Wholesale Trade (NAICS 42) Profile

One profile, the whole American wholesale sector, stitched together from the federal statistical system. Data USA — the project built by Deloitte with Datawheel over Census ACS/PUMS microdata, BLS employment statistics and BEA input-output tables — maintains a sector profile for NAICS 42, and it behaves like a control panel more than a report: headline counters for the workforce (3,428,164 people, average age 44.5), pay ($80,294 average salary, about $10,416 above the national average of $69,878), and outlook (a 3.39% projected job growth over ten years against 30.1% projected output growth).

Below the counters sit five working sections — Employment & Salaries, Occupations, Business, Diversity and Projections — and each renders its charts with the underlying rows reachable directly rather than trapped in an image. Occupation counts led by 643,856 wholesale and manufacturing sales representatives, establishment counts by state (California 56,104, Texas 33,635, Florida 30,998 in 2022), gender, race and education splits: the numbers behind the pictures, which is what makes this a dataset rather than a brochure.

Within Datadory's catalog of 1,744 datasets across 159 viable industries it scores 7/10 on the quality rubric with field definitions verified during research, ranking tenth among the industry's 16 primary records.

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Three annual rows from the workforce slice, flat exactly as the fields arrive:

# Row 1 — the wholesale labor force, start of the verified series
year                 : 2014
industry_sector      : Wholesale Trade
workforce_population : 4788396
average_wage         : 46315.73

# Row 2 — a year later: wages up, headcount flat
year                 : 2015
industry_sector      : Wholesale Trade
workforce_population : 4776310
average_wage         : 47438.63

# Row 3 — same slice, one sparse cell
year                 : 2016
industry_sector      : Wholesale Trade
workforce_population : -
average_wage         : 48978.77

The rows settle the questions buyers ask first. Trajectory: average wages climb from $46,315.73 to $48,978.77 across the three prints while headcount holds near 4.8 million — the sector's recent history is pay growth on a stable base, visible in two columns with no derivation. Sparsity: row three's dash is real, not an error. Aggregated survey slices don't promise every measure in every year, so a production feed scopes which slices and years ship — exactly the decision a sample request pins down before anything recurring starts.

Seven documented columns, definitions verified during research, grouped into three families. Time and classification: Year, the reference year riding on every row; and Industry Sector, the classification level where key 42 denotes wholesale trade. People: Total Population, the workforce estimate drawn from ACS PUMS microdata; Average Wage, the mean annual wage for whoever sits in the slice; and Employees, the industry employee count carried on the BLS employment-statistics side — the payroll-survey view of the same sector. Business: Establishments, the count of business establishments published by state; and Median Earnings, the state-level median printed beside them.

Read together, those seven already answer who works in wholesale trade, what they take home, and how many firms employ them — the skeleton that every richer question hangs on.

The profile page renders far more than seven columns; the extra measures live in sibling slices and ride along at sampling:

• Occupation counts — the full ladder behind the headline: 643,856 wholesale and manufacturing sales representatives, 389,065 firstline supervisors of nonretail sales workers, 271,525 driver/sales workers and truck drivers.
• Demographic splits — gender, race and education cuts, including the series behind the 1.28x male-to-female wage ratio ($85,975 versus $67,256).
• Geographic drilldowns — state, county, metro, place, zip-code, congressional-district and PUMA depths, whichever the chosen slice supports.
• Projection series — the 3.39% ten-year job-growth and 30.1% output-growth trajectories as pullable rows rather than chart annotations.
• Monthly employment — the month-stepped employment series selectable back to 2008.

Ask for any of these with your sample and they land in the same row shape — no second schema, no re-plumbing on your side.

Geography — the United States at several altitudes: nation, states, counties, metropolitan areas, places, zip codes, congressional districts and PUMAs all appear across the collection of slices, so a national briefing and a metro-level siting study draw from the same record.

Temporal — layered rather than single-dated. Headline counters stamp 2024; the monthly employment charts step back to 2008; the verified workforce-wage series runs from 2014; the establishment-by-state counts print 2022. Mixing vintages in one analysis just means labeling each series with its own reference year, and the dictionary makes that cheap because Year travels on every row.

Granularity — aggregated person and business counts, never microdata: one row per slice, keyed by geography, year, industry, occupation, gender, race or education. That aggregation is what keeps the row shape small enough to drop into a warehouse table without a modeling step in between.

API, files, or your warehouse. Daily, weekly, or hourly.

Name the measures and the geography when you request the sample — the full profile, a single section such as Occupations, or one state's establishment counts. The sample ships first either way; the ongoing feed lands on whatever cadence your models and dashboards need, shaped to the scope you named rather than dumped whole.

Seven verified columns and five profile sections earn their keep in six jobs:

• Distribution market sizing — a 3.43-million-person workforce and state-level establishment counts give TAM models denominators nobody argued with in the meeting; see market-sizing use cases.
• Compensation benchmarking — $80,294 against a $69,878 national average, with occupation counts alongside, frames wholesale pay bands before offers go out.
• Workforce and location planning — where California's 56,104, Texas's 33,635 and Florida's 30,998 establishments sit is where hiring plans start.
• Diversity and pay-equity studies — gender, race and education cuts quantify the 1.28x wage gap directly.
• Sector forecasting — the 3.39% jobs and 30.1% output projections become assumption rows in macro models.
• Citation-grade research — every counter traces to a named federal program, which is what keeps a footnote defensible; the pattern is the one behind citation-grade research.

Market Researchers & Consultants get official denominators for wholesale market studies — workforce, establishments and pay in one consistent frame. Data Scientists & ML Engineers get typed rows with Year on everything and verified definitions, so ingestion is configuration. Sales & Growth Teams get territory math before outreach spend: which states hold the establishments, which occupations carry the buying conversations. Investors & Quants get employment, earnings and projections for a cyclical-leading sector as series rather than screenshots. Start from the trading companies & distributors data hub, then read the persona cuts for market researchers, data scientists and sales growth teams.

Provenance note — compiled by Data USA, the Deloitte-and-Datawheel project built over Census ACS/PUMS microdata, BLS employment statistics and BEA input-output tables. One editorial hand across all of them is why a 2024 headline and a 2014 wage print read consistently in the same record.

Classification note — NAICS 42 covers merchant wholesalers plus wholesale electronic markets and agents and brokers. The companion code directory supplies the classification tree itself; this record supplies the people and firms operating inside it, and the merchant wholesalers glossary entry draws the boundary in plain language.

Completeness note — Datadory scores this record 7/10 with field definitions verified during research and three real sample rows shipping with it. The honest caveats are vintage mixing across sections and the occasional sparse cell — both handled explicitly when a sample is scoped.

Where to go next — the flows beside these stocks: the Monthly Wholesale Trade Survey adds monthly sales against inventories; the NAICS 42 code directory lays out the classification; ONS reads the same trade for the UK; and the comparison against WTO Stats takes this profile field by field against a trade-flows record.