Recipe2: Employer side Payroll Tax Offset#
This recipe is to introduce the usage of employer_payroll_offset function from payrolloffset.py, when calling the function to conduct analysis upon employer side payroll tax reform
import taxcalcpayroll as tcp
from taxcalcpayroll.payrolloffset import employer_payroll_offset
import pandas as pd
import numpy as np
recs = tcp.Records(data = 'puf.csv')
pol = tcp.Policy()
calc0 = tcp.Calculator(policy = pol, records = recs)
(1) total tax revenue after the reform (without offset)#
recs = tcp.Records(data = 'puf.csv')
pol1 = tcp.Policy()
pol1.implement_reform(reform1, print_warnings=True, raise_errors=True)
calc1 = tcp.Calculator(policy = pol1, records = recs)
calc1.advance_to_year(2025)
calc1.calc_all()
print("In 2025, combined tax revenue under reformed policy (without offset) is: ", calc1.weighted_total('combined') / 10**9)
In 2025, combined tax revenue under reformed policy (without offset) is: $4053.78 Billion
(2) total tax revenue after the reform, considering the offset#
calc0.advance_to_year(2025)
calc0.calc_all()
/Users/bodiyang/Desktop/taxcalc/Tax-Calculator/taxcalc/records.py:353: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
self.e00300 *= self.ADJ['INT{}'.format(year)][self.agi_bin].values
Function usage:#
Input for offset function: baseline calculator (its policy and records object) and reform policy
Output for offset function: returns a dataframe of output variables
offdf = employer_payroll_offset(reform1, calc0, pol, recs, dump=False)
print the result of calculated combined tax revenue
print("In 2025, combined tax revenue under reformed policy, after the offset, is: ", (offdf['combined'] * offdf['s006']).sum() / 10**9)
In 2025, combined tax revenue under reformed policy, after the offset, is: 4023.508250234729
In 2025, combined tax revenue under reformed policy, after the offset, is: $4023.51 Billion
Muti year usage#
Comparison of total tax revenue calculation of baseline policy, reform policy (without offset), and reform policy considering offset
# setup some tool list to help printing result
dtable = pd.DataFrame([])
year_list = []
cur_list = []
reform_list = []
offset_list = []
base_ref_diff = []
ref_offset_diff = []
base_offset_diff = []
cur_sum = 0
reform_sum = 0
offset_sum = 0
base_ref_sum = 0
ref_offset_sum = 0
base_offset_sum = 0
recs = tcp.Records(data = 'puf.csv')
pol = tcp.Policy()
calc0 = tcp.Calculator(policy = pol, records = recs)
recs = tcp.Records(data = 'puf.csv')
pol1 = tcp.Policy()
pol1.implement_reform(reform1, print_warnings=True, raise_errors=True)
calc1 = tcp.Calculator(policy = pol1, records = recs)
# multi year
for year in range(2024, 2034):
year_list.append(year)
# baseline
calc0.advance_to_year(year)
calc0.calc_all()
itax_rev0 = calc0.weighted_total('combined')
itax_rev0 = (itax_rev0/10**9).round(2)
cur_list.append(itax_rev0)
cur_sum += itax_rev0
# reform before offset
calc1.advance_to_year(year)
calc1.calc_all()
itax_rev1 = calc1.weighted_total('combined')
itax_rev1 = (itax_rev1/10**9).round(2)
reform_list.append(itax_rev1)
br_diff = itax_rev1 - itax_rev0
base_ref_diff.append(br_diff)
reform_sum += itax_rev1
base_ref_sum += br_diff
# offset
offset_df = employer_payroll_offset(reform1, calc0, pol, recs, dump=False)
itax_revbr = (offset_df['combined'] * offset_df['s006']).sum()
itax_revbr = (itax_revbr/10**9).round(2)
offset_list.append(itax_revbr)
ro_diff = itax_revbr - itax_rev1
ref_offset_diff.append(ro_diff)
bo_diff = itax_revbr - itax_rev0
base_offset_diff.append(bo_diff)
offset_sum += itax_revbr
ref_offset_sum += ro_diff
base_offset_sum += bo_diff
year_list.append("10 year 2024-2033")
cur_list.append(cur_sum)
reform_list.append(reform_sum)
base_ref_diff.append(base_ref_sum)
offset_list.append(offset_sum)
ref_offset_diff.append(ref_offset_sum)
base_offset_diff.append(base_offset_sum)
df = pd.DataFrame({'Year': year_list, 'Baseline Tototal Revenue': cur_list, 'Reform Tototal Revenue (no offset)': reform_list,
'Reform Tototal Revenue with offset': offset_list, "Diff: Reform - Baseline": base_ref_diff,
'Diff: Offset - Reform': ref_offset_diff, "Diff: Offset - Baseline": base_offset_diff
})
df = df.style.hide()
display(df)
| Year | Baseline Tototal Revenue | Reform Tototal Revenue (no offset) | Reform Tototal Revenue with offset | Diff: Reform - Baseline | Diff: Offset - Reform | Diff: Offset - Baseline |
|---|---|---|---|---|---|---|
| 2024 | 3757.610000 | 3757.610000 | 3757.610000 | 0.000000 | 0.000000 | 0.000000 |
| 2025 | 3952.990000 | 4053.780000 | 4023.510000 | 100.790000 | -30.270000 | 70.520000 |
| 2026 | 4353.220000 | 4458.410000 | 4423.900000 | 105.190000 | -34.510000 | 70.680000 |
| 2027 | 4543.830000 | 4653.240000 | 4617.230000 | 109.410000 | -36.010000 | 73.400000 |
| 2028 | 4737.260000 | 4850.960000 | 4813.430000 | 113.700000 | -37.530000 | 76.170000 |
| 2029 | 4935.730000 | 5053.900000 | 5014.790000 | 118.170000 | -39.110000 | 79.060000 |
| 2030 | 5139.730000 | 5262.540000 | 5221.780000 | 122.810000 | -40.760000 | 82.050000 |
| 2031 | 5348.200000 | 5475.840000 | 5433.370000 | 127.640000 | -42.470000 | 85.170000 |
| 2032 | 5549.990000 | 5682.670000 | 5638.450000 | 132.680000 | -44.220000 | 88.460000 |
| 2033 | 5781.670000 | 5918.520000 | 5872.840000 | 136.850000 | -45.680000 | 91.170000 |
| 10 year 2024-2033 | 48100.230000 | 49167.470000 | 48816.910000 | 1067.240000 | -350.560000 | 716.680000 |