Massachusetts tax and benefit system

This notebook shows how the state and federal tax and benefit system affects Massachusetts residents holistically.

Examples

Consider a set of Massachusetts family types, each with $1,000 monthly rent and $50 monthly broadband costs, and varying in terms of marital status and number of children. Their net income—after state and federal taxes and benefits modeled by PolicyEngine US—is shown in the graph below.

The cliff is due to Massachusetts’ emergency SNAP allotment, which entitles SNAP-eligible households to the maximum benefit for their household size; this also affects other benefits through categorical eligibility.

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, rent=12_000)
    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)
    # $1,000 monthly rent, $50 monthly broadband.
    sim.add_spm_unit(name="spm_unit", members=members, broadband_cost=600)
    sim.add_household(name="household", members=members, state_code="MA")
    sim.vary("employment_income", max=100_000, step=100)
    employment_income = sim.calc("employment_income")[0]
    spm_unit_net_income = sim.calc("spm_unit_net_income")[0].round()
    mtr = 1 - sim.deriv(
        "spm_unit_net_income", "employment_income", wrt_target="head"
    )
    return pd.DataFrame(
        dict(
            employment_income=employment_income,
            spm_unit_net_income=spm_unit_net_income,
            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",
    spm_unit_net_income="Net income",
    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",
    "spm_unit_net_income",
    color="children",
    animation_frame="adults",
    labels=LABELS,
    title="Net income for a Massachusetts household",
    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 range from below -50% to above 50%.

fig = px.line(
    df,
    "employment_income",
    "mtr",
    color="children",
    animation_frame="adults",
    labels=LABELS,
    title="Marginal tax rate for a Massachusetts household",
    color_discrete_map=COLOR_MAP,
)
fig.update_layout(
    xaxis_tickformat="$,",
    yaxis_tickformat=".1%",
    yaxis_range=[-1, 1],
    plot_bgcolor="white",
    xaxis_gridcolor=LIGHT_GRAY,
    yaxis_gridcolor=LIGHT_GRAY,
)
fig.show()

Other situations

A single person who is age 65 and holds less than $2,000 in assets will be eligible for additional tax exemptions and federal Supplemental Security Income (SSI).

sim = IndividualSim(year=2022)
sim.add_person(name="head", age=65)
sim.add_tax_unit(name="tax_unit", members=["head"], premium_tax_credit=0)
sim.add_spm_unit(name="spmu", members=["head"])
sim.add_household(name="h", members=["head"], state_code="MA")

sim.vary("employment_income", max=100_000, step=100)
employment_income = sim.calc("employment_income")[0]
spm_unit_net_income = sim.calc("spm_unit_net_income")[0].round()
mtr = 1 - sim.deriv(
    "spm_unit_net_income", "employment_income", wrt_target="head"
)
df = pd.DataFrame(
    dict(
        employment_income=employment_income,
        spm_unit_net_income=spm_unit_net_income,
        mtr=mtr,
    )
)

fig = px.line(
    df,
    "employment_income",
    "spm_unit_net_income",
    labels=LABELS,
    title="Net income for a single person aged 65 in Massachusetts",
)

fig.update_layout(
    xaxis_tickformat="$,",
    yaxis_tickformat="$,",
    plot_bgcolor="white",
    xaxis_gridcolor=LIGHT_GRAY,
    yaxis_gridcolor=LIGHT_GRAY,
)
fig.show()

This single 65-year-old faces marginal tax rates between 57 and 80 percent when they earn between $1,100 and $21,300. Earnings beyond that, up to $100,000, are subject to marginal tax rates between 22 and 35 percent, except for a cliff when they lose SNAP eligibility at $25,700 earnings.

fig = px.line(
    df,
    "employment_income",
    "mtr",
    labels=LABELS,
    title="Marginal tax rate for a single person aged 65 in Massachusetts",
)

fig.update_layout(
    xaxis_tickformat="$,",
    yaxis_tickformat=".1%",
    yaxis_range=[0, 1],
    plot_bgcolor="white",
    xaxis_gridcolor=LIGHT_GRAY,
    yaxis_gridcolor=LIGHT_GRAY,
)
fig.show()