This is an advanced recipe that should be followed only after mastering the basic recipe. This recipe shows how to estimate the reform response in charitable giving when the response elasticities vary by earnings group. It employs the groupby technique used in the Creating a Custom Table recipe, so you might want to read that recipe first.
import sys
if 'google.colab' in sys.modules:
!pip install -q condacolab &> /dev/null # dev/null suppresses output
import condacolab
condacolab.install()import taxcalc as tc
# use publicly-available CPS input file
recs = tc.Records.cps_constructor()
# specify Calculator object for static analysis of current-law policy
pol = tc.Policy()
calc1 = tc.Calculator(policy=pol, records=recs)
CYR = 2020
# calculate current-law tax liabilities for CYR
calc1.advance_to_year(CYR)
calc1.calc_all()
# calculate marginal tax rate wrt cash charitable giving
(_, _, mtr1) = calc1.mtr('e19800', calc_all_already_called=True,
wrt_full_compensation=False)
# specify Calculator object for static analysis of reform policy
# TODO: Move this reform online so it can be read non-locally.
pol.implement_reform(tc.Policy.read_json_reform('github://PSLmodels:Tax-Calculator@master/docs/recipes/_static/reformB.json'))
calc2 = tc.Calculator(policy=pol, records=recs)
# calculate reform tax liabilities for CYR
calc2.advance_to_year(CYR)
calc2.calc_all()
# calculate marginal tax rate wrt cash charitable giving
(_, _, mtr2) = calc2.mtr('e19800', calc_all_already_called=True,
wrt_full_compensation=False)
# extract variables needed for quantity_response function
# (note the aftertax price is 1+mtr because mtr wrt charity is non-positive)
vdf = calc1.dataframe(['s006', 'e19800', 'e00200'])
vdf['price1'] = 1.0 + mtr1
vdf['price2'] = 1.0 + mtr2
vdf['atinc1'] = calc1.array('aftertax_income')
vdf['atinc2'] = calc2.array('aftertax_income')
# group filing units into earnings groups with different response elasticities
# (note earnings groups are just an example based on no empirical results)
earnings_bins = [-9e99, 50e3, 9e99] # two groups: below and above $50,000
vdf = tc.add_income_table_row_variable(vdf, 'e00200', earnings_bins)
gbydf = vdf.groupby('table_row', as_index=False, observed=False)
# compute percentage response in charitable giving
# (note elasticity values are just an example based on no empirical results)
PRICE_ELASTICITY = [-0.1, -0.4]
INCOME_ELASTICITY = [0.1, 0.1]
print('\nResponse in Charitable Giving by Earnings Group')
results = '{:18s}\t{:8.3f}\t{:8.3f}\t{:8.2f}'
colhead = '{:18s}\t{:>8s}\t{:>8s}\t{:>8s}'
print(colhead.format('Earnings Group', 'Num(#M)', 'Resp($B)', 'Resp(%)'))
tot_funits = 0.
tot_response = 0.
tot_baseline = 0.
idx = 0
for grp_interval, grp in gbydf:
funits = grp['s006'].sum() * 1e-6
tot_funits += funits
response = tc.quantity_response(grp['e19800'],
PRICE_ELASTICITY[idx],
grp['price1'],
grp['price2'],
INCOME_ELASTICITY[idx],
grp['atinc1'],
grp['atinc2'])
grp_response = (response * grp['s006']).sum() * 1e-9
tot_response += grp_response
grp_baseline = (grp['e19800'] * grp['s006']).sum() * 1e-9
tot_baseline += grp_baseline
pct_response = 100. * grp_response / grp_baseline
glabel = '[{:.8g}, {:.8g})'.format(grp_interval.left, grp_interval.right)
print(results.format(glabel, funits, grp_response, pct_response))
idx += 1
pct_response = 100. * tot_response / tot_baseline
print(results.format('ALL', tot_funits, tot_response, pct_response))
Response in Charitable Giving by Earnings Group
Earnings Group Num(#M) Resp($B) Resp(%)
[-9e+99, 50000) 151.185 0.656 0.40
[50000, 9e+99) 53.299 3.080 1.99
ALL 204.484 3.736 1.17