CREAT: Census Research Exploration and Analysis Tool

LBOs, Debt And R&D Intensity

February 1993

Working Paper Number:

CES-93-03

Abstract

This paper details the impact of debt on R&D intensity for firms undergoing a leveraged buyout (LBO). We develop seven hypotheses based on capital market imperfection theories and agency theory. To test these hypotheses, we compare 72 R&D performing LBOs with 3329 non-LBO control observations and 126 LBOs with little or no R&D expenditures. The regressions yield four statistically significant major findings. First, pre-LBO R&D intensity is roughly one-half of the overall manufacturing mean and two-thirds of the firm's industry mean. Second, LBOs cause R&D intensity to drop by 40 percent. Third, large firms tend to have smaller LBO- related declines in R&D intensity. Fourth, R&D intensive LBOs outperform both their non-LBO industry peers and other LBOs without R&D expenditures.

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.
:
investment, sale, quarterly, takeover, acquisition, financial, investing, finance, financing, leverage, shareholder, expenditure, depreciation, stock, debt, equity, contract, spending, liquidation

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.
:
National Science Foundation, Standard Industrial Classification

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 'LBOs, Debt And R&D Intensity' are listed below in order of similarity.