The Tool Desk
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What card spending data can show
Transaction records can reveal changes in captured purchases sooner than many official statistics. Depending on the dataset, analysts may be able to compare activity across days, industries, or regions. That makes card data useful for detecting a sudden disruption, tracking whether activity appears to recover, or identifying that a local area is behaving differently from a national trend.
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The central advantage is timeliness. A 2019 Federal Reserve paper by Aditya Aladangady, Shifrah Aron-Dine, Wendy Dunn, Laura Feiveson, Paul Lengermann, and Claudia Sahm described anonymized transaction-based daily estimates available within a few days of transactions. The researchers found that national monthly growth patterns in their dataset were similar to Census statistics, and used the estimates to monitor the 2019 government shutdown and measure local spending effects of Hurricanes Harvey and Irma in 2017. Similar growth patterns support the data’s value as a signal; they do not mean the two sources captured identical purchases. Federal Reserve, “The Effect of the 2013 Government Shutdown on Economic Activity: Evidence from Daily Data”.
That speed matters because official measures arrive later and may be revised. It can help analysts follow a short-lived shock or look for differences that a monthly national average would conceal. Federal Reserve Governor Adriana Kugler described credit and debit card transaction data as “a helpful, private-sector tool that economists use to understand consumer behavior in various sectors or regions in close to real time” in a July 16, 2024 speech. Federal Reserve, Kugler speech.
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Consumer spending deserves close attention: a 2019 Federal Reserve paper noted that personal consumption expenditures (PCE) make up nearly 70 percent of U.S. GDP. That is contextual information from the paper, not a newly measured 2026 estimate. Federal Reserve paper.
Why a card-data trend is not the whole economy
The dataset covers only its captured transactions
A card-based measure omits purchases made with cash and other payment channels. It may also represent some merchants or customers better than others. A provider’s transaction totals therefore should not be read as all purchases by households, all retail sales, or the full value of consumption.
The Federal Reserve’s 2026 release of initial 2025 Payments Study results offers useful context about payment methods, but it is not a measure of total consumer spending. It reports 236.6 billion noncash payments in 2024, combining consumer and business payments across noncash methods. Cards accounted for more than three-quarters of noncash payments by number, and debit remained the majority of card payments. Those figures describe payment counts, not spending dollars or the economy as a whole. Federal Reserve Payments Study, 2024 detailed data.
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Payment value tells a different story from payment count. ACH transfers made up almost three-quarters of noncash payment value in 2024, according to the same study. A payment method can dominate by number of transactions without accounting for a comparable share of dollars. Federal Reserve Payments Study, 2024 detailed data.
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Merchant coverage and online sales can be uneven
The Bureau of Economic Analysis (BEA) warned that its experimental Near Real-Time Spending series was not necessarily representative of each industry, even after reweighting. BEA said its underlying card information primarily covered brick-and-mortar merchants and that its representation of e-commerce in the nonstore category was unclear. A reported change in one industry or sales channel may therefore reflect what the provider captured as well as what consumers did. BEA, Near Real-Time Spending.
Merchants leaving the sample can hide declines
BEA’s experimental series relied on merchants that contributed data continuously over a 13-month period. Merchants that stopped contributing entirely were excluded, so their lost sales could not appear in the estimates. In a downturn, that kind of sample attrition could make declines look smaller than they were. BEA, Near Real-Time Spending.
People can change how they pay
If shoppers move from cash to card or other electronic payments, captured card activity can rise even if their total spending does not. The same shift can make a decline in total purchases look less severe in card data. BEA flagged this payment-substitution issue as a limitation of its experimental series. BEA, Near Real-Time Spending.
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Transaction providers do not necessarily have the same merchant mix, customer mix, or sample stability. A 2025 BEA paper evaluated several real-time spending sources against national benchmarks, tested their forecasting ability, and compared geographic trends. It found that combining sources could mitigate source-specific noise or bias and strengthen the signal, while also emphasizing unresolved concerns about representativeness and stability. Combining data is not a guarantee that the result is unbiased. BEA, 2025 paper on real-time spending data.
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Card transactions, retail sales, payments, and PCE are different measures
These terms can overlap in what they capture, but they answer different questions. Before comparing two numbers, identify whether each is counting transactions, measuring payment activity, or estimating the value of goods and services purchased.
| Measure | What it describes | How to interpret it |
|---|---|---|
| Card transaction data | Transactions captured by a particular provider’s dataset. | A potentially timely signal whose coverage depends on the provider’s merchants, customers, and payment channels. |
| Federal Reserve Payments Study | Aggregate trends in noncash payments, based on voluntary surveys of financial institutions, card networks, processors, and issuers. | A periodic benchmark of payment systems—not daily consumer spending or a direct total-spending measure. Federal Reserve Payments Study resources. |
| Retail sales statistics | Official retail-sales estimates, which the 2019 Federal Reserve paper used as a comparison for national monthly growth patterns. | A benchmark for comparison, but similarity in growth rates does not establish identical coverage. Federal Reserve paper. |
| BEA personal consumption expenditures (PCE) | The value of goods and services purchased by or on behalf of U.S. residents. | A broad consumption measure available monthly, quarterly, and annually. More detailed PCE estimates are more likely than higher-level aggregates to rely on judgmental trends or less reliable source data. BEA, consumer spending. |
Do not treat growth in card transactions as interchangeable with growth in retail sales or PCE. Differences may arise from coverage, payment habits, sampling, and the spending categories included. Even official statistics have different scopes: the Payments Study describes noncash payments, while PCE estimates consumption by or on behalf of U.S. residents. Federal Reserve Payments Study resources; BEA, consumer spending.
