def test_proportional_change_in_gdp(cps_subsample):
"""
Test correct and incorrect calls to proportional_change_in_gdp function.
"""
rec = Records.cps_constructor(data=cps_subsample, no_benefits=True)
pol = Policy()
calc1 = Calculator(policy=pol, records=rec)
reform = {2015: {'_II_em': [0.0]}} # reform increases taxes and MTRs
pol.implement_reform(reform)
calc2 = Calculator(policy=pol, records=rec)
assert calc1.current_year == calc2.current_year
assert calc1.current_year == 2014 # because using CPS data
gdpc = proportional_change_in_gdp(2014, calc1, calc2, elasticity=0.36)
assert gdpc == 0.0 # no effect for first data year
gdpc = proportional_change_in_gdp(2015, calc1, calc2, elasticity=0.36)
assert gdpc == 0.0 # no effect in first year of reform
calc1.increment_year()
calc2.increment_year()
assert calc1.current_year == 2015
gdp_pchg = 100.0 * proportional_change_in_gdp(
2016, calc1, calc2, elasticity=0.36)
exp_pchg = -0.54 # higher MTRs imply negative expected GDP percent change
abs_diff_pchg = abs(gdp_pchg - exp_pchg)
if abs_diff_pchg > 0.01:
msg = 'year,gdp_pchg,exp_pchg= {} {:.3f} {:.3f}'.format(
2016, gdp_pchg, exp_pchg)
assert msg == 'ERROR: gdp_pchg not close to exp_pchg'
# skip calcN.increment_year to 2016, so calcN.current_year is still 2015
with pytest.raises(ValueError):
proportional_change_in_gdp(2017, calc1, calc2, elasticity=0.36)
def run_nth_year_gdp_elast_model(year_n,
start_year,
use_puf_not_cps,
use_full_sample,
user_mods,
gdp_elasticity,
return_dict=True):
"""
The run_nth_year_gdp_elast_model function assumes user_mods is a dictionary
returned by the Calculator.read_json_param_objects() function.
Setting use_puf_not_cps=True implies use puf.csv input file;
otherwise, use cps.csv input file.
Setting use_full_sample=False implies use sub-sample of input file;
otherwsie, use the complete sample.
"""
# pylint: disable=too-many-arguments,too-many-locals
# calculate gdp_effect
fyear = check_years_return_first_year(year_n, start_year, use_puf_not_cps)
if (start_year + year_n) > fyear:
# create calc1 and calc2 calculated for year_n - 1
(calc1, calc2, _) = calculate((year_n - 1),
start_year,
use_puf_not_cps,
use_full_sample,
user_mods,
behavior_allowed=False)
# compute GDP effect given specified gdp_elasticity
gdp_effect = proportional_change_in_gdp((start_year + year_n), calc1,
calc2, gdp_elasticity)
else:
gdp_effect = 0.0
# return gdp_effect results
if return_dict:
gdp_df = pd.DataFrame(data=[gdp_effect], columns=['col0'])
gdp_elast_names_n = [
x + '_' + str(year_n) for x in GDP_ELAST_ROW_NAMES
]
gdp_elast_total = create_dict_table(gdp_df,
row_names=gdp_elast_names_n,
num_decimals=5)
gdp_elast_total = dict((k, v[0]) for k, v in gdp_elast_total.items())
return gdp_elast_total
else:
return gdp_effect
