CREAT: Census Research Exploration and Analysis Tool

Gross Job Creation and Destruction: Microeconomic Evidence and Macroeconomic Implications

September 1990

Working Paper Number:

CES-90-10

Abstract

This paper investigates the connection between the heterogeneity of establishment-level employment changes and aggregate fluctuations at business cycle frequencies. The empirical work exploits a rich data set with approximately 860,000 annual observations and 3.4 million quarterly observations on 160,000 manufacturing establishments to calculate rates of gross job creation, gross job destruction, and their sum, gross job reallocation. The central messages that emerge from the research in this paper are: (1) Establishment-level employment changes exhibit tremendous heterogeneity, even within narrowly defined sectors of the economy. This heterogeneity manifests itself in terms of high rates of gross job creation, destruction, and reallocation. Further, the magnitude of this heterogeneity varies significantly over time, most of the variation is due to time variation in the idiosyncratic component of establishment growth rates, and the variation is significantly countercyclical. (2) The theoretical model of employment reallocation and business cycles is suggestive of how both aggregate and allocative disturbances can drive fluctuations in job creation, job destruction, unemployment, productivity, and output. (3) The empirical analysis of the joint dynamics of job creation and job destruction supports the view that allocative disturbances were a major driving force behind movements in jobs creation, job destruction, job reallocation and net employment growth in the U.S. manufacturing sector during the 1972 to 1986 period.

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.
:

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.
:

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 'Gross Job Creation and Destruction: Microeconomic Evidence and Macroeconomic Implications' are listed below in order of similarity.