A $300-per-month UBI would cut Veteran poverty in half

By Max Ghenis and Nate Golden, 2020-11-11

Today we honor over 19 million Americans who have served in the military, of whom one in 13 is in poverty. This is a third lower than the poverty rate among non-Veterans, thanks to higher incomes and Veteran benefits, but still amounts to over 1.4 million people who lack resources to cover basic needs. Universal basic income would help.

Existing assistance programs cut Veteran poverty substantially. Two million Veterans receive refundable tax credits—the Earned Income Tax Credit and the refundable portion of the Child Tax Credit—and 1.3 million receive SNAP (formerly food stamps). Certain veteran households are also eligible to receive up to $36,387 per year through the Veterans Pension program, depending on their household size, income, net worth, age, service period, and disability status. Veterans with disabilities related to their active-duty service can also receive up to $44,208 per year, depending on their household size and severity of their disability through the Veterans Disability Compensation program. The Veterans Benefit Administration provides a number of other benefits around health, education, and other areas, especially for elderly Veterans.

Yet these programs fail to reach all Veterans in need. As the Department of Veterans Affairs found in 2012, veterans with disabilities have a higher official poverty rate than non-veterans with disabilities for all age groups except over age 65, and half of Veterans don’t use VA programs. 20 percent of Veterans have a service-connected disability, and as we wrote in July, one in five people with disabilities is in poverty—nearly double the rate of people without disabilities. In that analysis, we also showed that a $300-per-month universal basic income (UBI) would halve poverty among people with disabilities. What would UBI do for all Veterans?

In this analysis, we modeled a UBI funded by a flat tax on adjusted gross income (AGI). In this model, every $100 monthly UBI requires a 3.3 percent tax on AGI. To fund a $300-per-month UBI, the federal government would have to levy a 9.8 percent tax. But like we found in the disability analysis, this would also reduce Veteran poverty by half. Poverty among children in Veteran households would be cut by 60 percent.

# Import Libraries
import microdf as mdf
import numpy as np
import pandas as pd
import plotly.express as px

# Import data
raw = pd.read_csv("https://github.com/UBICenter/Veteran-s_Day/raw/main/VeteranData.gz")

# Create Demographic Columns
person = raw.copy(deep=True)
person.columns = person.columns.str.lower()
person["child"] = person.age < 18
person["adult"] = person.age >= 18
person["veteran"] = (person.vetstat == 2) & person.adult
person["non_veteran"] = (person.vetstat == 1) & person.adult

veterans = person.groupby(["spmfamunit"])[["veteran"]].sum()
veterans.columns = ["total_veterans"]
person = person.merge(veterans.reset_index(), on=["spmfamunit"])

person["child_with_vet"] = (person.child) & (person.total_veterans > 0)
person["child_with_no_vet"] = (person.child) & (person.total_veterans == 0)

# Show total veterans in millions
total_veterans = (person.veteran * person.asecwt).sum()
total_veterans / 1_000_000

# Calculate total AGI
person["adjginc"].replace({99999999: 0}, inplace=True)
population = person.asecwt.sum()
person["weighted_agi"] = person.adjginc * person.asecwt
total_agi = person.weighted_agi.sum()

# Calculate AGI tax rate per dollar of UBI
fed_tax_rate_per_dollar_ubi_monthly = (population * 12) / total_agi

# Create table showing tax amounts
tax_rates = pd.DataFrame(np.arange(0, 1001, 50))
tax_rates.columns = ["monthly_ubi"]

def tax(monthly_ubi):
    return (monthly_ubi * fed_tax_rate_per_dollar_ubi_monthly * 100).round(1)

def tax_row(row):
    return tax(row.monthly_ubi)

tax_rates["tax_rate"] = tax_rates.apply(tax_row, axis=1)
tax_rates.columns = ["Monthly UBI", "Flat Tax Rate on AGI"]

def ubi(status, monthly_ubi):
    """At a given UBI level, calculate the poverty rate, median resources,
    mean resources, and percent of people better off for:
    * Veterans
    * Non-Veterans
    * Children living with Veteran
    * Children not living with Veterans
        status: A person's Veteran status.
            For this simulation their are 4 categories:
            * Veteran
            * Non-Veteran
            * Child living with a Veteran
            * Children not living with a Veteran
        monthly_ubi: the monthly cash transfer given to each person
        pandas Series with four elements for the selected group:
        * Poverty rate
        * Median resources per person
        * Mean resources per person
        * Percent of people better off

