Employee benefits and company performance: Evidence from a high-dimensional machine learning model

Abstract

By incorporating novel social media data, we analyze in detail how US companies offer different employee benefits and how they are associated with several company performance measures. Benefits such as 401(k), employee discounts, parking, and vision/dental healthcare are the most commonly provided, while free food -related benefits and family-related benefits are the most scarcely offered. Furthermore, with the aid of efficient machine learning -based models and tools from explainable artificial intelligence, we discover that family-related benefits are often associated with the most satisfied employees and best-performing companies. Our findings indicate that high-growth companies tend to provide a broad array of benefits to their employees. In contrast, highly profitable companies often concentrate on delivering a more limited and specialized set of benefits. We argue that companies offer rare and highly sought benefits to keep and recruit high-performers.