CREAT: Census Research Exploration and Analysis Tool

Mergers and Acquisitions and Productivity in the U.S. Meat Products Industries: Evidence from the Micro Data

March 2002

Working Paper Number:

CES-02-07

Abstract

This paper investigates the motives for mergers and acquisitions in the U.S. meat products industry from1977-92. Results show that acquired meat and poultry plants were highly productive before mergers, and that meat plants significantly improved productivity growth in the post-merger periods, but poultry plants did not.

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, sale, product, merger, acquisition, produce, acquirer, agriculture, agricultural, profit, competitor, mergers acquisitions, meat

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, Longitudinal Research Database, Total Factor Productivity, Department of Agriculture

Similar Working Papers Similarity between working papers are determined by an unsupervised neural network model know as Doc2Vec.

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.

The 10 most similar working papers to the working paper 'Mergers and Acquisitions and Productivity in the U.S. Meat Products Industries: Evidence from the Micro Data' are listed below in order of similarity.