Food Distributors Data for Investors & Quant Researchers

Cadence is the binding constraint: no record in this slice beats monthly except a product-listing scanner, so everything below behaves like a benchmark input rather than an alpha feed.

Three of these five records form a coherent margin-and-volume panel. The USDA ERS Food Dollar Series tracks farm-versus-retail margin shares annually, which maps into food inflation views and packaged-food margin calls: when the farm share compresses, downstream processors and distributors capture more of each dollar. U.S. Economic Census - Wholesale Trade (NAICS 424) sets the denominator - wholesale trade totals for NAICS 424, refreshed on the five-year census cycle - so growth rates you model for distributors get checked against an economy-wide benchmark rather than a vendor's estimate. The IFDA library adds the only monthly pulse in the slice, reading the US foodservice distribution economy to time positions in distributors and restaurant suppliers ahead of quarterly prints.

The remaining two serve adjacent workflows. AIES validates end-market sizes and expense structures when you diligence a private wholesaler with no filings to lean on, and RangeMe turns daily new-brand listings into a qualitative formation signal for CPG pipelines feeding those same distributors. Two caveats belong in any factor spec built here: the census-based series are restated benchmarks, not point-in-time observations, and coverage follows classification codes - a distributor that reclassifies out of NAICS 424 drops out of the panel without ever leaving the market.

The long runs belong to the government series. NAICS 424 wholesale trade benchmarks go back across successive five-yearly economic censuses and are static between releases; the Food Dollar Series publishes annually; AIES continues the former AWTS on an annual cycle. IFDA's monthly foodservice distribution readings are the timeliest funded series here, and RangeMe's listing index is the only daily record - but it carries no financials, so it cannot enter a numeric backtest at all.

Point-in-time discipline matters more than usual because everything quantitative is revised - treat the census and survey vintages as restated history, and date-stamp any observation you carry into a factor file.

Not in this industry slice. It holds no satellite imagery, card-panel, web-traffic or location feed - the alternative-data categories investors usually pair with consumer and logistics names sit outside food distribution's qualifying set. The nearest high-frequency proxy is RangeMe's daily new-brand listing scan, which is directional and unpriced rather than a measurable demand series.