Apparel, Accessories & Luxury Goods Data: Macro Series, ML Corpora and Live Commerce Intelligence · Head-to-head
World Bank DataBank & API - World Development Indicators vs BLS Consumer Price Index - Apparel (CUSR0000SAA) Timeseries
Which apparel, accessories & luxury goods data: macro series, ml corpora and live commerce intelligence data fits your job: World Bank DataBank & API - World Development Indicators, or BLS Consumer Price Index - Apparel Timeseries. API, files, or your warehouse. Daily, weekly, or hourly.
World Bank DataBank & API - World Development Indicators
BLS Consumer Price Index - Apparel (CUSR0000SAA) Timeseries
Coverage, side by side
| World Bank DataBank & API - World Development Indicators | BLS Consumer Price Index - Apparel Timeseries | |
|---|---|---|
| Geographic | 265 economies, including countries and World Bank aggregates | United States, U.S. city average (national level) |
| Temporal | 66 time periods; annual series generally reach back to 1960 | January 1947 to the current month |
| Granularity | One observation: economy x indicator x period | One observation: a calendar month of a single national index |
What each contains
Pick by fit, not by loyalty.
| World Bank DataBank & API - World Development Indicators | BLS Consumer Price Index - Apparel Timeseries | |
|---|---|---|
| Publisher | World Bank | U.S. Bureau of Labor Statistics |
| Subject lens | Country-level development statistics with an apparel entry point: textiles and clothing value added, trade, consumption and GDP | Consumer price inflation for apparel, U.S. city average, seasonally adjusted |
| Unit of analysis | One observation: economy x indicator x period | One observation: a calendar month of a single national index |
| Geographic coverage | 265 economies, including countries and World Bank aggregates | United States, U.S. city average (national level) |
| Temporal reach | 66 time periods; annual series generally reach back to 1960 | January 1947 to the current month |
| Scale | 1,498 indicators x 265 economies; the complete extract runs tens of megabytes compressed | ~950 monthly observations per series; dozens of related apparel sub-series under adjacent IDs |
| Documented fields | 10 | 8 |
| Datadory rubric | 9/10, field definitions verified | 8/10, field definitions verified |
| Best for | Cross-country sizing, sourcing screens, trade and demand context on a shared country-year grain | U.S. apparel price levels, deflators and margin analysis on a monthly cadence |
What each does better
World Bank DataBank & API - World Development Indicators
Cross-economy breadth no single-country series can match. 265 economies, countries and World Bank aggregates alike, across 66 time periods with annual series generally reaching back to 1960 - an economy x indicator x year grid you can group by region, income tier or sourcing footprint.
An apparel-specific manufacturing lens. NV.MNF.TXTL.ZS.UN tracks textiles and clothing as a share of manufacturing value added under ISIC revision 3 divisions 17-19 - manufacture of textiles, apparel, dyeing of fur and tanning of leather - so the series measures the production side of the industry, not just its retail echo. The 2022 sample cut shows how a row reads: indicator, economy, ISO3 code, year, value.
Demand and trade context wired in around it. Merchandise exports and imports in current US dollars (sample row: TX.VAL.MRCH.CD.WT, Africa Eastern and Southern, 2022 = 273,195,683,896.1, with Africa Western and Central at 169,725,000,000.0), alongside household final consumption expenditure, GDP and per-capita measures - all sharing one country-year grain.
Scale that survives slicing. 1,498 indicators x 265 economies, with the complete extract running tens of megabytes compressed - small enough to hold the whole grid in memory, large enough to drown a spreadsheet habit.
BLS Consumer Price Index - Apparel Timeseries
Depth on exactly one market. Roughly 950 monthly observations per series since January 1947 - the sample cut runs from 38.4 in January 1947 to 136.434 in July 2026, with June 2026 at 136.313. Decade-scale apparel inflation history without a single imputed month of guesswork.
