# Basic income would shrink racial poverty disparities¶

By Max Ghenis, Connor Tragesser, and Nate Golden, 2021-01-18

Dr. Martin Luther King Jr. is remembered chiefly for his leadership of the civil rights movement, but toward the end of his life, King extended this leadership to the cause of poverty. In his final book, he wrote:

The time has come for us to civilize ourselves by the total, direct and immediate abolition of poverty […] I’m now convinced that the simplest approach will prove to be the most effective — the solution to poverty is to abolish it directly by a now widely discussed measure: the guaranteed income.

—Dr. Martin Luther King, Jr., Where Do We Go From Here: Chaos or Community? (1967)

We honor King’s call by showing how a universal basic income (UBI), funded by a flat income tax, would not only reduce overall poverty, but also shrink the poverty disparities between Black and White people.

Black Americans today are 75 percent more likely to be in poverty than White Americans, with a rate of 18.4 percent compared to 10.5 percent. A $250 monthly UBI would cut both Black and White poverty roughly in half (this is similar to what we found in a July 2020 post, which used older data and did not simulate taxes to fund the UBI). A$1,000 monthly UBI funded by a flat income tax would reduce poverty for both White and Black people to about 1 percent.

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

SPM_COLS = [
"spm_" + i for i in ["id", "weight", "povthreshold", "resources", "numper"]
]
"https://github.com/MaxGhenis/datarepo/raw/master/pppub20.csv.gz",
usecols=["PRDTRACE", "MARSUPWT", "AGI"] + [i.upper() for i in SPM_COLS],
)
person = raw.copy(deep=True)
person.columns = person.columns.str.lower()
person["weight"] = person.marsupwt / 100
person.spm_weight /= 100
person = person.rename(columns={"prdtrace": "race"})
# Add indicators for white only and black only (not considering other races).
person["white"] = person.race == 1
person["black"] = person.race == 2
# Limit to positive AGI.
person["agi_pos"] = np.maximum(person.agi, 0)
# Need total population to calculate UBI and total AGI for required tax rate.
total_population = person.weight.sum()
total_agi = mdf.weighted_sum(person, "agi_pos", "weight")
# Sum up AGI for each SPM unit and merge that back to person level.
spm = person.groupby(SPM_COLS)[["agi_pos", "white", "black"]].sum()
spm.columns = ["spm_" + i for i in spm.columns]
# Merge these back to person to calculate population in White and Black spmus.
person = person.merge(spm, on="spm_id")
pop_in_race_spmu = pd.Series(
{
"Black": person[person.spm_black > 0].weight.sum(),
"White": person[person.spm_white > 0].weight.sum(),
}
)
spm.reset_index(inplace=True)

def pov_gap(df, resources, threshold, weight):
# df: Should be SPM-unit level.
gaps = np.maximum(df[threshold] - df[resources], 0)
return (gaps * df[weight]).sum()

def pov(race, monthly_ubi):
# Total cost and associated tax rate.
cost = monthly_ubi * total_population * 12
tax_rate = cost / total_agi
# Calculate new tax, UBI and resources per SPM unit.
spm["new_spm_resources"] = (
spm.spm_resources -
(tax_rate * spm.spm_agi_pos) +  # New tax
(12 * monthly_ubi * spm.spm_numper))  # UBI
# Merge back to person.
person2 = person.merge(spm[["spm_id", "new_spm_resources"]], on="spm_id")
# Based on new resources, calculate
person2["new_poor"] = person2.new_spm_resources < person2.spm_povthreshold
# Calculate poverty rate for specified race.
poverty_rate = mdf.weighted_mean(
person2[person2[race.lower()]], "new_poor", "weight"
)
# Calculate poverty gap for specified race.
poverty_gap = pov_gap(
spm[spm["spm_" + race.lower()] > 0], "new_spm_resources",
"spm_povthreshold", "spm_weight"
)
poverty_gap_per_capita = (poverty_gap / pop_in_race_spmu[race])

return pd.Series({
"poverty_rate": poverty_rate,
"poverty_gap_per_capita": poverty_gap_per_capita
})

def pov_row(row):
return pov(row.race, row.monthly_ubi)

summary = mdf.cartesian_product(
{"race": ["White", "Black"], "monthly_ubi": np.arange(0, 1001, 50)}
)
summary = pd.concat([summary, summary.apply(pov_row, axis=1)], axis=1)
# Format results.
summary.poverty_rate = 100 * summary.poverty_rate.round(3)
summary.poverty_gap_per_capita = summary.poverty_gap_per_capita.round(0)
wide = summary.pivot_table(
["poverty_rate", "poverty_gap_per_capita"], "monthly_ubi", "race"
)
wide.columns = ["pg_black", "pg_white", "pr_black", "pr_white"]
wide["pg_ratio"] = (wide.pg_black / wide.pg_white).round(2)
wide["pr_ratio"] = (wide.pr_black / wide.pr_white).round(2)
wide.reset_index(inplace=True)
ratios = wide.melt(id_vars="monthly_ubi", value_vars=["pr_ratio", "pg_ratio"])
# Change for chart.
ratios.variable.replace({"pr_ratio": "Poverty rate",
"pg_ratio": "Poverty gap per capita"},
inplace=True)

dict(
source="https://raw.githubusercontent.com/UBICenter/blog/master/jb/_static/ubi_center_logo_wide_blue.png",
# See https://github.com/plotly/plotly.py/issues/2975.
# source="../_static/ubi_center_logo_wide_blue.png",
xref="paper", yref="paper",
x=x, y=y,
sizex=0.12, sizey=0.12,
xanchor="right", yanchor="bottom"
)
)

def line_graph(
df,
x,
y,
color,
title,
xaxis_title,
yaxis_title,
color_discrete_map,
yaxis_ticksuffix,
yaxis_tickprefix,
):
"""Style for line graphs.

Arguments
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 rate.
color: The string representing the UBI type.
xaxis_title: The string represnting the xaxis-title.
yaxis_title: The string representing the yaxis-title.

Returns
Nothing. Shows the plot.
"""
fig = px.line(
df, x=x, y=y, color=color, color_discrete_map=color_discrete_map
)
fig.update_layout(
title=title,
xaxis_title=xaxis_title,
yaxis_title=yaxis_title,
yaxis_ticksuffix=yaxis_ticksuffix,
yaxis_tickprefix=yaxis_tickprefix,
font=dict(family="Roboto"),
hovermode="x",
xaxis_tickprefix="\$",
plot_bgcolor="white",
legend_title_text="",
height=600,
width=1000,
)

fig.update_layout(
legend=dict(yanchor="top", y=0.99, xanchor="left", x=0.9)
)

fig.update_traces(mode="markers+lines", hovertemplate=None)

return fig

DARK_BLUE = "#1565C0"
GRAY = "#9E9E9E"
DARK_GREEN = "#388E3C"
LIGHT_GREEN = "#66BB6A"
CONFIG = {"displayModeBar": False}

line_graph(
df=summary,
x="monthly_ubi",
y="poverty_rate",
color="race",
title="Black and White poverty rate by UBI amount",
xaxis_title="Monthly universal basic income funded by flat income tax",
yaxis_title="SPM poverty rate (2019)",
color_discrete_map={"White": GRAY, "Black": DARK_BLUE},
yaxis_ticksuffix="%",
yaxis_tickprefix="",
).show(config=CONFIG)