Does Ageist Language in Job Ads Predict Age Discrimination in Hiring?

Area of Science:

• Empirical economics
• Computational linguistics
• Industrial psychology

Background:

• Ageist stereotypes are prevalent in society and may influence hiring decisions.
• Previous research has not fully explored the link between job ad language and age discrimination in hiring.
• A field experiment provides a unique opportunity to study this relationship.

Purpose of the Study:

• To investigate the relationship between ageist stereotypes in job ad language and age discrimination in hiring.
• To apply computational linguistics and machine learning to analyze textual data in an empirical economics context.
• To develop a framework for analyzing text data to uncover underlying biases.

Main Methods:

• A resume (correspondence study) field experiment was conducted.
• Textual analysis of job ads using computational linguistics and machine learning.
• Comparison of callback rates for older and younger job applicants.

Main Results:

• Language reflecting ageist stereotypes in job ads was found to predict age discrimination.
• For men, stereotypes concerning health, personality, and skill predicted discrimination.
• For women, personality stereotypes predicted discrimination; overall, stronger evidence for men.

Conclusions:

• Job ad language can reflect and perpetuate ageist stereotypes, leading to hiring discrimination.
• Computational methods offer valuable tools for analyzing text data in economic research.
• Findings align with existing industrial psychology literature on age stereotypes, particularly for men.