Also check whether the reported change is in nominal dollars or adjusted for inflation. Those are different analytical questions: a rise in dollar totals does not by itself tell you whether the amount of goods and services purchased increased. Ask what adjustment, if any, was made before comparing a card series with an official statistic. Likewise, compare like with like: national monthly growth is not a direct test of a daily regional series, and a broad category may not match an individual industry.
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When a report says spending rose or fell, ask what was measured and how comparable the underlying data are. This checklist helps distinguish a meaningful signal from a number that may be driven by sample changes or a different definition.
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- Identify the measure. Is it transaction count, nominal dollars, or inflation-adjusted spending? Is the figure about card payments, retail sales, or a broader consumption estimate?
- Check who and what is covered. Ask which merchants and consumers appear in the sample, what payment types are included, and whether online sales are represented. Do not assume that a source captures all businesses or payment channels.
- Look for sample stability. Find out whether merchants or customers can enter or leave the dataset and how the provider handles exits. A change in the sample can resemble a change in spending.
- Match the comparison. Compare the same time frequency, geography, industry, and category. A daily card series and a monthly official statistic are not directly comparable without aligning their periods and definitions.
- Consider revisions and release timing. Check how quickly the figure is published and whether earlier estimates can change. Fast release is valuable, but an initial estimate and a later official benchmark may differ for methodological reasons.
- Cross-check with independent sources. Compare the card signal with official retail statistics, PCE, or other data that measure related activity. Agreement strengthens confidence in the direction of a trend; disagreement is a reason to investigate coverage and definitions rather than automatically choosing one figure.
- Keep geography and detail in perspective. Fine-grained regional or industry figures can reveal variation that national averages miss, but detailed estimates may be less stable or less representative. BEA cautions that even detailed official PCE estimates can rely more heavily on judgmental trends or less reliable sources than higher-level aggregates. BEA, consumer spending.
For a useful benchmark check, compare the card series with the official measure at the same frequency and in the closest matching category or geography available. A broad national match can support using a dataset to follow direction, as in the Federal Reserve researchers’ comparison with Census statistics, but it does not validate every local or industry estimate. If two sources disagree, first ask whether they cover the same merchants, channels, period, and type of spending; the difference may be informative about scope rather than evidence that one number is simply wrong.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What official and experimental data add
Federal Reserve research shows the timeliness advantage
The 2019 Federal Reserve study used anonymized transactions from a large electronic payments technology company to create daily retail-spending estimates for detailed geographies. Their near-real-time availability let the researchers examine specific events, including the 2019 government shutdown and the local effects of Hurricanes Harvey and Irma in 2017. Their comparison with Census statistics found similar national monthly growth patterns, a useful validation of the signal but not evidence that transaction records cover every purchase. Federal Reserve paper.
BEA’s experimental series is no longer produced
BEA’s Near Real-Time Spending estimates combined credit, debit, and gift-card transactions collected by Fiserv and aggregated them to state and national levels. The series adjusted for day of week, month, holidays, and broad annual trends. Its event-study framing compared spending with expected pre-pandemic levels. BEA last updated the product on May 7, 2024, and says it is no longer produced because of budget constraints; its charts should not be described as a current live indicator. BEA also documented the representativeness, merchant-exit, e-commerce, and payment-substitution limitations discussed above. BEA, Near Real-Time Spending.
Multiple sources can improve context, not guarantee certainty
BEA’s 2025 evaluation of several real-time spending sources assessed their relationship to national benchmarks, forecasting potential, and finer geographic trends. The analysis found that combining sources could reduce source-specific noise or bias and strengthen the signal. It did not establish that any particular combined measure is free of bias, so coverage and stability still matter. BEA, 2025 paper on real-time spending data.
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A current official comparison is not a card-spending reading
As of the BEA release dated September 30, 2026, its consumer-spending page showed PCE growth of 0.9% for August 2026 and 0.1% for July 2026. These are official monthly PCE figures, not credit-card spending figures; they should be checked against the latest BEA release before being used as current context. BEA, consumer spending.
Frequently Asked Questions
What do credit card spending numbers show?
They show changes in the transactions captured by a particular provider, potentially broken out by time, industry, or place. They are a useful timely signal, not a count of every purchase or a complete measure of consumer spending.
Are credit card spending data a good measure of the economy?
They can help track short-term changes and local or sector patterns, but they are not sufficient on their own. Cash and other payment channels may be missing, provider coverage can be uneven, and changes in who contributes data or how people pay can affect the trend. Compare them with official statistics and independent sources.
Why might card spending data disagree with official spending figures?
The sources may cover different merchants, payment types, categories, geographies, and time periods. Card activity can also change when people switch payment methods, and provider samples can change. Align definitions and compare at the same frequency and category before interpreting a gap.
The Bottom Line
Credit card data can provide a fast, detailed view of some spending activity, but they cannot establish total consumer spending or the health of the economy by themselves. Treat them as a supplementary signal: check what the dataset includes, compare like with like, and cross-check the trend against official statistics such as retail sales or PCE.
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