CREAT: Census Research Exploration and Analysis Tool

A Portrait of U.S. Factoryless Goods Producers

October 2018

Written by: Fariha Kamal

Working Paper Number:

CES-18-43

Abstract

This paper evaluates the U.S. Census Bureau's most recent data collection efforts to classify business entities that engage in an extreme form of production fragmentation called 'factoryless' goods production. 'Factoryless' goods-producing entities outsource physical transformation activities while retaining ownership of the intellectual property and control of sales to customers. Responses to a special inquiry on the incidence of purchases of contract manufacturing services in combination with data on production inputs and outputs, intellectual property, and international trade is used to identify and document characteristics of 'factoryless' firms in the U.S. economy.

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.
:
production, manufacturing, enterprise, industrial, sale, export, manufacturer, factory, multinational, warehousing, wholesale, firms export, outsourcing, outsourced

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.
:
Census of Manufactures, Center for Economic Studies, Office of Management and Budget, Bureau of Economic Analysis, Company Organization Survey, Foreign Direct Investment, Longitudinal Business Database, Economic Census, Census of Services, Wholesale Trade, North American Industry Classification System, Patent and Trademark Office, Technical Services, Census Bureau Business Register, Business Register, 2010 Census, Business R&D and Innovation Survey, Business Research and Development and Innovation Survey, Annual Survey of Entrepreneurs, Annual Business Survey

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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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