Board of Governors of the Federal Reserve System

October 11, 2024

A Better Way of Understanding the US Consumer: Decomposing Retail Spending by Household Income

Sinem Hacıoğlu Hoke, Leo Feler, and Jack Chylak^*

Changes in retail spending reflect changes in consumer demand for goods. For the past several months, retail sales estimates published by the Census Bureau have indicated that consumer demand for retail goods remains resilient. However, published measures do not provide details on which consumers' spending has remained resilient. Using a detailed micro dataset, we construct a measure of average retail spending for low-, middle- and high-income households. Our measure of spending by income shows that middle- and high-income households have been fueling the strong demand for retail goods in the economy while low-income households' spending has been stable in the post-pandemic period. We also show the heterogeneity in spending by education, generation, and housing status.

The current understanding of retail spending

Every month, the Census Bureau publishes the Advance Monthly Sales for Retail and Food Services report (MARTS) to provide an estimate of retail and food services sales.¹ Economists and financial markets track the results of this report as one of the indicators of the state of the US consumer.

To prepare the MARTS report, the Census Bureau collects the dollar value of sales directly from retailers by fielding establishment-level surveys and estimating total sales and sales by relevant 3-digit NAICS categories. Due to survey design, with establishments reporting monthly sales rather than consumers reporting monthly spending, there is no way to decompose retail sales into purchases by households of different demographic groups.

Figure 1 shows retail sales excluding motor vehicles and parts since 2015, indexed to 100 in 2019 for ease of interpretation.² In the post-pandemic period, nominal retail sales (shown in blue) increased at a faster pace. Nominal retail sales are not adjusted for price changes, so while pent-up demand, Covid-era fiscal stimulus, and excess savings all boosted sales, higher prices also contributed to the increase.

Figure 1. Retail sales: total excluding motor vehicles and parts (seasonally adjusted)

To account for price changes and obtain a clearer picture of the real increase in retail sales, the orange dashed line shows retail sales adjusted for inflation. There was a jump in inflation-adjusted retail sales in March 2021 following the roll-out of vaccines, the pandemic re-opening, and the last fiscal stimulus program. Since then, inflation-adjusted retail spending has grown more slowly and is almost back to its pre-pandemic trend (shown in red).

A micro dataset to address a macro question: constructing a retail spending measure for different income groups

To understand retail sales from the bottom-up – looking at household spending rather than retailers' sales – we use data from a panel of 150,000 representative US households from Numerator, a consumer data company.³ The Numerator panel data are collected at the household level. The data include survey information on household income, age, education, zip code, household size, race/ethnicity, and various other measures. The data also include information on households' purchases from paper receipts, online receipts, and transaction records.⁴ The purchase information contains the quantity and price of each item purchased, the overall total on the receipt, and where and when the purchase took place. These details allow us to classify households' spending into the relevant 3-digit NAICS categories and aggregate them into a monthly total retail spending measure just as in the Census MARTS report. Moreover, we can investigate how spending differs between low, middle, and high-income households.

First, we establish the reliability of Numerator's retail spending measure. Figure 2 plots three lines, all seasonally-adjusted and inflation-adjusted, starting in 2018.⁵ The solid red line indicates the Census Bureau's official seasonally-adjusted measure for total retail sales excluding motor vehicles and parts, which we then inflation-adjust using the chain indexed PCE deflator for goods and food services excluding motor vehicles. The dashed blue line is the retail spending measure we construct from Numerator data, which we seasonally-adjust using the Census Bureau's X13-ARIMA-SEATS package and inflation-adjust as described previously.⁶ The dashed gray line is the non-seasonally adjusted Census series that we then seasonally-adjust and inflation-adjust using the same methodology we use for Numerator data. The purpose of the gray line is to show that our seasonal adjustment methodology closely aligns with the Census Bureau's methodology.⁷

Figure 2. Retail sales, total excluding motor vehicles and parts constructed using Census Bureau estimates and Numerator panel data (seasonally-adjusted and inflation-adjusted)

The correlation between the Numerator series and the Census Bureau's official seasonally-adjusted retail sales series is 0.94 in levels and 0.83 when looking at monthly percent changes. Figure 2 allows us to verify that the bottom-up construction of the Numerator data, captured from household purchases, closely matches the top-down construction of the Census Bureau's retail sales series, captured from surveys of retail and food service establishments.⁸

Decomposing retail spending by household income

Having established that the aggregated Numerator retail spending series closely matches the Census Bureau's retail sales series, we now examine spending patterns based on household income using the Numerator series.

Figure 3 plots growth since January 2018 in average monthly household spending, adjusted for inflation and seasonality, overall and by three income groups:

• overall, shown in red,
• for low-income households ($0-$60K in annual household income), shown in purple,
• for middle-income households ($60K-$100K), shown in gray,
• and for high-income households ($100K+), shown in green.

Figure 3. Growth of average retail spending overall and decomposed by household income (seasonally-adjusted and inflation-adjusted)

A few observations stand out in Figure 3:

1. The growth in average household retail spending is similar across all income groups in the pre-pandemic era. Following the decline at the beginning of the pandemic, average household spending for all income groups, adjusted for inflation and seasonality, is now higher than before the pandemic.
2. Low-income households appear to benefit more from pandemic-era stimulus. In percentage terms, low-income households increase their spending by more than middle and high-income households in 2020 and early 2021. The large jump in the average spending of low-income households in early 2021 coincides with the last stimulus program.
3. The spending behavior of low, middle, and high-income households begins to diverge in mid-2021. Between mid-2021 and mid-2023, middle and high-income households maintain or increase their real average spending; low-income households reduce their real average spending compared with mid-2021 levels.
4. Since mid-2023, low, middle, and high-income households have all been increasing their real average spending.
5. As of August 2024, real average spending by low-income households is up 7.9% relative to January 2018, real average spending by middle-income households is up 13.3%, and real average spending by high-income households is up 16.7%.⁹
6. Overall average retail spending (shown in red) better captures the spending behavior of middle- and high-income households than it does low-income households. While the overall average suggests consumers have remained resilient, similar to the narrative one obtains when looking at the Census Bureau's Advance Monthly Sales for Retail and Food Services reports, our decomposition suggests consumer resilience has been driven by middle- and high-income households, while low-income households have pulled back since mid-2021 through mid-2023 and only recently recovered to their mid-2021 levels of real average retail spending.

