Pennsylvania#
This notebook shows how Pennsylvania’s income tax, including credits, operates holistically.
Examples#
from policyengine_us import IndividualSim
import pandas as pd
import plotly.express as px
LIGHT_GRAY = "#F5F5F5"
GRAY = "#BDBDBD"
BLUE = "#5091cc"
LIGHT_BLUE = "lightblue"
DARK_BLUE = "darkblue"
def make_tax(adults, children):
sim = IndividualSim(year=2022)
sim.add_person(name="head", age=25)
members = ["head"]
if adults == 2:
sim.add_person(name="spouse", age=25)
members += ["spouse"]
for i in range(children):
child = "child{}".format(i)
sim.add_person(name=child, age=6)
members += [child]
sim.add_tax_unit(name="tax_unit", members=members, premium_tax_credit=0)
sim.add_household(name="household", members=members, state_code="PA")
sim.vary("employment_income", max=100_000, step=1_000)
employment_income = sim.calc("employment_income")[0]
state_income_tax = sim.calc("state_income_tax")[0].round()
mtr = sim.deriv("state_income_tax", "employment_income", wrt_target="head")
return pd.DataFrame(
dict(
employment_income=employment_income,
state_income_tax=state_income_tax,
mtr=mtr,
adults=adults,
children=str(children),
)
)
# Make a table of state taxes for different numbers of adults and children.
l = []
for adults in range(1, 3):
for children in range(0, 4):
l.append(make_tax(adults, children))
df = pd.concat(l)
LABELS = dict(
employment_income="Employment income",
state_income_tax="Pennsylvania income tax",
mtr="Marginal tax rate",
adults="Adults",
children="Children",
)
COLOR_MAP = {"0": GRAY, "1": LIGHT_BLUE, "2": BLUE, "3": DARK_BLUE}
fig = px.line(
df,
"employment_income",
"state_income_tax",
color="children",
animation_frame="adults",
labels=LABELS,
title="Pennsylvania income tax",
color_discrete_map=COLOR_MAP,
)
fig.update_layout(
xaxis_tickformat="$,",
yaxis_tickformat="$,",
plot_bgcolor="white",
xaxis_gridcolor=LIGHT_GRAY,
yaxis_gridcolor=LIGHT_GRAY,
)
fig.show()
Total marginal tax rates are typically 3.07%, though the forgiveness raises them as high as 55%.
fig = px.line(
df,
"employment_income",
"mtr",
color="children",
animation_frame="adults",
labels=LABELS,
title="Marginal tax rate for a Pennsylvania household",
color_discrete_map=COLOR_MAP,
)
fig.update_layout(
xaxis_tickformat="$,",
yaxis_tickformat=".1%",
yaxis_range=[0, 0.6],
plot_bgcolor="white",
xaxis_gridcolor=LIGHT_GRAY,
yaxis_gridcolor=LIGHT_GRAY,
)
fig.show()
Based on 2020 Current Population Survey, the 2022 Pennsylvania income tax code would have raised $13.4 billion.
This falls 26% below Pennsylvania’s Independent Fiscal Office’s June 2022 estimate of $18.1 billion in personal income tax revenue for FY2021, and 14% below their June 2021 estimate of $15.5 billion.
from policyengine_us import Microsimulation
ms = Microsimulation(dataset_year=2020)
(ms.calc("pa_income_tax", period=2022).sum() / 1e6).round()
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/tmp/ipykernel_3850/373537429.py in <cell line: 0>()
1 from policyengine_us import Microsimulation
2
----> 3 ms = Microsimulation(dataset_year=2020)
4 (ms.calc("pa_income_tax", period=2022).sum() / 1e6).round()
~/work/policyengine-us/policyengine-us/policyengine_us/system.py in __init__(self, *args, **kwargs)
136
137 def __init__(self, *args, **kwargs):
--> 138 super().__init__(*args, **kwargs)
139
140 reform = create_structural_reforms_from_parameters(
TypeError: Simulation.__init__() got an unexpected keyword argument 'dataset_year'