Search Results
AI Hype or Reality? Shifts in Corporate Investment after ChatGPT
An analysis of earnings calls shows a sharp rise in AI-related chatter among U.S. corporate executives. But this increase doesn’t appear to be matched by a similar rise in capital and R&D spending.
Does Worker Scarcity Spur Investment, Automation and Productivity? Evidence from Earnings Calls
An analysis suggests labor issues like higher wages and hiring difficulties have prompted some firms to invest in automation, leading to productivity growth.
AI and Productivity Growth: Evidence from Historical Developments in Other Technologies
An analysis of the diffusion of PCs, smart devices, cloud computing and 3D printing suggests that AI may spread in a pattern similar to those of PCs and cloud computing.
Working Paper
Economic Surveillance using Corporate Text
Full and correct order of authors: Tarek A. Hassan, Stephan Hollander, Aakash Kalyani, Laurence van Lent, Markus Schwedeler, and Ahmed Tahoun. This article applies simple methods from computational linguistics to analyze unstructured corporate texts for economic surveillance. We apply text-as-data approaches to earnings conference call transcripts, patent texts, and job postings to uncover unique insights into how markets and firms respond to economic shocks, such as a nuclear disaster or a geopolitical event---insights that often elude traditional data sources. This method enhances our ...
AI Optimism and Uncertainty: What Can Earnings Calls Tell Us Post-ChatGPT?
An analysis of earnings calls shows that America’s corporate leaders are talking much more about AI. It also reveals an increase in perceived risk in this new technology.
Can Earnings Calls Be Used to Gauge Labor Market Tightness?
An index that uses textual analysis of earnings calls to track labor issues appears to be highly correlated to one measure of labor market tightness.
Working Paper
The Diffusion of New Technologies
CORRECT ORDER OF AUTHORS: Aakash Kalyani, Nicholas Bloom, Marcela Carvalho, Tarek Hassan, Josh Lerner, and Ahmed Tahoun. We identify phrases associated with novel technologies using textual analysis of patents, job postings, and earnings calls, enabling us to identify four stylized facts on the diffusion of jobs relating to new technologies. First, the development of economically impactful new technologies is geographically highly concentrated, more so even than overall patenting: 56% of the most economically impactful technologies come from just two U.S. locations, Silicon Valley and the ...
Working Paper
Economic Surveillance using Corporate Text
FULL AND CORRECT ORDER OF AUTHORS: Tarek A. Hassan, Stephan Hollander, Aakash Kalyani, Laurence van Lent, Markus Schwedeler, and Ahmed Tahoun. This article applies simple methods from computational linguistics to analyze unstructured corporate texts for economic surveillance. We apply text-as-data approaches to earnings conference call transcripts, patent texts, and job postings to uncover unique insights into how markets and firms respond to economic shocks, such as a nuclear disaster or a geopolitical event---insights that often elude traditional data sources. This method enhances our ...
Working Paper
Text as Data in Economic Analysis
FULL AND CORRECT ORDER OF AUTHORS: Tarek A. Hassan, Stephan Hollander, Aakash Kalyani, Laurence van Lent, Markus Schwedeler, and Ahmed Tahoun. This article discusses how to apply computational linguistics techniques to analyze largely unstructured corporate-generated text for economic analysis. As a core example, we illustrate how textual analysis of earnings conference call transcripts can provide insights into how markets and individual firms respond to economic shocks, such as a nuclear disaster or a geopolitical event: insights that often elude traditional non-text data sources. This ...
Working Paper
Theory Meets Textual Analysis: Measuring Firm-Level Labor Cost Pressures and Inflation Pass-Through
We develop a novel measure of firm-level marginal labor cost and investigate its pass-through to inflation. To construct this measure, we apply textual analysis to earnings calls to identify discussions of labor-related topics such as higher costs, shortages, and hiring. Leveraging the theoretical principle that cost-minimizing firms equate marginal costs across variable inputs, we project changes in firms intermediate input revenue shares onto the intensity of labor-related discussions to quantify their contributions to marginal labor costs. This approach provides an economically-motivated ...