NBER-CES Manufacturing Industry Database

NBER-CES Manufacturing Industry Database is an annual panel of U.S. manufacturing industries, a joint project of the NBER and the U.S. Census Bureau’s Center for Economic Studies (CES). Each row is one industry in one year and carries output, employment, payroll, capital, materials, energy, inventories, price deflators, and total factor productivity. It is maintained by Randy A. Becker, Wayne B. Gray, and Jordan Marvakov (and predecessors). It is free and public; no API key. Used in, for example, Grigoris & Segal, which uses it to validate the link between input price uncertainty and supplier return volatility (an upstream/downstream price-correlation check).

• Cost: free, public.
• API key: none required.
• Coverage: U.S. manufacturing industries, annual. The NAICS-2012 vintage pulled here (nberces5818v1) covers 1958 to 2018. A separate SIC-based vintage also exists.
• Home: https://www.nber.org/research/data/nber-ces-manufacturing-industry-database

Access

Files are served as plain CSV from the NBER data directory with no authentication.

Step 1: download the CSV

# Download the NAICS-2012 vintage (1958-2018), no key
curl -sL -o nberces5818v1_n2012.csv \
  "https://data.nber.org/nberces/nberces5818v1/nberces5818v1_n2012.csv"

The file is about 5 MB. The directory https://data.nber.org/nberces/ lists each vintage and, within a vintage, both the NAICS version and the SIC version plus a data dictionary.

Step 2: load in Python

import pandas as pd

df = pd.read_csv(
    "nberces5818v1_n2012.csv",
    dtype={"naics": str},   # keep industry codes as strings
)
df["year"] = df["year"].astype(int)

Parse naics as a string so industry codes keep their structure and join cleanly to other NAICS-coded data; parse year as an integer.

Gotchas (the ones that bite pipelines)

• NAICS and SIC vintages are not directly comparable. Industry definitions differ between the two bases. Pick one basis and stay on it for a given study; do not mix rows from a NAICS file with rows from a SIC file.
• Industry definitions change across NAICS revisions. A given code can map to a different industry in different vintages or revision years. Cross-walk codes before pooling long panels across revisions.
• Deflators are price indices, not dollars. The pi* series are price indices (deflators); deflate the nominal series yourself. Confirm the base year and the dollar units (the data dictionary describes nominal dollar variables in millions, per the data dictionary) in the documentation rather than assuming.
• Two TFP definitions, each as a rate and a level. TFP comes in a 4-factor variant (dtfp4, tfp4) and a 5-factor variant (dtfp5, tfp5), and each appears both as a growth rate (dtfp*) and as an index level (tfp*). Do not mix the 4-factor and 5-factor definitions, and do not mix the growth rate with the index.
• This vintage ends in 2018. nberces5818v1 is not updated annually, so do not expect recent years. Check the directory for a newer vintage before pinning one.
• naics reads as an integer by default. read_csv will infer it as an integer, which discards any leading zeros and breaks joins to other NAICS-coded data. Parse it as a string as shown above.

Columns

Header row of nberces5818v1_n2012.csv, verbatim:

Column	Meaning
naics	NAICS industry code
year	Year
emp	Employment (thousands)
pay	Total payroll
prode	Production-worker employment
prodh	Production-worker hours
prodw	Production-worker wages
vship	Value of shipments
matcost	Cost of materials
vadd	Value added
invest	Capital investment
invent	End-of-year inventories
energy	Cost of electricity and fuels
cap	Total real capital stock
equip	Real equipment capital
plant	Real structures/plant capital
piship	Price deflator for shipments
pimat	Price deflator for materials
piinv	Price deflator for investment
pien	Price deflator for energy
dtfp5	5-factor TFP growth
tfp5	5-factor TFP index
dtfp4	4-factor TFP growth
tfp4	4-factor TFP index

Nominal dollar variables are in millions of nominal dollars per the data dictionary; confirm units and the deflator base year in the documentation for your vintage.

Citation

Cite the database, the specific vintage and basis, the maintainers, the NBER home URL, and the access date, for example: Becker, Randy A., Wayne B. Gray, and Jordan Marvakov, NBER-CES Manufacturing Industry Database (vintage nberces5818v1, NAICS-2012 basis, 1958-2018), National Bureau of Economic Research, https://www.nber.org/research/data/nber-ces-manufacturing-industry-database, accessed YYYY-MM-DD. For reproducibility, pin the exact vintage filename (nberces5818v1_n2012.csv) so the pull can be repeated.