New York EITC#
The New York EITC equals 30 percent of the federal EITC, minus the New York household credit.
Examples#
The New York EITC provided up to $2,000 in 2021, for a joint filer with three children and earnings between about $25,200 and $26,100. It is not smooth because it subtracts the New York household credit, which falls with income in a stepped manner.
from policyengine_us import IndividualSim, Microsimulation
import pandas as pd
import plotly.express as px
LIGHT_GRAY = "#F5F5F5"
GRAY = "#BDBDBD"
BLUE = "#5091cc"
LIGHT_BLUE = "lightblue"
DARK_BLUE = "darkblue"
COLOR_MAP = {"0": GRAY, "1": LIGHT_BLUE, "2": BLUE, "3": DARK_BLUE}
def make_eitc(adults, children):
sim = IndividualSim(year=2021)
sim.add_person(name="head", age=25)
members = ["head"]
if adults == 2:
sim.add_person(name="spouse")
members += ["spouse"]
for i in range(children):
child = "child{}".format(i)
sim.add_person(name=child, age=6)
members += [child]
sim.add_tax_unit(name="tax_unit", members=members)
sim.add_spm_unit(name="spm_unit", members=members)
sim.add_household(name="household", members=members, state_code="NY")
sim.vary("employment_income", max=60_000, step=100)
return pd.DataFrame(
dict(
employment_income=sim.calc("employment_income")[0],
ny_eitc=sim.calc("ny_eitc")[0].round(),
mtr=-sim.deriv("ny_eitc", "employment_income", wrt_target="head"),
adults=adults,
children=str(children),
)
)
# Make a table of EITCs for different numbers of adults and children.
l = []
for adults in range(1, 3):
for children in range(0, 4):
l.append(make_eitc(adults, children))
df = pd.concat(l)
LABELS = dict(
employment_income="Employment income",
mtr="Marginal tax rate of NY EITC",
adults="Adults",
children="Children",
ny_eitc="New York EITC",
)
fig = px.line(
df,
"employment_income",
"ny_eitc",
color="children",
animation_frame="adults",
labels=LABELS,
title="New York Earned Income Tax Credit",
color_discrete_map=COLOR_MAP,
)
fig.update_layout(
xaxis_tickformat="$,",
yaxis_tickformat="$,",
plot_bgcolor="white",
xaxis_gridcolor=LIGHT_GRAY,
yaxis_gridcolor=LIGHT_GRAY,
)
fig.show()
The NY EITC creates marginal tax rates ranging from -13.5 percent to +6.3 percent, plus downward spikes corresponding to the NY household credit cliffs.
fig = px.line(
df,
"employment_income",
"mtr",
color="children",
animation_frame="adults",
labels=LABELS,
title="NY EITC marginal tax rate",
color_discrete_map=COLOR_MAP,
)
fig.update_layout(
xaxis_tickformat="$,",
yaxis_tickformat=".1%",
plot_bgcolor="white",
xaxis_gridcolor=LIGHT_GRAY,
yaxis_gridcolor=LIGHT_GRAY,
)
fig.show()
Budgetary impact#
Applying the New York EITC logic to the 2020 Current Population Survey March Supplement shows that the program cost an estimated $533 million in 2020.
from policyengine_us.model_api import *
def budgetary_impacts(
variable: str, data_year: int = 2020, policy_year: int = 2022
) -> Tuple:
"""Calculates the budgetary impact of a variable for a given policy year.
Args:
variable (str): The variable to calculate the budgetary impact of.
data_year (int): The year of the data to use.
policy_year (int): The year of the policy to use.
Returns:
Tuple: A tuple of the total cost and budgetary impact of repealing the variable,
respectively, in millions.
"""
class ignore_reported(Reform):
def apply(self):
self.neutralize_variable("spm_unit_net_income_reported")
class neutralize(Reform):
def apply(self):
self.neutralize_variable(variable)
sim = Microsimulation(ignore_reported, year=data_year)
sim_neutralized = Microsimulation(
(ignore_reported, neutralize), year=data_year
)
program_value = sim.calc(variable, period=policy_year).sum()
baseline_net_income = sim.calc(
"spm_unit_net_income", period=policy_year
).sum()
neutralized_net_income = sim_neutralized.calc(
"spm_unit_net_income", period=policy_year
).sum()
budgetary_impact = neutralized_net_income - baseline_net_income
return round(program_value / 1e6), round(budgetary_impact / 1e6)
budgetary_impacts("ny_eitc")
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/tmp/ipykernel_3635/1540646120.py in <cell line: 0>()
40
41
---> 42 budgetary_impacts("ny_eitc")
/tmp/ipykernel_3635/1540646120.py in budgetary_impacts(variable, data_year, policy_year)
25 self.neutralize_variable(variable)
26
---> 27 sim = Microsimulation(ignore_reported, year=data_year)
28 sim_neutralized = Microsimulation(
29 (ignore_reported, neutralize), year=data_year
~/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 'year'