This paper uses machine learning (ML) to estimate hedonic price indices at scale from item-level transaction and product characteristics. The procedure uses state-of-the-art approaches from hedonic econometrics and implements them with a neural network ML approach. Applying the methodology to...

Using longitudinal data on the location of mobile devices, we provide new evidence on the evolution of onsite work (OSW) over the course of the pandemic and its aftermath. We start with a large sample of individuals who, based on their mobile device activity, had a job at which they worked...

An important gap in most empirical studies of establishment-level productivity is the limited information about workers' characteristics and their tasks. Skill-adjusted labor input measures have been shown to be important for aggregate productivity measurement. Moreover, the theoretical...

This paper describes the adoption of automation technologies by US firms across all economic sectors by leveraging a new module introduced in the 2019 Annual Business Survey, conducted by the US Census Bureau in partnership with the National Center for Science and Engineering Statistics (NCSES)....

Recent evidence suggests the U.S. business environment is changing, with rising market concentration and markups. The most prominent and extensive evidence backs out firm-level markups from the first-order conditions for variable factors. The markup is identified as the ratio of the variable...

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...