects calc__1 and calc__2
for specified mtr_of income type and specified tax_type.
"""
assert tax_type in ('combined', 'iitax')
_, iitax1, combined1 = calc__1.mtr(mtr_of, wrt_full_compensation=True)
_, iitax2, combined2 = calc__2.mtr(mtr_of, wrt_full_compensation=True)
if tax_type == 'combined':
return (combined1, combined2)
return (iitax1, iitax2)
# End nested functions used only in this response function
# Begin main logic of response function
calc1.calc_all()
calc2.calc_all()
assert calc1.array_len == calc2.array_len
assert calc1.current_year == calc2.current_year
pvalue = tc.Parameters.param_dict_for_year(calc1.current_year,
behavior, PARAM_INFO)
mtr_cap = 0.99
if dump:
dvars = list(tc.Records.USABLE_READ_VARS | tc.Records.CALCULATED_VARS)
# Calculate sum of substitution and income effects
if pvalue['BE_sub'] == 0.0 and pvalue['BE_inc'] == 0.0:
zero_sub_and_inc = True
if dump:
wage_mtr1 = np.zeros(calc1.array_len)
wage_mtr2 = np.zeros(calc2.array_len)
else:
zero_sub_and_inc = False
# calculate marginal combined tax rates on taxpayer wages+salary
# (e00200p is taxpayer's wages+salary)
wage_mtr1, wage_mtr2 = _mtr12(calc1, calc2,
mtr_of='e00200p',
tax_type='combined')
# calculate magnitude of substitution effect
if pvalue['BE_sub'] == 0.0:
sub = np.zeros(calc1.array_len)
else:
# proportional change in marginal net-of-tax rates on earnings
mtr1 = np.where(wage_mtr1 > mtr_cap, mtr_cap, wage_mtr1)
mtr2 = np.where(wage_mtr2 > mtr_cap, mtr_cap, wage_mtr2)
pch = ((1. - mtr2) / (1. - mtr1)) - 1.
# Note: c04800 is filing unit's taxable income and
# p23250 is filing units' long-term capital gains
taxinc_less_ltcg = calc1.array('c04800') - calc1.array('p23250')
sub = (pvalue['BE_sub'] * pch * taxinc_less_ltcg)
# calculate magnitude of income effect
if pvalue['BE_inc'] == 0.0:
inc = np.zeros(calc1.array_len)
else:
# dollar change in after-tax income
# Note: combined is f.unit's income+payroll tax liability
dch = calc1.array('combined') - calc2.array('combined')
inc = pvalue['BE_inc'] * dch
# calculate sum of substitution and income effects
si_chg = sub + inc
# Calculate long-term capital-gains effect
if pvalue['BE_cg'] == 0.0:
ltcg_chg = np.zeros(calc1.array_len)
else:
# calculate marginal tax rates on long-term capital gains
# p23250 is filing units' long-term capital gains
ltcg_mtr1, ltcg_mtr2 = _mtr12(calc1, calc2,
mtr_of='p23250',
tax_type='iitax')
rch = ltcg_mtr2 - ltcg_mtr1
exp_term = np.exp(pvalue['BE_cg'] * rch)
new_ltcg = calc1.array('p23250') * exp_term
ltcg_chg = new_ltcg - calc1.array('p23250')
# Extract dataframe from calc1
if dump:
df1 = calc1.dataframe(dvars)
df1.drop('mtr_inctax', axis='columns', inplace=True)
df1.drop('mtr_paytax', axis='columns', inplace=True)
df1['mtr_combined'] = wage_mtr1 * 100
else:
df1 = calc1.distribution_table_dataframe()
del calc1
# Add behavioral-response changes to income sources
calc2_behv = copy.deepcopy(calc2)
del calc2
if not zero_sub_and_inc:
calc2_behv = _update_ordinary_income(si_chg, calc2_behv)
calc2_behv = _update_cap_gain_income(ltcg_chg, calc2_behv)
# Recalculate post-reform taxes incorporating behavioral responses
calc2_behv.calc_all()
# Extract dataframe from calc2_behv
if dump:
df2 = calc2_behv.dataframe(dvars)
df2.drop('mtr_inctax', axis='columns', inplace=True)
df2.drop('mtr_paytax', axis='columns', inplace=True)
df2['mtr_combined'] = wage_mtr2 * 100
else:
df2 = calc2_behv.distribution_table_dataframe()
del calc2_behv
# Return the two dataframes
return (df1, df2)