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Author:Kalyani, Aakash 

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.
On the Economy

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.
On the Economy

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.
On the Economy

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.
On the Economy

Journal Article
The Innovation Puzzle: Patents and Productivity Growth

Understanding the pace and nature of technological progress is key for evaluating innovation policy and projecting economic growth.
Economic Synopses , Issue 7

Journal Article
Uneven Innovation in the U.S.

The San Jose and San Francisco areas don’t make the U.S. top 10 in terms of population, and they each contain merely 1% of the national workforce. But jointly they produce about 20% of all innovation output.
Economic Synopses , Issue 11 , Pages 2 pages

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.
On the Economy

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 Papers , Paper 2024-022

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 ...
Working Papers , Paper 2025-021

Working Paper
New Technologies and the College Premium

This paper shows that the pace of technology creation is a key driver of wage inequality. It develops a model where college-educated workers learn how to use new technologies faster than others, and where this advantage dissipates over time as technologies become standardized and easier to use. In equilibrium, the college wage premium is determined by the interplay between the pace of technology creation and standardization. A heightened pace of technology creation causes a long-lasting increase in the college premium. We calibrate the model using novel text-based data on new technologies and ...
Working Papers , Paper 2025-022

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