# Poverty and inequality effects of new unemployment benefits, payroll tax cuts, and universal payments¶

By Max Ghenis and Nate Golden

July 2020

The expiration of the $600-per-week Federal Pandemic Unemployment Compensation (FPUC) program at the end of July 2020 has spurred discussions for new Covid-19 relief legislation in the United States. Proposals for this bill include extending FPUC throughout 2020, cutting payroll taxes, and unconditional payments. We use data from the Current Population Survey between 2009 and 2018 to estimate the poverty and inequality effects of a hypothetical FPUC and FPUC extension, compared to budget-neutral payroll tax cuts and universal payments, respectively. Our simulations show that a universal payment would have lowered poverty and inequality less than April-to-July FPUC (i.e., the policy that’s expiring). However, universal payments would have reduced poverty and inequality more than August-to-December FPUC extensions; for example, it would have reduced poverty 24 percent more in 2009. These deltas are larger for the poverty rate of children, Black people, and people with disabilities. Payroll tax cuts would have had much smaller effects than either policy, and universal payments that include children are consistently more effective than adult-only payments. ## Background¶ The CARES Act established two major new unemployment insurance reforms: Pandemic Unemployment Assistance (PUA), which expands unemployment benefits to more worker categories, and Federal Pandemic Unemployment Compensation (FPUC), which adds a federally-funded$600 per week to all unemployment benefits. While PUA is available for up to 39 weeks through the end of the year, FPUC runs only from April through July.

Given FPUC’s expiration and the Congressional Budget Office’s July 2020 projections that the unemployment rate would remain above 10 percent throughout 2020, federal lawmakers are preparing for a new relief package. Congressional Democrats are seeking to extend FPUC in full through January, as specified in the House-passed HEROES Act. The Trump administration has proposed cutting payroll taxes, and members of both parties have spoken of another round of direct payments, following the Recovery Rebates in the CARES Act.

FPUC’s work disincentives have made it controversial: it replaces over 100 percent of wages for all workers earning below $21 per hour, and depending on the state, for workers earning up to$33 per hour.1 Capping the wage replacement rate was infeasible due to limitations with state unemployment benefit computer systems. Its proponents emphasize income stabilization and the need to reduce work to prevent Covid infections. To the extent that FPUC may be making it harder to find workers, payroll tax cuts can offset that effect.

Distributional analysis has been less prominent in the debates over Covid relief policies. Columbia University’s Center on Poverty and Social Policy estimated that the CARES Act roughly offsets the poverty caused by the pandemic’s reduction in market incomes.

Our analysis estimates FPUC’s poverty effects in the CARES Act period, as well as FPUC’s potential expansion through the rest of the year, compared to budget-neutral payroll tax cuts and universal payments.

## Data¶

We use the Current Population Survey March Supplement from 2009 to 2018, obtained from IPUMS. The Supplemental Poverty Measure was introduced in 2009, while 2018 is the latest survey year available as of this writing.

We extract fields used for tax and poverty analysis, as well as the “number of weeks, in single weeks, that the respondent looked for work or was on layoff during the preceding calendar year.” This field allows us to estimate the total FPUC benefits for each respondent. We also extract the disability status (i.e., whether the respondent reports any physical or cognitive difficulty), which the CPS collects only for civilians aged 15+.

All dollar values are scaled to 2020 dollars using FRED’s CPI-U series.

## Methodology¶

The CPS only provides the number of weeks each respondent was unemployed, not the specific calendar weeks. Since the specific time periods—April to July to model past policy, and August to December to model proposed policy—are relevant for the simulation, we provide each person $600 per week of unemployment, multiplied by the number of weeks in the program (17 for FPUC, 22 for FPUC extension) divided by 52 total weeks. This is the asymptotic equivalent to imputing specific weeks among the total weeks unemployed.2 ### Tax simulation¶ We use the open-source Tax-Calculator software for simulating FPUC tax liabilities. This involves grouping people into tax units3 and calculating tax-relevant fields from each tax unit’s aggregate CPS responses. We then calculate each tax unit’s federal tax liability with and without FPUC—applying 2020 tax law for all years—and store the difference as the FPUC tax. To identify the net-of-tax FPUC benefits for each person,4 we allocate the tax unit’s FPUC tax across individuals in proportion to their FPUC benefits. This leaves us with the net-of-tax FPUC benefits for each person in the sample. ### Calculating budget-neutral alternatives¶ For each year, we calculated the total cost of FPUC and expanding FPUC by adding up the costs for each person. This table shows these costs, along with other quantities from the CPS that factor into the budget-neutral parameters, for each year. As the economy improved from 2009 to 2018, and specifically as the unemployment rate fell, the cost of FPUC and expanding FPUC fell by roughly half. For example, enacting FPUC from April to July of 2009 would have cost an estimated$68 billion (total), while it would have cost $32 billion in 2018. """ SETUP """ import numpy as np import pandas as pd import taxcalc as tc import microdf as mdf import plotly.express as px import pandas_datareader as pdr # For CPI series. person = pd.read_csv('fpuc/data/cps.csv.gz') # These hold functions. %run 'fpuc/convert_asec_taxcalc.py' %run 'fpuc/make_tax_units.py' # This runs execution, and also includes more functions. # -i gives it access to the active environment (person and prior functions). %run -i 'fpuc/run.py'  Matplotlib is building the font cache; this may take a moment.  # Print overall summary print("All figures in millions.") (year_summary.rename(columns={'FLPDYR': 'Year'}).set_index( 'Year')[OVERALL_YEARLY_METRICS].rename( columns=DISPLAY_METRICS) / 1e6).style.format("{:,.0f}")  All figures in millions.  Cost of FPUC Cost of expanding FPUC Population Adult population Total FICA Year 2009 67,500 85,630 304 230 565,285 2010 63,310 80,186 307 232 554,978 2011 60,172 76,143 309 235 430,923 2012 55,853 70,642 311 237 429,108 2013 51,203 64,709 313 239 580,207 2014 42,712 54,093 316 242 590,995 2015 39,791 50,398 319 245 643,590 2016 36,919 46,757 320 246 654,402 2017 32,277 40,924 323 249 672,941 2018 31,933 40,440 324 251 687,864 These figures produce the budget-neutral policy parameters for each year by division. For example, to calculate the universal payment associated with FPUC in 2009, we can divide the cost of FPUC in that year ($68 billion) by the population in that year (304 million), and arrive at $222 per person. Similarly, the associated FICA cut would be the total FICA collections ($68 billion) divided by \$118 billion, or 12 percent (note this is the cut for FICA through the full year).

