CREAT: Census Research Exploration and Analysis Tool

The Span of the Effect of R&D in the Firm and Industry

May 1994

Working Paper Number:

CES-94-07

Abstract

Previous studies have found that the firm's own research and spillovers of research by related firms increase firm productivity. In contrast, in this paper we explore the impact of firm R&D on the productivity of its individual plants. We carry out this investigation of within firm R&D effects using a unique set of Census data. The data, which are from the chemicals industry, are a match of plant level productivity and other characteristics with firm level data on R&D of the parent company, cross-classified by location and applied product field. We explore three aspects of the span of effect of the firm's R&D: (i), the degree to which its R&D is "public" across plants; (ii), the extent of its localization in geographic space, and (iii), the breadth of its relevance outside the applied product area in which it is classified. We find that (i), firm R&D acts more like a private input which is strongly amortized by the number of plants in the firm; (ii), firm R&D is geographically localized, and exerts greater influence on productivity when it is conducted nearer to the plant; and (iii), firm R&D in a given applied product area is of limited relevance to plants producing outside that product area. Moreover, we find that while geographic localization remains significant, it diminishes over time. This trend is consistent with the effect of improved telecommunications on increased information flows within organizations. Finally, we consider spillovers of R&D from the rest of industry, finding that the marginal product of industry R&D on plant productivity, though positive and significant, is far smaller than the marginal product of parent firm's R&D.

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.
:
econometrically, analysis, production, econometric, macroeconomic, manufacturing, sale, company, growth, corporate, firms plants, subsidiary, firms census, diversification, innovation, patent, spillover, plant productivity, plants firms, plants industry, productivity firms, geographically, industry concentration, firms productivity, locality, plants industries, industry variation

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.
:
Annual Survey of Manufactures, Longitudinal Research Database, National Science Foundation, Ordinary Least Squares, Total Factor Productivity, National Bureau of Economic Research, Bureau of Economic Analysis, Journal of Political Economy, American Economic Review, Journal of Econometrics, University of Chicago, MIT Press, Michigan Institute for Teaching and Research in Economics

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 Span of the Effect of R&D in the Firm and Industry' are listed below in order of similarity.