app.py

import streamlit as st
from policyengine_us import Simulation
from policyengine_core.reforms import Reform
from policyengine_core.periods import instant
import plotly.express as px
from policyengine_core.charts import format_fig
import pandas as pd


# Title and subtitle.
st.title("How would the TRAFWA Child Tax Credit impact you?")
st.markdown(
    """
The [Tax Relief for American Families and Workers Act](https://www.congress.gov/bill/118th-congress/house-bill/7024) would make four changes to the Child Tax Credit:
- Raise the maximum refundable credit to \$1,800 in 2023, \$1,900 in 2024, and \$2,100 in 2025.
- Phase in the refundable credit on a per-child basis from 2023 to 2025.
- Allow the refundable credit credit to be claimed based on current or prior year earnings in 2024 and 2025.
- Inflation-adjust the non-refundable credit beginning in 2024.
            
This calculator shows how TRAFWA would affect your Child Tax Credit from 2023 to 2025.
#### Your tax filing unit
"""
)


def modify_parameters(parameters):
    parameters.gov.contrib.congress.wyden_smith.actc_lookback.update(
        start=instant("2023-01-01"), stop=instant("2025-12-31"), value=True
    )
    parameters.gov.contrib.congress.wyden_smith.per_child_actc_phase_in.update(
        start=instant("2023-01-01"), stop=instant("2025-12-31"), value=True
    )
    parameters.gov.irs.credits.ctc.refundable.individual_max.update(
        start=instant("2023-01-01"), stop=instant("2023-12-31"), value=1800
    )
    parameters.gov.irs.credits.ctc.refundable.individual_max.update(
        start=instant("2024-01-01"), stop=instant("2024-12-31"), value=1900
    )
    parameters.gov.irs.credits.ctc.refundable.individual_max.update(
        start=instant("2025-01-01"), stop=instant("2025-12-31"), value=2100
    )
    parameters.gov.irs.credits.ctc.amount.base[0].amount.update(
        start=instant("2024-01-01"), stop=instant("2025-12-31"), value=2100
    )
    return parameters


class reform(Reform):
    def apply(self):
        self.modify_parameters(modify_parameters)


DEFAULT_ADULT_AGE = 40

# Collecting the number of CTC-eligible children
ctc_eligible_children = st.number_input(
    "How many CTC-eligible children (under age 17) do you have?",
    min_value=0,
    max_value=10,
    value=1,
)

# Collecting marital status
is_married = st.radio("Are you married?", ("Yes", "No")) == "Yes"


# Collecting earnings for each year from 2023 to 2025
# Function to format the input as a dollar amount
def format_dollar(amount):
    if amount is not None:
        return "${:,.0f}".format(amount)
    return ""


def get_earnings(year):
    # Ask how much you earned if in 2023, otherwise how much you expect to earn.
    if year == 2023:
        question = f"How much did you earn in {year}?"
    else:
        question = f"How much do you expect to earn in {year}?"
    earnings_input = st.text_input(question, value="$0")
    return int("".join(filter(str.isdigit, earnings_input)))


earnings = {}
for year in range(2023, 2026):
    earnings[year] = get_earnings(year)

submit = st.button("Calculate")


# Construct the household situation
situation = {
    "people": {
        "you": {
            "age": {"2023": DEFAULT_ADULT_AGE},
            "employment_income": {
                "2023": earnings[2023],
                "2024": earnings[2024],
                "2025": earnings[2025],
            },
            "employment_income_last_year": {
                "2023": 0,
                "2024": earnings[2023],
                "2025": earnings[2024],
            },
        },
    },
    "tax_units": {
        "your tax unit": {
            # Zero out some quantities that won't change between baseline and reform
            # This will improve performance.
            "premium_tax_credit": {"2023": 0},
            "tax_unit_itemizes": {"2023": False},
            "taxable_income_deductions_if_itemizing": {"2023": 0},
            "alternative_minimum_tax": {"2023": 0},
            "net_investment_income_tax": {"2023": 0},
            "eitc": {"2023": 0},
        }
    },
    "households": {"your household": {"state_code_str": "TX"}},
}
members = ["you"]

if is_married:
    situation["people"]["spouse"] = {"age": {"2023": DEFAULT_ADULT_AGE}}
    members.append("spouse")
for i in range(ctc_eligible_children):
    situation["people"][f"child{i}"] = {"age": {"2023": 0}}
    members.append(f"child{i}")