This table shows the full set of parameters that match the total cost of both the original FPUC and its expansion (“FPUC2”).

# Print reform parameter summary.
(year_summary.rename(columns={'FLPDYR': 'Year'}).set_index('Year')[
FPUC_YEARLY_METRICS + FPUC2_YEARLY_METRICS].rename(
columns=DISPLAY_METRICS)).style.format("{:,.0f}")

Universal one-time payment (FPUC) Adult one-time payment (FPUC) FICA % cut (FPUC) Universal one-time payment (FPUC2) Adult one-time payment (FPUC2) FICA % cut (FPUC2)
Year
2009 222 294 12 281 373 15
2010 207 273 11 262 345 14
2011 195 256 14 247 324 18
2012 180 236 13 227 298 16
2013 163 214 9 206 270 11
2014 135 176 7 171 223 9
2015 125 163 6 158 206 8
2016 115 150 6 146 190 7
2017 100 130 5 127 164 6
2018 98 127 5 125 161 6

### Poverty estimation¶

SPM poverty rates are calculated by grouping individuals into SPM units, which are sub-household groups that the Bureau of Labor Statistics identifies as sharing resources. Each SPM unit then has its own poverty threshold—based on composition and housing situation—and resources—based on income, taxes, mandatory spending categories, and transfers. SPM units are determined to classified as poor if their poverty threshold exceeds their resources.

To calculate this, we group each person into their SPM unit (each year) and aggregate resources under each of the nine conditions:

• Baseline

• FPUC (April through July) and budget-neutral alternatives:

• Payroll tax cut budget

• Equal per-person payment

• FPUC extension (August through December) and budget-neutral alternatives:

• Payroll tax cut budget

• Equal per-person payment

These SPM-unit resource totals facilitate calculation of the poverty gap (the total amount of money it would take to end poverty, if perfectly targeted). Calculating the poverty rate requires merging resource back to the person level on SPM unit ID, as the poverty rate is calculated as the share of people (not SPM units) in poverty.

We compute inequality as the per-person Gini index, based on their SPM unit’s per-capita resources.

## Results¶

We begin by showing the effects of FPUC from April to July, compared to budget-neutral payroll tax cuts and universal payments, and then show the effects of expanding FPUC from August to December.

Policies are evaluated against the following metrics:

• Poverty rate: The share of the population in SPM units whose poverty threshold exceeds their resources.

• Poverty gap: The total dollar amount required to raise each SPM unit to its poverty threshold (this is a measure of poverty depth).

• Gini index: Measure of inequality, calculated from the person-level distribution of SPM unit total resources.

### FPUC thus far¶

FPUC and universal payments would have been more progressive than payroll tax cuts, and in most outcomes and years, FPUC would have been slightly more progressive than universal payments, which would have been about 20 percent more progressive than adult-only payments.

#### Poverty rate¶

In most of the last decade, FPUC would have reduced poverty than the alternatives. However, in 2009, when economic conditions most closely resembled today’s, universal transfers were nearly equivalently effective. The antipoverty effect of all policies shrinks over time, as unemployment has fallen and therefore a hypothetical FPUC’s budgetary impact has shrunk.

In all years, universal transfers and FPUC outperformed payments to adults only, and especially outperformed the payroll tax cut.

# Round for the hover text.
line_graph(pov_rates2[(pov_rates2.age_group == 'All') &
(pov_rates2.race == 'All') &
(pov_rates2.disability_filter == False)],
group='fpuc', y='pov_pc',
yaxis_title='Change in SPM poverty rate',
title='Poverty reduction by reform')