Industry employment trend

Get a multi-year monthly employment time series for one NAICS industry. The example uses NAICS 3254 — Pharmaceutical and Medicine Manufacturing because it has dense coverage and tracks a sector with visible macro dynamics.

Problem

You want to plot employment and wages for a single industry, month by month, across several years. The BLS CES survey publishes this directly, and Thesma exposes it without the CSV-download dance.

curl — 5-year monthly series

Use from_date and to_date (YYYY-MM) to bound the window:

curl -H "X-API-Key: $THESMA_API_KEY" \
  "https://api.thesma.dev/v1/us/bls/industries/3254/employment?from_date=2020-01&to_date=2025-12" \
  | jq '.data[] | {period, employment, avg_hourly_earnings}'

Python SDK

from thesma import ThesmaClient

client = ThesmaClient()

trend = client.bls.industries("3254").employment(
    from_date="2020-01",
    to_date="2025-12",
)

for row in trend.data:
    print(f"{row.period}  employment={row.employment:,}  wages=${row.avg_hourly_earnings:.2f}/hr")

Expected response shape

{
  "data": [
    {
      "period": "2020-01",
      "employment": 173100,
      "employment_yoy": 0.021,
      "avg_hourly_earnings": 37.42,
      "earnings_yoy": 0.034,
      "avg_weekly_hours": 41.2,
      "adjustment": "SA",
      "preliminary": false
    },
    {
      "period": "2020-02",
      "employment": 173400,
      "employment_yoy": 0.022,
      "avg_hourly_earnings": 37.55,
      "earnings_yoy": 0.036,
      "avg_weekly_hours": 41.1,
      "adjustment": "SA",
      "preliminary": false
    }
    /* ... 70 more monthly rows through 2025-12 ... */
  ],
  "pagination": {
    "page": 1,
    "per_page": 72,
    "total": 72,
    "total_pages": 1
  },
  "metadata": {
    "naics": "3254",
    "naics_title": "Pharmaceutical and Medicine Manufacturing",
    "source": "BLS CES",
    "adjustment_default": "SA"
  }
}

Field types are stable; monthly counts drift as BLS publishes revisions to preliminary months.

NAICS codes with dense coverage

Good targets for industry examples:

NAICS	Industry
`3254`	Pharmaceutical and Medicine Manufacturing
`3344`	Semiconductor and Other Electronic Component Manufacturing
`5112`	Software Publishers
`5415`	Computer Systems Design and Related Services
`5221`	Depository Credit Intermediation
`6211`	Offices of Physicians
`7225`	Restaurants and Other Eating Places

Avoid very granular 6-digit NAICS for monthly CES series — CES is published at aggregate NAICS levels (typically 3-4 digit). 6-digit granularity is available in QCEW (quarterly) but not CES.

Gotchas

SIC vs NAICS. BLS CES uses NAICS. If you have a SIC code (e.g., SIC 2834 for pharmaceutical preparations), map it to the corresponding NAICS (3254 is the closest pharma equivalent). The BLS dataset page has the endpoint catalogue.
Seasonal adjustment default. CES values default to seasonally-adjusted (adjustment: "SA"). For NSA series, pass adjustment=NSA explicitly. Don’t mix SA and NSA in the same chart.
Preliminary revisions. The most-recent two or three months often have preliminary: true. Values get revised in subsequent releases — re-pull if you need final-revised data.
YoY deltas are computed server-side. employment_yoy compares this month to the same month a year prior. It’s null for the first 12 months of a new NAICS code’s coverage history.