Maryland Standard Deduction#

Maryland’s Earned Income Tax Credit (EITC) matches a percentage of the federal EITC depending on income and family structure. In 2021, single childless filers received 100% of the federal EITC up to $530, while other filers received 50% as a non-refundable credit and 45% as a refundable credit.

Examples#

Consider a set of Maryland households whose income is solely from earnings.

from policyengine_us import IndividualSim
import pandas as pd
import plotly.express as px
import numpy as np


def make(adults, children):
    sim = IndividualSim(year=2022)
    sim.add_person(name="head", age=25, is_tax_unit_head=True)
    members = ["head"]
    if adults > 1:
        sim.add_person(name="spouse", is_tax_unit_head=False)
        members += ["spouse"]
    for i in range(children):
        child = "child{}".format(i)
        sim.add_person(name=child, age=5)
        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="MD")
    sim.vary("employment_income", max=60_000, step=100)
    return pd.DataFrame(
        dict(
            employment_income=sim.calc("employment_income")[0],
            standard_deduction=sim.calc("md_standard_deduction")[0].round(),
            adults=adults,
            children=children,
        )
    )


l = []
for adults in range(1, 3):
    l.append(make(adults, 0))

df = pd.concat(l)

df["Filing Status"] = np.where(df.adults == 1, "Single", "Other")

LABELS = dict(
    employment_income="Employment income",
    standard_deduction="MD Standard Deduction",
    adults="Adults",
    children="Children",
)

fig = px.line(
    df,
    "employment_income",
    "standard_deduction",
    color="Filing Status",
    labels=LABELS,
    title="Maryland Standard Deduction",
)
fig.update_layout(
    xaxis_tickformat="$,",
    yaxis_tickformat="$,",
    yaxis_range=[0, df.standard_deduction.max() * 1.05],
)
fig.show()