    # Create a copy of the person DataFrame
    target_persons = person.copy(deep=True)

    # Calculate a person's tax increase
    target_persons["tax_increase"] = (
        fed_tax_rate_per_dollar_ubi_monthly * monthly_ubi * target_persons.adjginc

    # Calculate the total UBI per SPM unit.
    target_persons["total_ubi"] = target_persons.spmnpers * 12 * monthly_ubi

    # Calculate the total tax increase of an SPM unit
    spmu = target_persons.groupby(["spmfamunit"])[["tax_increase"]].sum()
    spmu.columns = ["total_tax_increase"]
    target_persons = target_persons.merge(
        spmu, left_on=["spmfamunit"], right_index=True

    # Calculate each SPM unit's tax rate person
    target_persons["new_spm_resources"] = (
        + target_persons.total_ubi
        - target_persons.total_tax_increase

    # Calculate the new resources per person of each SPM unit
    target_persons["new_resources_per_person"] = (
        target_persons.new_spm_resources / target_persons.spmnpers

    # Slice the data based on Race input
    if status == "veteran":
        target_persons = target_persons[target_persons.veteran]
    if status == "non_veteran":
        target_persons = target_persons[target_persons.non_veteran]

    if status == "veteran_child":
        target_persons = target_persons[target_persons.child_with_vet]
    if status == "non_veteran_child":
        target_persons = target_persons[target_persons.child_with_no_vet]

    # Calculate the change in poverty rate
    target_persons["poor"] = target_persons.new_spm_resources < target_persons.spmthresh
    total_poor = (target_persons.poor * target_persons.asecwt).sum()
    target_pop = target_persons.asecwt.sum()

    # Calculate percent better off
    target_persons["better_off"] = (
        target_persons.new_spm_resources > target_persons.spmtotres
    total_better_off = (target_persons.better_off * target_persons.asecwt).sum()
    percent = total_better_off / target_pop * 100

    return pd.Series(
                target_persons, "new_resources_per_person", "asecwt"
                target_persons, "new_resources_per_person", "asecwt"
            (total_poor / target_pop * 100).round(1),

def ubi_row(row):
    """ run the ubi function across the rows of a DataFrame.
    row: the row of the DataFrame containing a person's race and the monthly UBI amount
    The poverty rate for the selected row.
    The median resources per person for the selected row.
    The mean resources per person for the selected row.
    The percent of people better off under the program for the selected row.
    return ubi(row.status, row.monthly_ubi)

# Create a DataFrame that has each the each monthly UBI amount for each race input
summary = mdf.cartesian_product(
        "monthly_ubi": np.arange(0, 1001, 50),
        "status": ["veteran", "non_veteran", "veteran_child", "non_veteran_child"],

# Calculate the poverty rate for each row of the summary DataFrame
] = summary.apply(ubi_row, axis=1)

summary2 = summary[summary["status"] != "non_veteran_child"]
summary2 = summary2[summary2["status"] != "veteran_child"]
# Format text
center = {
    "med_resources_per_person": "Median resources",
    "mean_resources_per_person": "Mean resources",
status = {
    "veteran": "Veterans",
    "non_veteran": "Non-Veteran adults",
    "veteran_child": "Children in Veteran households",
    "non_veteran_child": "Children in non-Veteran households",

summary["status"] = summary["status"].map(status)
summary2["status"] = summary2["status"].map(status)

# Colors from https://material.io/design/color/the-color-system.html
BLUE = "#1976D2"
DARK_BLUE = "#0D47A1"
GRAY = "#9E9E9E"

    "Veterans": BLUE,
    "Non-Veteran adults": GRAY,
    "Children in Veteran households": BARELY_BLUE,
    "Children in non-Veteran households": LIGHT_GRAY,

def line_graph(df, x, y, color, title, xaxis_title, yaxis_title):
    """Style for line graphs.
        df: DataFrame with data to be plotted.
        x: The string representing the column in df that holds the new
            spending in billions.
        y: The string representing the column in df that holds the poverty
        color: The string representing the UBI type.
        xaxis_title: The string represnting the xaxis-title.
        yaxis_title: The string representing the yaxis-title.
        Nothing. Shows the plot.
    fig = px.line(df, x=x, y=y, color=color, color_discrete_map=COLOR_MAP)
    fig.update_traces(mode="markers+lines", hovertemplate=None)

    hide_line = ["Children in Veteran households", "Children in non-Veteran households"]

        lambda trace: trace.update(visible="legendonly")
        if trace.name in hide_line
        else ()

    fig.show(config={"displayModeBar": False})

    title="The impact of a UBI on Veterans and their families",
    xaxis_title="Monthly UBI",
    yaxis_title="SPM poverty rate",