NETS: National Establishment Time Series (licensed)

Licensed: not exercised here

NETS is a licensed commercial dataset (Walls & Associates, built from Dun & Bradstreet source data), so it carries no provenance badge: the access path below was not run in this session (no Walls & Associates credentials were available). The page documents the access route and the gotchas; treat it as unverified until someone exercises it through a licensed account. This is the honest grade under the institute’s Verified discipline.

NETS (the National Establishment Time Series) is an establishment-level panel built by Walls & Associates from annual snapshots of the Dun & Bradstreet (D&B) business database. Each establishment carries a location (geocoded to county and census tract), industry code, employment, sales, and ownership and headquarters links tracked over time, which lets researchers follow establishment births, deaths, relocations, and ownership changes. It is a common source in establishment-level research: used in, for example Barkai & Panageas (establishment-level employment and ownership/acquirer-age changes, 1998 to 2014, 213,792 acquisitions), and Kruttli, Roth Tran & Watugala (firm establishment locations by county, annual, to construct a hurricane landfall-region exposure measure).

• Cost: licensed, purchased extract. No free tier or standard academic subscription path.
• Vendor: Walls & Associates (from Dun & Bradstreet source data).
• Coverage: US establishments annually from the early 1990s; tens of millions of establishments per vintage.

Access (when licensed)

• Directly from Walls & Associates. NETS is purchased as a dataset extract, often as a one-time vintage. Contact Walls & Associates for current pricing and extraction options.
• No standard WRDS path. Unlike many licensed academic datasets, NETS does not flow through WRDS; the purchase is a direct arrangement with the vendor.
• A license agreement is required. Keep any credentials or delivery tokens in .env, never hard-coded.

Gotchas (the ones that bite pipelines)

These are the failure modes to expect; they are documented, not verified here.

• Sales and employment are often imputed, not reported. Walls & Associates derives establishment-level sales and employment from D&B industry-level ratios when the underlying D&B record lacks a reported figure. Establishment- level sales growth cannot be measured cleanly and level comparisons are noisy; Barkai & Panageas flag exactly this limitation.
• D&B updates lag and carry stale records. Establishment births and deaths are measured with delay and error because D&B does not instantly remove defunct entities or add new ones; treat event timing as approximate.
• Periodic snapshot stitched into a time series. Within-establishment changes over time are more reliable than cross-section levels in any given year; design tests accordingly.
• DUNS and ownership links change with corporate restructurings. The establishment identifier (DUNS) and headquarters/parent ownership links are point-in-time; treat them as such rather than as stable longitudinal keys.
• Geocoding precision varies by vintage. County-level assignment is generally stable; tract-level precision degrades in older vintages and for less-populous areas.
• Coverage and accuracy are debated. The literature has noted discrepancies relative to County Business Patterns (CBP) and the Quarterly Census of Employment and Wages (QCEW), particularly for aggregate employment counts. Benchmark NETS figures against CBP or QCEW before drawing aggregate conclusions.

Citation

Cite NETS by vendor and source, stating the vintage, whether employment and sales figures are reported or imputed, and how establishment births and deaths were defined in your sample: e.g., NETS (National Establishment Time Series), Walls & Associates, from Dun & Bradstreet source data, vintage YYYY; employment figures are largely imputed from industry ratios. Report the DUNS-based identifier scheme and any ownership-link vintages used.