# Maryland Child and Dependent Care Credit#

Maryland’s Child and Dependent Care Tax Credit (CDCC) provides up to 32% of the federal CDCC, depending on income and filing status.

## Examples#

Consider a single parent in Maryland with one child and $8,000 child care expenses (the maximum for one child in 2021). Since the federal CDCC changed in 2021 (2022 returns to 2020 law), this chart shows all three years via the slider. The example is limited to single parents because the federal CDCC is capped by the lower of a head and spouse’s earnings. from policyengine_us import IndividualSim import pandas as pd import plotly.express as px def make_cdcc(children, year): sim = IndividualSim(year=year) sim.add_person(name="head", is_tax_unit_head=True) members = ["head"] for i in range(children): child = "child{}".format(i) sim.add_person(name=child, age=5) members += [child] sim.add_tax_unit( name="tax_unit", members=members, tax_unit_childcare_expenses=8_000 * children, ) sim.add_spm_unit(name="spm_unit", members=members) sim.add_household(name="household", members=members, state_code="MD") sim.vary("employment_income", max=100_000, step=100) return pd.DataFrame( dict( employment_income=sim.calc("employment_income")[0], md_cdcc=sim.calc("md_cdcc")[0].round(), cdcc_mtr=-sim.deriv( "md_cdcc", "employment_income", wrt_target="head", ), year=year, children=children, ) ) # Make a table of state taxes for different numbers of adults and children. l = [] for year in [2020, 2021, 2022]: for children in range(1, 3): l.append(make_cdcc(children, year)) df = pd.concat(l) LABELS = dict( employment_income="Employment income", md_cdcc="MD CDCC", scb_mtr="MD CDCC Marginal Tax Rate", year="Year", children="Children", ) fig = px.line( df, "employment_income", "md_cdcc", color="children", animation_frame="year", labels=LABELS, title="Maryland CDCC", ) fig.update_layout( xaxis_tickformat="$,",
yaxis_tickformat="\$,",
yaxis_range=[0, df.md_cdcc.max() * 1.05],
)
fig.show()