We develop measures of labor-saving and labor-augmenting technology exposure using textual analysis of patents and job tasks. Using US administrative data, we show that both measures negatively predict earnings growth of individual incumbent workers. While labor-saving technologies predict...

We study the early adoption and diffusion of five AI-related technologies (automated-guided vehicles, machine learning, machine vision, natural language processing, and voice recognition) as documented in the 2018 Annual Business Survey of 850,000 firms across the United States. We find that...

We introduce a new survey module intended to complement and expand research on the causes and consequences of advanced technology adoption. The 2018 Annual Business Survey (ABS), conducted by the Census Bureau in partnership with the National Center for Science and Engineering Statistics...

We construct new technology indicators using textual analysis of patent documents and occupation task descriptions that span almost two centuries (1850-2010). At the industry level, improvements in technology are associated with higher labor productivity but a decline in the labor share....

We address three core questions about the hypothesized role of newly emerging job categories ('new work') in counterbalancing the erosive effect of task-displacing automation on labor demand: what is the substantive content of new work; where does it come from; and what effect does it have on...

Timely and accurate measurement of AI use by firms is both challenging and crucial for understanding the impacts of AI on the U.S. economy. We provide new, real-time estimates of current and expected future use of AI for business purposes based on the Business Trends and Outlook Survey for...