This article provides an overview of the methods used for algorithmic text analysis in
economics, with a focus on three key contributions. First, we introduce methods for …

The advent of large-scale datasets that trace the workings of science has encouraged
researchers from many different disciplinary backgrounds to turn scientific methods into …

We develop a method that identifies the attention paid by earnings call participants to firms'
climate change exposures. The method adapts a machine learning keyword discovery …

We answer three core questions about the hypothesized role of newly emerging job
categories (“new work”) in counterbalancing the erosive effect of task-displacing automation …

What are the effects of recent advances in Generative AI on the value of firms? Our study
offers a quantitative answer to this question for US publicly traded companies based on the …

An ever-increasing share of human interaction, communication, and culture is recorded as
digital text. We provide an introduction to the use of text as an input to economic research …

We collect data on the size distribution of US businesses for 100 years, and use these data
to estimate the concentration of production (eg, asset share or sales share of top …

The covid‐19 pandemic induced a major shift in the prevalence of remote and hybrid work
arrangements. This review article studies the effects of this remote work revolution for …

This review presents machine learning (ML) approaches from an applied economist's
perspective. We first introduce the key ML methods drawing connections to econometric …

In this paper, we address the question of 'Why do firms publish scientific papers?'. Research
examining the competitive advantages that firms accrue from investment in R&D has …