CREAT: Census Research Exploration and Analysis Tool

Investment and Subjective Uncertainty

November 2022

Working Paper Number:

CES-22-52

Abstract

A longstanding challenge in evaluating the impact of uncertainty on investment is obtaining measures of managers' subjective uncertainty. We address this challenge by using a detailed new survey measure of subjective uncertainty collected by the U.S. Census Bureau for approximately 25,000 manufacturing plants. We find three key results. First, investment is strongly and robustly negatively associated with higher uncertainty, with a two standard deviation increase in uncertainty associated with about a 6% reduction in investment. Second, uncertainty is also negatively related to employment growth and overall shipments (sales) growth, which highlights the damaging impact of uncertainty on firm growth. Third, flexible inputs like rental capital and temporary workers show a positive relationship to uncertainty, demonstrating that businesses switch from less flexible to more flexible factor inputs at higher levels of uncertainty.

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, econometric, macroeconomic, manufacturing, earnings, export, financial, finance, recession, stock, invest, risk


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 'Investment and Subjective Uncertainty' are listed below in order of similarity.