def test_ce_aftertax_income(cps_subsample):
# test certainty_equivalent() function with con>cmin
con = 5000
cmin = 1000
assert con == round(certainty_equivalent(con, 0, cmin), 6)
assert con > round(certainty_equivalent((math.log(con) - 0.1), 1, cmin), 6)
# test certainty_equivalent() function with con<cmin
con = 500
cmin = 1000
assert con == round(certainty_equivalent(con, 0, cmin), 6)
# test with require_no_agg_tax_change equal to False
rec = Records.cps_constructor(data=cps_subsample, no_benefits=True)
cyr = 2020
# specify calc1 and calc_all() for cyr
pol = Policy()
calc1 = Calculator(policy=pol, records=rec)
calc1.advance_to_year(cyr)
calc1.calc_all()
# specify calc2 and calc_all() for cyr
reform = {2019: {'_II_em': [1000]}}
pol.implement_reform(reform)
calc2 = Calculator(policy=pol, records=rec)
calc2.advance_to_year(cyr)
calc2.calc_all()
df1 = calc1.dataframe(['s006', 'combined', 'expanded_income'])
df2 = calc2.dataframe(['s006', 'combined', 'expanded_income'])
cedict = ce_aftertax_expanded_income(df1,
df2,
require_no_agg_tax_change=False)
assert isinstance(cedict, dict)
np.allclose(cedict['ceeu1'], [55641, 27167, 5726, 2229, 1565],
atol=0.5,
rtol=0.0)
np.allclose(cedict['ceeu2'], [54629, 26698, 5710, 2229, 1565],
atol=0.5,
rtol=0.0)
# test with require_no_agg_tax_change equal to True
with pytest.raises(ValueError):
ce_aftertax_expanded_income(df1, df2, require_no_agg_tax_change=True)
# test with require_no_agg_tax_change equal to False and custom_params
params = {'crra_list': [0, 2], 'cmin_value': 2000}
with pytest.raises(ValueError):
ce_aftertax_expanded_income(df1,
df2,
require_no_agg_tax_change=True,
custom_params=params)
def test_ce_aftertax_income(cps_subsample):
# test certainty_equivalent() function with con>cmin
con = 5000
cmin = 1000
assert con == round(certainty_equivalent(con, 0, cmin), 6)
assert con > round(certainty_equivalent((math.log(con) - 0.1), 1, cmin), 6)
# test certainty_equivalent() function with con<cmin
con = 500
cmin = 1000
assert con == round(certainty_equivalent(con, 0, cmin), 6)
# test with require_no_agg_tax_change equal to False
rec = Records.cps_constructor(data=cps_subsample)
cyr = 2020
# specify calc1 and calc_all() for cyr
pol = Policy()
calc1 = Calculator(policy=pol, records=rec)
calc1.advance_to_year(cyr)
calc1.calc_all()
# specify calc2 and calc_all() for cyr
reform = {'II_em': {2019: 1000}}
pol.implement_reform(reform)
calc2 = Calculator(policy=pol, records=rec)
calc2.advance_to_year(cyr)
calc2.calc_all()
df1 = calc1.dataframe(['s006', 'combined', 'expanded_income'])
df2 = calc2.dataframe(['s006', 'combined', 'expanded_income'])
cedict = ce_aftertax_expanded_income(df1, df2,
require_no_agg_tax_change=False)
assert isinstance(cedict, dict)
np.allclose(cedict['ceeu1'], [55641, 27167, 5726, 2229, 1565],
atol=0.5, rtol=0.0)
np.allclose(cedict['ceeu2'], [54629, 26698, 5710, 2229, 1565],
atol=0.5, rtol=0.0)
# test with require_no_agg_tax_change equal to True
with pytest.raises(ValueError):
ce_aftertax_expanded_income(df1, df2, require_no_agg_tax_change=True)
# test with require_no_agg_tax_change equal to False and custom_params
params = {'crra_list': [0, 2], 'cmin_value': 2000}
with pytest.raises(ValueError):
ce_aftertax_expanded_income(df1, df2, require_no_agg_tax_change=True,
custom_params=params)
