# 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"

sim = IndividualSim(year=2022)
members += ["spouse"]
for i in range(children):
child = "child{}".format(i)
members += [child]
# $1,000 monthly rent,$50 monthly broadband.
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(
)
return pd.DataFrame(
dict(
employment_income=employment_income,
spm_unit_net_income=spm_unit_net_income,
mtr=mtr,
children=str(children),
)
)

# Make a table of state taxes for different numbers of adults and children.
l = []
for children in range(0, 4):

df = pd.concat(l)

LABELS = dict(
employment_income="Employment income",
spm_unit_net_income="Net income",
mtr="Marginal tax rate",
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",
xaxis_tickformat="$,", yaxis_tickformat="$,",