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U.S. retail-sales data is a monthly estimate of sales made by retail and food-service businesses to final consumers. It is one of the fastest recurring indicators of demand for goods, but it is not a complete measure of everything households spend. The Census Bureau’s survey covers selected businesses, adjusts for seasonal patterns, and is revised as better information arrives. To interpret a release correctly, identify its coverage, vintage, adjustment status, industry mix and relationship to broader measures such as personal consumption expenditures (PCE).
What the monthly retail-sales report measures
The Census Bureau’s Monthly Retail Trade and Food Services Survey (MRTS) estimates dollar sales and end-of-month inventories for businesses classified in retail trade and food services. Results are published by North American Industry Classification System (NAICS) industry. Each release has seasonally adjusted and unadjusted series.
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The survey is designed around sales to final consumers. It therefore captures transactions through businesses such as motor-vehicle dealers, grocery stores, department stores, building-material retailers, restaurants and online merchants that fall within the covered industries. It does not attempt to measure every purchase made by a household or every business transaction in the economy.
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Sales and inventories are separate concepts
MRTS asks firms for sales during the reference month and inventories at the end of that month. Sales indicate the value of transactions recorded by the covered businesses. Inventory data help analysts assess whether a change in sales is accompanied by stock building or drawdowns, but inventory is not added to the sales total.
It is a survey estimate, not a store-by-store count
The monthly sample contains roughly 13,000 paid-employee retail businesses. About 2,500 units are selected with certainty because they are among the largest businesses in their industry or size group. The figures describe the survey design; they are not a census of every store, outlet or seller.
As of the April 2025 annual revision, nonemployer businesses were removed from the MRTS time series to align it with the Annual Integrated Economic Survey. That change affects comparisons with older vintages, so long historical runs should be identified by the applicable vintage.
How Census collects and estimates the data
A stratified, one-stage sample
MRTS uses a stratified, one-stage design. Census divides the frame into 85 primary industry strata and then into multiple annual-sales-size strata. Within each industry, the largest firms are selected with certainty. Other firms are selected randomly within their size and industry strata.
Stratification makes the sample more efficient than a simple random draw. Large firms can account for a substantial share of an industry’s sales, so including them with certainty reduces the chance that a major national chain is missed. Randomly selected smaller firms provide information about the rest of the industry.
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Reported, imputed and weighted observations
Businesses provide reported observations when they respond. Census imputes values for eligible units that do not provide a current report, using the survey’s estimation procedures and available information. Reported and imputed observations are then weighted to represent the businesses in their strata.
The resulting weighted totals are benchmarked to the latest annual-survey totals. Benchmarking keeps the monthly path connected to a more comprehensive annual measurement, but it also means historical monthly values can change when annual benchmarks or other inputs are updated.
Keeping the sample current
Business births and deaths are incorporated each quarter. New weights are phased in over three months rather than introduced all at once. This approach helps the estimates reflect a changing retail industry while avoiding an abrupt break caused by a single-period redesign.
When releases arrive and why the first number is not final
Census publishes two related monthly products. The Advance Monthly Retail Trade Survey (MARTS) provides an early estimate of total sales and the month-to-month change. It is released approximately 10 business days after the reference month ends. About one month later, Census revises the advance result using MRTS estimates. The regular MRTS sales and inventory release arrives approximately six weeks after the reference month.
| Feature | Advance MARTS | Regular MRTS |
|---|---|---|
| Typical timing | About 10 business days after the reference month | About six weeks after the reference month |
| Primary purpose | Fast read on total sales and monthly change | More complete sales and inventory estimates by industry |
| Estimator and information | Link-relative estimator based on current-to-previous-month weighted sales ratios; less current detail | Uses MRTS reporting and imputation with the regular survey estimation process |
| Revision risk | Higher, because it is an early estimate | Still subject to later revisions and annual benchmarking |
| Best use | Monitoring turning points when speed matters | Industry analysis and historical comparisons after allowing for later updates |
A responsible news or analytical reference should label the vintage: advance or revised, seasonally adjusted or unadjusted, and the release date. A percentage change copied from an advance release should not be presented later as though it were an unchanged final observation.
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Seasonally adjusted versus unadjusted sales
Retailing has powerful recurring patterns. Holiday shopping, school calendars, weather, pay cycles and the number of trading days can move sales even when underlying demand has not changed. Census uses X-13ARIMA-SEATS with an X-11 filter-based procedure to account for recurring seasonal, holiday and trading-day effects.
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| Series | What is removed or retained | Best question to ask | Main caution |
|---|---|---|---|
| Seasonally adjusted | Recurring seasonal, holiday and trading-day patterns are estimated and removed | Did underlying momentum change from the previous month? | It is an estimate that can change when seasonal factors and history are recalculated |
| Unadjusted | Actual reported seasonal pattern remains | How many dollars were sold in this month, or how does this season compare with another? | A month-to-month move may mostly reflect a normal calendar pattern |
Use the seasonally adjusted series for month-to-month signal detection when the question is underlying momentum. Use unadjusted data for actual dollar levels or for comparisons in which the seasonal pattern is itself relevant. Never mix the two in a chart or calculation without labeling the change.
Does retail sales equal consumer spending?
No. Retail sales are a major input to the national accounts, but they cover a narrower slice of household outlays than total consumer spending.
Coverage is concentrated in retail and food services
Many household purchases are services outside the retail-and-food-services industries. Housing services, health care, financial services, insurance, education, travel services and many other categories are not represented by a retail-sales total. A strong retail report can therefore coexist with weak or strong spending elsewhere in household budgets.
