CREAT: Census Research Exploration and Analysis Tool

The Human Factor in Acquisitions: Cross-Industry Labor Mobility and Corporate Diversification

September 2015

Written by: Geoffrey Tate, Liu Yang

Working Paper Number:

CES-15-31

Abstract

Internal labor markets facilitate cross-industry worker reallocation and collaboration, and the resulting benefits are largest when the markets include industries that utilize similar worker skills. We construct a matrix of industry pair-wise human capital transferability using information obtained from more than 11 million job changes. We show that diversifying acquisitions occur more frequently among industry pairs with higher human capital transferability. Such acquisitions result in larger labor productivity gains and are less often undone in subsequent divestitures. Moreover, acquirers retain more high skill workers and they exploit the real option to move workers from the target firm to jobs in other industries inside the merged firm. Overall, our results identify human capital as a source of value from corporate diversification and provide an explanation for seemingly unrelated acquisitions.

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.
:
restructuring, takeover, corporate, merger, acquisition, diversification, diversified, acquirer, workforce, opportunity, prospect, diversify, corp


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 'The Human Factor in Acquisitions: Cross-Industry Labor Mobility and Corporate Diversification' are listed below in order of similarity.