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    • Measurement and Effects of Supply Chain Bottlenecks Using Natural Language Processing

    Metadata Updated: December 18, 2024

    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.

    Access & Use Information

    Public: This dataset is intended for public access and use. License: No license information was provided. If this work was prepared by an officer or employee of the United States government as part of that person's official duties it is considered a U.S. Government Work.

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    Dates

    Metadata Created Date December 18, 2024
    Metadata Updated Date December 18, 2024

    Metadata Source

    Harvested from Federal Reserve

    Other Data Resources

    Additional Metadata

    Resource Type Dataset
    Metadata Created Date December 18, 2024
    Metadata Updated Date December 18, 2024
    Publisher Board of Governors of the Federal Reserve System
    Maintainer
    Identifier FRBC0001
    Data Last Modified 2024-10-24
    Public Access Level public
    Bureau Code 920:00
    Schema Version https://project-open-data.cio.gov/v1.1/schema
    Harvest Object Id e3ee4662-07b5-4ebb-8954-2efda70caa79
    Harvest Source Id ed4f9073-f069-4049-8ee0-17ff5c98cd46
    Harvest Source Title Federal Reserve
    Homepage URL https://www.federalreserve.gov/econres/notes/feds-notes/measurement-and-effects-of-supply-chain-bottlenecks-using-natural-language-processing-20230206.html
    Program Code 920:000
    Source Datajson Identifier True
    Source Hash d3a60e7b27bf50bb664c39f85ab8549328410e192541e1834a5928fab7e106f4
    Source Schema Version 1.1

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