Retail sales are recorded by place of sale
Retail sales record the value of sales made through covered businesses, not where the goods were produced. An imported television sold by a U.S. retailer appears in retail sales just as a domestically produced television does. The Bureau of Economic Analysis (BEA) states that “Personal consumption of goods is based on retail sales data, which do not distinguish between sales of domestically produced goods versus imported goods.”
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That distinction matters for gross domestic product (GDP). GDP’s expenditure identity is C + I + G + X − M: consumption plus investment plus government spending plus exports minus imports. Imported goods can raise measured consumption while the import subtraction prevents their foreign production from being counted as U.S. domestic output.
Retail sales feed into PCE estimates
BEA uses Census monthly and annual retail surveys in its retail-control method. Sales by kind of business help extrapolate many goods components of personal consumption expenditures. PCE, however, combines those goods estimates with a much broader set of service expenditures and other source data. PCE also follows a different release and revision schedule.
| Measure | Coverage | How source data are used | Import treatment and interpretation |
|---|---|---|---|
| MRTS retail sales | Retail trade and food services, primarily goods demand plus food-service sales | Monthly business survey of sales and inventories | Sales by place of sale; does not identify domestic versus imported goods |
| Personal consumption expenditures (PCE) | Goods plus a much broader range of household services | BEA integrates retail data with other administrative, industry and spending sources | Designed for national-accounts consumption; not the same series or vintage as MRTS |
| GDP consumption component | Consumption contribution within domestic production accounting | BEA’s expenditure framework, including imports and exports | Imports are subtracted in GDP even when imported products appear in retail sales |
How to interpret a monthly headline
A single percentage change is a starting point, not a complete consumer-demand diagnosis. Read the release in a fixed sequence.
- Identify the vintage. Record the reference month, release date, and whether the observation is advance or revised.
- Choose the adjustment. Start with seasonally adjusted month-to-month data for momentum; consult unadjusted dollars when seasonal levels matter.
- Check a longer comparison. Pair the monthly move with a year-over-year change. The two views can diverge when a monthly result is affected by a difficult base, calendar timing or a temporary event.
- Inspect the industry mix. Determine whether the total reflects broad-based changes or a few large categories such as motor vehicles, gasoline, grocery stores or restaurants.
- Consider prices. Retail sales are dollar values. A higher total can reflect higher prices, more units sold, or both. Use appropriate price measures before claiming that households bought more physical volume.
- Review revisions and uncertainty. Compare the current estimate with the prior vintage and consider the survey’s stated margin of sampling error and known nonsampling risks.
- Separate retail demand from total consumption. Use PCE or other national-accounts measures when the question includes services outside retail and food services.
Why category composition changes the story
Two months can have the same total growth while implying different conditions. A gain concentrated in gasoline retailers may primarily reflect fuel prices. A gain concentrated in restaurants says something different from a gain in furniture or electronics. Census publishes industry detail so users can examine these contributions instead of treating the headline as a complete account of household behavior.
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Revisions are a normal feature of a sample survey, not evidence that the original release was deliberately misleading.
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- Advance-to-regular update: The early MARTS estimate is replaced about a month later with information from MRTS.
- Late or imputed reports: A business that did not report in the first processing cycle may provide data later, reducing reliance on imputation.
- Benchmarking: Monthly estimates are aligned with the latest annual-survey totals.
- Business turnover: Quarterly updates for births, deaths and phased-in weights alter the represented business population.
- Seasonal-factor updates: Re-estimation of seasonal, holiday and trading-day patterns can change adjusted history.
- Methodological changes: The April 2025 annual revision removed nonemployers from the series to align with the Annual Integrated Economic Survey.
When evaluating a trend, preserve the vintage used in the original analysis and state when a later release changes it. A historical chart assembled from different vintages can show artificial breaks if its source status is not documented.
How accurate is the estimate?
Census identifies both sampling and nonsampling error. Sampling error exists because the survey measures a sample rather than every business. Census describes its margin of sampling error as a 90 percent confidence interval; a small change may therefore be statistically indistinguishable from no change.
Nonsampling risks can arise from nonresponse, overcoverage, undercoverage, misreporting, recording or coding mistakes, misunderstanding of a question and processing errors. These risks are not summarized by the sampling interval alone.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsPractical safeguards include reporting the adjustment status, avoiding false precision, looking at category detail and checking whether a move survives later revisions. The confidence interval is not a guarantee that the true value lies within a fixed range; it describes the survey’s sampling uncertainty under the stated statistical framework.
A practical workflow for analysts, reporters and readers
- Save the release metadata. Note the reference month, release date, series name, seasonal status and whether it is MARTS or MRTS.
- Read the total and its categories. Flag unusually large movements and identify which industries account for them.
- Compare month-to-month and year-over-year views. Use the first for momentum and the second for a broader base comparison.
- Check dollar versus volume interpretation. If prices moved sharply, do not describe a nominal increase as a proportional increase in quantities purchased.
- Place the result beside PCE when the question is total household spending. Explain that services and national-accounts adjustments make PCE broader.
- Revisit the number after the next release. Keep a revision log if the result informs a forecast, investment decision or policy brief.
Bottom line for consumer-spending questions
Retail sales provide a timely, industry-detailed view of demand recorded by U.S. retail and food-service businesses. The estimate comes from a stratified sample, weighted and benchmarked to annual information, with seasonal adjustment and regular revisions. It is especially useful for tracking goods demand and near-term momentum. It should not be treated as a complete household-spending measure: services outside the covered industries are missing, dollar sales include price effects, and imported goods appear in sales even though GDP subtracts imports. The most defensible reading names the release vintage, uses the appropriate seasonal series, checks category concentration and uncertainty, and turns to PCE for the broader consumption picture.
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