Does Public Ownership and Accountability Increase Diversity?: Evidence From IPOs

Koning, Rembrand; Ferguson, John‐Paul

doi:10.2139/ssrn.3316178

Cited by 1 publication

(1 citation statement)

References 70 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The underlying micro-data, at the firm level, are confidential but available to BLS-approved researchers, and the OES micro-data have been used to study occupational differences across firms by Ayyagari and Maksimovic (2017), Handwerker (2020) and Ma, Ouimet, and Simintzi (2019). As another example, data from the Equal Employment Opportunity Commission (EEOC) allow researchers to study occupational-level details within firms (e.g., Ferguson & Koning, 2018;Koning & Ferguson, 2019;Tomaskovic-Devey et al, 2016). Finally, the Burning Glass Technologies (BG) data that we use in one of our validation exercises can be used to study occupational differences across firms.…”

Section: Competitive and Corporate Strategymentioning

confidence: 99%

Occupational, industry, and geographic exposure to artificial intelligence: A novel dataset and its potential uses

Felten

Raj

Seamans

2021

Strategic Management Journal

118

View full text Add to dashboard Cite

We create and validate a new measure of an occupation's exposure to AI that we call the AI Occupational Exposure (AIOE). We use the AIOE to construct a measure of AI exposure at the industry level, which we call the AI Industry Exposure (AIIE) and a measure of AI exposure at the county level, which we call the AI Geographic Exposure (AIGE). We also describe several ways in which the AIOE can be used to create firm level measures of AI exposure. We validate the measures and describe how they can be used in different applications by management, organization and strategy scholars. Managerial Summary: Although artificial intelligence (AI) promises to spur economic growth, there is widespread concern that it could displace workers, alter industry trajectories, and reshape organizations. Despite the interest in this area, we have limited ability to study the effects of AI on occupations, firms, industries, and geographies because of limited availability of data that measures exposure to AI. To address this limitation, we create and validate a new measure of an occupation's exposure to AI that we call the AI Occupational Exposure (AIOE). We use the AIOE to construct a measure of AI exposure at the industry level (AIIE)

show abstract