Use of LinkedIn Data and Machine Learning to Analyze Gender Differences in Construction Career Paths

Abstract

Will women and men follow distinctively different paths to achieve executive engineering leadership positions in the US architecture, engineering, and construction (AEC) industry? Using Engineering News Record’s (ENR’s) 2019 Top 400 list, this research analyzed LinkedIn profiles for over 2,800 executives to assess career differences between genders. Statistical comparisons of important features, highlighted by number of companies, titles, education, and network size, revealed a significant impact of gender on individual career paths. A key finding was that men ascend to leadership with a single firm throughout their career, outpacing women almost fourfold (37% to 10%). Applying random forest (RF) as an ensemble classifier, researchers successfully predicted profile gender with accuracy of 98.95% for training and 89.53% for testing samples. Collating and categorizing the activities and milestones of individual and collective executives offer insight regarding successful experiences, skills, and choices to reach leadership roles. This creates a roadmap for current and future early and midlevel professionals to model their own vocational journey and accelerate progression up the corporate ladder. From an industry perspective, firms deprive themselves and customers of the proven wide-ranging benefits of diversity.