Employee benefits and company performance: Evidence from a high-dimensional machine learning model
利用社交媒体数据，通过机器学习模型分析美国公司提供的不同员工福利与公司绩效的关系，发现家庭相关福利与员工满意度和公司表现正相关，高增长公司提供广泛福利，高利润公司则聚焦于特定福利。
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.