Seasonal adjustment baked into the headline. The R in CU-R-0000-SAA decodes to seasonally adjusted, so deflator and margin work can use the series directly instead of re-adjusting a raw index and defending the method later.
A calendar-shaped dictionary. Year, twelve monthly columns (M01-M12), HALF1/HALF2 half-year aggregates and an annual-average column sit beside a documented Base Period field (1982-84=100) - the table's shape matches how pricing teams already think, not how statisticians store things.
Honest gaps, flagged per observation. October 2025 carries footnote code X - data unavailable due to the 2025 lapse in appropriations - and dozens of adjacent apparel sub-series, footwear included, live under nearby IDs when the category needs splitting.
Where they're equivalent
Same shelf, same grade. Both are primary members of the apparel, accessories & luxury goods slice, both clear the catalog's 7.81 average rubric score, and both passed field-definition verification in the same August 2026 research pass.
A shared observation spine. Every record on either side resolves to place x period x value - countryiso3code, date and value on the WDI side; U.S. city average, year-month and index value on the CPI side. That spine is what makes the two stackable rather than competing, and it is where a join gets decided.
Documented examples on every field. Neither dictionary asks you to guess a format: WDI documents its observation-status and decimal-hint conventions, CUSR0000SAA documents its footnote codes, and sample rows ship with each dataset for validation before anything ships.
Long memories. Annuals reaching to 1960 on one side, monthlies to 1947 on the other - either one carries a multi-decade backtest without patchwork splicing.
The verdict
Verdict: sample both, pick by fit - the variable in your model decides.
Take WDI when geography is the variable: screening sourcing markets, sizing demand across regions, comparing textiles-and-clothing value-added shares between candidate countries, or anchoring an expansion case in consumption and trade totals.
Three quick tests settle most cases. Need hundreds of economies? Only WDI has them. Need a monthly U.S. number? Only CUSR0000SAA publishes one. Need global demand valued in dollars that hold their purchasing power? That is the both-of-them case, and in apparel planning it is the common one.
Sample both, pick by fit. See World Bank DataBank & API - World Development Indicators · See BLS Consumer Price Index - Apparel Timeseries
Or take both in one feed
A market-entry team can rank candidate economies on textiles-and-clothing value added, merchandise trade and household consumption, then deflate revenue plans with the U.S. apparel index; a merchandising team can run the same index against its own sell-through to separate category inflation from real growth.
Two practical notes from the records. Mind the grain - WDI observes economy x indicator x year while CUSR0000SAA observes months, so aggregate the monthly index to annual averages before joining on year, and remember the two values live on different scales: percent-of-value-added shares versus index points on a 1982-84=100 base.
Or take both in one feed. Datadory normalizes each record to its documented dictionary, attaches sample rows for validation, and ships them beside the rest of the apparel, accessories & luxury goods catalog - delivered daily, weekly, or hourly, your call.
API, files, or your warehouse. Daily, weekly, or hourly.
Fair questions
What do sample rows from the two datasets look like?
WDI rows read economy x indicator x year: TX.VAL.MRCH.CD.WT, Africa Eastern and Southern, 2022 = 273,195,683,896.1. CUSR0000SAA rows read calendar and index: July 2026 = 136.434, June 2026 = 136.313, January 1947 = 38.4 - and October 2025 shows a dash with an X footnote marking the unavailable month.
How current can the delivered apparel data be?
As current as your project needs: Datadory delivers either dataset daily, weekly, or hourly - your call. Content-wise, the CUSR0000SAA sample cut runs through July 2026, while WDI's grid extends across 265 economies with the most recent periods subject to each indicator's own reporting cycle.
Can Datadory deliver both datasets together?
Yes. Sample both and pick by fit, or take both in one feed - normalized to their documented dictionaries (10 fields on the WDI side, 8 on the CPI side), aligned on the year grain you choose, and validated against sample rows before anything ships.