Additional results: decomposing retail spending by education, generation, and housing status

In addition to decomposing retail spending by household income, we can decompose retail spending by education, generation, and housing status.

Education
In Figure 4, we show average household retail spending by education. Education is based on the self-reported level of education of the primary shopper of a household in Numerator panel data. One benefit of decomposing retail sales by education rather than income is that education remains broadly stable between 2018 and today, whereas household incomes increase with inflation. The stability of the education groups over time allows us to check whether our results based on household income might have just been driven by compositional changes in income groups (e.g., if more households become high-income over time and fewer households remain low-income, that affects the composition of households in each income group and their average monthly retail spending). Our results for education are broadly similar to our results for household income, with households with lower education levels (high school completed or less) experiencing a pullback in real average household spending on retail goods and food services between mid-2021 and mid-2023, similar to households with lower income levels, while households with middle and higher education levels (some college or undergraduate degree and some graduate school or graduate degree) showing resilience and sustaining higher levels of retail spending on goods and food services since mid-2021.

Figure 4. Growth of average retail spending overall and decomposed by education (seasonally-adjusted and inflation-adjusted)

Generation
In Figure 5, we show average household retail spending by generation. Generation is based on the self-reported birth year of the primary shopper of a household in Numerator panel data. When decomposing average household spending by generation, we observe the following:

1. Boomers (shown in purple) cut back the most at the onset of the pandemic. This makes sense considering older cohorts experienced higher mortality rates and may have wanted to limit their exposure by reducing in-person shopping more than younger cohorts.
2. Boomers have increased their real retail spending the least relative to 2018 levels while Millennials (shown in green) have increased their spending the most. Again, these differences make sense considering the surge of retirements among Boomers at the onset of the pandemic and the fact that Boomers are increasingly on fixed-incomes. In contrast, Millennials are aging into their peak earning and spending years and have been able to benefit more from the strong labor market.

Figure 5. Growth of average retail spending overall and decomposed by generation (seasonally-adjusted and inflation-adjusted)

Housing Status
The Numerator data contain several different household attributes we could use to decompose retail spending.¹⁰ For this note, as a final result, we focus on housing status to test a hypothesis that households with the opportunity to lock-in low fixed-rate mortgages before 2022 have been better able to sustain higher retail spending.

In Figure 6, we show average household retail spending by whether households have owned their current homes since 2021 or prior ("long own"), whether households have only recently owned their current homes since 2022 to the present ("recent own"), and whether households are renters ("rent"). We observe households who are renters experienced the largest increase in retail spending at the onset of the pandemic. This makes sense considering renters tend to be lower-income, and as with low-income households shown in Figure 3, stimulus and support payments at the onset of the pandemic represented a greater percentage increase in both their incomes and spending. Households that are long-term owners had the opportunity to lock-in low fixed-rate mortgages in 2020 and 2021, allowing them to save on monthly housing payments and have more disposable income to sustain higher retail spending on goods and food services. Lastly, we see that those households that became homeowners since 2022 or more recently may exhibit self-selection. Those who can afford the higher monthly payments associated with higher mortgage rates and higher home prices are those who were able to maintain higher average real spending on retail goods and food services since mid-2021. However, real retail spending for these recent-owner households has been declining since mid-2022 to the present, which corresponds to the time when they purchased their homes, whereas spending has been increasing for other groups.

Figure 6. Growth of average retail spending overall and decomposed by housing status (seasonally-adjusted and inflation-adjusted)

Discussion

The purpose of this note is to demonstrate a better way of understanding consumer spending on retail goods and food services by decomposing spending based on various household attributes such as income, education, generation, and housing status.

While our analysis does not offer evidence for the differences in the spending we observe for low-, middle-, and high-income households, we offer a few hypotheses for why these differences have emerged:

1. Low-income households depleted pandemic-era excess savings earlier as documented by Abdelrahman, Oliveira, and Shapiro (2024).
2. Government support programs such as SNAP emergency allotments, enhanced unemployment insurance, and enhanced child tax credits that disproportionately benefited low-income households expired and no longer provide a boost to these households' spending.
3. High-income households might experience a wealth effect as their homes and investments increase in value, while they also receive more interest and investment income during periods of higher interest rates, all providing a stimulus for sustained levels of spending.

Our analysis and inflation adjustment likely undercounts the extent to which low-income households experienced inflation. Cavallo and Kryvtsov (2024) document that the prices of products low-income households tend to purchase increased at a faster pace. If there is heterogeneity in households' inflation experience, with low-income households experiencing higher inflation, low-income households' spending after the appropriate inflation adjustment would be lower than what our analysis has shown.

References

Cavallo, Alberto and Oleksiy Kryvtsov (2024). "Price Discounts and Cheapflation During the Post-Pandemic Inflation Surge," Journal of Monetary Economics.

Hamza, Abdelrahman, Luiz Edgard Oliveira, and Adam Shapiro (2024). "Pandemic-Era Liquid Wealth Is Running Dry," Federal Reserve Bank of San Francisco Economic Letter.