Measurement and Effects of Supply Chain Bottlenecks Using Natural Language Processing
The Supply Chain Bottleneck Sentiment (SCB Sentiment) index measures supply chain bottlenecks by analyzing narratives from the Federal Reserve's Beige Books using machine learning and natural language processing techniques. The Beige Book summarizes the economic condition of each of the twelve Federal Reserve districts and aggregates narratives that are collected from business contacts.
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Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| bureauCode |
[ "920:00" ] |
| contactPoint |
{ "fn": "Katherine Tom", "hasEmail": "mailto:ogda-data@frb.gov" } |
| description | The Supply Chain Bottleneck Sentiment (SCB Sentiment) index measures supply chain bottlenecks by analyzing narratives from the Federal Reserve's Beige Books using machine learning and natural language processing techniques. The Beige Book summarizes the economic condition of each of the twelve Federal Reserve districts and aggregates narratives that are collected from business contacts. |
| identifier | FRBC0001 |
| keyword |
[ "Data resource", "FRS research data", "Macroeconomic activity", "Manufacturing", "Sentiment" ] |
| landingPage | https://www.federalreserve.gov/econres/notes/feds-notes/measurement-and-effects-of-supply-chain-bottlenecks-using-natural-language-processing-20230206.html |
| modified | 2025-10-16 |
| programCode |
[ "920:000" ] |
| publisher |
{ "name": "Board of Governors of the Federal Reserve System" } |
| title | Measurement and Effects of Supply Chain Bottlenecks Using Natural Language Processing |
