CREAT: Census Research Exploration and Analysis Tool

The Industry R&D Survey: Patent Database Link Project

November 2006

Written by: Shihe Fu, William Kerr

Working Paper Number:

CES-06-28

Abstract

This paper details the construction of a firm-year panel dataset combining the NBER Patent Dataset with the Industry R&D Survey conducted by the Census Bureau and National Science Foundation. The developed platform offers an unprecedented view of the R&D-to-patenting innovation process and a close analysis of the strengths and limitations of the Industry R&D Survey. The files are linked through a name-matching algorithm customized for uniting the firm names to which patents are assigned with the firm names in Census Bureau's SSEL business registry. Through the Census Bureau's file structure, this R&D platform can be linked to the operating performances of each firm's establishments, further facilitating innovation-to-productivity studies.

Document Tags and Keywords

Keywords Keywords are automatically generated using KeyBERT, a powerful and innovative keyword extraction tool that utilizes BERT embeddings to ensure high-quality and contextually relevant keywords.

By analyzing the content of working papers, KeyBERT identifies terms and phrases that capture the essence of the text, highlighting the most significant topics and trends. This approach not only enhances searchability but provides connections that go beyond potentially domain-specific author-defined keywords.
:
manufacturing, industrial, company, invention, manufacturer, corporation, corporate, venture, incorporated, innovation, inventory, patent, patenting, trademark, developed, innovative, innovation productivity, corp

Tags Tags are automatically generated using a pretrained language model from spaCy, which excels at several tasks, including entity tagging.

The model is able to label words and phrases by part-of-speech, including "organizations." By filtering for frequent words and phrases labeled as "organizations", papers are identified to contain references to specific institutions, datasets, and other organizations.
:
Metropolitan Statistical Area, Standard Statistical Establishment List, Standard Industrial Classification, Service Annual Survey, Longitudinal Research Database, National Science Foundation, Current Population Survey, Longitudinal Business Database, Chicago Census Research Data Center, Patent and Trademark Office, Business Register, Limited Liability Company

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Doc2Vec is a model that represents entire documents as fixed-length vectors, allowing for the capture of semantic meaning in a way that relates to the context of words within the document. The model learns to associate a unique vector with each document while simultaneously learning word vectors, enabling tasks such as document classification, clustering, and similarity detection by preserving the order and structure of words. The document vectors are compared using cosine similarity/distance to determine the most similar working papers. Papers identified with 🔥 are in the top 20% of similarity.

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