# Update the rest of the situation entities accordingly
situation["families"] = {"your family": {"members": members}}
situation["marital_units"] = {
    "your marital unit": {"members": members if is_married else ["you"]}
}
situation["tax_units"]["your tax unit"]["members"] = members
situation["spm_units"] = {"your spm_unit": {"members": members}}
situation["households"]["your household"]["members"] = members


def get_income_change(year, baseline_simulation, reform_simulation):
    baseline_tax = baseline_simulation.calculate(
        "income_tax_before_refundable_credits", year
    )[0]
    baseline_actc = baseline_simulation.calculate("refundable_ctc", year)[0]

    reform_tax = reform_simulation.calculate(
        "income_tax_before_refundable_credits", year
    )[0]
    reform_actc = reform_simulation.calculate("refundable_ctc", year)[0]

    actc_change = reform_actc - baseline_actc
    tax_change = reform_tax - baseline_tax
    return actc_change - tax_change


if submit:
    # Avoid computing benefits by only computing taxes and credits.
    # This will speed up the simulation.
    baseline_simulation = Simulation(situation=situation)
    reform_simulation = Simulation(reform=reform, situation=situation)

    # Dictionary to store income changes for each year
    income_changes = {}

    for year in range(2023, 2026):
        income_changes[year] = get_income_change(
            year, baseline_simulation, reform_simulation
        )

    total_income_change = sum(income_changes.values())

    # Create a DataFrame from the income_changes dictionary
    df_income_changes = pd.DataFrame(
        list(income_changes.items()), columns=["Year", "Income Change"]
    )

    # Convert 'Year' to string if it isn't already, to ensure it displays correctly on the x-axis
    df_income_changes["Year"] = df_income_changes["Year"].astype(str)

    # Sum the total benefit over the three years
    total_ctc_benefit = sum(df_income_changes["Income Change"])

    # Update the chart title to include the total CTC benefit
    if total_ctc_benefit > 0:
        chart_title = f"TRAFWA would increase your Child Tax Credit by ${total_ctc_benefit:,.0f}"
    else:
        chart_title = "TRAFWA would not affect your Child Tax Credit"

    # Use markdown for the chart title so it can be bigger.

    st.markdown(
        f"""
        #### {chart_title}
        From 2023 to 2025
        """
    )

    # Create a bar chart using Plotly Express with a single color for all bars
    fig = px.bar(
        df_income_changes,
        x="Year",
        y="Income Change",
        text="Income Change",
        color_discrete_sequence=["#003f5c"],
    )

    # Update the layout to remove the legend and add the total to the title
    fig.update_layout(
        showlegend=False,
        xaxis=dict(title="", showgrid=False),
        yaxis=dict(
            title="TRAFWA CTC Impact",
            tickprefix="$",
            tickformat=",",
            showgrid=False,
        ),
        font=dict(family="Roboto, sans-serif"),
    )

    # Add the dollar sign to the bar text and set the text position
    fig.update_traces(texttemplate="$%{text:,.0f}", textposition="outside")

    # Style the figure
    fig = format_fig(fig)

    # Display the Plotly bar chart in Streamlit
    st.plotly_chart(fig)

# Add a section for caveats at the bottom of the app
st.markdown(
    """
#### Notes
This simulation assumes that:
- All earnings are from the tax filer's wages and salaries.
- The filer has no other taxable income.
- The filer takes the standard deduction.
- All children are eligible for the Child Tax Credit in each of 2023, 2024, and 2025.
- Married couples file jointly.
- [The inflation adjustment takes effect in 2024.](https://github.com/PolicyEngine/policyengine-us/discussions/3726)

To estimate how TRAFWA would affect you with more flexibility, describe your household in the [full PolicyEngine app](https://policyengine.org/us/household?reform=46315&focus=intro).
            
We do not store any personal data entered into this app.
"""
)