Kim Elsesser

Senior Contributor

AS SEEN ON

Preston's Summary

Kim Elsesser is a Senior Contributor at Forbes. A gender bias expert, she investigates critical issues surrounding the gender pay gap and women's representation in the workplace, drawing from her experience as an author and UCLA lecturer. Kim's insights have also been featured in the New York Times, Los Angeles Times, and she was recognized as one of CNN’s Most Intriguing People.

Preston is the artificial intelligence that powers the Intelligent Relations PR platform. Meet Preston

Geo Focus

Coverage Attributes:

Beta
Cites Data: 59 %
Government Announcement: 13 %
Legal Policy Regulation: 11 %
Breaking News: 2 %
Evolving Stories: 2 %

Themes Covered:

Not enough data icon

Not enough data

Most Recent Topics:

  • Campus Life
  • College & University
  • University Research
  • Corporate Finance
  • Corporate Sustainability
  • Corporations
  • Entrepreneurship
  • Incubators & Accelerators
  • Startups
  • Career Pathing
  • Professional Training & Development
  • Upskilling

Pitching Insights

Kim Elsesser's articles focus on gender equality, workplace equality and research findings related to these topics. Her coverage attributes indicate a reliance on data and government announcements.

When reaching out to Kim, consider providing access to relevant data or newsworthy government announcements that pertain to gender equality, women's rights, and workplace fairness. Additionally, if you have insights from credible research studies in these areas or can provide expert commentary based on substantial evidence, it may be of interest to her.

As Kim covers a wide range of themes within politics, sports, human resources & employment which are centered around the topic of gender equality and women's rights. Consider tailoring pitches towards providing unique perspectives within the broader context of these themes.

This information evolves through artificial intelligence and human feedback. Improve this profile .

Journalists With Similar Coverage:

Based on similarity of content.
Publications
Most recent topics
Not enough data
Publications
Most recent topics
Not enough data
Publications
Most recent topics
Not enough data
Most recent topics
Not enough data