Government intervention has been one of the major tailwinds keeping the economy afloat amid massive unemployment and depressed spending during the COVID-19 pandemic.  Several companies have commented on the surge in spend they saw after stimulus checks were first issued in mid-April, and also on slowing growth they’ve seen since additional unemployment benefits lapsed at the end of July.  Given the current government stalemate over a new stimulus, it is important for all businesses to understand the impact that the initial round of benefits had on their low income customers, and how that trajectory may change if a new deal isn’t reached.

Since the beginning of the pandemic, low income spend from those making less than $40,000 per year generally remained higher than that of any other group when compared to each group’s spend levels the second week of March before stay-at-home orders and business closures were enacted in most states.  This spend did begin to show an even larger outperformance the week ending 4/19/2020 as stimulus checks were issued.  However, consistent with commentary from many retailers, low income spend fell off in the beginning of August when additional unemployment payments were pulled back, and trailed every income group except the highest in the weeks ending 8/16/2020 and 8/23/2020.  Spend changes from the highest income group making more than $150,000 per year has notably trailed that of other groups since the pandemic began.

Total Panel Spend by Income

Spend by Income Group, Indexed to Week Ending March 15, 2020 Chart

A regional view provides further insight.  Using CE Vision’s unique capability to dig into a cross-section of demographic and geographic characteristics, it becomes clear that certain regions have been seeing more strength in low income spend than others.  Specifically, the South, where closures of low-wage businesses like retail and restaurants have been less pervasive and shorter in duration, has seen outperformance in low income spend growth for much of the pandemic.  The region saw not only less impact than other geographies at the beginning of the pandemic, but also a stronger boost from stimulus checks.  The South has also been the most resilient since the lapse of additional unemployment benefits.  The West, which contains several states that were among the first to shut down and have suffered from several reclosures even after opening businesses, has trailed other regions in low income spend growth.

Low Income Spend by Census Region

Low Income Year over year Spend Growth by Region Chart
Note: Among those making less than $40,000 per year

There are specific subcategories that have been especially hard hit by the lapsed benefits.  Looking at change in spend for low income shoppers versus the entire panel (to control for sectoral growth due to reopenings) in the first full three weeks of August versus the first full three weeks of July, declines in spend seem to skew towards services and online category specialists.  Education and health services were among the most impacted.  Travel also suffered, with larger-than-average declines in spend for both lodging and ground transportation.  Declines in oil & gas spend may also be travel-related.  Subindustries such as sporting goods, hobby, toy, & game and home furnishings/home improvement where executives had specifically mentioned a stimulus check boost also saw a relative slowdown in low income spend in August.  And, both full-service and limited-service restaurants saw low income spend change underperform the overall population by greater than 1% in August versus July.

Spend by Subcategory

Delta in Spend Change After Extra Unemployment Lapsed, Low Income vs. Overall Panel Chart
Note: % spend change 8/3/2020-8/23/2020 vs. 7/6/2020-7/26/2020 for each income group; chart shows delta subtracting spend change for overall panel from spend change for low income (less than $40,000 per year) shoppers

With CE Vision, users can track how macro events and government policies affect spending patterns for their key demographic groups.  They can cut this data by geography to see which stores or markets are most affected.  They can also look at the data for their subindustry as a whole to see if they are being impacted more or less than competitors.